Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

USGS Publications Warehouse

Horstmann, Tobias; Harrington, Rebecca M.; Cochran, Elizabeth S.

2013-01-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low‒amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross‒correlation technique, followed by a Self‒Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being “semiautomated”. We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal‒to‒noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal‒to‒noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

NASA Astrophysics Data System (ADS)

Horstmann, T.; Harrington, R. M.; Cochran, E. S.

2013-09-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low-amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross-correlation technique, followed by a Self-Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being "semiautomated". We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal-to-noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal-to-noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Function Clustering Self-Organization Maps (FCSOMs) for mining differentially expressed genes in Drosophila and its correlation with the growth medium.

PubMed

Liu, L L; Liu, M J; Ma, M

2015-09-28

The central task of this study was to mine the gene-to-medium relationship. Adequate knowledge of this relationship could potentially improve the accuracy of differentially expressed gene mining. One of the approaches to differentially expressed gene mining uses conventional clustering algorithms to identify the gene-to-medium relationship. Compared to conventional clustering algorithms, self-organization maps (SOMs) identify the nonlinear aspects of the gene-to-medium relationships by mapping the input space into another higher dimensional feature space. However, SOMs are not suitable for huge datasets consisting of millions of samples. Therefore, a new computational model, the Function Clustering Self-Organization Maps (FCSOMs), was developed. FCSOMs take advantage of the theory of granular computing as well as advanced statistical learning methodologies, and are built specifically for each information granule (a function cluster of genes), which are intelligently partitioned by the clustering algorithm provided by the DAVID_6.7 software platform. However, only the gene functions, and not their expression values, are considered in the fuzzy clustering algorithm of DAVID. Compared to the clustering algorithm of DAVID, these experimental results show a marked improvement in the accuracy of classification with the application of FCSOMs. FCSOMs can handle huge datasets and their complex classification problems, as each FCSOM (modeled for each function cluster) can be easily parallelized.

An incremental anomaly detection model for virtual machines.

PubMed

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

An incremental anomaly detection model for virtual machines

PubMed Central

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

Tropospheric Ozonesonde Profiles at Long-term U.S. Monitoring Sites: 1. A Climatology Based on Self-Organizing Maps

NASA Technical Reports Server (NTRS)

Stauffer, Ryan M.; Thompson, Anne M.; Young, George S.

2016-01-01

Sonde-based climatologies of tropospheric ozone (O3) are vital for developing satellite retrieval algorithms and evaluating chemical transport model output. Typical O3 climatologies average measurements by latitude or region, and season. A recent analysis using self-organizing maps (SOM) to cluster ozonesondes from two tropical sites found that clusters of O3 mixing ratio profiles are an excellent way to capture O3variability and link meteorological influences to O3 profiles. Clusters correspond to distinct meteorological conditions, e.g., convection, subsidence, cloud cover, and transported pollution. Here the SOM technique is extended to four long-term U.S. sites (Boulder, CO; Huntsville, AL; Trinidad Head, CA; and Wallops Island, VA) with4530 total profiles. Sensitivity tests on k-means algorithm and SOM justify use of 3 3 SOM (nine clusters). Ateach site, SOM clusters together O3 profiles with similar tropopause height, 500 hPa height temperature, and amount of tropospheric and total column O3. Cluster means are compared to monthly O3 climatologies.For all four sites, near-tropopause O3 is double (over +100 parts per billion by volume; ppbv) the monthly climatological O3 mixing ratio in three clusters that contain 1316 of profiles, mostly in winter and spring.Large midtropospheric deviations from monthly means (6 ppbv, +710 ppbv O3 at 6 km) are found in two of the most populated clusters (combined 3639 of profiles). These two clusters contain distinctly polluted(summer) and clean O3 (fall-winter, high tropopause) profiles, respectively. As for tropical profiles previously analyzed with SOM, O3 averages are often poor representations of U.S. O3 profile statistics.

Tropospheric ozonesonde profiles at long-term U.S. monitoring sites: 1. A climatology based on self-organizing maps

PubMed Central

Stauffer, Ryan M.; Thompson, Anne M.; Young, George S.

2018-01-01

Sonde-based climatologies of tropospheric ozone (O3) are vital for developing satellite retrieval algorithms and evaluating chemical transport model output. Typical O3 climatologies average measurements by latitude or region, and season. Recent analysis using self-organizing maps (SOM) to cluster ozonesondes from two tropical sites found clusters of O3 mixing ratio profiles are an excellent way to capture O3 variability and link meteorological influences to O3 profiles. Clusters correspond to distinct meteorological conditions, e.g. convection, subsidence, cloud cover, and transported pollution. Here, the SOM technique is extended to four long-term U.S. sites (Boulder, CO; Huntsville, AL; Trinidad Head, CA; Wallops Island, VA) with 4530 total profiles. Sensitivity tests on k-means algorithm and SOM justify use of 3×3 SOM (nine clusters). At each site, SOM clusters together O3 profiles with similar tropopause height, 500 hPa height/temperature, and amount of tropospheric and total column O3. Cluster means are compared to monthly O3 climatologies. For all four sites, near-tropopause O3 is double (over +100 parts per billion by volume; ppbv) the monthly climatological O3 mixing ratio in three clusters that contain 13 – 16% of profiles, mostly in winter and spring. Large mid-tropospheric deviations from monthly means (−6 ppbv, +7 – 10 ppbv O3 at 6 km) are found in two of the most populated clusters (combined 36 – 39% of profiles). These two clusters contain distinctly polluted (summer) and clean O3 (fall-winter, high tropopause) profiles, respectively. As for tropical profiles previously analyzed with SOM, O3 averages are often poor representations of U.S. O3 profile statistics. PMID:29619288

Tropospheric ozonesonde profiles at long-term U.S. monitoring sites: 1. A climatology based on self-organizing maps.

PubMed

Stauffer, Ryan M; Thompson, Anne M; Young, George S

2016-02-16

Sonde-based climatologies of tropospheric ozone (O 3 ) are vital for developing satellite retrieval algorithms and evaluating chemical transport model output. Typical O 3 climatologies average measurements by latitude or region, and season. Recent analysis using self-organizing maps (SOM) to cluster ozonesondes from two tropical sites found clusters of O 3 mixing ratio profiles are an excellent way to capture O 3 variability and link meteorological influences to O 3 profiles. Clusters correspond to distinct meteorological conditions, e.g. convection, subsidence, cloud cover, and transported pollution. Here, the SOM technique is extended to four long-term U.S. sites (Boulder, CO; Huntsville, AL; Trinidad Head, CA; Wallops Island, VA) with 4530 total profiles. Sensitivity tests on k-means algorithm and SOM justify use of 3×3 SOM (nine clusters). At each site, SOM clusters together O 3 profiles with similar tropopause height, 500 hPa height/temperature, and amount of tropospheric and total column O 3 . Cluster means are compared to monthly O 3 climatologies. For all four sites, near-tropopause O 3 is double (over +100 parts per billion by volume; ppbv) the monthly climatological O 3 mixing ratio in three clusters that contain 13 - 16% of profiles, mostly in winter and spring. Large mid-tropospheric deviations from monthly means (-6 ppbv, +7 - 10 ppbv O 3 at 6 km) are found in two of the most populated clusters (combined 36 - 39% of profiles). These two clusters contain distinctly polluted (summer) and clean O 3 (fall-winter, high tropopause) profiles, respectively. As for tropical profiles previously analyzed with SOM, O 3 averages are often poor representations of U.S. O 3 profile statistics.

Network-based spatial clustering technique for exploring features in regional industry

NASA Astrophysics Data System (ADS)

Chou, Tien-Yin; Huang, Pi-Hui; Yang, Lung-Shih; Lin, Wen-Tzu

2008-10-01

In the past researches, industrial cluster mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. Industrial cluster could generate three kinds of spillover effects, including knowledge, labor market pooling, and input sharing. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model, GeoSOM, that combines DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SOM (Self-Organizing Map) was developed for analyzing industrial cluster. Different from former distance-based algorithm for industrial cluster, the proposed GeoSOM model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity between firms based on SOM clustering analysis. The demonstrative data sets, the manufacturers around Taichung County in Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that GeoSOM is suitable for evaluating spatial industrial cluster.

Russian Character Recognition using Self-Organizing Map

NASA Astrophysics Data System (ADS)

Gunawan, D.; Arisandi, D.; Ginting, F. M.; Rahmat, R. F.; Amalia, A.

2017-01-01

The World Tourism Organization (UNWTO) in 2014 released that there are 28 million visitors who visit Russia. Most of the visitors might have problem in typing Russian word when using digital dictionary. This is caused by the letters, called Cyrillic that used by the Russian and the countries around it, have different shape than Latin letters. The visitors might not familiar with Cyrillic. This research proposes an alternative way to input the Cyrillic words. Instead of typing the Cyrillic words directly, camera can be used to capture image of the words as input. The captured image is cropped, then several pre-processing steps are applied such as noise filtering, binary image processing, segmentation and thinning. Next, the feature extraction process is applied to the image. Cyrillic letters recognition in the image is done by utilizing Self-Organizing Map (SOM) algorithm. SOM successfully recognizes 89.09% Cyrillic letters from the computer-generated images. On the other hand, SOM successfully recognizes 88.89% Cyrillic letters from the image captured by the smartphone’s camera. For the word recognition, SOM successfully recognized 292 words and partially recognized 58 words from the image captured by the smartphone’s camera. Therefore, the accuracy of the word recognition using SOM is 83.42%

Improving clustering with metabolic pathway data.

PubMed

Milone, Diego H; Stegmayer, Georgina; López, Mariana; Kamenetzky, Laura; Carrari, Fernando

2014-04-10

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

PubMed

Van Gassen, Sofie; Callebaut, Britt; Van Helden, Mary J; Lambrecht, Bart N; Demeester, Piet; Dhaene, Tom; Saeys, Yvan

2015-07-01

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor. © 2015 International Society for Advancement of Cytometry.

Autonomous Data Collection Using a Self-Organizing Map.

PubMed

Faigl, Jan; Hollinger, Geoffrey A

2018-05-01

The self-organizing map (SOM) is an unsupervised learning technique providing a transformation of a high-dimensional input space into a lower dimensional output space. In this paper, we utilize the SOM for the traveling salesman problem (TSP) to develop a solution to autonomous data collection. Autonomous data collection requires gathering data from predeployed sensors by moving within a limited communication radius. We propose a new growing SOM that adapts the number of neurons during learning, which also allows our approach to apply in cases where some sensors can be ignored due to a lower priority. Based on a comparison with available combinatorial heuristic algorithms for relevant variants of the TSP, the proposed approach demonstrates improved results, while also being less computationally demanding. Moreover, the proposed learning procedure can be extended to cases where particular sensors have varying communication radii, and it can also be extended to multivehicle planning.

A novel constructive-optimizer neural network for the traveling salesman problem.

PubMed

Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami

2007-08-01

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

PubMed Central

2011-01-01

Background Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering. Results The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions. Conclusions The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources. PMID:21569575

Clavulanic acid production estimation based on color and structural features of Streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm.

PubMed

Nurmohamadi, Maryam; Pourghassem, Hossein

2014-05-01

The utilization of antibiotics produced by Clavulanic acid (CA) is an increasing need in medicine and industry. Usually, the CA is created from the fermentation of Streptomycen Clavuligerus (SC) bacteria. Analysis of visual and morphological features of SC bacteria is an appropriate measure to estimate the growth of CA. In this paper, an automatic and fast CA production level estimation algorithm based on visual and structural features of SC bacteria instead of statistical methods and experimental evaluation by microbiologist is proposed. In this algorithm, structural features such as the number of newborn branches, thickness of hyphal and bacterial density and also color features such as acceptance color levels are extracted from the SC bacteria. Moreover, PH and biomass of the medium provided by microbiologists are considered as specified features. The level of CA production is estimated by using a new application of Self-Organizing Map (SOM), and a hybrid model of genetic algorithm with back propagation network (GA-BPN). The proposed algorithm is evaluated on four carbonic resources including malt, starch, wheat flour and glycerol that had used as different mediums of bacterial growth. Then, the obtained results are compared and evaluated with observation of specialist. Finally, the Relative Error (RE) for the SOM and GA-BPN are achieved 14.97% and 16.63%, respectively. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Nonlinear projection methods for visualizing Barcode data and application on two data sets.

PubMed

Olteanu, Madalina; Nicolas, Violaine; Schaeffer, Brigitte; Denys, Christiane; Missoup, Alain-Didier; Kennis, Jan; Larédo, Catherine

2013-11-01

Developing tools for visualizing DNA sequences is an important issue in the Barcoding context. Visualizing Barcode data can be put in a purely statistical context, unsupervised learning. Clustering methods combined with projection methods have two closely linked objectives, visualizing and finding structure in the data. Multidimensional scaling (MDS) and Self-organizing maps (SOM) are unsupervised statistical tools for data visualization. Both algorithms map data onto a lower dimensional manifold: MDS looks for a projection that best preserves pairwise distances while SOM preserves the topology of the data. Both algorithms were initially developed for Euclidean data and the conditions necessary to their good implementation were not satisfied for Barcode data. We developed a workflow consisting in four steps: collapse data into distinct sequences; compute a dissimilarity matrix; run a modified version of SOM for dissimilarity matrices to structure the data and reduce dimensionality; project the results using MDS. This methodology was applied to Astraptes fulgerator and Hylomyscus, an African rodent with debated taxonomy. We obtained very good results for both data sets. The results were robust against unbalanced species. All the species in Astraptes were well displayed in very distinct groups in the various visualizations, except for LOHAMP and FABOV that were mixed up. For Hylomyscus, our findings were consistent with known species, confirmed the existence of four unnamed taxa and suggested the existence of potentially new species. © 2013 John Wiley & Sons Ltd.

Machine Learning Method for Pattern Recognition in Volcano Seismic Spectra

NASA Astrophysics Data System (ADS)

Radic, V.; Unglert, K.; Jellinek, M.

2016-12-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as Self-Organizing Maps (SOM), Principal Component Analysis (PCA) and clustering methods can help to quickly and automatically identify important patterns related to impending eruptions. In this study we develop and evaluate an algorithm applied on a set of synthetic volcano seismic spectra as well as observed spectra from Kılauea Volcano, Hawai`i. Our goal is to retrieve a set of known spectral patterns that are associated with dominant phases of volcanic tremor before, during, and after periods of volcanic unrest. The algorithm is based on training a SOM on the spectra and then identifying local maxima and minima on the SOM 'topography'. The topography is derived from the first two PCA modes so that the maxima represent the SOM patterns that carry most of the variance in the spectra. Patterns identified in this way reproduce the known set of spectra. Our results show that, regardless of the level of white noise in the spectra, the algorithm can accurately reproduce the characteristic spectral patterns and their occurrence in time. The ability to rapidly classify spectra of volcano seismic data without prior knowledge of the character of the seismicity at a given volcanic system holds great potential for real time or near-real time applications, and thus ultimately for eruption forecasting.

Computer-aided classification of rheumatoid arthritis in finger joints using frequency domain optical tomography

NASA Astrophysics Data System (ADS)

Klose, C. D.; Kim, H. K.; Netz, U.; Blaschke, S.; Zwaka, P. A.; Mueller, G. A.; Beuthan, J.; Hielscher, A. H.

2009-02-01

Novel methods that can help in the diagnosis and monitoring of joint disease are essential for efficient use of novel arthritis therapies that are currently emerging. Building on previous studies that involved continuous wave imaging systems we present here first clinical data obtained with a new frequency-domain imaging system. Three-dimensional tomographic data sets of absorption and scattering coefficients were generated for 107 fingers. The data were analyzed using ANOVA, MANOVA, Discriminant Analysis DA, and a machine-learning algorithm that is based on self-organizing mapping (SOM) for clustering data in 2-dimensional parameter spaces. Overall we found that the SOM algorithm outperforms the more traditional analysis methods in terms of correctly classifying finger joints. Using SOM, healthy and affected joints can now be separated with a sensitivity of 0.97 and specificity of 0.91. Furthermore, preliminary results suggest that if a combination of multiple image properties is used, statistical significant differences can be found between RA-affected finger joints that show different clinical features (e.g. effusion, synovitis or erosion).

An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network

PubMed Central

Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

2009-01-01

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3–11.3 μm; IR2, 11.5–12.5 μm and WV 6.3–7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products. PMID:22346714

An Improved Cloud Classification Algorithm for China's FY-2C Multi-Channel Images Using Artificial Neural Network.

PubMed

Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

2009-01-01

The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3-11.3 μm; IR2, 11.5-12.5 μm and WV 6.3-7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.

Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

NASA Astrophysics Data System (ADS)

Karaca, Yeliz; Cattani, Carlo

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

An Information-Theoretic-Cluster Visualization for Self-Organizing Maps.

PubMed

Brito da Silva, Leonardo Enzo; Wunsch, Donald C

2018-06-01

Improved data visualization will be a significant tool to enhance cluster analysis. In this paper, an information-theoretic-based method for cluster visualization using self-organizing maps (SOMs) is presented. The information-theoretic visualization (IT-vis) has the same structure as the unified distance matrix, but instead of depicting Euclidean distances between adjacent neurons, it displays the similarity between the distributions associated with adjacent neurons. Each SOM neuron has an associated subset of the data set whose cardinality controls the granularity of the IT-vis and with which the first- and second-order statistics are computed and used to estimate their probability density functions. These are used to calculate the similarity measure, based on Renyi's quadratic cross entropy and cross information potential (CIP). The introduced visualizations combine the low computational cost and kernel estimation properties of the representative CIP and the data structure representation of a single-linkage-based grouping algorithm to generate an enhanced SOM-based visualization. The visual quality of the IT-vis is assessed by comparing it with other visualization methods for several real-world and synthetic benchmark data sets. Thus, this paper also contains a significant literature survey. The experiments demonstrate the IT-vis cluster revealing capabilities, in which cluster boundaries are sharply captured. Additionally, the information-theoretic visualizations are used to perform clustering of the SOM. Compared with other methods, IT-vis of large SOMs yielded the best results in this paper, for which the quality of the final partitions was evaluated using external validity indices.

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †.

PubMed

Dafonte, Carlos; Garabato, Daniel; Álvarez, Marco A; Manteiga, Minia

2018-05-03

Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

Using Self-Organizing Neural Network Map Combined with Ward's Clustering Algorithm for Visualization of Students' Cognitive Structural Models about Aliveness Concept

PubMed Central

Ugulu, Ilker; Aydin, Halil

2016-01-01

We propose an approach to clustering and visualization of students' cognitive structural models. We use the self-organizing map (SOM) combined with Ward's clustering to conduct cluster analysis. In the study carried out on 100 subjects, a conceptual understanding test consisting of open-ended questions was used as a data collection tool. The results of analyses indicated that students constructed the aliveness concept by associating it predominantly with human. Motion appeared as the most frequently associated term with the aliveness concept. The results suggest that the aliveness concept has been constructed using anthropocentric and animistic cognitive structures. In the next step, we used the data obtained from the conceptual understanding test for training the SOM. Consequently, we propose a visualization method about cognitive structure of the aliveness concept. PMID:26819579

Multi Robot Path Planning for Budgeted Active Perception with Self-Organising Maps

DTIC Science & Technology

2016-10-04

Multi- Robot Path Planning for Budgeted Active Perception with Self-Organising Maps Graeme Best1, Jan Faigl2 and Robert Fitch1 Abstract— We propose a...optimise paths for a multi- robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting...regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes

Lava Lake Thermal Pattern Classification Using Self-Organizing Maps and Relationships to Eruption Processes at Kīlauea Volcano, Hawaii

NASA Astrophysics Data System (ADS)

Burzynski, A. M.; Anderson, S. W.; Morrison, K.; LeWinter, A. L.; Patrick, M. R.; Orr, T. R.; Finnegan, D. C.

2014-12-01

Nested within the Halema'uma'u Crater on the summit of Kīlauea Volcano, the active lava lake of Overlook Crater poses hazards to local residents and Hawaii Volcanoes National Park visitors. Since its formation in March 2008, the lava lake has enlarged to +28,500 m2 and has been closely monitored by researchers at the USGS Hawaiian Volcano Observatory (HVO). Time-lapse images, collected via visible and thermal infrared cameras, reveal thin crustal plates, separated by incandescent cracks, moving across the lake surface as lava circulates beneath. We hypothesize that changes in size, shape, velocity, and patterns of these crustal plates are related to other eruption processes at the volcano. Here we present a methodology to identify characteristic lava lake surface patterns from thermal infrared video footage using a self-organizing maps (SOM) algorithm. The SOM is an artificial neural network that performs unsupervised clustering and enables us to visualize the relationships between groups of input patterns on a 2-dimensional grid. In a preliminary trial, we input ~4 hours of thermal infrared time-lapse imagery collected on December 16-17, 2013 during a transient deflation-inflation deformation event at a rate of one frame every 10 seconds. During that same time period, we also acquired a series of one-second terrestrial laser scans (TLS) every 30 seconds to provide detailed topography of the lava lake surface. We identified clusters of characteristic thermal patterns using a self-organizing maps algorithm within the Matlab SOM Toolbox. Initial results from two SOMs, one large map (81 nodes) and one small map (9 nodes), indicate 4-6 distinct groups of thermal patterns. We compare these surface patterns with lava lake surface slope and crustal plate velocities derived from concurrent TLS surveys and with time series of other eruption variables, including outgassing rates and inflation-deflation events. This methodology may be applied to the continuous stream of thermal video footage at Kīlauea to expand the breadth of eruption information we are able to obtain from a remote thermal infrared camera and may potentially allow for the recognition of lava lake patterns as a proxy for other eruption parameters.

Spherical self-organizing map using efficient indexed geodesic data structure.

PubMed

Wu, Yingxin; Takatsuka, Masahiro

2006-01-01

The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However, existing data structures for such SOMs are either not space efficient or are time consuming when searching the neighborhood. We introduce a 2D rectangular grid data structure to store the icosahedron-based geodesic dome. Vertices relationships are maintained by their positions in the data structure rather than by immediate neighbor pointers or an adjacency list. Increasing the number of neurons can be done efficiently because the overhead caused by pointer updates is reduced. Experiments show that the spherical SOM using our data structure, called a GeoSOM, runs with comparable speed to the conventional 2D SOM. The GeoSOM also reduces data distortion due to removal of the boundaries. Furthermore, we developed an interface to project the GeoSOM onto the 2D plane using a cartographic approach, which gives users a global view of the spherical data map. Users can change the center of the 2D data map interactively. In the end, we compare the GeoSOM to the other spherical SOMs by space complexity and time complexity.

Neural network analysis of crosshole tomographic images: The seismic signature of gas hydrate bearing sediments in the Mackenzie Delta (NW Canada)

NASA Astrophysics Data System (ADS)

Bauer, K.; Pratt, R. G.; Haberland, C.; Weber, M.

2008-10-01

Crosshole seismic experiments were conducted to study the in-situ properties of gas hydrate bearing sediments (GHBS) in the Mackenzie Delta (NW Canada). Seismic tomography provided images of P velocity, anisotropy, and attenuation. Self-organizing maps (SOM) are powerful neural network techniques to classify and interpret multi-attribute data sets. The coincident tomographic images are translated to a set of data vectors in order to train a Kohonen layer. The total gradient of the model vectors is determined for the trained SOM and a watershed segmentation algorithm is used to visualize and map the lithological clusters with well-defined seismic signatures. Application to the Mallik data reveals four major litho-types: (1) GHBS, (2) sands, (3) shale/coal interlayering, and (4) silt. The signature of seismic P wave characteristics distinguished for the GHBS (high velocities, strong anisotropy and attenuation) is new and can be used for new exploration strategies to map and quantify gas hydrates.

Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

NASA Astrophysics Data System (ADS)

Unglert, K.; Radić, V.; Jellinek, A. M.

2016-06-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

A Self-Organizing Map-Based Approach to Generating Reduced-Size, Statistically Similar Climate Datasets

NASA Astrophysics Data System (ADS)

Cabell, R.; Delle Monache, L.; Alessandrini, S.; Rodriguez, L.

2015-12-01

Climate-based studies require large amounts of data in order to produce accurate and reliable results. Many of these studies have used 30-plus year data sets in order to produce stable and high-quality results, and as a result, many such data sets are available, generally in the form of global reanalyses. While the analysis of these data lead to high-fidelity results, its processing can be very computationally expensive. This computational burden prevents the utilization of these data sets for certain applications, e.g., when rapid response is needed in crisis management and disaster planning scenarios resulting from release of toxic material in the atmosphere. We have developed a methodology to reduce large climate datasets to more manageable sizes while retaining statistically similar results when used to produce ensembles of possible outcomes. We do this by employing a Self-Organizing Map (SOM) algorithm to analyze general patterns of meteorological fields over a regional domain of interest to produce a small set of "typical days" with which to generate the model ensemble. The SOM algorithm takes as input a set of vectors and generates a 2D map of representative vectors deemed most similar to the input set and to each other. Input predictors are selected that are correlated with the model output, which in our case is an Atmospheric Transport and Dispersion (T&D) model that is highly dependent on surface winds and boundary layer depth. To choose a subset of "typical days," each input day is assigned to its closest SOM map node vector and then ranked by distance. Each node vector is treated as a distribution and days are sampled from them by percentile. Using a 30-node SOM, with sampling every 20th percentile, we have been able to reduce 30 years of the Climate Forecast System Reanalysis (CFSR) data for the month of October to 150 "typical days." To estimate the skill of this approach, the "Measure of Effectiveness" (MOE) metric is used to compare area and overlap of statistical exceedance between the reduced data set and the full 30-year CFSR dataset. Using the MOE, we find that our SOM-derived climate subset produces statistics that fall within 85-90% overlap with the full set while using only 15% of the total data length, and consequently, 15% of the computational time required to run the T&D model for the full period.

Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

PubMed

Demirhan, Ayşe; Toru, Mustafa; Guler, Inan

2015-07-01

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.

Exploration of a Capability-Focused Aerospace System of Systems Architecture Alternative with Bilayer Design Space, Based on RST-SOM Algorithmic Methods

PubMed Central

Li, Zhifei; Qin, Dongliang

2014-01-01

In defense related programs, the use of capability-based analysis, design, and acquisition has been significant. In order to confront one of the most challenging features of a huge design space in capability based analysis (CBA), a literature review of design space exploration was first examined. Then, in the process of an aerospace system of systems design space exploration, a bilayer mapping method was put forward, based on the existing experimental and operating data. Finally, the feasibility of the foregoing approach was demonstrated with an illustrative example. With the data mining RST (rough sets theory) and SOM (self-organized mapping) techniques, the alternative to the aerospace system of systems architecture was mapping from P-space (performance space) to C-space (configuration space), and then from C-space to D-space (design space), respectively. Ultimately, the performance space was mapped to the design space, which completed the exploration and preliminary reduction of the entire design space. This method provides a computational analysis and implementation scheme for large-scale simulation. PMID:24790572

Exploration of a capability-focused aerospace system of systems architecture alternative with bilayer design space, based on RST-SOM algorithmic methods.

PubMed

Li, Zhifei; Qin, Dongliang; Yang, Feng

2014-01-01

In defense related programs, the use of capability-based analysis, design, and acquisition has been significant. In order to confront one of the most challenging features of a huge design space in capability based analysis (CBA), a literature review of design space exploration was first examined. Then, in the process of an aerospace system of systems design space exploration, a bilayer mapping method was put forward, based on the existing experimental and operating data. Finally, the feasibility of the foregoing approach was demonstrated with an illustrative example. With the data mining RST (rough sets theory) and SOM (self-organized mapping) techniques, the alternative to the aerospace system of systems architecture was mapping from P-space (performance space) to C-space (configuration space), and then from C-space to D-space (design space), respectively. Ultimately, the performance space was mapped to the design space, which completed the exploration and preliminary reduction of the entire design space. This method provides a computational analysis and implementation scheme for large-scale simulation.

Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Li, Deng; Lu, Xiaoming; Cheng, Xinyi; Wang, Liwei

2014-10-01

Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance γ interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 ±0.17 mm and 1.92 ±0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.

Multiscale visual quality assessment for cluster analysis with self-organizing maps

NASA Astrophysics Data System (ADS)

Bernard, Jürgen; von Landesberger, Tatiana; Bremm, Sebastian; Schreck, Tobias

2011-01-01

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

Visualized analysis of mixed numeric and categorical data via extended self-organizing map.

PubMed

Hsu, Chung-Chian; Lin, Shu-Han

2012-01-01

Many real-world datasets are of mixed types, having numeric and categorical attributes. Even though difficult, analyzing mixed-type datasets is important. In this paper, we propose an extended self-organizing map (SOM), called MixSOM, which utilizes a data structure distance hierarchy to facilitate the handling of numeric and categorical values in a direct, unified manner. Moreover, the extended model regularizes the prototype distance between neighboring neurons in proportion to their map distance so that structures of the clusters can be portrayed better on the map. Extensive experiments on several synthetic and real-world datasets are conducted to demonstrate the capability of the model and to compare MixSOM with several existing models including Kohonen's SOM, the generalized SOM and visualization-induced SOM. The results show that MixSOM is superior to the other models in reflecting the structure of the mixed-type data and facilitates further analysis of the data such as exploration at various levels of granularity.

The dynamics of ant mosaics in tropical rainforests characterized using the Self-Organizing Map algorithm.

PubMed

Dejean, Alain; Azémar, Frédéric; Céréghino, Régis; Leponce, Maurice; Corbara, Bruno; Orivel, Jérôme; Compin, Arthur

2016-08-01

Ants, the most abundant taxa among canopy-dwelling animals in tropical rainforests, are mostly represented by territorially dominant arboreal ants (TDAs) whose territories are distributed in a mosaic pattern (arboreal ant mosaics). Large TDA colonies regulate insect herbivores, with implications for forestry and agronomy. What generates these mosaics in vegetal formations, which are dynamic, still needs to be better understood. So, from empirical research based on 3 Cameroonian tree species (Lophira alata, Ochnaceae; Anthocleista vogelii, Gentianaceae; and Barteria fistulosa, Passifloraceae), we used the Self-Organizing Map (SOM, neural network) to illustrate the succession of TDAs as their host trees grow and age. The SOM separated the trees by species and by size for L. alata, which can reach 60 m in height and live several centuries. An ontogenic succession of TDAs from sapling to mature trees is shown, and some ecological traits are highlighted for certain TDAs. Also, because the SOM permits the analysis of data with many zeroes with no effect of outliers on the overall scatterplot distributions, we obtained ecological information on rare species. Finally, the SOM permitted us to show that functional groups cannot be selected at the genus level as congeneric species can have very different ecological niches, something particularly true for Crematogaster spp., which include a species specifically associated with B. fistulosa, nondominant species and TDAs. Therefore, the SOM permitted the complex relationships between TDAs and their growing host trees to be analyzed, while also providing new information on the ecological traits of the ant species involved. © 2015 Institute of Zoology, Chinese Academy of Sciences.

Community health assessment using self-organizing maps and geographic information systems

PubMed Central

Basara, Heather G; Yuan, May

2008-01-01

Background From a public health perspective, a healthier community environment correlates with fewer occurrences of chronic or infectious diseases. Our premise is that community health is a non-linear function of environmental and socioeconomic effects that are not normally distributed among communities. The objective was to integrate multivariate data sets representing social, economic, and physical environmental factors to evaluate the hypothesis that communities with similar environmental characteristics exhibit similar distributions of disease. Results The SOM algorithm used the intrinsic distributions of 92 environmental variables to classify 511 communities into five clusters. SOM determined clusters were reprojected to geographic space and compared with the distributions of several health outcomes. ANOVA results indicated that the variability between community clusters was significant with respect to the spatial distribution of disease occurrence. Conclusion Our study demonstrated a positive relationship between environmental conditions and health outcomes in communities using the SOM-GIS method to overcome data and methodological challenges traditionally encountered in public health research. Results demonstrated that community health can be classified using environmental variables and that the SOM-GIS method may be applied to multivariate environmental health studies. PMID:19116020

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach

PubMed Central

van Diest, Mike; Stegenga, Jan; Wörtche, Heinrich J.; Roerdink, Jos B. T. M; Verkerke, Gijsbertus J.; Lamoth, Claudine J. C.

2015-01-01

Background Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user’s balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. Methods Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. Results Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. Conclusions Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training. PMID:26230655

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

PubMed

van Diest, Mike; Stegenga, Jan; Wörtche, Heinrich J; Roerdink, Jos B T M; Verkerke, Gijsbertus J; Lamoth, Claudine J C

2015-01-01

Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

Self-organizing maps based on limit cycle attractors.

PubMed

Huang, Di-Wei; Gentili, Rodolphe J; Reggia, James A

2015-03-01

Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent with the rhythmic oscillatory activity observed in the brain. Here we develop and study an alternative encoding scheme that instead uses sparsely-coded limit cycles to represent external input patterns/sequences. We establish conditions under which learned limit cycle representations arise reliably and dominate the dynamics in a SOM. These limit cycles tend to be relatively unique for different inputs, robust to perturbations, and fairly insensitive to timing. In spite of the continually changing activity in the map layer when a limit cycle representation is used, map formation continues to occur reliably. In a two-SOM architecture where each SOM represents a different sensory modality, we also show that after learning, limit cycles in one SOM can correctly evoke corresponding limit cycles in the other, and thus there is the potential for multi-SOM systems using limit cycles to work effectively as hetero-associative memories. While the results presented here are only first steps, they establish the viability of SOM models based on limit cycle activity patterns, and suggest that such models merit further study. Copyright © 2014 Elsevier Ltd. All rights reserved.

Robust spike classification based on frequency domain neural waveform features.

PubMed

Yang, Chenhui; Yuan, Yuan; Si, Jennie

2013-12-01

We introduce a new spike classification algorithm based on frequency domain features of the spike snippets. The goal for the algorithm is to provide high classification accuracy, low false misclassification, ease of implementation, robustness to signal degradation, and objectivity in classification outcomes. In this paper, we propose a spike classification algorithm based on frequency domain features (CFDF). It makes use of frequency domain contents of the recorded neural waveforms for spike classification. The self-organizing map (SOM) is used as a tool to determine the cluster number intuitively and directly by viewing the SOM output map. After that, spike classification can be easily performed using clustering algorithms such as the k-Means. In conjunction with our previously developed multiscale correlation of wavelet coefficient (MCWC) spike detection algorithm, we show that the MCWC and CFDF detection and classification system is robust when tested on several sets of artificial and real neural waveforms. The CFDF is comparable to or outperforms some popular automatic spike classification algorithms with artificial and real neural data. The detection and classification of neural action potentials or neural spikes is an important step in single-unit-based neuroscientific studies and applications. After the detection of neural snippets potentially containing neural spikes, a robust classification algorithm is applied for the analysis of the snippets to (1) extract similar waveforms into one class for them to be considered coming from one unit, and to (2) remove noise snippets if they do not contain any features of an action potential. Usually, a snippet is a small 2 or 3 ms segment of the recorded waveform, and differences in neural action potentials can be subtle from one unit to another. Therefore, a robust, high performance classification system like the CFDF is necessary. In addition, the proposed algorithm does not require any assumptions on statistical properties of the noise and proves to be robust under noise contamination.

Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace.

PubMed

Zhu, Daqi; Huang, Huan; Yang, S X

2013-04-01

For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

PubMed

Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung

2018-05-04

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modeling Poroelastic Wave Propagation in a Real 2-D Complex Geological Structure Obtained via Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Itzá Balam, Reymundo; Iturrarán-Viveros, Ursula; Parra, Jorge O.

2018-03-01

Two main stages of seismic modeling are geological model building and numerical computation of seismic response for the model. The quality of the computed seismic response is partly related to the type of model that is built. Therefore, the model building approaches become as important as seismic forward numerical methods. For this purpose, three petrophysical facies (sands, shales and limestones) are extracted from reflection seismic data and some seismic attributes via the clustering method called Self-Organizing Maps (SOM), which, in this context, serves as a geological model building tool. This model with all its properties is the input to the Optimal Implicit Staggered Finite Difference (OISFD) algorithm to create synthetic seismograms for poroelastic, poroacoustic and elastic media. The results show a good agreement between observed and 2-D synthetic seismograms. This demonstrates that the SOM classification method enables us to extract facies from seismic data and allows us to integrate the lithology at the borehole scale with the 2-D seismic data.

Identification of large-scale meteorological patterns associated with extreme precipitation in the US northeast

NASA Astrophysics Data System (ADS)

Agel, Laurie; Barlow, Mathew; Feldstein, Steven B.; Gutowski, William J.

2018-03-01

Patterns of daily large-scale circulation associated with Northeast US extreme precipitation are identified using both k-means clustering (KMC) and Self-Organizing Maps (SOM) applied to tropopause height. The tropopause height provides a compact representation of the upper-tropospheric potential vorticity, which is closely related to the overall evolution and intensity of weather systems. Extreme precipitation is defined as the top 1% of daily wet-day observations at 35 Northeast stations, 1979-2008. KMC is applied on extreme precipitation days only, while the SOM algorithm is applied to all days in order to place the extreme results into the overall context of patterns for all days. Six tropopause patterns are identified through KMC for extreme day precipitation: a summertime tropopause ridge, a summertime shallow trough/ridge, a summertime shallow eastern US trough, a deeper wintertime eastern US trough, and two versions of a deep cold-weather trough located across the east-central US. Thirty SOM patterns for all days are identified. Results for all days show that 6 SOM patterns account for almost half of the extreme days, although extreme precipitation occurs in all SOM patterns. The same SOM patterns associated with extreme precipitation also routinely produce non-extreme precipitation; however, on extreme precipitation days the troughs, on average, are deeper and the downstream ridges more pronounced. Analysis of other fields associated with the large-scale patterns show various degrees of anomalously strong moisture transport preceding, and upward motion during, extreme precipitation events.

Large-Scale Meteorological Patterns Associated with Extreme Precipitation in the US Northeast

NASA Astrophysics Data System (ADS)

Agel, L. A.; Barlow, M. A.

2016-12-01

Patterns of daily large-scale circulation associated with Northeast US extreme precipitation are identified using both k-means clustering (KMC) and Self-Organizing Maps (SOM) applied to tropopause height. Tropopause height provides a compact representation of large-scale circulation patterns, as it is linked to mid-level circulation, low-level thermal contrasts and low-level diabatic heating. Extreme precipitation is defined as the top 1% of daily wet-day observations at 35 Northeast stations, 1979-2008. KMC is applied on extreme precipitation days only, while the SOM algorithm is applied to all days in order to place the extreme results into a larger context. Six tropopause patterns are identified on extreme days: a summertime tropopause ridge, a summertime shallow trough/ridge, a summertime shallow eastern US trough, a deeper wintertime eastern US trough, and two versions of a deep cold-weather trough located across the east-central US. Thirty SOM patterns for all days are identified. Results for all days show that 6 SOM patterns account for almost half of the extreme days, although extreme precipitation occurs in all SOM patterns. The same SOM patterns associated with extreme precipitation also routinely produce non-extreme precipitation; however, on extreme precipitation days the troughs, on average, are deeper and the downstream ridges more pronounced. Analysis of other fields associated with the large-scale patterns show various degrees of anomalously strong upward motion during, and moisture transport preceding, extreme precipitation events.

Cross-entropy embedding of high-dimensional data using the neural gas model.

PubMed

Estévez, Pablo A; Figueroa, Cristián J; Saito, Kazumi

2005-01-01

A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural Gas (NG) quantizer algorithm, into a low-dimensional output space. The aim of this approach is to preserve the relationship defined by the NG neighborhood function for each pair of input and codebook vectors. A cost function based on the cross-entropy between input and output probabilities is minimized by using a Newton-Raphson method. The new approach is compared with Sammon's non-linear mapping (NLM) and the hierarchical approach of combining a vector quantizer such as the self-organizing feature map (SOM) or NG with the NLM recall algorithm. In comparison with these techniques, our method delivers a clear visualization of both data points and codebooks, and it achieves a better mapping quality in terms of the topology preservation measure q(m).

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

Comparative analysis of model behaviour for flood prediction purposes using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Casper, M. C.; Grundmann, J.; Buchholz, O.

2009-03-01

Distributed watershed models constitute a key component in flood forecasting systems. It is widely recognized that models because of their structural differences have varying capabilities of capturing different aspects of the system behaviour equally well. Of course, this also applies to the reproduction of peak discharges by a simulation model which is of particular interest regarding the flood forecasting problem. In our study we use a Self-Organizing Map (SOM) in combination with index measures which are derived from the flow duration curve in order to examine the conditions under which three different distributed watershed models are capable of reproducing flood events present in the calibration data. These indices are specifically conceptualized to extract data on the peak discharge characteristics of model output time series which are obtained from Monte-Carlo simulations with the distributed watershed models NASIM, LARSIM and WaSIM-ETH. The SOM helps to analyze this data by producing a discretized mapping of their distribution in the index space onto a two dimensional plane such that their pattern and consequently the patterns of model behaviour can be conveyed in a comprehensive manner. It is demonstrated how the SOM provides useful information about details of model behaviour and also helps identifying the model parameters that are relevant for the reproduction of peak discharges and thus for flood prediction problems. It is further shown how the SOM can be used to identify those parameter sets from among the Monte-Carlo data that most closely approximate the peak discharges of a measured time series. The results represent the characteristics of the observed time series with partially superior accuracy than the reference simulation obtained by implementing a simple calibration strategy using the global optimization algorithm SCE-UA. The most prominent advantage of using SOM in the context of model analysis is that it allows to comparatively evaluating the data from two or more models. Our results highlight the individuality of the model realizations in terms of the index measures and shed a critical light on the use and implementation of simple and yet too rigorous calibration strategies.

Quality assessment of data discrimination using self-organizing maps.

PubMed

Mekler, Alexey; Schwarz, Dmitri

2014-10-01

One of the important aspects of the data classification problem lies in making the most appropriate selection of features. The set of variables should be small and, at the same time, should provide reliable discrimination of the classes. The method for the discriminating power evaluation that enables a comparison between different sets of variables will be useful in the search for the set of variables. A new approach to feature selection is presented. Two methods of evaluation of the data discriminating power of a feature set are suggested. Both of the methods implement self-organizing maps (SOMs) and the newly introduced exponents of the degree of data clusterization on the SOM. The first method is based on the comparison of intraclass and interclass distances on the map. Another method concerns the evaluation of the relative number of best matching unit's (BMUs) nearest neighbors of the same class. Both methods make it possible to evaluate the discriminating power of a feature set in cases when this set provides nonlinear discrimination of the classes. Current algorithms in program code can be downloaded for free at http://mekler.narod.ru/Science/Articles_support.html, as well as the supporting data files. Copyright © 2014 Elsevier Inc. All rights reserved.

An artificial neural network model for the prediction of mechanical and barrier properties of biodegradable films.

PubMed

Nobrega, Marcelo Medre; Bona, Evandro; Yamashita, Fabio

2013-10-01

Nowadays, the production of biodegradable starch-based films is of great interest because of the growing environmental concerns regarding pollution and the need to reduce dependence on the plastics industry. A broad view of the role of different components, added to starch-based films to improve their properties, is required to guide the future development. The self-organizing maps (SOMs) provide comparisons that initially were complicated due to the large volume of the data. Furthermore, the construction of a model capable of predicting the mechanical and barrier properties of these films will accelerate the development of films with improved characteristics. The water vapor permeability (WVP) analysis using the SOM algorithm showed that the presence of glycerol is very important for films with low amounts of poly (butylene adipate co-terephthalate) and confirms the role of the equilibrium relative humidity in the determination of WVP. Considering the mechanical properties, the SOM analysis emphasizes the important role of poly (butylene adipate co-terephthalate) in thermoplastic starch based films. The properties of biodegradable films were predicted and optimized by using a multilayer perceptron coupled with a genetic algorithm, presenting a great correlation between the experimental and theoretical values with a maximum error of 24%. To improve the response of the model and to ensure the compatibility of the components more information will be necessary. © 2013.

Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry.

PubMed

Pandini, Alessandro; Fraccalvieri, Domenico; Bonati, Laura

2013-01-01

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry.

Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

2017-04-01

The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed integration time (typically 1 min, 1 hour or 1 day). In this study we used the concept of rain event. In fact, the discrete and intermittent natures of rain processes make the definition of some features inadequate when defined on a fixed duration. Long integration times (hour, day) lead to mix rainy and clear air periods in the same sample. Small integration time (seconds, minutes) will lead to noisy data with a great sensibility to detector characteristics. The analysis on the whole rain event instead of individual short duration samples of a fixed duration allows to clarify relationships between features, in particular between macro physical and microphysical ones. This approach allows suppressing the intra-event variability partly due to measurement uncertainties and allows focusing on physical processes. An algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is developed to obtain a parsimonious characterisation of rain events using a minimal set of variables. The use of self-organizing map (SOM) is justified by the fact that it allows to map a high dimensional data space in a two-dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained SOM allows providing the dependencies between variables and consequently removing redundant variables leading to a minimal subset of only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5). To confirm relevance of the five selected features the corresponding SOM is analyzed. This analysis shows clearly the existence of relationships between features. It also shows the independence of the inter-event time (IETp) feature or the weak dependence of the Dry percentage in event (Dd%e) feature. This confirms that a rain time series can be considered by an alternation of independent rain event and no rain period. The five selected feature are used to perform a hierarchical clustering of the events. The well-known division between stratiform and convective events appears clearly. This classification into two classes is then refined in 5 fairly homogeneous subclasses. The data driven analysis performed on whole rain events instead of fixed length samples allows identifying strong relationships between macrophysics (based on rain rate) and microphysics (based on raindrops) features. We show that among the 5 identified subclasses some of them have specific microphysics characteristics. Obtaining information on microphysical characteristics of rainfall events from rain gauges measurement suggests many implications in development of the quantitative precipitation estimation (QPE), for the improvement of rain rate retrieval algorithm in remote sensing context.

Application of a novel hybrid method for spatiotemporal data imputation: A case study of the Minqin County groundwater level

NASA Astrophysics Data System (ADS)

Zhang, Zhongrong; Yang, Xuan; Li, Hao; Li, Weide; Yan, Haowen; Shi, Fei

2017-10-01

The techniques for data analyses have been widely developed in past years, however, missing data still represent a ubiquitous problem in many scientific fields. In particular, dealing with missing spatiotemporal data presents an enormous challenge. Nonetheless, in recent years, a considerable amount of research has focused on spatiotemporal problems, making spatiotemporal missing data imputation methods increasingly indispensable. In this paper, a novel spatiotemporal hybrid method is proposed to verify and imputed spatiotemporal missing values. This new method, termed SOM-FLSSVM, flexibly combines three advanced techniques: self-organizing feature map (SOM) clustering, the fruit fly optimization algorithm (FOA) and the least squares support vector machine (LSSVM). We employ a cross-validation (CV) procedure and FOA swarm intelligence optimization strategy that can search available parameters and determine the optimal imputation model. The spatiotemporal underground water data for Minqin County, China, were selected to test the reliability and imputation ability of SOM-FLSSVM. We carried out a validation experiment and compared three well-studied models with SOM-FLSSVM using a different missing data ratio from 0.1 to 0.8 in the same data set. The results demonstrate that the new hybrid method performs well in terms of both robustness and accuracy for spatiotemporal missing data.

Self-organizing map (SOM) of space acceleration measurement system (SAMS) data.

PubMed

Sinha, A; Smith, A D

1999-01-01

In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from STS-50 and STS-57 missions are presented. Following issues are discussed in details: impact of number of neurons, global ordering of SOM weight vectors, effectiveness of a SOM in data classification, and effects of shifting time windows in the generation of input patterns. The concept of 'cascade of SOM networks' is also developed and tested. It has been found that a SOM network can successfully classify SAMS data obtained during STS-50 and STS-57 missions.

Self-organizing map (SOM) of space acceleration measurement system (SAMS) data

NASA Technical Reports Server (NTRS)

Sinha, A.; Smith, A. D.

1999-01-01

In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from STS-50 and STS-57 missions are presented. Following issues are discussed in details: impact of number of neurons, global ordering of SOM weight vectors, effectiveness of a SOM in data classification, and effects of shifting time windows in the generation of input patterns. The concept of 'cascade of SOM networks' is also developed and tested. It has been found that a SOM network can successfully classify SAMS data obtained during STS-50 and STS-57 missions.

Exploring the behavior of Caenorhabditis Elegans by using a self-organizing map and hidden Markov model

NASA Astrophysics Data System (ADS)

Kang, Seung-Ho; Lee, Sang-Hee; Chon, Tae-Soo

2012-02-01

In recent decades, the behavior of Caenorhabditis elegans ( C. elegans) has been extensively studied to understand the respective roles of neural control and biomechanics. Thus far, however, only a few studies on the simulation modeling of C. elegans swimming behavior have been conducted because it is mathematically difficult to describe its complicated behavior. In this study, we built two hidden Markov models (HMMs), corresponding to the movements of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 ppm), respectively. The movement was characterized by a series of shape patterns of the organism, taken every 0.25 s for 40 min. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into seven patterns by using the self-organizing map (SOM) and the k-means clustering algorithm. The HMM coupled with the SOM was successful in accurately explaining the organism's behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems for real-time applications to determine water quality.

Implementation of Self Organizing Map (SOM) as decision support: Indonesian telematics services MSMEs empowerment

NASA Astrophysics Data System (ADS)

Tosida, E. T.; Maryana, S.; Thaheer, H.; Hardiani

2017-01-01

Information technology and communication (telematics) is one of the most rapidly developing business sectors in Indonesia. It has strategic position in its contribution towards planning and implementation of developmental, economics, social, politics and defence strategies in business, communication and education. Aid absorption for the national telecommunication SMEs is relatively low; therefore, improvement is needed using analysis on business support cluster of which basis is types of business. In the study, the business support cluster analysis is specifically implemented for Indonesian telecommunication service. The data for the business are obtained from the National Census of Economic (Susenas 2006). The method used to develop cluster model is an Artificial Neural Network (ANN) system called Self-Organizing Maps (SOM) algorithm. Based on Index of Davies Bouldin (IDB), the accuracy level of the cluster model is 0.37 or can be categorized as good. The cluster model is developed to find out telecommunication business clusters that has influence towards the national economy so that it is easier for the government to supervise telecommunication business.

Multistrategy Self-Organizing Map Learning for Classification Problems

PubMed Central

Hasan, S.; Shamsuddin, S. M.

2011-01-01

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test. PMID:21876686

Local Geographic Variation of Public Services Inequality: Does the Neighborhood Scale Matter?

PubMed Central

Wei, Chunzhu; Cabrera-Barona, Pablo; Blaschke, Thomas

2016-01-01

This study aims to explore the effect of the neighborhood scale when estimating public services inequality based on the aggregation of social, environmental, and health-related indicators. Inequality analyses were carried out at three neighborhood scales: the original census blocks and two aggregated neighborhood units generated by the spatial “k”luster analysis by the tree edge removal (SKATER) algorithm and the self-organizing map (SOM) algorithm. Then, we combined a set of health-related public services indicators with the geographically weighted principal components analyses (GWPCA) and the principal components analyses (PCA) to measure the public services inequality across all multi-scale neighborhood units. Finally, a statistical test was applied to evaluate the scale effects in inequality measurements by combining all available field survey data. We chose Quito as the case study area. All of the aggregated neighborhood units performed better than the original census blocks in terms of the social indicators extracted from a field survey. The SKATER and SOM algorithms can help to define the neighborhoods in inequality analyses. Moreover, GWPCA performs better than PCA in multivariate spatial inequality estimation. Understanding the scale effects is essential to sustain a social neighborhood organization, which, in turn, positively affects social determinants of public health and public quality of life. PMID:27706072

FPGA implementation of self organizing map with digital phase locked loops.

PubMed

Hikawa, Hiroomi

2005-01-01

The self-organizing map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small.

Analysis of Induced Polarization effects in airborne TEM data - a case study from central East Greenland

NASA Astrophysics Data System (ADS)

Maack Rasmussen, Thorkild; Brethes, Anaïs; Pierpaolo Guarnieri, Pierpaolo; Bauer, Tobias

2017-04-01

Data from a high-resolution airborne SkyTEM time-domain electromagnetic survey conducted in central East Greenland were analysed. An analysis based on utilization of a Self Organizing Map procedure for response curve characterization and analyses based on data inversion and modelling are presented. The survey was flown in 2013 along the eastern margin of the Jameson Land basin with the purpose of base metal exploration and with sulphide mineralization as target. The survey area comprises crystalline basement to the East and layered Early Triassic to Jurassic sediments to the West. The layers are dipping a few degrees towards West. The Triassic sequence is 1 to 2 km thick and mostly of continental origin. The fluviatile Early Triassic arkoses and conglomerates, the Upper Triassic grey limestone and black shale beds and overlying gypsiferous sandstones and mudstones are known to host disseminated sulphides. E-W oriented lines were flown with an average terrain clearance of 30m and a separation of 300m. The data were initially processed and inverted by SkyTEM Aps. The conductivity models showed some conductive layers as well as induced polarization (IP) effects in the data. IP effects in TEM data reflect the relaxation of polarized charges in the ground which can be good indicators of the presence of metallic particles. Some of these locations were drilled during the following field season but unfortunately did not reveal the presence of mineralization. The aim of this study is therefore to understand the possible causes of these IP effects. Electrical charge accumulation in the ground can be related to the presence of sulphides, oxides or graphite or to the presence of clays or fibrous minerals. Permafrost may also cause IP effects and is then expected to be associated with a highly resistive subsurface. Several characteristics of the transient curves (IP indicators) of the SkyTEM survey were extracted and analysed by using the Kohonen Self-Organizing Map (SOM) technique. SOM is a type of neural network algorithm developed for analysis of non-linear relationships in multivariate data. The basic idea of SOM is to provide a method for easy visualizing of multi-dimensional data. The SOM may be viewed as a two-dimensional grid onto which multi-dimensional input data are projected or mapped from a multi-dimensional space. The space dimension is equal to the number of analysed variables containing the geo-referenced IP indicators that characterise the transient curves. Input data that are similar or close to each other, irrespectively of their geographic location, are mapped to the same or adjacent position in the SOM. Data that belongs to a particular cluster in the SOM space are afterwards mapped into a geographical space. Characteristics of the IP effects can therefore be mapped and spatially compared with the geology of the area. Once IP were identified and located, Cole-Cole parameters were recovered from the airborne TEM data in specific locations by inversion of the TEM data. An interpretation of the derived models is discussed in relation to possible causes of the observed IP effects.

Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps.

PubMed

Mortazavi, Ali; Pepke, Shirley; Jansen, Camden; Marinov, Georgi K; Ernst, Jason; Kellis, Manolis; Hardison, Ross C; Myers, Richard M; Wold, Barbara J

2013-12-01

We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.

Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps

PubMed Central

Mortazavi, Ali; Pepke, Shirley; Jansen, Camden; Marinov, Georgi K.; Ernst, Jason; Kellis, Manolis; Hardison, Ross C.; Myers, Richard M.; Wold, Barbara J.

2013-01-01

We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity. PMID:24170599

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

NASA Astrophysics Data System (ADS)

Hoomod, Haider K.; Kareem Jebur, Tuka

2018-05-01

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

PubMed

Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D

2017-01-01

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches

PubMed Central

Bernard, Jessica A.; Seidler, Rachael D.; Hassevoort, Kelsey M.; Benson, Bryan L.; Welsh, Robert C.; Wiggins, Jillian Lee; Jaeggi, Susanne M.; Buschkuehl, Martin; Monk, Christopher S.; Jonides, John; Peltier, Scott J.

2012-01-01

The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into “motor” and “non-motor” regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure. PMID:22907994

Predictive Multiple Model Switching Control with the Self-Organizing Map

NASA Technical Reports Server (NTRS)

Motter, Mark A.

2000-01-01

A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Extraction of Intraseasonal Phases of the East Asia Summer Monsoon using Self Organizing Maps and their ENSO effects

NASA Astrophysics Data System (ADS)

Chu, J.; Ha, K.; Hameed, S. N.

2011-12-01

We advance the hypothesis that regional characteristics of the East Asian Summer Monsoon (EASM) result from the presence of non-linear coupled features that modulate the seasonal circulation and rainfall at the intraseasonal timescale. To examine this hypothesis, we undertake the analysis of daily EASM variability using a non-linear multivariate data classifying algorithm known as Self Organizing Maps (SOM). SOM is used to locate archetypal circulation states present in a circulation state vector composed of important indices representing subtropical high pressure regions, the lower and upper level wind vectors and vertical and horizontal shear. These so-called nodes on the SOM identify prominent modes of temporal variations across the region Based on an analysis of the various SOM nodes, we identify 4 major intraseasonal phases of the EASM that are located at the far corners of the SOM. The first node describes a circulation state corresponding to weak tropical and subtropical pressure systems, weakened monsoonal winds, and cyclonic upper level vorticity. This mode that is related with large rainfall anomalies in South East China and Southern Japan occurs a few weeks prior to the onset of Changma rains in Korea. Based on its various characteristics, we identify it is as the Meiyu-Baiu phase. At the diagonally opposite corner from the node representing the Meiyu-Baiu phase, the circulation vector is its mirror image. Copious rains occur over Korea during this phase, which we term the post-Changma phase. The third node selected for this analysis represents the Changma-proper over Korea and occurs with a distinct circulation state corresponding to strengthened subtropical high, monsoonal winds and anticyclonic upper level vorticity to the southeast of Korea. The fourth node is diagonally opposite to this one and features a mirror image of the circulation vector. As Korea experiences a dry-spell associated with this SOM node, we refer to it as the dry-spell phase. We further demonstrate that a strong modulation of the Changma and dry-spell phases on the interannual timescales occurs during El Nino and La Nina years. Our results imply that the key to the predictability of the EASM on interannual timescales may lie with the analysis and exploitation of its non-linear characteristics.

A semi-automatic method for analysis of landscape elements using Shuttle Radar Topography Mission and Landsat ETM+ data

NASA Astrophysics Data System (ADS)

Ehsani, Amir Houshang; Quiel, Friedrich

2009-02-01

In this paper, we demonstrate artificial neural networks—self-organizing map (SOM)—as a semi-automatic method for extraction and analysis of landscape elements in the man and biosphere reserve "Eastern Carpathians". The Shuttle Radar Topography Mission (SRTM) collected data to produce generally available digital elevation models (DEM). Together with Landsat Thematic Mapper data, this provides a unique, consistent and nearly worldwide data set. To integrate the DEM with Landsat data, it was re-projected from geographic coordinates to UTM with 28.5 m spatial resolution using cubic convolution interpolation. To provide quantitative morphometric parameters, first-order (slope) and second-order derivatives of the DEM—minimum curvature, maximum curvature and cross-sectional curvature—were calculated by fitting a bivariate quadratic surface with a window size of 9×9 pixels. These surface curvatures are strongly related to landform features and geomorphological processes. Four morphometric parameters and seven Landsat-enhanced thematic mapper (ETM+) bands were used as input for the SOM algorithm. Once the network weights have been randomly initialized, different learning parameter sets, e.g. initial radius, final radius and number of iterations, were investigated. An optimal SOM with 20 classes using 1000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. The effect of randomization of initial weights for optimal SOM was also studied. Feature space analysis, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of landform, based on morphometric analysis, and land use, based on spectral properties. Results were displayed as thematic map of landscape elements according to form, cover and slope. Spectral and morphometric signature analysis with corresponding zoom samples superimposed by contour lines were compared in detail to clarify the role of morphometric parameters to separate landscape elements. The results revealed the efficiency of SOM to integrate SRTM and Landsat data in landscape analysis. Despite the stochastic nature of SOM, the results in this particular study are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible for the same application with consistent results.

Ranked centroid projection: a data visualization approach with self-organizing maps.

PubMed

Yen, G G; Wu, Z

2008-02-01

The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2-D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.

The Classification of Diabetes Mellitus Using Kernel k-means

NASA Astrophysics Data System (ADS)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.

PubMed

Cattinelli, Isabella; Bolzoni, Elena; Barbieri, Carlo; Mari, Flavio; Martin-Guerrero, José David; Soria-Olivas, Emilio; Martinez-Martinez, José Maria; Gomez-Sanchis, Juan; Amato, Claudia; Stopper, Andrea; Gatti, Emanuele

2012-03-01

The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self-Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy-readable informative form. A SOM is a computational technique that allows projecting high-dimensional datasets to a two-dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self-organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high-dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30-dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.

Mapping forest soil organic matter on New Jersey's coastal plain

Treesearch

Brian J. Clough; Edwin J. Green; Richard B. Lathrop

2012-01-01

Managing forest soil organic matter (SOM) stocks is a vital strategy for reducing the impact of anthropogenic carbon dioxide emissions. However, the SOM pool is highly variable, and developing accurate estimates to guide management decisions has remained a difficult task. We present the results of a spatial model designed to map soil organic matter for all forested...

Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps.

PubMed

Mei, Paulo Afonso; de Carvalho Carneiro, Cleyton; Fraser, Stephen J; Min, Li Li; Reis, Fabiano

2015-12-15

To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis. Self-Organizing Maps (SOM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge. We used SOM to analyze T1, T2 and FLAIR acquisitions from two MRI machines in our service from 14 patients with brain tumors confirmed by biopsies--three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SOM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters. Damaged tissue was separated from the normal tissue using the SOM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor--like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger. Our results showed that SOM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors. Copyright © 2015. Published by Elsevier B.V.

Using self-organizing maps to classify humpback whale song units and quantify their similarity.

PubMed

Allen, Jenny A; Murray, Anita; Noad, Michael J; Dunlop, Rebecca A; Garland, Ellen C

2017-10-01

Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002 to 2014, a subset of vocal signals was acoustically measured and then classified using a Self-Organizing Map (SOM). The SOM created (1) an acoustic dictionary of units representing the song's repertoire, and (2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets and can be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification.

Application of self-organizing maps to the study of U-Zr-Ti-Nb distribution in sandstone-hosted uranium ores

NASA Astrophysics Data System (ADS)

Klus, Jakub; Pořízka, Pavel; Prochazka, David; Mikysek, Petr; Novotný, Jan; Novotný, Karel; Slobodník, Marek; Kaiser, Jozef

2017-05-01

This paper presents a novel approach for processing the spectral information obtained from high-resolution elemental mapping performed by means of Laser-Induced Breakdown Spectroscopy. The proposed methodology is aimed at the description of possible elemental associations within a heterogeneous sample. High-resolution elemental mapping provides a large number of measurements. Moreover, typical laser-induced plasma spectrum consists of several thousands of spectral variables. Analysis of heterogeneous samples, where valuable information is hidden in a limited fraction of sample mass, requires special treatment. The sample under study is a sandstone-hosted uranium ore that shows irregular distribution of ore elements such as zirconium, titanium, uranium and niobium. Presented processing methodology shows the way to reduce the dimensionality of data and retain the spectral information by utilizing self-organizing maps (SOM). The spectral information from SOM is processed further to detect either simultaneous or isolated presence of elements. Conclusions suggested by SOM are in good agreement with geological studies of mineralization phases performed at the deposit. Even deeper investigation of the SOM results enables discrimination of interesting measurements and reveals new possibilities in the visualization of chemical mapping information. Suggested approach improves the description of elemental associations in mineral phases, which is crucial for the mining industry.

Using patient data similarities to predict radiation pneumonitis via a self-organizing map

NASA Astrophysics Data System (ADS)

Chen, Shifeng; Zhou, Sumin; Yin, Fang-Fang; Marks, Lawrence B.; Das, Shiva K.

2008-01-01

This work investigates the use of the self-organizing map (SOM) technique for predicting lung radiation pneumonitis (RP) risk. SOM is an effective method for projecting and visualizing high-dimensional data in a low-dimensional space (map). By projecting patients with similar data (dose and non-dose factors) onto the same region of the map, commonalities in their outcomes can be visualized and categorized. Once built, the SOM may be used to predict pneumonitis risk by identifying the region of the map that is most similar to a patient's characteristics. Two SOM models were developed from a database of 219 lung cancer patients treated with radiation therapy (34 clinically diagnosed with Grade 2+ pneumonitis). The models were: SOMall built from all dose and non-dose factors and, for comparison, SOMdose built from dose factors alone. Both models were tested using ten-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Models SOMall and SOMdose yielded ten-fold cross-validated ROC areas of 0.73 (sensitivity/specificity = 71%/68%) and 0.67 (sensitivity/specificity = 63%/66%), respectively. The significant difference between the cross-validated ROC areas of these two models (p < 0.05) implies that non-dose features add important information toward predicting RP risk. Among the input features selected by model SOMall, the two with highest impact for increasing RP risk were: (a) higher mean lung dose and (b) chemotherapy prior to radiation therapy. The SOM model developed here may not be extrapolated to treatment techniques outside that used in our database, such as several-field lung intensity modulated radiation therapy or gated radiation therapy.

An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.

PubMed

Hsu, Arthur L; Tang, Sen-Lin; Halgamuge, Saman K

2003-11-01

Current Self-Organizing Maps (SOMs) approaches to gene expression pattern clustering require the user to predefine the number of clusters likely to be expected. Hierarchical clustering methods used in this area do not provide unique partitioning of data. We describe an unsupervised dynamic hierarchical self-organizing approach, which suggests an appropriate number of clusters, to perform class discovery and marker gene identification in microarray data. In the process of class discovery, the proposed algorithm identifies corresponding sets of predictor genes that best distinguish one class from other classes. The approach integrates merits of hierarchical clustering with robustness against noise known from self-organizing approaches. The proposed algorithm applied to DNA microarray data sets of two types of cancers has demonstrated its ability to produce the most suitable number of clusters. Further, the corresponding marker genes identified through the unsupervised algorithm also have a strong biological relationship to the specific cancer class. The algorithm tested on leukemia microarray data, which contains three leukemia types, was able to determine three major and one minor cluster. Prediction models built for the four clusters indicate that the prediction strength for the smaller cluster is generally low, therefore labelled as uncertain cluster. Further analysis shows that the uncertain cluster can be subdivided further, and the subdivisions are related to two of the original clusters. Another test performed using colon cancer microarray data has automatically derived two clusters, which is consistent with the number of classes in data (cancerous and normal). JAVA software of dynamic SOM tree algorithm is available upon request for academic use. A comparison of rectangular and hexagonal topologies for GSOM is available from http://www.mame.mu.oz.au/mechatronics/journalinfo/Hsu2003supp.pdf

Data-driven cluster reinforcement and visualization in sparsely-matched self-organizing maps.

PubMed

Manukyan, Narine; Eppstein, Margaret J; Rizzo, Donna M

2012-05-01

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of current methods for displaying interneuron distances. In this brief, we introduce a new cluster reinforcement (CR) phase for sparsely-matched SOMs. The CR phase amplifies within-cluster similarity in an unsupervised, data-driven manner. Discontinuities in the resulting map correspond to between-cluster distances and are stored in a boundary (B) matrix. We describe a new hierarchical visualization of cluster boundaries displayed directly on feature maps, which requires no further clustering beyond what was implicitly accomplished during self-organization in SOM training. We use a synthetic benchmark problem and previously published microbial community profile data to demonstrate the benefits of the proposed methods.

Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps

PubMed Central

Cheu, Ruey Long; Guo, Xiucheng; Romo, Alicia

2014-01-01

A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower's velocity, relative velocity, and gap) while the output signals represented the response (the follower's acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity. PMID:25538767

[Assessment of skin aging grading based on computer vision].

PubMed

Li, Lingyu; Xue, Jinxia; He, Xiangqian; Zhang, Sheng; Fan, Chu

2017-06-01

Skin aging is the most intuitive and obvious sign of the human aging processes. Qualitative and quantitative determination of skin aging is of particular importance for the evaluation of human aging and anti-aging treatment effects. To solve the problem of subjectivity of conventional skin aging grading methods, the self-organizing map (SOM) network was used to explore an automatic method for skin aging grading. First, the ventral forearm skin images were obtained by a portable digital microscope and two texture parameters, i.e. , mean width of skin furrows and the number of intersections were extracted by image processing algorithm. Then, the values of texture parameters were taken as inputs of SOM network to train the network. The experimental results showed that the network achieved an overall accuracy of 80.8%, compared with the aging grading results by human graders. The designed method appeared to be rapid and objective, which can be used for quantitative analysis of skin images, and automatic assessment of skin aging grading.

Guasom Analysis Of The Alhambra Survey

NASA Astrophysics Data System (ADS)

Garabato, Daniel; Manteiga, Minia; Dafonte, Carlos; Álvarez, Marco A.

2017-10-01

GUASOM is a data mining tool designed for knowledge discovery in large astronomical spectrophotometric archives developed in the framework of Gaia DPAC (Data Processing and Analysis Consortium). Our tool is based on a type of unsupervised learning Artificial Neural Networks named Self-organizing maps (SOMs). SOMs permit the grouping and visualization of big amount of data for which there is no a priori knowledge and hence they are very useful for analyzing the huge amount of information present in modern spectrophotometric surveys. SOMs are used to organize the information in clusters of objects, as homogeneously as possible according to their spectral energy distributions, and to project them onto a 2D grid where the data structure can be visualized. Each cluster has a representative, called prototype which is a virtual pattern that better represents or resembles the set of input patterns belonging to such a cluster. Prototypes make easier the task of determining the physical nature and properties of the objects populating each cluster. Our algorithm has been tested on the ALHAMBRA survey spectrophotometric observations, here we present our results concerning the survey segmentation, visualization of the data structure, separation between types of objects (stars and galaxies), data homogeneity of neurons, cluster prototypes, redshift distribution and crossmatch with other databases (Simbad).

Visualizing Intrapopulation Hematopoietic Cell Heterogeneity with Self-Organizing Maps of SIMS Data.

PubMed

Mirshafiee, Vahid; Harley, Brendan A C; Kraft, Mary L

2018-05-07

Characterization of the heterogeneity within stem cell populations, which affects their differentiation potential, is necessary for the design of artificial cultures for stem cell expansion. In this study, we assessed whether self-organizing maps (SOMs) of single-cell time-of-flight secondary ion mass spectrometry (TOF-SIMS) data provide insight into the spectral, and thus the related functional heterogeneity between and within three hematopoietic cell populations. SOMs were created of TOF-SIMS data from individual hematopoietic stem and progenitor cells (HSPCs), lineage-committed common lymphoid progenitors (CLPs), and fully differentiated B cells that had been isolated from murine bone marrow via conventional flow cytometry. The positions of these cells on the SOMs and the spectral variation between adjacent map units, shown on the corresponding unified distance matrix (U-matrix), indicated the CLPs exhibited the highest intrapopulation spectral variation, regardless of the age of the donor mice. SOMs of HSPCs, CLPs, and B cells isolated from young and old mice using the same surface antigen profiles revealed the HSPCs exhibited the most age-related spectral variation, whereas B cells exhibited the least. These results demonstrate that SOMs of single-cell spectra enable characterizing the heterogeneity between and within cell populations that lie along distinct differentiation pathways.

Spatial distribution of pH and organic matter in urban soils and its implications on site-specific land uses in Xuzhou, China.

PubMed

Mao, Yingming; Sang, Shuxun; Liu, Shiqi; Jia, Jinlong

2014-05-01

The spatial variation of soil pH and soil organic matter (SOM) in the urban area of Xuzhou, China, was investigated in this study. Conventional statistics, geostatistics, and a geographical information system (GIS) were used to produce spatial distribution maps and to provide information about land use types. A total of 172 soil samples were collected based on grid method in the study area. Soil pH ranged from 6.47 to 8.48, with an average of 7.62. SOM content was very variable, ranging from 3.51 g/kg to 17.12 g/kg, with an average of 8.26 g/kg. Soil pH followed a normal distribution, while SOM followed a log-normal distribution. The results of semi-variograms indicated that soil pH and SOM had strong (21%) and moderate (44%) spatial dependence, respectively. The variogram model was spherical for soil pH and exponential for SOM. The spatial distribution maps were achieved using kriging interpolation. The high pH and high SOM tended to occur in the mixed forest land cover areas such as those in the southwestern part of the urban area, while the low values were found in the eastern and the northern parts, probably due to the effect of industrial and human activities. In the central urban area, the soil pH was low, but the SOM content was high, which is mainly attributed to the disturbance of regional resident activities and urban transportation. Furthermore, anthropogenic organic particles are possible sources of organic matter after entering the soil ecosystem in urban areas. These maps provide useful information for urban planning and environmental management. Copyright © 2014 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Arctic Ocean CO2 uptake: an improved multiyear estimate of the air-sea CO2 flux incorporating chlorophyll a concentrations

NASA Astrophysics Data System (ADS)

Yasunaka, Sayaka; Siswanto, Eko; Olsen, Are; Hoppema, Mario; Watanabe, Eiji; Fransson, Agneta; Chierici, Melissa; Murata, Akihiko; Lauvset, Siv K.; Wanninkhof, Rik; Takahashi, Taro; Kosugi, Naohiro; Omar, Abdirahman M.; van Heuven, Steven; Mathis, Jeremy T.

2018-03-01

We estimated monthly air-sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014. This was done by mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll a concentration (Chl a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. We applied new algorithms for extracting Chl a from satellite remote sensing reflectance with close examination of uncertainty of the obtained Chl a values. The overall relationship between pCO2w and Chl a was negative, whereas the relationship varied among seasons and regions. The addition of Chl a as a parameter in the SOM process enabled us to improve the estimate of pCO2w, particularly via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. As a result of the inclusion of Chl a, the uncertainty in the CO2 flux estimate was reduced, with a net annual Arctic Ocean CO2 uptake of 180 ± 130 Tg C yr-1. Seasonal to interannual variation in the CO2 influx was also calculated.

Time-space and cognition-space transformations for transportation network analysis based on multidimensional scaling and self-organizing map

NASA Astrophysics Data System (ADS)

Hong, Zixuan; Bian, Fuling

2008-10-01

Geographic space, time space and cognition space are three fundamental and interrelated spaces in geographic information systems for transportation. However, the cognition space and its relationships to the time space and geographic space are often neglected. This paper studies the relationships of these three spaces in urban transportation system from a new perspective and proposes a novel MDS-SOM transformation method which takes the advantages of the techniques of multidimensional scaling (MDS) and self-organizing map (SOM). The MDS-SOM transformation framework includes three kinds of mapping: the geographic-time transformation, the cognition-time transformation and the time-cognition transformation. The transformations in our research provide a better understanding of the interactions of these three spaces and beneficial knowledge is discovered to help the transportation analysis and decision supports.

Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

Efficient hyperspectral image segmentation using geometric active contour formulation

NASA Astrophysics Data System (ADS)

Albalooshi, Fatema A.; Sidike, Paheding; Asari, Vijayan K.

2014-10-01

In this paper, we present a new formulation of geometric active contours that embeds the local hyperspectral image information for an accurate object region and boundary extraction. We exploit self-organizing map (SOM) unsupervised neural network to train our model. The segmentation process is achieved by the construction of a level set cost functional, in which, the dynamic variable is the best matching unit (BMU) coming from SOM map. In addition, we use Gaussian filtering to discipline the deviation of the level set functional from a signed distance function and this actually helps to get rid of the re-initialization step that is computationally expensive. By using the properties of the collective computational ability and energy convergence capability of the active control models (ACM) energy functional, our method optimizes the geometric ACM energy functional with lower computational time and smoother level set function. The proposed algorithm starts with feature extraction from raw hyperspectral images. In this step, the principal component analysis (PCA) transformation is employed, and this actually helps in reducing dimensionality and selecting best sets of the significant spectral bands. Then the modified geometric level set functional based ACM is applied on the optimal number of spectral bands determined by the PCA. By introducing local significant spectral band information, our proposed method is capable to force the level set functional to be close to a signed distance function, and therefore considerably remove the need of the expensive re-initialization procedure. To verify the effectiveness of the proposed technique, we use real-life hyperspectral images and test our algorithm in varying textural regions. This framework can be easily adapted to different applications for object segmentation in aerial hyperspectral imagery.

On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

PubMed Central

Abdelsamea, Mohammed M.; Gnecco, Giorgio; Gaber, Mohamed Medhat; Elyan, Eyad

2015-01-01

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses. PMID:25960736

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective.

PubMed

Faigl, Jan

2016-01-01

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

PubMed Central

Faigl, Jan

2016-01-01

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning. PMID:27340395

Self-Organizing Maps: A Data Mining Tool for the Analysis of Airborne Geophysical Data Collected over the Brazilian Amazon

NASA Astrophysics Data System (ADS)

Carneiro, C.; Fraser, S. J.; Crosta, A. P.; Silva, A.; Barros, C.

2011-12-01

Regional airborne geophysical data sets are being collected worldwide to promote mineral exploration and resource development. These data sets often are collected over highly prospective terranes, where access is limited or there are environmental concerns. Such regional surveys typically consist of two or more sensor packages being flown in an aircraft over the survey area and vast amounts of near-continuous data can be acquired in a relatively short time. Increasingly, there is also a need to process such data in a timely fashion to demonstrate the data's value and indicate the potential return or value of the survey to the funding agency. To assist in the timely analysis of such regional data sets, we have used an exploratory data mining approach: the Self Organizing Map (SOM). Because SOM is based on vector quantization and measures of vector similarity, it is an ideal tool to analyze a data set consisting of disparate geophysical input parameters to look for relationships and trends. We report on our use of SOM to analyze part of a regional airborne geophysical survey collected over the prospective Anapu-Tuere region of the Brazilian Amazon. Magnetic and spectrometric gamma ray data were used as input to our SOM analysis, and the results used to discriminate and identify various rock types and produce a "pseudo" geological map over the study area. The ability of SOM to define discrete domains of rock-types with similar properties allowed us to expand upon existing geological knowledge of the area for mapping purposes; and, often it was the combination of the magnetic and radiometric responses that identified a lithology's unique response. One particular unit was identified that had an association with known gold mineralization, which consequently highlighted the prospectivity of that unit elsewhere in the survey area. Our results indicate that SOM can be used for the semi-automatic analysis of regional airborne geophysical data to assist in geological mapping and the identification of potentially significant relationships.

SOM Classification of Martian TES Data

NASA Technical Reports Server (NTRS)

Hogan, R. C.; Roush, T. L.

2002-01-01

A classification scheme based on unsupervised self-organizing maps (SOM) is described. Results from its application to the ASU mineral spectral database are presented. Applications to the Martian Thermal Emission Spectrometer data are discussed. Additional information is contained in the original extended abstract.

Antarctic Sea Ice-Atmosphere Interactions: A Self-organizing Map-based Perspective

NASA Astrophysics Data System (ADS)

Reusch, D. B.

2005-12-01

Interactions between the ocean, sea ice and the atmosphere are a significant component of the dynamic nature of the Earth's climate system. Self-organizing maps (SOMs), an analysis tool from the field of artificial neural networks, have been used to study variability in Antarctic sea ice extent and the West Antarctic atmospheric circulation, plus the relationship and interactions between these two systems. Self-organizing maps enable unsupervised classification of large, multivariate/multidimensional data sets, e.g., time series of the atmospheric circulation or sea-ice extent, into a fixed number of distinct generalized states or modes, organized spatially as a two-dimensional grid, that are representative of the input data. When applied to atmospheric data, the analysis yields a nonlinear classification of the continuum of atmospheric conditions. In contrast to principal component analysis, SOMs do not force orthogonality or require subjective rotations to produce interpretable patterns. Twenty four years (1973-96) of monthly sea ice extent data (10 deg longitude bands; Simmonds and Jacka, 1995) were analyzed with a 30-node SOM. The resulting set of generalized patterns concisely captures the spatial and temporal variability in this data. An example of the former is variability in the longitudinal region of greatest extent. The SOM patterns readily show that there are multiple spatial patterns corresponding to "greatest extent conditions". Temporal variability is examined by creating frequency maps (i.e., which patterns occur most often) by month. With the annual cycle still in the data, the monthly frequency maps show a cycle moving from least extent, through expansion to greatest extent and back through retreat. When plotted in "SOM space", month-to-month transitions occur at different rates of change, suggesting that there is variability in the rate of change in extent at different times of the year, e.g., retreat in January is faster than November. Twenty five years (1977-2001) of monthly 500 mb temperature and pressure data (from the ECMWF 40-year reanalysis, ERA-40) from a region centered on the Antarctic Peninsula were analyzed independently for a separate SOMs-based study. Dominant SOM temperature patterns include the expected summer warmth and winter cold, plus "dipoles" of warm Atlantic (Pacific) and cold Pacific (Atlantic) sectors (with corresponding pressure patterns). Temporally, there is the expected annual progression from warmth, through cooling and back to warmth, with no particularly predominant patterns in many of the monthly frequency maps when the full record is used. Stratifying by high/low values of the Southern Oscillation Index (SOI) suggests that the spatial patterns of cooling and warming may be related to conditions in the tropical Pacific: in a low SOI year (1987), cooling and warming both begin in the Atlantic sector, with the opposite true in a high SOI year (1989). Further study of this aspect is planned. In addition to direct comparisons of the SOM analysis results from each study, a joint SOM analysis will be done on the combined data sets, exploiting the flexibility and power of this technique. We anticipate additional useful insights into the joint variability and relationships between Antarctic sea ice and the overlying atmosphere through this expanded analysis.

Organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in Cyprinidae fish: Towards hints of their arrangements using advanced classification methods.

PubMed

Romanić, Snježana Herceg; Vuković, Gordana; Klinčić, Darija; Sarić, Marijana Matek; Župan, Ivan; Antanasijević, Davor; Popović, Aleksandar

2018-05-18

To tackle the ever-present global concern regarding human exposure to persistent organic pollutants (POPs) via food products, this study strived to indicate associations between organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in lake-fish tissue depending on the species and sampling season. Apart from the monitoring initiatives recommended in the Global Monitoring Plan for POPs, the study discussed 7 OCPs and 18 PCB congeners determined in three Cyprinidae species (rudd, carp, and Prussian carp) from Vransko Lake (Croatia), which are widely domesticated and reared as food fish across Europe and Asia. We exploit advanced classification algorithms, the Kohonen self-organizing maps (SOM) and Decision Trees (DT), to search for POP patterns typical for the investigated species. As indicated by SOM, some of the dioxin-like and non-dioxin-like PCBs (PCB-28, PCB-74, PCB-52, PCB-101, PCB-105, PCB-114, PCB-118, PCB-156 and PCB-157), α-HCH and β-HCH caused dissimilarities among fish species, but regardless of their weight and length. To support these suggestions, DT analysis sequenced the fish species and seasons based on the concentration of heavier congeners. The presented assumptions indicated that the supplemental application of SOM and DT offers advantageous features over the usually rough interpretation of POPs pattern and over the single use of the methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

2017-04-01

Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of rain events. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

Digital terrain model generalization incorporating scale, semantic and cognitive constraints

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Papadogiorgaki, Maria

2014-05-01

Cartographic generalization is a well-known process accommodating spatial data compression, visualization and comprehension under various scales. In the last few years, there are several international attempts to construct tangible GIS systems, forming real 3D surfaces using a vast number of mechanical parts along a matrix formation (i.e., bars, pistons, vacuums). Usually, moving bars upon a structured grid push a stretching membrane resulting in a smooth visualization for a given surface. Most of these attempts suffer either in their cost, accuracy, resolution and/or speed. Under this perspective, the present study proposes a surface generalization process that incorporates intrinsic constrains of tangible GIS systems including robotic-motor movement and surface stretching limitations. The main objective is to provide optimized visualizations of 3D digital terrain models with minimum loss of information. That is, to minimize the number of pixels in a raster dataset used to define a DTM, while reserving the surface information. This neighborhood type of pixel relations adheres to the basics of Self Organizing Map (SOM) artificial neural networks, which are often used for information abstraction since they are indicative of intrinsic statistical features contained in the input patterns and provide concise and characteristic representations. Nevertheless, SOM remains more like a black box procedure not capable to cope with possible particularities and semantics of the application at hand. E.g. for coastal monitoring applications, the near - coast areas, surrounding mountains and lakes are more important than other features and generalization should be "biased"-stratified to fulfill this requirement. Moreover, according to the application objectives, we extend the SOM algorithm to incorporate special types of information generalization by differentiating the underlying strategy based on topologic information of the objects included in the application. The final research scheme comprises of the combination of SOM with the variations of other widely used generalization algorithms. For instance, an adaptation of the Douglas-Peucker line simplification method in 3D data is used in order to reduce the initial nodes, while maintaining their actual coordinates. Furthermore, additional methods are deployed, aiming to corroborate and verify the significance of each node, such as mathematical algorithms exploiting the pixel's nearest neighbors. Finally, besides the quantitative evaluation of error vs information preservation in a DTM, cognitive inputs from geoscience experts are incorporated in order to test, fine-tune and advance our algorithm. Under the described strategy that incorporates mechanical, topology, semantic and cognitive restrains, results demonstrate the necessity to integrate these characteristics in describing raster DTM surfaces. Acknowledgements: This work is partially supported under the framework of the "Cooperation 2011" project ATLANTAS (11_SYN_6_1937) funded from the Operational Program "Competitiveness and Entrepreneurship" (co-funded by the European Regional Development Fund (ERDF)) and managed by the Greek General Secretariat for Research and Technology.

Automated Classification of Thermal Infrared Spectra Using Self-organizing Maps

NASA Technical Reports Server (NTRS)

Roush, Ted L.; Hogan, Robert

2006-01-01

Existing and planned space missions to a variety of planetary and satellite surfaces produce an ever increasing volume of spectral data. Understanding the scientific informational content in this large data volume is a daunting task. Fortunately various statistical approaches are available to assess such data sets. Here we discuss an automated classification scheme based on Kohonen Self-organizing maps (SOM) we have developed. The SUM process produces an output layer were spectra having similar properties lie in close proximity to each other. One major effort is partitioning this output layer into appropriate regions. This is prefonned by defining dosed regions based upon the strength of the boundaries between adjacent cells in the SOM output layer. We use the Davies-Bouldin index as a measure of the inter-class similarities and intra-class dissimilarities that determines the optimum partition of the output layer, and hence number of SOM clusters. This allows us to identify the natural number of clusters formed from the spectral data. Mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) are used to test and evaluate the classification scheme. We label the library sample spectra in a hierarchical scheme with class, subclass, and mineral group names. We use a portion of the spectra to train the SOM, i.e. produce the output layer, while the remaining spectra are used to test the SOM. The test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. We then evaluate these assignments to assess the scientific meaning and accuracy of the derived SOM classes as they relate to the labels. We demonstrate that unsupervised classification by SOMs can be a useful component in autonomous systems designed to identify mineral species from reflectance and emissivity spectra in the therrnal IR.

Condition monitoring of 3G cellular networks through competitive neural models.

PubMed

Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

2005-09-01

We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

Attractors in Sequence Space: Agent-Based Exploration of MHC I Binding Peptides.

PubMed

Jäger, Natalie; Wisniewska, Joanna M; Hiss, Jan A; Freier, Anja; Losch, Florian O; Walden, Peter; Wrede, Paul; Schneider, Gisbert

2010-01-12

Ant Colony Optimization (ACO) is a meta-heuristic that utilizes a computational analogue of ant trail pheromones to solve combinatorial optimization problems. The size of the ant colony and the representation of the ants' pheromone trails is unique referring to the given optimization problem. In the present study, we employed ACO to generate novel peptides that stabilize MHC I protein on the plasma membrane of a murine lymphoma cell line. A jury of feedforward neural network classifiers served as fitness function for peptide design by ACO. Bioactive murine MHC I H-2K(b) stabilizing as well as nonstabilizing octapeptides were designed, synthesized and tested. These peptides reveal residue motifs that are relevant for MHC I receptor binding. We demonstrate how the performance of the implemented ACO algorithm depends on the colony size and the size of the search space. The actual peptide design process by ACO constitutes a search path in sequence space that can be visualized as trajectories on a self-organizing map (SOM). By projecting the sequence space on a SOM we visualize the convergence of the different solutions that emerge during the optimization process in sequence space. The SOM representation reveals attractors in sequence space for MHC I binding peptides. The combination of ACO and SOM enables systematic peptide optimization. This technique allows for the rational design of various types of bioactive peptides with minimal experimental effort. Here, we demonstrate its successful application to the design of MHC-I binding and nonbinding peptides which exhibit substantial bioactivity in a cell-based assay. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multi-Scale Voxel Segmentation for Terrestrial Lidar Data within Marshes

NASA Astrophysics Data System (ADS)

Nguyen, C. T.; Starek, M. J.; Tissot, P.; Gibeaut, J. C.

2016-12-01

The resilience of marshes to a rising sea is dependent on their elevation response. Terrestrial laser scanning (TLS) is a detailed topographic approach for accurate, dense surface measurement with high potential for monitoring of marsh surface elevation response. The dense point cloud provides a 3D representation of the surface, which includes both terrain and non-terrain objects. Extraction of topographic information requires filtering of the data into like-groups or classes, therefore, methods must be incorporated to identify structure in the data prior to creation of an end product. A voxel representation of three-dimensional space provides quantitative visualization and analysis for pattern recognition. The objectives of this study are threefold: 1) apply a multi-scale voxel approach to effectively extract geometric features from the TLS point cloud data, 2) investigate the utility of K-means and Self Organizing Map (SOM) clustering algorithms for segmentation, and 3) utilize a variety of validity indices to measure the quality of the result. TLS data were collected at a marsh site along the central Texas Gulf Coast using a Riegl VZ 400 TLS. The site consists of both exposed and vegetated surface regions. To characterize structure of the point cloud, octree segmentation is applied to create a tree data structure of voxels containing the points. The flexibility of voxels in size and point density makes this algorithm a promising candidate to locally extract statistical and geometric features of the terrain including surface normal and curvature. The characteristics of the voxel itself such as the volume and point density are also computed and assigned to each point as are laser pulse characteristics. The features extracted from the voxelization are then used as input for clustering of the points using the K-means and SOM clustering algorithms. Optimal number of clusters are then determined based on evaluation of cluster separability criterions. Results for different combinations of the feature space vector and differences between K-means and SOM clustering will be presented. The developed method provides a novel approach for compressing TLS scene complexity in marshes, such as for vegetation biomass studies or erosion monitoring.

Visualizing the Topical Structure of the Medical Sciences: A Self-Organizing Map Approach

PubMed Central

Skupin, André; Biberstine, Joseph R.; Börner, Katy

2013-01-01

Background We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues. Methodology Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains. Conclusions Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid. PMID:23554924

SOMKE: kernel density estimation over data streams by sequences of self-organizing maps.

PubMed

Cao, Yuan; He, Haibo; Man, Hong

2012-08-01

In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to obtain well-defined data clusters to estimate the underlying probability distributions of incoming data streams. The main idea of this paper is to build a series of SOMs over the data streams via two operations, that is, creating and merging the SOM sequences. The creation phase produces the SOM sequence entries for windows of the data, which obtains clustering information of the incoming data streams. The size of the SOM sequences can be further reduced by combining the consecutive entries in the sequence based on the measure of Kullback-Leibler divergence. Finally, the probability density functions over arbitrary time periods along the data streams can be estimated using such SOM sequences. We compare SOMKE with two other KDE methods for data streams, the M-kernel approach and the cluster kernel approach, in terms of accuracy and processing time for various stationary data streams. Furthermore, we also investigate the use of SOMKE over nonstationary (evolving) data streams, including a synthetic nonstationary data stream, a real-world financial data stream and a group of network traffic data streams. The simulation results illustrate the effectiveness and efficiency of the proposed approach.

A new look at patient satisfaction: learning from self-organizing maps.

PubMed

Voutilainen, Ari; Kvist, Tarja; Sherwood, Paula R; Vehviläinen-Julkunen, Katri

2014-01-01

To some extent, results always depend on the methods used, and the complete picture of the phenomenon of interest can be drawn only by combining results of different data processing techniques. This emphasizes the use of a wide arsenal of methods for processing and analyzing patient satisfaction surveys. The purpose of this study was to introduce the self-organizing map (SOM) to nursing science and to illustrate the use of the SOM with patient satisfaction data. The SOM is a widely used artificial neural network suitable for clustering and exploring all kind of data sets. The study was partly a secondary analysis of data collected for the Attractive and Safe Hospital Study from four Finnish hospitals in 2008 and 2010 using the Revised Humane Caring Scale. The sample consisted of 5,283 adult patients. The SOM was used to cluster the data set according to (a) respondents and (b) questionnaire items. The SOM was also used as a preprocessor for multinomial logistic regression. An analysis of missing data was carried out to improve the data interpretation. Combining results of the two SOMs and the logistic regression revealed associations between the level of satisfaction, different components of satisfaction, and item nonresponse. The common conception that the relationship between patient satisfaction and age is positive may partly be due to positive association between the tendency of item nonresponse and age. The SOM proved to be a useful method for clustering a questionnaire data set even when the data set was low dimensional per se. Inclusion of empty responses in analyses may help to detect possible misleading noncausative relationships.

A Graphical, Self-Organizing Approach to Classifying Electronic Meeting Output.

ERIC Educational Resources Information Center

Orwig, Richard E.; Chen, Hsinchun; Nunamaker, Jay F., Jr.

1997-01-01

Describes research using an artificial intelligence approach in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information…

Application of Clustering Algorithms to Observed and Simulated Daily Precipitation over the Tropical and Southern Pacific Ocean

NASA Astrophysics Data System (ADS)

Pike, M.; Lintner, B. R.

2017-12-01

We apply two data organization methods, self-organizing maps (SOMs) and k-means clustering with linear unidimensional scaling (k-means+LUS), to identify and organize the spatial patterns inherent in daily austral summer (December-January-February or DJF) rainfall over the tropical and southern Pacific Ocean basins from Tropical Rainfall Measuring Mission (TRMM) satellite observations. For either a 2x2 SOM or k = 4 clustering of all available DJFs from 1998-2013, we find an El Niño/Southern Oscillation (ENSO) signature, with pairs of maps reflecting either El Niño or La Niña phase conditions. Within each of the ENSO-phase pairs, one map favors Intertropical Convergence Zone (ITCZ)-active conditions, in which precipitation is more intense over the ITCZ region compared to the South Pacific Convergence Zone (SPCZ) region, while the remaining one is SPCZ-active. The SPCZ-active maps show a spatial translation of the principal SPCZ diagonal consistent with the impacts of El Niño/Southern Oscillation (ENSO) or analogous low-frequency modes of variability on the SPCZ as shown in prior studies. Because of the dominant impact of ENSO, we further apply these methods separately on subsets of rainfall data for each ENSO phase. While the overall position of the SPCZ is sensitive to the phase of ENSO, within each phase, more- or less-steeply sloped SPCZ diagonals may occur. Thus, while the mean position of the SPCZ is largely controlled by ENSO phase, the distinct orientations of the SPCZ within the same ENSO phase point to higher-frequency modulation of SPCZ slope. To investigate the nature of these further, we construct composites of pressure-level winds and specific humidity from the Climate Forecast System Reanalysis (CFSR) associated with the rainfall patterns. For either SOM or kmeans-based composites, we find large-scale dynamics and moisture signatures that are consistent with the rainfall patterns and which we interpret in terms of previously described mechanisms of SPCZ variability. By progressively increasing the number of clusters, patterns reminiscent of Rossby wave propagation begin to emerge. To further investigate the connection to propagation, we examine upper air vorticity composites in relationship to the periodic enhancements of SPCZ precipitation which appear to be independent of ENSO.

Identifying localized and scale-specific multivariate controls of soil organic matter variations using multiple wavelet coherence.

PubMed

Zhao, Ruiying; Biswas, Asim; Zhou, Yin; Zhou, Yue; Shi, Zhou; Li, Hongyi

2018-06-23

Environmental factors have shown localized and scale-dependent controls over soil organic matter (SOM) distribution in the landscape. Previous studies have explored the relationships between SOM and individual controlling factors; however, few studies have indicated the combined control from multiple environmental factors. In this study, we compared the localized and scale-dependent univariate and multivariate controls of SOM along two long transects (northeast, NE transect and north, N transect) from China. Bivariate wavelet coherence (BWC) between SOM and individual factors and multiple wavelet coherence (MWC) between SOM and factor combinations were calculated. Average wavelet coherence (AWC) and percent area of significant coherence (PASC) were used to assess the relative dominance of individual and a combination of factors to explain SOM variations at different scales and locations. The results showed that (in BWC analysis) mean annual temperature (MAT) with the largest AWC (0.39) and PASC (16.23%) was the dominant factor in explaining SOM variations along the NE transect. The topographic wetness index (TWI) was the dominant factor (AWC = 0.39 and PASC = 20.80%) along the N transect. MWC identified the combination of Slope, net primary production (NPP) and mean annual precipitation (MAP) as the most important combination in explaining SOM variations along the NE transect with a significant increase in AWC and PASC at different scales and locations (e.g. AWC = 0.91 and PASC = 58.03% at all scales). The combination of TWI, NPP and normalized difference vegetation index (NDVI) was the most influential along the N transect (AWC = 0.83 and PASC = 32.68% at all scales). The results indicated that the combined controls of environmental factors on SOM variations at different scales and locations in a large area can be identified by MWC. This is promising for a better understanding of the multivariate controls in SOM variations at larger spatial scales and may improve the capability of digital soil mapping. Copyright © 2018 Elsevier B.V. All rights reserved.

Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lai, Wen-li; Wang, Hong-rui; Wang, Cheng

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated withmore » waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.« less

Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

DOE PAGES

Lai, Wen-li; Wang, Hong-rui; Wang, Cheng; ...

2017-05-05

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated withmore » waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.« less

Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps.

PubMed

Hadjisolomou, Ekaterini; Stefanidis, Konstantinos; Papatheodorou, George; Papastergiadou, Evanthia

2018-03-19

During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA), cluster analysis, and a self-organizing map (SOM) were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters' relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl - a), water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.

Cloud fraction at the ARM SGP site: reducing uncertainty with self-organizing maps

NASA Astrophysics Data System (ADS)

Kennedy, Aaron D.; Dong, Xiquan; Xi, Baike

2016-04-01

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997-2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. This result may be due to a manifestation of seasonally dependent biases in NARR. With use of the SOMs, the average uncertainty in monthly CF is reduced in half from the values calculated in Kennedy et al. (Theor Appl Climatol 115:91-105, 2014).

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2009-03-01

Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2008-12-01

Hydrological model evaluation and identification essentially depends on the extraction of information from model time series and its processing. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by a distributed conceptual watershed model. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Registratiom of TM data to digital elevation models

NASA Technical Reports Server (NTRS)

1984-01-01

Several problems arise when attempting to register LANDSAT thematic mapper data to U.S. B Geological Survey digital elevation models (DEMs). The TM data are currently available only in a rotated variant of the Space Oblique Mercator (SOM) map projection. Geometric transforms are thus; required to access TM data in the geodetic coordinates used by the DEMs. Due to positional errors in the TM data, these transforms require some sort of external control. The spatial resolution of TM data exceeds that of the most commonly DEM data. Oversampling DEM data to TM resolution introduces systematic noise. Common terrain processing algorithms (e.g., close computation) compound this problem by acting as high-pass filters.

Adaptive training of cortical feature maps for a robot sensorimotor controller.

PubMed

Adams, Samantha V; Wennekers, Thomas; Denham, Sue; Culverhouse, Phil F

2013-08-01

This work investigates self-organising cortical feature maps (SOFMs) based upon the Kohonen Self-Organising Map (SOM) but implemented with spiking neural networks. In future work, the feature maps are intended as the basis for a sensorimotor controller for an autonomous humanoid robot. Traditional SOM methods require some modifications to be useful for autonomous robotic applications. Ideally the map training process should be self-regulating and not require predefined training files or the usual SOM parameter reduction schedules. It would also be desirable if the organised map had some flexibility to accommodate new information whilst preserving previous learnt patterns. Here methods are described which have been used to develop a cortical motor map training system which goes some way towards addressing these issues. The work is presented under the general term 'Adaptive Plasticity' and the main contribution is the development of a 'plasticity resource' (PR) which is modelled as a global parameter which expresses the rate of map development and is related directly to learning on the afferent (input) connections. The PR is used to control map training in place of a traditional learning rate parameter. In conjunction with the PR, random generation of inputs from a set of exemplar patterns is used rather than predefined datasets and enables maps to be trained without deciding in advance how much data is required. An added benefit of the PR is that, unlike a traditional learning rate, it can increase as well as decrease in response to the demands of the input and so allows the map to accommodate new information when the inputs are changed during training. Copyright © 2013 Elsevier Ltd. All rights reserved.

Ice Shape Characterization Using Self-Organizing Maps

NASA Technical Reports Server (NTRS)

McClain, Stephen T.; Tino, Peter; Kreeger, Richard E.

2011-01-01

A method for characterizing ice shapes using a self-organizing map (SOM) technique is presented. Self-organizing maps are neural-network techniques for representing noisy, multi-dimensional data aligned along a lower-dimensional and possibly nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. In information processing, the intent of SOM methods is to transmit the codebook vectors, which contains far fewer elements and requires much less memory or bandwidth, than the original noisy data set. When applied to airfoil ice accretion shapes, the properties of the codebook vectors and the statistical nature of the SOM methods allows for a quantitative comparison of experimentally measured mean or average ice shapes to ice shapes predicted using computer codes such as LEWICE. The nature of the codebook vectors also enables grid generation and surface roughness descriptions for use with the discrete-element roughness approach. In the present study, SOM characterizations are applied to a rime ice shape, a glaze ice shape at an angle of attack, a bi-modal glaze ice shape, and a multi-horn glaze ice shape. Improvements and future explorations will be discussed.

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

NASA Astrophysics Data System (ADS)

Suzuki, Yutaka; Fukasawa, Mizuya; Sakata, Osamu; Kato, Hatsuhiro; Hattori, Asobu; Kato, Takaya

Vascular access for hemodialysis is a lifeline for over 280,000 chronic renal failure patients in Japan. Early detection of stenosis may facilitate long-term use of hemodialysis shunts. Stethoscope auscultation of vascular murmurs has some utility in the assessment of access patency; however, the sensitivity of this diagnostic approach is skill dependent. This study proposes a novel diagnosis support system to detect stenosis by using vascular murmurs. The system is based on a self-organizing map (SOM) and short-time maximum entropy method (STMEM) for data analysis. SOM is an artificial neural network, which is trained using unsupervised learning to produce a feature map that is useful for visualizing the analogous relationship between input data. The author recorded vascular murmurs before and after percutaneous transluminal angioplasty (PTA). The SOM-based classification was consistent with to the classification based on MEM spectral and spectrogram characteristics. The ratio of pre-PTA murmurs in the stenosis category was much higher than the post-PTA murmurs. The results suggest that the proposed method may be an effective tool in the determination of shunt stenosis.

Using self-organizing maps to develop ambient air quality classifications: a time series example

PubMed Central

2014-01-01

Background Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies. Objective Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles. Methods Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques. Results Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships. Conclusion We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies. PMID:24990361

Neural network analysis for geological interpretation of tomographic images beneath the Japan Islands

NASA Astrophysics Data System (ADS)

Kuwatani, T.; Toriumi, M.

2009-12-01

Recent advances in methodologies of geophysical observations, such as seismic tomography, seismic reflection method and geomagnetic method, provide us a large amount and a wide variety of data for physical properties of a crust and upper mantle (e.g. Matsubara et al. (2008)). However, it has still been difficult to specify a rock type and its physical conditions, mainly because (1) available data usually have a lot of error and uncertainty, and (2) physical properties of rocks are greatly affected by fluid and microstructures. The objective interpretation and quantitative evaluation for lithology and fluid-related structure require the statistical analyses of integrated geophysical and geological data. Self-Organizing Maps (SOMs) are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data (Kohonen 2001). SOMs are powerful neural network techniques to classify and interpret multiattribute data sets. Results of SOM classifications can be represented as 2D images, called feature maps which illustrate the complexity and interrelationships among input data sets. Recently, some works have used SOM in order to interpret multidimensional, non-linear, and highly noised geophysical data for purposes of geological prediction (e.g. Klose 2006; Tselentis et al. 2007; Bauer et al. 2008). This paper describes the application of SOM to the 3D velocity structure beneath the whole Japan islands (e.g. Matsubara et al. 2008). From the obtained feature maps, we can specify the lithology and qualitatively evaluate the effect of fluid-related structures. Moreover, re-projection of feature maps onto the 3D velocity structures resulted in detailed images of the structures within the plates. The Pacific plate and the Philippine Sea plate subducting beneath the Eurasian plate can be imaged more clearly than the original P- and S-wave velocity structures. In order to understand more precise prediction of lithology and its structure, we will use the additional input data sets, such as tomographic images of random velocity fluctuation (Takahashi et al. 2009) and b-value mapping data. Additionally, different kinds of data sets, including the experimental and petrological results (e.g. Christensen 1991; Hacker et al. 2003) can be applied to our analyses.

Clustering self-organizing maps (SOM) method for human papillomavirus (HPV) DNA as the main cause of cervical cancer disease

NASA Astrophysics Data System (ADS)

Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.

2017-07-01

One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map

PubMed Central

An, Yan; Zou, Zhihong; Li, Ranran

2016-01-01

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data. PMID:26761018

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map.

PubMed

An, Yan; Zou, Zhihong; Li, Ranran

2016-01-08

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps

DOE PAGES

Kennedy, Aaron D.; Dong, Xiquan; Xi, Baike

2015-02-15

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrumentmore » record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. Lastly, this result may be due to a manifestation of seasonally dependent biases in NARR.« less

Transit shapes and self-organizing maps as a tool for ranking planetary candidates: application to Kepler and K2

NASA Astrophysics Data System (ADS)

Armstrong, D. J.; Pollacco, D.; Santerne, A.

2017-03-01

A crucial step in planet hunting surveys is to select the best candidates for follow-up observations, given limited telescope resources. This is often performed by human 'eyeballing', a time consuming and statistically awkward process. Here, we present a new, fast machine learning technique to separate true planet signals from astrophysical false positives. We use self-organizing maps (SOMs) to study the transit shapes of Kepler and K2 known and candidate planets. We find that SOMs are capable of distinguishing known planets from known false positives with a success rate of 87.0 per cent, using the transit shape alone. Furthermore, they do not require any candidate to be dispositioned prior to use, meaning that they can be used early in a mission's lifetime. A method for classifying candidates using a SOM is developed, and applied to previously unclassified members of the Kepler Objects of Interest (KOI) list as well as candidates from the K2 mission. The method is extremely fast, taking minutes to run the entire KOI list on a typical laptop. We make PYTHON code for performing classifications publicly available, using either new SOMs or those created in this work. The SOM technique represents a novel method for ranking planetary candidate lists, and can be used both alone or as part of a larger autovetting code.

Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps

PubMed Central

Stefanidis, Konstantinos; Papatheodorou, George

2018-01-01

During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA), cluster analysis, and a self-organizing map (SOM) were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters’ relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl-a), water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication. PMID:29562675

Motion planning with complete knowledge using a colored SOM.

PubMed

Vleugels, J; Kok, J N; Overmars, M

1997-01-01

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.

A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM).

PubMed

Iwasaki, Yuki; Abe, Takashi; Wada, Kennosuke; Wada, Yoshiko; Ikemura, Toshimichi

2013-11-20

With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM) is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM), which allowed classification of sequence fragments (e.g., 1 kb) according to phylotypes, solely depending on oligonucleotide composition. Metagenomics studies of uncultivable microorganisms in clinical and environmental samples should allow extensive surveys of genes important in life sciences. BLSOM is most suitable for phylogenetic assignment of metagenomic sequences, because fragmental sequences can be clustered according to phylotypes, solely depending on oligonucleotide composition. We first constructed oligonucleotide BLSOMs for all available sequences from genomes of known species, and by mapping metagenomic sequences on these large-scale BLSOMs, we can predict phylotypes of individual metagenomic sequences, revealing a microbial community structure of uncultured microorganisms, including viruses. BLSOM has shown that influenza viruses isolated from humans and birds clearly differ in oligonucleotide composition. Based on this host-dependent oligonucleotide composition, we have proposed strategies for predicting directional changes of virus sequences and for surveilling potentially hazardous strains when introduced into humans from non-human sources.

Can Selforganizing Maps Accurately Predict Photometric Redshifts?

NASA Technical Reports Server (NTRS)

Way, Michael J.; Klose, Christian

2012-01-01

We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods

Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: A case study for Sentinel-2

NASA Astrophysics Data System (ADS)

Zurita-Milla, R.; Laurent, V. C. E.; van Gijsel, J. A. E.

2015-12-01

Monitoring biophysical and biochemical vegetation variables in space and time is key to understand the earth system. Operational approaches using remote sensing imagery rely on the inversion of radiative transfer models, which describe the interactions between light and vegetation canopies. The inversion required to estimate vegetation variables is, however, an ill-posed problem because of variable compensation effects that can cause different combinations of soil and canopy variables to yield extremely similar spectral responses. In this contribution, we present a novel approach to visualise the ill-posed problem using self-organizing maps (SOM), which are a type of unsupervised neural network. The approach is demonstrated with simulations for Sentinel-2 data (13 bands) made with the Soil-Leaf-Canopy (SLC) radiative transfer model. A look-up table of 100,000 entries was built by randomly sampling 14 SLC model input variables between their minimum and maximum allowed values while using both a dark and a bright soil. The Sentinel-2 spectral simulations were used to train a SOM of 200 × 125 neurons. The training projected similar spectral signatures onto either the same, or contiguous, neuron(s). Tracing back the inputs that generated each spectral signature, we created a 200 × 125 map for each of the SLC variables. The lack of spatial patterns and the variability in these maps indicate ill-posed situations, where similar spectral signatures correspond to different canopy variables. For Sentinel-2, our results showed that leaf area index, crown cover and leaf chlorophyll, water and brown pigment content are less confused in the inversion than variables with noisier maps like fraction of brown canopy area, leaf dry matter content and the PROSPECT mesophyll parameter. This study supports both educational and on-going research activities on inversion algorithms and might be useful to evaluate the uncertainties of retrieved canopy biophysical and biochemical state variables.

Self-Organizing Maps method in recent Adriatic Sea environmental studies: applications and perspectives

NASA Astrophysics Data System (ADS)

Mihanovic, H.; Vilibic, I.

2014-12-01

Herein we present three recent oceanographic studies performed in the Adriatic Sea (the northernmost arm of the Mediterranean Sea), where Self-Organizing Maps (SOM) method, an unsupervised neural network method capable of recognizing patterns in various types of datasets, was applied to environmental data. The first study applied the SOM method to a long (50 years) series of thermohaline, dissolved oxygen and nutrient data measured over a deep (1200 m) Southern Adriatic Pit, in order to extract characteristic deep water mass patterns and their temporal variability. Low-dimensional SOM solutions revealed that the patterns were not sensitive to nutrients but were determined mostly by temperature, salinity and DO content; therefore, the water masses in the region can be traced by using no nutrient data. The second study encompassed the classification of surface current patterns measured by HF radars over the northernmost part of the Adriatic, by applying the SOM method to the HF radar data and operational mesoscale meteorological model surface wind fields. The major output from this study was a high correlation found between characteristic ocean current distribution patterns with and without wind data introduced to the SOM, implying the dominant wind driven dynamics over a local scale. That nominates the SOM method as a basis for generating very fast real-time forecast models over limited domains, based on the existing atmospheric forecasts and basin-oriented ocean experiments. The last study classified the sea ambient noise distributions in a habitat area of bottlenose dolphin, connecting it to the man-made noise generated by different types of vessels. Altogether, the usefulness of the SOM method has been recognized in different aspects of basin-scale ocean environmental studies, and may be a useful tool in future investigations of understanding of the multi-disciplinary dynamics over a basin, including the creation of operational environmental forecasting systems.

Oscillating Adriatic temperature and salinity regimes mapped using the Self-Organizing Maps method

NASA Astrophysics Data System (ADS)

Matić, Frano; Kovač, Žarko; Vilibić, Ivica; Mihanović, Hrvoje; Morović, Mira; Grbec, Branka; Leder, Nenad; Džoić, Tomislav

2017-01-01

This paper aims to document salinity and temperature regimes in the middle and south Adriatic Sea by applying the Self-Organizing Maps (SOM) method to the available long-term temperature and salinity series. The data were collected on a seasonal basis between 1963 and 2011 in two dense water collecting depressions, Jabuka Pit and Southern Adriatic Pit, and over the Palagruža Sill. Seasonality was removed prior to the analyses. Salinity regimes have been found to oscillate rapidly between low-salinity and high-salinity SOM solutions, ascribed to the advection of Western and Eastern Mediterranean waters, respectively. Transient salinity regimes normally lasted less than a season, while temperature transient regimes lasted longer. Salinity regimes prolonged their duration after the major basin-wide event, the Eastern Mediterranean Transient, in the early 1990s. A qualitative relationship between high-salinity regimes and dense water formation and dynamics has been documented. The SOM-based analyses have a large capacity for classifying the oscillating ocean regimes in a basin, which, in the case of the Adriatic Sea, beside climate forcing, is an important driver of biogeochemical changes that impacts trophic relations, appearance and abundance of alien organisms, and fisheries, etc.

Using Self-Organizing Map (SOM) Clusters of Ozonesonde Profiles to Evaluate Climatologies and Create Linkages between Meteorology and Pollution

NASA Astrophysics Data System (ADS)

Stauffer, R. M.; Thompson, A. M.; Young, G. S.; Oltmans, S. J.; Johnson, B.

2016-12-01

Ozone (O3) climatologies are typically created by averaging ozonesonde profiles on a monthly or seasonal basis, either for specific regions or zonally. We demonstrate the advantages of using a statistical clustering technique, self-organizing maps (SOM), over this simple averaging, through analysis of more than 4500 sonde profiles taken from the long-term US sites at Boulder, CO; Huntsville, AL; Trinidad Head, CA; and Wallops Island, VA. First, we apply SOM to O3 mixing ratios from surface to 12 km amsl. At all four sites, profiles in SOM clusters exhibit similar tropopause height, 500 hPa height and temperature, and total and tropospheric column O3. Second, when profiles from each SOM cluster are compared to monthly O3 means, near-tropopause O3 in three of the clusters is double (over +100 ppbv) the climatological O3 mixing ratio. The three clusters include 13-16% of all profiles, mostly from winter and spring. Large mid-tropospheric deviations from monthly means are found in two highly-populated clusters that represent either distinctly polluted (summer) or clean O3 (fall-winter, high tropopause) profiles. Thus, SOM indeed appear to represent US O3 profile statistics better than conventional climatologies. In the case of Trinidad Head, SOM clusters of O3 profile data from the lower troposphere (surface-6 km amsl) can discriminate background vs polluted O3 and the meteorology associated with each. Two of nine O3 clusters exhibit thin layers ( 100s of m thick) of high O3, typically between 1 and 4 km. Comparisons between clusters and downwind, high-altitude surface O3 measurements display a marked impact of the elevated tropospheric O­­3. Days corresponding to the high O3 clusters exhibit hourly surface O3 anomalies at surface sites of +5 -10 ppbv compared to a climatology; the anomalies can last up to four days. We also explore applications of SOM to tropical ozonesonde profiles, where tropospheric O3 variability is generally smaller.

Building regional early flood warning systems by AI techniques

NASA Astrophysics Data System (ADS)

Chang, F. J.; Chang, L. C.; Amin, M. Z. B. M.

2017-12-01

Building early flood warning system is essential for the protection of the residents against flood hazards and make actions to mitigate the losses. This study implements AI technology for forecasting multi-step-ahead regional flood inundation maps during storm events. The methodology includes three major schemes: (1) configuring the self-organizing map (SOM) to categorize a large number of regional inundation maps into a meaningful topology; (2) building dynamic neural networks to forecast multi-step-ahead average inundated depths (AID); and (3) adjusting the weights of the selected neuron in the constructed SOM based on the forecasted AID to obtain real-time regional inundation maps. The proposed models are trained, and tested based on a large number of inundation data sets collected in regions with the most frequent and serious flooding in the river basin. The results appear that the SOM topological relationships between individual neurons and their neighbouring neurons are visible and clearly distinguishable, and the hybrid model can continuously provide multistep-ahead visible regional inundation maps with high resolution during storm events, which have relatively small RMSE values and high R2 as compared with numerical simulation data sets. The computing time is only few seconds, and thereby leads to real-time regional flood inundation forecasting and make early flood inundation warning system. We demonstrate that the proposed hybrid ANN-based model has a robust and reliable predictive ability and can be used for early warning to mitigate flood disasters.

Deriving photometric redshifts using fuzzy archetypes and self-organizing maps - I. Methodology

NASA Astrophysics Data System (ADS)

Speagle, Joshua S.; Eisenstein, Daniel J.

2017-07-01

We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large number of galaxy spectral templates into a corresponding collection of 'fuzzy archetypes' using a suitable set of perturbative priors designed to account for empirical variation in dust attenuation and emission-line strengths. To bypass widely separated degeneracies in parameter space (e.g. the redshift-reddening degeneracy), we train self-organizing maps (SOMs) on large 'model catalogues' generated from Monte Carlo sampling of our fuzzy archetypes to cluster the predicted observables in a topologically smooth fashion. Subsequent sampling over the SOM then allows full reconstruction of the relevant probability distribution functions (PDFs). This combined approach enables the multimodal exploration of known variation among galaxy spectral energy distributions with minimal modelling assumptions. We demonstrate the power of this approach to recover full redshift PDFs using discrete Markov chain Monte Carlo sampling methods combined with SOMs constructed from Large Synoptic Survey Telescope ugrizY and Euclid YJH mock photometry.

Application of self-organizing maps for PCDD/F pattern recognition of environmental and biological samples to evaluate the impact of a hazardous waste incinerator.

PubMed

Mari, Montse; Nadal, Martí; Schuhmacher, Marta; Domingo, José L

2010-04-15

Kohonen's self-organizing maps (SOM) is one of the most popular artificial neural network models. In this study, SOM were used to assess the potential relationships between polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) congener profiles in environmental (soil, herbage, and ambient air) and biological (plasma, adipose tissue, and breast milk) samples, and the emissions of a hazardous waste incinerator (HWI) in Spain. The visual examination of PCDD/F congener profiles of most environmental and biological samples did not allow finding out any differences between monitors. However, the global SOM analysis of environmental and biological samples showed that the weight of the PCDD/F stack emissions of the HWI on the environmental burden and on the exposure of the individuals living in the surroundings was not significant in relation to the background levels. The results confirmed the small influence of the HWI emissions of PCDD/Fs on the environment and the population living in the neighborhood.

A neural circuit for gamma-band coherence across the retinotopic map in mouse visual cortex

PubMed Central

Hakim, Richard; Shamardani, Kiarash

2018-01-01

Cortical gamma oscillations have been implicated in a variety of cognitive, behavioral, and circuit-level phenomena. However, the circuit mechanisms of gamma-band generation and synchronization across cortical space remain uncertain. Using optogenetic patterned illumination in acute brain slices of mouse visual cortex, we define a circuit composed of layer 2/3 (L2/3) pyramidal cells and somatostatin (SOM) interneurons that phase-locks ensembles across the retinotopic map. The network oscillations generated here emerge from non-periodic stimuli, and are stimulus size-dependent, coherent across cortical space, narrow band (30 Hz), and depend on SOM neuron but not parvalbumin (PV) neuron activity; similar to visually induced gamma oscillations observed in vivo. Gamma oscillations generated in separate cortical locations exhibited high coherence as far apart as 850 μm, and lateral gamma entrainment depended on SOM neuron activity. These data identify a circuit that is sufficient to mediate long-range gamma-band coherence in the primary visual cortex. PMID:29480803

Self organizing map neural networks approach for lithologic interpretation of nuclear and electrical well logs in basaltic environment, Southern Syria.

PubMed

Asfahani, J; Ahmad, Z; Ghani, B Abdul

2018-07-01

An approach based on self organizing map (SOM) artificial neural networks is proposed herewith oriented towards interpreting nuclear and electrical well logging data. The well logging measurements of Kodana well in Southern Syria have been interpreted by applying the proposed approach. Lithological cross-section model of the basaltic environment has been derived and four different kinds of basalt have been consequently distinguished. The four basalts are hard massive basalt, hard basalt, pyroclastic basalt and the alteration basalt products- clay. The results obtained by SOM artificial neural networks are in a good agreement with the previous published results obtained by other different techniques. The SOM approach is practiced successfully in the case study of the Kodana well logging data, and can be therefore recommended as a suitable and effective approach for handling huge well logging data with higher number of variables required for lithological discrimination purposes. Copyright © 2018 Elsevier Ltd. All rights reserved.

How territoriality and host-tree taxa determine the structure of ant mosaics.

PubMed

Dejean, Alain; Ryder, Suzanne; Bolton, Barry; Compin, Arthur; Leponce, Maurice; Azémar, Frédéric; Céréghino, Régis; Orivel, Jérôme; Corbara, Bruno

2015-06-01

Very large colonies of territorially dominant arboreal ants (TDAAs), whose territories are distributed in a mosaic pattern in the canopies of many tropical rainforests and tree crop plantations, have a generally positive impact on their host trees. We studied the canopy of an old Gabonese rainforest (ca 4.25 ha sampled, corresponding to 206 "large" trees) at a stage just preceding forest maturity (the Caesalpinioideae dominated; the Burseraceae were abundant). The tree crowns sheltered colonies from 13 TDAAs plus a co-dominant species out of the 25 ant species recorded. By mapping the TDAAs' territories and using a null model co-occurrence analysis, we confirmed the existence of an ant mosaic. Thanks to a large sampling set and the use of the self-organizing map algorithm (SOM), we show that the distribution of the trees influences the structure of the ant mosaic, suggesting that each tree taxon attracts certain TDAA species rather than others. The SOM also improved our knowledge of the TDAAs' ecological niches, showing that these ant species are ecologically distinct from each other based on their relationships with their supporting trees. Therefore, TDAAs should not systematically be placed in the same functional group even when they belong to the same genus. We conclude by reiterating that, in addition to the role played by TDAAs' territorial competition, host trees contribute to structuring ant mosaics through multiple factors, including host-plant selection by TDAAs, the age of the trees, the presence of extrafloral nectaries, and the taxa of the associated hemipterans.

Identifying Local Scale Climate Zones of Urban Heat Island from HJ-1B Satellite Data Using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Wei, C. Z.; Blaschke, T.

2016-10-01

With the increasing acceleration of urbanization, the degeneration of the environment and the Urban Heat Island (UHI) has attracted more and more attention. Quantitative delineation of UHI has become crucial for a better understanding of the interregional interaction between urbanization processes and the urban environment system. First of all, our study used medium resolution Chinese satellite data-HJ-1B as the Earth Observation data source to derive parameters, including the percentage of Impervious Surface Areas, Land Surface Temperature, Land Surface Albedo, Normalized Differential Vegetation Index, and object edge detector indicators (Mean of Inner Border, Mean of Outer border) in the city of Guangzhou, China. Secondly, in order to establish a model to delineate the local climate zones of UHI, we used the Principal Component Analysis to explore the correlations between all these parameters, and estimate their contributions to the principal components of UHI zones. Finally, depending on the results of the PCA, we chose the most suitable parameters to classify the urban climate zones based on a Self-Organization Map (SOM). The results show that all six parameters are closely correlated with each other and have a high percentage of cumulative (95%) in the first two principal components. Therefore, the SOM algorithm automatically categorized the city of Guangzhou into five classes of UHI zones using these six spectral, structural and climate parameters as inputs. UHI zones have distinguishable physical characteristics, and could potentially help to provide the basis and decision support for further sustainable urban planning.

Analyzing the effectiveness of flare dispensing programs against pulse width modulation seekers using self-organizing maps

NASA Astrophysics Data System (ADS)

Şahingil, Mehmet C.; Aslan, Murat Š.

2013-10-01

Infrared guided missile seekers utilizing pulse width modulation in target tracking is one of the threats against air platforms. To be able to achieve a "soft-kill" protection of own platform against these type of threats, one needs to examine carefully the seeker operating principle with its special electronic counter-counter measure (ECCM) capability. One of the cost-effective ways of soft kill protection is to use flare decoys in accordance with an optimized dispensing program. Such an optimization requires a good understanding of the threat seeker, capabilities of the air platform and engagement scenario information between them. Modeling and simulation is very powerful tool to achieve a valuable insight and understand the underlying phenomenology. A careful interpretation of simulation results is crucial to infer valuable conclusions from the data. In such an interpretation there are lots of factors (features) which affect the results. Therefore, powerful statistical tools and pattern recognition algorithms are of special interest in the analysis. In this paper, we show how self-organizing maps (SOMs), which is one of those powerful tools, can be used in analyzing the effectiveness of various flare dispensing programs against a PWM seeker. We perform several Monte Carlo runs for a typical engagement scenario in a MATLAB-based simulation environment. In each run, we randomly change the flare dispending program and obtain corresponding class: "successful" or "unsuccessful", depending on whether the corresponding flare dispensing program deceives the seeker or not, respectively. Then, in the analysis phase, we use SOMs to interpret and visualize the results.

How territoriality and host-tree taxa determine the structure of ant mosaics

NASA Astrophysics Data System (ADS)

Dejean, Alain; Ryder, Suzanne; Bolton, Barry; Compin, Arthur; Leponce, Maurice; Azémar, Frédéric; Céréghino, Régis; Orivel, Jérôme; Corbara, Bruno

2015-06-01

Very large colonies of territorially dominant arboreal ants (TDAAs), whose territories are distributed in a mosaic pattern in the canopies of many tropical rainforests and tree crop plantations, have a generally positive impact on their host trees. We studied the canopy of an old Gabonese rainforest (ca 4.25 ha sampled, corresponding to 206 "large" trees) at a stage just preceding forest maturity (the Caesalpinioideae dominated; the Burseraceae were abundant). The tree crowns sheltered colonies from 13 TDAAs plus a co-dominant species out of the 25 ant species recorded. By mapping the TDAAs' territories and using a null model co-occurrence analysis, we confirmed the existence of an ant mosaic. Thanks to a large sampling set and the use of the self-organizing map algorithm (SOM), we show that the distribution of the trees influences the structure of the ant mosaic, suggesting that each tree taxon attracts certain TDAA species rather than others. The SOM also improved our knowledge of the TDAAs' ecological niches, showing that these ant species are ecologically distinct from each other based on their relationships with their supporting trees. Therefore, TDAAs should not systematically be placed in the same functional group even when they belong to the same genus. We conclude by reiterating that, in addition to the role played by TDAAs' territorial competition, host trees contribute to structuring ant mosaics through multiple factors, including host-plant selection by TDAAs, the age of the trees, the presence of extrafloral nectaries, and the taxa of the associated hemipterans.

Risk groups in children under six months of age using self-organizing maps.

PubMed

Schilithz, A O C; Kale, P L; Gama, S G N; Nobre, F F

2014-06-01

Fetal and infant growth tends to follow irregular patterns and, particularly in developing countries, these patterns are greatly influenced by unfavorable living conditions and interactions with complications during pregnancy. The aim of this study was to identify groups of children with different risk profiles for growth development. The study sample comprised 496 girls and 508 boys under six months of age from 27 pediatric primary health care units in the city of Rio de Janeiro, Brazil. Data were obtained through interviews with the mothers and by reviewing each child's health card. An unsupervised learning, know as a self-organizing map (SOM) and a K-means algorithm were used for cluster analysis to identify groups of children. Four groups of infants were identified. The first (139) consisted of infants born exclusively by cesarean delivery, and their mothers were exclusively multiparous; the highest prevalences of prematurity and low birthweight, a high prevalence of exclusive breastfeeding and a low proportion of hospitalization were observed for this group. The second (247 infants) and the third (298 infants) groups had the best and worst perinatal and infant health indicators, respectively. The infants of the fourth group (318) were born heavier, had a low prevalence of exclusive breastfeeding, and had a higher rate of hospitalization. Using a SOM, it was possible to identify children with common features, although no differences between groups were found with respect to the adequacy of postnatal weight. Pregnant women and children with characteristics similar to those of group 3 require early intervention and more attention in public policy. Copyright © 2014. Published by Elsevier Ireland Ltd.

Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site

NASA Astrophysics Data System (ADS)

Karmakar, Mampi; Maiti, Saumen; Singh, Amrita; Ojha, Maheswar; Maity, Bhabani Sankar

2017-07-01

Modeling and classification of the subsurface lithology is very important to understand the evolution of the earth system. However, precise classification and mapping of lithology using a single framework are difficult due to the complexity and the nonlinearity of the problem driven by limited core sample information. Here, we implement a joint approach by combining the unsupervised and the supervised methods in a single framework for better classification and mapping of rock types. In the unsupervised method, we use the principal component analysis (PCA), K-means cluster analysis (K-means), dendrogram analysis, Fuzzy C-means (FCM) cluster analysis and self-organizing map (SOM). In the supervised method, we use the Bayesian neural networks (BNN) optimized by the Hybrid Monte Carlo (HMC) (BNN-HMC) and the scaled conjugate gradient (SCG) (BNN-SCG) techniques. We use P-wave velocity, density, neutron porosity, resistivity and gamma ray logs of the well U1343E of the Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. While the SOM algorithm allows us to visualize the clustering results in spatial domain, the combined classification schemes (supervised and unsupervised) uncover the different patterns of lithology such of as clayey-silt, diatom-silt and silty-clay from an un-cored section of the drilled hole. In addition, the BNN approach is capable of estimating uncertainty in the predictive modeling of three types of rocks over the entire lithology section at site U1343. Alternate succession of clayey-silt, diatom-silt and silty-clay may be representative of crustal inhomogeneity in general and thus could be a basis for detail study related to the productivity of methane gas in the oceans worldwide. Moreover, at the 530 m depth down below seafloor (DSF), the transition from Pliocene to Pleistocene could be linked to lithological alternation between the clayey-silt and the diatom-silt. The present results could provide the basis for the detailed study to get deeper insight into the Bering Sea' sediment deposition and sequence.

Molecular subtyping of bladder cancer using Kohonen self-organizing maps

PubMed Central

Borkowska, Edyta M; Kruk, Andrzej; Jedrzejczyk, Adam; Rozniecki, Marek; Jablonowski, Zbigniew; Traczyk, Magdalena; Constantinou, Maria; Banaszkiewicz, Monika; Pietrusinski, Michal; Sosnowski, Marek; Hamdy, Freddie C; Peter, Stefan; Catto, James WF; Kaluzewski, Bogdan

2014-01-01

Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6–5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3–11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering. PMID:25142434

Advancing assessment and design of stormwater monitoring programs using a self-organizing map: characterization of trace metal concentration profiles in stormwater runoff.

PubMed

Ki, Seo Jin; Kang, Joo-Hyon; Lee, Seung Won; Lee, Yun Seok; Cho, Kyung Hwa; An, Kwang-Guk; Kim, Joon Ha

2011-08-01

Stormwater runoff poses a great challenge to the scientific assessment of the effects of diffuse pollution sources on receiving waters. In this study, a self-organizing map (SOM), a research tool for analyzing specific patterns in a large array of data, was applied to the monitoring data obtained from a stormwater monitoring survey to acquire new insights into stream water quality profiles under different rainfall conditions. The components of the input data vectors used by the SOM included concentrations of 10 metal elements, river discharge, and rainfall amount which were collected at the inlet and endpoint of an urban segment of the Yeongsan River, Korea. From the study, it was found that the SOM displayed significant variability in trace metal concentrations for different monitoring sites and rainfall events, with a greater impact of stormwater runoff on stream water quality at the upstream site than at the downstream site, except under low rainfall conditions (≤ 4 mm). In addition, the SOM clearly determined the water quality characteristics for "non-storm" and "storm" data, where the parameters nickel and arsenic and the parameters chromium, cadmium, and lead played an important role in reflecting the spatial and temporal water quality, respectively. When the SOM was used to examine the efficacy of stormwater quality monitoring programs, between 34 and 64% of the sample size in the current data set was shown to be sufficient for estimating the stormwater pollutant loads. The observed errors were small, generally being below 10, 6, and 20% for load estimation, map resolution, and clustering accuracy, respectively. Thus, the method recommended may be used to minimize monitoring costs if both the efficiency and accuracy are further determined by examining a large existing data set. Copyright © 2011 Elsevier Ltd. All rights reserved.

Combined interpretation of 3D seismic reflection attributes for geothermal exploration in the Polish Basin using self-organizing maps

NASA Astrophysics Data System (ADS)

Bauer, Klaus; Pussak, Marcin; Stiller, Manfred; Bujakowski, Wieslaw

2014-05-01

Self-organizing maps (SOM) are neural network techniques which can be used for the joint interpretation of multi-disciplinary data sets. In this investigation we apply SOM within a geothermal exploration project using 3D seismic reflection data. The study area is located in the central part of the Polish basin. Several sedimentary target horizons were identified at this location based on fluid flow rate measurements in the geothermal research well Kompina-2. The general objective is a seismic facies analysis and characterization of the major geothermal target reservoir. A 3D seismic reflection experiment with a sparse acquisition geometry was carried out around well Kompina-2. Conventional signal processing (amplitude corrections, filtering, spectral whitening, deconvolution, static corrections, muting) was followed by normal-moveout (NMO) stacking, and, alternatively, by common-reflection-surface (CRS) stacking. Different signal attributes were then derived from the stacked images including root-mean-square (RMS) amplitude, instantaneous frequency and coherency. Furthermore, spectral decomposition attributes were calculated based on the continuous wavelet transform. The resulting attribute maps along major target horizons appear noisy after the NMO stack and clearly structured after the CRS stack. Consequently, the following SOM-based multi-parameter signal attribute analysis was applied only to the CRS images. We applied our SOM work flow, which includes data preparation, unsupervised learning, segmentation of the trained SOM using image processing techniques, and final application of the learned knowledge. For the Lower Jurassic target horizon Ja1 we derived four different clusters with distinct seismic attribute signatures. As the most striking feature, a corridor parallel to a fault system was identified, which is characterized by decreased RMS amplitudes and low frequencies. In our interpretation we assume that this combination of signal properties can be explained by increased fracture porosity and enhanced fluid saturation within this part of the Lower Jurassic sandstone horizon. Hence, we suggest that a future drilling should be carried out within this compartment of the reservoir.

Towards Automated Ecosystem-based Management: A case study of Northern Gulf of Mexico Water

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Lary, D. J.; Allee, R.; Gould, R.; Ko, D.

2012-12-01

The vast and dynamic nature of large systems limit the feasibility of the frequent in situ sampling needed to establish a robust long-term database. Satellite remote sensing offers an alternative to in situ sampling and is possibly the best solution to address the data collection needs at a regional scale. In this context, we have used an unsupervised machine learning (ML) technique, called a self-organizing map (SOM), to objectively provide a classification of the US Gulf of Mexico water using a suite of ocean data products. The input data that we used in this study were the sea surface temperature, the surface chlorophyll concentration, the sea surface salinity, the euphotic depth and the temperature difference between the sea surface and the sea floor. The SOM method uses the multivariate signature of the data records to classify the data into a specified number of classes. The output of the analysis is essentially a comprehensive two-dimensional map of the Gulf of Mexico. We analyzed the individual SOM classes over a five-year period from 2005 to 2009. We then used the machine learning results to established a correspondence between the SOM classification and the completely independent Coastal and Marine Ecological Classification Standard (CMECS), which accommodates the physical, biological, and chemical information to collectively characterize marine and coastal ecosystems. The CMECS water column component information is then fused with fish count data from the Southeast Area Monitoring and Assessment Program (SEAMAP) to produce an interactive map. The results can be used in providing online decision-support system, and tools for Ecosystem-based management.Figures shows the fish count distribution with respect to the SOM classes. The fish preference can be inferred from the plot. This information can be used to construct an online decision-support system for conservation as well as commercial purposes.

Interannual rainfall variability and SOM-based circulation classification

NASA Astrophysics Data System (ADS)

Wolski, Piotr; Jack, Christopher; Tadross, Mark; van Aardenne, Lisa; Lennard, Christopher

2018-01-01

Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disaggregated SOM (7 × 9) of prognostic atmospheric variables, including specific humidity, air temperature and geostrophic winds, captures only 20-45% of interannual local rainfall variability, and that the residual variance contains a strong systematic component. Utilising a multivariate linear regression framework demonstrates that this residual variance can largely be explained using synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location.

Adaptive awareness for personal and small group decision making.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perano, Kenneth J.; Tucker, Steve; Pancerella, Carmen M.

2003-12-01

Many situations call for the use of sensors monitoring physiological and environmental data. In order to use the large amounts of sensor data to affect decision making, we are coupling heterogeneous sensors with small, light-weight processors, other powerful computers, wireless communications, and embedded intelligent software. The result is an adaptive awareness and warning tool, which provides both situation awareness and personal awareness to individuals and teams. Central to this tool is a sensor-independent architecture, which combines both software agents and a reusable core software framework that manages the available hardware resources and provides services to the agents. Agents can recognizemore » cues from the data, warn humans about situations, and act as decision-making aids. Within the agents, self-organizing maps (SOMs) are used to process physiological data in order to provide personal awareness. We have employed a novel clustering algorithm to train the SOM to discern individual body states and activities. This awareness tool has broad applicability to emergency teams, military squads, military medics, individual exercise and fitness monitoring, health monitoring for sick and elderly persons, and environmental monitoring in public places. This report discusses our hardware decisions, software framework, and a pilot awareness tool, which has been developed at Sandia National Laboratories.« less

Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps.

PubMed

Mendoza-Carranza, Manuel; Ejarque, Elisabet; Nagelkerke, Leopold A J

2018-01-01

Tropical small-scale fisheries are typical for providing complex multivariate data, due to their diversity in fishing techniques and highly diverse species composition. In this paper we used for the first time a supervised Self-Organizing Map (xyf-SOM), to recognize and understand the internal heterogeneity of a tropical marine small-scale fishery, using as model the fishery fleet of San Pedro port, Tabasco, Mexico. We used multivariate data from commercial logbooks, including the following four factors: fish species (47), gear types (bottom longline, vertical line+shark longline and vertical line), season (cold, warm), and inter-annual variation (2007-2012). The size of the xyf-SOM, a fundamental characteristic to improve its predictive quality, was optimized for the minimum distance between objects and the maximum prediction rate. The xyf-SOM successfully classified individual fishing trips in relation to the four factors included in the model. Prediction percentages were high (80-100%) for bottom longline and vertical line + shark longline, but lower prediction values were obtained for vertical line (51-74%) fishery. A confusion matrix indicated that classification errors occurred within the same fishing gear. Prediction rates were validated by generating confidence interval using bootstrap. The xyf-SOM showed that not all the fishing trips were targeting the most abundant species and the catch rates were not symmetrically distributed around the mean. Also, the species composition is not homogeneous among fishing trips. Despite the complexity of the data, the xyf-SOM proved to be an excellent tool to identify trends in complex scenarios, emphasizing the diverse and complex patterns that characterize tropical small scale-fishery fleets.

Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps

NASA Astrophysics Data System (ADS)

Chen, I.-Ting; Chang, Li-Chiu; Chang, Fi-John

2018-01-01

In this study, we propose a soft-computing methodology to visibly explore the spatio-temporal groundwater variations of the Kuoping River basin in southern Taiwan. The self-organizing map (SOM) is implemented to investigate the interactive mechanism between surface water and groundwater over the river basin based on large high-dimensional data sets coupled with their occurrence times. We find that extracting the occurrence time from each 30-day moving average data set in the clustered neurons of the SOM is a crucial step to learn the spatio-temporal interaction between surface water and groundwater. We design 2-D Topological Bubble Map to summarize all the groundwater values of four aquifers in a neuron, which can visibly explore the major features of the groundwater in the vertical direction. The constructed SOM topological maps nicely display that: (1) the groundwater movement, in general, extends from the eastern area to the western, where groundwater in the eastern area can be easily recharged from precipitation in wet seasons and discharged into streams during dry seasons due to the high permeability in this area; (2) the water movements in the four aquifers of the study area are quite different, and the seasonal variations of groundwater in the second and third aquifers are larger than those of the others; and (3) the spatial distribution and seasonal variations of groundwater and surface water are comprehensively linked together over the constructed maps to present groundwater characteristics and the interrelation between groundwater and surface water. The proposed modeling methodology not only can classify the large complex high-dimensional data sets into visible topological maps to effectively facilitate the quantitative status of regional groundwater resources but can also provide useful elaboration for future groundwater management.

Self-organizing map analysis using multivariate data from theophylline powders predicted by a thin-plate spline interpolation.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Kikuchi, Shingo; Takayama, Kozo

2010-11-01

The quality by design concept in pharmaceutical formulation development requires establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline powders were prepared based on the standard formulation. The angle of repose, compressibility, cohesion, and dispersibility were measured as the response variables. These responses were predicted quantitatively on the basis of a nonlinear TPS. A large amount of data on these powders was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the powders could be classified into several distinctive clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and powder characteristics. For instance, the quantities of microcrystalline cellulose (MCC) and magnesium stearate (Mg-St) were classified distinctly into each cluster, indicating that the quantities of MCC and Mg-St were crucial for determining the powder characteristics. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline powder formulations. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Self organising maps for visualising and modelling

PubMed Central

2012-01-01

The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring. PMID:22594434

Response of Microbial Soil Carbon Mineralization Rates to Oxygen Limitations

NASA Astrophysics Data System (ADS)

Keiluweit, M.; Denney, A.; Nico, P. S.; Fendorf, S. E.

2014-12-01

The rate of soil organic matter (SOM) mineralization is known to be controlled by climatic factors as well as molecular structure, mineral-organic associations, and physical protection. What remains elusive is to what extent oxygen (O2) limitations impact overall rates of microbial SOM mineralization (oxidation) in soils. Even within upland soils that are aerobic in bulk, factors limiting O2 diffusion such as texture and soil moisture can result in an abundance of anaerobic microsites in the interior of soil aggregates. Variation in ensuing anaerobic respiration pathways can further impact SOM mineralization rates. Using a combination of (first) aggregate model systems and (second) manipulations of intact field samples, we show how limitations on diffusion and carbon bioavailability interact to impose anaerobic conditions and associated respiration constraints on SOM mineralization rates. In model aggregates, we examined how particle size (soil texture) and amount of dissolved organic carbon (bioavailable carbon) affect O2 availability and distribution. Monitoring electron acceptor profiles (O2, NO3-, Mn and Fe) and SOM transformations (dissolved, particulate, mineral-associated pools) across the resulting redox gradients, we then determined the distribution of operative microbial metabolisms and their cumulative impact on SOM mineralization rates. Our results show that anaerobic conditions decrease SOM mineralization rates overall, but those are partially offset by the concurrent increases in SOM bioavailability due to transformations of protective mineral phases. In intact soil aggregates collected from soils varying in texture and SOM content, we mapped the spatial distribution of anaerobic microsites. Optode imaging, microsensor profiling and 3D tomography revealed that soil texture regulates overall O2 availability in aggregate interiors, while particulate SOM in biopores appears to control the fine-scale distribution of anaerobic microsites. Collectively, our results suggest that texture and particulate organic matter content are useful predictors for the impact of O2 limitations on SOM mineralization rates.

Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India.

PubMed

Murty, Upadhyayula Suryanarayana; Srinivasa Rao, Mutheneni; Misra, Sunil

2008-09-01

Due to the availability of a huge amount of epidemiological and public health data that require analysis and interpretation by using appropriate mathematical tools to support the existing method to control the mosquito and mosquito-borne diseases in a more effective way, data-mining tools are used to make sense from the chaos. Using data-mining tools, one can develop predictive models, patterns, association rules, and clusters of diseases, which can help the decision-makers in controlling the diseases. This paper mainly focuses on the applications of data-mining tools that have been used for the first time to prioritize the malaria endemic regions in Manipur state by using Self Organizing Maps (SOM). The SOM results (in two-dimensional images called Kohonen maps) clearly show the visual classification of malaria endemic zones into high, medium and low in the different districts of Manipur, and will be discussed in the paper.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Identification of lithofacies using Kohonen self-organizing maps

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.

2002-01-01

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

Molecular subtyping of bladder cancer using Kohonen self-organizing maps.

PubMed

Borkowska, Edyta M; Kruk, Andrzej; Jedrzejczyk, Adam; Rozniecki, Marek; Jablonowski, Zbigniew; Traczyk, Magdalena; Constantinou, Maria; Banaszkiewicz, Monika; Pietrusinski, Michal; Sosnowski, Marek; Hamdy, Freddie C; Peter, Stefan; Catto, James W F; Kaluzewski, Bogdan

2014-10-01

Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6-5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3-11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering. © 2014 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish

PubMed Central

Russo, Tommaso; Scardi, Michele; Cataudella, Stefano

2014-01-01

We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods. PMID:24466185

Spatiotemporal classification of environmental monitoring data in the Yeongsan River basin, Korea, using self-organizing maps.

PubMed

Jin, Y-H; Kawamura, A; Park, S-C; Nakagawa, N; Amaguchi, H; Olsson, J

2011-10-01

Environmental monitoring data for planning, implementing and evaluating the Total Maximum Daily Loads (TMDL) management system have been measured at about 8-day intervals in a number of rivers in Korea since 2004. In the present study, water quality parameters such as Suspended Solids (SS), Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP) and the corresponding runoff were collected from six stations in the Yeongsan River basin for six years and transformed into monthly mean values. With the primary objective to understand spatiotemporal characteristics of the data, a methodologically systematic application of a Self-Organizing Map (SOM) was made. The SOM application classified the environmental monitoring data into nine clusters showing exclusively distinguishable patterns. Data frequency at each station on a monthly basis identified the spatiotemporal distribution for the first time in the study area. Consequently, the SOM application provided useful information that the sub-basin containing a metropolitan city is associated with deteriorating water quality and should be monitored and managed carefully during spring and summer for water quality improvement in the river basin.

Toward a new task assignment and path evolution (TAPE) for missile defense system (MDS) using intelligent adaptive SOM with recurrent neural networks (RNNs).

PubMed

Wang, Chi-Hsu; Chen, Chun-Yao; Hung, Kun-Neng

2015-06-01

In this paper, a new adaptive self-organizing map (SOM) with recurrent neural network (RNN) controller is proposed for task assignment and path evolution of missile defense system (MDS). We address the problem of N agents (defending missiles) and D targets (incoming missiles) in MDS. A new RNN controller is designed to force an agent (or defending missile) toward a target (or incoming missile), and a monitoring controller is also designed to reduce the error between RNN controller and ideal controller. A new SOM with RNN controller is then designed to dispatch agents to their corresponding targets by minimizing total damaging cost. This is actually an important application of the multiagent system. The SOM with RNN controller is the main controller. After task assignment, the weighting factors of our new SOM with RNN controller are activated to dispatch the agents toward their corresponding targets. Using the Lyapunov constraints, the weighting factors for the proposed SOM with RNN controller are updated to guarantee the stability of the path evolution (or planning) system. Excellent simulations are obtained using this new approach for MDS, which show that our RNN has the lowest average miss distance among the several techniques.

Mapping Patterns of Multiple Deprivation and Well-Being Using Self-Organizing Maps: An Application to Swiss Household Panel Data

ERIC Educational Resources Information Center

Lucchini, Mario; Assi, Jenny

2013-01-01

The aim of this paper is to propose multidimensional measures of deprivation and wellbeing in contemporary Switzerland, in order to overcome the limitations of standard approaches. More precisely, we have developed self organising maps (SOM) using data drawn from the 2009 Swiss Household Panel wave, in order to identify highly homogeneous clusters…

A limit-cycle self-organizing map architecture for stable arm control.

PubMed

Huang, Di-Wei; Gentili, Rodolphe J; Katz, Garrett E; Reggia, James A

2017-01-01

Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks. We show through computer simulations that our architecture generalizes well, achieves accurate, fast, and smooth arm movements, and is robust in the face of arm perturbations, map damage, and variations of internal timing parameters controlling the flow of activity. A robotic implementation is evaluated successfully without further training, demonstrating for the first time that limit cycle maps can control a physical robot arm. We conclude that architectures based on limit cycle maps can be organized to function effectively as neural controllers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics.

PubMed

Weber, Marc; Teeling, Hanno; Huang, Sixing; Waldmann, Jost; Kassabgy, Mariette; Fuchs, Bernhard M; Klindworth, Anna; Klockow, Christine; Wichels, Antje; Gerdts, Gunnar; Amann, Rudolf; Glöckner, Frank Oliver

2011-05-01

Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions, but have not been applied to broad-scale NGS-based metagenomics yet. Here, we provide a novel implementation, demonstrate its potential and practicability, and provide a web-based service for public usage. Evaluation with published data sets mimicking varyingly complex habitats resulted into classification specificities and sensitivities of close to 100% to above 90% from phylum to genus level for assemblies exceeding 8 kb for low and medium complexity data. When applied to five real-world metagenomes of medium complexity from direct pyrosequencing of marine subsurface waters, classifications of assemblies above 2.5 kb were in good agreement with fluorescence in situ hybridizations, indicating that biodiversity was mostly retained within the metagenomes, and confirming high classification specificities. This was validated by two protein-based classifications (PBCs) methods. SOMs were able to retrieve the relevant taxa down to the genus level, while surpassing PBCs in resolution. In order to make the approach accessible to a broad audience, we implemented a feature-rich web-based SOM application named TaxSOM, which is freely available at http://www.megx.net/toolbox/taxsom. TaxSOM can classify reads or assemblies exceeding 2.5 kb with high accuracy and thus assists in linking biodiversity and functions in metagenome studies, which is a precondition to study microbial ecology in a holistic fashion.

Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps

PubMed Central

Ejarque, Elisabet; Nagelkerke, Leopold A. J.

2018-01-01

Tropical small-scale fisheries are typical for providing complex multivariate data, due to their diversity in fishing techniques and highly diverse species composition. In this paper we used for the first time a supervised Self-Organizing Map (xyf-SOM), to recognize and understand the internal heterogeneity of a tropical marine small-scale fishery, using as model the fishery fleet of San Pedro port, Tabasco, Mexico. We used multivariate data from commercial logbooks, including the following four factors: fish species (47), gear types (bottom longline, vertical line+shark longline and vertical line), season (cold, warm), and inter-annual variation (2007–2012). The size of the xyf-SOM, a fundamental characteristic to improve its predictive quality, was optimized for the minimum distance between objects and the maximum prediction rate. The xyf-SOM successfully classified individual fishing trips in relation to the four factors included in the model. Prediction percentages were high (80–100%) for bottom longline and vertical line + shark longline, but lower prediction values were obtained for vertical line (51–74%) fishery. A confusion matrix indicated that classification errors occurred within the same fishing gear. Prediction rates were validated by generating confidence interval using bootstrap. The xyf-SOM showed that not all the fishing trips were targeting the most abundant species and the catch rates were not symmetrically distributed around the mean. Also, the species composition is not homogeneous among fishing trips. Despite the complexity of the data, the xyf-SOM proved to be an excellent tool to identify trends in complex scenarios, emphasizing the diverse and complex patterns that characterize tropical small scale-fishery fleets. PMID:29782501

Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng

2016-10-01

Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.

SELF-ORGANIZING MAPS FOR INTEGRATED ASSESSMENT OF THE MID-ATLANTIC REGION

EPA Science Inventory

A. new method was developed to perform an environmental assessment for the
Mid-Atlantic Region (MAR). This was a combination of the self-organizing map (SOM) neural network and principal component analysis (PCA). The method is capable of clustering ecosystems in terms of envi...

Self-organizing map analysis using multivariate data from theophylline tablets predicted by a thin-plate spline interpolation.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Yamamoto, Rie; Takayama, Kozo

2013-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared based on a standard formulation. The tensile strength, disintegration time, and stability of these variables were measured as response variables. These responses were predicted quantitatively based on nonlinear TPS. A large amount of data on these tablets was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the tablets were classified into several distinct clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and tablet characteristics. The results of this study suggest that increasing the proportion of microcrystalline cellulose (MCC) improved the tensile strength and the stability of tensile strength of these theophylline tablets. In addition, the proportion of MCC has an optimum value for disintegration time and stability of disintegration. Increasing the proportion of magnesium stearate extended disintegration time. Increasing the compression force improved tensile strength, but degraded the stability of disintegration. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulations.

Data-driven modeling of background and mine-related acidity and metals in river basins

USGS Publications Warehouse

Friedel, Michael J

2013-01-01

A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (>98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies–Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.

Accumulation in coastal West Antarctic ice core records and the role of cyclone activity

NASA Astrophysics Data System (ADS)

Hosking, J. Scott; Fogt, Ryan; Thomas, Elizabeth R.; Moosavi, Vahid; Phillips, Tony; Coggins, Jack; Reusch, David

2017-09-01

Cyclones are an important component of Antarctic climate variability, yet quantifying their impact on the polar environment is challenging. We assess how cyclones which pass through the Bellingshausen Sea affect accumulation over Ellsworth Land, West Antarctica, where we have two ice core records. We use self-organizing maps (SOMs), an unsupervised machine learning technique, to group cyclones into nine SOM nodes differing by their trajectories (1980-2015). The annual frequency of cyclones associated with the first SOM node (SOM1, which generally originate from lower latitudes over the South Pacific Ocean) is significantly (p < 0.001) correlated with annual accumulation, with the highest seasonal correlations (p < 0.001) found during autumn. While significant (p < 0.01) increases in vertically integrated water vapor over the South Pacific Ocean coincide with this same group of cyclones, we find no indication that this has led to an increase in moisture advection into, nor accumulation over, Ellsworth Land over this short time period.

SOM guided fuzzy logic prospectivity model for gold in the Häme Belt, southwestern Finland

NASA Astrophysics Data System (ADS)

Leväniemi, Hanna; Hulkki, Helena; Tiainen, Markku

2017-04-01

This study investigated gold prospectivity in the Paleoproterozoic Häme Belt, located in southwestern Finland. The Häme Belt comprises calc-alkaline and tholeitic volcanic rocks, migmatites, granitoids, and mafic to ultramafic intrusions. Mineral exploration in the region has resulted in the discovery of several gold occurrences during recent decades; however, no prospectivity modeling for gold has yet been conducted. This study integrated till geochemical and geophysical data to examine and extract data characteristics critical for gold occurrences. Modeling was guided by self-organizing map (SOM) analysis to define essential data associations and to aid in model input data selection and generation. The final fuzzy logic prospectivity model map yielded high predictability values for most known Au or Cu-Au occurrences, but also highlighted new targets for exploration.

Cropland Area Extraction in China with Multi-Temporal MODIS Data

NASA Astrophysics Data System (ADS)

Bagan, H.; Baruah, P. J.; Wang, Q.; Yasuoka, Y.

2007-12-01

: extracting the area of cropland in China is very important for agricultural management, land degradation and ecosystem assessment. In this study we investigate the potential and the methodology for the cropland area extraction using multi-temporal MODIS EVI data and some ancillary data. A 16-day composite EVI time-series data for 2003 (6 March 2003 - 2 December 2003) with a spatial resolution of 500 m, and the ancillary data included Land-use GIS data, Landsat TM/ETM, ASTER data, and county-level cultivated land statistical data of year 2000. The Self-Organizing Map (SOM) neural network classification algorithm was applied to the EVI data set. To focus on agricultural and desertification, we designed 9 land-cover types: 1) water, 2) woodland, 3) grassland, 4) dry cropland, 5) sandy, 6) paddy, 7) wetland, 8) urban/bare, and 9) snow/ice. The overall classification accuracy was 85% with a kappa coefficient of 0.84. The EVI data sets were sensitive and performed well in distinguishing the majority of land cover types. We also used county-level cultivated land statistical data from the year 2000 to evaluate the accuracy of the agricultural area from classification results, and found that the correlation coefficient was high in most counties. The result of this study shows that the methodology used in this study is, in general, feasible for cropland extraction in China. Keywords: MODIS, EVI, SOM, Cropland, land cover.

Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks.

PubMed

Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele

2011-10-09

Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. Copyright © 2011 Elsevier B.V. All rights reserved.

Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach

USGS Publications Warehouse

Friedel, Michael J.

2011-01-01

Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios.

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.

A novel bioinformatics method for efficient knowledge discovery by BLSOM from big genomic sequence data.

PubMed

Bai, Yu; Iwasaki, Yuki; Kanaya, Shigehiko; Zhao, Yue; Ikemura, Toshimichi

2014-01-01

With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional data such as oligonucleotide composition on one map. By modifying the conventional SOM, we have previously developed Batch-Learning SOM (BLSOM), which allows classification of sequence fragments according to species, solely depending on the oligonucleotide composition. In the present study, we introduce the oligonucleotide BLSOM used for characterization of vertebrate genome sequences. We first analyzed pentanucleotide compositions in 100 kb sequences derived from a wide range of vertebrate genomes and then the compositions in the human and mouse genomes in order to investigate an efficient method for detecting differences between the closely related genomes. BLSOM can recognize the species-specific key combination of oligonucleotide frequencies in each genome, which is called a "genome signature," and the specific regions specifically enriched in transcription-factor-binding sequences. Because the classification and visualization power is very high, BLSOM is an efficient powerful tool for extracting a wide range of information from massive amounts of genomic sequences (i.e., big sequence data).

Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

PubMed

Leflaive, Joséphine; Céréghino, Régis; Danger, Michaël; Lacroix, Gérard; Ten-Hage, Loïc

2005-07-01

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

Interpretation of fingerprint image quality features extracted by self-organizing maps

NASA Astrophysics Data System (ADS)

Danov, Ivan; Olsen, Martin A.; Busch, Christoph

2014-05-01

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

Lithostratigraphic interpretation from joint analysis of seismic tomography and magnetotelluric resistivity models using self-organizing map techniques

NASA Astrophysics Data System (ADS)

Bauer, K.; Muñoz, G.; Moeck, I.

2012-12-01

The combined interpretation of different models as derived from seismic tomography and magnetotelluric (MT) inversion represents a more efficient approach to determine the lithology of the subsurface compared with the separate treatment of each discipline. Such models can be developed independently or by application of joint inversion strategies. After the step of model generation using different geophysical methodologies, a joint interpretation work flow includes the following steps: (1) adjustment of a joint earth model based on the adapted, identical model geometry for the different methods, (2) classification of the model components (e.g. model blocks described by a set of geophysical parameters), and (3) re-mapping of the classified rock types to visualise their distribution within the earth model, and petrophysical characterization and interpretation. One possible approach for the classification of multi-parameter models is based on statistical pattern recognition, where different models are combined and translated into probability density functions. Classes of rock types are identified in these methods as isolated clusters with high probability density function values. Such techniques are well-established for the analysis of two-parameter models. Alternatively we apply self-organizing map (SOM) techniques, which have no limitations in the number of parameters to be analysed in the joint interpretation. Our SOM work flow includes (1) generation of a joint earth model described by so-called data vectors, (2) unsupervised learning or training, (3) analysis of the feature map by adopting image processing techniques, and (4) application of the knowledge to derive a lithological model which is based on the different geophysical parameters. We show the usage of the SOM work flow for a synthetic and a real data case study. Both tests rely on three geophysical properties: P velocity and vertical velocity gradient from seismic tomography, and electrical resistivity from MT inversion. The synthetic data are used as a benchmark test to demonstrate the performance of the SOM method. The real data were collected along a 40 km profile across parts of the NE German basin. The lithostratigraphic model from the joint SOM interpretation consists of eight litho-types and covers Cenozoic, Mesozoic and Paleozoic sediments down to 5 km depth. There is a remarkable agreement between the SOM based model and regional marker horizons interpolated from surrounding 2D industrial seismic data. The most interesting results include (1) distinct properties of the Jurassic (low P velocity gradients, low resistivities) interpreted as the signature of shaly clastics, and (2) a pattern within the Upper Permian Zechstein with decreased resistivities and increased P velocities within the salt depressions on the one hand, and increased resistivities and decreased P velocities in the salt pillows on the other hand. In our interpretation this pattern is related with flow of less dense salt matrix components into the pillows and remaining brittle evaporites within the depressions.

Prudentia: A Medical School's Solution to Curriculum Mapping and Curriculum Management

ERIC Educational Resources Information Center

Steketee, Carole

2015-01-01

During early accreditation visits by the Australian Medical Council (AMC), staff in the School of Medicine (SoM) were asked to demonstrate how and when AMC student outcome statements were being integrated into the MBBS course. As a result, the School Executive committed to developing a curriculum mapping system (CMS) that could systematically…

A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map.

PubMed

Tsai, Wen-Ping; Huang, Shih-Pin; Cheng, Su-Ting; Shao, Kwang-Tsao; Chang, Fi-John

2017-02-01

The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water quality and the fish species of stream habitat units. Copyright © 2016 Elsevier B.V. All rights reserved.

Dealing with missing data in remote sensing images within land and crop classification

NASA Astrophysics Data System (ADS)

Skakun, Sergii; Kussul, Nataliia; Basarab, Ruslan

Optical remote sensing images from space provide valuable data for environmental monitoring, disaster management [1], agriculture mapping [2], so forth. In many cases, a time-series of satellite images is used to discriminate or estimate particular land parameters. One of the factors that influence the efficiency of satellite imagery is the presence of clouds. This leads to the occurrence of missing data that need to be addressed. Numerous approaches have been proposed to fill in missing data (or gaps) and can be categorized into inpainting-based, multispectral-based, and multitemporal-based. In [3], ancillary MODIS data are utilized for filling gaps and predicting Landsat data. In this paper we propose to use self-organizing Kohonen maps (SOMs) for missing data restoration in time-series of satellite imagery. Such approach was previously used for MODIS data [4], but applying this approach for finer spatial resolution data such as Sentinel-2 and Landsat-8 represents a challenge. Moreover, data for training the SOMs are selected manually in [4] that complicates the use of the method in an automatic mode. SOM is a type of artificial neural network that is trained using unsupervised learning to produce a discretised representation of the input space of the training samples, called a map. The map seeks to preserve the topological properties of the input space. The reconstruction of satellite images is performed for each spectral band separately, i.e. a separate SOM is trained for each spectral band. Pixels that have no missing values in the time-series are selected for training. Selecting the number of training pixels represent a trade-off, in particular increasing the number of training samples will lead to the increased time of SOM training while increasing the quality of restoration. Also, training data sets should be selected automatically. As such, we propose to select training samples on a regular grid of pixels. Therefore, the SOM seeks to project a large number of non-missing data to the subspace vectors in the map. Restoration of the missing values is performed in the following way. The multi-temporal pixel values (with gaps) are put to the neural network. A neuron-winner (or a best matching unit, BMU) in the SOM is selected based on the distance metric (for example, Euclidian). It should be noted that missing values are omitted from metric estimation when selecting BMU. When the BMU is selected, missing values are substituted by corresponding components of the BMU values. The efficiency of the proposed approach was tested on a time-series of Landsat-8 images over the JECAM test site in Ukraine and Sich-2 images over Crimea (Sich-2 is Ukrainian remote sensing satellite acquiring images at 8m spatial resolution). Landsat-8 images were first converted to the TOA reflectance, and then were atmospherically corrected so each pixel value represents a surface reflectance in the range from 0 to 1. The error of reconstruction (error of quantization) on training data was: band-2: 0.015; band-3: 0.020; band-4: 0.026; band-5: 0.070; band-6: 0.060; band-7: 0.055. The reconstructed images were also used for crop classification using a multi-layer perceptron (MLP). Overall accuracy was 85.98% and Cohen's kappa was 0.83. References. 1. Skakun, S., Kussul, N., Shelestov, A. and Kussul, O. “Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia,” Risk Analysis, 2013, doi: 10.1111/risa.12156. 2. Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., Kussul, O. “Efficiency assessment of using satellite data for crop area estimation in Ukraine,” International Journal of Applied Earth Observation and Geoinformation, vol. 29, pp. 22-30, 2014. 3. Roy D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., and Lindquist, E., “Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data,” Remote Sensing of Environment, 112(6), pp. 3112-3130, 2008. 4. Latif, B.A., and Mercier, G., “Self-Organizing maps for processing of data with missing values and outliers: application to remote sensing images,” Self-Organizing Maps. InTech, pp. 189-210, 2010.

Global Establishment Risk of Economically Important Fruit Fly Species (Tephritidae)

PubMed Central

Qin, Yujia; Paini, Dean R.; Wang, Cong; Fang, Yan; Li, Zhihong

2015-01-01

The global invasion of Tephritidae (fruit flies) attracts a great deal of attention in the field of plant quarantine and invasion biology because of their economic importance. Predicting which one in hundreds of potential invasive fruit fly species is most likely to establish in a region presents a significant challenge, but can be facilitated using a self organising map (SOM), which is able to analyse species associations to rank large numbers of species simultaneously with an index of establishment. A global presence/absence dataset including 180 economically significant fruit fly species in 118 countries was analysed using a SOM. We compare and contrast ranked lists from six countries selected from each continent, and also show that those countries geographically close were clustered together by the SOM analysis because they have similar fruit fly assemblages. These closely clustered countries therefore represent greater threats to each other as sources of invasive fruit fly species. Finally, we indicate how this SOM method could be utilized as an initial screen to support prioritizing fruit fly species for further research into their potential to invade a region. PMID:25588025

The novel approach to the biomonitor survey using one- and two-dimensional Kohonen networks.

PubMed

Deljanin, Isidora; Antanasijević, Davor; Urošević, Mira Aničić; Tomašević, Milica; Perić-Grujić, Aleksandra; Ristić, Mirjana

2015-10-01

To compare the applicability of the leaves of horse chestnut (Aesculus hippocastanum) and linden (Tilia spp.) as biomonitors of trace element concentrations, a coupled approach of one- and two-dimensional Kohonen networks was applied for the first time. The self-organizing networks (SONs) and the self-organizing maps (SOMs) were applied on the database obtained for the element accumulation (Cr, Fe, Ni, Cu, Zn, Pb, V, As, Cd) and the SOM for the Pb isotopes in the leaves for a multiyear period (2002-2006). A. hippocastanum seems to be a more appropriate biomonitor since it showed more consistent results in the analysis of trace elements and Pb isotopes. The SOM proved to be a suitable and sensitive tool for assessing differences in trace element concentrations and for the Pb isotopic composition in leaves of different species. In addition, the SON provided more clear data on seasonal and temporal accumulation of trace elements in the leaves and could be recommended complementary to the SOM analysis of trace elements in biomonitoring studies.

Summarising climate and air quality (ozone) data on self-organising maps: a Sydney case study.

PubMed

Jiang, Ningbo; Betts, Alan; Riley, Matt

2016-02-01

This paper explores the classification and visualisation utility of the self-organising map (SOM) method in the context of New South Wales (NSW), Australia, using gridded NCEP/NCAR geopotential height reanalysis for east Australia, together with multi-site meteorological and air quality data for Sydney from the NSW Office of Environment and Heritage Air Quality Monitoring Network. A twice-daily synoptic classification has been derived for east Australia for the period of 1958-2012. The classification has not only reproduced the typical synoptic patterns previously identified in the literature but also provided an opportunity to visualise the subtle, non-linear change in the eastward-migrating synoptic systems influencing NSW (including Sydney). The summarisation of long-term, multi-site air quality/meteorological data from the Sydney basin on the SOM plane has identified a set of typical air pollution/meteorological spatial patterns in the region. Importantly, the examination of these patterns in relation to synoptic weather types has provided important visual insights into how local and synoptic meteorological conditions interact with each other and affect the variability of air quality in tandem. The study illustrates that while synoptic circulation types are influential, the within-type variability in mesoscale flows plays a critical role in determining local ozone levels in Sydney. These results indicate that the SOM can be a useful tool for assessing the impact of weather and climatic conditions on air quality in the regional airshed. This study further promotes the use of the SOM method in environmental research.

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

PubMed Central

Weber, Marc; Teeling, Hanno; Huang, Sixing; Waldmann, Jost; Kassabgy, Mariette; Fuchs, Bernhard M; Klindworth, Anna; Klockow, Christine; Wichels, Antje; Gerdts, Gunnar; Amann, Rudolf; Glöckner, Frank Oliver

2011-01-01

Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions, but have not been applied to broad-scale NGS-based metagenomics yet. Here, we provide a novel implementation, demonstrate its potential and practicability, and provide a web-based service for public usage. Evaluation with published data sets mimicking varyingly complex habitats resulted into classification specificities and sensitivities of close to 100% to above 90% from phylum to genus level for assemblies exceeding 8 kb for low and medium complexity data. When applied to five real-world metagenomes of medium complexity from direct pyrosequencing of marine subsurface waters, classifications of assemblies above 2.5 kb were in good agreement with fluorescence in situ hybridizations, indicating that biodiversity was mostly retained within the metagenomes, and confirming high classification specificities. This was validated by two protein-based classifications (PBCs) methods. SOMs were able to retrieve the relevant taxa down to the genus level, while surpassing PBCs in resolution. In order to make the approach accessible to a broad audience, we implemented a feature-rich web-based SOM application named TaxSOM, which is freely available at http://www.megx.net/toolbox/taxsom. TaxSOM can classify reads or assemblies exceeding 2.5 kb with high accuracy and thus assists in linking biodiversity and functions in metagenome studies, which is a precondition to study microbial ecology in a holistic fashion. PMID:21160538

Predicting Invasive Fungal Pathogens Using Invasive Pest Assemblages: Testing Model Predictions in a Virtual World

PubMed Central

Paini, Dean R.; Bianchi, Felix J. J. A.; Northfield, Tobin D.; De Barro, Paul J.

2011-01-01

Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96–98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1–10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84–98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk. PMID:22016773

Predicting invasive fungal pathogens using invasive pest assemblages: testing model predictions in a virtual world.

PubMed

Paini, Dean R; Bianchi, Felix J J A; Northfield, Tobin D; De Barro, Paul J

2011-01-01

Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96-98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1-10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84-98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk.

Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing

NASA Astrophysics Data System (ADS)

Liao, Zhiheng; Sun, Jiaren; Yao, Jialin; Liu, Li; Li, Haowen; Liu, Jian; Xie, Jielan; Wu, Dui; Fan, Shaojia

2018-05-01

Self-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013-2017) radiosonde-based virtual potential temperature profiles. The classified ABL types are then examined in relation to near-surface pollutant concentrations to understand the modulation effects of the changing ABL meteorology on Beijing's air quality. Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification technique, and each is characterized by distinct dynamic and thermodynamic conditions. In general, the self-organized ABL types are able to distinguish between high and low loadings of near-surface pollutants. The average concentrations of PM2.5, NO2 and CO dramatically increased from the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9) during all seasons except for summer. Since extremely strong stability can isolate the near-surface observations from the influence of elevated SO2 pollution layers, the highest average SO2 concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O3 shows an opposite dependence on atmospheric stability, with the lowest average concentration in Node 9. Analysis of three typical pollution months (i.e., January 2013, December 2015 and December 2016) suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM2.5 loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM2.5 levels are estimated to be 58.3 % (44.4 µg m-3) in January 2013, 46.4 % (22.2 µg m-3) in December 2015 and 73.3 % (34.6 µg m-3) in December 2016.

Self-Organizing Neural Network Map for the Purpose of Visualizing the Concept Images of Students on Angles

ERIC Educational Resources Information Center

Kaya, Deniz

2017-01-01

The purpose of the study is to perform a less-dimensional thorough visualization process for the purpose of determining the images of the students on the concept of angle. The Ward clustering analysis combined with Self-Organizing Neural Network Map (SOM) has been used for the dimension process. The Conceptual Understanding Tool, which consisted…

Mapping underwater sound noise and assessing its sources by using a self-organizing maps method.

PubMed

Rako, Nikolina; Vilibić, Ivica; Mihanović, Hrvoje

2013-03-01

This study aims to provide an objective mapping of the underwater noise and its sources over an Adriatic coastal marine habitat by applying the self-organizing maps (SOM) method. Systematic sampling of sea ambient noise (SAN) was carried out at ten predefined acoustic stations between 2007 and 2009. Analyses of noise levels were performed for 1/3 octave band standard centered frequencies in terms of instantaneous sound pressure levels averaged over 300 s to calculate the equivalent continuous sound pressure levels. Data on vessels' presence, type, and distance from the monitoring stations were also collected at each acoustic station during the acoustic sampling. Altogether 69 noise surveys were introduced to the SOM predefined 2 × 2 array. The overall results of the analysis distinguished two dominant underwater soundscapes, associating them mainly to the seasonal changes in the nautical tourism and fishing activities within the study area and to the wind and wave action. The analysis identified recreational vessels as the dominant anthropogenic source of underwater noise, particularly during the tourist season. The method demonstrated to be an efficient tool in predicting the SAN levels based on the vessel distribution, indicating also the possibility of its wider implication for marine conservation.

Response of SOM Decomposition to Anthropogenic N Deposition: Simulations From the PnET-SOM Model.

NASA Astrophysics Data System (ADS)

Tonitto, C.; Goodale, C. L.; Ollinger, S. V.; Jenkins, J. P.

2008-12-01

Anthropogenic forcing of the C and N cycles has caused rapid change in atmospheric CO2 and N deposition, with complex and uncertain effects on forest C and N balance. With some exceptions, models of forest ecosystem response to anthropogenic perturbation have historically focused more on aboveground than belowground processes; the complexity of soil organic matter (SOM) is often represented with abstract or incomplete SOM pools, and remains difficult to quantify. We developed a model of SOM dynamics in northern hardwood forests with explicit feedbacks between C and N cycles. The soil model is linked to the aboveground dynamics of the PnET model to form PnET-SOM. The SOM model includes: 1) physically measurable SOM pools, including humic and mineral-associated SOM in O, A, and B soil horizons, 2) empirical soil turnover times based on 14C data, 3) alternative SOM decomposition algorithms with and without explicit microbial processing, and 4) soluble element transport explicitly linked to the hydrologic cycle. We tested model sensitivity to changes in litter decomposition rate (k) and completeness of decomposition (limit value) by altering these parameters based on experimental observations from long-term litter decomposition experiments with N fertilization treatments. After a 100 year simulation, the Oe+Oa horizon SOC pool was reduced by 15 % and the A-horizon humified SOC was reduced by 7 % for N deposition scenarios relative to forests without N fertilization. In contrast, predictions for slower time-scale pools showed negligible variation in response to variation in the limit values tested, with A-horizon mineral SOC pools reduced by < 3 % and B-horizon mineral SOC reduced by 0.1 % for N deposition scenarios relative to forests without N fertilization. The model was also used to test the effect of varying initial litter decomposition rate to simulate response to N deposition. In contrast to the effect of varying limit values, simulations in which only k-values were varied did not drastically alter the predicted SOC pool distribution throughout the soil profile, but did significantly alter the Oi SOC pool. These results suggest that describing soil response to N deposition via alteration of the limit value alone, or as a combined alteration of limit value and the initial decomposition rate, can lead to significant variation in predicted long-term C storage.

A new service offered by rural environment to the city: stormwater reception.

NASA Astrophysics Data System (ADS)

Chiaradia, Enrico Antonio; Weber, Enrico; Masseroni, Daniele; Battista Bischetti, Gian; Gandolfi, Claudio

2017-04-01

Stormwaters are the main cause of urban floods in many urbanized areas. Historically, stormwater management practices have been focused on building infrastructures that achieve runoff attenuation through the storage of water volumes in large detention basins. However, this approach has proven to be insufficient to resolve the problem as well as it is difficult to implement in areas with a dense urban fabric. Nowadays, around the world, water managers are increasingly embracing "soft path" approaches, that aim to manage the excess of urban runoff through Green Infrastructures, where detention capacities are provided by the retention proprieties of soil and vegetation elements. Along the line of these new sustainable stormwater management practices, the aim of this study is to promote a further paradigm-shift with respect to the traditional practices i.e. to investigate the possibility to use the already existing green infrastructures of the peri-urban rural areas as reception element of the surplus of urban runoff. Many territories in Northern Italy, for example. are characterized by a high density of irrigation canals and agricultural fields that, in some cases, are isolated or pent-up inside urbanized areas. Both these elements may represent storage volumes for accumulating stormwater from urban areas. In this work, we implemented a holistic framework, based on Self Organized Map technique (SOM), with the objective to produce a spatial map of the stormwater reception level that can be provided by the rural environment. We elaborated physiographic characteristics of irrigation canals and agricultural fields through the SOM algorithm obtaining as output a series of cluster groups with the same level of receptivity. This procedure was applied on an area of 1933 km2 around the city of Milan and a map of 250x250m resolution was obtained with three different levels of stormwater reception capacity. About 50% of rural environment has a good level of reception and only 30% and 20% of rural areas have respectively a moderate and scarce level of reception. By the results we can conclude that the rural environment could become a valuable structural alternative to the traditional stormwater control methods, ascribing the rural environment to a new role in urban flood protection from.

Investigating the effect of landfill leachates on the characteristics of dissolved organic matter in groundwater using excitation-emission matrix fluorescence spectra coupled with fluorescence regional integration and self-organizing map.

PubMed

He, Xiao-Song; Fan, Qin-Dong

2016-11-01

For the purpose of investigating the effect of landfill leachate on the characteristics of organic matter in groundwater, groundwater samples were collected near and in a landfill site, and dissolved organic matter (DOM) was extracted from the groundwater samples and characterized by excitation-emission matrix (EEM) fluorescence spectra combined with fluorescence regional integration (FRI) and self-organizing map (SOM). The results showed that the groundwater DOM comprised humic-, fulvic-, and protein-like substances. The concentration of humic-like matter showed no obvious variation for all groundwater except the sample collected in the landfill site. Fulvic-like substance content decreased when the groundwater was polluted by landfill leachates. There were two kinds of protein-like matter in the groundwater. One kind was bound to humic-like substances, and its content did not change along with groundwater pollution. However, the other kind was present as "free" molecules or else bound in proteins, and its concentration increased significantly when the groundwater was polluted by landfill leachates. The FRI and SOM methods both can characterize the composition and evolution of DOM in the groundwater. However, the SOM analysis can identify whether protein-like moieties was bound to humic-like matter.

A novel approach combining self-organizing map and parallel factor analysis for monitoring water quality of watersheds under non-point source pollution

PubMed Central

Zhang, Yixiang; Liang, Xinqiang; Wang, Zhibo; Xu, Lixian

2015-01-01

High content of organic matter in the downstream of watersheds underscored the severity of non-point source (NPS) pollution. The major objectives of this study were to characterize and quantify dissolved organic matter (DOM) in watersheds affected by NPS pollution, and to apply self-organizing map (SOM) and parallel factor analysis (PARAFAC) to assess fluorescence properties as proxy indicators for NPS pollution and labor-intensive routine water quality indicators. Water from upstreams and downstreams was sampled to measure dissolved organic carbon (DOC) concentrations and excitation-emission matrix (EEM). Five fluorescence components were modeled with PARAFAC. The regression analysis between PARAFAC intensities (Fmax) and raw EEM measurements indicated that several raw fluorescence measurements at target excitation-emission wavelength region could provide similar DOM information to massive EEM measurements combined with PARAFAC. Regression analysis between DOC concentration and raw EEM measurements suggested that some regions in raw EEM could be used as surrogates for labor-intensive routine indicators. SOM can be used to visualize the occurrence of pollution. Relationship between DOC concentration and PARAFAC components analyzed with SOM suggested that PARAFAC component 2 might be the major part of bulk DOC and could be recognized as a proxy indicator to predict the DOC concentration. PMID:26526140

Segmentation of Natural Gas Customers in Industrial Sector Using Self-Organizing Map (SOM) Method

NASA Astrophysics Data System (ADS)

Masbar Rus, A. M.; Pramudita, R.; Surjandari, I.

2018-03-01

The usage of the natural gas which is non-renewable energy, needs to be more efficient. Therefore, customer segmentation becomes necessary to set up a marketing strategy to be right on target or to determine an appropriate fee. This research was conducted at PT PGN using one of data mining method, i.e. Self-Organizing Map (SOM). The clustering process is based on the characteristic of its customers as a reference to create the customer segmentation of natural gas customers. The input variables of this research are variable of area, type of customer, the industrial sector, the average usage, standard deviation of the usage, and the total deviation. As a result, 37 cluster and 9 segment from 838 customer data are formed. These 9 segments then employed to illustrate the general characteristic of the natural gas customer of PT PGN.

Identifying regions of interest in medical images using self-organizing maps.

PubMed

Teng, Wei-Guang; Chang, Ping-Lin

2012-10-01

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

A Study on Regional Frequency Analysis using Artificial Neural Network - the Sumjin River Basin

NASA Astrophysics Data System (ADS)

Jeong, C.; Ahn, J.; Ahn, H.; Heo, J. H.

2017-12-01

Regional frequency analysis means to make up for shortcomings in the at-site frequency analysis which is about a lack of sample size through the regional concept. Regional rainfall quantile depends on the identification of hydrologically homogeneous regions, hence the regional classification based on hydrological homogeneous assumption is very important. For regional clustering about rainfall, multidimensional variables and factors related geographical features and meteorological figure are considered such as mean annual precipitation, number of days with precipitation in a year and average maximum daily precipitation in a month. Self-Organizing Feature Map method which is one of the artificial neural network algorithm in the unsupervised learning techniques solves N-dimensional and nonlinear problems and be shown results simply as a data visualization technique. In this study, for the Sumjin river basin in South Korea, cluster analysis was performed based on SOM method using high-dimensional geographical features and meteorological factor as input data. then, for the results, in order to evaluate the homogeneity of regions, the L-moment based discordancy and heterogeneity measures were used. Rainfall quantiles were estimated as the index flood method which is one of regional rainfall frequency analysis. Clustering analysis using SOM method and the consequential variation in rainfall quantile were analyzed. This research was supported by a grant(2017-MPSS31-001) from Supporting Technology Development Program for Disaster Management funded by Ministry of Public Safety and Security(MPSS) of the Korean government.

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

NASA Astrophysics Data System (ADS)

Prochazka, D.; Mazura, M.; Samek, O.; Rebrošová, K.; Pořízka, P.; Klus, J.; Prochazková, P.; Novotný, J.; Novotný, K.; Kaiser, J.

2018-01-01

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods.

Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches.

PubMed

de Luca, Aurélie; Horvath, Dragos; Marcou, Gilles; Solov'ev, Vitaly; Varnek, Alexandre

2012-09-24

This work addresses the problem of similarity search and classification of chemical reactions using Neighborhood Behavior (NB) and Condensed Graphs of Reaction (CGR) approaches. The CGR formalism represents chemical reactions as a classical molecular graph with dynamic bonds, enabling descriptor calculations on this graph. Different types of the ISIDA fragment descriptors generated for CGRs in combination with two metrics--Tanimoto and Euclidean--were considered as chemical spaces, to serve for reaction dissimilarity scoring. The NB method has been used to select an optimal combination of descriptors which distinguish different types of chemical reactions in a database containing 8544 reactions of 9 classes. Relevance of NB analysis has been validated in generic (multiclass) similarity search and in clustering with Self-Organizing Maps (SOM). NB-compliant sets of descriptors were shown to display enhanced mapping propensities, allowing the construction of better Self-Organizing Maps and similarity searches (NB and classical similarity search criteria--AUC ROC--correlate at a level of 0.7). The analysis of the SOM clusters proved chemically meaningful CGR substructures representing specific reaction signatures.

Characterizing UT/LS O3 from Global Ozonesonde Profiles Using a Clustering Technique and Meteorological Reanalyses

NASA Astrophysics Data System (ADS)

Stauffer, R. M.; Thompson, A. M.

2017-12-01

Previous studies employing the self-organizing map (SOM) clustering technique to US ozonesonde data proved valuable for quantifying UT/LS O3 variability, and linking meteorological and chemical drivers to the shape of the ozone (O3) profile from the troposphere to the lower stratosphere. Focus has thus far been limited to specific geographical regions, but SOM has demonstrated the advantages of clustering over monthly climatological O3 averages, which mask day-to-day variability in the O3 profile and the correspondence between O3 and meteorology. We expand SOM to a global set of ozonesonde profiles, mostly from WOUDC, spanning 1980-present from 30 sites to evaluate global O3 climatologies and quantify links to geophysical processes for various meteorological regimes. Four clusters of O3 mixing ratio profiles are generated for each site, which show dominant profile shapes that correspond to site latitude. Offsets among O3 profile clusters and monthly O3 climatologies are 100s of ppbv in the UT/LS at higher latitude sites with active dynamics. Examination of meteorological reanalyses reveals a clear relationship among SOM clusters and covarying meteorological fields (geopotential height, potential vorticity, and tropopause height) for most sites. Tropical SOM clusters show marked dependence on velocity potential anomalies calculated from reanalysis winds, with low UT/LS O3 amounts corresponding to enhanced upper-level divergence, and vice versa. In addition to creating SOM cluster-based O3 climatologies, these results are meant to inform future approaches to validation of chemical transport models and satellite retrievals, which often struggle in the UT/LS region.

Social-Ecological Patterns of Soil Heavy Metals Based on a Self-Organizing Map (SOM): A Case Study in Beijing, China

PubMed Central

Wang, Binwu; Li, Hong; Sun, Danfeng

2014-01-01

The regional management of trace elements in soils requires understanding the interaction between the natural system and human socio-economic activities. In this study, a social-ecological patterns of heavy metals (SEPHM) approach was proposed to identify the heavy metal concentration patterns and processes in different ecoregions of Beijing (China) based on a self-organizing map (SOM). Potential ecological risk index (RI) values of Cr, Ni, Zn, Hg, Cu, As, Cd and Pb were calculated for 1,018 surface soil samples. These data were averaged in accordance with 253 communities and/or towns, and compared with demographic, agriculture structure, geomorphology, climate, land use/cover, and soil-forming parent material to discover the SEPHM. Multivariate statistical techniques were further applied to interpret the control factors of each SEPHM. SOM application clustered the 253 towns into nine groups on the map size of 12 × 7 plane (quantization error 1.809; topographic error, 0.0079). The distribution characteristics and Spearman rank correlation coefficients of RIs were strongly associated with the population density, vegetation index, industrial and mining land percent and road density. The RIs were relatively high in which towns in a highly urbanized area with large human population density exist, while low RIs occurred in mountainous and high vegetation cover areas. The resulting dataset identifies the SEPHM of Beijing and links the apparent results of RIs to driving factors, thus serving as an excellent data source to inform policy makers for legislative and land management actions. PMID:24690947

Machine learning with neural networks - a case study of estimating thermal conductivity with ancient well-log data

NASA Astrophysics Data System (ADS)

Harrison, Benjamin; Sandiford, Mike; McLaren, Sandra

2016-04-01

Supervised machine learning algorithms attempt to build a predictive model using empirical data. Their aim is to take a known set of input data along with known responses to the data, and adaptively train a model to generate predictions for new data inputs. A key attraction to their use is the ability to perform as function approximators where the definition of an explicit relationship between variables is infeasible. We present a novel means of estimating thermal conductivity using a supervised self-organising map algorithm, trained on about 150 thermal conductivity measurements, and using a suite of five electric logs common to 14 boreholes. A key motivation of the study was to supplement the small number of direct measurements of thermal conductivity with the decades of borehole data acquired in the Gippsland Basin to produce more confident calculations of surface heat flow. A previous attempt to generate estimates from well-log data in the Gippsland Basin using classic petrophysical log interpretation methods was able to produce reasonable synthetic thermal conductivity logs for only four boreholes. The current study has extended this to a further ten boreholes. Interesting outcomes from the study are: the method appears stable at very low sample sizes (< ~100); the SOM permits quantitative analysis of essentially qualitative uncalibrated well-log data; and the method's moderate success at prediction with minimal effort tuning the algorithm's parameters.

Data Mining in Cyber Operations

DTIC Science & Technology

2014-07-01

information processing units intended to mimic the network of neurons in the human brain for performing pattern recognition  Self- organizing maps (SOM...patterns are mined from in order to influence the learning model . An exploratory attack does not alter the training process , but rather uses other...New Jersey: Prentice Hall. 21) Kohonen, T. (1982). Self- organized formation of topologically correct feature maps. Biological Cybernetics , 43, 59–69

Analysis of liver damage from radon, X-ray, or alcohol treatments in mice using a self-organizing map.

PubMed

Kanzaki, Norie; Kataoka, Takahiro; Etani, Reo; Sasaoka, Kaori; Kanagawa, Akihiro; Yamaoka, Kiyonori

2017-01-01

In our previous studies, we found that low-dose radiation inhibits oxidative stress-induced diseases due to increased antioxidants. Although these effects of low-dose radiation were demonstrated, further research was needed to clarify the effects. However, the analysis of oxidative stress is challenging, especially that of low levels of oxidative stress, because antioxidative substances are intricately involved. Thus, we proposed an approach for analysing oxidative liver damage via use of a self-organizing map (SOM)-a novel and comprehensive technique for evaluating hepatic and antioxidative function. Mice were treated with radon inhalation, irradiated with X-rays, or subjected to intraperitoneal injection of alcohol. We evaluated the oxidative damage levels in the liver from the SOM results for hepatic function and antioxidative substances. The results showed that the effects of low-dose irradiation (radon inhalation at a concentration of up to 2000 Bq/m 3 , or X-irradiation at a dose of up to 2.0 Gy) were comparable with the effect of alcohol administration at 0.5 g/kg bodyweight. Analysis using the SOM to discriminate small changes was made possible by its ability to 'learn' to adapt to unexpected changes. Moreover, when using a spherical SOM, the method comprehensively examined liver damage by radon, X-ray, and alcohol. We found that the types of liver damage caused by radon, X-rays, and alcohol have different characteristics. Therefore, our approaches would be useful as a method for evaluating oxidative liver damage caused by radon, X-rays and alcohol. © The Author 2016. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

Feature combination analysis in smart grid based using SOM for Sudan national grid

NASA Astrophysics Data System (ADS)

Bohari, Z. H.; Yusof, M. A. M.; Jali, M. H.; Sulaima, M. F.; Nasir, M. N. M.

2015-12-01

In the investigation of power grid security, the cascading failure in multicontingency situations has been a test because of its topological unpredictability and computational expense. Both system investigations and burden positioning routines have their limits. In this project, in view of sorting toward Self Organizing Maps (SOM), incorporated methodology consolidating spatial feature (distance)-based grouping with electrical attributes (load) to evaluate the vulnerability and cascading impact of various part sets in the force lattice. Utilizing the grouping result from SOM, sets of overwhelming stacked beginning victimized people to perform assault conspires and asses the consequent falling impact of their failures, and this SOM-based approach viably distinguishes the more powerless sets of substations than those from the conventional burden positioning and other bunching strategies. The robustness of power grids is a central topic in the design of the so called "smart grid". In this paper, to analyze the measures of importance of the nodes in a power grid under cascading failure. With these efforts, we can distinguish the most vulnerable nodes and protect them, improving the safety of the power grid. Also we can measure if a structure is proper for power grids.

Microscale soil structure development after glacial retreat - using machine-learning based segmentation of elemental distributions obtained by NanoSIMS

NASA Astrophysics Data System (ADS)

Schweizer, Steffen; Schlueter, Steffen; Hoeschen, Carmen; Koegel-Knabner, Ingrid; Mueller, Carsten W.

2017-04-01

Soil organic matter (SOM) is distributed on mineral surfaces depending on physicochemical soil properties that vary at the submicron scale. Nanoscale secondary ion mass spectrometry (NanoSIMS) can be used to visualize the spatial distribution of up to seven elements simultaneously at a lateral resolution of approximately 100 nm from which patterns of SOM coatings can be derived. Existing computational methods are mostly confined to visualization and lack spatial quantification measures of coverage and connectivity of organic matter coatings. This study proposes a methodology for the spatial analysis of SOM coatings based on supervised pixel classification and automatic image analysis of the 12C, 12C14N (indicative for SOM) and 16O (indicative for mineral surfaces) secondary ion distributions. The image segmentation of the secondary ion distributions into mineral particle surface and organic coating was done with a machine learning algorithm, which accounts for multiple features like size, color, intensity, edge and texture in all three ion distributions simultaneously. Our workflow allowed the spatial analysis of differences in the SOM coverage during soil development in the Damma glacier forefield (Switzerland) based on NanoSIMS measurements (n=121; containing ca. 4000 particles). The Damma chronosequence comprises several stages of soil development with increasing ice-free period (from ca. 15 to >700 years). To investigate mineral-associated SOM in the developing soil we obtained clay fractions (<2 μm) from two density fractions: light mineral (1.6 to 2.2 g cm3) and heavy mineral (>2.2 g cm3). We found increased coverage and a simultaneous development from patchy-distributed organic coatings to more connected coatings with increasing time after glacial retreat. The normalized N:C ratio (12C14N: (12C14N + 12C)) on the organic matter coatings was higher in the medium-aged soils than in the young and mature ones in both heavy and light mineral fraction. This reflects the sequential accumulation of proteinaceous SOM in the medium-aged soils and C-rich compounds in the mature soils. The results of our microscale image analysis correlated well with the SOM concentration of the fractions measured by elemental analyzer. Image analysis in combination with secondary ion distributions provides a powerful tool at the required microscale and enhances our mechanistic understanding of SOM stabilization in soil.

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

Representation of DNA sequences with virtual potentials and their processing by (SEQREP) Kohonen self-organizing maps.

PubMed

Aires-de-Sousa, João; Aires-de-Sousa, Luisa

2003-01-01

We propose representing individual positions in DNA sequences by virtual potentials generated by other bases of the same sequence. This is a compact representation of the neighbourhood of a base. The distribution of the virtual potentials over the whole sequence can be used as a representation of the entire sequence (SEQREP code). It is a flexible code, with a length independent of the sequence size, does not require previous alignment, and is convenient for processing by neural networks or statistical techniques. To evaluate its biological significance, the SEQREP code was used for training Kohonen self-organizing maps (SOMs) in two applications: (a) detection of Alu sequences, and (b) classification of sequences encoding for HIV-1 envelope glycoprotein (env) into subtypes A-G. It was demonstrated that SOMs clustered sequences belonging to different classes into distinct regions. For independent test sets, very high rates of correct predictions were obtained (97% in the first application, 91% in the second). Possible areas of application of SEQREP codes include functional genomics, phylogenetic analysis, detection of repetitions, database retrieval, and automatic alignment. Software for representing sequences by SEQREP code, and for training Kohonen SOMs is made freely available from http://www.dq.fct.unl.pt/qoa/jas/seqrep. Supplementary material is available at http://www.dq.fct.unl.pt/qoa/jas/seqrep/bioinf2002

Face recognition: a convolutional neural-network approach.

PubMed

Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

1997-01-01

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

Covariance Matrix Adaptation Evolutionary Strategy for Drift Correction of Electronic Nose Data

NASA Astrophysics Data System (ADS)

Di Carlo, S.; Falasconi, M.; Sanchez, E.; Sberveglieri, G.; Scionti, A.; Squillero, G.; Tonda, A.

2011-09-01

Electronic Noses (ENs) might represent a simple, fast, high sample throughput and economic alternative to conventional analytical instruments [1]. However, gas sensors drift still limits the EN adoption in real industrial setups due to high recalibration effort and cost [2]. In fact, pattern recognition (PaRC) models built in the training phase become useless after a period of time, in some cases a few weeks. Although algorithms to mitigate the drift date back to the early 90 this is still a challenging issue for the chemical sensor community [3]. Among other approaches, adaptive drift correction methods adjust the PaRC model in parallel with data acquisition without need of periodic calibration. Self-Organizing Maps (SOMs) [4] and Adaptive Resonance Theory (ART) networks [5] have been already tested in the past with fair success. This paper presents and discusses an original methodology based on a Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [6], suited for stochastic optimization of complex problems.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

Data analytics approach to create waste generation profiles for waste management and collection.

PubMed

Niska, Harri; Serkkola, Ari

2018-04-30

Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and in-time waste generation information for the basis of waste management and collection. In this paper, a data-based approach based on the self-organizing map (SOM) and the k-means algorithm is developed for creating a set of waste generation type profiles. The approach is demonstrated using the extensive container-level waste weighting data collected in the metropolitan area of Helsinki, Finland. The results obtained highlight the potential of advanced data analytic approaches in producing more detailed waste generation information e.g. for the basis of tailored feedback services for waste producers and the planning and optimization of waste collection and recycling. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods.

PubMed

Mustonen, Satu M; Tissari, Soile; Huikko, Laura; Kolehmainen, Mikko; Lehtola, Markku J; Hirvonen, Arja

2008-05-01

The distribution of drinking water generates soft deposits and biofilms in the pipelines of distribution systems. Disturbances in water distribution can detach these deposits and biofilms and thus deteriorate the water quality. We studied the effects of simulated pressure shocks on the water quality with online analysers. The study was conducted with copper and composite plastic pipelines in a pilot distribution system. The online data gathered during the study was evaluated with Self-Organising Map (SOM) and Sammon's mapping, which are useful methods in exploring large amounts of multivariate data. The objective was to test the usefulness of these methods in pinpointing the abnormal water quality changes in the online data. The pressure shocks increased temporarily the number of particles, turbidity and electrical conductivity. SOM and Sammon's mapping were able to separate these situations from the normal data and thus make those visible. Therefore these methods make it possible to detect abrupt changes in water quality and thus to react rapidly to any disturbances in the system. These methods are useful in developing alert systems and predictive applications connected to online monitoring.

MISR Level 1 Near Real Time Products

Atmospheric Science Data Center

2016-10-31

Level 1 Near Real Time The MISR Near Real Time Level 1 data products ... km MISR swath and projected onto a Space-Oblique Mercator (SOM) map grid. The Ellipsoid-projected and Terrain-projected top-of-atmosphere (TOA) radiance products provide measurements respectively resampled onto the ...

Wind regimes and their relation to synoptic variables using self-organizing maps

NASA Astrophysics Data System (ADS)

Berkovic, Sigalit

2018-01-01

This study exemplifies the ability of the self-organizing maps (SOM) method to directly define well known wind regimes over Israel during the entire year, except summer period, at 12:00 UTC. This procedure may be applied at other hours and is highly relevant to future automatic climatological analysis and applications. The investigation is performed by analysing surface wind measurements from 53 Israel Meteorological Service stations. The relation between the synoptic variables and the wind regimes is revealed from the averages of ECMWF ERA-INTERIM reanalysis variables for each SOM wind regime. The inspection of wind regimes and their average geopotential anomalies has shown that wind regimes relate to the gradient of the pressure anomalies, rather than to the specific isobars pattern. Two main wind regimes - strong western and the strong eastern or northern - are well known over this region. The frequencies of the regimes according to seasons is verified. Strong eastern regimes are dominant during winter, while strong western regimes are frequent in all seasons.

Lead spatio-temporal pattern identification in urban microenvironments using moss bags and the Kohonen self-organizing maps

NASA Astrophysics Data System (ADS)

Deljanin, Isidora; Antanasijević, Davor; Vuković, Gordana; Urošević, Mira Aničić; Tomašević, Milica; Perić-Grujić, Aleksandra; Ristić, Mirjana

2015-09-01

The first investigation of the use of the Kohonen self-organizing map (SOM) which includes lead concentration and its isotopic composition in moss bags to assess the spatial and temporal patterns of lead in the urban microenvironments is presented in this paper. The moss bags experiment was carried out during 2011 in the city tunnel in Belgrade, as well as in street canyons at different heights (4, 8 and 16 m) and in public garages. The moss bags were exposed for 5 and 10 weeks. The results revealed that the 10 weeks period represents suitable exposure time in screening Pb isotopic composition in active biomonitoring analysis. The obtained results showed that the SOM analysis, by recognizing slight differences among moss samples regarding exposure time, horizontal and vertical spatial distribution, with both, contribution of stable lead isotopes and Pb concentration, could be recommended in biomonitoring analysis of lead distribution in urban microenvironments.

Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin.

PubMed

Nkiaka, E; Nawaz, N R; Lovett, J C

2016-07-01

Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.

Classification of MEC with the ALLTEM at Camp Stanley, Texas

USGS Publications Warehouse

Asch, T.; Moulton, C.; Smith, D.V.

2011-01-01

The ALLTEM is a multi-axis electromagnetic induction system designed for unexploded ordnance UXO applications. It uses a continuous triangle-wave excitation and provides good late-time signal-to-noise ratio SNR especially for ferrous targets. Multi-axis transmitter Tx and receiver Rx systems such as ALLTEM provide a richer data set from which to invert for the target parameters required to distinguish between clutter and UXO. Inversions of field data acquired between 2006 and 2010 over the Army's UXO Standardized Test sites at the Yuma Proving Ground YPG in Arizona and at the Aberdeen Proving Ground APG in Maryland have produced reasonable and generally repeatable results for many UXO items buried at different orientations and depths. In February-March 2011 ALLTEM data was acquired at two locations on the Camp Stanley Storage Activity CSSA just north of San Antonio, Texas. Camp Stanley is used to store munitions as well as test, fire, and overhaul munitions components. Site B-20 is an open burn/open detonation OBOD area and Site B-27 consists of narrow trenches blasted into limestone containing buried range and munitions debris and possibly MEC. The processing, analysis, and classification techniques developed at the controlled environments of YPG and APG have been applied to these two "live" sites at Camp Stanley. ALLTEM data analysis includes both classical numerical inversion of data from each anomaly and clustering of the raw data by means of a self-organizing map SOM via generalized neural network algorithms. Final classification consists of an integration of both the numerical and SOM results. ?? 2011 Society of Exploration Geophysicists.

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions.

PubMed

Jang, Eun-Hye; Park, Byoung-Jun; Park, Mi-Sook; Kim, Sang-Hyeob; Sohn, Jin-Hun

2015-06-18

The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7% is obtained by using DFA. This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.

A Neural-Network Clustering-Based Algorithm for Privacy Preserving Data Mining

NASA Astrophysics Data System (ADS)

Tsiafoulis, S.; Zorkadis, V. C.; Karras, D. A.

The increasing use of fast and efficient data mining algorithms in huge collections of personal data, facilitated through the exponential growth of technology, in particular in the field of electronic data storage media and processing power, has raised serious ethical, philosophical and legal issues related to privacy protection. To cope with these concerns, several privacy preserving methodologies have been proposed, classified in two categories, methodologies that aim at protecting the sensitive data and those that aim at protecting the mining results. In our work, we focus on sensitive data protection and compare existing techniques according to their anonymity degree achieved, the information loss suffered and their performance characteristics. The ℓ-diversity principle is combined with k-anonymity concepts, so that background information can not be exploited to successfully attack the privacy of data subjects data refer to. Based on Kohonen Self Organizing Feature Maps (SOMs), we firstly organize data sets in subspaces according to their information theoretical distance to each other, then create the most relevant classes paying special attention to rare sensitive attribute values, and finally generalize attribute values to the minimum extend required so that both the data disclosure probability and the information loss are possibly kept negligible. Furthermore, we propose information theoretical measures for assessing the anonymity degree achieved and empirical tests to demonstrate it.

Ants as biological indicators of Wayana Amerindian land use in French Guiana.

PubMed

Delabie, Jacques H C; Céréghino, Régis; Groc, Sarah; Dejean, Andrea; Gibernau, Marc; Corbara, Bruno; Dejean, Alain

2009-07-01

We examined the ecological impact of traditional land use by Wayana Amerindians in French Guiana using ants as bio-indicators. Ants were sampled through a rapid assessment method and the core results analyzed using Kohonen's self-organizing maps (SOM). Our sample sites included: (1) a Wayana village; (2) a cassava plantation; (3) an abandoned cassava plantation; (4) a forest fragment near the village; (5) a riparian forest; and (6) a primary terra firma forest. The ant diversity decreases according to the degree to which the habitat is disturbed. The SOM allowed us to compare the ecological succession between the six habitats. The protocol used is robust since the same conclusions were drawn using partial data.

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

PubMed

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Function approximation using combined unsupervised and supervised learning.

PubMed

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Differentiating the Spatiotemporal Distribution of Natural and Anthropogenic Processes on River Water-Quality Variation Using a Self-Organizing Map With Factor Analysis.

PubMed

Wang, Yeuh-Bin; Liu, Chen-Wuing; Lee, Jin-Jing

2015-08-01

To elucidate the historical improvement and advanced measure of river water quality in the Taipei metropolitan area, this study applied the self-organizing map (SOM) technique with factor analysis (FA) to differentiate the spatiotemporal distribution of natural and anthropogenic processes on river water-quality variation spanning two decades. The SOM clustered river water quality into five groups: very low pollution, low pollution, moderate pollution, high pollution, and very high pollution. FA was then used to extract four latent factors that dominated water quality from 1991 to 2011 including three anthropogenic process factors (organic, industrial, and copper pollution) and one natural process factor [suspended solids (SS) pollution]. The SOM revealed that the water quality improved substantially over time. However, the downstream river water quality was still classified as high pollution because of an increase in anthropogenic activity. FA showed the spatiotemporal pattern of each factor score decreasing over time, but the organic pollution factor downstream of the Tamsui River, as well as the SS factor scores in the upstream major tributary (the Dahan Stream), remained within the high pollution level. Therefore, we suggest that public sewage-treatment plants should be upgraded from their current secondary biological processing to advanced treatment processing. The conservation of water and soil must also be reinforced to decrease the SS loading of the Dahan Stream from natural erosion processes in the future.

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.

PubMed

Higuera, Clara; Gardiner, Katheleen J; Cios, Krzysztof J

2015-01-01

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

PubMed Central

Higuera, Clara; Gardiner, Katheleen J.; Cios, Krzysztof J.

2015-01-01

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets. PMID:26111164

Catchment classification by runoff behaviour with self-organizing maps (SOM)

NASA Astrophysics Data System (ADS)

Ley, R.; Casper, M. C.; Hellebrand, H.; Merz, R.

2011-09-01

Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Catchment classification by runoff behaviour with self-organizing maps (SOM)

NASA Astrophysics Data System (ADS)

Ley, R.; Casper, M. C.; Hellebrand, H.; Merz, R.

2011-03-01

Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Application of growing hierarchical SOM for visualisation of network forensics traffic data.

PubMed

Palomo, E J; North, J; Elizondo, D; Luque, R M; Watson, T

2012-08-01

Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of these attacks and large amount of data. Therefore, software tools that can help with these digital investigations are in great demand. In this paper, a novel approach to analysing and visualising network traffic data based on growing hierarchical self-organising maps (GHSOM) is presented. The self-organising map (SOM) has been shown to be successful for the analysis of highly-dimensional input data in data mining applications as well as for data visualisation in a more intuitive and understandable manner. However, the SOM has some problems related to its static topology and its inability to represent hierarchical relationships in the input data. The GHSOM tries to overcome these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relationships among them. Moreover, the proposed GHSOM has been modified to correctly treat the qualitative features that are present in the traffic data in addition to the quantitative features. Experimental results show that this approach can be very useful for a better understanding of network traffic data, making it easier to search for evidence of attacks or anomalous behaviour in a network environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Data splitting for artificial neural networks using SOM-based stratified sampling.

PubMed

May, R J; Maier, H R; Dandy, G C

2010-03-01

Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance; however, the choice of sampling methodology is rarely given due consideration by ANN modellers. Increased confidence in the sampling is of paramount importance, since the hold-out sampling is generally performed only once during ANN development. This paper considers the variability in the quality of subsets that are obtained using different data splitting approaches. A novel approach to stratified sampling, based on Neyman sampling of the self-organizing map (SOM), is developed, with several guidelines identified for setting the SOM size and sample allocation in order to minimize the bias and variance in the datasets. Using an example ANN function approximation task, the SOM-based approach is evaluated in comparison to random sampling, DUPLEX, systematic stratified sampling, and trial-and-error sampling to minimize the statistical differences between data sets. Of these approaches, DUPLEX is found to provide benchmark performance with good model performance, with no variability. The results show that the SOM-based approach also reliably generates high-quality samples and can therefore be used with greater confidence than other approaches, especially in the case of non-uniform datasets, with the benefit of scalability to perform data splitting on large datasets. Copyright 2009 Elsevier Ltd. All rights reserved.

Intelligent estimation of spatially distributed soil physical properties

USGS Publications Warehouse

Iwashita, F.; Friedel, M.J.; Ribeiro, G.F.; Fraser, Stephen J.

2012-01-01

Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave-convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75. cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave-convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data. ?? 2011 Elsevier B.V.

Discovery of Possible Gene Relationships through the Application of Self-Organizing Maps to DNA Microarray Databases

PubMed Central

Chavez-Alvarez, Rocio; Chavoya, Arturo; Mendez-Vazquez, Andres

2014-01-01

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms. PMID:24699245

Estimation of austral summer net community production in the Amundsen Sea: Self-organizing map analysis approach

NASA Astrophysics Data System (ADS)

Park, K.; Hahm, D.; Lee, D. G.; Rhee, T. S.; Kim, H. C.

2014-12-01

The Amundsen Sea, Antarctica, has been known for one of the most susceptible region to the current climate change such as sea ice melting and sea surface temperature change. In the Southern Ocean, a predominant amount of primary production is occurring in the continental shelf region. Phytoplankton blooms take place during the austral summer due to the limited sunlit and sea ice cover. Thus, quantifying the variation of summer season net community production (NCP) in the Amundsen Sea is essential to analyze the influence of climate change to the variation of biogeochemical cycle in the Southern Ocean. During the past three years of 2011, 2012 and 2014 in austral summer, we have conducted underway observations of ΔO2/Ar and derived NCP of the Amundsen Sea. Despite the importance of NCP for understanding biological carbon cycle of the ocean, the observations are rather limited to see the spatio-temporal variation in the Amundsen Sea. Therefore, we applied self-organizing map (SOM) analysis to expand our observed data sets and estimate the NCP during the summer season. SOM analysis, a type of artificial neural network, has been proved to be a useful method for extracting and classifying features in geoscience. In oceanography, SOM has applied for the analysis of various properties of the seawater such as sea surface temperature, chlorophyll concentration, pCO2, and NCP. Especially it is useful to expand a spatial coverage of direct measurements or to estimate properties whose satellite observations are technically or spatially limited. In this study, we estimate summer season NCP and find a variables set which optimally delineates the NCP variation in the Amundsen Sea as well. Moreover, we attempt to analyze the interannual variation of the Amundsen Sea NCP by taking climatological factors into account for the SOM analysis.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database.

PubMed

Ikeda, Shun; Abe, Takashi; Nakamura, Yukiko; Kibinge, Nelson; Hirai Morita, Aki; Nakatani, Atsushi; Ono, Naoaki; Ikemura, Toshimichi; Nakamura, Kensuke; Altaf-Ul-Amin, Md; Kanaya, Shigehiko

2013-05-01

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations.

PubMed

Grossberg, Stephen; Pilly, Praveen K

2014-02-05

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

Integration of Satellite Tracking Data and Satellite Images for Detailed Characteristics of Wildlife Habitats

NASA Astrophysics Data System (ADS)

Dobrynin, D. V.; Rozhnov, V. V.; Saveliev, A. A.; Sukhova, O. V.; Yachmennikova, A. A.

2017-12-01

Methods of analysis of the results got from satellite tracking of large terrestrial mammals differ in the level of their integration with additional geographic data. The reliable fine-scale cartographic basis for assessing specific wildlife habitats can be developed through the interpretation of multispectral remote sensing data and extrapolation of the results to the entire estimated species range. Topographic maps were ordinated according to classified features using self-organizing maps (Kohonen's SOM). The satellite image of the Ussuriiskyi Nature Reserve area was interpreted for the analysis of movement conditions for seven wild Amur tigers ( Panthera tigris altaica) equipped with GPS collars. 225 SOM classes for cartographic visualization are sufficient for the detailed mapping of all natural complexes that were identified as a result of interpretation. During snow-free periods, tigers preferred deciduous and shrub associations at lower elevations, as well as mixed forests in the valleys of streams that are adjacent to sparse forests and shrub watershed in the mountain ranges; during heavy snow periods, the animals preferred the entire range of plant communities in different relief types, except for open sites in meadows and abandoned fields at foothills. The border zones of different biotopes were typically used by the tigers during all seasons. Amur tigers preferred coniferous forests for long-term movements.

Seismic facies analysis based on self-organizing map and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Du, Hao-kun; Cao, Jun-xing; Xue, Ya-juan; Wang, Xing-jian

2015-01-01

Seismic facies analysis plays an important role in seismic interpretation and reservoir model building by offering an effective way to identify the changes in geofacies inter wells. The selections of input seismic attributes and their time window have an obvious effect on the validity of classification and require iterative experimentation and prior knowledge. In general, it is sensitive to noise when waveform serves as the input data to cluster analysis, especially with a narrow window. To conquer this limitation, the Empirical Mode Decomposition (EMD) method is introduced into waveform classification based on SOM. We first de-noise the seismic data using EMD and then cluster the data using 1D grid SOM. The main advantages of this method are resolution enhancement and noise reduction. 3D seismic data from the western Sichuan basin, China, are collected for validation. The application results show that seismic facies analysis can be improved and better help the interpretation. The powerful tolerance for noise makes the proposed method to be a better seismic facies analysis tool than classical 1D grid SOM method, especially for waveform cluster with a narrow window.

Land Cover Change Detection using Neural Network and Grid Cells Techniques

NASA Astrophysics Data System (ADS)

Bagan, H.; Li, Z.; Tangud, T.; Yamagata, Y.

2017-12-01

In recent years, many advanced neural network methods have been applied in land cover classification, each of which has both strengths and limitations. In which, the self-organizing map (SOM) neural network method have been used to solve remote sensing data classification problems and have shown potential for efficient classification of remote sensing data. In SOM, both the distribution and the topology of features of the input layer are identified by using an unsupervised, competitive, neighborhood learning method. The high-dimensional data are then projected onto a low-dimensional map (competitive layer), usually as a two-dimensional map. The neurons (nodes) in the competitive layer are arranged by topological order in the input space. Spatio-temporal analyses of land cover change based on grid cells have demonstrated that gridded data are useful for obtaining spatial and temporal information about areas that are smaller than municipal scale and are uniform in size. Analysis based on grid cells has many advantages: grid cells all have the same size allowing for easy comparison; grids integrate easily with other scientific data; grids are stable over time and thus facilitate the modelling and analysis of very large multivariate spatial data sets. This study chose time-series MODIS and Landsat images as data sources, applied SOM neural network method to identify the land utilization in Inner Mongolia Autonomous Region of China. Then the results were integrated into grid cell to get the dynamic change maps. Land cover change using MODIS data in Inner Mongolia showed that urban area increased more than fivefold in recent 15 years, along with the growth of mining area. In terms of geographical distribution, the most obvious place of urban expansion is Ordos in southwest Inner Mongolia. The results using Landsat images from 1986 to 2014 in northeastern part of the Inner Mongolia show degradation in grassland from 1986 to 2014. Grid-cell-based spatial correlation analysis also confirmed a strong negative correlation between grassland and barren land, indicating that grassland degradation in this region is due to the urbanization and coal mining activities over the past three decades.

Climate change predicted to negatively influence surface soil health of dryland cropping systems in the Inland Pacific Northwest

USDA-ARS?s Scientific Manuscript database

Soil organic matter (SOM) is a key indicator of agricultural productivity and overall soil health. Currently, dryland cropping systems of the inland Pacific Northwest (iPNW) span a large gradient in mean annual temperature (MAT) and precipitation (MAP). These climatic drivers are major determinants ...

Soil Fertility Map for Food Legumes Production Areas in China

NASA Astrophysics Data System (ADS)

Li, Ling; Yang, Tao; Redden, Robert; He, Weifeng; Zong, Xuxiao

2016-05-01

Given the limited resources of fossil energy, and the environmental risks of excess fertilizer on crops, it is time to reappraise the potential role of food legume biological nitrogen fixation (BNF) as sources of nitrogen for cropping systems in China. 150 soil samples across 17 provinces and 2 municipalities of China were collected and analyzed. A distribution map of the soil fertilities and their patterns of distribution was constructed. The pH results indicated that soils were neutral to slightly alkaline overall. The soil organic matter (SOM) and the available nitrogen (AN) content were relatively low, while the available phosphorus (AP) and available potassium (AK) contents were from moderate to high. Production areas of food legumes (faba bean, pea, adzuki bean, mung bean and common bean) were clearly separated into 4 soil fertility type clusters. In addition, regions with SOM, AN, AP and AK deficiency, high acidity and high alkalinity were listed as target areas for further soil improvement. The potential was considered for biological nitrogen fixation to substitute for the application of mineral nitrogen fertiliser.

Variability of Changjiang Diluted Water revealed by a 45-year long-term ocean hindcast and Self-Organizing Maps analysis

NASA Astrophysics Data System (ADS)

Zeng, Xiangming; He, Ruoying; Zong, Haibo

2017-08-01

Based on long-term realistic ocean circulation hindcast for in the Bohai, Yellow, and East China Seas, 45 years (1961-2005) of sea surface salinity data were analyzed using Self-Organizing Maps (SOM) to have a better understanding of the Changjiang Diluted Water (CDW) variation. Three spatial patterns were revealed by the SOM: normal, transition, and extension. The normal pattern mainly occurs from December to May while the CDW hugs China's east coast closely and flows southward. The extension pattern is dominant from June to October when the CDW extends northwestward toward Jeju Island in an omega shape. The transition pattern prevails for the rest of the year. Pattern-averaged temperature, circulation, and chlorophyll-a concentration show significant differences. CDW area and its eastern most extension were explored as a function of the Changjiang runoff and regional upwelling index. We found that Changjiang runoff and upwelling index can be reasonable predictors for the overall CDW area, while ambient circulation determines the distribution and structure of the CDW, and thus the CDW eastern most extension.

Soil Fertility Map for Food Legumes Production Areas in China

PubMed Central

Li, Ling; Yang, Tao; Redden, Robert; He, Weifeng; Zong, Xuxiao

2016-01-01

Given the limited resources of fossil energy, and the environmental risks of excess fertilizer on crops, it is time to reappraise the potential role of food legume biological nitrogen fixation (BNF) as sources of nitrogen for cropping systems in China. 150 soil samples across 17 provinces and 2 municipalities of China were collected and analyzed. A distribution map of the soil fertilities and their patterns of distribution was constructed. The pH results indicated that soils were neutral to slightly alkaline overall. The soil organic matter (SOM) and the available nitrogen (AN) content were relatively low, while the available phosphorus (AP) and available potassium (AK) contents were from moderate to high. Production areas of food legumes (faba bean, pea, adzuki bean, mung bean and common bean) were clearly separated into 4 soil fertility type clusters. In addition, regions with SOM, AN, AP and AK deficiency, high acidity and high alkalinity were listed as target areas for further soil improvement. The potential was considered for biological nitrogen fixation to substitute for the application of mineral nitrogen fertiliser. PMID:27212262

Associations Across Caregiver and Care Recipient Symptoms: Self-Organizing Map and Meta-analysis.

PubMed

Voutilainen, Ari; Ruokostenpohja, Nora; Välimäki, Tarja

2018-03-19

The main objective of this study was to reveal generalizable associations across caregiver burden (CGB), caregiver depression (CGD), care recipient cognitive ability (CRCA), and care recipient behavioral and psychological symptoms of dementia (BPSD). Studies published between 2004 and 2014 and reporting CGB and/or CGD together with CRCA and/or BPSD were included. Only 95 out of 1,955 studies provided enough data for data clustering with the Self-Organizing Map (SOM) and 27 of them for meta-analyses based on correlation coefficients. Caregiver and care recipient symptoms were not tightly associated with each other, except for the CGB-BPSD interaction at the individual level. SOM emphasized the cluster comprising studies reporting low CGB, low CGD, high CRCA, and few BPSD. Meta-analyses indicated high heterogeneity between the original studies. Relationships between caregiver and care recipient symptoms should be treated as situation-specific phenomena, at least when the symptoms are moderate at most. Dementia caregiving per se should not be understood as a source of stress and mental health problems. More systematic and coherent use of measures is necessary to enable a comprehensive analysis of caregiving.

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

Allen, J. I.; Somerfield, P. J.; Gilbert, F. J.

2007-01-01

Marine ecosystem models are becoming increasingly complex and sophisticated, and are being used to estimate the effects of future changes in the earth system with a view to informing important policy decisions. Despite their potential importance, far too little attention has been, and is generally, paid to model errors and the extent to which model outputs actually relate to real-world processes. With the increasing complexity of the models themselves comes an increasing complexity among model results. If we are to develop useful modelling tools for the marine environment we need to be able to understand and quantify the uncertainties inherent in the simulations. Analysing errors within highly multivariate model outputs, and relating them to even more complex and multivariate observational data, are not trivial tasks. Here we describe the application of a series of techniques, including a 2-stage self-organising map (SOM), non-parametric multivariate analysis, and error statistics, to a complex spatio-temporal model run for the period 1988-1989 in the Southern North Sea, coinciding with the North Sea Project which collected a wealth of observational data. We use model output, large spatio-temporally resolved data sets and a combination of methodologies (SOM, MDS, uncertainty metrics) to simplify the problem and to provide tractable information on model performance. The use of a SOM as a clustering tool allows us to simplify the dimensions of the problem while the use of MDS on independent data grouped according to the SOM classification allows us to validate the SOM. The combination of classification and uncertainty metrics allows us to pinpoint the variables and associated processes which require attention in each region. We recommend the use of this combination of techniques for simplifying complex comparisons of model outputs with real data, and analysis of error distributions.

Direct Analysis in Real Time-Mass Spectrometry & Kohonen Artificial Neural Networks for the Rapid Species Identification of Larvae, Pupae and Adult Life Stages of Carrion Insects.

PubMed

Beyramysoltan, Samira; Giffen, Justine E; Rosati, Jennifer Y; Musah, Rabi Ann

2018-06-20

Species determination of the various life stages of flies (order: Diptera) is challenging, particularly for the immature forms, because analogous life stages of different species are difficult to differentiate morphologically. It is demonstrated here that DART high-resolution mass spectrometry (DART-HRMS) combined with supervised Kohonen Self-Organizing Maps (SOM) enables accomplishment of species-level identification of larvae, pupae and adult life stages of carrion flies. DART-HRMS data for each life stage were acquired from analysis of ethanol suspensions representing Calliphoridae, Phoridae and Sarcophagidae families, without additional sample preparation. After preprocessing, the data were subjected to a combination of minimum Redundancy Maximal Relevance (mRMR) and Sparse Discriminant Analysis (SDA) methods to select the most significant variables for creating accurate SOM models. The resulting data were divided into training and validation sets, and then analyzed by the SOM method to define the proper discrimination models. The 5-fold venetian blind cross-validation misclassification error was below 7% for all life stages, and the validation samples were correctly identified in all cases. The multiclass SOM model also revealed which chemical components were the most significant markers for each species, with several of these being amino acids. The results show that processing of DART-HRMS data using artificial neural networks (ANNs) based on the Kohonen SOM approach enables rapid discrimination and identification of fly species even for the immature life stages. The ANNs can be continuously expanded to include a larger number of species, and can be used to screen DART-HRMS data from unknowns to rapidly determine species identity.

Innovative use of self-organising maps (SOMs) in model validation.

NASA Astrophysics Data System (ADS)

Jolly, Ben; McDonald, Adrian; Coggins, Jack

2016-04-01

We present an innovative combination of techniques for validation of numerical weather prediction (NWP) output against both observations and reanalyses using two classification schemes, demonstrated by a validation of the operational NWP 'AMPS' (the Antarctic Mesoscale Prediction System). Historically, model validation techniques have centred on case studies or statistics at various time scales (yearly/seasonal/monthly). Within the past decade the latter technique has been expanded by the addition of classification schemes in place of time scales, allowing more precise analysis. Classifications are typically generated for either the model or the observations, then used to create composites for both which are compared. Our method creates and trains a single self-organising map (SOM) on both the model output and observations, which is then used to classify both datasets using the same class definitions. In addition to the standard statistics on class composites, we compare the classifications themselves between the model and the observations. To add further context to the area studied, we use the same techniques to compare the SOM classifications with regimes developed for another study to great effect. The AMPS validation study compares model output against surface observations from SNOWWEB and existing University of Wisconsin-Madison Antarctic Automatic Weather Stations (AWS) during two months over the austral summer of 2014-15. Twelve SOM classes were defined in a '4 x 3' pattern, trained on both model output and observations of 2 m wind components, then used to classify both training datasets. Simple statistics (correlation, bias and normalised root-mean-square-difference) computed for SOM class composites showed that AMPS performed well during extreme weather events, but less well during lighter winds and poorly during the more changeable conditions between either extreme. Comparison of the classification time-series showed that, while correlations were lower during lighter wind periods, AMPS actually forecast the existence of those periods well suggesting that the correlations may be unfairly low. Further investigation showed poor temporal alignment during more changeable conditions, highlighting problems AMPS has around the exact timing of events. There was also a tendency for AMPS to over-predict certain wind flow patterns at the expense of others. In order to gain a larger scale perspective, we compared our mesoscale SOM classification time-series with synoptic scale regimes developed by another study using ERA-Interim reanalysis output and k-means clustering. There was good alignment between the regimes and the observations classifications (observations/regimes), highlighting the effect of synoptic scale forcing on the area. However, comparing the alignment between observations/regimes and AMPS/regimes showed that AMPS may have problems accurately resolving the strength and location of cyclones in the Ross Sea to the north of the target area.

Using parallel factor analysis modeling (PARAFAC) and self-organizing maps to track senescence-induced patterns in leaf litter leachate

NASA Astrophysics Data System (ADS)

Wheeler, K. I.; Levia, D. F., Jr.; Hudson, J. E.

2017-12-01

As trees undergo autumnal processes such as resorption, senescence, and leaf abscission, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams changes. However, little research has investigated how the fluorescent DOM (FDOM) changes throughout the autumn and how this differs inter- and intraspecifically. Two of the major impacts of global climate change on forested ecosystems include altering phenology and causing forest community species and subspecies composition restructuring. We examined changes in FDOM in leachate from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and yellow poplar (Liriodendron tulipifera L.) leaves from Maryland throughout three different phenophases: green, senescing, and freshly abscissed. Beech leaves from Maryland and Rhode Island have previously been identified as belonging to the same distinct genetic cluster and beech trees from Vermont and the study site in North Carolina from the other. FDOM in samples was characterized using excitation-emission matrices (EEMs) and a six-component parallel factor analysis (PARAFAC) model was created to identify components. Self-organizing maps (SOMs) were used to visualize variation and patterns in the PARAFAC component proportions of the leachate samples. Phenophase and species had the greatest influence on determining where a sample mapped on the SOM when compared to genetic clusters and geographic origin. Throughout senescence, FDOM from all the trees transitioned from more protein-like components to more humic-like ones. Percent greenness of the sampled leaves and the proportion of the tyrosine-like component 1 were found to significantly differ between the two genetic beech clusters. This suggests possible differences in photosynthesis and resorption between the two genetic clusters of beech. The use of SOMs to visualize differences in patterns of senescence between the different species and genetic populations proved to be useful in ways that other multivariate analysis techniques lack.

Assessment of Ice Shape Roughness Using a Self-Orgainizing Map Approach

NASA Technical Reports Server (NTRS)

Mcclain, Stephen T.; Kreeger, Richard E.

2013-01-01

Self-organizing maps are neural-network techniques for representing noisy, multidimensional data aligned along a lower-dimensional and nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. Prior investigations of ice shapes have focused on using self-organizing maps to characterize mean ice forms. The Icing Research Branch has recently acquired a high resolution three dimensional scanner system capable of resolving ice shape surface roughness. A method is presented for the evaluation of surface roughness variations using high-resolution surface scans based on a self-organizing map representation of the mean ice shape. The new method is demonstrated for 1) an 18-in. NACA 23012 airfoil 2 AOA just after the initial ice coverage of the leading 5 of the suction surface of the airfoil, 2) a 21-in. NACA 0012 at 0AOA following coverage of the leading 10 of the airfoil surface, and 3) a cold-soaked 21-in.NACA 0012 airfoil without ice. The SOM method resulted in descriptions of the statistical coverage limits and a quantitative representation of early stages of ice roughness formation on the airfoils. Limitations of the SOM method are explored, and the uncertainty limits of the method are investigated using the non-iced NACA 0012 airfoil measurements.

Constructing Ozone Profile Climatologies with Self-Organizing Maps: Illustrations with CONUS Ozonesonde Data

NASA Astrophysics Data System (ADS)

Thompson, A. M.; Stauffer, R. M.; Young, G. S.

2015-12-01

Ozone (O3) trends analysis is typically performed with monthly or seasonal averages. Although this approach works well for stratospheric or total O3, uncertainties in tropospheric O3 amounts may be large due to rapid meteorological changes near the tropopause and in the lower free troposphere (LFT) where pollution has a days-weeks lifetime. We use self-organizing maps (SOM), a clustering technique, as an alternative for creating tropospheric climatologies from O3 soundings. In a previous study of 900 tropical ozonesondes, clusters representing >40% of profiles deviated > 1-sigma from mean O­3. Here SOM are based on 15 years of data from four sites in the contiguous US (CONUS; Boulder, CO; Huntsville, AL; Trinidad Head, CA; Wallops Island, VA). Ozone profiles from 2 - 12 km are used to evaluate the impact of tropopause variability on climatology; 2 - 6 km O3 profile segments are used for the LFT. Near-tropopause O­3 is twice the mean O­3 mixing ratio in three clusters of 2 - 12 km O3, representing > 15% of profiles at each site. Large mid and lower-tropospheric O3 deviations from monthly means are found in clusters of both 2 - 12 and 2 - 6 km O3. Positive offsets result from pollution and stratosphere-to-troposphere exchange. In the LFT the lowest tropospheric O3 is associated with subtropical air. Some clusters include profiles with common seasonality but other factors, e.g., tropopause height or LFT column amount, characterize other SOM nodes. Thus, as for tropical profiles, CONUS O­3 averages can be a poor choice for a climatology.

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

2017-12-01

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

Abstracting GIS Layers from Hyperspectral Imagery

DTIC Science & Technology

2009-03-01

Difference Vegetative Index ( NDVI ) 2-20 2.2.10 Separating Trees from Grass . . . . . . . . . . . 2-22 2.3 Spatial Analysis...2-18 2.10. Example of the Normalized Difference Vegetation Index ( NDVI ) applied to a hyperspectral image. . . . . . . . . . . . . . . . . . 2-20...3.5. Example of applying NDVI to a SOM. . . . . . . . . . . . . . . 3-8 3.6. Visualization of the NIR scatter tree ID algorithm. . . . . . . . 3-9 ix

Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing

NASA Astrophysics Data System (ADS)

Lin, Bin; An, Jubai; Brown, Carl E.; Chen, Weiwei

2003-05-01

In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.

Organizing Books and Authors by Multilayer SOM.

PubMed

Zhang, Haijun; Chow, Tommy W S; Wu, Q M Jonathan

2016-12-01

This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.

The Segmentation of Point Clouds with K-Means and ANN (artifical Neural Network)

NASA Astrophysics Data System (ADS)

Kuçak, R. A.; Özdemir, E.; Erol, S.

2017-05-01

Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

PubMed

Eslamizadeh, Gholamhossein; Barati, Ramin

2017-05-01

Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling and Inverse Controller Design for an Unmanned Aerial Vehicle Based on the Self-Organizing Map

NASA Technical Reports Server (NTRS)

Cho, Jeongho; Principe, Jose C.; Erdogmus, Deniz; Motter, Mark A.

2005-01-01

The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a SOM-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.

Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

ERIC Educational Resources Information Center

Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

2012-01-01

Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

Fire-climate interactions in forests of the American Pacific Coast

Treesearch

Valerie Trouet; Alan H. Taylor; Andrew M. Carleton; Carl N. Skinner

2006-01-01

We investigate relationships between climate and wildfire activity between 1929 and 2004 in Pacific coast forests of the United States. Self-Organizing Mapping (SOM) of annual area burned in National Forests (NF) in California, Oregon, and Washington identifies three contiguous NF groups and a fourth group of NF traversed by major highways. Large fire years in all...

Climate change predicted to negatively influence surface soil organic matter of dryland cropping systems in the Inland Pacific Northwest, USA

USDA-ARS?s Scientific Manuscript database

Soil organic matter (SOM) is a key indicator of agricultural productivity and overall soil health. Currently, dryland cropping systems of the inland Pacific Northwest (iPNW) span a large gradient in mean annual temperature (MAT) and precipitation (MAP).These climatic drivers are major determinants o...

Using self-organizing maps to determine observation threshold limit predictions in highly variant data

USGS Publications Warehouse

Paganoni, C.A.; Chang, K.C.; Robblee, M.B.

2006-01-01

A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

Using self-organizing maps to determine observation threshold limit predictions in highly variant data

NASA Astrophysics Data System (ADS)

Paganoni, Christopher A.; Chang, K. C.; Robblee, Michael B.

2006-05-01

A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

Inference of Surface Parameters from Near-Infrared Spectra of Crystalline H2O Ice with Neural Learning

NASA Astrophysics Data System (ADS)

Zhang, Lili; Merényi, Erzsébet; Grundy, William M.; Young, Eliot F.

2010-07-01

The near-infrared spectra of icy volatiles collected from planetary surfaces can be used to infer surface parameters, which in turn may depend on the recent geologic history. The high dimensionality and complexity of the spectral data, the subtle differences between the spectra, and the highly nonlinear interplay between surface parameters make it often difficult to accurately derive these surface parameters. We use a neural machine, with a Self-Organizing Map (SOM) as its hidden layer, to infer the latent physical parameters, temperature and grain size from near-infrared spectra of crystalline H2O ice. The output layer of the SOM-hybrid machine is customarily trained with only the output from the SOM winner. We show that this scheme prevents simultaneous achievement of high prediction accuracies for both parameters. We propose an innovative neural architecture we call Conjoined Twins that allows multiple (k) SOM winners to participate in the training of the output layer and in which the customization of k can be limited automatically to a small range. With this novel machine we achieve scientifically useful accuracies, 83.0 ± 2.7% and 100.0 ± 0.0%, for temperature and grain size, respectively, from simulated noiseless spectra. We also show that the performance of the neural model is robust under various noisy conditions. A primary application of this prediction capability is planned for spectra returned from the Pluto-Charon system by New Horizons.

Evaluation of the NASA GISS AR5 SCM/GCM at the ARM SGP Site using Self Organizing Maps

NASA Astrophysics Data System (ADS)

Kennedy, A. D.; Dong, X.; Xi, B.; Del Genio, A. D.; Wolf, A.

2011-12-01

Understanding and improving clouds in climate models requires moving beyond comparing annual and seasonal means. Errors can offset resulting in models getting the right long-term solution for the wrong reasons. For example, cloud parameterization errors may be balanced by the model incorrectly simulating the frequency distribution of atmospheric states. To faithfully evaluate climate models it is necessary to partition results into specific regimes. This has been completed in the past by evaluating models by their ability to produce cloud regimes as determined by observational products from satellites. An alternative approach is to first classify meteorological regimes (i.e., synoptic pattern and forcing) and then determine what types of clouds occur for each class. In this study, a competitive neural network known as the Self Organizing Map (SOM) is first used to classify synoptic patterns from a reanalysis over the Southern Great Plains (SGP) region during the period 1999-2008. These results are then used to evaluate simulated clouds from the AR5 version of the NASA GISS Model E Single Column Model (SCM). Unlike past studies that narrowed classes into several categories, this study assumes that the atmosphere is capable of producing an infinite amount of states. As a result, SOMs were generated with a large number of classes for specific months when model errors were found. With nearly ten years of forcing data, an adequate number of samples have been used to determine how cloud fraction varies across the SOM and to distinguish cloud errors. Barring major forcing errors, SCM studies can be thought of as what the GCM would simulate if the dynamics were perfect. As a result, simulated and observed CFs frequently occur for the same atmospheric states. For example, physically deep clouds during the winter months occur for a small number of classes in the SOM. Although the model produces clouds during the correct states, CFs are consistently too low. Instead, the model has a positive bias of thinner clouds during these classes that were associated with low-pressure systems and fronts. To determine if this and other SCM errors are present in the GCM, the Atmospheric Model Intercomparison Project (AMIP) run for the NASA GISS GCM will also be investigated. The SOM will be used to classify atmospheric states within the GCM to determine how well the GCM captures the PDF of observed atmospheric states. Together, these comparisons will allow for a thorough evaluation of the model at the ARM SGP site.

Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

Enabling technology for human collaboration.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murphy, Tim Andrew; Jones, Wendell Bruce; Warner, David Jay

2003-11-01

This report summarizes the results of a five-month LDRD late start project which explored the potential of enabling technology to improve the performance of small groups. The purpose was to investigate and develop new methods to assist groups working in high consequence, high stress, ambiguous and time critical situations, especially those for which it is impractical to adequately train or prepare. A testbed was constructed for exploratory analysis of a small group engaged in tasks with high cognitive and communication performance requirements. The system consisted of five computer stations, four with special devices equipped to collect physiologic, somatic, audio andmore » video data. Test subjects were recruited and engaged in a cooperative video game. Each team member was provided with a sensor array for physiologic and somatic data collection while playing the video game. We explored the potential for real-time signal analysis to provide information that enables emergent and desirable group behavior and improved task performance. The data collected in this study included audio, video, game scores, physiological, somatic, keystroke, and mouse movement data. The use of self-organizing maps (SOMs) was explored to search for emergent trends in the physiological data as it correlated with the video, audio and game scores. This exploration resulted in the development of two approaches for analysis, to be used concurrently, an individual SOM and a group SOM. The individual SOM was trained using the unique data of each person, and was used to monitor the effectiveness and stress level of each member of the group. The group SOM was trained using the data of the entire group, and was used to monitor the group effectiveness and dynamics. Results suggested that both types of SOMs were required to adequately track evolutions and shifts in group effectiveness. Four subjects were used in the data collection and development of these tools. This report documents a proof of concept study, and its observations are preliminary. Its main purpose is to demonstrate the potential for the tools developed here to improve the effectiveness of groups, and to suggest possible hypotheses for future exploration.« less

The use of self-organising maps for anomalous behaviour detection in a digital investigation.

PubMed

Fei, B K L; Eloff, J H P; Olivier, M S; Venter, H S

2006-10-16

The dramatic increase in crime relating to the Internet and computers has caused a growing need for digital forensics. Digital forensic tools have been developed to assist investigators in conducting a proper investigation into digital crimes. In general, the bulk of the digital forensic tools available on the market permit investigators to analyse data that has been gathered from a computer system. However, current state-of-the-art digital forensic tools simply cannot handle large volumes of data in an efficient manner. With the advent of the Internet, many employees have been given access to new and more interesting possibilities via their desktop. Consequently, excessive Internet usage for non-job purposes and even blatant misuse of the Internet have become a problem in many organisations. Since storage media are steadily growing in size, the process of analysing multiple computer systems during a digital investigation can easily consume an enormous amount of time. Identifying a single suspicious computer from a set of candidates can therefore reduce human processing time and monetary costs involved in gathering evidence. The focus of this paper is to demonstrate how, in a digital investigation, digital forensic tools and the self-organising map (SOM)--an unsupervised neural network model--can aid investigators to determine anomalous behaviours (or activities) among employees (or computer systems) in a far more efficient manner. By analysing the different SOMs (one for each computer system), anomalous behaviours are identified and investigators are assisted to conduct the analysis more efficiently. The paper will demonstrate how the easy visualisation of the SOM enhances the ability of the investigators to interpret and explore the data generated by digital forensic tools so as to determine anomalous behaviours.

Explore the Impacts of River Flow and Water Quality on Fish Communities

NASA Astrophysics Data System (ADS)

Tsai, W. P.; Chang, F. J.; Lin, C. Y.; Hu, J. H.; Yu, C. J.; Chu, T. J.

2015-12-01

Owing to the limitation of geographical environment in Taiwan, the uneven temporal and spatial distribution of rainfall would cause significant impacts on river ecosystems. To pursue sustainable water resources development, integrity and rationality is important to water management planning. The water quality and the flow regimes of rivers are closely related to each other and affect river ecosystems simultaneously. Therefore, this study collects long-term observational heterogeneity data, which includes water quality parameters, stream flow and fish species in the Danshui River of norther Taiwan, and aims to explore the complex impacts of water quality and flow regime on fish communities in order to comprehend the situations of the eco-hydrological system in this river basin. First, this study improves the understanding of the relationship between water quality parameters, flow regime and fish species by using artificial neural networks (ANNs). The Self-organizing feature map (SOM) is an unsupervised learning process used to cluster, analyze and visualize a large number of data. The results of SOM show that nine clusters (3x3) forms the optimum map size based on the local minimum values of both quantization error (QE) and topographic error (TE). Second, the fish diversity indexes are estimated by using the Adapted network-based fuzzy inference system (ANFIS) based on key input factors determined by the Gamma Test (GT), which is a useful tool for reducing model dimension and the structure complexity of ANNs. The result reveals that the constructed models can effectively estimate fish diversity indexes and produce good estimation performance based on the 9 clusters identified by the SOM, in which RMSE is 0.18 and CE is 0.84 for the training data set while RMSE is 0.20 and CE is 0.80 for the testing data set.

Time-resolved metabolomics reveals metabolic modulation in rice foliage

PubMed Central

Sato, Shigeru; Arita, Masanori; Soga, Tomoyoshi; Nishioka, Takaaki; Tomita, Masaru

2008-01-01

Background To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. However, the results were unsatisfactory and effective data-mining techniques that apply appropriate comprehensive datasets are needed. Results Using capillary electrophoresis mass spectrometry (CE-MS) and capillary electrophoresis diode-array detection (CE-DAD), we analyzed the dynamic changes in the level of 56 basic metabolites in plant foliage (Oryza sativa L. ssp. japonica) at hourly intervals over a 24-hr period. Unsupervised clustering of comprehensive metabolic profiles using Kohonen's self-organizing map (SOM) allowed classification of the biochemical pathways activated by the light and dark cycle. The carbon and nitrogen (C/N) metabolism in both periods was also visualized as a phenotypic linkage map that connects network modules on the basis of traditional metabolic pathways rather than pairwise correlations among metabolites. The regulatory networks of C/N assimilation/dissimilation at each time point were consistent with previous works on plant metabolism. In response to environmental stress, glutathione and spermidine fluctuated synchronously with their regulatory targets. Adenine nucleosides and nicotinamide coenzymes were regulated by phosphorylation and dephosphorylation. We also demonstrated that SOM analysis was applicable to the estimation of unidentifiable metabolites in metabolome analysis. Hierarchical clustering of a correlation coefficient matrix could help identify the bottleneck enzymes that regulate metabolic networks. Conclusion Our results showed that our SOM analysis with appropriate metabolic time-courses effectively revealed the synchronous dynamics among metabolic modules and elucidated the underlying biochemical functions. The application of discrimination of unidentified metabolites and the identification of bottleneck enzymatic steps even to non-targeted comprehensive analysis promise to facilitate an understanding of large-scale interactions among components in biological systems. PMID:18564421

Self-organizing maps in geothermal exploration-A new approach for understanding geochemical processes and fluid evolution

NASA Astrophysics Data System (ADS)

Brehme, Maren; Bauer, Klaus; Nukman, Mochamad; Regenspurg, Simona

2017-04-01

Understanding geochemical processes is an important part of geothermal exploration to get information about the source and evolution of geothermal fluids. However, in most cases knowledge of fluid properties is based on few parameters determined in samples from the shallow subsurface. This study presents a new approach that allows to conclude from the combination of a variety of these data on processes occurring at depth in a geothermal reservoir. The neural network clustering technique called "self-organizing maps" (SOMs) successfully distinguished two different geothermal settings based on a hydrochemical database and disclosed the source, evolution and flow pathways of geothermal fluids. Scatter plots, as shown in this study, are appropriate presentations of element concentrations and the chemical interaction of water and rock at depth. One geological setting presented here is marked by fault dominated fluid pathways and minor influence of volcanic affected fluids with high concentrations of HCO3, Ca and Sr. The second is a magmatically dominated setting showing strong alteration features in volcanic rocks and accommodates acidic fluids with high SO4 and Si concentrations. Former studies, i.e., Giggenbach (1988), suggested Cl, HCO3 and SO4 to be generally the most important elements for understanding hydrochemical processes in geothermal reservoirs. Their relation has been widely used to classify different water types in geothermal fields. However, this study showed that non-standard elements are at least of same importance to reveal different fluid types in geothermal systems. Therefore, this study is an extended water classification approach using SOM for element correlations. SOM have been proven to be a successful method for analyzing even relatively small hydrochemical datasets in geothermal applications.

Changes in balance coordination and transfer to an unlearned balance task after slackline training: a self-organizing map analysis.

PubMed

Serrien, Ben; Hohenauer, Erich; Clijsen, Ron; Taube, Wolfgang; Baeyens, Jean-Pierre; Küng, Ursula

2017-11-01

How humans maintain balance and change postural control due to age, injury, immobility or training is one of the basic questions in motor control. One of the problems in understanding postural control is the large set of degrees of freedom in the human motor system. Therefore, a self-organizing map (SOM), a type of artificial neural network, was used in the present study to extract and visualize information about high-dimensional balance strategies before and after a 6-week slackline training intervention. Thirteen subjects performed a flamingo and slackline balance task before and after the training while full body kinematics were measured. Range of motion, velocity and frequency of the center of mass and joint angles from the pelvis, trunk and lower leg (45 variables) were calculated and subsequently analyzed with an SOM. Subjects increased their standing time significantly on the flamingo (average +2.93 s, Cohen's d = 1.04) and slackline (+9.55 s, d = 3.28) tasks, but the effect size was more than three times larger in the slackline. The SOM analysis, followed by a k-means clustering and marginal homogeneity test, showed that the balance coordination pattern was significantly different between pre- and post-test for the slackline task only (χ 2  = 82.247; p < 0.001). The shift in balance coordination on the slackline could be characterized by an increase in range of motion and a decrease in velocity and frequency in nearly all degrees of freedom simultaneously. The observation of low transfer of coordination strategies to the flamingo task adds further evidence for the task-specificity principle of balance training, meaning that slackline training alone will be insufficient to increase postural control in other challenging situations.

Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued monetary bills adversely affect the daily operation of automated teller machines (ATMs). In order to make the classification of fatigued bills more efficient, the development of an automatic fatigued monetary bill classification method is desirable. We propose a new method by which to estimate the fatigue level of monetary bills from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves the estimation accuracy of the fatigue level of monetary bills by adding frequency domain information to the acoustic energy pattern. The experimental results with real monetary bill samples reveal the effectiveness of the proposed method.

LiDAR-derived topographic indices to inform sampling and mapping of soil moisture at the plot to field scale

NASA Astrophysics Data System (ADS)

Kaleita, A. L.

2013-12-01

Identifying field-scale soil moisture patterns, and quantifying their impact on hydrology and nutrient flux, is currently limited by the time and resources required to do sufficient monitoring. A small number of monitoring locations or occasions may not be sufficient to capture the true spatial and temporal dynamics of these patterns. While process models can help to fill in data gaps, it is often difficult if not impossible to effectively parameterize them at the field and sub-field scale. Thus, empirical methods that can optimize sampling and mapping of soil moisture by using a minimal amount of readily available data may be of significant value. LiDAR is one source of such readily available data. Various topographic indices, including relative elevation, land slope, curvature, and slope aspect are known to influence soil moisture patterns, though the exact nature of that relationship appears to vary from study to study. The objective of this study was to use these data to identify critical sampling locations for mapping soil moisture, and to upscale point measurements at those locations to both a single field-average value, and to a high-resolution pattern map for the field. This study analyzed in-situ soil moisture measurements from the working agricultural field in Story County, Iowa. Theta probe soil moisture measurement values were taken every 50 meters on a 300 x 250 meter grid (~18 acres) during the summer growing seasons of 2004, 2005, 2007, and 2008. The elevation in the field varies by approximately 5 meters and the grid covers six different soil types and a variety of different landscape positions throughout the field. We used self-organizing maps (SOMs) and K-means clustering algorithms to split apart the field study area into distinct categories of similarly-characterized locations. We then used the SOM and clustering metrics to identify locations within each group that were representative of the behavior of that group of locations. We developed a weighted upscaling process to estimate a whole-field average soil moisture content from these few critical samples, and we compared the results to those obtained through the more traditional 'temporal stability' approach. The cluster-based approach was as good as and often better than the temporal stability approach, with the significant advantage that the former does not require any initial period of exhaustive soil moisture monitoring, whereas the latter does. A second objective was to use the classification results of the landscape data to interpolate these sparse critical sampling point data over the whole field. Using what we term 'feature-space interpolation' we were able to re-create a high-resolution soil moisture map for the field using only three measurements, by giving locations with similar landscape characteristics similar soil moisture values. The results showed a small but significant statistical improvement over traditional distance-based interpolation methods, and the resulting patterns also had stronger correlation with end-of-season yield, suggesting this approach may have valuable applications in production agriculture decision-making and assessment.

Bearing performance degradation assessment based on time-frequency code features and SOM network

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

2017-04-01

Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data.

Data-driven modeling of surface temperature anomaly and solar activity trends

USGS Publications Warehouse

Friedel, Michael J.

2012-01-01

A novel two-step modeling scheme is used to reconstruct and analyze surface temperature and solar activity data at global, hemispheric, and regional scales. First, the self-organizing map (SOM) technique is used to extend annual modern climate data from the century to millennial scale. The SOM component planes are used to identify and quantify strength of nonlinear relations among modern surface temperature anomalies (<150 years), tropical and extratropical teleconnections, and Palmer Drought Severity Indices (0–2000 years). Cross-validation of global sea and land surface temperature anomalies verifies that the SOM is an unbiased estimator with less uncertainty than the magnitude of anomalies. Second, the quantile modeling of SOM reconstructions reveal trends and periods in surface temperature anomaly and solar activity whose timing agrees with published studies. Temporal features in surface temperature anomalies, such as the Medieval Warm Period, Little Ice Age, and Modern Warming Period, appear at all spatial scales but whose magnitudes increase when moving from ocean to land, from global to regional scales, and from southern to northern regions. Some caveats that apply when interpreting these data are the high-frequency filtering of climate signals based on quantile model selection and increased uncertainty when paleoclimatic data are limited. Even so, all models find the rate and magnitude of Modern Warming Period anomalies to be greater than those during the Medieval Warm Period. Lastly, quantile trends among reconstructed equatorial Pacific temperature profiles support the recent assertion of two primary El Niño Southern Oscillation types. These results demonstrate the efficacy of this alternative modeling approach for reconstructing and interpreting scale-dependent climate variables.

Effect of high-temperature on the swellable organically-modified silica (SOMS) and its application to gas-phase hydrodechlorination of trichloroethylene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sohn, Hyuntae; Celik, Gokhan; Gunduz, Seval

Pd catalysts supported on swellable organically-modified silica (SOMS) and high-temperature-treated swellable organically-modified silica (H-SOMS) were characterized and tested for gas-phase hydrodechlorination (HDC) of trichloroethylene (TCE) conditions. The high-temperature treatment on SOMS resulted in an increase in surface area and pore diameter as well as significant improvement of Pd dispersion on H-SOMS with smaller Pd particle sizes compared to the Pd/SOMS catalyst. Although the high-temperature treatment led to some alteration of the SOMS polysiloxane network, the hydrophobicity and organic vapor adsorption characteristics of SOMS were preserved. The reduction and oxidation characteristics of Pd on SOMS and HSOMS were investigated in situmore » using XANES technique. It was found that the Pd sites in the pores of SOMS was accessible to small molecules such as H 2, facilitating the reduction of PdO x, whereas oxidation of metallic Pd was limited even at higher temperatures when O 2 was used. This effect was only observed over Pd/SOMS catalyst. For Pd/H-SOMS, because the pores were more widely open than Pd/SOMS, both reduction and oxidation of Pd were observed. Finally, the catalytic activity of Pd/H-SOMS for gas-phase HDC of TCE was significantly better than Pd/SOMS. When water was added to the reactant stream (TCE + H 2O), both Pd/SOMS and Pd/H-SOMS maintained its catalytic performances due to hydrophobic property of the supports.« less

Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

NASA Astrophysics Data System (ADS)

Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

2017-07-01

Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed on randomized training sets. Results: We obtain excellent results (about 5% global error rate) with classification into light curve morphology classes on the Hipparcos data. The classification into system morphology classes using the Catalog and Atlas of Eclipsing binaries (CALEB) has a higher error rate (about 10.5%), most importantly due to the (sometimes strong) similarity of the photometric light curves originating from physically different systems. When trained on CALEB and then applied to Kepler-detected eclipsing binaries subsampled according to Gaia observing times, LDA and SOM provide tractable, easy-to-visualize subspaces of the full (functional) space of light curves that summarize the most important phenomenological elements of the individual light curves. The sequence of light curves ordered by their first linear discriminant coefficient is compared to results obtained using local linear embedding. The SOM method proves able to find a 2-dimensional embedded surface in the space of the light curves which separates the system morphology classes in its different regions, and also identifies a few other phenomena, such as the asymmetry of the light curves due to spots, eccentric systems, and systems with a single eclipse. Furthermore, when data from other surveys are projected to the same SOM surface, the resulting map yields a good overview of the general biases and distortions due to differences in time sampling or population.

The mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex: an activity-dependent developmental model.

PubMed

Philips, Ryan T; Chakravarthy, V Srinivasa

2015-01-01

Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Theoretically such a map has been approximated by a complex log map. Neural models with correlational learning have explained the development of other visual maps like orientation maps and ocular-dominance maps. In this paper it is demonstrated that activity based mechanisms can drive a self-organizing map (SOM) into such a configuration that dilations and rotations of a particular image (in this case a rectangular bar) are mapped onto orthogonal axes. We further demonstrate using the Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model, with an appropriate boundary and realistic initial conditions, that a retinotopic map which maps eccentricity and meridional angle to the horizontal and vertical axes respectively can be developed. This developed map bears a strong resemblance to the complex log map. We also simulated lesion studies which indicate that the lateral excitatory connections play a crucial role in development of the retinotopic map.

Interactive Medical Volume Visualization for Surgical Operations

DTIC Science & Technology

2001-10-25

the preprocessing and processing stages, related medical brain tissues, which are skull, white matter, gray matter and pathology ( tumor ), are segmented ...from 12 or 16 bit data depths. NMR segmentation plays an important role in our work, because, classifying brain tissues from NMR slices requires an...performing segmentation of brain structures. Our segmentation process uses Self Organizing Feature Maps (SOFM) [12]. In SOM, on the contrary to Feedback

Assessment of Social Vulnerability Identification at Local Level around Merapi Volcano - A Self Organizing Map Approach

NASA Astrophysics Data System (ADS)

Lee, S.; Maharani, Y. N.; Ki, S. J.

2015-12-01

The application of Self-Organizing Map (SOM) to analyze social vulnerability to recognize the resilience within sites is a challenging tasks. The aim of this study is to propose a computational method to identify the sites according to their similarity and to determine the most relevant variables to characterize the social vulnerability in each cluster. For this purposes, SOM is considered as an effective platform for analysis of high dimensional data. By considering the cluster structure, the characteristic of social vulnerability of the sites identification can be fully understand. In this study, the social vulnerability variable is constructed from 17 variables, i.e. 12 independent variables which represent the socio-economic concepts and 5 dependent variables which represent the damage and losses due to Merapi eruption in 2010. These variables collectively represent the local situation of the study area, based on conducted fieldwork on September 2013. By using both independent and dependent variables, we can identify if the social vulnerability is reflected onto the actual situation, in this case, Merapi eruption 2010. However, social vulnerability analysis in the local communities consists of a number of variables that represent their socio-economic condition. Some of variables employed in this study might be more or less redundant. Therefore, SOM is used to reduce the redundant variable(s) by selecting the representative variables using the component planes and correlation coefficient between variables in order to find the effective sample size. Then, the selected dataset was effectively clustered according to their similarities. Finally, this approach can produce reliable estimates of clustering, recognize the most significant variables and could be useful for social vulnerability assessment, especially for the stakeholder as decision maker. This research was supported by a grant 'Development of Advanced Volcanic Disaster Response System considering Potential Volcanic Risk around Korea' [MPSS-NH-2015-81] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of Korea. Keywords: Self-organizing map, Component Planes, Correlation coefficient, Cluster analysis, Sites identification, Social vulnerability, Merapi eruption 2010

Current developments in soil organic matter modeling and the expansion of model applications: a review

NASA Astrophysics Data System (ADS)

Campbell, Eleanor E.; Paustian, Keith

2015-12-01

Soil organic matter (SOM) is an important natural resource. It is fundamental to soil and ecosystem functions across a wide range of scales, from site-specific soil fertility and water holding capacity to global biogeochemical cycling. It is also a highly complex material that is sensitive to direct and indirect human impacts. In SOM research, simulation models play an important role by providing a mathematical framework to integrate, examine, and test the understanding of SOM dynamics. Simulation models of SOM are also increasingly used in more ‘applied’ settings to evaluate human impacts on ecosystem function, and to manage SOM for greenhouse gas mitigation, improved soil health, and sustainable use as a natural resource. Within this context, there is a need to maintain a robust connection between scientific developments in SOM modeling approaches and SOM model applications. This need forms the basis of this review. In this review we first provide an overview of SOM modeling, focusing on SOM theory, data-model integration, and model development as evidenced by a quantitative review of SOM literature. Second, we present the landscape of SOM model applications, focusing on examples in climate change policy. We conclude by discussing five areas of recent developments in SOM modeling including: (1) microbial roles in SOM stabilization; (2) modeling SOM saturation kinetics; (3) temperature controls on decomposition; (4) SOM dynamics in deep soil layers; and (5) SOM representation in earth system models. Our aim is to comprehensively connect SOM model development to its applications, revealing knowledge gaps in need of focused interdisciplinary attention and exposing pitfalls that, if avoided, can lead to best use of SOM models to support policy initiatives and sustainable land management solutions.

Current developments in soil organic matter modeling and the expansion of model applications. A review

DOE PAGES

Campbell, Eleanor E.; Paustian, Keith

2015-12-23

It is important to note that Soil organic matter (SOM) is a great natural resource. It is fundamental to soil and ecosystem functions across a wide range of scales, from site-specific soil fertility and water holding capacity to global biogeochemical cycling. It is also a highly complex material that is sensitive to direct and indirect human impacts. In our SOM research, simulation models play an important role by providing a mathematical framework to integrate, examine, and test the understanding of SOM dynamics. Simulation models of SOM are also increasingly used in more ‘applied’ settings to evaluate human impacts on ecosystemmore » function, and to manage SOM for greenhouse gas mitigation, improved soil health, and sustainable use as a natural resource. Within this context, there is a need to maintain a robust connection between scientific developments in SOM modeling approaches and SOM model applications. This need forms the basis of this review. In this review we first provide an overview of SOM modeling, focusing on SOM theory, data-model integration, and model development as evidenced by a quantitative review of SOM literature. Second, we present the landscape of SOM model applications, focusing on examples in climate change policy. Finally, we conclude by discussing five areas of recent developments in SOM modeling including: (1) microbial roles in SOM stabilization; (2) modeling SOM saturation kinetics; (3) temperature controls on decomposition; (4)SOM dynamics in deep soil layers; and (5)SOM representation in earth system models. Our aim is to comprehensively connect SOM model development to its applications, revealing knowledge gaps in need of focused interdisciplinary attention and exposing pitfalls that, if avoided, can lead to best use of SOM models to support policy initiatives and sustainable land management solutions.« less

Environmental Composites for Bomb Cyclones of the Western North Atlantic in Reanalysis, 1948-2016.

NASA Astrophysics Data System (ADS)

Adams, R.; Sheridan, S. C.

2017-12-01

"Bomb" cyclones represent a small subset of mid-latitude cyclones characterized by rapid intensification and frequently are associated with extreme weather conditions along the eastern coast of North America. Like other extreme phenomena, bomb cyclone predictions are prone to error leading to inadequate or untimely hazard warnings. The rare nature of bomb cyclones and the uniqueness of their evolutions has made it difficult for researchers to make meaningful generalizations on bomb cyclone events. This paper describes bomb cyclone climatology for the western North Atlantic, using data from the NCEP-NCAR Reanalysis for 1948-2016, and uses a synoptic climatological analysis to relate these bombs to their associated atmospheric environments. A self-organizing map (SOM) of 300-hPa geopotential height tendency is created to partition the regional atmospheric environment. Thermodynamic fields are contrasted by each 300-hPa geopotential height tendency pattern for both bomb and non-bomb events in composite difference maps. The SOM patterns most significantly associated with western North Atlantic bomb cyclogenesis are characterized by both strongly and weakly negative height tendencies along the eastern United States. In both cases, these patterns exhibit strong meridional flow, a distinction marked by the weakening and breaking down of the polar vortex in the boreal Winter. The composite maps for each pattern show the mean differences in low-mid level ascent and near surface thermodynamics for bomb environments contrasted with non-bomb environments, resulting in diverse spatiotemporal distributions of bombs in the western North Atlantic.

Explore the interrelationships of the WFE Nexus by relational topological mapping techniques - a case in northern Taiwan

NASA Astrophysics Data System (ADS)

Liao, Y. Y.; Tsai, W. P.; Chang, F. J.

2016-12-01

In recent years, Water-Food-Energy Nexus (WFE Nexus) has become a global issue due to the scarcity of natural resources. Besides, the resource management of water, energy and food at spatial and temporal scales raises multiple interwoven concerns ranging from co-opetitive relationship to resource accessibility. Over the past few decades, the socio-economic development of Taiwan has experienced drastic transformation because of population growth, urbanization and excessive consumption of natural resources. Therefore, our research intends to explore the interrelationships of the WFE Nexus by means of data mining techniques, such as artificial intelligence (AI). Our study area is the Tamsui River watershed in northern Taiwan. At first, we collect and analyze long-term WFE-related multidimensional heterogeneous observational data, such as water demand, food consumption and energy requirements. Then in order to explore the interrelationships and interactions of the WFE Nexus, the self-organizing map (SOM) is used to classify multidimensional heterogeneous data for building two-dimensional topological maps for clustering purpose. Consequently, we aim to establish the interrelationships in the WFE Nexus based on the obtained SOM topologies. This research will also explore the collaborative synergies of resource allocation and utilization that may influence the development of urbanization in the future. Ultimately, based on our proposed methodology and findings, the considerable impacts of the WFE Nexus on resource supply and demand and how the potential collaborative synergies would promote sustainable socio-economic development will be illuminated.

Bomb Cyclones Of The Western North Atlantic

NASA Astrophysics Data System (ADS)

Adams, Ryan E.

"Bomb" cyclones represent a small subset of mid-latitude cyclones characterized by rapid intensification and frequently are associated with extreme weather conditions along the eastern coast of North America. Like other extreme phenomena, bomb cyclone predictions are prone to error leading to inadequate or untimely hazard warnings. The rare nature of bomb cyclones and the uniqueness of their evolutions has made it difficult for researchers to make meaningful generalizations on bomb cyclone events. This paper describes bomb cyclone climatology for the western North Atlantic, using data from the NCEP-NCAR Reanalysis for 1948-2016, and uses a synoptic climatological analysis to relate these bombs to their associated atmospheric environments. A self-organizing map (SOM) of 300-hPa geopotential height tendency is created to partition the regional atmospheric environment. Thermodynamic fields are contrasted by each 300-hPa geopotential height tendency pattern for both bomb and non-bomb events in composite difference maps. The SOM patterns most significantly associated with western North Atlantic bomb cyclogenesis are characterized by both strongly and weakly negative height tendencies along the eastern United States. In both cases, these patterns exhibit strong meridional flow, a distinction marked by the weakening and breaking down of the polar vortex in the boreal Winter. The composite maps for each pattern show the mean differences in low-mid level ascent and near surface thermodynamics for bomb environments contrasted with non-bomb environments, resulting in diverse spatiotemporal distributions of bombs in the western North Atlantic.

A constraint optimization based virtual network mapping method

NASA Astrophysics Data System (ADS)

Li, Xiaoling; Guo, Changguo; Wang, Huaimin; Li, Zhendong; Yang, Zhiwen

2013-03-01

Virtual network mapping problem, maps different virtual networks onto the substrate network is an extremely challenging work. This paper proposes a constraint optimization based mapping method for solving virtual network mapping problem. This method divides the problem into two phases, node mapping phase and link mapping phase, which are all NP-hard problems. Node mapping algorithm and link mapping algorithm are proposed for solving node mapping phase and link mapping phase, respectively. Node mapping algorithm adopts the thinking of greedy algorithm, mainly considers two factors, available resources which are supplied by the nodes and distance between the nodes. Link mapping algorithm is based on the result of node mapping phase, adopts the thinking of distributed constraint optimization method, which can guarantee to obtain the optimal mapping with the minimum network cost. Finally, simulation experiments are used to validate the method, and results show that the method performs very well.

SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation

NASA Astrophysics Data System (ADS)

Xie, Xiaomin; Wang, Tingting

2017-02-01

In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.

Approximate solution of the multiple watchman routes problem with restricted visibility range.

PubMed

Faigl, Jan

2010-10-01

In this paper, a new self-organizing map (SOM) based adaptation procedure is proposed to address the multiple watchman route problem with the restricted visibility range in the polygonal domain W. A watchman route is represented by a ring of connected neuron weights that evolves in W, while obstacles are considered by approximation of the shortest path. The adaptation procedure considers a coverage of W by the ring in order to attract nodes toward uncovered parts of W. The proposed procedure is experimentally verified in a set of environments and several visibility ranges. Performance of the procedure is compared with the decoupled approach based on solutions of the art gallery problem and the consecutive traveling salesman problem. The experimental results show the suitability of the proposed procedure based on relatively simple supporting geometrical structures, enabling application of the SOM principles to watchman route problems in W.

Characterization of heavy-metal-contaminated sediment by using unsupervised multivariate techniques and health risk assessment.

PubMed

Wang, Yeuh-Bin; Liu, Chen-Wuing; Wang, Sheng-Wei

2015-03-01

This study characterized the sediment quality of the severely contaminated Erjen River in Taiwan by using multivariate analysis methods-including factor analysis (FA), self-organizing maps (SOMs), and positive matrix factorization (PMF)-and health risk assessment. The SOMs classified the dataset with similar heavy-metal-contaminated sediment into five groups. FA extracted three major factors-traditional electroplating and metal-surface processing factor, nontraditional heavy-metal-industry factor, and natural geological factor-which accounted for 80.8% of the variance. The SOMs and FA revealed the heavy-metal-contaminated-sediment hotspots in the middle and upper reaches of the major tributary in the dry season. The hazardous index value for health risk via ingestion was 0.302. PMF further qualified the source apportionment, indicating that traditional electroplating and metal-surface-processing industries comprised 47% of the health risk posed by heavy-metal-contaminated sediment. Contaminants discharged from traditional electroplating and metal-surface-processing industries in the middle and upper reaches of the major tributary must be eliminated first to improve the sediment quality in Erjen River. The proposed assessment framework for heavy-metal-contaminated sediment can be applied to contaminated-sediment river sites in other regions. Copyright © 2014 Elsevier Inc. All rights reserved.

MALDI-typing of infectious algae of the genus Prototheca using SOM portraits.

PubMed

Wirth, Henry; von Bergen, Martin; Murugaiyan, Jayaseelan; Rösler, Uwe; Stokowy, Tomasz; Binder, Hans

2012-01-01

MALDI-typing has become a frequently used approach for the identification of microorganisms and recently also of invertebrates. Similarity-comparisons are usually based on single-spectral data. We apply self-organizing maps (SOM) to portray the MS-spectral data with individual resolution and to improve the typing of Prototheca algae by using meta-spectra representing prototypes of groups of similar-behaving single spectra. The MALDI-TOF peaklists of more than 300 algae extracts referring to five Prototheca species were transformed into colored mosaic images serving as molecular portraits of the individual samples. The portraits visualize the algae-specific distribution of high- and low-amplitude peaks in two dimensions. Species-specific pattern of MS intensities were readily discernable in terms of unique single spots of high amplitude MS-peaks which collect characteristic fingerprint spectra. The spot patterns allow the visual identification of groups of samples referring to different species, genotypes or isolates. The use of meta-peaks instead of single-peaks reduces the dimension of the data and leads to an increased discriminating power in downstream analysis. We expect that our SOM portray method improves MS-based classifications and feature selection in upcoming applications of MALDI-typing based species identifications especially of closely related species. Copyright © 2011. Published by Elsevier B.V.

Comparison of empirical and data driven hydrometeorological hazard models on coastal cities of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Koga-Vicente, A.; Friedel, M. J.

2010-12-01

Every year thousands of people are affected by floods and landslide hazards caused by rainstorms. The problem is more serious in tropical developing countries because of the susceptibility as a result of the high amount of available energy to form storms, and the high vulnerability due to poor economic and social conditions. Predictive models of hazards are important tools to manage this kind of risk. In this study, a comparison of two different modeling approaches was made for predicting hydrometeorological hazards in 12 cities on the coast of São Paulo, Brazil, from 1994 to 2003. In the first approach, an empirical multiple linear regression (MLR) model was developed and used; the second approach used a type of unsupervised nonlinear artificial neural network called a self-organized map (SOM). By using twenty three independent variables of susceptibility (precipitation, soil type, slope, elevation, and regional atmospheric system scale) and vulnerability (distribution and total population, income and educational characteristics, poverty intensity, human development index), binary hazard responses were obtained. Model performance by cross-validation indicated that the respective MLR and SOM model accuracy was about 67% and 80%. Prediction accuracy can be improved by the addition of information, but the SOM approach is preferred because of sparse data and highly nonlinear relations among the independent variables.

Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary. Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters. This methodology shows promise for the identification of conditions in general populations and supports the current focus on the potential importance of bowel symptoms and the gut in mental health research.

Examining the NZESM Cloud representation with Self Organizing Maps

NASA Astrophysics Data System (ADS)

Schuddeboom, Alex; McDonald, Adrian; Parsons, Simon; Morgenstern, Olaf; Harvey, Mike

2017-04-01

Several different cloud regimes are identified from MODIS satellite data and the representation of these regimes within the New Zealand Earth System Model (NZESM) is examined. For the development of our cloud classification we utilize a neural network algorithm known as self organizing maps (SOMs) on MODIS cloud top pressure - cloud optical thickness joint histograms. To evaluate the representation of the cloud within NZESM, the frequency and geographical distribution of the regimes is compared between the NZESM and satellite data. This approach has the advantage of not only identifying differences, but also potentially giving additional information about the discrepancy such as in which regions or phases of cloud the differences are most prominent. To allow for a more direct comparison between datasets, the COSP satellite simulation software is applied to NZESM output. COSP works by simulating the observational processes linked to a satellite, within the GCM, so that data can be generated in a way that shares the particular observational bias of specific satellites. By taking the COSP joint histograms and comparing them to our existing classifications we can easily search for discrepancies between the observational data and the simulations without having to be cautious of biases introduced by the satellite. Preliminary results, based on data for 2008, show a significant decrease in overall cloud fraction in the NZESM compared to the MODIS satellite data. To better understand the nature of this discrepancy, the cloud fraction related to different cloud heights and phases were also analysed.

Automatic lithofacies segmentation from well-logs data. A comparative study between the Self-Organizing Map (SOM) and Walsh transform

NASA Astrophysics Data System (ADS)

Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar

2013-04-01

The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

PubMed

Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

2017-09-01

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.

Seismic data classification and artificial neural networks: can software replace eyeballs?

NASA Astrophysics Data System (ADS)

Reusch, D. B.; Larson, A. M.

2006-05-01

Modern seismic datasets are providing many new opportunities for furthering our understanding of our planet, ranging from the deep earth to the sub-ice sheet interface. With many geophysical applications, the large volume of these datasets raises issues of manageability in areas such as quality control (QC) and event identification (EI). While not universally true, QC can be a labor intensive, subjective (and thus not entirely reproducible) and uninspiring task when such datasets are involved. The EI process shares many of these drawbacks but has the benefit of (usually) being closer to interesting science-based questions. Here we explore two techniques from the field of artificial neural networks (ANNs) that seek to reduce the time requirements and increase the objectivity of QC and EI on seismic datasets. In particular, we focus on QC of receiver functions from broadband seismic data collected by the 2000-2003 Transantarctic Mountains Seismic Experiment (TAMSEIS). Self-organizing maps (SOMs) enable unsupervised classification of large, complex geophysical data sets (e.g., time series of the atmospheric circulation) into a fixed number of distinct generalized patterns or modes representing the probability distribution function of the input data. These patterns are organized spatially as a two-dimensional grid such that distances represent similarity (adjacent patterns will be most similar). After training, input data are matched to their most similar generalized pattern to produce frequency maps, i.e., what fraction of the data is represented best by each individual SOM pattern. Given a priori information on data quality (from previous manual grading) or event type, a probabilistic classification can be developed that gives a likelihood for each category of interest for each SOM pattern. New data are classified by identifying the closest matching pattern (without retraining) and examining the associated probabilities. Feed-forward ANNs (FFNNs) are a supervised classification tool that has been successfully used in a number of seismic applications (e.g., Langer et al, 2003; Del Pezzo et al 2003). FFNNs require a correct answer for each training record so that the transfer functions between input predictors and output predictions can be developed during training. After training, applying new input data to the FFNNs classifies the input based on the existing transfer functions. Key to the success of both approaches is the selection of proper predictor variables that reflect, to varying degrees, the criteria humans use when doing these tasks manually. SOMs also have the potential to assist in this selection process. Because SOMs and FFNNs are used in different ways, they can address different aspects of the overall data classification problem in complementary ways. While not the first application of computers to these problems, ANN-based tools bring unique characteristics to the problem of capturing human decision-making processes.

String tightening as a self-organizing phenomenon.

PubMed

Banerjee, Bonny

2007-09-01

The phenomenon of self-organization has been of special interest to the neural network community throughout the last couple of decades. In this paper, we study a variant of the self-organizing map (SOM) that models the phenomenon of self-organization of the particles forming a string when the string is tightened from one or both of its ends. The proposed variant, called the string tightening self-organizing neural network (STON), can be used to solve certain practical problems, such as computation of shortest homotopic paths, smoothing paths to avoid sharp turns, computation of convex hull, etc. These problems are of considerable interest in computational geometry, robotics path-planning, artificial intelligence (AI) (diagrammatic reasoning), very large scale integration (VLSI) routing, and geographical information systems. Given a set of obstacles and a string with two fixed terminal points in a 2-D space, the STON model continuously tightens the given string until the unique shortest configuration in terms of the Euclidean metric is reached. The STON minimizes the total length of a string on convergence by dynamically creating and selecting feature vectors in a competitive manner. Proof of correctness of this anytime algorithm and experimental results obtained by its deployment have been presented in the paper.

Multimedia Classifier

NASA Astrophysics Data System (ADS)

Costache, G. N.; Gavat, I.

2004-09-01

Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processing steps realized for the interesting multimedia objects are: first, the parameterization, by analysis, in order to obtain a description based on features, forming the parameter vector; second, a classification, generally with a hierarchical structure to make the necessary decisions. For audio signals, both speech and music, the derived perceptual features are the melcepstral (MFCC) and the perceptual linear predictive (PLP) coefficients. For images, the derived features are the geometric parameters of the speaker mouth. The hierarchical classifier consists generally in a clustering stage, based on the Kohonnen Self-Organizing Maps (SOM) and a final stage, based on a powerful classification algorithm called Support Vector Machines (SVM). The system, in specific variants, is applied with good results in two tasks: the first, is a bimodal speech recognition which uses features obtained from speech signal fused to features obtained from speaker's image and the second is a music retrieval from large music database.

Assessing Heavy and Trace Metal Contamination in Surface Materials near the Ambaji and Zawar mines in Gujurat and Rajasthan, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) Data

NASA Astrophysics Data System (ADS)

Farrand, W. H.

2017-12-01

An investigation has begun into effects on water quality in waters coming from a pair of mines, and their surrounding drainage basins, in western India. The study areas are the Ambaji and Zawar mines in the Indian states of, respectively, Gujurat and Rajasthan. The Ambaji mine is situated in Precambrian-aged metasediments and metavolcanics of the Delhi Supergroup. Sulfide mineralization at Ambaji is hosted by hydrothermally altered felsic metavolcanics rocks with ferric oxide and oxyhydroxide as well as copper carbonate surface indicator minerals. The Zawar zinc mine is part of the Precambrian Aravalli Supergroup and lies amidst surface exposures of dolomites and quartzites. Hyperspectral visible through short-wave infrared (VSWIR) data from the Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) was collected in February 2016 over these sites as part of a joint campaign between NASA and the Indian Space Research Organization (ISRO). The AVIRIS-NG data is being used to detect, map, and characterize surface mineralogy in the area. Data discovery is being carried out using a self-organizing map (SOM) methodology with mineral endmembers being mapped initially with a support vector machine (SVM) classifier and a planned more comprehensive mapping using the USGS Material Identification and Characterization Algorithm (MICA). Results of the mineral mapping will be field checked and rock, soil, and water samples will be collected and examined for heavy and trace metal contamination. Past studies have shown changes in the shape of the 2.2 mm Al-OH vibrational overtone feature as well as in blue-red spectral ratios that were directly correlated with the concentration of heavy and trace metals that had been adsorbed into the structure of the affected minerals. Early analysis of the Zawar area scenes indicates the presence of Al-OH clay minerals which might have been affected by the adsorption of trace metals. Scenes from the Ambaji area have more extensive surface exposures of carbonate minerals. Future work will focus more closely on detailed spectral feature mapping of absorption features that have been affected by heavy and trace metal adsorption.

Assessing the pollution risk of soil Chromium based on loading capacity of paddy soil at a regional scale

PubMed Central

Qu, Mingkai; Li, Weidong; Zhang, Chuanrong; Huang, Biao; Zhao, Yongcun

2015-01-01

The accumulation of a trace metal in rice grain is not only affected by the total concentration of the soil trace metal, but also by crop variety and related soil properties, such as soil pH, soil organic matter (SOM) and so on. However, these factors were seldom considered in previous studies on mapping the pollution risk of trace metals in paddy soil at a regional scale. In this study, the spatial nonstationary relationships between rice-Cr and a set of perceived soil properties (soil-Cr, soil pH and SOM) were explored using geographically weighted regression; and the relationships were then used for calculating the critical threshold (CT) of soil-Cr concentration that may ensure the concentration of rice-Cr being below the permissible limit. The concept of “loading capacity” (LC) for Cr in paddy soil was then defined as the difference between the CT and the real concentration of Cr in paddy soil, so as to map the pollution risk of soil-Cr to rice grain and assess the risk areas in Jiaxing city, China. Compared with the information of the concentration of the total soil-Cr, such results are more valuable for spatial decision making in reducing the accumulation of rice-Cr at a regional scale. PMID:26675587

Assessing the pollution risk of soil Chromium based on loading capacity of paddy soil at a regional scale.

PubMed

Qu, Mingkai; Li, Weidong; Zhang, Chuanrong; Huang, Biao; Zhao, Yongcun

2015-12-17

The accumulation of a trace metal in rice grain is not only affected by the total concentration of the soil trace metal, but also by crop variety and related soil properties, such as soil pH, soil organic matter (SOM) and so on. However, these factors were seldom considered in previous studies on mapping the pollution risk of trace metals in paddy soil at a regional scale. In this study, the spatial nonstationary relationships between rice-Cr and a set of perceived soil properties (soil-Cr, soil pH and SOM) were explored using geographically weighted regression; and the relationships were then used for calculating the critical threshold (CT) of soil-Cr concentration that may ensure the concentration of rice-Cr being below the permissible limit. The concept of "loading capacity" (LC) for Cr in paddy soil was then defined as the difference between the CT and the real concentration of Cr in paddy soil, so as to map the pollution risk of soil-Cr to rice grain and assess the risk areas in Jiaxing city, China. Compared with the information of the concentration of the total soil-Cr, such results are more valuable for spatial decision making in reducing the accumulation of rice-Cr at a regional scale.

Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

NASA Astrophysics Data System (ADS)

Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

NASA Astrophysics Data System (ADS)

Sarparandeh, Mohammadali; Hezarkhani, Ardeshir

2017-12-01

The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) - including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and self-organizing map (SOM) - were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed methods and geological studies leads to finding some hidden information, and this approach has the best results compared to using only one of them.

Can Global Weed Assemblages Be Used to Predict Future Weeds?

PubMed Central

Morin, Louise; Paini, Dean R.; Randall, Roderick P.

2013-01-01

Predicting which plant taxa are more likely to become weeds in a region presents significant challenges to both researchers and government agencies. Often it is done in a qualitative or semi-quantitative way. In this study, we explored the potential of using the quantitative self-organising map (SOM) approach to analyse global weed assemblages and estimate likelihoods of plant taxa becoming weeds before and after they have been moved to a new region. The SOM approach examines plant taxa associations by analysing where a taxon is recorded as a weed and what other taxa are recorded as weeds in those regions. The dataset analysed was extracted from a pre-existing, extensive worldwide database of plant taxa recorded as weeds or other related status and, following reformatting, included 187 regions and 6690 plant taxa. To assess the value of the SOM approach we selected Australia as a case study. We found that the key and most important limitation in using such analytical approach lies with the dataset used. The classification of a taxon as a weed in the literature is not often based on actual data that document the economic, environmental and/or social impact of the taxon, but mostly based on human perceptions that the taxon is troublesome or simply not wanted in a particular situation. The adoption of consistent and objective criteria that incorporate a standardized approach for impact assessment of plant taxa will be necessary to develop a new global database suitable to make predictions regarding weediness using methods like SOM. It may however, be more realistic to opt for a classification system that focuses on the invasive characteristics of plant taxa without any inference to impacts, which to be defined would require some level of research to avoid bias from human perceptions and value systems. PMID:23393591

Extracción de conocimiento en bases de datos astronómicas mediante redes de neuronas artificiales: aplicaciones en la misión Gaia

NASA Astrophysics Data System (ADS)

Fustes Villadóniga, Diego

2014-02-01

In the so-called IT era, the capabilities of data acquisition systems have increased to such an extent that it has become difficult to store all the information they produce, and analyse it. This explosion of data has recently appeared in the field of Astronomy, where an increasing number of objects are being observed on a regular basis. An example of this is the upcoming Gaia mission, which will pick up multiple properties of a billion stars, whose information will have a volume of approximately a petabyte. The analysis of a similar amount of information inevitably requires the development of new data analysis methods to extract all the knowledge it contains. This thesis is devoted to the development of data analysis methods to be integrated in the Gaia pipeline, such that knowledge can be extracted from the data collected by the mission. In order to analyze the data from the Gaia mission, the European Space Agency organized the Data Processing and Analysis Consortium (DPAC) which is composed of hundreds of scientists and engineers. DPAC is divided into eight Coordination Units (CUs). This thesis is dedicated to algorithm development in CU8, which is responsible for source classification and astrophysical parameters (AP) estimation. Methods based on Artificial Neural Networks (ANNs) are developed to perform the tasks related to two different work packages in CU8: the GSP-Spec package (GWP-823), and the OA package (GWP-836). The GSP-Spec package is responsible for estimating stellar APs by means of the Radial Velocity Spectrograph (RVS) spectrum. This work presents the development of one of the GSP-Spec modules, which is based on the application of feed-forward ANNs. A methodology is described, based on the optimization of genetic algorithms and aimed at obtaining an optimal set of configuration parameters for the ANN in each case, depending on the signal to noise ratio (SNR) in the RVS spectrum and on the type of star to parameterize. Furthermore, in order to improve the AP estimates, wavelet signal processing techniques, applied to the RVS spectrum, are studied. Despite the effectiveness shown by ANNs in estimating APs, in principle they lack the ability to provide an uncertainty value on these estimates, making it impossible to determine their reliability. Because of this, a new architecture for the ANN is presented in which the inputs and outputs are reversed, so that the ANN estimates the RVS spectrum from the APs. Such an architecture is called Generative ANN (GANN) and is applied to the AP estimation of a set of simulated RVS spectra for the Gaia mission, where it is more effective than the conventional ANN model, in the case of faint stars with low SNR. Finally, the GANN can be applied for obtaining the posterior probability of each of the APs according to the RVS spectrum, allowing for their more complete analysis. Given the nature of the Gaia mission, which is the first astronomical mission that will observe, in an unbiased way, the entire sky up to magnitude 20, a large number of outliers are expected. The OA package in CU8 handles the processing of this type of objects, which are defined as those that could not be reliably classified by the methods in the upstream classification packages. OA methods are based on the unsupervised learning of all outliers. Such learning has two parts: clustering and dimensionality reduction. The Self-Organizing Map (SOM) algorithm is selected as a basis for this learning. Its effectiveness is demonstrated when it is applied, with an optimal configuration, to the Gaia simulations. Furthermore, the algorithm is applied to real outliers from the SDSS catalog. Since a subsequent identification of the clusters obtained by the SOM is necessary, two different methods of identification are applied. The first method is based on the similarity between the SOM prototypes and the Gaia simulations, and the second method is based on the recovery of stored classifications in the SIMBAD catalog by cross-matching celestial coordinates. Thanks to the visualization of the SOM planes, and to both methods of identification, it is possible to distinguish between valid observations and observational artifacts. Furthermore, the method allows for the selection of objects of interest for follow-up observations, in order to determine their nature.

Massive processing of pyro-chromatogram mass spectra (py-GCMS) of soil samples using the PARAFAC2 algorithm

NASA Astrophysics Data System (ADS)

Cécillon, Lauric; Quénéa, Katell; Anquetil, Christelle; Barré, Pierre

2015-04-01

Due to its large heterogeneity at all scales (from soil core to the globe), several measurements are often mandatory to get a meaningful value of a measured soil property. A large number of measurements can therefore be needed to study a soil property whatever the scale of the study. Moreover, several soil investigation techniques produce large and complex datasets, such as pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) which produces complex 3-way data. In this context, straightforward methods designed to speed up data treatments are needed to deal with large datasets. GC-MS pyrolysis (py-GCMS) is a powerful and frequently used tool to characterize soil organic matter (SOM). However, the treatment of the results of a py-GCMS analysis of soil sample is time consuming (number of peaks, co-elution, etc.) and the treatment of large data set of py-GCMS results is rather laborious. Moreover, peak position shifts and baseline drifts between analyses make the automation of GCMS programs data treatment difficult. These problems can be fixed using the Parallel Factor Analysis 2 (PARAFAC 2, Kiers et al., 1999; Bro et al., 1999). This algorithm has been applied frequently on chromatography data but has never been applied to analyses of SOM. We developed a Matlab routine based on existing Matlab packages dedicated to the simultaneous treatment of dozens of pyro-chromatograms mass spectra. We applied this routine on 40 soil samples. The benefits and expected improvements of our method will be discussed in our poster. References Kiers et al. (1999) PARAFAC2 - PartI. A direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics, 13: 275-294. Bro et al. (1999) PARAFAC2 - PartII. Modeling chromatographic data with retention time shifts. Journal of Chemometrics, 13: 295-309.

Interaction of polar and nonpolar organic pollutants with soil organic matter: sorption experiments and molecular dynamics simulation.

PubMed

Ahmed, Ashour A; Thiele-Bruhn, Sören; Aziz, Saadullah G; Hilal, Rifaat H; Elroby, Shaaban A; Al-Youbi, Abdulrahman O; Leinweber, Peter; Kühn, Oliver

2015-03-01

The fate of organic pollutants in the environment is influenced by several factors including the type and strength of their interactions with soil components especially SOM. However, a molecular level answer to the question "How organic pollutants interact with SOM?" is still lacking. In order to explore mechanisms of this interaction, we have developed a new SOM model and carried out molecular dynamics (MD) simulations in parallel with sorption experiments. The new SOM model comprises free SOM functional groups (carboxylic acid and naphthalene) as well as SOM cavities (with two different sizes), simulating the soil voids, containing the same SOM functional groups. To examine the effect of the hydrophobicity on the interaction, the organic pollutants hexachlorobenzene (HCB, non-polar) and sulfanilamide (SAA, polar) were considered. The experimental and theoretical investigations explored four major points regarding sorption of SAA and HCB on soil, yielding the following results. 1--The interaction depends on the SOM chemical composition more than the SOM content. 2--The interaction causes a site-specific adsorption on the soil surfaces. 3--Sorption hysteresis occurs, which can be explained by inclusion of these pollutants inside soil voids. 4--The hydrophobic HCB is adsorbed on soil stronger than the hydrophilic SAA. Moreover, the theoretical results showed that HCB forms stable complexes with all SOM models in the aqueous solution, while most of SAA-SOM complexes are accompanied by dissociation into SAA and the free SOM models. The SOM-cavity modeling had a significant effect on binding of organic pollutants to SOM. Both HCB and SAA bind to the SOM models in the order of models with a small cavity>a large cavity>no cavity. Although HCB binds to all SOM models stronger than SAA, the latter is more affected by the presence of the cavity. Finally, HCB and SAA bind to the hydrophobic functional group (naphthalene) stronger than to the hydrophilic one (carboxylic acid) for all SOM models containing a cavity. For models without a cavity, SAA binds to carboxylic acid stronger than to naphthalene. Copyright © 2014 Elsevier B.V. All rights reserved.

Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Patterning ecological risk of pesticide contamination at the river basin scale.

PubMed

Faggiano, Leslie; de Zwart, Dick; García-Berthou, Emili; Lek, Sovan; Gevrey, Muriel

2010-05-01

Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called "Lot et Garonne". The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale. Copyright 2010 Elsevier B.V. All rights reserved.

Visualization of multiple influences on ocellar flight control in giant honeybees with the data-mining tool Viscovery SOMine.

PubMed

Kastberger, G; Kranner, G

2000-02-01

Viscovery SOMine is a software tool for advanced analysis and monitoring of numerical data sets. It was developed for professional use in business, industry, and science and to support dependency analysis, deviation detection, unsupervised clustering, nonlinear regression, data association, pattern recognition, and animated monitoring. Based on the concept of self-organizing maps (SOMs), it employs a robust variant of unsupervised neural networks--namely, Kohonen's Batch-SOM, which is further enhanced with a new scaling technique for speeding up the learning process. This tool provides a powerful means by which to analyze complex data sets without prior statistical knowledge. The data representation contained in the trained SOM is systematically converted to be used in a spectrum of visualization techniques, such as evaluating dependencies between components, investigating geometric properties of the data distribution, searching for clusters, or monitoring new data. We have used this software tool to analyze and visualize multiple influences of the ocellar system on free-flight behavior in giant honeybees. Occlusion of ocelli will affect orienting reactivities in relation to flight target, level of disturbance, and position of the bee in the flight chamber; it will induce phototaxis and make orienting imprecise and dependent on motivational settings. Ocelli permit the adjustment of orienting strategies to environmental demands by enforcing abilities such as centering or flight kinetics and by providing independent control of posture and flight course.

Identification of land degradation evidences in an organic farm using probability maps (Croatia)

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Bogunovic, Igor; Estebaranz, Ferran

2017-04-01

Land degradation is a biophysical process with important impacts on society, economy and policy. Areas affected by land degradation do not provide services in quality and with capacity to full-field the communities that depends on them (Amaya-Romero et al., 2015; Beyene, 2015; Lanckriet et al., 2015). Agricultural activities are one of the main causes of land degradation (Kraaijvanger and Veldkamp, 2015), especially when they decrease soil organic matter (SOM), a crucial element for soil fertility. In temperate areas, the critical level of SOM concentration in agricultural soils is 3.4%. Below this level there is a potential decrease of soil quality (Loveland and Weeb, 2003). However, no previous work was carried out in other environments, such as the Mediterranean. The spatial distribution of potential degraded land is important to be identified and mapped, in order to identify the areas that need restoration (Brevik et al., 2016; Pereira et al., 2017). The aim of this work is to assess the spatial distribution of areas with evidences of land degradation (SOM bellow 3.4%) using probability maps in an organic farm located in Croatia. In order to find the best method, we compared several probability methods, such as Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), Indicator Kriging (IK), Probability Kriging (PK) and Disjunctive Kriging (DK). The study area is located on the Istria peninsula (45°3' N; 14°2' E), with a total area of 182 ha. One hundred eighty-two soil samples (0-30 cm) were collected during July of 2015 and SOM was assessed using wet combustion procedure. The assessment of the best probability method was carried out using leave one out cross validation method. The probability method with the lowest Root Mean Squared Error (RMSE) was the most accurate. The results showed that the best method to predict the probability of potential land degradation was SK with an RMSE of 0.635, followed by DK (RMSE=0.636), UK (RMSE=0.660), OK (RMSE=0.660), IK (RMSE=0.722) and PK (RMSE=1.661). According to the most accurate method, it is observed that the majority of the area studied has a high probability to be degraded. Measures are needed to restore this area. References Amaya-Romero, M., Abd-Elmabod, S., Munoz-Rojas, M., Castellano, G., Ceacero, C., Alvarez, S., Mendez, M., De la Rosa, D. (2015) Evaluating soil threats under climate change scenarios in the Andalusia region, Southern Spain. Land Degradation and Development, 26, 441-449. Beyene, F. (2015) Incentives and challenges in community based rangeland management: Evidence from Eastern Ethiopia. Land Degradation and Development, 26, 502-509. Brevik, E., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Baumgarten, A., Jordán, A. (2016) Historical perspectives and future needs in soil mapping, classification and pedological modelling, Geoderma, 264, Part B, 256-274. Kraaijvanger, R., Veldkamp, T. (2015) Grain productivity, fertilizer response and nutrient balance of farming systems in Tigray, Ethiopia: A Multiprespective view in relation do soil fertility degradation. Land Degradation and Development, 26, 701-710. Lanckriet, S., Derudder, B., Naudts, J., Bauer, H., Deckers, J., Haile, M., Nyssen, J. (2015) A political ecology perspective of land degradation in the North Ethiopian Highlands. Land Degradation and Development, 26, 521-530. Loveland, P., Weeb, J. (2003) Is there a critical level of organic matter in the agricultural soils of temperate regions: a review. Soil & Tillage Research, 70, 1-18. Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B., Smetanova, A., Depellegrin, D., Misiune, I., Novara, A., Cerda, A. Soil mapping and process modelling for sustainable land management. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B. (Eds.) Soil mapping and process modelling for sustainable land use management (Elsevier Publishing House) ISBN: 9780128052006

The origin of soil organic matter controls its composition and bioreactivity across a mesic boreal forest latitudinal gradient

NASA Astrophysics Data System (ADS)

Kohl, L.; Philben, M. J.; Edwards, K. A.; Podrebarac, F. A.; Jamie, W.; Ziegler, S. E.

2017-12-01

Warmer climates have been associated with reduced soil organic matter (SOM) bioreactivity, lower respiration rates at a given temperature, which is typically attributed to the presence of more decomposed SOM. Cross site studies, however, indicate that ecosystem regime shifts associated with long-term climate warming can affect SOM properties through changes in vegetation and plant litter inputs to soils. The relative importance of these two controls, diagenesis and inputs, on SOM properties as ecosystems experience climate warming remains poorly understood. To address this, we characterized the elemental, chemical (nuclear magnetic resonance spectroscopy and total hydrolysable amino acids), and isotopic composition of plant litter and SOM across a well-constrained mesic boreal forest latitudinal transect in Atlantic Canada. Results across forest sites within each of three climate regions indicated that (1) climate history and diagenesis affect distinct parameters of SOM chemistry, (2) increases in SOM bioreactivity with latitude were associated with elevated proportions of carbohydrates relative to plant waxes and lignin, and (3) despite the common forest type across regions, differences in SOM chemistry by climate region were associated with chemically distinct litter inputs and not different degrees of diagenesis. Climate effects on vascular plant litter chemistry explained only part of the regional differences in SOM chemistry, most notably the higher protein content of SOM from warmer regions. Greater proportions of lignin and aliphatic compounds and smaller proportions of carbohydrates in warmer sites' soils were explained by the higher proportion of vascular plant relative to moss litter in the warmer forests. These results indicate that a climate induced decrease in the proportion of moss inputs will not only impact SOM chemistry but also increase the resistance of SOM to decomposition, thus significantly altering SOM cycling in these boreal forest soils.

Cell type-specific long-range connections of basal forebrain circuit.

PubMed

Do, Johnny Phong; Xu, Min; Lee, Seung-Hee; Chang, Wei-Cheng; Zhang, Siyu; Chung, Shinjae; Yung, Tyler J; Fan, Jiang Lan; Miyamichi, Kazunari; Luo, Liqun; Dan, Yang

2016-09-19

The basal forebrain (BF) plays key roles in multiple brain functions, including sleep-wake regulation, attention, and learning/memory, but the long-range connections mediating these functions remain poorly characterized. Here we performed whole-brain mapping of both inputs and outputs of four BF cell types - cholinergic, glutamatergic, and parvalbumin-positive (PV+) and somatostatin-positive (SOM+) GABAergic neurons - in the mouse brain. Using rabies virus -mediated monosynaptic retrograde tracing to label the inputs and adeno-associated virus to trace axonal projections, we identified numerous brain areas connected to the BF. The inputs to different cell types were qualitatively similar, but the output projections showed marked differences. The connections to glutamatergic and SOM+ neurons were strongly reciprocal, while those to cholinergic and PV+ neurons were more unidirectional. These results reveal the long-range wiring diagram of the BF circuit with highly convergent inputs and divergent outputs and point to both functional commonality and specialization of different BF cell types.

Cell-type specific circuit connectivity of hippocampal CA1 revealed through Cre-dependent rabies tracing

PubMed Central

Sun, Yanjun; Nguyen, Amanda; Nguyen, Joseph; Le, Luc; Saur, Dieter; Choi, Jiwon; Callaway, Edward M.; Xu, Xiangmin

2014-01-01

Summary We applied a new Cre-dependent, genetically modified rabies-based tracing system to map direct synaptic connections to CA1 excitatory and inhibitory neuron types in mouse hippocampus. We found common inputs to excitatory and inhibitory CA1 neurons from CA3, CA2, entorhinal cortex and the medial septum (MS), and unexpectedly also from the subiculum. Excitatory CA1 neurons receive inputs from both cholinergic and GABAergic MS neurons while inhibitory CA1 neurons receive a great majority of input from GABAergic MS neurons; both cell types also receive weaker input from glutamatergic MS neurons. Comparisons of inputs to CA1 PV+ interneurons versus SOM+ interneurons showed similar strengths of input from the subiculum, but PV+ interneurons receive much stronger input than SOM+ neurons from CA3, entorhinal cortex and MS. Differential input from CA3 to specific CA1 cell types was also demonstrated functionally using laser scanning photostimulation and whole cell recordings. PMID:24656815

Anomaly Detection of Electromyographic Signals.

PubMed

Ijaz, Ahsan; Choi, Jongeun

2018-04-01

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

Assessment of WRF Simulated Precipitation by Meteorological Regimes

NASA Astrophysics Data System (ADS)

Hagenhoff, Brooke Anne

This study evaluated warm-season precipitation events in a multi-year (2007-2014) database of Weather Research and Forecasting (WRF) simulations over the Northern Plains and Southern Great Plains. These WRF simulations were run daily in support of the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) by the National Severe Storms Laboratory (NSSL) for operational forecasts. Evaluating model skill by synoptic pattern allows for an understanding of how model performance varies with particular atmospheric states and will aid forecasters with pattern recognition. To conduct this analysis, a competitive neural network known as the Self-Organizing Map (SOM) was used. SOMs allow the user to represent atmospheric patterns in an array of nodes that represent a continuum of synoptic categorizations. North American Regional Reanalysis (NARR) data during the warm season (April-September) was used to perform the synoptic typing over the study domains. Simulated precipitation was evaluated against observations provided by the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis.

An invasive wetland grass primes deep soil carbon pools.

PubMed

Bernal, Blanca; Megonigal, J Patrick; Mozdzer, Thomas J

2017-05-01

Understanding the processes that control deep soil carbon (C) dynamics and accumulation is of key importance, given the relevance of soil organic matter (SOM) as a vast C pool and climate change buffer. Methodological constraints of measuring SOM decomposition in the field prevent the addressing of real-time rhizosphere effects that regulate nutrient cycling and SOM decomposition. An invasive lineage of Phragmites australis roots deeper than native vegetation (Schoenoplectus americanus and Spartina patens) in coastal marshes of North America and has potential to dramatically alter C cycling and accumulation in these ecosystems. To evaluate the effect of deep rooting on SOM decomposition we designed a mesocosm experiment that differentiates between plant-derived, surface SOM-derived (0-40 cm, active root zone of native marsh vegetation), and deep SOM-derived mineralization (40-80 cm, below active root zone of native vegetation). We found invasive P. australis allocated the highest proportion of roots in deeper soils, differing significantly from the native vegetation in root : shoot ratio and belowground biomass allocation. About half of the CO 2 produced came from plant tissue mineralization in invasive and native communities; the rest of the CO 2 was produced from SOM mineralization (priming). Under P. australis, 35% of the CO 2 was produced from deep SOM priming and 9% from surface SOM. In the native community, 9% was produced from deep SOM priming and 44% from surface SOM. SOM priming in the native community was proportional to belowground biomass, while P. australis showed much higher priming with less belowground biomass. If P. australis deep rooting favors the decomposition of deep-buried SOM accumulated under native vegetation, P. australis invasion into a wetland could fundamentally change SOM dynamics and lead to the loss of the C pool that was previously sequestered at depth under the native vegetation, thereby altering the function of a wetland as a long-term C sink. © 2016 John Wiley & Sons Ltd.

Deposition of sedimentary organic matter in black shale facies indicated by the geochemistry and petrography of high-resolution samples, blake nose, western North Atlantic

USGS Publications Warehouse

Barker, C.E.; Pawlewicz, M.; Cobabe, E.A.

2001-01-01

A transect of three holes drilled across the Blake Nose, western North Atlantic Ocean, retrieved cores of black shale facies related to the Albian Oceanic Anoxic Events (OAE) lb and ld. Sedimentary organic matter (SOM) recovered from Ocean Drilling Program Hole 1049A from the eastern end of the transect showed that before black shale facies deposition organic matter preservation was a Type III-IV SOM. Petrography reveals that this SOM is composed mostly of degraded algal debris, amorphous SOM and a minor component of Type III-IV terrestrial SOM, mostly detroinertinite. When black shale facies deposition commenced, the geochemical character of the SOM changed from a relatively oxygen-rich Type III-IV to relatively hydrogen-rich Type II. Petrography, biomarker and organic carbon isotopic data indicate marine and terrestrial SOM sources that do not appear to change during the transition from light-grey calcareous ooze to the black shale facies. Black shale subfacies layers alternate from laminated to homogeneous. Some of the laminated and the poorly laminated to homogeneous layers are organic carbon and hydrogen rich as well, suggesting that at least two SOM depositional processes are influencing the black shale facies. The laminated beds reflect deposition in a low sedimentation rate (6m Ma-1) environment with SOM derived mostly from gravity settling from the overlying water into sometimes dysoxic bottom water. The source of this high hydrogen content SOM is problematic because before black shale deposition, the marine SOM supplied to the site is geochemically a Type III-IV. A clue to the source of the H-rich SOM may be the interlayering of relatively homogeneous ooze layers that have a widely variable SOM content and quality. These relatively thick, sometimes subtly graded, sediment layers are thought to be deposited from a Type II SOM-enriched sediment suspension generated by turbidities or direct turbidite deposition.

Ultimate compression after impact load prediction in graphite/epoxy coupons using neural network and multivariate statistical analyses

NASA Astrophysics Data System (ADS)

Gregoire, Alexandre David

2011-07-01

The goal of this research was to accurately predict the ultimate compressive load of impact damaged graphite/epoxy coupons using a Kohonen self-organizing map (SOM) neural network and multivariate statistical regression analysis (MSRA). An optimized use of these data treatment tools allowed the generation of a simple, physically understandable equation that predicts the ultimate failure load of an impacted damaged coupon based uniquely on the acoustic emissions it emits at low proof loads. Acoustic emission (AE) data were collected using two 150 kHz resonant transducers which detected and recorded the AE activity given off during compression to failure of thirty-four impacted 24-ply bidirectional woven cloth laminate graphite/epoxy coupons. The AE quantification parameters duration, energy and amplitude for each AE hit were input to the Kohonen self-organizing map (SOM) neural network to accurately classify the material failure mechanisms present in the low proof load data. The number of failure mechanisms from the first 30% of the loading for twenty-four coupons were used to generate a linear prediction equation which yielded a worst case ultimate load prediction error of 16.17%, just outside of the +/-15% B-basis allowables, which was the goal for this research. Particular emphasis was placed upon the noise removal process which was largely responsible for the accuracy of the results.

The characteristic analysis of Korean August rainfall using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Lee, S. H.; Seo, K. H.; Kim, J.

2016-12-01

The characteristics of the low-level pressure pattern during Korean August rainfall have been investigated using a neural network-based cluster analysis called self-organizing map (SOM). On the basis of various SOM mode analyses, five major phases of low-level pressure pattern are dynamically identified. The first mode occurs with a distinct circulation state corresponding to a strengthened subtropical high to the south of Korea and migratory low passing though north of Korea. The cold, dry inflow from the north by the cyclonic anomaly and warm, moist air produced by the WNPSH demonstrate the convective instability that provides reasonably intense precipitation over the Korean Peninsula. The second mode represents that low-level anticyclonic anomaly is located to the south of Korea and low-level anticyclonic anomaly is located over the Sea of Okhotsk. The two high pressure pattern conflict with each other forming front, which is identified as the frontal precipitation. The third mode represents local instability with no specific large-scale environmental condition; weak low-level jets, weak upper-level jets, no front, and no typhoon. The fourth mode is typhoon near Taiwan suppling a lot of water vapor in the Korean peninsula to be unloaded precipitation. This can be represented as an indirect-typhoon mode. The fifth mode can be classified as direct-typhoon mode, which typhoon passes though the Korea.

Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls

DOE PAGES

Kallenbach, Cynthia M.; Frey, Serita D.; Grandy, A. Stuart

2016-11-28

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that microbial residues account for the chemistry, stability and abundance of SOM is still lacking. Further, emerging models emphasize the stabilization of microbial-derived SOM by abiotic mechanisms, while the effects of microbial physiology on microbial residue production remain unclear. Here we provide the first direct evidence that soil microbes producemore » chemically diverse, stable SOM. As a result, we show that SOM accumulation is driven by distinct microbial communities more so than clay mineralogy, where microbial-derived SOM accumulation is greatest in soils with higher fungal abundances and more efficient microbial biomass production.« less

Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kallenbach, Cynthia M.; Frey, Serita D.; Grandy, A. Stuart

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that microbial residues account for the chemistry, stability and abundance of SOM is still lacking. Further, emerging models emphasize the stabilization of microbial-derived SOM by abiotic mechanisms, while the effects of microbial physiology on microbial residue production remain unclear. Here we provide the first direct evidence that soil microbes producemore » chemically diverse, stable SOM. As a result, we show that SOM accumulation is driven by distinct microbial communities more so than clay mineralogy, where microbial-derived SOM accumulation is greatest in soils with higher fungal abundances and more efficient microbial biomass production.« less

Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls

NASA Astrophysics Data System (ADS)

Kallenbach, Cynthia M.; Frey, Serita D.; Grandy, A. Stuart

2016-11-01

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that microbial residues account for the chemistry, stability and abundance of SOM is still lacking. Further, emerging models emphasize the stabilization of microbial-derived SOM by abiotic mechanisms, while the effects of microbial physiology on microbial residue production remain unclear. Here we provide the first direct evidence that soil microbes produce chemically diverse, stable SOM. We show that SOM accumulation is driven by distinct microbial communities more so than clay mineralogy, where microbial-derived SOM accumulation is greatest in soils with higher fungal abundances and more efficient microbial biomass production.

Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls.

PubMed

Kallenbach, Cynthia M; Frey, Serita D; Grandy, A Stuart

2016-11-28

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that microbial residues account for the chemistry, stability and abundance of SOM is still lacking. Further, emerging models emphasize the stabilization of microbial-derived SOM by abiotic mechanisms, while the effects of microbial physiology on microbial residue production remain unclear. Here we provide the first direct evidence that soil microbes produce chemically diverse, stable SOM. We show that SOM accumulation is driven by distinct microbial communities more so than clay mineralogy, where microbial-derived SOM accumulation is greatest in soils with higher fungal abundances and more efficient microbial biomass production.

Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma.

PubMed

Arnold, David T; Rowen, Donna; Versteegh, Matthijs M; Morley, Anna; Hooper, Clare E; Maskell, Nicholas A

2015-01-23

In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published. Health related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed. The dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset. This study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

Soil Carbon Losses after Rainforest Conversion to Oil Palm and Rubber Plantations: Processes and Sensitivity of Soil Fertility Indicators Assessed by a New Approach

NASA Astrophysics Data System (ADS)

Guillaume, T.; Maranguit, D.; Murtilaksono, K.; Kuzyakov, Y.

2015-12-01

Tropical forest conversion to agricultural land leads to strong decrease of soil organic matter (SOM). Nonetheless, the magnitude of SOM losses and their impacts on soil fertility in oil palm and rubber plantations remain unclear, despite the large scale extension of such land-use types. We quantified SOM losses, and estimated soil erosion and changes in SOM turnover using SOM δ13C values in forest, oil palm plantations, extensive rubber plantations and rubber monocultures on Sumatra Island (Indonesia). Further, we assessed the response of biological (basal respiration, microbial biomass, acid phosphatase) and chemical fertility indicators (light fraction, DOC, total N, available P) to SOM losses. We used a new approach based on (non-)linear regressions between SOM losses and the indices standardized to natural ecosystem. Carbon contents in the Ah horizon under oil palm and rubber plantations were strongly reduced: up to 70% and 62%, respectively. The decrease was lower under extensive rubber (41%). The estimated erosion was the strongest in oil palm (35±8 cm) and rubber (33±10 cm) plantations. The SOM 13C enrichment used as a proxy of its turnover indicates a decrease of SOM turnover under oil palm after forest conversion. The negative impact of land-use changes on all measured indicators increased in the following sequence: forest > extensive rubber > rubber > oil palm. The basal respiration, microbial biomass and nutrients were comparatively resistant to SOM losses, whereas the light fraction was lost faster than the SOM. The resistance of the microbial activity to SOM losses is an indication that the microbial functions sustain SOM losses. However, responses of basal respiration and microbial biomass to SOM losses were non-linear. Below 2.7 % C content, the relationship was reversed. The basal respiration decreased faster than the SOM, resulting in a stronger drop of microbial activity under oil palm compared to rubber despite small difference in C content. We conclude that the new approach allows a quantitative assessment of the sensitivity and threshold of various soil functions to land-use changes and consequently, can be used to assess resilience of agroecosystem of gradual use intensity.

A Novel Color Image Encryption Algorithm Based on Quantum Chaos Sequence

NASA Astrophysics Data System (ADS)

Liu, Hui; Jin, Cong

2017-03-01

In this paper, a novel algorithm of image encryption based on quantum chaotic is proposed. The keystreams are generated by the two-dimensional logistic map as initial conditions and parameters. And then general Arnold scrambling algorithm with keys is exploited to permute the pixels of color components. In diffusion process, a novel encryption algorithm, folding algorithm, is proposed to modify the value of diffused pixels. In order to get the high randomness and complexity, the two-dimensional logistic map and quantum chaotic map are coupled with nearest-neighboring coupled-map lattices. Theoretical analyses and computer simulations confirm that the proposed algorithm has high level of security.

Liquid–liquid phase separation in particles containing secondary organic material free of inorganic salts

DOE PAGES

Song, Mijung; Liu, Pengfei; Martin, Scot T.; ...

2017-09-25

Particles containing secondary organic material (SOM) are ubiquitous in the atmosphere and play a role in climate and air quality. Recently, research has shown that liquid–liquid phase separation (LLPS) occurs at high relative humidity (RH) (greater than ~95 %) in α-pinene-derived SOM particles free of inorganic salts, while LLPS does not occur in isoprene-derived SOM particles free of inorganic salts. We expand on these findings by investigating LLPS at 290 ± 1 K in SOM particles free of inorganic salts produced from ozonolysis of β-caryophyllene, ozonolysis of limonene, and photo-oxidation of toluene. LLPS was observed at greater than ~95 %more » RH in the biogenic SOM particles derived from β-caryophyllene and limonene while LLPS was not observed in the anthropogenic SOM particles derived from toluene. This work combined with the earlier work on LLPS in SOM particles free of inorganic salts suggests that the occurrence of LLPS in SOM particles free of inorganic salts is related to the oxygen-to-carbon elemental ratio (O : C) of the organic material. These results help explain the difference between the hygroscopic parameter κ of SOM particles measured above and below water saturation in the laboratory and field, and have implications for predicting the cloud condensation nucleation properties of SOM particles.« less

Liquid–liquid phase separation in particles containing secondary organic material free of inorganic salts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Mijung; Liu, Pengfei; Martin, Scot T.

Particles containing secondary organic material (SOM) are ubiquitous in the atmosphere and play a role in climate and air quality. Recently, research has shown that liquid–liquid phase separation (LLPS) occurs at high relative humidity (RH) (greater than ~95 %) in α-pinene-derived SOM particles free of inorganic salts, while LLPS does not occur in isoprene-derived SOM particles free of inorganic salts. We expand on these findings by investigating LLPS at 290 ± 1 K in SOM particles free of inorganic salts produced from ozonolysis of β-caryophyllene, ozonolysis of limonene, and photo-oxidation of toluene. LLPS was observed at greater than ~95 %more » RH in the biogenic SOM particles derived from β-caryophyllene and limonene while LLPS was not observed in the anthropogenic SOM particles derived from toluene. This work combined with the earlier work on LLPS in SOM particles free of inorganic salts suggests that the occurrence of LLPS in SOM particles free of inorganic salts is related to the oxygen-to-carbon elemental ratio (O : C) of the organic material. These results help explain the difference between the hygroscopic parameter κ of SOM particles measured above and below water saturation in the laboratory and field, and have implications for predicting the cloud condensation nucleation properties of SOM particles.« less

Liquid-liquid phase separation in particles containing secondary organic material free of inorganic salts

NASA Astrophysics Data System (ADS)

Song, Mijung; Liu, Pengfei; Martin, Scot T.; Bertram, Allan K.

2017-09-01

Particles containing secondary organic material (SOM) are ubiquitous in the atmosphere and play a role in climate and air quality. Recently, research has shown that liquid-liquid phase separation (LLPS) occurs at high relative humidity (RH) (greater than ˜ 95 %) in α-pinene-derived SOM particles free of inorganic salts, while LLPS does not occur in isoprene-derived SOM particles free of inorganic salts. We expand on these findings by investigating LLPS at 290 ± 1 K in SOM particles free of inorganic salts produced from ozonolysis of β-caryophyllene, ozonolysis of limonene, and photo-oxidation of toluene. LLPS was observed at greater than ˜ 95 % RH in the biogenic SOM particles derived from β-caryophyllene and limonene while LLPS was not observed in the anthropogenic SOM particles derived from toluene. This work combined with the earlier work on LLPS in SOM particles free of inorganic salts suggests that the occurrence of LLPS in SOM particles free of inorganic salts is related to the oxygen-to-carbon elemental ratio (O : C) of the organic material. These results help explain the difference between the hygroscopic parameter κ of SOM particles measured above and below water saturation in the laboratory and field, and have implications for predicting the cloud condensation nucleation properties of SOM particles.

The effect of composition on stability ((14)C activity) of soil organic matter fractions from the albic and black soils.

PubMed

Jin, Jie; Sun, Ke; Wang, Ziying; Han, Lanfang; Wu, Fengchang; Xing, Baoshan

2016-01-15

The importance of the composition of soil organic matter (SOM) for carbon (C) cycling is still under debate. Here a single soil source was used to examine the specific influence of its composition on stability ((14)C activity) of SOM fractions while constraining other influential C turnover factors such as mineral, climate and plant input. The following SOM fractions were isolated from two soil samples: four humic acids, two humins, non-hydrolyzable carbon, and the demineralized fraction. We examined the isotope ratios of SOM fractions in relation to composition (such as aliphatic and aromatic C content) using solid state (13)C nuclear magnetic resonance (NMR) and thermal analysis. The Δ(14)C values of the fractions isolated from both an albic soil (SOMs-A) and a black soil (SOMs-B) correlated negatively with their peak temperature of decomposition and the temperature where half of the total heat of reaction was evolved, implying a potential link between thermal and biogeochemical stability of SOM fractions. Aryl C contents of SOMs-A determined using (13)C NMR varied inversely with δ(15)N values and directly with δ(13)C values, suggesting that part of aryl C of SOMs-A might be fire-derived. The Δ(14)C values of SOMs-A correlated positively with aliphatic C content and negatively with aromatic C content. We therefore concluded that fire-derived aromatic C in SOMs-A appeared to be more stable than microbially-derived aliphatic C. The greater decomposition of SOMs-B fractions weakened the relationship of their Δ(14)C values with alkyl and aryl C contents. Hence, the role of the composition of SOM fractions in regulating stability might be dependent on the source of specific C forms and their stage of decomposition. Copyright © 2015 Elsevier B.V. All rights reserved.

The origin of soil organic matter controls its composition and bioreactivity across a mesic boreal forest latitudinal gradient.

PubMed

Kohl, Lukas; Philben, Michael; Edwards, Kate A; Podrebarac, Frances A; Warren, Jamie; Ziegler, Susan E

2018-02-01

Warmer climates have been associated with reduced bioreactivity of soil organic matter (SOM) typically attributed to increased diagenesis; the combined biological and physiochemical transformation of SOM. In addition, cross-site studies have indicated that ecosystem regime shifts, associated with long-term climate warming, can affect SOM properties through changes in vegetation and plant litter production thereby altering the composition of soil inputs. The relative importance of these two controls, diagenesis and inputs, on SOM properties as ecosystems experience climate warming, however, remains poorly understood. To address this issue we characterized the elemental, chemical (nuclear magnetic resonance spectroscopy and total hydrolysable amino acids analysis), and isotopic composition of plant litter and SOM across a well-constrained mesic boreal forest latitudinal transect in Atlantic Canada. Results across forest sites within each of three climate regions indicated that (1) climate history and diagenesis affect distinct parameters of SOM chemistry, (2) increases in SOM bioreactivity with latitude were associated with elevated proportions of carbohydrates relative to plant waxes and lignin, and (3) despite the common forest type across regions, differences in SOM chemistry by climate region were associated with chemically distinct litter inputs and not different degrees of diagenesis. The observed climate effects on vascular plant litter chemistry, however, explained only part of the regional differences in SOM chemistry, most notably the higher protein content of SOM from warmer regions. Greater proportions of lignin and aliphatic compounds and smaller proportions of carbohydrates in warmer sites' soils were explained by the higher proportion of vascular plant relative to moss litter in the warmer relative to cooler forests. These results indicate that climate change induced decreases in the proportion of moss inputs not only impacts SOM chemistry but also increases the resistance of SOM to decomposition, thus significantly altering SOM cycling in these boreal forest soils. © 2017 John Wiley & Sons Ltd.

Plasmid mapping computer program.

PubMed Central

Nolan, G P; Maina, C V; Szalay, A A

1984-01-01

Three new computer algorithms are described which rapidly order the restriction fragments of a plasmid DNA which has been cleaved with two restriction endonucleases in single and double digestions. Two of the algorithms are contained within a single computer program (called MPCIRC). The Rule-Oriented algorithm, constructs all logical circular map solutions within sixty seconds (14 double-digestion fragments) when used in conjunction with the Permutation method. The program is written in Apple Pascal and runs on an Apple II Plus Microcomputer with 64K of memory. A third algorithm is described which rapidly maps double digests and uses the above two algorithms as adducts. Modifications of the algorithms for linear mapping are also presented. PMID:6320105

Observations of liquid-liquid phase separation in several types of secondary organic materials free of inorganic salts

NASA Astrophysics Data System (ADS)

Song, M.; Liu, P.; Martin, S. T.; Bertram, A. K.; Ham, S.

2016-12-01

Particles consisting of secondary organic materials (SOMs) are ubiquitous in the atmosphere. In order to predict the role of these particles in climate, visibility, and atmospheric chemistry, knowledge of the phase states of the particles is required. However, the phase states of the SOMs are still poorly understood. Herein we focused on liquid-liquid phase separation in different types of SOM particles free of inorganic salts produced by the ozonolysis of β-caryophyllene, ozonolysis of limonene, photo-oxidation of isoprene, and photo-oxidation of toluene. Liquid-liquid phase separation was investigated using optical microscopy and SOM particle mass concentrations ranging from 15 µg·m-3 to 7000 µg·m-3. During humidity cycles, liquid-liquid phase separation was observed in β-caryophyllene-derived SOM and limonene-derived SOM particles while no liquid-liquid phase separation was observed in isoprene-derived SOM and toluene-derived SOM particles. Results from the studies will be presented.

From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

PubMed Central

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed. PMID:25879067

From determinism and probability to chaos: chaotic evolution towards philosophy and methodology of chaotic optimization.

PubMed

Pei, Yan

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

Analyzing Sub-Classifications of Glaucoma via SOM Based Clustering of Optic Nerve Images.

PubMed

Yan, Sanjun; Abidi, Syed Sibte Raza; Artes, Paul Habib

2005-01-01

We present a data mining framework to cluster optic nerve images obtained by Confocal Scanning Laser Tomography (CSLT) in normal subjects and patients with glaucoma. We use self-organizing maps and expectation maximization methods to partition the data into clusters that provide insights into potential sub-classification of glaucoma based on morphological features. We conclude that our approach provides a first step towards a better understanding of morphological features in optic nerve images obtained from glaucoma patients and healthy controls.

ABA-insensitive3, ABA-insensitive5, and DELLAs Interact to activate the expression of SOMNUS and other high-temperature-inducible genes in imbibed seeds in Arabidopsis.

PubMed

Lim, Soohwan; Park, Jeongmoo; Lee, Nayoung; Jeong, Jinkil; Toh, Shigeo; Watanabe, Asuka; Kim, Junghyun; Kang, Hyojin; Kim, Dong Hwan; Kawakami, Naoto; Choi, Giltsu

2013-12-01

Seeds monitor the environment to germinate at the proper time, but different species respond differently to environmental conditions, particularly light and temperature. In Arabidopsis thaliana, light promotes germination but high temperature suppresses germination. We previously reported that light promotes germination by repressing SOMNUS (SOM). Here, we examined whether high temperature also regulates germination through SOM and found that high temperature activates SOM expression. Consistent with this, som mutants germinated more frequently than the wild type at high temperature. The induction of SOM mRNA at high temperature required abscisic acid (ABA) and gibberellic acid biosynthesis, and ABA-insensitive3 (ABI3), ABI5, and DELLAs positively regulated SOM expression. Chromatin immunoprecipitation assays indicated that ABI3, ABI5, and DELLAs all target the SOM promoter. At the protein level, ABI3, ABI5, and DELLAs all interact with each other, suggesting that they form a complex on the SOM promoter to activate SOM expression at high temperature. We found that high-temperature-inducible genes frequently have RY motifs and ABA-responsive elements in their promoters, some of which are targeted by ABI3, ABI5, and DELLAs in vivo. Taken together, our data indicate that ABI3, ABI5, and DELLAs mediate high-temperature signaling to activate the expression of SOM and other high-temperature-inducible genes, thereby inhibiting seed germination.

Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system

NASA Technical Reports Server (NTRS)

Lee, C. S. G.; Chen, C. L.

1989-01-01

Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed.

Enhancing the usability and performance of structured association mapping algorithms using automation, parallelization, and visualization in the GenAMap software system

PubMed Central

2012-01-01

Background Structured association mapping is proving to be a powerful strategy to find genetic polymorphisms associated with disease. However, these algorithms are often distributed as command line implementations that require expertise and effort to customize and put into practice. Because of the difficulty required to use these cutting-edge techniques, geneticists often revert to simpler, less powerful methods. Results To make structured association mapping more accessible to geneticists, we have developed an automatic processing system called Auto-SAM. Auto-SAM enables geneticists to run structured association mapping algorithms automatically, using parallelization. Auto-SAM includes algorithms to discover gene-networks and find population structure. Auto-SAM can also run popular association mapping algorithms, in addition to five structured association mapping algorithms. Conclusions Auto-SAM is available through GenAMap, a front-end desktop visualization tool. GenAMap and Auto-SAM are implemented in JAVA; binaries for GenAMap can be downloaded from http://sailing.cs.cmu.edu/genamap. PMID:22471660

The origin of soil organic matter dictates its composition and bioreactivity across a mesic boreal forest latitudinal gradient

NASA Astrophysics Data System (ADS)

Kohl, Lukas; Philben, Michael; Edwards, Kate A.; Podrebarac, Frances A.; Warren, Jamie; Ziegler, Susan E.

2017-04-01

Climate transect studies and soil warming experiments have shown that soil organic matter (SOM) formed under a warmer climate is typically more resistant to microbial decomposition, as indicated by lower decomposition rates at a given temperature (bioreactivity). However, it remains unclear how climate impacts SOM via its effect on vegetation and thus litter inputs to soils, or on decomposition and thus how SOM changes over time (diagenesis). We addressed this question by studying how the chemical and biological properties of SOM vary with decomposition (depth) and climate history (latitude) in mesic boreal forests of Atlantic Canada. SOM bioreactivity, measured in a 15-months decomposition experiment, decreased from cold to warm regions, and from the topmost (L) to the deepest horizon studied (H). The variations in SOM bioreactivity with climate history and depth, however, were associated with distinct parameters of SOM chemistry. More decomposed SOM with depth was associated with lower proportions of %N as total hydrolysable amino acids (THAA), and a different THAA-based degradation index signifying a more degraded state. However, SOM from the warmer region exhibited higher lignin to carbohydrate ratios, as detected by NMR. None of the measured parameters associated with regional differences in SOM chemistry increased with depth. Together, these results indicate that the regional differences in SOM chemistry and bioreactivity in these soils did not result from significant differences in the degree of degradation, but rather resulted from chemically distinct litter inputs. The comparison of SOM and plant litter chemistry allowed us to identify how climate affects litter inputs in these forests. Vascular plant litter collected in litter traps, unlike SOM, exhibited largely similar chemical composition across all transect regions. Litter traps, however, do not collect moss litter, which is chemically distinct from vascular plant litter. We, therefore, assessed the proportions of identifiable moss to vascular plant material in the L horizon, and found that the proportion of moss litter decreases from north to south consistent with moss cover estimates across this transect. Furthermore, SOM was more similar to vascular plant litter in more southern sites, while properties of moss litter (high carbohydrate, low lignin and plant wax concentrations) were associated with the more bioreactive SOM found in the northern sites. Despite the slow rates of moss litter decomposition relative to vascular plant litter observed in many studies, these results suggest that moss derived SOM is decomposed more rapidly than vascular plant derived SOM. This is consistent with (1) initially rapid decomposition of vascular plant litter contributing to more slow turnover SOM, and (2) the role of antimicrobial compounds in reducing surface moss litter decomposition which may be reduced at depth due to their removal or inactivation over time. The decrease of SOM bioreactivity due to lower proportions of moss inputs in the warmer forests studied here signifies an important and understudied ecosystem mechanism that can control the cycling of C in these, and likely other boreal forest soils in a warmer future.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Assessment for water quality by artificial neural network in Daya Bay, South China Sea.

PubMed

Wu, Mei-Lin; Wang, You-Shao; Gu, Ji-Dong

2015-10-01

In this study, artificial neural network such as a self-organizing map (SOM) was used to assess for the effects caused by climate change and human activities on the water quality in Daya Bay, South China Sea. SOM has identified the anthropogenic effects and seasonal characters of water quality. SOM grouped the four seasons as four groups (winter, spring, summer and autumn). The Southeast Asian monsoons, northeasterly from October to the next April and southwesterly from May to September have also an important influence on the water quality in Daya Bay. Spatial pattern is mainly related to anthropogenic activities and hydrodynamics conditions. In spatial characteristics, the water quality in Daya Bay was divided into two groups by chemometrics. The monitoring stations (S3, S8, S10 and S11) were in these area (Dapeng Ao, Aotou Harbor) and northeast parts of Daya Bay, which are areas of human activity. The thermal pollution has been observed near water body in Daya Bay Nuclear Power Plant (S5). The rest of the monitoring sites were in the south, central and eastern parts of Daya Bay, which are areas that experience water exchanges from South China Sea. The results of this study may provide information on the spatial and temporal patterns in Daya Bay. Further research will be carry out more research concerning functional changes in the bay ecology with respect to changes in climatic factor, human activities and bay morphology in Daya Bay.

Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

2017-05-01

Accurate nitrate load predictions can elevate decision management of water quality of watersheds which affects to environment and drinking water. In this paper, two scenarios were considered for Multi-Station (MS) nitrate load modeling of the Little River watershed. In the first scenario, Markovian characteristics of streamflow-nitrate time series were proposed for the MS modeling. For this purpose, feature extraction criterion of Mutual Information (MI) was employed for input selection of artificial intelligence models (Feed Forward Neural Network, FFNN and least square support vector machine). In the second scenario for considering seasonality-based characteristics of the time series, wavelet transform was used to extract multi-scale features of streamflow-nitrate time series of the watershed's sub-basins to model MS nitrate loads. Self-Organizing Map (SOM) clustering technique which finds homogeneous sub-series clusters was also linked to MI for proper cluster agent choice to be imposed into the models for predicting the nitrate loads of the watershed's sub-basins. The proposed MS method not only considers the prediction of the outlet nitrate but also covers predictions of interior sub-basins nitrate load values. The results indicated that the proposed FFNN model coupled with the SOM-MI improved the performance of MS nitrate predictions compared to the Markovian-based models up to 39%. Overall, accurate selection of dominant inputs which consider seasonality-based characteristics of streamflow-nitrate process could enhance the efficiency of nitrate load predictions.

Hybrid modeling of spatial continuity for application to numerical inverse problems

USGS Publications Warehouse

Friedel, Michael J.; Iwashita, Fabio

2013-01-01

A novel two-step modeling approach is presented to obtain optimal starting values and geostatistical constraints for numerical inverse problems otherwise characterized by spatially-limited field data. First, a type of unsupervised neural network, called the self-organizing map (SOM), is trained to recognize nonlinear relations among environmental variables (covariates) occurring at various scales. The values of these variables are then estimated at random locations across the model domain by iterative minimization of SOM topographic error vectors. Cross-validation is used to ensure unbiasedness and compute prediction uncertainty for select subsets of the data. Second, analytical functions are fit to experimental variograms derived from original plus resampled SOM estimates producing model variograms. Sequential Gaussian simulation is used to evaluate spatial uncertainty associated with the analytical functions and probable range for constraining variables. The hybrid modeling of spatial continuity is demonstrated using spatially-limited hydrologic measurements at different scales in Brazil: (1) physical soil properties (sand, silt, clay, hydraulic conductivity) in the 42 km2 Vargem de Caldas basin; (2) well yield and electrical conductivity of groundwater in the 132 km2 fractured crystalline aquifer; and (3) specific capacity, hydraulic head, and major ions in a 100,000 km2 transboundary fractured-basalt aquifer. These results illustrate the benefits of exploiting nonlinear relations among sparse and disparate data sets for modeling spatial continuity, but the actual application of these spatial data to improve numerical inverse modeling requires testing.

Evaluating synoptic systems in the CMIP5 climate models over the Australian region

NASA Astrophysics Data System (ADS)

Gibson, Peter B.; Uotila, Petteri; Perkins-Kirkpatrick, Sarah E.; Alexander, Lisa V.; Pitman, Andrew J.

2016-10-01

Climate models are our principal tool for generating the projections used to inform climate change policy. Our confidence in projections depends, in part, on how realistically they simulate present day climate and associated variability over a range of time scales. Traditionally, climate models are less commonly assessed at time scales relevant to daily weather systems. Here we explore the utility of a self-organizing maps (SOMs) procedure for evaluating the frequency, persistence and transitions of daily synoptic systems in the Australian region simulated by state-of-the-art global climate models. In terms of skill in simulating the climatological frequency of synoptic systems, large spread was observed between models. A positive association between all metrics was found, implying that relative skill in simulating the persistence and transitions of systems is related to skill in simulating the climatological frequency. Considering all models and metrics collectively, model performance was found to be related to model horizontal resolution but unrelated to vertical resolution or representation of the stratosphere. In terms of the SOM procedure, the timespan over which evaluation was performed had some influence on model performance skill measures, as did the number of circulation types examined. These findings have implications for selecting models most useful for future projections over the Australian region, particularly for projections related to synoptic scale processes and phenomena. More broadly, this study has demonstrated the utility of the SOMs procedure in providing a process-based evaluation of climate models.

The role of atmospheric circulation patterns on short-term sea-level fluctuations along the eastern seaboard of the US

NASA Astrophysics Data System (ADS)

Sheridan, S. C.; Lee, C. C.; Pirhalla, D.; Ransi, V.

2017-12-01

Sea-level fluctuations over time are a product of short-term weather events, as well as long-term secular trends in sea-level rise. With sea-levl rise, these fluctuations increasingly have substantial impacts upon coastal ecosystems and impact society through coastal flooding events. In this research, we assess the impact of short-term events, combined with sea-level rise, through synoptic climatological analysis, exploring whether circulation pattern identification can be used to enhance probabilistic forecasts of flood likelihood. Self-organizing maps (SOMs) were created for two discrete atmospheric variables: 700-hPa geopotential height (700z) and sea-level pressure (SLP). For each variable, a SOM array of patterns was created based on data spanning 25°-50°N and 60°-90°W for the period 1979-2014. Sea-level values were derived from tidal gauges between Cape May, New Jersey and Charleston, South Carolina, along the mid-Atlantic coast of the US. Both anomalous sea-level values, as well as nuisance flood occurrence (defined using the local gauge threshold), were assessed. Results show the impacts of both the inverted barometer effect as well as surface wind forcing on sea levels. With SLP, higher sea levels are associated with either patterns that were indicative of on-shore flow or cyclones. At 700z, ridges situated along the east coast are associated with higher sea levels. As the SOM matrix arranges atmospheric patterns in a continuum, the nodes of each SOM show a clear spatial pattern in terms of anomalous sea level, including some significant sea-level anomalies associated with relatively ambiguous pressure patterns. Further, multi-day transitions are also analyzed, showing rapidly deepening cyclones, or persistent onshore flow, can be associated with the greatest likelihood of nuisance floods. Results are weaker with 700z than SLP; however, in some cases, it is clear that the mid-tropospheric circulation can modulate the connection between sea-level anomalies and surface circulation.

Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique

NASA Astrophysics Data System (ADS)

Nakaoka, S.; Telszewski, M.; Nojiri, Y.; Yasunaka, S.; Miyazaki, C.; Mukai, H.; Usui, N.

2013-09-01

This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters - sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) - are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

A new image enhancement algorithm with applications to forestry stand mapping

NASA Technical Reports Server (NTRS)

Kan, E. P. F. (Principal Investigator); Lo, J. K.

1975-01-01

The author has identified the following significant results. Results show that the new algorithm produced cleaner classification maps in which holes of small predesignated sizes were eliminated and significant boundary information was preserved. These cleaner post-processed maps better resemble true life timber stand maps and are thus more usable products than the pre-post-processing ones: Compared to an accepted neighbor-checking post-processing technique, the new algorithm is more appropriate for timber stand mapping.

Mapping chemicals in air using an environmental CAT scanning system: evaluation of algorithms

NASA Astrophysics Data System (ADS)

Samanta, A.; Todd, L. A.

A new technique is being developed which creates near real-time maps of chemical concentrations in air for environmental and occupational environmental applications. This technique, we call Environmental CAT Scanning, combines the real-time measuring technique of open-path Fourier transform infrared spectroscopy with the mapping capabilitites of computed tomography to produce two-dimensional concentration maps. With this system, a network of open-path measurements is obtained over an area; measurements are then processed using a tomographic algorithm to reconstruct the concentrations. This research focussed on the process of evaluating and selecting appropriate reconstruction algorithms, for use in the field, by using test concentration data from both computer simultation and laboratory chamber studies. Four algorithms were tested using three types of data: (1) experimental open-path data from studies that used a prototype opne-path Fourier transform/computed tomography system in an exposure chamber; (2) synthetic open-path data generated from maps created by kriging point samples taken in the chamber studies (in 1), and; (3) synthetic open-path data generated using a chemical dispersion model to create time seires maps. The iterative algorithms used to reconstruct the concentration data were: Algebraic Reconstruction Technique without Weights (ART1), Algebraic Reconstruction Technique with Weights (ARTW), Maximum Likelihood with Expectation Maximization (MLEM) and Multiplicative Algebraic Reconstruction Technique (MART). Maps were evaluated quantitatively and qualitatively. In general, MART and MLEM performed best, followed by ARTW and ART1. However, algorithm performance varied under different contaminant scenarios. This study showed the importance of using a variety of maps, particulary those generated using dispersion models. The time series maps provided a more rigorous test of the algorithms and allowed distinctions to be made among the algorithms. A comprehensive evaluation of algorithms, for the environmental application of tomography, requires the use of a battery of test concentration data before field implementation, which models reality and tests the limits of the algorithms.

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

NASA Astrophysics Data System (ADS)

Madokoro, H.; Tsukada, M.; Sato, K.

2013-07-01

This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

Numerical Conformal Mapping Using Cross-Ratios and Delaunay Triangulation

NASA Technical Reports Server (NTRS)

Driscoll, Tobin A.; Vavasis, Stephen A.

1996-01-01

We propose a new algorithm for computing the Riemann mapping of the unit disk to a polygon, also known as the Schwarz-Christoffel transformation. The new algorithm, CRDT, is based on cross-ratios of the prevertices, and also on cross-ratios of quadrilaterals in a Delaunay triangulation of the polygon. The CRDT algorithm produces an accurate representation of the Riemann mapping even in the presence of arbitrary long, thin regions in the polygon, unlike any previous conformal mapping algorithm. We believe that CRDT can never fail to converge to the correct Riemann mapping, but the correctness and convergence proof depend on conjectures that we have so far not been able to prove. We demonstrate convergence with computational experiments. The Riemann mapping has applications to problems in two-dimensional potential theory and to finite-difference mesh generation. We use CRDT to produce a mapping and solve a boundary value problem on long, thin regions for which no other algorithm can solve these problems.

The influence of impression management scales on the Personality Assessment Inventory in the epilepsy monitoring unit.

PubMed

Purdom, Catherine L; Kirlin, Kristin A; Hoerth, Matthew T; Noe, Katherine H; Drazkowski, Joseph F; Sirven, Joseph I; Locke, Dona E C

2012-12-01

The Somatic Complaints scale (SOM) and Conversion subscale (SOM-C) of the Personality Assessment Inventory perform best in classifying psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES); however, the impact of positive impression management (PIM) and negative impression management (NIM) scales on SOM and SOM-C classification has not been examined. We studied 187 patients from an epilepsy monitoring unit with confirmed PNES or ES. On SOM, the best cut score was 72.5 T when PIM was elevated and 69.5 T when there was no bias. On SOM-C, when PIM was elevated, the best cut score was 67.5 T and 76.5 T when there was no bias. Negative impression management elevations (n=9) were too infrequent to analyze separately. Despite similarities in classification accuracy, there were differences in sensitivity and specificity with and without PIM, impacting positive and negative predictive values. The presence of PIM bias generally increases positive predictive power of SOM and SOM-C but decreases negative predictive power. Copyright © 2012 Elsevier Inc. All rights reserved.

Mineral surface-reactive metabolites secreted during fungal decomposition contribute to the formation of soil organic matter.

PubMed

Wang, Tao; Tian, Zhaomo; Bengtson, Per; Tunlid, Anders; Persson, Per

2017-12-01

Soil organic matter (SOM) constitutes the largest terrestrial C pool. An emerging, untested, view is that oxidation and depolymerization of SOM by microorganisms promote the formation of SOM-mineral associations that is critical for SOM stabilization. To test this hypothesis, we performed laboratory-scale experiments involving one ectomycorrhizal and one saprotrophic fungus that represent the two major functional groups of microbial decomposers in the boreal forest soils. Fungal decomposition enhanced the retention of SOM on goethite, partly because of oxidative modifications of organic matter (OM) by the fungi. Moreover, both fungi secreted substantial amounts (> 10% new biomass C) of aromatic metabolites that also contributed to an enhanced mineral retention of OM. Our study demonstrates that soil fungi can form mineral-stabilized SOM not only by oxidative conversion of the SOM but also by synthesizing mineral surface-reactive metabolites. Metabolites produced by fungal decomposers can play a yet overlooked role in the formation and stabilization of SOM. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

New segmentation-based tone mapping algorithm for high dynamic range image

NASA Astrophysics Data System (ADS)

Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

2017-07-01

The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

Pattern recognition and classification of vibrational spectra by artificial neural networks

NASA Astrophysics Data System (ADS)

Yang, Husheng

1999-10-01

A drawback of current open-path Fourier transform infrared (OP/FT-IR) systems is that they need a human expert to determine those compounds that may be quantified from a given spectrum. In this study, three types of artificial neural networks were used to alleviate this problem. Firstly, multi-layer feed-forward neural networks were used to automatically recognize compounds in an OP/FT-IR spectrum. Each neural network was trained to recognize one compound in the presence of up to ten interferents in an OP/FT-IR spectrum. The networks were successfully used to recognize five alcohols and two chlorinated compounds in field-measured controlled-release OP/FT-IR spectra of mixtures of these compounds. It has also been demonstrated that a neural network could correctly identify a spectrum in the presence of an interferent that was not included in the training set and could also reject interferents it has not seen before. Secondly, the possibility of using one- and two- dimensional Kohonen self-organizing maps (SOMs) to recognize similarities in low-resolution vapor-phase infrared spectra without any additional information has been investigated. Both full-range reference spectra and open-path window reference spectra were used to train the networks and the trained networks were then used to classify the reference spectra into several groups. The results showed that the SOMs obtained from the two different training sets were quite different, and it is more appropriate to use the second SOM in OP/FT-IR spectrometry. Thirdly, vapor-phase FT-IR reference spectra of five alcohols along with four baseline spectra were encoded as prototype vectors for a Hopfield network. Inclusion of the baseline spectra allowed the network to classify spectra as unknowns, when the reference spectra of these compounds were not stored as prototype vectors in the network. The network could identify each of the 5 alcohols correctly even in the presence of noise and interfering compounds. Finally, one- and two-dimensional Kohonen SOMs were also successfully used for the unsupervised differentiation of the Fourier transform Raman spectra of hardwoods from softwoods. A semi-quantitative method that is based on the Euclidean distances of the weight matrix has been developed to assist the automatic clustering of the neurons in a two-dimensional SOM.

ABA-INSENSITIVE3, ABA-INSENSITIVE5, and DELLAs Interact to Activate the Expression of SOMNUS and Other High-Temperature-Inducible Genes in Imbibed Seeds in Arabidopsis[W

PubMed Central

Lim, Soohwan; Park, Jeongmoo; Lee, Nayoung; Jeong, Jinkil; Toh, Shigeo; Watanabe, Asuka; Kim, Junghyun; Kang, Hyojin; Kim, Dong Hwan; Kawakami, Naoto; Choi, Giltsu

2013-01-01

Seeds monitor the environment to germinate at the proper time, but different species respond differently to environmental conditions, particularly light and temperature. In Arabidopsis thaliana, light promotes germination but high temperature suppresses germination. We previously reported that light promotes germination by repressing SOMNUS (SOM). Here, we examined whether high temperature also regulates germination through SOM and found that high temperature activates SOM expression. Consistent with this, som mutants germinated more frequently than the wild type at high temperature. The induction of SOM mRNA at high temperature required abscisic acid (ABA) and gibberellic acid biosynthesis, and ABA-INSENSITIVE3 (ABI3), ABI5, and DELLAs positively regulated SOM expression. Chromatin immunoprecipitation assays indicated that ABI3, ABI5, and DELLAs all target the SOM promoter. At the protein level, ABI3, ABI5, and DELLAs all interact with each other, suggesting that they form a complex on the SOM promoter to activate SOM expression at high temperature. We found that high-temperature-inducible genes frequently have RY motifs and ABA-responsive elements in their promoters, some of which are targeted by ABI3, ABI5, and DELLAs in vivo. Taken together, our data indicate that ABI3, ABI5, and DELLAs mediate high-temperature signaling to activate the expression of SOM and other high-temperature-inducible genes, thereby inhibiting seed germination. PMID:24326588

Temperature Responses of Soil Organic Matter Components With Varying Recalcitrance

NASA Astrophysics Data System (ADS)

Simpson, M. J.; Feng, X.

2007-12-01

The response of soil organic matter (SOM) to global warming remains unclear partly due to the chemical heterogeneity of SOM composition. In this study, the decomposition of SOM from two grassland soils was investigated in a one-year laboratory incubation at six different temperatures. SOM was separated into solvent- extractable compounds, suberin- and cutin-derived compounds, and lignin monomers by solvent extraction, base hydrolysis, and CuO oxidation, respectively. These SOM components had distinct chemical structures and recalcitrance, and their decomposition was fitted by a two-pool exponential decay model. The stability of SOM components was assessed using geochemical parameters and kinetic parameters derived from model fitting. Lignin monomers exhibited much lower decay rates than solvent-extractable compounds and a relatively low percentage of lignin monomers partitioned into the labile SOM pool, which confirmed the generally accepted recalcitrance of lignin compounds. Suberin- and cutin-derived compounds had a poor fitting for the exponential decay model, and their recalcitrance was shown by the geochemical degradation parameter which stabilized during the incubation. The aliphatic components of suberin degraded faster than cutin-derived compounds, suggesting that cutin-derived compounds in the soil may be at a higher stage of degradation than suberin- derived compounds. The temperature sensitivity of decomposition, expressed as Q10, was derived from the relationship between temperature and SOM decay rates. SOM components exhibited varying temperature responses and the decomposition of the recalcitrant lignin monomers had much higher Q10 values than soil respiration or the solvent-extractable compounds decomposition. Our study shows that the decomposition of recalcitrant SOM is highly sensitive to temperature, more so than bulk soil mineralization. This observation suggests a potential acceleration in the degradation of the recalcitrant SOM pool with global warming.

Microbial control of soil organic matter mineralization responses to labile carbon in subarctic climate change treatments.

PubMed

Rousk, Kathrin; Michelsen, Anders; Rousk, Johannes

2016-12-01

Half the global soil carbon (C) is held in high-latitude systems. Climate change will expose these to warming and a shift towards plant communities with more labile C input. Labile C can also increase the rate of loss of native soil organic matter (SOM); a phenomenon termed 'priming'. We investigated how warming (+1.1 °C over ambient using open top chambers) and litter addition (90 g m -2  yr -1 ) treatments in the subarctic influenced the susceptibility of SOM mineralization to priming, and its microbial underpinnings. Labile C appeared to inhibit the mineralization of C from SOM by up to 60% within hours. In contrast, the mineralization of N from SOM was stimulated by up to 300%. These responses occurred rapidly and were unrelated to microbial successional dynamics, suggesting catabolic responses. Considered separately, the labile C inhibited C mineralization is compatible with previously reported findings termed 'preferential substrate utilization' or 'negative apparent priming', while the stimulated N mineralization responses echo recent reports of 'real priming' of SOM mineralization. However, C and N mineralization responses derived from the same SOM source must be interpreted together: This suggested that the microbial SOM-use decreased in magnitude and shifted to components richer in N. This finding highlights that only considering SOM in terms of C may be simplistic, and will not capture all changes in SOM decomposition. The selective mining for N increased in climate change treatments with higher fungal dominance. In conclusion, labile C appeared to trigger catabolic responses of the resident microbial community that shifted the SOM mining to N-rich components; an effect that increased with higher fungal dominance. Extrapolating from these findings, the predicted shrub expansion in the subarctic could result in an altered microbial use of SOM, selectively mining it for N-rich components, and leading to a reduced total SOM-use. © 2016 John Wiley & Sons Ltd.

Rapid soil formation after glacial retreat shaped by spatial patterns of organic matter accrual in microaggregates.

PubMed

Schweizer, Steffen A; Hoeschen, Carmen; Schlüter, Steffen; Kögel-Knabner, Ingrid; Mueller, Carsten W

2018-04-01

Global change contributes to the retreat of glaciers at unprecedented rates. The deglaciation facilitates biogeochemical processes on glacial deposits with initiating soil formation as an important driver of evolving ecosystems. The underlying mechanisms of soil formation and the association of soil organic matter (SOM) with mineral particles remain unclear, although further insights are critical to understand carbon sequestration in soils. We investigated the microspatial arrangement of SOM coatings at intact soil microaggregate structures during various stages of ecosystem development from 15 to >700 years after deglaciation in the proglacial environment of the Damma glacier (Switzerland). The functionally important clay-sized fraction (<2 μm) was separated into two density fractions with different amounts of organo-mineral associations: light (1.6-2.2 g/cm 3 ) and heavy (>2.2 g/cm 3 ). To quantify how SOM extends across the surface of mineral particles (coverage) and whether SOM coatings are distributed in fragmented or connected patterns (connectivity), we developed an image analysis protocol based on nanoscale secondary ion mass spectrometry (NanoSIMS). We classified SOM and mineral areas depending on the 16 O - , 12 C - , and 12 C 14 N - distributions. With increasing time after glacial retreat, the microspatial coverage and connectivity of SOM increased rapidly. The rapid soil formation led to a succession of patchy distributed to more connected SOM coatings on soil microaggregates. The maximum coverage of 55% at >700 years suggests direct evidence for SOM sequestration being decoupled from the mineral surface, as it was not completely masked by SOM and retained its functionality as an ion exchange site. The chemical composition of SOM coatings showed a rapid change toward a higher CN:C ratio already at 75 years after glacial retreat, which was associated with microbial succession patterns reflecting high N assimilation. Our results demonstrate that rapid SOM sequestration drives the microspatial succession of SOM coatings in soils, a process that can stabilize SOM for the long term. © 2017 John Wiley & Sons Ltd.

Integrating plant-microbe interactions to understand soil C stabilization with the MIcrobial-MIneral Carbon Stabilization model (MIMICS)

NASA Astrophysics Data System (ADS)

Grandy, Stuart; Wieder, Will; Kallenbach, Cynthia; Tiemann, Lisa

2014-05-01

If soil organic matter is predominantly microbial biomass, plant inputs that build biomass should also increase SOM. This seems obvious, but the implications fundamentally change how we think about the relationships between plants, microbes and SOM. Plant residues that build microbial biomass are typically characterized by low C/N ratios and high lignin contents. However, plants with high lignin contents and high C/N ratios are believed to increase SOM, an entrenched idea that still strongly motivates agricultural soil management practices. Here we use a combination of meta-analysis with a new microbial-explicit soil biogeochemistry model to explore the relationships between plant litter chemistry, microbial communities, and SOM stabilization in different soil types. We use the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, newly built upon the Community Land Model (CLM) platform, to enhance our understanding of biology in earth system processes. The turnover of litter and SOM in MIMICS are governed by the activity of r- and k-selected microbial groups and temperature sensitive Michaelis-Menten kinetics. Plant and microbial residues are stabilized short-term by chemical recalcitrance or long-term by physical protection. Fast-turnover litter inputs increase SOM by >10% depending on temperature in clay soils, and it's only in sandy soils devoid of physical protection mechanisms that recalcitrant inputs build SOM. These results challenge centuries of lay knowledge as well as conventional ideas of SOM formation, but are they realistic? To test this, we conducted a meta-analysis of the relationships between the chemistry of plant liter inputs and SOM concentrations. We find globally that the highest SOM concentrations are associated with plant inputs containing low C/N ratios. These results are confirmed by individual tracer studies pointing to greater stabilization of low C/N ratio inputs, particularly in clay soils. Our model and meta-analysis results suggest that current ideas about plant-microbe-SOM relationships are unraveling. If so, our reconsideration of the mechanisms stabilizing SOM will also challenge long-held views about how to optimize plant community management to increase SOM.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Improving depth maps of plants by using a set of five cameras

NASA Astrophysics Data System (ADS)

Kaczmarek, Adam L.

2015-03-01

Obtaining high-quality depth maps and disparity maps with the use of a stereo camera is a challenging task for some kinds of objects. The quality of these maps can be improved by taking advantage of a larger number of cameras. The research on the usage of a set of five cameras to obtain disparity maps is presented. The set consists of a central camera and four side cameras. An algorithm for making disparity maps called multiple similar areas (MSA) is introduced. The algorithm was specially designed for the set of five cameras. Experiments were performed with the MSA algorithm and the stereo matching algorithm based on the sum of sum of squared differences (sum of SSD, SSSD) measure. Moreover, the following measures were included in the experiments: sum of absolute differences (SAD), zero-mean SAD (ZSAD), zero-mean SSD (ZSSD), locally scaled SAD (LSAD), locally scaled SSD (LSSD), normalized cross correlation (NCC), and zero-mean NCC (ZNCC). Algorithms presented were applied to images of plants. Making depth maps of plants is difficult because parts of leaves are similar to each other. The potential usability of the described algorithms is especially high in agricultural applications such as robotic fruit harvesting.

Automatic Boosted Flood Mapping from Satellite Data

NASA Technical Reports Server (NTRS)

Coltin, Brian; McMichael, Scott; Smith, Trey; Fong, Terrence

2016-01-01

Numerous algorithms have been proposed to map floods from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, most require human input to succeed, either to specify a threshold value or to manually annotate training data. We introduce a new algorithm based on Adaboost which effectively maps floods without any human input, allowing for a truly rapid and automatic response. The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input. We evaluate Adaboost, as well as numerous previously proposed flood mapping algorithms, on multiple MODIS flood images, as well as on hundreds of non-flood MODIS lake images, demonstrating its effectiveness across a wide variety of conditions.

Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time.

PubMed

Dhar, Amrit; Minin, Vladimir N

2017-05-01

Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences.

Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time

PubMed Central

Dhar, Amrit

2017-01-01

Abstract Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences. PMID:28177780

Complex assessment of urban housing energy sustainability

NASA Astrophysics Data System (ADS)

Popova, Olga; Glebova, Julia; Karakozova, Irina

2018-03-01

The article presents the results of a complex experimental-analytical research of residential development energy parameters - survey of construction sites and determination of calculated energy parameters (resistance to heat transfer) considering their technical condition. The authors suggest a methodology for assessing residential development energy parameters on the basis of construction project's structural analysis with the use of advanced intelligent collection systems, processing (self-organizing maps - SOM) and data visualization (geo-informational systems - GIS). SOM clustering permitted to divide the housing stock (on the example of Arkhangelsk city) into groups with similar technical-operational and energy parameters. It is also possible to measure energy parameters of construction project of each cluster by comparing them with reference (normative) measures and also with each other. The authors propose mechanisms for increasing the area's energy stability level by implementing a set of reproduction activities for residential development of various groups. The analysis showed that modern multilevel and high-rise construction buildings have the least heat losses. At present, however, ow-rise wood buildings is the dominant styles of buildings of Arkhangelsk city. Data visualisation on the created heat map showed that such housing stock covers the largest urban area. The development strategies for depressed areas is in a high-rise building, which show the economic, social and environmental benefits of upward growth of the city. An urban regeneration programme for severely rundown urban housing estates is in a high-rise construction building, which show the economic, social and environmental benefits of upward growth of the city.

Self-organizing map network-based precipitation regionalization for the Tibetan Plateau and regional precipitation variability

NASA Astrophysics Data System (ADS)

Wang, Nini; Yin, Jianchuan

2017-12-01

A precipitation-based regionalization for the Tibetan Plateau (TP) was investigated for regional precipitation trend analysis and frequency analysis using data from 1113 grid points covering the period 1900-2014. The results utilizing self-organizing map (SOM) network suggest that four clusters of precipitation coherent zones can be identified, including the southwestern edge, the southern edge, the southeastern region, and the north central region. Regionalization results of the SOM network satisfactorily represent the influences of the atmospheric circulation systems such as the East Asian summer monsoon, the south Asian summer monsoon, and the mid-latitude westerlies. Regionalization results also well display the direct impacts of physical geographical features of the TP such as orography, topography, and land-sea distribution. Regional-scale annual precipitation trend as well as regional differences of annual and seasonal total precipitation were investigated by precipitation index such as precipitation concentration index (PCI) and Standardized Anomaly Index (SAI). Results demonstrate significant negative long-term linear trends in southeastern TP and the north central part of the TP, indicating arid and semi-arid regions in the TP are getting drier. The empirical mode decomposition (EMD) method shows an evolution of the main cycle with 4 and 12 months for all the representative grids of four sub-regions. The cross-wavelet analysis suggests that predominant and effective period of Indian Ocean Dipole (IOD) on monthly precipitation is around ˜12 months, except for the representative grid of the northwestern region.

Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China

NASA Astrophysics Data System (ADS)

Wu, Meilin; Wang, Youshao; Dong, Junde; Sun, Fulin; Wang, Yutu; Hong, Yiguo

2017-03-01

A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary (PRE). Chemometrics such as Principal Component Analysis (PCA), Cluster analysis (CA) and Self-Organizing Map (SOM) were employed to identify anthropogenic and natural influences on estuary water quality. The scores of stations in the surface layer in the first principal component (PC1) were related to NH4-N, PO4-P, NO2-N, NO3-N, TP, and Chlorophyll a while salinity, turbidity, and SiO3-Si in the second principal component (PC2). Similarly, the scores of stations in the bottom layers in PC1 were related to PO4-P, NO2-N, NO3-N, and TP, while salinity, Chlorophyll a, NH4-N, and SiO3-Si in PC2. Results of the PCA identified the spatial distribution of the surface and bottom water quality, namely the Guangzhou urban reach, Middle reach, and Lower reach of the estuary. Both cluster analysis and PCA produced the similar results. Self-organizing map delineated the Guangzhou urban reach of the Pearl River that was mainly influenced by human activities. The middle and lower reaches of the PRE were mainly influenced by the waters in the South China Sea. The information extracted by PCA, CA, and SOM would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.

A novel algorithm for fully automated mapping of geospatial ontologies

NASA Astrophysics Data System (ADS)

Chaabane, Sana; Jaziri, Wassim

2018-01-01

Geospatial information is collected from different sources thus making spatial ontologies, built for the same geographic domain, heterogeneous; therefore, different and heterogeneous conceptualizations may coexist. Ontology integrating helps creating a common repository of the geospatial ontology and allows removing the heterogeneities between the existing ontologies. Ontology mapping is a process used in ontologies integrating and consists in finding correspondences between the source ontologies. This paper deals with the "mapping" process of geospatial ontologies which consist in applying an automated algorithm in finding the correspondences between concepts referring to the definitions of matching relationships. The proposed algorithm called "geographic ontologies mapping algorithm" defines three types of mapping: semantic, topological and spatial.

Late Glacial to Holocene evolution and sea-level history of Gulf of Gemlik, Sea of Marmara, Turkey

NASA Astrophysics Data System (ADS)

Sabuncu, Asen; Kadir Eriş, K.; Kaslilar, Ayse; Namık Çaǧatay, M.; Gasperini, Luca; Filikçi, Betül

2016-04-01

The Gulf of Gemlik is an E-W elongated trans-tensional basin with a maximum depth of 113 m, located on the middle strand of the North Anatolian Fault (NAF) in the south eastern part of the Sea of Marmara (SoM). While during the Holocene the sea level in the Gulf of Gemlik changed in tandem with the water level changes in the SoM, it may have been different in the late glacial when the Sea of Marmara was lacustrine. Beside the tectonic activity related to the NAFZ, eustatic sea level changes would have controlled the basin evolution and consequent sedimentary history during the different paleocanographic phases of the SoM. Considering the limited studies on the late glacial-Holocene stratigraph of the Gulf of Gemlik, this study aims to investigate the depositional units and their environments with respect to different allogenic and autogenic controls. For these purposes, we analyzed over 300 2 - 7 kHz bandwidth high-resolution gridded seismic sub-bottom CHIRP profiles together with 70 kHz high resolution multibeam bathymetry with backscatter data. Four seismic stratigraphic units were defined and correlated with chronstratigraphic units in five piston cores covering the last 15.8 ka BP according to radiocarbon ages (14C). The depth-scale accuracy of chronostratigraphic units in cores is of key importance for the precise calculation of sedimentation rates. Correlation between the seismic profiles and cores were made by matching Multi-Sensor Core-Logger (MSCL) data and seismic reflection coefficients and amplitudes for different stratigraphic units. The impedance data derived from the logger were used to generate a synthetic seismogram. We used an approach to display, estimate, and correct the depth-scale discrepancies due to oversampling affecting the upper part of sedimentary series during piston coring. The method is based on the resynchronization of synthetic seismograms computed from high-quality physical property logs to the corresponding CHIRP profiles. Each sequence boundary represented by different reflection coefficient and various amplitude values were mapped for the whole gulf area from the pseudo-3D seismic data. Isopach and isochron maps were generated using 2-D cubic B-spline interpolation method to reconstruct basin evolution models through late glacial to Holocene. Each map shows various depositional period with respect to water level changes that has been controlled by sea level fluctuations in the SoM. The seismic units labeled as Unit S1-S4 from top to bottom display different seismic facies and geometries. Unit S1 is a transgressive marine mud drape younger than 10.6 ka BP, which lacustrine sediments, Unit S2 is a parallel bedded mud drape in the basin and progradational clinoforms on the shelf edge. It is dated between 13.9-10.6 ka BP, Unit S3 is characterized by erosional gullies and a clinoform architecture indicating a deltaic system dated between 15.8-13.9 ka BP. Unit S4 represents mounded sediments that are truncated by erosional gullies and dated >15.8 ka BP. Key words: Gulf of Gemlik, Seismic Stratigraphy, Numerical Modelling, Late Pleistocene to Holocene

The surface-pore integrated effect of soil organic matter on retention and transport of pharmaceuticals and personal care products in soils.

PubMed

Qin, Qin; Chen, Xijuan; Zhuang, Jie

2017-12-01

This study examines a surface-pore integrated mechanism that allows soil organic matter (SOM) to influence the retention and transport of three representative pharmaceuticals and personal care products (PPCPs)-ibuprofen, carbamazepine, and bisphenol A-in agricultural soil. A series of sorption-desorption batch tests and breakthrough column experiments were conducted using manured and non-manured soils. Results show that SOM could substantially influence the environmental behaviors of PPCPs via two mechanisms: surface-coating and pore-filling. Surface-coating with molecular SOM decreases the sorption of dissociated PPCPs (e.g., ibuprofen) but increases the sorption of non-dissociated PPCPs (e.g., carbamazepine and bisphenol A), while pore-filling with colloidal SOM enhances the retention of all the PPCPs by providing nano-/micro-pores that limit diffusion. The higher retention and lower mobility of PPCPs in soil microaggregates than in bulk soils suggest that SOM content and SOM-altered soil pore structure could exert a coupled effect on PPCP retention. Differences in the elution of PPCPs with low surface tension solution (i.e., 20% ethanol) in the presence and absence of SOM indicate that PPCPs prefer to remain in SOM-filled pores. Overall, ibuprofen has a high environmental risk, whereas carbamazepine and bisphenol A could be readily retarded in agricultural soils (with a loamy clay texture). This study implies that SOM accrual (particularly pore-filling SOM) has a high potential for reducing the off-site risks of PPCPs by increasing soil nano-/micro-porosity. Copyright © 2017 Elsevier B.V. All rights reserved.

Reactive mineral removal relative to soil organic matter heterogeneity and implications for organic contaminant sorption.

PubMed

Li, Fangfang; Pan, Bo; Liang, Ni; Chang, Zhaofeng; Zhou, Yuwei; Wang, Lin; Li, Hao; Xing, Baoshan

2017-08-01

Soil organic matter (SOM) is generally treated as a static compartment of soil in pollutant fate studies. However, SOM might be altered or fractionated in soil systems, and the details of SOM property/composition changes when coupled with contaminant behavior are unknown. In this study, a mild acid treatment was adopted to remove reactive minerals and partially remove SOM components. After acid treatment, biomarker signatures showed that lignin-derived phenols were released and black carbon (as suggested by benzene-polycarboxylic acids) and lipids were enriched. The biomarker information was consistent with common bulk chemical characterization. The sorption coefficient K d for PHE was two times higher after acid treatment, whereas K d for OFL was three times lower. The organic carbon normalized sorption coefficient K OC values for PHE were higher for soils after acid treatment, indicating stronger interactions between PHE and SOM. The linear regression line between K d and f OC for OFL showed lower intercepts and slopes after reactive mineral removal, suggesting a decreased contribution of minerals and reduced dependence on SOM. These results were attributed to the release of polar compositions in SOM accompanied by reactive mineral removal. Our results suggest that the mobility of ionic organic contaminants increases, whereas that of hydrophobic organic contaminants decreases after acid treatment with respect to reactive mineral depletion. This study emphasized that new insights into the coupling of SOM dynamics should be incorporated into organic contaminant behavior studies. SOM molecular biomarkers offer a useful technique for correlating SOM composition and sorption property changes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

PubMed Central

de Carvalho Rocha, Werickson Fortunato; Schantz, Michele M.; Sheen, David A.; Chu, Pamela M.; Lippa, Katrice A.

2017-01-01

As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties. PMID:28603295

Evaluating Mineral-Associated Soil Organic Matter Pools as Indicators of Forest Harvesting Disturbance

NASA Astrophysics Data System (ADS)

Kellman, L. M.; Gabriel, C. E.

2015-12-01

Soil organic matter (SOM) in northern forest soils is associated with a suite of minerals that can confer SOM stability, resulting in the potential for long-term storage of carbon. Increasingly, evidence is suggesting that SOM in certain mineral phases is dynamic and vulnerable to soil disturbance. The objective of this research was to investigate changes in a suite of mineral-associated pools of SOM through depth in a temperate forest soil to determine which mineral-associated carbon pools are most sensitive to forest harvesting disturbance. Sequential selective dissolutions representing increasingly stable SOM pools (soluble minerals (deionized water); humus-mineral complexes (Na-pyrophosphate); poorly crystalline minerals (HCl hydroxylamine); and crystalline secondary minerals (Na-dithionite + HCl)) of mineral soils through depth to 50 cm were carried out in podzolic soils sampled from temperate red spruce forests of contrasting stand age in Nova Scotia, Canada. Results of this analysis point to a loss of carbon from SOM within the B-horizon of the most recently harvested site from the pyrophosphate-extracted humus mineral complexed SOM, suggesting that it is this exchangeable pool that appears to be destabilized following clearcut harvesting at these study sites. This suggests that recovery from this landuse disturbance is dependent upon increasing storage of this SOM pool, and that mineral-associated pools, particularly pyrophosphate-extractable SOM, may be a useful indicator of changes to soil carbon storage following land use change.

Temperature responses of individual soil organic matter components

NASA Astrophysics Data System (ADS)

Feng, Xiaojuan; Simpson, Myrna J.

2008-09-01

Temperature responses of soil organic matter (SOM) remain unclear partly due to its chemical and compositional heterogeneity. In this study, the decomposition of SOM from two grassland soils was investigated in a 1-year laboratory incubation at six different temperatures. SOM was separated into solvent extractable compounds, suberin- and cutin-derived compounds, and lignin-derived monomers by solvent extraction, base hydrolysis, and CuO oxidation, respectively. These SOM components have distinct chemical structures and stabilities and their decomposition patterns over the course of the experiment were fitted with a two-pool exponential decay model. The stability of SOM components was also assessed using geochemical parameters and kinetic parameters derived from model fitting. Compared with the solvent extractable compounds, a low percentage of lignin monomers partitioned into the labile SOM pool. Suberin- and cutin-derived compounds were poorly fitted by the decay model, and their recalcitrance was shown by the geochemical degradation parameter (ω - C16/∑C16), which was observed to stabilize during the incubation. The temperature sensitivity of decomposition, expressed as Q10, was derived from the relationship between temperature and SOM decay rates. SOM components exhibited varying temperature responses and the decomposition of lignin monomers exhibited higher Q10 values than the decomposition of solvent extractable compounds. Our study shows that Q10 values derived from soil respiration measurements may not be reliable indicators of temperature responses of individual SOM components.

Increased precipitation accelerates soil organic matter turnover associated with microbial community composition in topsoil of alpine grassland on the eastern Tibetan Plateau.

PubMed

Han, Conghai; Wang, Zongli; Si, Guicai; Lei, Tianzhu; Yuan, Yanli; Zhang, Gengxin

2017-10-01

Large quantities of carbon are stored in alpine grassland of the Tibetan Plateau, which is extremely sensitive to climate change. However, it remains unclear whether soil organic matter (SOM) in different layers responds to climate change analogously, and whether microbial communities play vital roles in SOM turnover of topsoil. In this study we measured and collected SOM turnover by the 14 C method in alpine grassland to test climatic effects on SOM turnover in soil profiles. Edaphic properties and microbial communities in the northwestern Qinghai Lake were investigated to explore microbial influence on SOM turnover. SOM turnover in surface soil (0-10 cm) was more sensitive to precipitation than that in subsurface layers (10-40 cm). Precipitation also imposed stronger effects on the composition of microbial communities in the surface layer than that in deeper soil. At the 5-10 cm depth, the SOM turnover rate was positively associated with the bacteria/fungi biomass ratio and the relative abundance of Acidobacteria, both of which are related to precipitation. Partial correlation analysis suggested that increased precipitation could accelerate the SOM turnover rate in topsoil by structuring soil microbial communities. Conversely, carbon stored in deep soil would be barely affected by climate change. Our results provide valuable insights into the dynamics and storage of SOM in alpine grasslands under future climate scenarios.

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-05

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-01

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088

Applications of artificial intelligence in safe human-robot interactions.

PubMed

Najmaei, Nima; Kermani, Mehrdad R

2011-04-01

The integration of industrial robots into the human workspace presents a set of unique challenges. This paper introduces a new sensory system for modeling, tracking, and predicting human motions within a robot workspace. A reactive control scheme to modify a robot's operations for accommodating the presence of the human within the robot workspace is also presented. To this end, a special class of artificial neural networks, namely, self-organizing maps (SOMs), is employed for obtaining a superquadric-based model of the human. The SOM network receives information of the human's footprints from the sensory system and infers necessary data for rendering the human model. The model is then used in order to assess the danger of the robot operations based on the measured as well as predicted human motions. This is followed by the introduction of a new reactive control scheme that results in the least interferences between the human and robot operations. The approach enables the robot to foresee an upcoming danger and take preventive actions before the danger becomes imminent. Simulation and experimental results are presented in order to validate the effectiveness of the proposed method.

Comparative study of feature selection with ensemble learning using SOM variants

NASA Astrophysics Data System (ADS)

Filali, Ameni; Jlassi, Chiraz; Arous, Najet

2017-03-01

Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for every cluster from a dataset. The proposed method is another extension of the Random Forests approach using self-organizing maps (SOM) variants to unlabeled data that estimates the out-of-bag feature importance from a set of partitions. Every partition is created using a various bootstrap sample and a random subset of the features. Then, we show that the process internal estimates are used to measure variable pertinence in Random Forests are also applicable to feature selection in unsupervised learning. This approach aims to the dimensionality reduction, visualization and cluster characterization at the same time. Hence, we provide empirical results on nineteen benchmark data sets indicating that RFS can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art unsupervised methods, with a very limited subset of features. The approach proves promise to treat with very broad domains.

Real-time camera-based face detection using a modified LAMSTAR neural network system

NASA Astrophysics Data System (ADS)

Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

2003-03-01

This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

Soil texture analysis revisited: Removal of organic matter matters more than ever

PubMed Central

Schjønning, Per; Watts, Christopher W.; Christensen, Bent T.; Munkholm, Lars J.

2017-01-01

Exact estimates of soil clay (<2 μm) and silt (2–20 μm) contents are crucial as these size fractions impact key soil functions, and as pedotransfer concepts based on clay and silt contents are becoming increasingly abundant. We examined the effect of removing soil organic matter (SOM) by H2O2 before soil dispersion and determination of clay and silt. Soil samples with gradients in SOM were retrieved from three long-term field experiments each with uniform soil mineralogy and texture. For soils with less than 2 g C 100 g-1 minerals, clay estimates were little affected by SOM. Above this threshold, underestimation of clay increased dramatically with increasing SOM content. Silt contents were systematically overestimated when SOM was not removed; no lower SOM threshold was found for silt, but the overestimation was more pronounced for finer textured soils. When exact estimates of soil particles <20 μm are needed, SOM should always be removed before soil dispersion. PMID:28542416

Soil texture analysis revisited: Removal of organic matter matters more than ever.

PubMed

Jensen, Johannes Lund; Schjønning, Per; Watts, Christopher W; Christensen, Bent T; Munkholm, Lars J

2017-01-01

Exact estimates of soil clay (<2 μm) and silt (2-20 μm) contents are crucial as these size fractions impact key soil functions, and as pedotransfer concepts based on clay and silt contents are becoming increasingly abundant. We examined the effect of removing soil organic matter (SOM) by H2O2 before soil dispersion and determination of clay and silt. Soil samples with gradients in SOM were retrieved from three long-term field experiments each with uniform soil mineralogy and texture. For soils with less than 2 g C 100 g-1 minerals, clay estimates were little affected by SOM. Above this threshold, underestimation of clay increased dramatically with increasing SOM content. Silt contents were systematically overestimated when SOM was not removed; no lower SOM threshold was found for silt, but the overestimation was more pronounced for finer textured soils. When exact estimates of soil particles <20 μm are needed, SOM should always be removed before soil dispersion.

Manifold absolute pressure estimation using neural network with hybrid training algorithm

PubMed Central

Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value. PMID:29190779

Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species

NASA Astrophysics Data System (ADS)

Peña, M.; Calise, L.

2016-04-01

To acoustically assess the biomass of multiple species or taxa within a survey region, the volume backscatter data should be apportioned to the constituent sound scatterers. Typically, measured backscatter is attributed to certain species using predictions at different frequencies, mostly based on the difference in scattering at the frequencies of 38 and 120 kHz (dual frequency method). We used the full version of the stochastic distortedwave Born approximation (SDWBA) model to predict backscatter spectra for Meganyctiphanes norvegica and to explore the sensitivities of ΔMVBS to the model parameters, e.g. acoustic frequency and incidence angle, and animal density and sound speed contrast, length, and shape. The orientation is almost the unique parameter responsible for variation, with fatness affecting longer lengths. We present a summary of ΔMVBS that can serve as the basis for identification algorithms. Next, we simulate the scenario encountered in the Balearic Sea (western Mediterranean) where Northern krill are mixed with mesopelagic fish species (bristlemouths and lanternfishes), which are modeled with a prolate spheroid model. Simulated numerical data are employed to emulate the discrimination process with the most common identification techniques and typical survey frequencies. The importance of using density-independent techniques for acoustic classification is highlighted. Finally, an unsupervised neural network, the Self-Organizing Map (SOM), is used to cluster these theoretical data and identify the frequencies that provide, in this case, the most classification potential. The simulation results confirm that pairs of frequencies spanning the Rayleigh and geometric scattering regimes of the targets are the most useful for clustering; a minimum of four frequencies are necessary to separate the three species, while three frequencies are able to differentiate krill from mesopelagic fish species.

Mapped Landmark Algorithm for Precision Landing

NASA Technical Reports Server (NTRS)

Johnson, Andrew; Ansar, Adnan; Matthies, Larry

2007-01-01

A report discusses a computer vision algorithm for position estimation to enable precision landing during planetary descent. The Descent Image Motion Estimation System for the Mars Exploration Rovers has been used as a starting point for creating code for precision, terrain-relative navigation during planetary landing. The algorithm is designed to be general because it handles images taken at different scales and resolutions relative to the map, and can produce mapped landmark matches for any planetary terrain of sufficient texture. These matches provide a measurement of horizontal position relative to a known landing site specified on the surface map. Multiple mapped landmarks generated per image allow for automatic detection and elimination of bad matches. Attitude and position can be generated from each image; this image-based attitude measurement can be used by the onboard navigation filter to improve the attitude estimate, which will improve the position estimates. The algorithm uses normalized correlation of grayscale images, producing precise, sub-pixel images. The algorithm has been broken into two sub-algorithms: (1) FFT Map Matching (see figure), which matches a single large template by correlation in the frequency domain, and (2) Mapped Landmark Refinement, which matches many small templates by correlation in the spatial domain. Each relies on feature selection, the homography transform, and 3D image correlation. The algorithm is implemented in C++ and is rated at Technology Readiness Level (TRL) 4.

Optimal mapping of neural-network learning on message-passing multicomputers

NASA Technical Reports Server (NTRS)

Chu, Lon-Chan; Wah, Benjamin W.

1992-01-01

A minimization of learning-algorithm completion time is sought in the present optimal-mapping study of the learning process in multilayer feed-forward artificial neural networks (ANNs) for message-passing multicomputers. A novel approximation algorithm for mappings of this kind is derived from observations of the dominance of a parallel ANN algorithm over its communication time. Attention is given to both static and dynamic mapping schemes for systems with static and dynamic background workloads, as well as to experimental results obtained for simulated mappings on multicomputers with dynamic background workloads.

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE PAGES

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto; ...

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

PubMed

Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

How similar are forest disturbance maps derived from different Landsat time series algorithms?

Treesearch

Warren B. Cohen; Sean P. Healey; Zhiqiang Yang; Stephen V. Stehman; C. Kenneth Brewer; Evan B. Brooks; Noel Gorelick; Chengqaun Huang; M. Joseph Hughes; Robert E. Kennedy; Thomas R. Loveland; Gretchen G. Moisen; Todd A. Schroeder; James E. Vogelmann; Curtis E. Woodcock; Limin Yang; Zhe Zhu

2017-01-01

Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal...

Persistence of soil organic matter as an ecosystem property.

PubMed

Schmidt, Michael W I; Torn, Margaret S; Abiven, Samuel; Dittmar, Thorsten; Guggenberger, Georg; Janssens, Ivan A; Kleber, Markus; Kögel-Knabner, Ingrid; Lehmann, Johannes; Manning, David A C; Nannipieri, Paolo; Rasse, Daniel P; Weiner, Steve; Trumbore, Susan E

2011-10-05

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily--and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.

Persistence of soil organic matter as an ecosystem property

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmidt, M.W.; Torn, M. S.; Abiven, S.

2011-08-15

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily—and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.

Effect of varying experimental conditions on the viscosity of α-pinene derived secondary organic material

DOE PAGES

Grayson, James W.; Zhang, Yue; Mutzel, Anke; ...

2016-05-18

Knowledge of the viscosity of particles containing secondary organic material (SOM) is useful for predicting reaction rates and diffusion in SOM particles. In this study we investigate the viscosity of SOM particles as a function of relative humidity and SOM particle mass concentration, during SOM synthesis. The SOM was generated via the ozonolysis of α-pinene at < 5 % relative humidity (RH). Experiments were carried out using the poke-and-flow technique, which measures the experimental flow time ( τ exp, flow) of SOM after poking the material with a needle. In the first set of experiments, we show that τ exp,more » flow increased by a factor of 3600 as the RH increased from < 0.5 RH to 50 % RH, for SOM with a production mass concentration of 121 µg m -3. Based on simulations, the viscosities of the particles were between 6 × 10 5 and 5 × 10 7 Pa s at < 0.5 % RH and between 3 × 10 2 and 9 × 10 3 Pa s at 50 % RH. In the second set of experiments we show that under dry conditions τ exp, flow decreased by a factor of 45 as the production mass concentration increased from 121 to 14 000 µg m -3. From simulations of the poke-and-flow experiments, the viscosity of SOM with a production mass concentration of 14 000 µg m -3 was determined to be between 4 × 10 4 and 1.5 × 10 6 Pa s compared to between 6 × 10 5 and 5 × 10 7 Pa s for SOM with a production mass concentration of 121 µg m -3. The results can be rationalized by a dependence of the chemical composition of SOM on production conditions. Lastly, these results emphasize the shifting characteristics of SOM, not just with RH and precursor type, but also with the production conditions, and suggest that production mass concentration and the RH at which the viscosity was determined should be considered both when comparing laboratory results and when extrapolating these results to the atmosphere.« less

Stabilization of enzymatically polymerized phenolic chemicals in a model soil organic matter-free geomaterial.

PubMed

Palomo, Mónica; Bhandari, Alok

2012-01-01

A variety of remediation methods, including contaminant transformation by peroxidase-mediated oxidative polymerization, have been proposed to manage soils and groundwater contaminated with chlorinated phenols. Phenol stabilization has been successfully observed during cross polymerization between phenolic polymers and soil organic matter (SOM) for soils with SOM >3%. This study evaluates peroxidase-mediated transformation and removal of 2,4-dichlorophenol (DCP) from an aqueous phase in contact with a natural geomaterial modified to contain negligible (<0.3%) SOM. The results are compared with those for soils with higher SOM. The SOM-free sorbent was generated by removing SOM using a NaOCl oxidation. When horseradish peroxidase (HRP) was used to induce polymerization of DCP, the soil-water phase distribution relationship (PDR) of DCP polymerization products (DPP) was complete within 1 d and PDRs did not significantly change over the 28 d of study. The conversion of DCP to DPP was close to 95% efficient. Extractable solute consisted entirely of DPP with 5% or less of unreacted DCP. The aqueous extractability of DPP from SOM-free geomaterial decreased at longer contact times and at smaller residual aqueous concentrations of DPP. DCP stabilization appeared to have resulted from a combination of sorption, precipitation, and ligand exchange between oligomeric products and the exposed mineral surfaces. Modification of the mineral surface through coverage with DPP enhanced the time-dependent retention of the oligomers. DPP stabilization in SOM-free geomaterial was comparable with that reported in the literature with soil containing SOM contents >1%. Results from this study suggest that the effectiveness of HRP-mediated stabilization of phenolic compounds not only depends on the cross-coupling with SOM, but also on the modification of the surface of the sorbent that can augment affinity with oligomers and enhance stabilization. Coverage of the mineral surface by phenolic oligomers may be analogous to SOM that can potentially sorb other xenobiotics. HRP- mediated reactions can be used to stabilize DCP associated with low SOM mineral soils or aquifer media, thereby restricting the transport of phenolic contaminants in the soil environment. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grayson, James W.; Zhang, Yue; Mutzel, Anke

Knowledge of the viscosity of particles containing secondary organic material (SOM) is useful for predicting reaction rates and diffusion in SOM particles. In this study we investigate the viscosity of SOM particles as a function of relative humidity and SOM particle mass concentration, during SOM synthesis. The SOM was generated via the ozonolysis of α-pinene at < 5 % relative humidity (RH). Experiments were carried out using the poke-and-flow technique, which measures the experimental flow time ( τ exp, flow) of SOM after poking the material with a needle. In the first set of experiments, we show that τ exp,more » flow increased by a factor of 3600 as the RH increased from < 0.5 RH to 50 % RH, for SOM with a production mass concentration of 121 µg m -3. Based on simulations, the viscosities of the particles were between 6 × 10 5 and 5 × 10 7 Pa s at < 0.5 % RH and between 3 × 10 2 and 9 × 10 3 Pa s at 50 % RH. In the second set of experiments we show that under dry conditions τ exp, flow decreased by a factor of 45 as the production mass concentration increased from 121 to 14 000 µg m -3. From simulations of the poke-and-flow experiments, the viscosity of SOM with a production mass concentration of 14 000 µg m -3 was determined to be between 4 × 10 4 and 1.5 × 10 6 Pa s compared to between 6 × 10 5 and 5 × 10 7 Pa s for SOM with a production mass concentration of 121 µg m -3. The results can be rationalized by a dependence of the chemical composition of SOM on production conditions. Lastly, these results emphasize the shifting characteristics of SOM, not just with RH and precursor type, but also with the production conditions, and suggest that production mass concentration and the RH at which the viscosity was determined should be considered both when comparing laboratory results and when extrapolating these results to the atmosphere.« less

Implementation of spectral clustering on microarray data of carcinoma using k-means algorithm

NASA Astrophysics Data System (ADS)

Frisca, Bustamam, Alhadi; Siswantining, Titin

2017-03-01

Clustering is one of data analysis methods that aims to classify data which have similar characteristics in the same group. Spectral clustering is one of the most popular modern clustering algorithms. As an effective clustering technique, spectral clustering method emerged from the concepts of spectral graph theory. Spectral clustering method needs partitioning algorithm. There are some partitioning methods including PAM, SOM, Fuzzy c-means, and k-means. Based on the research that has been done by Capital and Choudhury in 2013, when using Euclidian distance k-means algorithm provide better accuracy than PAM algorithm. So in this paper we use k-means as our partition algorithm. The major advantage of spectral clustering is in reducing data dimension, especially in this case to reduce the dimension of large microarray dataset. Microarray data is a small-sized chip made of a glass plate containing thousands and even tens of thousands kinds of genes in the DNA fragments derived from doubling cDNA. Application of microarray data is widely used to detect cancer, for the example is carcinoma, in which cancer cells express the abnormalities in his genes. The purpose of this research is to classify the data that have high similarity in the same group and the data that have low similarity in the others. In this research, Carcinoma microarray data using 7457 genes. The result of partitioning using k-means algorithm is two clusters.

Input of easily available organic C and N stimulates microbial decomposition of soil organic matter in arctic permafrost soil

PubMed Central

Wild, Birgit; Schnecker, Jörg; Alves, Ricardo J. Eloy; Barsukov, Pavel; Bárta, Jiří; Čapek, Petr; Gentsch, Norman; Gittel, Antje; Guggenberger, Georg; Lashchinskiy, Nikolay; Mikutta, Robert; Rusalimova, Olga; Šantrůčková, Hana; Shibistova, Olga; Urich, Tim; Watzka, Margarete; Zrazhevskaya, Galina; Richter, Andreas

2014-01-01

Rising temperatures in the Arctic can affect soil organic matter (SOM) decomposition directly and indirectly, by increasing plant primary production and thus the allocation of plant-derived organic compounds into the soil. Such compounds, for example root exudates or decaying fine roots, are easily available for microorganisms, and can alter the decomposition of older SOM (“priming effect”). We here report on a SOM priming experiment in the active layer of a permafrost soil from the central Siberian Arctic, comparing responses of organic topsoil, mineral subsoil, and cryoturbated subsoil material (i.e., poorly decomposed topsoil material subducted into the subsoil by freeze–thaw processes) to additions of 13C-labeled glucose, cellulose, a mixture of amino acids, and protein (added at levels corresponding to approximately 1% of soil organic carbon). SOM decomposition in the topsoil was barely affected by higher availability of organic compounds, whereas SOM decomposition in both subsoil horizons responded strongly. In the mineral subsoil, SOM decomposition increased by a factor of two to three after any substrate addition (glucose, cellulose, amino acids, protein), suggesting that the microbial decomposer community was limited in energy to break down more complex components of SOM. In the cryoturbated horizon, SOM decomposition increased by a factor of two after addition of amino acids or protein, but was not significantly affected by glucose or cellulose, indicating nitrogen rather than energy limitation. Since the stimulation of SOM decomposition in cryoturbated material was not connected to microbial growth or to a change in microbial community composition, the additional nitrogen was likely invested in the production of extracellular enzymes required for SOM decomposition. Our findings provide a first mechanistic understanding of priming in permafrost soils and suggest that an increase in the availability of organic carbon or nitrogen, e.g., by increased plant productivity, can change the decomposition of SOM stored in deeper layers of permafrost soils, with possible repercussions on the global climate. PMID:25089062

Crosstalks between kisspeptin neurons and somatostatin neurons are not photoperiod dependent in the ewe hypothalamus.

PubMed

Dufourny, Laurence; Lomet, Didier

2017-12-01

Seasonal reproduction is under the control of gonadal steroid feedback, itself synchronized by day-length or photoperiod. As steroid action on GnRH neurons is mostly indirect and therefore exerted through interneurons, we looked for neuroanatomical interactions between kisspeptin (KP) neurons and somatostatin (SOM) neurons, two populations targeted by sex steroids, in three diencephalic areas involved in the central control of ovulation and/or sexual behavior: the arcuate nucleus (ARC), the preoptic area (POA) and the ventrolateral part of the ventromedial hypothalamus (VMHvl). KP is the most potent secretagogue of GnRH secretion while SOM has been shown to centrally inhibit LH pulsatile release. Notably, hypothalamic contents of these two neuropeptides vary with photoperiod in specific seasonal species. Our hypothesis is that SOM inhibits KP neuron activity and therefore indirectly modulate GnRH release and that this effect may be seasonally regulated. We used sections from ovariectomized estradiol-replaced ewes killed after photoperiodic treatment mimicking breeding or anestrus season. We performed triple immunofluorescent labeling to simultaneously detect KP, SOM and synapsin, a marker for synaptic vesicles. Sections from the POA and from the mediobasal hypothalamus were examined using a confocal microscope. Randomly selected KP or SOM neurons were observed in the POA and ARC. SOM neurons were also observed in the VMHvl. In both the ARC and POA, nearly all KP neurons presented numerous SOM contacts. SOM neurons presented KP terminals more frequently in the ARC than in the POA and VMHvl. Quantitative analysis failed to demonstrate major seasonal variations of KP and SOM interactions. Our data suggest a possible inhibitory action of SOM on all KP neurons in both photoperiodic statuses. On the other hand, the physiological significance of KP modulation of SOM neuron activity and vice versa remain to be determined. Copyright © 2017 Elsevier Inc. All rights reserved.

Recovery of arctic tundra from thermal erosion disturbance is constrained by nutrient accumulation: a modeling analysis.

PubMed

Pearce, A R; Rastetter, E B; Kwiatkowski, B L; Bowden, W B; Mack, M C; Jiang, Y

2015-07-01

Abstract. We calibrated the Multiple Element Limitation (MEL) model to Alaskan arctic tundra to simulate recovery of thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could significantly alter regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as the climate warms. We simulated recovery following TEF stabilization and did not address initial, short-term losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we modeled a range of post-stabilization conditions by varying the initial size of SOM stocks and nutrient supply rates. Simulations indicate that nitrogen (N) losses after the TEF stabilizes are small, but phosphorus (P) losses continue. Vegetation biomass recovered 90% of its undisturbed C, N, and P stocks in 100 years using nutrients mineralized from SOM. Because of low litter inputs but continued decomposition, younger SOM continued to be lost for 10 years after the TEF began to recover, but recovered to about 84% of its undisturbed amount in 100 years. The older recalcitrant SOM in mineral soil continued to be lost throughout the 100-year simulation. Simulations suggest that biomass recovery depended on the amount of SOM remaining after disturbance. Recovery was initially limited by the photosynthetic capacity of vegetation but became co-limited by N and P once a plant canopy developed. Biomass and SOM recovery was enhanced by increasing nutrient supplies, but the magnitude, source, and controls on these supplies are poorly understood. Faster mineralization of nutrients from SOM (e.g., by warming) enhanced vegetation recovery but delayed recovery of SOM. Taken together, these results suggest that although vegetation and surface SOM on TEFs recovered quickly (25 and 100 years, respectively), the recovery of deep, mineral soil SOM took centuries and represented a major ecosystem C loss.

Plants mediate soil organic matter decomposition in response to sea level rise.

PubMed

Mueller, Peter; Jensen, Kai; Megonigal, James Patrick

2016-01-01

Tidal marshes have a large capacity for producing and storing organic matter, making their role in the global carbon budget disproportionate to land area. Most of the organic matter stored in these systems is in soils where it contributes 2-5 times more to surface accretion than an equal mass of minerals. Soil organic matter (SOM) sequestration is the primary process by which tidal marshes become perched high in the tidal frame, decreasing their vulnerability to accelerated relative sea level rise (RSLR). Plant growth responses to RSLR are well understood and represented in century-scale forecast models of soil surface elevation change. We understand far less about the response of SOM decomposition to accelerated RSLR. Here we quantified the effects of flooding depth and duration on SOM decomposition by exposing planted and unplanted field-based mesocosms to experimentally manipulated relative sea level over two consecutive growing seasons. SOM decomposition was quantified as CO2 efflux, with plant- and SOM-derived CO2 separated via δ(13) CO2 . Despite the dominant paradigm that decomposition rates are inversely related to flooding, SOM decomposition in the absence of plants was not sensitive to flooding depth and duration. The presence of plants had a dramatic effect on SOM decomposition, increasing SOM-derived CO2 flux by up to 267% and 125% (in 2012 and 2013, respectively) compared to unplanted controls in the two growing seasons. Furthermore, plant stimulation of SOM decomposition was strongly and positively related to plant biomass and in particular aboveground biomass. We conclude that SOM decomposition rates are not directly driven by relative sea level and its effect on oxygen diffusion through soil, but indirectly by plant responses to relative sea level. If this result applies more generally to tidal wetlands, it has important implications for models of SOM accumulation and surface elevation change in response to accelerated RSLR. © 2015 John Wiley & Sons Ltd.

Humic Acid Composition and Characteristics of Soil Organic Matter in Relation to the Elevation Gradient of Moso Bamboo Plantations.

PubMed

Wang, Hsueh-Ching; Chou, Chiao-Ying; Chiou, Chyi-Rong; Tian, Guanglong; Chiu, Chih-Yu

2016-01-01

Studying the influence of climatic and/or site-specific factors on soil organic matter (SOM) along an elevation gradient is important for understanding the response of SOM to global warming. We evaluated the composition of SOM and structure of humic acids along an altitudinal gradient from 600 to 1400 m in moso bamboo (Phyllostachys edulis) plantations in central Taiwan using NMR spectroscopy and photometric analysis. Total organic C and total nitrogen (N) content increased with increasing elevation. Aromaticity decreased and ΔlogK (the logarithm of the absorbance ratio of humic acids at 400 and 600 nm) increased with increasing elevation, which suggests that SOM humification decreased with increasing elevation. High temperature at low elevations seemed to enhance the decomposition (less accumulation of total organic C and N) and humification (high aromaticity and low ΔlogK). The alkyl-C/O-alkyl-C (A/O-A) ratio of humic acids increased with increasing elevation, which suggests that SOM humification increased with increasing elevation; this finding was contrary to the trend observed for ΔlogK and aromaticity. Such a discrepancy might be due to the relatively greater remaining of SOM derived from high alkyl-C broadleaf litter of previous forest at high elevations. The ratio of recalcitrant C to total organic C was low at low elevations, possibly because of enhanced decomposition of recalcitrant SOM from the previous broadleaf forest during long-term intensive cultivation and high temperature. Overall, the change in SOM pools and in the rate of humification with elevation was primarily affected by changes in climatic conditions along the elevation gradient in these bamboo plantations. However, when the composition of SOM, as assessed by NMR spectroscopy and photometric analysis was considered, site-specific factors such as residual SOM from previous forest and intensive cultivation history could also have an important effect on the humic acid composition and humification of SOM.

Humic Acid Composition and Characteristics of Soil Organic Matter in Relation to the Elevation Gradient of Moso Bamboo Plantations

PubMed Central

Wang, Hsueh-Ching; Chou, Chiao-Ying; Chiou, Chyi-Rong; Tian, Guanglong

2016-01-01

Studying the influence of climatic and/or site-specific factors on soil organic matter (SOM) along an elevation gradient is important for understanding the response of SOM to global warming. We evaluated the composition of SOM and structure of humic acids along an altitudinal gradient from 600 to 1400 m in moso bamboo (Phyllostachys edulis) plantations in central Taiwan using NMR spectroscopy and photometric analysis. Total organic C and total nitrogen (N) content increased with increasing elevation. Aromaticity decreased and ΔlogK (the logarithm of the absorbance ratio of humic acids at 400 and 600 nm) increased with increasing elevation, which suggests that SOM humification decreased with increasing elevation. High temperature at low elevations seemed to enhance the decomposition (less accumulation of total organic C and N) and humification (high aromaticity and low ΔlogK). The alkyl-C/O-alkyl-C (A/O-A) ratio of humic acids increased with increasing elevation, which suggests that SOM humification increased with increasing elevation; this finding was contrary to the trend observed for ΔlogK and aromaticity. Such a discrepancy might be due to the relatively greater remaining of SOM derived from high alkyl-C broadleaf litter of previous forest at high elevations. The ratio of recalcitrant C to total organic C was low at low elevations, possibly because of enhanced decomposition of recalcitrant SOM from the previous broadleaf forest during long-term intensive cultivation and high temperature. Overall, the change in SOM pools and in the rate of humification with elevation was primarily affected by changes in climatic conditions along the elevation gradient in these bamboo plantations. However, when the composition of SOM, as assessed by NMR spectroscopy and photometric analysis was considered, site-specific factors such as residual SOM from previous forest and intensive cultivation history could also have an important effect on the humic acid composition and humification of SOM. PMID:27583451

Quantification of soil fauna metabolites and dead mass as humification sources in forest soils

NASA Astrophysics Data System (ADS)

Chertov, O. G.

2016-01-01

The analysis of publications on soil food webs (FWs) allowed calculation of the contents of soil fauna metabolites and dead mass, which can serve as materials for humification. Excreta production of FWmicrofauna reaches 570 kg/ha annually, but the liquid excreta of protozoa and nematodes compose about 25%. The soil fauna dead mass can be also maximally about 580 kg/ha per year. However, up to 70% of this material is a dead mass of bacteria, protozoa, and nematodes. The undecomposed forest floor (L) has low values of these metabolites in comparison with the raw humus organic layer (F + H). The mass of these metabolites is twice lower in Ah. Theoretical assessment of earthworms' role in SOM formation shows that the SOM amount in fresh coprolites can be 1.4 to 4.5-fold higher than SOM in the bulk soil in dependence on food assimilation efficiency, the soil: litter ratio in the earthworms' ration, and SOM quantity in the bulk soil. Excreta production varies from 0.2 to 1.9% of the total SOM pool annually, including 0.15-1.5% of excrements of arthropods and enchytraeidae, but the amount of arthropods' dead mass comprises 0.2-0.4%. The calculated values of the SOM increase due to earthworms' coprolites are of the same order (0.9-2.7% of SOM pool annually). These values of SOM-forming biota metabolites and dead mass are close to the experimental and simulated data on labile and stable SOM fractions decomposition in forest soils (about 2% annually). Therefore, these biota's products can play a role to restock SOM decrease due to mineralization.

Conditional Random Field-Based Offline Map Matching for Indoor Environments

PubMed Central

Bataineh, Safaa; Bahillo, Alfonso; Díez, Luis Enrique; Onieva, Enrique; Bataineh, Ikram

2016-01-01

In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF). The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters. The algorithm was applied to several real and simulated trajectories of different lengths. The results were then refined and matched with the map using the CRF algorithm. PMID:27537892

Conditional Random Field-Based Offline Map Matching for Indoor Environments.

PubMed

Bataineh, Safaa; Bahillo, Alfonso; Díez, Luis Enrique; Onieva, Enrique; Bataineh, Ikram

2016-08-16

In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF). The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters. The algorithm was applied to several real and simulated trajectories of different lengths. The results were then refined and matched with the map using the CRF algorithm.

Range image registration based on hash map and moth-flame optimization

NASA Astrophysics Data System (ADS)

Zou, Li; Ge, Baozhen; Chen, Lei

2018-03-01

Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.

Somatostatin-Expressing Inhibitory Interneurons in Cortical Circuits

PubMed Central

Yavorska, Iryna; Wehr, Michael

2016-01-01

Cortical inhibitory neurons exhibit remarkable diversity in their morphology, connectivity, and synaptic properties. Here, we review the function of somatostatin-expressing (SOM) inhibitory interneurons, focusing largely on sensory cortex. SOM neurons also comprise a number of subpopulations that can be distinguished by their morphology, input and output connectivity, laminar location, firing properties, and expression of molecular markers. Several of these classes of SOM neurons show unique dynamics and characteristics, such as facilitating synapses, specific axonal projections, intralaminar input, and top-down modulation, which suggest possible computational roles. SOM cells can be differentially modulated by behavioral state depending on their class, sensory system, and behavioral paradigm. The functional effects of such modulation have been studied with optogenetic manipulation of SOM cells, which produces effects on learning and memory, task performance, and the integration of cortical activity. Different classes of SOM cells participate in distinct disinhibitory circuits with different inhibitory partners and in different cortical layers. Through these disinhibitory circuits, SOM cells help encode the behavioral relevance of sensory stimuli by regulating the activity of cortical neurons based on subcortical and intracortical modulatory input. Associative learning leads to long-term changes in the strength of connectivity of SOM cells with other neurons, often influencing the strength of inhibitory input they receive. Thus despite their heterogeneity and variability across cortical areas, current evidence shows that SOM neurons perform unique neural computations, forming not only distinct molecular but also functional subclasses of cortical inhibitory interneurons. PMID:27746722

Evaluating the fidelity of CMIP5 models in producing large-scale meteorological patterns over the Northwestern United States

NASA Astrophysics Data System (ADS)

Lintner, B. R.; Loikith, P. C.; Pike, M.; Aragon, C.

2017-12-01

Climate change information is increasingly required at impact-relevant scales. However, most state-of-the-art climate models are not of sufficiently high spatial resolution to resolve features explicitly at such scales. This challenge is particularly acute in regions of complex topography, such as the Pacific Northwest of the United States. To address this scale mismatch problem, we consider large-scale meteorological patterns (LSMPs), which can be resolved by climate models and associated with the occurrence of local scale climate and climate extremes. In prior work, using self-organizing maps (SOMs), we computed LSMPs over the northwestern United States (NWUS) from daily reanalysis circulation fields and further related these to the occurrence of observed extreme temperatures and precipitation: SOMs were used to group LSMPs into 12 nodes or clusters spanning the continuum of synoptic variability over the regions. Here this observational foundation is utilized as an evaluation target for a suite of global climate models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Evaluation is performed in two primary ways. First, daily model circulation fields are assigned to one of the 12 reanalysis nodes based on minimization of the mean square error. From this, a bulk model skill score is computed measuring the similarity between the model and reanalysis nodes. Next, SOMs are applied directly to the model output and compared to the nodes obtained from reanalysis. Results reveal that many of the models have LSMPs analogous to the reanalysis, suggesting that the models reasonably capture observed daily synoptic states.

Fluorescence characterization of fractionated dissolved organic matter in the five tributaries of Poyang Lake, China.

PubMed

Yan, Caixia; Liu, Huihui; Sheng, Yanru; Huang, Xian; Nie, Minghua; Huang, Qi; Baalousha, Mohammed

2018-10-01

Characterization of natural colloids is the key to understand pollutant fate and transport in the environment. The present study investigates the relationship between size and fluorescence properties of colloidal organic matter (COM) from five tributaries of Poyang Lake. Colloids were size-fractionated using cross-flow ultrafiltration and their fluorescence properties were measured by three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM). Parallel factor analysis (PARAFAC) and/or Self-organizing map (SOM) were applied to assess fluorescence properties as proxy indicators for the different size of colloids. PARAFAC analysis identified four fluorescence components including three humic-like components (C1-C3) and a protein-like component (C4). These four fluorescence components, and in particular the protein-like component, are primarily present in <1 kDa phase. For the colloidal fractions (1-10 kDa, 10-100 kDa, and 100 kDa-0.7 μm), the majority of fluorophores are associated with the smallest size fraction. SOM analysis demonstrated that relatively high fluorescence intensity and aromaticity occur primarily in <1 kDa phase, followed by 1-10 kDa colloids. Coupling PARAFAC and SOM facilitate the visualization and interpretation of the relationship between colloidal size and fluorescence properties with fewer input variables, shorter running time, higher reliability, and nondestructive results. Fluorescence indices analysis reveals that the smallest colloidal fraction (1-10 kDa) was dominated by higher humified and less autochthonous COM. Copyright © 2018 Elsevier B.V. All rights reserved.

Identification and Control of Aircrafts using Multiple Models and Adaptive Critics

NASA Technical Reports Server (NTRS)

Principe, Jose C.

2007-01-01

We compared two possible implementations of local linear models for control: one approach is based on a self-organizing map (SOM) to cluster the dynamics followed by a set of linear models operating at each cluster. Therefore the gating function is hard (a single local model will represent the regional dynamics). This simplifies the controller design since there is a one to one mapping between controllers and local models. The second approach uses a soft gate using a probabilistic framework based on a Gaussian Mixture Model (also called a dynamic mixture of experts). In this approach several models may be active at a given time, we can expect a smaller number of models, but the controller design is more involved, with potentially better noise rejection characteristics. Our experiments showed that the SOM provides overall best performance in high SNRs, but the performance degrades faster than with the GMM for the same noise conditions. The SOM approach required about an order of magnitude more models than the GMM, so in terms of implementation cost, the GMM is preferable. The design of the SOM is straight forward, while the design of the GMM controllers, although still reasonable, is more involved and needs more care in the selection of the parameters. Either one of these locally linear approaches outperform global nonlinear controllers based on neural networks, such as the time delay neural network (TDNN). Therefore, in essence the local model approach warrants practical implementations. In order to call the attention of the control community for this design methodology we extended successfully the multiple model approach to PID controllers (still today the most widely used control scheme in the industry), and wrote a paper on this subject. The echo state network (ESN) is a recurrent neural network with the special characteristics that only the output parameters are trained. The recurrent connections are preset according to the problem domain and are fixed. In a nutshell, the states of the reservoir of recurrent processing elements implement a projection space, where the desired response is optimally projected. This architecture trades training efficiency by a large increase in the dimension of the recurrent layer. However, the power of the recurrent neural networks can be brought to bear on practical difficult problems. Our goal was to implement an adaptive critic architecture implementing Bellman s approach to optimal control. However, we could only characterize the ESN performance as a critic in value function evaluation, which is just one of the pieces of the overall adaptive critic controller. The results were very convincing, and the simplicity of the implementation was unparalleled.

Using adversary text to detect adversary phase changes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

2009-05-01

The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

Self-organizing maps for learning the edit costs in graph matching.

PubMed

Neuhaus, Michel; Bunke, Horst

2005-06-01

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.

Use of the self-organizing feature map to diagnose abnormal engineering change

NASA Astrophysics Data System (ADS)

Lu, Ruei-Shan; Wu, Zhi-Ting; Peng, Kuo-Wei; Yu, Tai-Yi

2015-07-01

This study established identification manners with self-organizing feature map (SOM) to achieve the goal of monitoring Engineering Change (EC) based on historical data of a company that specializes in computers and peripherals. The product life cycle of this company is 3-6 months. The historical data were divided into three parts, each covering four months. The first part, comprising 2,343 records from January to April (the training period), comprise the Control Group. The second and third parts comprise Experimental Groups (EG) 1 and 2, respectively. For EG 1 and 2, the successful rate of recognizing information on abnormal ECs was approximately 96% and 95%, respectively. This paper shows the importance and screening procedures of abnormal engineering change for a particular company specializing in computers and peripherals.

Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment.

PubMed

Pandey, Mayank; Pandey, Ashutosh Kumar; Mishra, Ashutosh; Tripathi, B D

2015-09-01

Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation study in river sediments revealed that exchangeable, reducible and oxidizable fractions were dominant in all the studied metals (Cr, Ni, Cu, Zn, Cd, Pb) except Mn and Fe. High pollution load index (1.64-3.89) recommends urgent need of mitigation measures. Self-organizing Map-Artificial Neural Network (SOM-ANN) was applied to the data set for the prediction of major point sources of pollution in the river Ganga. Copyright © 2015 Elsevier Ltd. All rights reserved.

Energy-Efficient and Environmentally Friendly Solid Oxide Membrane Electrolysis Process for Magnesium Oxide Reduction: Experiment and Modeling

NASA Astrophysics Data System (ADS)

Guan, Xiaofei; Pal, Uday B.; Powell, Adam C.

2014-06-01

This paper reports a solid oxide membrane (SOM) electrolysis experiment using an LSM(La0.8Sr0.2MnO3-δ)-Inconel inert anode current collector for production of magnesium and oxygen directly from magnesium oxide at 1423 K (1150 °C). The electrochemical performance of the SOM cell was evaluated by means of various electrochemical techniques including electrochemical impedance spectroscopy, potentiodynamic scan, and electrolysis. Electronic transference numbers of the flux were measured to assess the magnesium dissolution in the flux during SOM electrolysis. The effects of magnesium solubility in the flux on the current efficiency and the SOM stability during electrolysis are discussed. An inverse correlation between the electronic transference number of the flux and the current efficiency of the SOM electrolysis was observed. Based on the experimental results, a new equivalent circuit of the SOM electrolysis process is presented. A general electrochemical polarization model of SOM process for magnesium and oxygen gas production is developed, and the maximum allowable applied potential to avoid zirconia dissociation is calculated as well. The modeling results suggest that a high electronic resistance of the flux and a relatively low electronic resistance of SOM are required to achieve membrane stability, high current efficiency, and high production rates of magnesium and oxygen.

Estimation of the ICBM/2 Organic Matter Simulation Model parameters for biogas digestate mineralisaion in soil using Near Infrared Data.

NASA Astrophysics Data System (ADS)

Cabassi, Giovanni; Cavalli, Daniele; Borrelli, Lamberto; Degano, Luigi; Marino Gallina, Pietro

2014-05-01

The use of simulation models to study the turnover of soil organic matter (SOM) can support experimental data interpretation and the optimization of manure management. Icbm/2 (Katter, 2001) is a SOM simulation model that describes the turnover of SOM with three pools : one for old humified SOM (CO) and two for added manure, CL ( labile "young" C) and CS (stable "young" C). C outflows from CL and CR to be humified (h) and lost as CO2-C (1-h). All pools decay with firs-order kinetics with parameter kYL, kYR and kO (fig. 1).With this model of SOM turnover, during manure decomposition into the soil, only the evolved CO2 can be easily measured. Near infrared spectroscopy has been proved to be a useful technique for soil C evaluation. Since different soil C pools are expected to have different chemical composition, it was proven that NIR can be used as a cheap technique to develop calibration models to estimate the amount of C belonging to different pools). The aim of this work was compare the calibration of ICBM/2 using C respiration data or optimal NIR prediction of CO and CL pools. A total of six laboratory treatments were established using the same soil corresponding to the application of five fertilisers and a control treatment: 1) control without N fertilisation; 2) ammonium sulphate; 3) anaerobically digested dairy cow slurry (Digested slurry); 4-5) the liquid (Liquid fraction) and solid (Solid fraction) fractions after mechanical separation of Digested slurry; and 6) anaerobically stored dairy cow slurry (Stored slurry). The "nursery" method was used with 12 sampling dates. NIR analysis were performed on the air dried grounded soils. Spectra were collected using an FT-NIR Spectrometer. Parameters calibration was done separately for each soil using the downhill simplex method. For each manure, a C partitioning factor (Fi) was optimised. In each optimization step respiration measured data or NIR estimates CL and CO were used as imput for minimisation objective function. At the end the algorithm found those parameters that gave the lowest averaged RMSE between errors in the estimation of respired C. The model parameter extimations obtained using C respiration data and NIR predictions were comparable indicating a general ability of the NIR method to estimate model parameters together with a good prediction of C mineralisation.

Localization in Self-Healing Autonomous Sensor Networks (SASNet): Studies on Cooperative Localization of Sensor Nodes using Distributed Maps

DTIC Science & Technology

2008-01-01

CCA-MAP algorithm are analyzed. Further, we discuss the design considerations of the discussed cooperative localization algorithms to compare and...MAP and CCA-MAP to compare and evaluate their performance. Then a preliminary design analysis is given to address the implementation requirements and...plus précis, avec un nombre inférieur de nœuds ancres, comparativement aux autres types de schémas de localisation. En réalité, les algorithmes de

Use of photoacoustic mid-infrared spectroscopy to characterize soil properties and soil organic matter stability

NASA Astrophysics Data System (ADS)

Peltre, Clement; Bruun, Sander; Du, Changwen; Stoumann Jensen, Lars

2014-05-01

The persistence of soil organic matter (SOM) is recognized as a major ecosystem property due to its key role in earth carbon cycling, soil quality and ecosystem services. SOM stability is typically studied using biological methods such as measuring CO2-C evolution from microbial decomposition of SOM during laboratory incubation or by physical or chemical fractionation methods, allowing the separation of a labile fraction of SOM. However these methods are time consuming and there is still a need for developing reliable techniques to characterize SOM stability, providing both quantitative measurements and qualitative information, in order to improve our understanding of the mechanisms controlling SOM persistence. Several spectroscopic techniques have been used to characterize and predict SOM stability, such as near infrared reflectance spectroscopy (NIRS) and diffuse reflectance mid-infrared spectroscopy (DRIFT). The latter allows a proper identification of spectral regions corresponding to vibrations of specific molecular or functional groups associated with SOM lability. However, reflectance spectroscopy for soil analyses raises some difficulties related to the low reflectance of soils, and to the high influence of particle size. In the last three decades, the progresses in microphone sensitivity dramatically increased the performance of photoacoustic Fourier transform mid-infrared spectroscopy (FTIR-PAS). This technique offers benefits over reflectance spectroscopy techniques, because particle size and the level of sample reflectance have little effect of on the PAS signal, since FTIR-PAS is a direct absorption technique. Despite its high potential for soil analysis, only a limited number of studies have so far applied FTIR-PAS for soil characterization and its potential for determining SOM degradability still needs to be investigated. The objective of this study was to assess the potential of FTIR-PAS for the characterization of SOM decomposability during laboratory incubation and more classical soil parameters such as carbon and clay content for a range of 36 soils collected from various field experiments in Denmark. Partial least square (PLS) regression was used to correlate the collected FTIR-PAS spectra with the proportion of soil organic carbon mineralized after 34 weeks of incubation at 15° C and pF 2, taken as an indicator of the labile fraction of SOM. Results showed that it is possible to predict the labile fraction of SOM with FTIR PAS with similar accuracy as with NIRS (assessed in a previous study on the same soil set). FTIR-PAS offered the advantage over NIRS to allow identification of the chemical compounds positively or negatively correlated with the labile fraction of SOM. The band at 1612 cm-1 corresponding to polysaccharides, pectin and aromatic C=C was the band most positively correlated with labile SOM, which we attributed to the relative lability of fresh plant debris rich in polysaccharide and aromatic lignin. The band at 1560-1590 cm-1 assigned to N=H, C=N and aromatic C=C vibration was the band most negatively correlated with the labile fraction of SOM, confirming the abundance of nitrogenous and aromatic compounds in stabilized SOM. In conclusion, FTIR-PAS has proved to be a powerful tool to characterize the labile fraction of SOM, offering several benefits over reflectance spectroscopy techniques.

Persistence of soil organic matter in eroding versus depositional landform positions

USGS Publications Warehouse

Berhe, Asmeret Asefaw; Harden, Jennifer W.; Torn, Margaret S.; Kleber, Markus; Burton, Sarah D.; Harte, John

2012-01-01

Soil organic matter (SOM) processes in dynamic landscapes are strongly influenced by soil erosion and sedimentation. We determined the contribution of physical isolation of organic matter (OM) inside aggregates, chemical interaction of OM with soil minerals, and molecular structure of SOM in controlling storage and persistence of SOM in different types of eroding and depositional landform positions. By combining density fractionation with elemental and spectroscopic analyses, we showed that SOM in depositional settings is less transformed and better preserved than SOM in eroding landform positions. However, which environmental factors exert primary control on storage and persistence of SOM depended on the nature of the landform position considered. In an annual grassland watershed, protection of SOM by physical isolation inside aggregates and chemical association of organic matter (complexation) with soil minerals, as assessed by correlation with radiocarbon concentration, were more effective in the poorly drained, lowest-lying depositional landform positions, compared to well-drained landform positions in the upper parts of the watershed. Results of this study demonstrated that processes of soil erosion and deposition are important mechanisms of long-term OM stabilization.

Beyond clay: Towards an improved set of variables for predicting soil organic matter content

USGS Publications Warehouse

Rasmussen, Craig; Heckman, Katherine; Wieder, William R.; Keiluweit, Marco; Lawrence, Corey R.; Berhe, Asmeret Asefaw; Blankinship, Joseph C.; Crow, Susan E.; Druhan, Jennifer; Hicks Pries, Caitlin E.; Marin-Spiotta, Erika; Plante, Alain F.; Schadel, Christina; Schmiel, Joshua P.; Sierra, Carlos A.; Thompson, Aaron; Wagai, Rota

2018-01-01

Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.

Fast and Accurate Construction of Ultra-Dense Consensus Genetic Maps Using Evolution Strategy Optimization

PubMed Central

Mester, David; Ronin, Yefim; Schnable, Patrick; Aluru, Srinivas; Korol, Abraham

2015-01-01

Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time. PMID:25867943

Two-dimensional NMR spectroscopy as a tool to link soil organic matter composition to ecosystem processes

NASA Astrophysics Data System (ADS)

Soucemarianadin, Laure; Erhagen, Björn; Öquist, Mats; Nilsson, Mats; Schleucher, Jürgen

2014-05-01

Environmental factors (e.g. temperature and moisture) and the size and composition of soil microbial populations are often considered the main drivers of soil organic matter (SOM) mineralization. Less consideration is given to the role of SOM as a substrate for microbial metabolism and the importance of the organo-chemical composition of SOM on decomposition. In addition, a fraction of the SOM is often considered as recalcitrant to mineralization leading to accumulation of SOM. However, recently the concept of intrinsic recalcitrance of SOM to mineralization has been questioned. The challenge in investigating the role of SOM composition on its mineralization to a large extent stems from the difficulties in obtaining high resolution characterization of a very complex matrix. 13C nuclear magnetic resonance (NMR) spectroscopy is a widely used tool to characterize SOM. However, SOM is a very complex mixture and in the resulting 13C NMR spectra, the identified functional groups may represent different molecular fragments that appear in the same spectral region leading to broad peaks. These overlaps defy attempts to identify molecular moieties, and this makes it impossible to derive information at a resolution needed for evaluating e.g. recalcitrance of SOM. Here we applied a method, developed in wood science for the pulp paper industry, to achieve a better characterization of SOM. We directly dissolved finely ground organic layers of boreal forest floors-litters, fibric and humic horizons of both coniferous and broadleaved stands-in dimethyl sulfoxide and analyzed the resulting solution with a two-dimensional (2D) 1H-13C NMR experiment. We will discuss methodological aspects related to the ability to identify and quantify individual molecular moieties in SOM. We will demonstrate how the spectra resolve signals of CH groups in a 2D plane determined by the 13C and 1H chemical shifts, thereby vastly increasing the resolving power and information content of NMR spectra. The obtained 2D spectra resolve overlaps observed in 1D 13C spectra, so that hundreds of distinct CH moieties can be observed and many individual molecular fragments can be identified. For instance, in the aromatic spectral region, signals originating from various lignin monomers and unsaturated compounds can be resolved. This yields a detailed chemical fingerprint of the SOM samples, and valuable insights on molecular structures. We observed differences in the respective aromatic region of the 2D spectra of the litter layers and the fibric and humic horizons, in relation with humification processes. We were also able to relate the cross-peak complexity and abundance patterns of identifiable molecular moieties to variability in the temperature response of organic matter degradation, as assessed by Q10. To conclude, solution-state 2D NMR spectroscopy is a highly promising new tool to characterize SOM composition at the molecular level, which opens completely new possibilities to link SOM molecular composition to ecosystem processes, and their responses to environmental changes.

Decision-level fusion of SAR and IR sensor information for automatic target detection

NASA Astrophysics Data System (ADS)

Cho, Young-Rae; Yim, Sung-Hyuk; Cho, Hyun-Woong; Won, Jin-Ju; Song, Woo-Jin; Kim, So-Hyeon

2017-05-01

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

Soil Organic Matter (SOM): Molecular Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andersen, Amity

Molecular simulation is a powerful tool used to gain an atomistic, molecular, and nanoscale level understanding of the structure, dynamics, and interactions from adsorption on minerals and assembly in aggregates of soil organic matter (SOM). Given the importance of SOM fate and persistence in soils and the current knowledge gaps, applications of atomistic scale simulations to study the complex compounds in SOM and their interactions in self-assembled aggregates composed of different organic matter compounds and with mineral surfaces of different types common in soils are few and far between. Here, we describe various molecular simulation methods that are currently inmore » use in various areas and applicable to SOM research, followed by a brief survey of specific applications to SOM research and an illustration with our own recent efforts in this area. We conclude with an outlook and the challenges for future research in this area.« less

Continuous intensity map optimization (CIMO): A novel approach to leaf sequencing in step and shoot IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao Daliang; Earl, Matthew A.; Luan, Shuang

2006-04-15

A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases weremore » selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle{sup 3} treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hinks, Mallory L.; Brady, Monica V.; Lignell, Hanna

This work explores the effect of environmental conditions on the photodegradation rates of atmospherically relevant, photolabilie, organic molecules embedded in a film of secondary organic material (SOM). Three types of SOM were studied: a-pinene/O3 SOM (PSOM), limonene/O3 SOM (LSOM), and aged limonene/O3 obtained by exposure of LSOM to ammonia (brown LSOM). PSOM and LSOM were impregnated with 2,4-dinitrophenol (2,4-DNP), an atmospherically relevant molecule that photodegrades faster than either PSOM or LSOM alone, to serve as a probe of SOM matrix effects on photochemistry. Brown LSOM contains an unidentified chromophore that absorbs strongly at 510 nm and photobleaches upon irradiation. Thismore » chromophore served as a probe molecule for the brown LSOM experiments. In all experiments, the temperature and relative humidity (RH) surrounding the SOM films were varied. The extent of photochemical reaction in the samples was monitored using UV-Vis absorption spectroscopy. For all three model systems examined, the observed photodegradation rates were slower at lower temperatures and lower RH, under conditions that make SOM more viscous. Additionally, the activation energies for photodegradation of each system were positively correlated with the viscosity of the SOM matrix as measured in poke-flow experiments. These activation energies were calculated to be 50, 24, and 17 kJ/mol for 2,4-DNP in PSOM, 2,4-DNP in LSOM, and brown LSOM, respectively and PSOM was found to be the most viscous of the three. These results suggest that the increased viscosity is hindering the motion of the molecules in SOM and is slowing down photochemical reactions in which they participate.« less

Soil organic matter from pioneer species and its implications to phytostabilization of mined sites in the Sierra de Cartagena (Spain).

PubMed

Ottenhof, C J M; Faz Cano, A; Arocena, J M; Nierop, K G J; Verstraten, J M; van Mourik, J M

2007-11-01

Pioneer plant species were observed growing on mined areas despite unfavourable conditions such as extreme pH, high salinity and phytotoxic levels of several elements. This study evaluated the contribution of pioneer species to the accumulation of soil organic matter (SOM). We collected 51 samples from 17 non-vegetated, natural and pioneer-vegetated sites in five highly saline mined areas in the Sierra de Cartagena (Spain). The composition of SOM was determined using total C, N and S elemental anlayzer, pyrolysis and solid state (13)C NMR spectroscopy. Results showed that pioneer species like Lygeum spartum had contributed approximately 11 kg SOM kg(-1) soil into the Balsa Rosa sites since 1991; it will take approximately 120 years of continuous growth for this plant to increase the SOM level comparable to natural site. In the Portman Bay area, Sarconia ramosissima and Phragmites australis can contribute SOM equivalent to present day SOM in natural sites in the next 30 years. Low quality SOM (C/N>20) deposited by pioneer plants was dominated by lignin-derived organic compounds such as phenols, guaiacols, syringols and aromatics while polyssacharides and alkyls were the major components in high quality SOM (C/N<20). The addition of SOM to mine wastes is similar to early stages of soil formation and with time, we expect the formation of well-developed Ah horizon on the surface of mine wastes. The presence of P. australis on several sites makes it a very good candidate for successful revegetation of hostile conditions found in many mined sites.

The development of soil organic matter in restored biodiverse Jarrah forests of South-Western Australia as determined by ASE and GCMS.

PubMed

Lin, Deborah S; Greenwood, Paul F; George, Suman; Somerfield, Paul J; Tibbett, Mark

2011-08-01

Soil organic matter (SOM) is known to increase with time as landscapes recover after a major disturbance; however, little is known about the evolution of the chemistry of SOM in reconstructed ecosystems. In this study, we assessed the development of SOM chemistry in a chronosequence (space for time substitution) of restored Jarrah forest sites in Western Australia. Replicated samples were taken at the surface of the mineral soil as well as deeper in the profile at sites of 1, 3, 6, 9, 12, and 17 years of age. A molecular approach was developed to distinguish and quantify numerous individual compounds in SOM. This used accelerated solvent extraction in conjunction with gas chromatography mass spectrometry. A novel multivariate statistical approach was used to assess changes in accelerated solvent extraction (ASE)-gas chromatography-mass spectrometry (GCMS) spectra. This enabled us to track SOM developmental trajectories with restoration time. Results showed total carbon concentrations approached that of native forests soils by 17 years of restoration. Using the relate protocol in PRIMER, we demonstrated an overall linear relationship with site age at both depths, indicating that changes in SOM chemistry were occurring. The surface soils were seen to approach native molecular compositions while the deeper soil retained a more stable chemical signature, suggesting litter from the developing diverse plant community has altered SOM near the surface. Our new approach for assessing SOM development, combining ASE-GCMS with illuminating multivariate statistical analysis, holds great promise to more fully develop ASE for the characterisation of SOM.

Remote sensing of soil organic matter of farmland with hyperspectral image

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Wang, Lei; Yang, Guijun; Zhang, Liyan

2017-10-01

Monitoring soil organic matter (SOM) of cultivated land quantitively and mastering its spatial change are helpful for fertility adjustment and sustainable development of agriculture. The study aimed to analyze the response between SOM and reflectivity of hyperspectral image with different pixel size and develop the optimal model of estimating SOM with imaging spectral technology. The wavelet transform method was used to analyze the correlation between the hyperspectral reflectivity and SOM. Then the optimal pixel size and sensitive wavelet feature scale were screened to develop the inversion model of SOM. Result showed that wavelet transform of soil hyperspectrum was help to improve the correlation between the wavelet features and SOM. In the visible wavelength range, the susceptible wavelet features of SOM mainly concentrated 460 603 nm. As the wavelength increased, the wavelet scale corresponding correlation coefficient increased maximum and then gradually decreased. In the near infrared wavelength range, the susceptible wavelet features of SOM mainly concentrated 762 882 nm. As the wavelength increased, the wavelet scale gradually decreased. The study developed multivariate model of continuous wavelet transforms by the method of stepwise linear regression (SLR). The CWT-SLR models reached higher accuracies than those of univariate models. With the resampling scale increasing, the accuracies of CWT-SLR models gradually increased, while the determination coefficients (R2) fluctuated from 0.52 to 0.59. The R2 of 5*5 scale reached highest (0.5954), while the RMSE reached lowest (2.41 g/kg). It indicated that multivariate model based on continuous wavelet transform had better ability for estimating SOM than univariate model.

Assessment and visualization of uncertainty for countrywide soil organic matter map of Hungary using local entropy

NASA Astrophysics Data System (ADS)

Szatmári, Gábor; Pásztor, László

2016-04-01

Uncertainty is a general term expressing our imperfect knowledge in describing an environmental process and we are aware of it (Bárdossy and Fodor, 2004). Sampling, laboratory measurements, models and so on are subject to uncertainty. Effective quantification and visualization of uncertainty would be indispensable to stakeholders (e.g. policy makers, society). Soil related features and their spatial models should be stressfully targeted to uncertainty assessment because their inferences are further used in modelling and decision making process. The aim of our present study was to assess and effectively visualize the local uncertainty of the countrywide soil organic matter (SOM) spatial distribution model of Hungary using geostatistical tools and concepts. The Hungarian Soil Information and Monitoring System's SOM data (approximately 1,200 observations) and environmental related, spatially exhaustive secondary information (i.e. digital elevation model, climatic maps, MODIS satellite images and geological map) were used to model the countrywide SOM spatial distribution by regression kriging. It would be common to use the calculated estimation (or kriging) variance as a measure of uncertainty, however the normality and homoscedasticity hypotheses have to be refused according to our preliminary analysis on the data. Therefore, a normal score transformation and a sequential stochastic simulation approach was introduced to be able to model and assess the local uncertainty. Five hundred equally probable realizations (i.e. stochastic images) were generated. The number of the stochastic images is fairly enough to provide a model of uncertainty at each location, which is a complete description of uncertainty in geostatistics (Deutsch and Journel, 1998). Furthermore, these models can be applied e.g. to contour the probability of any events, which can be regarded as goal oriented digital soil maps and are of interest for agricultural management and decision making as well. A standardized measure of the local entropy was used to visualize uncertainty, where entropy values close to 1 correspond to high uncertainty, whilst values close to 0 correspond low uncertainty. The advantage of the usage of local entropy in this context is that it combines probabilities from multiple members into a single number for each location of the model. In conclusion, it is straightforward to use a sequential stochastic simulation approach to the assessment of uncertainty, when normality and homoscedasticity are violated. The visualization of uncertainty using the local entropy is effective and communicative to stakeholders because it represents the uncertainty through a single number within a [0, 1] scale. References: Bárdossy, Gy. & Fodor, J., 2004. Evaluation of Uncertainties and Risks in Geology. Springer-Verlag, Berlin Heidelberg. Deutsch, C.V. & Journel, A.G., 1998. GSLIB: geostatistical software library and user's guide. Oxford University Press, New York. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Smouldering Combustion of Soil Organic Matter: Inverse Modelling of the Thermal and Oxidative Degradation Kinetics

NASA Astrophysics Data System (ADS)

Huang, Xinyan; Rein, Guillermo

2013-04-01

Smouldering combustion of soil organic matter (SOM) such as peatlands leads to the largest fires on Earth and posses a possible positive feedback mechanism to climate change. In this work, a kinetic model, including 3-step chemical reactions and 1-step water evaporation is proposed to describe drying, pyrolysis and oxidation behaviour of peat. Peat is chosen as the most important type of SOM susceptible to smoudering, and a Chinese boreal peat sample is selected from the literature. A lumped model of mass loss based on four Arrhenius-type reactions is developed to predict its thermal and oxidative degradation under a range of heating rates. A genetic algorithm is used to solve the inverse problem, and find a group of kinetic and stoichiometric parameters for this peat that provides the best match to the thermogravimetric (TG) data from literature. A multi-objective fitness function is defined using the measurements of both mass loss and mass-loss rate in inert and normal atmospheres under a range of heating rates. Piece-wise optimization is conducted to separate the low temperature drying (<450 K) from the higher temperature pyrolysis and oxidation reaction (>450 K). Modelling results shows the proposed 3-step chemistry is the unique simplest scheme to satisfy all given TG data of this particular peat type. Afterward, this kinetic model and its kinetic parameters are incorporated into a simple one-dimensional species model to study the relative position of each reaction inside a smoulder front. Computational results show that the species model agrees with experimental observations. This is the first time that the smouldering kinetics of SOM is explained and predicted, thus helping to understanding this important natural and widespread phenomenon.

Definition and GIS-based characterization of an integral risk index applied to a chemical/petrochemical area.

PubMed

Nadal, Martí; Kumar, Vikas; Schuhmacher, Marta; Domingo, José L

2006-08-01

A risk map of the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain) was designed following a two-stage procedure. The first step was the creation of a ranking system (Hazard Index) for a number of different inorganic and organic pollutants: heavy metals, polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs) and polychlorinated aromatic hydrocarbons (PAHs) by applying self-organizing maps (SOM) to persistence, bioaccumulation and toxicity properties of the chemicals. PCBs seemed to be the most hazardous compounds, while the light PAHs showed the minimum values. Subsequently, an Integral Risk Index was developed taking into account the Hazard Index and the concentrations of all pollutants in soil samples collected in the assessed area of Tarragona. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a geographic information system (GIS). The results of the present study seem to indicate that the development of an integral risk map might be useful to help in making-decision processes concerning environmental pollutants.

Parallel algorithms for mapping pipelined and parallel computations

NASA Technical Reports Server (NTRS)

Nicol, David M.

1988-01-01

Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.

Large Scale Crop Classification in Ukraine using Multi-temporal Landsat-8 Images with Missing Data

NASA Astrophysics Data System (ADS)

Kussul, N.; Skakun, S.; Shelestov, A.; Lavreniuk, M. S.

2014-12-01

At present, there are no globally available Earth observation (EO) derived products on crop maps. This issue is being addressed within the Sentinel-2 for Agriculture initiative where a number of test sites (including from JECAM) participate to provide coherent protocols and best practices for various global agriculture systems, and subsequently crop maps from Sentinel-2. One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this abstract, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using non-missing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. For this, an ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for large scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine [1-2]. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy and kappa coefficient. The obtained classification map is also validated through estimated crop and forest areas and comparison to official statistics. 1. A.Yu. Shelestov et al., "Geospatial information system for agricultural monitoring," Cybernetics Syst. Anal., vol. 49, no. 1, pp. 124-132, 2013. 2. J. Gallego et al., "Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations," J. Autom. Inform. Scie., vol. 44, no. 5, pp. 67-80, 2012.

Indexing Permafrost Soil Organic Matter Degradation Using High-Resolution Mass Spectrometry

DOE PAGES

Mann, Benjamin F.; Chen, Hongmei; Herndon, Elizabeth M.; ...

2015-06-12

Microbial degradation of soil organic matter (SOM) is a key process for terrestrial carbon cycling, although the molecular details of these transformations remain unclear. This study reports the application of ultrahigh resolution mass spectrometry to profile the molecular composition of SOM and its degradation during a simulated warming experiment. A soil sample, collected near Barrow, Alaska, USA, was subjected to a 40-day incubation under anoxic conditions and analyzed before and after the incubation to determine changes of SOM composition. A CHO index based on molecular C, H, and O data was utilized to codify SOM components according to their observedmore » degradation potentials. Compounds with a CHO index score between –1 and 0 in a water-soluble fraction (WSF) demonstrated high degradation potential, with a highest shift of CHO index occurred in the N-containing group of compounds, while similar stoichiometries in a base-soluble fraction (BSF) did not. Additionally, compared with the classical H:C vs O:C van Krevelen diagram, CHO index allowed for direct visualization of the distribution of heteroatoms such as N in the identified SOM compounds. We demonstrate that CHO index is useful not only in characterizing arctic SOM at the molecular level but also enabling quantitative description of SOM degradation, thereby facilitating incorporation of the high resolution MS datasets to future mechanistic models of SOM degradation and prediction of greenhouse gas emissions.« less

Modulation of synovial cell function by somatostatin in patients with rheumatoid arthritis.

PubMed

Takeba, Y; Suzuki, N; Takeno, M; Asai, T; Tsuboi, S; Hoshino, T; Sakane, T

1997-12-01

To elucidate the role of neurologic, endocrine, and immune system interactions in the development of pathologic responses in patients with rheumatoid arthritis (RA), we studied somatostatin (SOM) production and somatostatin receptor (SOMR) expression in RA synovium and its function in patients with RA. The effects of SOM on proinflammatory cytokine (interleukin-6 [IL-6] and IL-8) and collagenase production by RA synovial cells were estimated by enzyme-linked immunosorbent assay, and their messenger RNA expression was assessed by reverse transcription-polymerase chain reaction (RT-PCR) using limiting dilutions of the complementary DNA. The expression of SOMR by RA synovial cells was also studied by RT-PCR. Local production of SOM was estimated by RT-PCR and immunohistochemical staining. Physiologic concentrations (approximately 10(-10)M) of SOM inhibited proliferation of RA synovial cells. The production of proinflammatory cytokines and matrix metalloproteinases by RA synovial cells was also modulated by SOM. SOMR subtypes 1 and 2 were expressed on fibroblast-like synovial cells, and the expression of SOMR-2 was up-regulated by proinflammatory cytokine treatment of the synovial cells from patients with RA. RA fibroblast-like cells synthesized SOM by themselves, suggesting that SOM acts as an autocrine regulator of synovial cell function in patients with RA. SOM inhibited aberrant synovial cell function in patients with RA, suggesting possible clinical applications of this neuropeptide.

Indexing Permafrost Soil Organic Matter Degradation Using High-Resolution Mass Spectrometry.

PubMed

Mann, Benjamin F; Chen, Hongmei; Herndon, Elizabeth M; Chu, Rosalie K; Tolic, Nikola; Portier, Evan F; Roy Chowdhury, Taniya; Robinson, Errol W; Callister, Stephen J; Wullschleger, Stan D; Graham, David E; Liang, Liyuan; Gu, Baohua

2015-01-01

Microbial degradation of soil organic matter (SOM) is a key process for terrestrial carbon cycling, although the molecular details of these transformations remain unclear. This study reports the application of ultrahigh resolution mass spectrometry to profile the molecular composition of SOM and its degradation during a simulated warming experiment. A soil sample, collected near Barrow, Alaska, USA, was subjected to a 40-day incubation under anoxic conditions and analyzed before and after the incubation to determine changes of SOM composition. A CHO index based on molecular C, H, and O data was utilized to codify SOM components according to their observed degradation potentials. Compounds with a CHO index score between -1 and 0 in a water-soluble fraction (WSF) demonstrated high degradation potential, with a highest shift of CHO index occurred in the N-containing group of compounds, while similar stoichiometries in a base-soluble fraction (BSF) did not. Additionally, compared with the classical H:C vs O:C van Krevelen diagram, CHO index allowed for direct visualization of the distribution of heteroatoms such as N in the identified SOM compounds. We demonstrate that CHO index is useful not only in characterizing arctic SOM at the molecular level but also enabling quantitative description of SOM degradation, thereby facilitating incorporation of the high resolution MS datasets to future mechanistic models of SOM degradation and prediction of greenhouse gas emissions.

Soil Organic Matter Stability and Soil Carbon Storage with Changes in Land Use Intensity in Uganda

NASA Astrophysics Data System (ADS)

Tiemann, L. K.; Grandy, S.; Hartter, J.

2014-12-01

As the foundation of soil fertility, soil organic matter (SOM) formation and break-down is a critical factor of agroecosystem sustainability. In tropical systems where soils are quickly weathered, the link between SOM and soil fertility is particularly strong; however, the mechanisms controlling the stabilization and destabilization of SOM are not well characterized in tropical soils. In western Uganda, we collected soil samples under different levels of land use intensity including maize fields, banana plantations and inside an un-cultivated native tropical forest, Kibale National Park (KNP). To better understand the link between land use intensity and SOM stability we measured total soil C and N, and respiration rates during a 369 d soil incubation. In addition, we separated soils into particle size fractions, and mineral adsorbed SOM in the silt (2-50 μm ) and clay (< 2 μm) fractions was dissociated, purified and chemically characterized via pyrolysis-GC/MS. Cultivated soil C and N have declined by 22 and 48%, respectively, in comparison to uncultivated KNP soils. Incubation data indicate that over the last decade, relatively accessible and labile soil organic carbon (SOC) pools have been depleted by 55-59% in cultivated soils. As a result of this depletion, the chemical composition of SOM has been altered such that clay and silt associated SOM differed significantly between agricultural fields and KNP. In particular, nitrogen containing compounds were in lower abundance in agricultural compared to KNP soils. This suggests that N depletion due to agriculture has advanced to pools of mineral associated organic N that are typically protected from break-down. In areas where land use intensity is relatively greater, increases in polysaccharides and lipids in maize fields compared to KNP indicate increases in microbial residues and decomposition by-products as microbes mine SOM for organic N. Chemical characterization of post-incubation SOM will help us better understand how microbes preferentially break-down SOM. Agricultural intensification over the past decade in western Uganda has depleted SOC, on average, by 1427 g m-2. In addition, depletion of organic nitrogen reserves in stable SOM pools corresponds with reported declines in crop yields and productivity in this region.

A real time QRS detection using delay-coordinate mapping for the microcontroller implementation.

PubMed

Lee, Jeong-Whan; Kim, Kyeong-Seop; Lee, Bongsoo; Lee, Byungchae; Lee, Myoung-Ho

2002-01-01

In this article, we propose a new algorithm using the characteristics of reconstructed phase portraits by delay-coordinate mapping utilizing lag rotundity for a real-time detection of QRS complexes in ECG signals. In reconstructing phase portrait the mapping parameters, time delay, and mapping dimension play important roles in shaping of portraits drawn in a new dimensional space. Experimentally, the optimal mapping time delay for detection of QRS complexes turned out to be 20 ms. To explore the meaning of this time delay and the proper mapping dimension, we applied a fill factor, mutual information, and autocorrelation function algorithm that were generally used to analyze the chaotic characteristics of sampled signals. From these results, we could find the fact that the performance of our proposed algorithms relied mainly on the geometrical property such as an area of the reconstructed phase portrait. For the real application, we applied our algorithm for designing a small cardiac event recorder. This system was to record patients' ECG and R-R intervals for 1 h to investigate HRV characteristics of the patients who had vasovagal syncope symptom and for the evaluation, we implemented our algorithm in C language and applied to MIT/BIH arrhythmia database of 48 subjects. Our proposed algorithm achieved a 99.58% detection rate of QRS complexes.

Advancing Analysis of Spatio-Temporal Variations of Soil Nutrients in the Water Level Fluctuation Zone of China’s Three Gorges Reservoir Using Self-Organizing Map

PubMed Central

Ye, Chen; Li, Siyue; Yang, Yuyi; Shu, Xiao; Zhang, Jiaquan; Zhang, Quanfa

2015-01-01

The ~350 km2 water level fluctuation zone (WLFZ) in the Three Gorges Reservoir (TGR) of China, situated at the intersection of terrestrial and aquatic ecosystems, experiences a great hydrological change with prolonged winter inundation. Soil samples were collected in 12 sites pre- (September 2008) and post submergence (June 2009) in the WLFZ and analyzed for soil nutrients. Self-organizing map (SOM) and statistical analysis including multi-way ANOVA, paired-T test, and stepwise least squares multiple regression were employed to determine the spatio-temporal variations of soil nutrients in relation to submergence, and their correlations with soil physical characteristics. Results showed significant spatial variability in nutrients along ~600 km long shoreline of the TGR before and after submergence. There were higher contents of organic matter, total nitrogen (TN), and nitrate (NO3-) in the lower reach and total phosphorus (TP) in the upper reach that were primarily due to the spatial variations in soil particle size composition and anthropogenic activities. Submergence enhanced soil available potassium (K), while significantly decreased soil N, possibly due to the alterations of soil particle size composition and increase in soil pH. In addition, SOM analysis determined important roles of soil pH value, bulk density, soil particle size (i.e., silt and sand) and nutrients (TP, TK, and AK) on the spatial and temporal variations in soil quality. Our results suggest that urban sewage and agricultural runoffs are primary pollutants that affect soil nutrients in the WLFZ of TGR. PMID:25789612

Tracking senescence-induced patterns in leaf litter leachate using parallel factor analysis (PARAFAC) modeling and self-organizing maps

NASA Astrophysics Data System (ADS)

Wheeler, K. I.; Levia, D. F.; Hudson, J. E.

2017-09-01

In autumn, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams in forested watersheds changes as trees undergo resorption, senescence, and leaf abscission. Despite its biogeochemical importance, little work has investigated how leaf litter leachate DOM changes throughout autumn and how any changes might differ interspecifically and intraspecifically. Since climate change is expected to cause vegetation migration, it is necessary to learn how changes in forest composition could affect DOM inputs via leaf litter leachate. We examined changes in leaf litter leachate fluorescent DOM (FDOM) from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and from yellow poplar (Liriodendron tulipifera L.) leaves from Maryland. FDOM in leachate samples was characterized by excitation-emission matrices (EEMs). A six-component parallel factor analysis (PARAFAC) model was created to identify components that accounted for the majority of the variation in the data set. Self-organizing maps (SOM) compared the PARAFAC component proportions of leachate samples. Phenophase and species exerted much stronger influence on the determination of a sample's SOM placement than geographic origin. As expected, FDOM from all trees transitioned from more protein-like components to more humic-like components with senescence. Percent greenness of sampled leaves and the proportion of tyrosine-like component 1 were found to be significantly different between the two genetic beech clusters, suggesting differences in photosynthesis and resorption. Our results highlight the need to account for interspecific and intraspecific variations in leaf litter leachate FDOM throughout autumn when examining the influence of allochthonous inputs to streams.

Influence of Soil Organic Matter Stabilization Mechanisms on Temperature Sensitivity of Soil Respiration

NASA Astrophysics Data System (ADS)

Gillabel, J.; de Gryze, S.; Six, J.; Merckx, R.

2007-12-01

Knowledge on the sensitivity of soil organic matter (SOM) respiration to changes in temperature is crucial for predicting future impacts of climate change on soil C stocks. Temperature sensitivity of respiration is determined by the chemical structure of the compound to be decomposed and by the availability of the organic matter for decomposers. Biochemically recalcitrant SOM has a higher temperature sensitivity than biochemically labile SOM. However, it is hypothesized that the stabilization of SOM by interaction with the soil matrix could be an important attenuating control on temperature sensitivity. We investigated the effect of different SOM stabilization mechanisms on temperature sensitivity of SOM respiration. Two main mechanisms were considered: chemical interactions of SOM with clay and silt particles, and physical protection inside aggregates. Soil samples from an agricultural silt loam soil were fractionated by wet-sieving into macroaggregates, microaggregates and silt+clay fractions. SOM stabilization in the silt+clay fraction occurs mainly chemically, whereas in aggregates physical protection of SOM is more important. Samples of each fraction and of bulk soil were incubated at two temperatures (20°C and 30°C) for one month. After 2% of total soil carbon was respired, temperature sensitivity was determined for respiration of the next 0.5% of total soil carbon. This was done by calculating a Q10 value as the ratio of the times needed at each temperature to respire that fraction of the soil C. This method allows determination of temperature sensitivity independent of C quality. Calculated Q10 values decreased in the order bulk soil > macroaggregates > microaggregates > silt+clay, with the difference between macroaggregate Q10 and silt+clay Q10 being the only significant difference. These results indicate that protection of SOM attenuates temperature sensitivity, with chemical protection (silt+clay) having a larger effect than physical protection (aggregates).

Enhancement of gamma-aminobutyric acid (GABA) levels using an autochthonous Lactobacillus futsaii CS3 as starter culture in Thai fermented shrimp (Kung-Som).

PubMed

Sanchart, Chatthaphisuth; Rattanaporn, Onnicha; Haltrich, Dietmar; Phukpattaranont, Pimpimol; Maneerat, Suppasil

2017-08-01

Gamma-aminobutyric acid (GABA) is a non-proteinogenic amino acid, which has a variety of well-characterized beneficial physiological functions. In order to improve GABA levels and the fermentation process of Thai fermented shrimp (Kung-Som), autochthonous Lactobacillus futsaii CS3 was inoculated as a starter culture into Kung-Som, and its effects on the quality of Kung-Som were studied. The optimal conditions for GABA production in Kung-Som as obtained by response surface methodology (RSM) using a central composite design (CCD) were an inoculum size of roughly 10 7 CFU/g (X 1 ) of L. futsaii cells together with the addition of 0.5% (w/w) monosodium glutamate (MSG) (X 2 ), resulting in maximum GABA levels of 10,500 mg per kg fresh product. Under these optimized conditions, the experimental GABA content of Kung-Som with an added starter culture was up to four times higher than that of the control (without starter culture) or commercial Kung-Som products (10,120 mg/kg product). Kung-Som produced by inoculation with L. futsaii CS3 but without addition of MSG showed a considerably increased GABA content of 7790 mg/kg compared to the control. Fermentation time was reduced to less than 1 week for these samples compared to the control batches, which took up to 19 days. Polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) revealed that L. futsaii CS3 remained prominently throughout the Kung-Som fermentation, and that lactic acid bacteria (LAB) rapidly dominated the total microflora because of this inoculation with L. futsaii CS3. Kung-Som samples with starter culture were accepted as well as commercial ones by 30 panelists (p > 0.05). In conclusion, L. futsaii CS3 is a good starter culture for GABA production, resulting in, improved microbiological safety as well as reduced fermentation time.

Warming-Induced Changes to the Molecular Composition of Soil Organic Matter

NASA Astrophysics Data System (ADS)

Feng, X.; Simpson, M. J.; Simpson, A. J.; Wilson, K. P.; Williams, D.

2007-12-01

Soil organic matter (SOM) contains two times more carbon than the atmosphere and the potential changes to SOM quantity and quality with global warming are a major concern. It is commonly believed that global warming will accelerate the decomposition of labile SOM compounds while refractory SOM constituents will remain stable. However, experimental evidence of molecular-level changes to SOM composition with global warming is currently lacking. Here we employ SOM biomarkers and nuclear magnetic resonance (NMR) spectroscopy to study SOM composition and degradation in a soil warming experiment in southern Ontario, Canada. The soil warming experiment consisted of a control and a treatment plot in a mixed forest that had a temperature difference of about 5 degrees C for 14 months. Before soil warming the control and treatment plots had the same organic carbon (OC) content and SOM composition. Soil warming significantly increased soil OC content and the abundance of cutin-derived carbon originating from leaf tissues and decreased carbohydrates that are regarded as easily degradable. Lignin components, which are believed to be part of the stable and slowly-cycling SOM, were observed to be in an advanced stage of degradation. This observation is corroborated by increases in fungal biomass in the warmed soil because fungi are considered the primary decomposer of lignin in the soil environment. An NMR study of SOM in the warmed and control plots indicates that alkyl carbon, mainly originating from plant cuticles in the soil, increased in the warmed soil while O-alkyl carbon, primarily occurring in carbohydrates, decreased. Aromatic and phenolic carbon regions, which include the main structures found in lignin, decreased in the warmed soil. These data collectively suggest that there is a great potential for lignin degradation with soil warming, and that the refractory (aromatic) soil carbon storage may be reduced as a result of increased fungal growth in a warmer climate.

Diffusivity of dicarboxylic acids molecules to secondary organic material governed by particle phase state

NASA Astrophysics Data System (ADS)

Han, Y.; Gong, Z.; Liu, P.; de Sá, S. S.; McKinney, K. A.; Martin, S. T.

2017-12-01

Atmospheric secondary organic material (SOM) from oxidation of volatile organic compounds can exist in amorphous solid, semisolid, and liquid states depending on a range of factors such as relative humidity (RH), temperature, and reaction history. The phase state of SOM affects the dynamic exchange and reactivity between particles and gas-phase molecules. Dicarboxylic acids are ubiquitous in ambient atmosphere and the uptake of which may lead to substantial changes in hygroscopicity, absorption property, and light scattering of aerosol particles. This study investigates the diffusivity of dicarboxylic acids to the matrix of SOM particles. SOM was generated from dark ozonolysis of a-pinene in Harvard Environmental Chamber. The produced SOM particles were passed through an ozone scrubber to remove gas-phase chemistry before being led into a flask reactor, where gas-phase dicarboxylic acid was injected continuously and RH was varied from 5% to 85%. The probe dicarboxylic acids molecules including malonic acid and a-ketoglutaric acid have been investigated for the uptake to SOM particles. Organic composition in the outflow of the flask was measured with a high-resolution time-of-flight aerosol mass spectrometer. The mass fractions of tracer ions in total organic mass for both malonic acid and a-ketoglutaric acid increased substantially with the increase of RH values. The tracer ions of malonic acid were also more abundant in a-pinene SOM particles with increased gas-phase concentrations. These results suggest that the diffusion of the studied dicarboxylic acids molecules to a-pinene SOM particles was enhanced at increased RH values, which is possibly due to the phase transition of a-pinene SOM particles from non-liquid to liquid states. Therefore, particle phase state may be an important factor governing the diffusivity of dicarboxylic acids molecules to a-pinene SOM. Further dicarboxylic acids with various functional groups will be investigated to understand the effects of volatility and structure on their diffusivity.

Lignin signatures show the effects of changes in heather and bracken cover on the composition of organic matter in a moorland soil profile.

PubMed

Hetherington, S L; Anderson, J M

1998-11-01

Bracken (Pteridium aquilinum) is aggressively displacing heather (Calluna vulgaris) on many moorlands in Britain. We investigated the use of lignin derivatives to identify the distribution of soil organic matter (SOM) derived from bracken in moorland soil profiles formed under heather. Phenylpropanoids extracted from recently senesced litters, roots and SOM, using alkaline CuO oxidation, showed distinct signatures for bracken and heather, with vanillyl moieties dominating bracken litter extracts and vanillyl and syringyl dominating heather litter extracts. Ratios of vanillyl and syringyl concentrations characterised the SOM derived from heather and bracken better than the concentrations of the individual moieties. The analysis showed up to a depth of 5 cm under pure bracken cover, and at the interface between heather and bracken, the SOM was largely derived from bracken litter but below that depth SOM was apparently derived from heather. The use of these methods to identify the plant origin of SOM not only enables understanding the effects of changing vegetation cover on organic matter dynamics in moorland soils but could also facilitate management techniques in moorland/heathland restoration which involve the removal of comparatively nutrient-rich SOM derived from bracken.

Cloudy Windows: What GCM Ensembles, Reanalyses and Observations Tell Us About Uncertainty in Greenland's Future Climate and Surface Melting

NASA Astrophysics Data System (ADS)

Reusch, D. B.

2016-12-01

Any analysis that wants to use a GCM-based scenario of future climate benefits from knowing how much uncertainty the GCM's inherent variability adds to the development of climate change predictions. This is extra relevant in the polar regions due to the potential of global impacts (e.g., sea level rise) from local (ice sheet) climate changes such as more frequent/intense surface melting. High-resolution, regional-scale models using GCMs for boundary/initial conditions in future scenarios inherit a measure of GCM-derived externally-driven uncertainty. We investigate these uncertainties for the Greenland ice sheet using the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Recent simulations are skill-tested against the ERA-Interim reanalysis and AWS observations with results informing future scenarios. We focus on key variables influencing surface melting through decadal climatologies, nonlinear analysis of variability with self-organizing maps (SOMs), regional-scale modeling (Polar WRF), and simple melt models. Relative to the ensemble average, spatially averaged climatological July temperature anomalies over a Greenland ice-sheet/ocean domain are mostly between +/- 0.2 °C. The spatial average hides larger local anomalies of up to +/- 2 °C. The ensemble average itself is 2 °C cooler than ERA-Interim. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. For CESMLE, the SOM patterns summarize the variability of multiple realizations of climate. Changes in pattern frequency by ensemble member show the influence of initial conditions. For example, basic statistical analysis of pattern frequency yields interquartile ranges of 2-4% for individual patterns across the ensemble. In climate terms, this tells us about climate state variability through the range of the ensemble, a potentially significant source of melt-prediction uncertainty. SOMs can also capture the different trajectories of climate due to intramodel variability over time. Polar WRF provides higher resolution regional modeling with improved, polar-centric model physics. Simple melt models allow us to characterize impacts of the upstream uncertainties on estimates of surface melting.

Soil organic carbon dynamics across a nitrogen deposition gradient: application of the PnET-SOM model to northeastern forest ecosystems

NASA Astrophysics Data System (ADS)

Tonitto, C.; Goodale, C. L.; Ollinger, S. V.; Jenkins, J.

2009-12-01

Anthropogenic forcing of the C and N cycles has caused rapid change in atmospheric CO2 and N deposition, with complex and uncertain effects on forest C and N balance. We developed the PnET-SOM model to enhance the model description of carbon and nitrogen coupling. Here we applied PnET-SOM to study changes to ecosystem carbon storage across a nitrogen deposition gradient. We designed the PnET-SOM model to: 1) represent SOM structured around measurable SOM pools, 2) expand simulated soil horizon complexity beyond the 1-box approach to hydrology and SOM structure used in PnET-CN, 3) model humified and mineral associated SOM using parameters derived from C14 field studies, and 4) couple C and N cycles to allow N-limitation of decomposition and plant growth. We explicitly modeled labile, biochemically recalcitrant (humified SOM), and physically-chemically protected (mineral associated SOM) C pools. These SOM pools are modeled in distinct soil horizons including: a forest floor, a mixed organic horizon, an A horizon, and a B horizon. Slow turnover pools of the A and B horizon constitute a significant proportion of SOC; explicitly modeling a deeper soil profile is important for estimating ecosystem SOC storage. In the latest version of PnET-SOM, we described N mineralization-immobilization in the forest floor based on equations derived in the LIDET synthesis study. Validation of the PnET-SOM model was conducted using 1) long-term water flux and nitrate leaching data from the Hubbard Brook LTER, 2) CO2 respiration observations from the Harvard Forest LTER, and 3) C and N stock and flux observations from the Harvard Forest LTER. In this work, we applied the PnET-SOM model to study the effects of an N deposition gradient on SOC dynamics over a 300 year simulation. We represented the effects of N deposition on litter decomposition by varying the exponential decay parameters of the litter layer based on observations from the Harvard Forest N addition experiment. We derived the change in limit value across the N deposition gradient simulated by applying a polynomial fit to limit values observed in the Harvard Forest N addition experimental plots. In PnET-SOM, changes in SOC dynamics under varying N deposition rates are derived from 1) change in litter decomposition rate, which directly affects OM inputs into soil pools, and 2) altered N availability which limits decomposition of OM throughout the soil profile. In our model application, the coarse woody debris pool decreased with increasing N deposition, while the forest floor pools increased. Relative to SOC pools under current N deposition rates at the Harvard Forest (8 gN/m2), deposition at a rate of 20 gN/m2 over a 300 year simulation resulted in a 3.4% increase in the O horizon humified SOC pool, a 2.3% increase in the A horizon humified SOC pool, a 1.3% increase in the A horizon mineral associated SOC pool, and a 0.14% increase in the B horizon mineral associated SOC pool.

An image-space parallel convolution filtering algorithm based on shadow map

NASA Astrophysics Data System (ADS)

Li, Hua; Yang, Huamin; Zhao, Jianping

2017-07-01

Shadow mapping is commonly used in real-time rendering. In this paper, we presented an accurate and efficient method of soft shadows generation from planar area lights. First this method generated a depth map from light's view, and analyzed the depth-discontinuities areas as well as shadow boundaries. Then these areas were described as binary values in the texture map called binary light-visibility map, and a parallel convolution filtering algorithm based on GPU was enforced to smooth out the boundaries with a box filter. Experiments show that our algorithm is an effective shadow map based method that produces perceptually accurate soft shadows in real time with more details of shadow boundaries compared with the previous works.

Which fraction of soil organic matter is more vulnerable to rhizosphere priming effect?

NASA Astrophysics Data System (ADS)

Zhu, B.; Cheng, W.

2016-12-01

Rhizosphere priming effect (RPE) is defined as the stimulation or suppression of soil organic matter (SOM) decomposition by living roots. It remains unclear which fraction of SOM is more vulnerable to rhizosphere priming. We conducted two experiments in continuous 13CO2 labeling growth chamber to compare the intensity of RPE for the active (or labile) vs. slow (or recalcitrant) SOM. A sandy loam (Alfisol) was incubated at 20oC and 80% water holding capacity for different periods, which created a gradient in the relative proportion of active vs. slow SOM in the remaining soils. We then grew sunflower (Helianthus annuus) and soybean (Glycine max) in these remaining soils for 50 days under the same environmental conditions to compare the RPE of these two plant species on the decomposition of soils that varied in the lability of SOM. In both experiments, as the incubation proceeded from 1 to 8 to 14 months (in experiment 1) and the soil changed from freshly-sampled soil to two-year-incubated soil (in experiment 2), the intensity of RPE increased significantly even after accounting for the changes in root biomass or root-derived CO2. This result suggests that the slow (or recalcitrant) fraction of SOM is likely more vulnerable to rhizosphere priming compared to the active (or labile) fraction of SOM. Although the underlying mechanisms of this finding await further investigation, our study clearly shows that the main component of SOM (slow or recalcitrant SOM, decadal turnover) is vulnerable to rhizosphere priming. Therefore, the RPE has the potential to substantially regulate both short-term and long-term soil carbon dynamics.

Long-term variation in above and belowground plant inputs alters soil organic matter biogeochemistry at the molecular-level

NASA Astrophysics Data System (ADS)

Simpson, M. J.; Pisani, O.; Lin, L.; Lun, O.; Simpson, A.; Lajtha, K.; Nadelhoffer, K. J.

2015-12-01

The long-term fate of soil carbon reserves with global environmental change remains uncertain. Shifts in moisture, altered nutrient cycles, species composition, or rising temperatures may alter the proportions of above and belowground biomass entering soil. However, it is unclear how long-term changes in plant inputs may alter the composition of soil organic matter (SOM) and soil carbon storage. Advanced molecular techniques were used to assess SOM composition in mineral soil horizons (0-10 cm) after 20 years of Detrital Input and Removal Treatment (DIRT) at the Harvard Forest. SOM biomarkers (solvent extraction, base hydrolysis and cupric (II) oxide oxidation) and both solid-state and solution-state nuclear magnetic resonance (NMR) spectroscopy were used to identify changes in SOM composition and stage of degradation. Microbial activity and community composition were assessed using phospholipid fatty acid (PLFA) analysis. Doubling aboveground litter inputs decreased soil carbon content, increased the degradation of labile SOM and enhanced the sequestration of aliphatic compounds in soil. The exclusion of belowground inputs (No roots and No inputs) resulted in a decrease in root-derived components and enhanced the degradation of leaf-derived aliphatic structures (cutin). Cutin-derived SOM has been hypothesized to be recalcitrant but our results show that even this complex biopolymer is susceptible to degradation when inputs entering soil are altered. The PLFA data indicate that changes in soil microbial community structure favored the accelerated processing of specific SOM components with littler manipulation. These results collectively reveal that the quantity and quality of plant litter inputs alters the molecular-level composition of SOM and in some cases, enhances the degradation of recalcitrant SOM. Our study also suggests that increased litterfall is unlikely to enhance soil carbon storage over the long-term in temperate forests.

Effects of somatostatin analog SOM230 on cell proliferation, apoptosis, and catecholamine levels in cultured pheochromocytoma cells.

PubMed

Pasquali, Daniela; Rossi, Valentina; Conzo, Giovanni; Pannone, Giuseppe; Bufo, Pantaleo; De Bellis, Annamaria; Renzullo, Andrea; Bellastella, Giuseppe; Colao, Annamaria; Vallone, Gianfranco; Bellastella, Antonio; Sinisi, Antonio A

2008-06-01

Surgery is the primary therapy for pheochromocytoma (PHEO), a catecholamine-producing tumor. Benign and malignant PHEO could develop recurrences, and the intraoperative risk of recurrent PHEO is an important unresolved issue. Non-surgical treatments of PHEO recurrence would therefore better prepare patients for reintervention as well as provide them with palliative management. We investigated the effects of the new somatostatin analog (pasireotide) SOM230 versus octreotide (OCT) in primary PHEO cell cultures (Pheo-c). Pheo-c from six benign surgical samples were set up and characterized by immunocytochemistry. Real-time PCR, using both PHEO tissues and Pheo-c, showed different levels of somatostatin receptor(1-5) mRNA expression. Cells treated with various doses of OCT or SOM230 for 48 and 72 h were analyzed to assess their effects on cell proliferation and apoptosis and catecholamine levels. Even if reduction of cell viability was observed in Pheo-c treated for 48 h with either OCT or SOM230 and this effect increased after 72 h, a more significant inhibition of cell growth as well as a significantly higher induction of apoptosis was seen in Pheo-c treated with SOM230 versus OCT. In particular, apoptosis in Pheo-c was detected after 48 h and was associated with increased expression and activation of caspase-3 and cleaved poly(ADP-ribose) polymerase. OCT 10(-6) M and SOM230 10(-7) M significantly reduced catecholamine levels. Our results indicate that while both OCT and SOM230 modulate cell growth and apoptosis and catecholamine levels in Pheo-c through specific receptors, SOM230 is more effective. This improves our knowledge on the mechanism of SOM230 action in PHEO and supports a possible therapeutic use in benign PHEO recurrence.

Soil organic matter as sole indicator of soil degradation.

PubMed

Obalum, S E; Chibuike, G U; Peth, S; Ouyang, Y

2017-04-01

Soil organic matter (SOM) is known to play vital roles in the maintenance and improvement of many soil properties and processes. These roles, which largely influence soil functions, are a pool of specific contributions of different components of SOM. The soil functions, in turn, normally define the level of soil degradation, viewed as quantifiable temporal changes in a soil that impairs its quality. This paper aims at providing a generalized assessment of the current state of knowledge on the usefulness of SOM in monitoring soil degradation, based on its influence on the physical, chemical and biological properties and processes of soils. Emphasis is placed particularly on the effect of SOM on soil structure and availability of plant nutrients. Although these properties are discussed separately, the soil system is of dynamic and interactive nature, and changes in one property will likely affect other soil properties as well. Thus, functions of SOM almost always affect various soil properties and processes and engage in multiple reactions. In view of its role in soil aggregation and erosion control, in availability of plant nutrients and in ameliorating other forms of soil degradation than erosion, SOM has proven to be an important indicator of soil degradation. It has been suggested, however, that rather than the absolute amount, temporal change and potential amount of SOM be considered in its use as indicator of soil degradation, and that SOM may not be an all-purpose indicator. Whilst SOM remains a candidate without substitute as long as a one-parameter indicator of soil degradation is needed, narrowing down to the use of its labile and microbial components could be more appropriate, since early detection is important in the control and management of soil degradation.

Arctic Tundra Soils: A Microbial Feast That Shrubs Will Cease

NASA Astrophysics Data System (ADS)

Machmuller, M.; Calderon, F.; Cotrufo, M. F.; Lynch, L.; Paul, E. A.; Wallenstein, M. D.

2016-12-01

Rapid climate warming may already be driving rapid decomposition of the vast stocks of carbon in Arctic tundra soils. However, stimulated decomposition may also release nitrogen and support increased plant productivity, potentially counteracting soil carbon losses. At the same time, these two processes interact, with plant derived carbon potentially fueling soil microbes to attack soil organic matter (SOM) to acquire nitrogen- a process known as priming. Thus, differences in the physiology, stoichiometry and microbial interactions among plant species could affect climate-carbon feedbacks. To reconcile these interactive mechanisms, we examined how vegetation type (Betula nana and Eriophorum vaginatum) and fertilization (short-term and long-term) influenced the decomposition of native SOM after labile carbon and nutrient addition. We hypothesized that labile carbon inputs would stimulate the loss of native SOM, but the magnitude of this effect would be indirectly related to soil nitrogen concentrations (e.g. SOM priming would be highest in N-limited soils). We added isotopically enriched (13C) glucose and ammonium nitrate to soils under shrub (B. nana) and tussock (E. vaginatum) vegetation. We found that nitrogen additions stimulated priming only in tussock soils, characterized by lower nutrient concentrations and microbial biomass (p<0.05). There was no evidence of priming in soils that had been fertilized for >20yrs. Rather, we found that long-term fertilization shifted SOM chemistry towards a greater abundance of recalcitrant SOM, lower microbial biomass, and decreased SOM respiration (p<0.05). Our results suggest that, in the short-term, the magnitude of SOM priming is dependent on vegetation and soil nitrogen concentrations, but this effect may not persist if shrubs increase in abundance under climate warming. Therefore, including nitrogen as a control on SOM decomposition and priming is critical to accurately model the effects of climate change on arctic carbon storage.

Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization.

PubMed

Wang, Changqing; Zhang, Xinyuan; Liu, Xiaoyun; He, Taigang; Chen, Wufan; Feng, Qianjin; Feng, Yanqiu

2018-08-01

To improve liver R2* mapping by incorporating adaptive neighborhood regularization into pixel-wise curve fitting. Magnetic resonance imaging R2* mapping remains challenging because of the serial images with low signal-to-noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel-wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter-based NLS algorithms on simulated, phantom, and in vivo data. Visually, the PCANR algorithm generates R2* maps with significantly reduced noise and well-preserved tiny structures. Quantitatively, the PCANR algorithm produces R2* maps with lower root mean square errors at varying R2* values and signal-to-noise-ratio levels compared with the NLS and nonlocal means filter-based NLS algorithms. For the high R2* values under low signal-to-noise-ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter-based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of R2* measurements in selected region of interests, respectively. The PCANR algorithm can reduce the effect of noise on liver R2* mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med 80:792-801, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.

Recursive approach to the moment-based phase unwrapping method.

PubMed

Langley, Jason A; Brice, Robert G; Zhao, Qun

2010-06-01

The moment-based phase unwrapping algorithm approximates the phase map as a product of Gegenbauer polynomials, but the weight function for the Gegenbauer polynomials generates artificial singularities along the edge of the phase map. A method is presented to remove the singularities inherent to the moment-based phase unwrapping algorithm by approximating the phase map as a product of two one-dimensional Legendre polynomials and applying a recursive property of derivatives of Legendre polynomials. The proposed phase unwrapping algorithm is tested on simulated and experimental data sets. The results are then compared to those of PRELUDE 2D, a widely used phase unwrapping algorithm, and a Chebyshev-polynomial-based phase unwrapping algorithm. It was found that the proposed phase unwrapping algorithm provides results that are comparable to those obtained by using PRELUDE 2D and the Chebyshev phase unwrapping algorithm.

The chimeric mapping problem: algorithmic strategies and performance evaluation on synthetic genomic data.

PubMed

Greenberg, D; Istrail, S

1994-09-01

The Human Genome Project requires better software for the creation of physical maps of chromosomes. Current mapping techniques involve breaking large segments of DNA into smaller, more-manageable pieces, gathering information on all the small pieces, and then constructing a map of the original large piece from the information about the small pieces. Unfortunately, in the process of breaking up the DNA some information is lost and noise of various types is introduced; in particular, the order of the pieces is not preserved. Thus, the map maker must solve a combinatorial problem in order to reconstruct the map. Good software is indispensable for quick, accurate reconstruction. The reconstruction is complicated by various experimental errors. A major source of difficulty--which seems to be inherent to the recombination technology--is the presence of chimeric DNA clones. It is fairly common for two disjoint DNA pieces to form a chimera, i.e., a fusion of two pieces which appears as a single piece. Attempts to order chimera will fail unless they are algorithmically divided into their constituent pieces. Despite consensus within the genomic mapping community of the critical importance of correcting chimerism, algorithms for solving the chimeric clone problem have received only passing attention in the literature. Based on a model proposed by Lander (1992a, b) this paper presents the first algorithms for analyzing chimerism. We construct physical maps in the presence of chimerism by creating optimization functions which have minimizations which correlate with map quality. Despite the fact that these optimization functions are invariably NP-complete our algorithms are guaranteed to produce solutions which are close to the optimum. The practical import of using these algorithms depends on the strength of the correlation of the function to the map quality as well as on the accuracy of the approximations. We employ two fundamentally different optimization functions as a means of avoiding biases likely to decorrelate the solutions from the desired map. Experiments on simulated data show that both our algorithm which minimizes the number of chimeric fragments in a solution and our algorithm which minimizes the maximum number of fragments per clone in a solution do, in fact, correlate to high quality solutions. Furthermore, tests on simulated data using parameters set to mimic real experiments show that that the algorithms have the potential to find high quality solutions with real data. We plan to test our software against real data from the Whitehead Institute and from Los Alamos Genomic Research Center in the near future.

Fall conservation deep tillage stabilizes maize residues into soil organic matter

USDA-ARS?s Scientific Manuscript database

Efforts for increasing soil organic matter (SOM) content under agricultural systems have primarily focused on management practices that reduce exposure of SOM to decomposition via minimum tillage. We assess an alternative approach, termed ‘fall conservation deep tillage’ (FCDT), to SOM stabilization...

Quasi-conformal mapping with genetic algorithms applied to coordinate transformations

NASA Astrophysics Data System (ADS)

González-Matesanz, F. J.; Malpica, J. A.

2006-11-01

In this paper, piecewise conformal mapping for the transformation of geodetic coordinates is studied. An algorithm, which is an improved version of a previous algorithm published by Lippus [2004a. On some properties of piecewise conformal mappings. Eesti NSV Teaduste Akademmia Toimetised Füüsika-Matemaakika 53, 92-98; 2004b. Transformation of coordinates using piecewise conformal mapping. Journal of Geodesy 78 (1-2), 40] is presented; the improvement comes from using a genetic algorithm to partition the complex plane into convex polygons, whereas the original one did so manually. As a case study, the method is applied to the transformation of the Spanish datum ED50 and ETRS89, and both its advantages and disadvantages are discussed herein.

Chance of Vulnerability Reduction in Application-Specific NoC through Distance Aware Mapping Algorithm

NASA Astrophysics Data System (ADS)

Janidarmian, Majid; Fekr, Atena Roshan; Bokharaei, Vahhab Samadi

2011-08-01

Mapping algorithm which means which core should be linked to which router is one of the key issues in the design flow of network-on-chip. To achieve an application-specific NoC design procedure that minimizes the communication cost and improves the fault tolerant property, first a heuristic mapping algorithm that produces a set of different mappings in a reasonable time is presented. This algorithm allows the designers to identify the set of most promising solutions in a large design space, which has low communication costs while yielding optimum communication costs in some cases. Another evaluated parameter, vulnerability index, is then considered as a principle of estimating the fault-tolerance property in all produced mappings. Finally, in order to yield a mapping which considers trade-offs between these two parameters, a linear function is defined and introduced. It is also observed that more flexibility to prioritize solutions within the design space is possible by adjusting a set of if-then rules in fuzzy logic.

Clustering of self-organizing map identifies five distinct medulloblastoma subgroups.

PubMed

Cao, Changjun; Wang, Wei; Jiang, Pucha

2016-01-01

Medulloblastoma is one the most malignant paediatric brain tumours. Molecular subgrouping these medulloblastomas will not only help identify specific cohorts for certain treatment but also improve confidence in prognostic prediction. Currently, there is a consensus of the existences of four distinct subtypes of medulloblastoma. We proposed a novel bioinformatics method, clustering of self-organizing map, to determine the subgroups and their molecular diversity. Microarray expression profiles of 46 medulloblastoma samples were analysed and five clusters with distinct demographics, clinical outcome and transcriptional profiles were identified. The previously reported Wnt subgroup was identified as expected. Three other novel subgroups were proposed for later investigation. Our findings underscore the value of SOM clustering for discovering the medulloblastoma subgroups. When the suggested subdivision has been confirmed in large cohorts, this method should serve as a part of routine classification of clinical samples.

Characterizing synoptic and cloud variability in the northern atlantic using self-organizing maps

NASA Astrophysics Data System (ADS)

Fish, Carly

Low-level clouds have a significant influence on the Earth's radiation budget and it is thus imperative to understand their behavior within the marine boundary layer (MBL). The cloud properties in the Northeast Atlantic region are highly variable in space and time and are a research focus for many atmospheric scientists. Characterizing the synoptic patterns in the region through the implementation of self-organizing maps (SOMs) enables a climatological grasp of cloud and atmospheric fields. ERA -- Interim and MODIS provide the platform to explore the variability in the Northeast Atlantic for over 30 years of data. Station data comes from CAP -- MBL on Graciosa Island in the Azores, which lies in a strong gradient of cloud and other atmospheric fields, offer an opportunity to incorporate an observational aspect for the years of 2009 and 2010.

Efficacy of Lens Protection Systems: Dependency on Different Cranial CT Scans in The Acute Stroke Setting.

PubMed

Guberina, Nika; Forsting, Michael; Ringelstein, Adrian

2017-06-15

To evaluate the dose-reduction potential with different lens protectors for patients undergoing cranial computed tomography (CT) scans. Eye lens dose was assessed in vitro (α-Al2O3:C thermoluminescence dosemeters) using an Alderson-Rando phantom® in cranial CT protocols at different CT scanners (SOMATOM-Definition-AS+®(CT1) and SOMATOM-Definition-Flash® (CT2)) using two different lens-protection systems (Somatex® (SOM) and Medical Imaging Systems® (MIS)). Summarised percentage of the transmitted photons: (1) CT1 (a) unenhanced CT (nCT) with gantry angulation: SOM = 103%, MIS = 111%; (2) CT2 (a) nCT without gantry angulation: SOM = 81%, MIS = 91%; (b) CT angiography (CTA) with automatic dose-modulation technique: SOM = 39%, MIS = 74%; (c) CTA without dose-modulation technique: SOM = 22%, MIS = 48%; (d) CT perfusion: SOM = 44%, MIS = 69%. SOM showed a higher dose-reduction potential than MIS maintaining equal image quality. Lens-protection systems are most effective in CTA protocols without dose-reduction techniques. Lens-protection systems lower the average eye lens dose during CT scans up to 1/3 (MIS) and 2/3 (SOM), respectively, if the eye lens is exposed to the direct beam of radiation. Considering both the CT protocol and the material of lens protectors, they seem to be mandatory for reducing the radiation exposure of the eye lens. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Toward an Improved Understanding of the Role of Soil Organic Matter in NOy Cycling through Investigation of Heterogeneous Reactions with NO2+

NASA Astrophysics Data System (ADS)

Engelhard, M.; Hansen, R. F.; Raff, J. D.

2017-12-01

Molecular level investigation of compositional changes due to heterogeneous reactions of nitrogen oxides (NOx, NOy) with soil organic matter (SOM) is important to develop a fundamental scientific understanding of the soil/atmosphere interface. In addition, interactions between NOx reservoir species and SOM play a more important role in NOx and NOy recycling than previously realized. Despite the importance of soil to the global terrestrial-atmospheric cycling of nitrogen, interactions of N2O5 with SOM are not well understood. Uncertainty in these processes is problematic because it means that NOy is not properly represented in the Earth-systems models used for prediction and regulation. The ultimate objective of this study is to investigate the production of NOy from the reaction of N2O5 with SOM and elucidate the mechanisms that return NOy back to the atmosphere, where NOy can contribute to aerosol and O3 formation. In the initial phase of this study, we reacted SOM standards with NO2+ (produced from concentrated HNO3), which is an intermediate in the heterogeneous reaction of N2O5 with SOM. We then characterized these reaction products using X-ray photoelectron spectroscopy (XPS). XPS was used to measure the nitrogen chemistry before and after reaction of SOM with NO2+. These results will be discussed along with the pros and cons using XPS to characterize SOM chemistry.

On quantifying active soil carbon using mid-infrared spectroscopy

EPA Science Inventory

Soil organic matter (SOM) is derived from plant or animal residues deposited to soil and is in various stages of decomposition and mineralization. Total SOM is a common measure of soil quality, although due to its heterogeneous composition SOM can vary dramatically in terms of i...

Text image authenticating algorithm based on MD5-hash function and Henon map

NASA Astrophysics Data System (ADS)

Wei, Jinqiao; Wang, Ying; Ma, Xiaoxue

2017-07-01

In order to cater to the evidentiary requirements of the text image, this paper proposes a fragile watermarking algorithm based on Hash function and Henon map. The algorithm is to divide a text image into parts, get flippable pixels and nonflippable pixels of every lump according to PSD, generate watermark of non-flippable pixels with MD5-Hash, encrypt watermark with Henon map and select embedded blocks. The simulation results show that the algorithm with a good ability in tampering localization can be used to authenticate and forensics the authenticity and integrity of text images

Flattening maps for the visualization of multibranched vessels.

PubMed

Zhu, Lei; Haker, Steven; Tannenbaum, Allen

2005-02-01

In this paper, we present two novel algorithms which produce flattened visualizations of branched physiological surfaces, such as vessels. The first approach is a conformal mapping algorithm based on the minimization of two Dirichlet functionals. From a triangulated representation of vessel surfaces, we show how the algorithm can be implemented using a finite element technique. The second method is an algorithm which adjusts the conformal mapping to produce a flattened representation of the original surface while preserving areas. This approach employs the theory of optimal mass transport. Furthermore, a new way of extracting center lines for vessel fly-throughs is provided.

Flattening Maps for the Visualization of Multibranched Vessels

PubMed Central

Zhu, Lei; Haker, Steven; Tannenbaum, Allen

2013-01-01

In this paper, we present two novel algorithms which produce flattened visualizations of branched physiological surfaces, such as vessels. The first approach is a conformal mapping algorithm based on the minimization of two Dirichlet functionals. From a triangulated representation of vessel surfaces, we show how the algorithm can be implemented using a finite element technique. The second method is an algorithm which adjusts the conformal mapping to produce a flattened representation of the original surface while preserving areas. This approach employs the theory of optimal mass transport. Furthermore, a new way of extracting center lines for vessel fly-throughs is provided. PMID:15707245