An effective self-assessment based on concept map extraction from test-sheet for personalized learning

NASA Astrophysics Data System (ADS)

Liew, Keng-Hou; Lin, Yu-Shih; Chang, Yi-Chun; Chu, Chih-Ping

2013-12-01

Examination is a traditional way to assess learners' learning status, progress and performance after a learning activity. Except the test grade, a test sheet hides some implicit information such as test concepts, their relationships, importance, and prerequisite. The implicit information can be extracted and constructed a concept map for considering (1) the test concepts covered in the same question means these test concepts have strong relationships, and (2) questions in the same test sheet means the test concepts are relative. Concept map has been successfully employed in many researches to help instructors and learners organize relationships among concepts. However, concept map construction depends on experts who need to take effort and time for the organization of the domain knowledge. In addition, the previous researches regarding to automatic concept map construction are limited to consider all learners of a class, which have not considered personalized learning. To cope with this problem, this paper proposes a new approach to automatically extract and construct concept map based on implicit information in a test sheet. Furthermore, the proposed approach also can help learner for self-assessment and self-diagnosis. Finally, an example is given to depict the effectiveness of proposed approach.

Comparison of Classifier Architectures for Online Neural Spike Sorting.

PubMed

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis.

PubMed

Sugii, Yuh; Kasai, Tomonari; Ikeda, Masashi; Vaidyanath, Arun; Kumon, Kazuki; Mizutani, Akifumi; Seno, Akimasa; Tokutaka, Heizo; Kudoh, Takayuki; Seno, Masaharu

2016-01-01

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

Analysis of 2D Phase Contrast MRI in Renal Arteries by Self Organizing Maps

NASA Astrophysics Data System (ADS)

Zöllner, Frank G.; Schad, Lothar R.

We present an approach based on self organizing maps to segment renal arteries from 2D PC Cine MR, images to measure blood velocity and flow. Such information are important in grading renal artery stenosis and support the decision on surgical interventions like percu-tan transluminal angioplasty. Results show that the renal arteries could be extracted automatically. The corresponding velocity profiles show high correlation (r=0.99) compared those from manual delineated vessels. Furthermore, the method could detect possible blood flow patterns within the vessel.

Universality in the Evolution of Orientation Columns in the Visual Cortex

PubMed Central

Kaschube, Matthias; Schnabel, Michael; Löwel, Siegrid; Coppola, David M.; White, Leonard E.; Wolf, Fred

2011-01-01

The brain’s visual cortex processes information concerning form, pattern, and motion within functional maps that reflect the layout of neuronal circuits. We analyzed functional maps of orientation preference in the ferret, tree shrew, and galago—three species separated since the basal radiation of placental mammals more than 65 million years ago—and found a common organizing principle. A symmetry-based class of models for the self-organization of cortical networks predicts all essential features of the layout of these neuronal circuits, but only if suppressive long-range interactions dominate development. We show mathematically that orientation-selective long-range connectivity can mediate the required interactions. Our results suggest that self-organization has canalized the evolution of the neuronal circuitry underlying orientation preference maps into a single common design. PMID:21051599

Probabilistic self-organizing maps for continuous data.

PubMed

Lopez-Rubio, Ezequiel

2010-10-01

The original self-organizing feature map did not define any probability distribution on the input space. However, the advantages of introducing probabilistic methodologies into self-organizing map models were soon evident. This has led to a wide range of proposals which reflect the current emergence of probabilistic approaches to computational intelligence. The underlying estimation theories behind them derive from two main lines of thought: the expectation maximization methodology and stochastic approximation methods. Here, we present a comprehensive view of the state of the art, with a unifying perspective of the involved theoretical frameworks. In particular, we examine the most commonly used continuous probability distributions, self-organization mechanisms, and learning schemes. Special emphasis is given to the connections among them and their relative advantages depending on the characteristics of the problem at hand. Furthermore, we evaluate their performance in two typical applications of self-organizing maps: classification and visualization.

Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity

PubMed Central

Srinivasa, Narayan; Jiang, Qin

2013-01-01

This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN) to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through ±180° along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex. PMID:23450808

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.

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.

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.

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

NASA Astrophysics Data System (ADS)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Topological mappings of video and audio data.

PubMed

Fyfe, Colin; Barbakh, Wesam; Ooi, Wei Chuan; Ko, Hanseok

2008-12-01

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM).(1) But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts.(2) We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels. Finally we note that we may dispense with the probabilistic underpinnings of the product of experts and derive the same algorithm as a minimisation of mean squared error between the prototypes and the data. This leads us to suggest a new algorithm which incorporates local and global information in the clustering. Both ot the new algorithms achieve better results than the standard Self-Organizing Map.

Self-Organizing Distributed Architecture Supporting Dynamic Space Expanding and Reducing in Indoor LBS Environment

PubMed Central

Jeong, Seol Young; Jo, Hyeong Gon; Kang, Soon Ju

2015-01-01

Indoor location-based services (iLBS) are extremely dynamic and changeable, and include numerous resources and mobile devices. In particular, the network infrastructure requires support for high scalability in the indoor environment, and various resource lookups are requested concurrently and frequently from several locations based on the dynamic network environment. A traditional map-based centralized approach for iLBSs has several disadvantages: it requires global knowledge to maintain a complete geographic indoor map; the central server is a single point of failure; it can also cause low scalability and traffic congestion; and it is hard to adapt to a change of service area in real time. This paper proposes a self-organizing and fully distributed platform for iLBSs. The proposed self-organizing distributed platform provides a dynamic reconfiguration of locality accuracy and service coverage by expanding and contracting dynamically. In order to verify the suggested platform, scalability performance according to the number of inserted or deleted nodes composing the dynamic infrastructure was evaluated through a simulation similar to the real environment. PMID:26016908

Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data.

PubMed

Wu, Hongle; Kato, Takafumi; Yamada, Tomomi; Numao, Masayuki; Fukui, Ken-Ichi

2017-07-01

We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

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.

Growing a hypercubical output space in a self-organizing feature map.

PubMed

Bauer, H U; Villmann, T

1997-01-01

Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective structure of the data in the input space. We here present a growth algorithm, called the GSOM or growing self-organizing map, which enhances a widespread map self-organization process, Kohonen's self-organizing feature map (SOFM), by an adaptation of the output space grid during learning. The GSOM restricts the output space structure to the shape of a general hypercubical shape, with the overall dimensionality of the grid and its extensions along the different directions being subject of the adaptation. This constraint meets the demands of many larger information processing systems, of which the neural map can be a part. We apply our GSOM-algorithm to three examples, two of which involve real world data. Using recently developed methods for measuring the degree of neighborhood preservation in neural maps, we find the GSOM-algorithm to produce maps which preserve neighborhoods in a nearly optimal fashion.

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

NASA Technical Reports Server (NTRS)

Motter, Mark A.

1998-01-01

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel 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. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

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

Beyond Academic Tracking: Using Cluster Analysis and Self-Organizing Maps to Investigate Secondary Students' Chemistry Self-Concept

ERIC Educational Resources Information Center

Nielsen, Sara E.; Yezierski, Ellen J.

2016-01-01

Academic tracking, placing students in different classes based on past performance, is a common feature of the American secondary school system. A longitudinal study of secondary students' chemistry self-concept scores was conducted, and one feature of the study was the presence of academic tracking. Though academic tracking is one way to group…

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.

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.

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.

An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

Kohonen's self-organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes, the quantization and dimensionality reduction of date. A short description of its history and its biological context is given. We show that the inherent classification properties of the feature map make it a suitable candidate for solving the classification task in power system areas like load forecasting, fault diagnosis and security assessment.

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.

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...

Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

NASA Astrophysics Data System (ADS)

Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

2017-08-01

This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

Complex Networks in Psychological Models

NASA Astrophysics Data System (ADS)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

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

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.

Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets.

PubMed

Franceschi, Pietro; Wehrens, Ron

2014-04-01

MS-based imaging approaches allow for location-specific identification of chemical components in biological samples, opening up possibilities of much more detailed understanding of biological processes and mechanisms. Data analysis, however, is challenging, mainly because of the sheer size of such datasets. This article presents a novel approach based on self-organizing maps, extending previous work in order to be able to handle the large number of variables present in high-resolution mass spectra. The key idea is to generate prototype images, representing spatial distributions of ions, rather than prototypical mass spectra. This allows for a two-stage approach, first generating typical spatial distributions and associated m/z bins, and later analyzing the interesting bins in more detail using accurate masses. The possibilities and advantages of the new approach are illustrated on an in-house dataset of apple slices. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Neighborhoods in Development: Human Development Index and Self-Organizing Maps

ERIC Educational Resources Information Center

Rende, Sevinc; Donduran, Murat

2013-01-01

The Human Development Index (HDI) has been instrumental in broadening the discussion of economic development beyond money-metric progress, in particular, by ranking a country against other countries in terms of the well being of their citizens. We propose self-organizing maps to explore similarities among countries using the components of the HDI…

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.

Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

NASA Astrophysics Data System (ADS)

Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

1996-04-01

The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

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.

Comparison between genetic algorithm and self organizing map to detect botnet network traffic

NASA Astrophysics Data System (ADS)

Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

2017-11-01

In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

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.

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.

Self-organizing map models of language acquisition

PubMed Central

Li, Ping; Zhao, Xiaowei

2013-01-01

Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061

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…

Micro-Laser-Induced Breakdown Spectroscopy (Micro-LIBS) Study on Ancient Roman Mortars.

PubMed

Pagnotta, Stefano; Lezzerini, Marco; Ripoll-Seguer, Laura; Hidalgo, Montserrat; Grifoni, Emanuela; Legnaioli, Stefano; Lorenzetti, Giulia; Poggialini, Francesco; Palleschi, Vincenzo

2017-04-01

The laser-induced breakdown spectroscopy (LIBS) technique was used for analyzing the composition of an ancient Roman mortar (5th century A.D.), exploiting an experimental setup which allows the determination of the compositions of binder and aggregate in few minutes, without the need for sample treatment. Four thousand LIBS spectra were acquired from an area of 10 mm 2 , with a 50 µm lateral resolution. The elements of interest in the mortar sample (H, C, O, Na, Mg, Al, Si, K, Ca, Ti, Mn, Fe) were detected and mapped. The collected data graphically shown as compositional images were interpreted using different statistical approaches for the determination of the chemical composition of the binder and aggregate fraction. The methods of false color imaging, blind separation, and self-organizing maps were applied and their results are discussed in this paper. In particular, the method based on the use of self-organizing maps gives well interpretable results in very short times, without any reduction in the dimensionality of the system.

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

PubMed Central

Pilly, Praveen K.; Grossberg, Stephen

2013-01-01

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning 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 these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. PMID:23577130

New adaptive color quantization method based on self-organizing maps.

PubMed

Chang, Chip-Hong; Xu, Pengfei; Xiao, Rui; Srikanthan, Thambipillai

2005-01-01

Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage overhead, which can be cut down by leveraging on existing encoder in an overall lossy compression scheme.

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

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

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.

Self-Organizing Map With Time-Varying Structure to Plan and Control Artificial Locomotion.

PubMed

Araujo, Aluizio F R; Santana, Orivaldo V

2015-08-01

This paper presents an algorithm, self-organizing map-state trajectory generator (SOM-STG), to plan and control legged robot locomotion. The SOM-STG is based on an SOM with a time-varying structure characterized by constructing autonomously close-state trajectories from an arbitrary number of robot postures. Each trajectory represents a cyclical movement of the limbs of an animal. The SOM-STG was designed to possess important features of a central pattern generator, such as rhythmic pattern generation, synchronization between limbs, and swapping between gaits following a single command. The acquisition of data for SOM-STG is based on learning by demonstration in which the data are obtained from different demonstrator agents. The SOM-STG can construct one or more gaits for a simulated robot with six legs, can control the robot with any of the gaits learned, and can smoothly swap gaits. In addition, SOM-STG can learn to construct a state trajectory form observing an animal in locomotion. In this paper, a dog is the demonstrator agent.

Semi-automatic mapping of linear-trending bedforms using 'Self-Organizing Maps' algorithm

NASA Astrophysics Data System (ADS)

Foroutan, M.; Zimbelman, J. R.

2017-09-01

Increased application of high resolution spatial data such as high resolution satellite or Unmanned Aerial Vehicle (UAV) images from Earth, as well as High Resolution Imaging Science Experiment (HiRISE) images from Mars, makes it necessary to increase automation techniques capable of extracting detailed geomorphologic elements from such large data sets. Model validation by repeated images in environmental management studies such as climate-related changes as well as increasing access to high-resolution satellite images underline the demand for detailed automatic image-processing techniques in remote sensing. This study presents a methodology based on an unsupervised Artificial Neural Network (ANN) algorithm, known as Self Organizing Maps (SOM), to achieve the semi-automatic extraction of linear features with small footprints on satellite images. SOM is based on competitive learning and is efficient for handling huge data sets. We applied the SOM algorithm to high resolution satellite images of Earth and Mars (Quickbird, Worldview and HiRISE) in order to facilitate and speed up image analysis along with the improvement of the accuracy of results. About 98% overall accuracy and 0.001 quantization error in the recognition of small linear-trending bedforms demonstrate a promising framework.

Identifying changes in dissolved organic matter content and characteristics by fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis during wastewater treatment.

PubMed

Yu, Huibin; Song, Yonghui; Liu, Ruixia; Pan, Hongwei; Xiang, Liancheng; Qian, Feng

2014-10-01

The stabilization of latent tracers of dissolved organic matter (DOM) of wastewater was analyzed by three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy coupled with self-organizing map and classification and regression tree analysis (CART) in wastewater treatment performance. DOM of water samples collected from primary sedimentation, anaerobic, anoxic, oxic and secondary sedimentation tanks in a large-scale wastewater treatment plant contained four fluorescence components: tryptophan-like (C1), tyrosine-like (C2), microbial humic-like (C3) and fulvic-like (C4) materials extracted by self-organizing map. These components showed good positive linear correlations with dissolved organic carbon of DOM. C1 and C2 were representative components in the wastewater, and they were removed to a higher extent than those of C3 and C4 in the treatment process. C2 was a latent parameter determined by CART to differentiate water samples of oxic and secondary sedimentation tanks from the successive treatment units, indirectly proving that most of tyrosine-like material was degraded by anaerobic microorganisms. C1 was an accurate parameter to comprehensively separate the samples of the five treatment units from each other, indirectly indicating that tryptophan-like material was decomposed by anaerobic and aerobic bacteria. EEM fluorescence spectroscopy in combination with self-organizing map and CART analysis can be a nondestructive effective method for characterizing structural component of DOM fractions and monitoring organic matter removal in wastewater treatment process. Copyright © 2014 Elsevier Ltd. All rights reserved.

The morphological classification of normal and abnormal red blood cell using Self Organizing Map

NASA Astrophysics Data System (ADS)

Rahmat, R. F.; Wulandari, F. S.; Faza, S.; Muchtar, M. A.; Siregar, I.

2018-02-01

Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets.

PubMed

Demartines, P; Herault, J

1997-01-01

We present a new strategy called "curvilinear component analysis" (CCA) for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization (VQ) of the submanifold in the data set (input space); and nonlinear projection (P) of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold. After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space.

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.

Heredity and self-organization: partners in the generation and evolution of phenotypes.

PubMed

Malagon, Nicolas; Larsen, Ellen

2015-01-01

In this review we examine the role of self-organization in the context of the evolution of morphogenesis. We provide examples to show that self-organized behavior is ubiquitous, and suggest it is a mechanism that can permit high levels of biodiversity without the invention of ever-increasing numbers of genes. We also examine the implications of self-organization for understanding the "internal descriptions" of organisms and the concept of a genotype-phenotype map. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Development of the SALdável programme to reduce salt intake among hypertensive Brazilian women: an intervention mapping approach.

PubMed

Cornélio, Marilia Estevam; Godin, Gaston; Rodrigues, Roberta; Agondi, Rúbia; Spana, Thaís; Gallani, Maria-Cecilia

2013-08-01

Despite strong evidence for a relationship between high salt intake and hypertension, plus the widespread recommendations for dietary salt restriction among hypertensive subjects, there are no nursing studies describing effective theory-based interventions. To describe a systematic process for development of a theory-based nursing intervention that is aimed at reducing salt intake among hypertensive women, by applying the 'intervention mapping' protocol. We developed our intervention following the six steps of the 'intervention mapping' protocol: assessing needs, creating a matrix of change objectives, selecting theoretical methods and practical applications, defining the intervention programme, organizing the adoption and implementation plan, and defining the evaluation plan. Addition of salt during cooking is identified as the main source for salt consumption, plus women are identified as the people responsible for cooking meals at home. In our study, the motivational predictors of this behaviour were self-efficacy and habit. Guided practice, verbal persuasion, coping barriers, consciousness-raising and counter-conditioning were the theoretical methods we selected for enhancing self-efficacy and promoting habit change, respectively. Brainstorming, role-playing, cookbook use, measuring spoon use, label reading, hands-on skill-building activities and reinforcement phone calls were the chosen practical applications. We designed our intervention programme, and then organized the adoption and implementation plans. Finally, we generated a plan to evaluate our intervention. 'Intervention mapping' was a feasible methodological framework to guide the development of a theory-based nursing intervention for dietary salt reduction among hypertensive women.

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.

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.

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.

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.

Characterization of suicidal behaviour with self-organizing maps.

PubMed

Leiva-Murillo, José M; López-Castromán, Jorge; Baca-García, Enrique

2013-01-01

The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

PubMed

Oyana, Tonny J; Achenie, Luke E K; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

[Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

PubMed

Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

2013-01-01

QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

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.

The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

PubMed Central

Oyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM. PMID:22481977

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.

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 Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?

PubMed Central

Philips, Ryan T.; Chakravarthy, V. Srinivasa

2017-01-01

A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial stimuli. The radial bias describes the preference of the visual system toward orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation (rc). The rc between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. The results also suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps. PMID:28111542

A Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?

PubMed

Philips, Ryan T; Chakravarthy, V Srinivasa

2016-01-01

A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial stimuli. The radial bias describes the preference of the visual system toward orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation ( r c ). The r c between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. The results also suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps.

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.

Self Organizing Maps for use in Deep Inelastic Scattering

NASA Astrophysics Data System (ADS)

Askanazi, Evan

2015-04-01

Self Organizing Maps are a type of artificial neural network that has been proven to be particularly useful in solving complex problems in neural biology, engineering, robotics and physics. We are attempting to use the Self Organizing Map to solve problems and probe phenomenological patterns in subatomic physics, specifically in Deep Inelastic Scattering (DIS). In DIS there is a cross section in electron hadron scattering that is dependent on the momentum fraction x of the partons in the hadron and the momentum transfer of the virtual photon exchanged. There is a soft cross part of this cross section that currently can only be found through experimentation; this soft part is comprised of Structure Functions which in turn are comprised of the Parton Distribution Functions (PDFs). We aim to use the Self Organizing Process, or SOP, to take theoretical models of these PDFs and fit it to the previous, known data. The SOP will also be used to probe the behavior of the PDFs in particular at large x values, in order to observe how they congregate. The ability of the SOPto take multidimensional data and convert it into two dimensional output is anticipated to be particularly useful in achieving this aim.

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

Self-organizing feature maps for dynamic control of radio resources in CDMA microcellular networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

1998-03-01

The application of artificial neural networks to the channel assignment problem for cellular code-division multiple access (CDMA) cellular networks has previously been investigated. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth-limited. Any reduction in interference in CDMA translates linearly into increased capacity. To satisfy the high demands for new services and improved connectivity for mobile communications, microcellular and picocellular systems are being introduced. For these systems, there is a need to develop robust and efficient management procedures for the allocation of power and spectrum to maximize radio capacity. Topology-conserving mappings play an important role in the biological processing of sensory inputs. The same principles underlying Kohonen's self-organizing feature maps (SOFMs) are applied to the adaptive control of radio resources to minimize interference, hence, maximize capacity in direct-sequence (DS) CDMA networks. The approach based on SOFMs is applied to some published examples of both theoretical and empirical models of DS/CDMA microcellular networks in metropolitan areas. The results of the approach for these examples are informally compared to the performance of algorithms, based on Hopfield- Tank neural networks and on genetic algorithms, for the channel assignment problem.

Communications and control for electric power systems: Power flow classification for static security assessment

NASA Technical Reports Server (NTRS)

Niebur, D.; Germond, A.

1993-01-01

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

A ground truth based comparative study on clustering of gene expression data.

PubMed

Zhu, Yitan; Wang, Zuyi; Miller, David J; Clarke, Robert; Xuan, Jianhua; Hoffman, Eric P; Wang, Yue

2008-05-01

Given the variety of available clustering methods for gene expression data analysis, it is important to develop an appropriate and rigorous validation scheme to assess the performance and limitations of the most widely used clustering algorithms. In this paper, we present a ground truth based comparative study on the functionality, accuracy, and stability of five data clustering methods, namely hierarchical clustering, K-means clustering, self-organizing maps, standard finite normal mixture fitting, and a caBIG toolkit (VIsual Statistical Data Analyzer--VISDA), tested on sample clustering of seven published microarray gene expression datasets and one synthetic dataset. We examined the performance of these algorithms in both data-sufficient and data-insufficient cases using quantitative performance measures, including cluster number detection accuracy and mean and standard deviation of partition accuracy. The experimental results showed that VISDA, an interactive coarse-to-fine maximum likelihood fitting algorithm, is a solid performer on most of the datasets, while K-means clustering and self-organizing maps optimized by the mean squared compactness criterion generally produce more stable solutions than the other methods.

Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders

NASA Astrophysics Data System (ADS)

Abdi, Abdi M.; Szu, Harold H.

2003-04-01

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

PubMed

Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

2016-08-03

Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization.

PubMed

Ferles, Christos; Beaufort, William-Scott; Ferle, Vanessa

2017-01-01

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge.

Visualizing human communication in business process simulations

NASA Astrophysics Data System (ADS)

Groehn, Matti; Jalkanen, Janne; Haho, Paeivi; Nieminen, Marko; Smeds, Riitta

1999-03-01

In this paper a description of business process simulation is given. Crucial part in the simulation of business processes is the analysis of social contacts between the participants. We will introduce a tool to collect log data and how this log data can be effectively analyzed using two different kind of methods: discussion flow charts and self-organizing maps. Discussion flow charts revealed the communication patterns and self-organizing maps are a very effective way of clustering the participants into development groups.

Cytoskeletal self-organization in neuromorphogenesis.

PubMed

Dehmelt, Leif

2014-01-01

Self-organization of dynamic microtubules via interactions with associated motors plays a critical role in spindle formation. The microtubule-based mechanisms underlying other aspects of cellular morphogenesis, such as the formation and development of protrusions from neuronal cells is less well understood. In a recent study, we investigated the molecular mechanism that underlies the massive reorganization of microtubules induced in non-neuronal cells by expression of the neuronal microtubule stabilizer MAP2c. In that study we directly observed cortical dynein complexes and how they affect the dynamic behavior of motile microtubules in living cells. We found that stationary dynein complexes transiently associate with motile microtubules near the cell cortex and that their rapid turnover facilitates efficient microtubule transport. Here, we discuss our findings in the larger context of cellular morphogenesis with specific focus on self-organizing principles from which cellular shape patterns such as the thin protrusions of neurons can emerge.

Cytoskeletal self-organization in neuromorphogenesis

PubMed Central

Dehmelt, Leif

2014-01-01

Self-organization of dynamic microtubules via interactions with associated motors plays a critical role in spindle formation. The microtubule-based mechanisms underlying other aspects of cellular morphogenesis, such as the formation and development of protrusions from neuronal cells is less well understood. In a recent study, we investigated the molecular mechanism that underlies the massive reorganization of microtubules induced in non-neuronal cells by expression of the neuronal microtubule stabilizer MAP2c. In that study we directly observed cortical dynein complexes and how they affect the dynamic behavior of motile microtubules in living cells. We found that stationary dynein complexes transiently associate with motile microtubules near the cell cortex and that their rapid turnover facilitates efficient microtubule transport. Here, we discuss our findings in the larger context of cellular morphogenesis with specific focus on self-organizing principles from which cellular shape patterns such as the thin protrusions of neurons can emerge. PMID:24847718

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.

Neural network-based multiple robot simultaneous localization and mapping.

PubMed

Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

2011-12-01

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

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.

Use of machine learning methods to classify Universities based on the income structure

NASA Astrophysics Data System (ADS)

Terlyga, Alexandra; Balk, Igor

2017-10-01

In this paper we discuss use of machine learning methods such as self organizing maps, k-means and Ward’s clustering to perform classification of universities based on their income. This classification will allow us to quantitate classification of universities as teaching, research, entrepreneur, etc. which is important tool for government, corporations and general public alike in setting expectation and selecting universities to achieve different goals.

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.

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.

Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

PubMed

Piastra, Marco

2013-05-01

Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

Organic–Inorganic Eu3+/Tb3+ codoped hybrid films for temperature mapping in integrated circuits

PubMed Central

Brites, Carlos D. S.; Lima, Patrícia P.; Silva, Nuno J. O.; Millán, Angel; Amaral, Vitor S.; Palacio, Fernando; Carlos, Luís D.

2013-01-01

The continuous decrease on the geometric size of electronic devices and integrated circuits generates higher local power densities and localized heating problems that cannot be characterized by conventional thermographic techniques. Here, a self-referencing intensity-based molecular thermometer involving a di-ureasil organic-inorganic hybrid thin film co-doped with Eu3+ and Tb3+ tris (β-diketonate) chelates is used to obtain the temperature map of a FR4 printed wiring board with spatio-temporal resolutions of 0.42 μm/4.8 ms. PMID:24790938

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.

Innovation and application of ANN in Europe demonstrated by Kohonen maps

NASA Technical Reports Server (NTRS)

Goser, Karl

1994-01-01

One of the most important contributions to neural networks comes from Kohonen, Helsinki/Espoo, Finland, who had the idea of self-organizating maps in 1981. He verified his idea by an algorithm of which many applications make use of. The impetus for this idea came from biology, a field where the Europeans have always been very active at several research laboratories. The challenge was to model the self-organization found in the brain. Today one goal is the development of more sophisticated neurons which model the biological neurons more exactly. They should come to a better performance of neural nets with only a few complex neurons instead of many simple ones. A lot of application concepts arise from this idea: Kohonen himself applied it to speech recognition, but the project did not overcome much more than the recognition of the numerals one to ten at that time. A more promising application for self-organizing maps is process control and process monitoring. Several proposals were made which concern parameter classification of semiconductor technologies, design of integrated circuits, and control of chemical processes. Self-organizing maps were applied to robotics. The neural concept was introduced into electric power systems. At Dortmund we are working on a system which has to monitor the quality and the reliability of gears and electrical motors in equipment installed in coal mines. The results are promising and the probability to apply the system in the field is very high. A special feature of the system is that linguistic rules which are embedded in a fuzzy controller analyze the data of the self-organizing map in regard to life expectation of the gears. It seems that the fuzzy technique will introduce the technology of neural networks in a tandem mode. These technologies together with the genetic algorithms start to form the attractive field of computational intelligence.

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…

A neural-network-based approach to the double traveling salesman problem.

PubMed

Plebe, Alessio; Anile, Angelo Marcello

2002-02-01

The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This application poses further constraints, like a collision-avoidance function. The proposed solution is based on a self-organizing map structure, initialized with as many artificial neurons as the number of targets to be reached. One of the key components of the process is the combination of competitive relaxation with a mechanism for deleting and creating artificial neurons. Moreover, in the competitive relaxation process, information about the trajectory connecting the neurons is combined with the distance of neurons from the target. This strategy prevents tangles in the trajectory and collisions between the two tours. Results of tests indicate that the proposed approach is efficient and reliable for harvest sequence planning. Moreover, the enhancements added to the pure self-organizing map concept are of wider importance, as proved by a traveling salesman problem version of the program, simplified from the double version for comparison.

Cube Kohonen self-organizing map (CKSOM) model with new equations in organizing unstructured data.

PubMed

Lim, Seng Poh; Haron, Habibollah

2013-09-01

Surface reconstruction by using 3-D data is used to represent the surface of an object and perform important tasks. The type of data used is important and can be described as either structured or unstructured. For unstructured data, there is no connectivity information between data points. As a result, incorrect shapes will be obtained during the imaging process. Therefore, the data should be reorganized by finding the correct topology so that the correct shape can be obtained. Previous studies have shown that the Kohonen self-organizing map (KSOM) could be used to solve data organizing problems. However, 2-D Kohonen maps are limited because they are unable to cover the whole surface of closed 3-D surface data. Furthermore, the neurons inside the 3-D KSOM structure should be removed in order to create a correct wireframe model. This is because only the outside neurons are used to represent the surface of an object. The aim of this paper is to use KSOM to organize unstructured data for closed surfaces. KSOM isused in this paper by testing its ability to organize medical image data because KSOM is mostly used in constructing engineering field data. Enhancements are added to the model by introducing class number and the index vector, and new equations are created. Various grid sizes and maximum iterations are tested in the experiments. Based on the results, the number of redundancies is found to be directly proportional to the grid size. When we increase the maximum iterations, the surface of the image becomes smoother. An area formula is used and manual calculations are performed to validate the results. This model is implemented and images are created using Dev C++ and GNUPlot.

Development of a chronic disease management program for stroke survivors using intervention mapping: The Stroke Coach

PubMed Central

Sakakibara, Brodie M.; Lear, Scott A.; Barr, Susan I.; Benavente, Oscar; Goldsmith, Charlie H.; Silverberg, Noah D.; Yao, Jennifer; Eng, Janice J.

2018-01-01

Objective To describe the systematic development of the Stroke Coach, a theory- and evidence-based intervention to improve control of lifestyle behaviour risk factors in stroke patients. Design Intervention development. Setting Community. Participants Individuals who have had a stroke. Intervention We used Intervention Mapping to guide the development of the Stroke Coach. Intervention Mapping is a systematic process used for intervention development and comprised of steps that progress from the integration of theory and evidence to the organization of realistic strategies to facilitate the development of a practical intervention supported by empirical evidence. Social Cognitive Theory was the underlying premise for behaviour change, while Control Theory methods were directed towards sustaining the changes to ensure long-term health benefits. Practical evidence-based strategies were linked to behavioural determinants to improve stroke risk factor control. Main outcome measures Not applicable. Results The Stroke Coach is a patient-centred, community-based, telehealth intervention to promote healthy lifestyles after stroke. Over six months, participants receive seven 30 to 60 minute telephone sessions with a lifestyle coach who provides education, facilitates motivation for lifestyle modification, and empowers participants to self-management their stroke risk factors. Participants also receive a self-management manual and a self-monitoring kit. Conclusion Through the use of Intervention Mapping we developed a theoretically sound and evidence-grounded intervention to improve risk factor control in stroke patients. If empirical evaluation of the Stroke Coach produces positive results, the next step will be to develop an implementation intervention to ensure successful uptake and delivery of the program in community and outpatient settings. PMID:28219685

Implementation of Fractal Dimension and Self-Organizing Map to Detect Toxic Effects of Toluene on Movement Tracks of Daphnia magna.

PubMed

Liu, Yuedan; Xia, Chunlei; Fan, Zhongya; Wu, Renren; Chen, Xianglin; Liu, Zuoyi

2018-01-01

Movement behaviors of an indicator species, Daphnia magna , in response to contaminants have been implemented to monitor environmental disturbances. Complexity in movement tracks of Daphnia magna was characterized by use of fractal dimension and self-organizing map. The individual movement tracks of D. magna were continuously recorded for 24 hours before and after treatments with toluene at the concentration of 10 mg/L, respectively. The general complexity in movement tracks (10 minutes) was characterized by fractal dimension. Results showed that average fractal dimension of movement tracks was decreased from 1.62 to 1.22 after treatments. The instantaneous movement parameters of movement segments in 5 s were input into the self-organizing map to investigate the swimming pattern changes under stresses of toluene. Abnormal behaviors of D. magna are more frequently observed after treatments than before treatments. Computational methods in ecological informatics could be utilized to obtain the useful information in behavioral data of D. magna and would be further applied as an in situ monitoring tool in water environment.

Stacked Multilayer Self-Organizing Map for Background Modeling.

PubMed

Zhao, Zhenjie; Zhang, Xuebo; Fang, Yongchun

2015-09-01

In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling.

PubMed

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic-semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory-semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

Asymmetric neighborhood functions accelerate ordering process of self-organizing maps

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

Ota, Kaiichiro; Aoki, Takaaki; Kurata, Koji

2011-02-15

A self-organizing map (SOM) algorithm can generate a topographic map from a high-dimensional stimulus space to a low-dimensional array of units. Because a topographic map preserves neighborhood relationships between the stimuli, the SOM can be applied to certain types of information processing such as data visualization. During the learning process, however, topological defects frequently emerge in the map. The presence of defects tends to drastically slow down the formation of a globally ordered topographic map. To remove such topological defects, it has been reported that an asymmetric neighborhood function is effective, but only in the simple case of mapping one-dimensionalmore » stimuli to a chain of units. In this paper, we demonstrate that even when high-dimensional stimuli are used, the asymmetric neighborhood function is effective for both artificial and real-world data. Our results suggest that applying the asymmetric neighborhood function to the SOM algorithm improves the reliability of the algorithm. In addition, it enables processing of complicated, high-dimensional data by using this algorithm.« less

Ge Detector Data Classification with Neural Networks

NASA Astrophysics Data System (ADS)

Wilson, Carly; Martin, Ryan; Majorana Collaboration

2014-09-01

The Majorana Demonstrator experiment is searching for neutrinoless double beta-decay using p-type point contact PPC germanium detectors at the Sanford Underground Research Facility, in South Dakota. Pulse shape discrimination can be used in PPC detectors to distinguish signal-like events from backgrounds. This research program explored the possibility of building a self-organizing map that takes data collected from germanium detectors and classifies the events as either signal or background. Self organizing maps are a type of neural network that are self-learning and less susceptible to being biased from imperfect training data. We acknowledge support from the Office of Nuclear Physics in the DOE Office of Science, the Particle and Nuclear Astrophysics Program of the National Science Foundation and the Russian Foundation for Basic Research.

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.

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.

Solving graph data issues using a layered architecture approach with applications to web spam detection.

PubMed

Scarselli, Franco; Tsoi, Ah Chung; Hagenbuchner, Markus; Noi, Lucia Di

2013-12-01

This paper proposes the combination of two state-of-the-art algorithms for processing graph input data, viz., the probabilistic mapping graph self organizing map, an unsupervised learning approach, and the graph neural network, a supervised learning approach. We organize these two algorithms in a cascade architecture containing a probabilistic mapping graph self organizing map, and a graph neural network. We show that this combined approach helps us to limit the long-term dependency problem that exists when training the graph neural network resulting in an overall improvement in performance. This is demonstrated in an application to a benchmark problem requiring the detection of spam in a relatively large set of web sites. It is found that the proposed method produces results which reach the state of the art when compared with some of the best results obtained by others using quite different approaches. A particular strength of our method is its applicability towards any input domain which can be represented as a graph. Copyright © 2013 Elsevier Ltd. All rights reserved.

Downscaled rainfall projections in south Florida using self-organizing maps.

PubMed

Sinha, Palash; Mann, Michael E; Fuentes, Jose D; Mejia, Alfonso; Ning, Liang; Sun, Weiyi; He, Tao; Obeysekera, Jayantha

2018-04-20

We make future projections of seasonal precipitation characteristics in southern Florida using a statistical downscaling approach based on Self Organized Maps. Our approach is applied separately to each three-month season: September-November; December-February; March-May; and June-August. We make use of 19 different simulations from the Coupled Model Inter-comparison Project, phase 5 (CMIP5) and generate an ensemble of 1500 independent daily precipitation surrogates for each model simulation, yielding a grand ensemble of 28,500 total realizations for each season. The center and moments (25%ile and 75%ile) of this distribution are used to characterize most likely scenarios and their associated uncertainties. This approach is applied to 30-year windows of daily mean precipitation for both the CMIP5 historical simulations (1976-2005) and the CMIP5 future (RCP 4.5) projections. For the latter case, we examine both the "near future" (2021-2050) and "far future" (2071-2100) periods for three scenarios (RCP2.6, RCP4.5, and RCP8.5). Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Deep SOMs for automated feature extraction and classification from big data streaming

NASA Astrophysics Data System (ADS)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

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.

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.

Generalized Self-Organizing Maps for Automatic Determination of the Number of Clusters and Their Multiprototypes in Cluster Analysis.

PubMed

Gorzalczany, Marian B; Rudzinski, Filip

2017-06-07

This paper presents a generalization of self-organizing maps with 1-D neighborhoods (neuron chains) that can be effectively applied to complex cluster analysis problems. The essence of the generalization consists in introducing mechanisms that allow the neuron chain--during learning--to disconnect into subchains, to reconnect some of the subchains again, and to dynamically regulate the overall number of neurons in the system. These features enable the network--working in a fully unsupervised way (i.e., using unlabeled data without a predefined number of clusters)--to automatically generate collections of multiprototypes that are able to represent a broad range of clusters in data sets. First, the operation of the proposed approach is illustrated on some synthetic data sets. Then, this technique is tested using several real-life, complex, and multidimensional benchmark data sets available from the University of California at Irvine (UCI) Machine Learning repository and the Knowledge Extraction based on Evolutionary Learning data set repository. A sensitivity analysis of our approach to changes in control parameters and a comparative analysis with an alternative approach are also performed.

A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data.

PubMed

Ray, Shubhra Sankar; Ganivada, Avatharam; Pal, Sankar K

2016-09-01

A new granular self-organizing map (GSOM) is developed by integrating the concept of a fuzzy rough set with the SOM. While training the GSOM, the weights of a winning neuron and the neighborhood neurons are updated through a modified learning procedure. The neighborhood is newly defined using the fuzzy rough sets. The clusters (granules) evolved by the GSOM are presented to a decision table as its decision classes. Based on the decision table, a method of gene selection is developed. The effectiveness of the GSOM is shown in both clustering samples and developing an unsupervised fuzzy rough feature selection (UFRFS) method for gene selection in microarray data. While the superior results of the GSOM, as compared with the related clustering methods, are provided in terms of β -index, DB-index, Dunn-index, and fuzzy rough entropy, the genes selected by the UFRFS are not only better in terms of classification accuracy and a feature evaluation index, but also statistically more significant than the related unsupervised methods. The C-codes of the GSOM and UFRFS are available online at http://avatharamg.webs.com/software-code.

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.

Development of a Chronic Disease Management Program for Stroke Survivors Using Intervention Mapping: The Stroke Coach.

PubMed

Sakakibara, Brodie M; Lear, Scott A; Barr, Susan I; Benavente, Oscar; Goldsmith, Charlie H; Silverberg, Noah D; Yao, Jennifer; Eng, Janice J

2017-06-01

To describe the systematic development of the Stroke Coach, a theory- and evidence-based intervention to improve control of lifestyle behavior risk factors in patients with stroke. Intervention development. Community. Individuals who have had a stroke. We used intervention mapping to guide the development of the Stroke Coach. Intervention mapping is a systematic process used for intervention development and composed of steps that progress from the integration of theory and evidence to the organization of realistic strategies to facilitate the development of a practical intervention supported by empirical evidence. Social cognitive theory was the underlying premise for behavior change, whereas control theory methods were directed toward sustaining the changes to ensure long-term health benefits. Practical evidence-based strategies were linked to behavioral determinants to improve stroke risk factor control. Not applicable. The Stroke Coach is a patient-centered, community-based, telehealth intervention to promote healthy lifestyles after stroke. Over 6 months, participants receive seven 30- to 60-minute telephone sessions with a lifestyle coach who provides education, facilitates motivation for lifestyle modification, and empowers participants to self-management their stroke risk factors. Participants also receive a self-management manual and a self-monitoring kit. Through the use of intervention mapping, we developed a theoretically sound and evidence-grounded intervention to improve risk factor control in patients with stroke. If empirical evaluation of the Stroke Coach produces positive results, the next step will be to develop an implementation intervention to ensure successful uptake and delivery of the program in community and outpatient settings. Copyright © 2017 American Congress of Rehabilitation Medicine. All rights reserved.

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.

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.

Neuronal networks and self-organizing maps: new computer techniques in the acoustic evaluation of the infant cry.

PubMed

Schönweiler, R; Kaese, S; Möller, S; Rinscheid, A; Ptok, M

1996-12-05

Neuronal networks are computer-based techniques for the evaluation and control of complex information systems and processes. So far, they have been used in engineering, telecommunications, artificial speech and speech recognition. A new approach in neuronal network is the self-organizing map (Kohonen map). In the phase of 'learning', the map adapts to the patterns of the primary signals. If, the phase of 'using the map', the input signal hits the field of the primary signals, it resembles them and is called a 'winner'. In our study, we recorded the cries of newborns and young infants using digital audio tape (DAT) and a high quality microphone. The cries were elicited by tactile stimuli wearing headphones. In 27 cases, delayed auditory feedback was presented to the children using a headphone and an additional three-head tape-recorder. Spectrographic characteristics of the cries were classified by 20-step bark spectra and then applied to the neuronal networks. It was possible to recognize similarities of different cries of the same children as well as interindividual differences, which are also audible to experienced listeners. Differences were obvious in profound hearing loss. We know much about the cries of both healthy and sick infants, but a reliable investigation regimen, which can be used for clinical routine purposes, has yet not been developed. If, in the future, it becomes possible to classify spectrographic characteristics automatically, even if they are not audible, neuronal networks may be helpful in the early diagnosis of infant diseases.

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.

Macromolecular target prediction by self-organizing feature maps.

PubMed

Schneider, Gisbert; Schneider, Petra

2017-03-01

Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

PubMed Central

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J.

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic–semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory–semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model. PMID:24688478

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.

Real-time detection of transients in OGLE-IV with application of machine learning

NASA Astrophysics Data System (ADS)

Klencki, Jakub; Wyrzykowski, Łukasz

2016-06-01

The current bottleneck of transient detection in most surveys is the problem of rejecting numerous artifacts from detected candidates. We present a triple-stage hierarchical machine learning system for automated artifact filtering in difference imaging, based on self-organizing maps. The classifier, when tested on the OGLE-IV Transient Detection System, accepts 97% of real transients while removing up to 97.5% of artifacts.

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

Feature-based alert correlation in security systems using self organizing maps

NASA Astrophysics Data System (ADS)

Kumar, Munesh; Siddique, Shoaib; Noor, Humera

2009-04-01

The security of the networks has been an important concern for any organization. This is especially important for the defense sector as to get unauthorized access to the sensitive information of an organization has been the prime desire for cyber criminals. Many network security techniques like Firewall, VPN Concentrator etc. are deployed at the perimeter of network to deal with attack(s) that occur(s) from exterior of network. But any vulnerability that causes to penetrate the network's perimeter of defense, can exploit the entire network. To deal with such vulnerabilities a system has been evolved with the purpose of generating an alert for any malicious activity triggered against the network and its resources, termed as Intrusion Detection System (IDS). The traditional IDS have still some deficiencies like generating large number of alerts, containing both true and false one etc. By automatically classifying (correlating) various alerts, the high-level analysis of the security status of network can be identified and the job of network security administrator becomes much easier. In this paper we propose to utilize Self Organizing Maps (SOM); an Artificial Neural Network for correlating large amount of logged intrusion alerts based on generic features such as Source/Destination IP Addresses, Port No, Signature ID etc. The different ways in which alerts can be correlated by Artificial Intelligence techniques are also discussed. . We've shown that the strategy described in the paper improves the efficiency of IDS by better correlating the alerts, leading to reduced false positives and increased competence of network administrator.

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%.

Example-based learning: comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge.

PubMed

Dyer, Joseph-Omer; Hudon, Anne; Montpetit-Tourangeau, Katherine; Charlin, Bernard; Mamede, Sílvia; van Gog, Tamara

2015-03-07

Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of problem-solving skills more than studying worked examples alone. Completion examples are worked examples in which some of the solution steps remain unsolved for learners to complete. Providing learners engaged in example-based learning with self-explanation prompts has been shown to foster increased meaningful learning compared to providing no self-explanation prompts. Concept mapping and concept map study are other instructional activities known to promote meaningful learning. This study compares the effects of self-explaining, completing a concept map and studying a concept map on conceptual knowledge and problem-solving skills among novice learners engaged in example-based learning. Ninety-one physiotherapy students were randomized into three conditions. They performed a pre-test and a post-test to evaluate their gains in conceptual knowledge and problem-solving skills (transfer performance) in intervention selection. They studied three pairs of worked/completion examples in a digital learning environment. Worked examples consisted of a written reasoning process for selecting an optimal physiotherapy intervention for a patient. The completion examples were partially worked out, with the last few problem-solving steps left blank for students to complete. The students then had to engage in additional self-explanation, concept map completion or model concept map study in order to synthesize and deepen their knowledge of the key concepts and problem-solving steps. Pre-test performance did not differ among conditions. Post-test conceptual knowledge was higher (P < .001) in the concept map study condition (68.8 ± 21.8%) compared to the concept map completion (52.8 ± 17.0%) and self-explanation (52.2 ± 21.7%) conditions. Post-test problem-solving performance was higher (P < .05) in the self-explanation (63.2 ± 16.0%) condition compared to the concept map study (53.3 ± 16.4%) and concept map completion (51.0 ± 13.6%) conditions. Students in the self-explanation condition also invested less mental effort in the post-test. Studying model concept maps led to greater conceptual knowledge, whereas self-explanation led to higher transfer performance. Self-explanation and concept map study can be combined with worked example and completion example strategies to foster intervention selection.

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.

Redox potential distribution of an organic-rich contaminated site obtained by the inversion of self-potential data

NASA Astrophysics Data System (ADS)

Abbas, M.; Jardani, A.; Soueid Ahmed, A.; Revil, A.; Brigaud, L.; Bégassat, Ph.; Dupont, J. P.

2017-11-01

Mapping the redox potential of shallow aquifers impacted by hydrocarbon contaminant plumes is important for the characterization and remediation of such contaminated sites. The redox potential of groundwater is indicative of the biodegradation of hydrocarbons and is important in delineating the shapes of contaminant plumes. The self-potential method was used to reconstruct the redox potential of groundwater associated with an organic-rich contaminant plume in northern France. The self-potential technique is a passive technique consisting in recording the electrical potential distribution at the surface of the Earth. A self-potential map is essentially the sum of two contributions, one associated with groundwater flow referred to as the electrokinetic component, and one associated with redox potential anomalies referred to as the electroredox component (thermoelectric and diffusion potentials are generally negligible). A groundwater flow model was first used to remove the electrokinetic component from the observed self-potential data. Then, a residual self-potential map was obtained. The source current density generating the residual self-potential signals is assumed to be associated with the position of the water table, an interface characterized by a change in both the electrical conductivity and the redox potential. The source current density was obtained through an inverse problem by minimizing a cost function including a data misfit contribution and a regularizer. This inversion algorithm allows the determination of the vertical and horizontal components of the source current density taking into account the electrical conductivity distribution of the saturated and non-saturated zones obtained independently by electrical resistivity tomography. The redox potential distribution was finally determined from the inverted residual source current density. A redox map was successfully built and the estimated redox potential values correlated well with in-situ measurements.

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.

Geography of community health information organization activity in the United States: Implications for the effectiveness of health information exchange.

PubMed

Vest, Joshua R

The United States has invested nearly a billion dollars in creating community health information organizations (HIOs) to foster health information exchange. Community HIOs provide exchange services to health care organizations within a distinct geographic area. While geography is a key organizing principle for community HIOs, it is unclear if geography is an effective method for organization or what challenges are created by a geography-based approach to health information exchange. This study describes the extent of reported community HIO coverage in the United States and explores the practical and policy implications of overlaps and gaps in HIO service areas. Furthermore, because self-reported service areas may not accurately reflect the true extent of HIOs activities, this study maps the actual markets for health services included in each HIO. An inventory of operational community HIOs that included self-reported geographic markets and participating organizations was face-validated using a crowd-sourcing approach. Aggregation of the participating hospitals' individual health care markets provided the total geographic market served by each community HIO. Mapping and overlay analyses using geographic information system methods described the extent of community HIO activity in the United States. Evidence suggests that community HIOs may be inefficiently distributed. Parts of the United States have multiple, overlapping HIOs, while others do not have any providing health information exchange services. In markets served by multiple community HIOs, 45% of hospitals were participants of only one HIO. The current geography of community HIO activity does not provide comprehensive patient information to providers, nor community-wide information for public health agencies. The discord between the self-reported and market geography of community HIOs raises concerns about the potential effectiveness of health information exchange, illustrates the limitations of geography as an organizing principle, and indicates operational challenges facing those leading and working with community HIOs.

What Do Medical Students Do for Self-Care? A Student-Centered Approach to Well-Being.

PubMed

Ayala, Erin E; Omorodion, Aisha M; Nmecha, Dennis; Winseman, Jeffrey S; Mason, Hyacinth R C

2017-01-01

Phenomenon: Despite the promotion of medical student health and wellness through recent program and curricular changes, research continues to show that medical education is associated with decreased well-being in medical students. Although many institutions have sought to more effectively assess and improve self-care in medical students, no self-care initiatives have been designed using the explicit perspectives of students themselves. Using concept mapping methodology, the research team created a student-generated taxonomy of self-care behaviors taken from a national sample of medical students in response to a brainstorming prompt. The research team examined how students' conceptualizations of self-care may be organized into a framework suitable for use in programming and curricular change in medical education. Ten clusters of self-care activities were identified: nourishment, hygiene, intellectual and creative health, physical activity, spiritual care, balance and relaxation, time for loved ones, big picture goals, pleasure and outside activities, and hobbies. Using results of the two-dimensional scaling analysis, students' individual self-care behaviors were organized within two orthogonal dimensions of self-care activities. Insights: This concept map of student-identified self-care activities provides a starting point for better understanding and ultimately improving medical student self-care. Students' brainstormed responses fit within a framework of varying levels of social engagement and physical-psychological health that included a wide range of solitary, social, physical, and mental health behaviors. As students' preferred self-care practices did not often include programmatic activities, medical educators may benefit from consulting this map as they plan new approaches to student self-care and in counseling individual students searching for more effective ways to ease the burdens of medical school.

A Neurocomputational Account of Taxonomic Responding and Fast Mapping in Early Word Learning

ERIC Educational Resources Information Center

Mayor, Julien; Plunkett, Kim

2010-01-01

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…

Self-Organizing-Map Program for Analyzing Multivariate Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Jacob, Joseph C.; Block, Gary L.; Braverman, Amy J.

2005-01-01

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

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

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.

Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China.

PubMed

Dai, Lijun; Wang, Lingqing; Li, Lianfang; Liang, Tao; Zhang, Yongyong; Ma, Chuanxin; Xing, Baoshan

2018-04-15

Heavy metals in lake sediment have become a great concern because their remobilization has frequently occurred under hydrodynamic disturbance in shallow lakes. In this study, heavy metals (Cr, Cu, Cd, Pb, and Zn) concentrations in the surface and core sediments of the largest freshwater lake in China, Poyang Lake, were investigated. Geostatistical prediction maps of heavy metals distribution in the surface sediment were completed as well as further data mining. Based on the prediction maps, the ranges of Cr, Cu, Cd, Pb, and Zn concentrations in the surface sediments of the entire lake were 96.2-175.2, 38.3-127.6, 0.2-2.3, 22.5-77.4, and 72.3-254.4mg/kg, respectively. A self-organizing map (SOM) was applied to find the inner element relation of heavy metals in the sediment cores. K-means clustering of the self-organizing map was also completed to define the Euclidian distance of heavy metals in the sediment cores. The geoaccumulation index (I geo ) for Poyang Lake indicated a varying degree of heavy metal contamination in the surface sediment, especially for Cu. The heavy metal contamination in the sediment profiles had similar pollution levels as those of surface sediment, except for Cd. Correlation matrix mapping and principal component analysis (PCA) were used to support the idea that Cr, Pb, and Zn may be mainly derived from both lithogenic and human activities, such as atmospheric and river inflow transportation, whereas Cu and Cd may be mainly contributed from anthropogenic sources, such as mining activities and fertilizer application. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Data-driven automated acoustic analysis of human infant vocalizations using neural network tools.

PubMed

Warlaumont, Anne S; Oller, D Kimbrough; Buder, Eugene H; Dale, Rick; Kozma, Robert

2010-04-01

Acoustic analysis of infant vocalizations has typically employed traditional acoustic measures drawn from adult speech acoustics, such as f(0), duration, formant frequencies, amplitude, and pitch perturbation. Here an alternative and complementary method is proposed in which data-derived spectrographic features are central. 1-s-long spectrograms of vocalizations produced by six infants recorded longitudinally between ages 3 and 11 months are analyzed using a neural network consisting of a self-organizing map and a single-layer perceptron. The self-organizing map acquires a set of holistic, data-derived spectrographic receptive fields. The single-layer perceptron receives self-organizing map activations as input and is trained to classify utterances into prelinguistic phonatory categories (squeal, vocant, or growl), identify the ages at which they were produced, and identify the individuals who produced them. Classification performance was significantly better than chance for all three classification tasks. Performance is compared to another popular architecture, the fully supervised multilayer perceptron. In addition, the network's weights and patterns of activation are explored from several angles, for example, through traditional acoustic measurements of the network's receptive fields. Results support the use of this and related tools for deriving holistic acoustic features directly from infant vocalization data and for the automatic classification of infant vocalizations.

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-organization and clustering algorithms

NASA Technical Reports Server (NTRS)

Bezdek, James C.

1991-01-01

Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed.

Self-organized criticality in single-neuron excitability

NASA Astrophysics Data System (ADS)

Gal, Asaf; Marom, Shimon

2013-12-01

We present experimental and theoretical arguments, at the single-neuron level, suggesting that neuronal response fluctuations reflect a process that positions the neuron near a transition point that separates excitable and unexcitable phases. This view is supported by the dynamical properties of the system as observed in experiments on isolated cultured cortical neurons, as well as by a theoretical mapping between the constructs of self-organized criticality and membrane excitability biophysics.

Modularity and the spread of perturbations in complex dynamical systems

NASA Astrophysics Data System (ADS)

Kolchinsky, Artemy; Gates, Alexander J.; Rocha, Luis M.

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

Modularity and the spread of perturbations in complex dynamical systems.

PubMed

Kolchinsky, Artemy; Gates, Alexander J; Rocha, Luis M

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

Unsupervised learning on scientific ocean drilling datasets from the South China Sea

NASA Astrophysics Data System (ADS)

Tse, Kevin C.; Chiu, Hon-Chim; Tsang, Man-Yin; Li, Yiliang; Lam, Edmund Y.

2018-06-01

Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar datasets, but using supervised learning methods which are designed to make predictions based on sample training data, unsupervised learning methods require no a priori information and focus only on the input data. In this study, popular unsupervised learning methods including K-means, self-organizing maps, hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters. The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales. K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales. This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Adaptive Classification of Landscape Process and Function: An Integration of Geoinformatics and Self-Organizing Maps

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

Coleman, Andre M.

2009-07-17

The advanced geospatial information extraction and analysis capabilities of a Geographic Information System (GISs) and Artificial Neural Networks (ANNs), particularly Self-Organizing Maps (SOMs), provide a topology-preserving means for reducing and understanding complex data relationships in the landscape. The Adaptive Landscape Classification Procedure (ALCP) is presented as an adaptive and evolutionary capability where varying types of data can be assimilated to address different management needs such as hydrologic response, erosion potential, habitat structure, instrumentation placement, and various forecast or what-if scenarios. This paper defines how the evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight intomore » complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Establishing relationships among high-dimensional datasets through neurocomputing based pattern recognition methods can help 1) resolve large volumes of data into a structured and meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape. Classification of hydrologic patterns in the landscape is demonstrated.« less

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.

Integrating sentiment analysis and term associations with geo-temporal visualizations on customer feedback streams

NASA Astrophysics Data System (ADS)

Hao, Ming; Rohrdantz, Christian; Janetzko, Halldór; Keim, Daniel; Dayal, Umeshwar; Haug, Lars-Erik; Hsu, Mei-Chun

2012-01-01

Twitter currently receives over 190 million tweets (small text-based Web posts) and manufacturing companies receive over 10 thousand web product surveys a day, in which people share their thoughts regarding a wide range of products and their features. A large number of tweets and customer surveys include opinions about products and services. However, with Twitter being a relatively new phenomenon, these tweets are underutilized as a source for determining customer sentiments. To explore high-volume customer feedback streams, we integrate three time series-based visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel idea of term associations that identify attributes, verbs, and adjectives frequently occurring together; and (3) new pixel cell-based sentiment calendars, geo-temporal map visualizations and self-organizing maps to identify co-occurring and influential opinions. We have combined these techniques into a well-fitted solution for an effective analysis of large customer feedback streams such as for movie reviews (e.g., Kung-Fu Panda) or web surveys (buyers).

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%.

Applying CBR to machine tool product configuration design oriented to customer requirements

NASA Astrophysics Data System (ADS)

Wang, Pengjia; Gong, Yadong; Xie, Hualong; Liu, Yongxian; Nee, Andrew Yehching

2017-01-01

Product customization is a trend in the current market-oriented manufacturing environment. However, deduction from customer requirements to design results and evaluation of design alternatives are still heavily reliant on the designer's experience and knowledge. To solve the problem of fuzziness and uncertainty of customer requirements in product configuration, an analysis method based on the grey rough model is presented. The customer requirements can be converted into technical characteristics effectively. In addition, an optimization decision model for product planning is established to help the enterprises select the key technical characteristics under the constraints of cost and time to serve the customer to maximal satisfaction. A new case retrieval approach that combines the self-organizing map and fuzzy similarity priority ratio method is proposed in case-based design. The self-organizing map can reduce the retrieval range and increase the retrieval efficiency, and the fuzzy similarity priority ratio method can evaluate the similarity of cases comprehensively. To ensure that the final case has the best overall performance, an evaluation method of similar cases based on grey correlation analysis is proposed to evaluate similar cases to select the most suitable case. Furthermore, a computer-aided system is developed using MATLAB GUI to assist the product configuration design. The actual example and result on an ETC series machine tool product show that the proposed method is effective, rapid and accurate in the process of product configuration. The proposed methodology provides a detailed instruction for the product configuration design oriented to customer requirements.

Self-organization in irregular landscapes: Detecting autogenic interactions from field data using descriptive statistics and dynamical systems theory

NASA Astrophysics Data System (ADS)

Larsen, L.; Watts, D.; Khurana, A.; Anderson, J. L.; Xu, C.; Merritts, D. J.

2015-12-01

The classic signal of self-organization in nature is pattern formation. However, the interactions and feedbacks that organize depositional landscapes do not always result in regular or fractal patterns. How might we detect their existence and effects in these "irregular" landscapes? Emergent landscapes such as newly forming deltaic marshes or some restoration sites provide opportunities to study the autogenic processes that organize landscapes and their physical signatures. Here we describe a quest to understand autogenic vs. allogenic controls on landscape evolution in Big Spring Run, PA, a landscape undergoing restoration from bare-soil conditions to a target wet meadow landscape. The contemporary motivation for asking questions about autogenic vs. allogenic controls is to evaluate how important initial conditions or environmental controls may be for the attainment of management objectives. However, these questions can also inform interpretation of the sedimentary record by enabling researchers to separate signals that may have arisen through self-organization processes from those resulting from environmental perturbations. Over three years at Big Spring Run, we mapped the dynamic evolution of floodplain vegetation communities and distributions of abiotic variables and topography. We used principal component analysis and transition probability analysis to detect associative interactions between vegetation and geomorphic variables and convergent cross-mapping on lidar data to detect causal interactions between biomass and topography. Exploratory statistics revealed that plant communities with distinct morphologies exerted control on landscape evolution through stress divergence (i.e., channel initiation) and promoting the accumulation of fine sediment in channels. Together, these communities participated in a negative feedback that maintains low energy and multiple channels. Because of the spatially explicit nature of this feedback, causal interactions could not be uncovered from convergent cross-mapping with this limited dataset, serving as a reminder that spatially explicit approaches for revealing causality are needed to reconstruct self-organizing mechanisms from data.

Construction of a reference molecular linkage map of globe artichoke (Cynara cardunculus var. scolymus).

PubMed

Portis, E; Mauromicale, G; Mauro, R; Acquadro, A; Scaglione, D; Lanteri, S

2009-12-01

The genome organization of globe artichoke (Cynara cardunculus var. scolymus), unlike other species belonging to Asteraceae (=Compositae) family (i.e. sunflower, lettuce and chicory), remains largely unexplored. The species is highly heterozygous and suffers marked inbreeding depression when forced to self-fertilize. Thus a two-way pseudo-testcross represents the optimal strategy for linkage analysis. Here, we report linkage maps based on the progeny of a cross between globe artichoke (C. cardunculus var. scolymus) and cultivated cardoon (C. cardunculus var. altilis). The population was genotyped using a variety of PCR-based marker platforms, resulting in the identification of 708 testcross markers suitable for map construction. The male map consisted of 177 loci arranged in 17 major linkage groups, spanning 1,015.5 cM, while female map was built with 326 loci arranged into 20 major linkage groups, spanning 1,486.8 cM. The presence of 84 loci shared between these maps and those previously developed from a cross within globe artichoke allowed for map alignment and the definition of 17 homologous linkage groups, corresponding to the haploid number of the species. This will provide a favourable property for QTL scanning; furthermore, as 25 mapped markers (8%) correspond to coding regions, it has an additional value as functional map and might represent an important genetic tool for candidate gene studies in globe artichoke.

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

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

PubMed Central

Beruvides, Gerardo

2017-01-01

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions. PMID:28906450

Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: a case study in central Taiwan.

PubMed

Chou, Wen-Chieh; Lin, Wen-Tzu; Lin, Chao-Yuan

2009-05-01

The catastrophic earthquake, 7.3 on the Richter scale, occurred on September 21, 1999 in Central Taiwan. Much of standing vegetation on slopes was eliminated and massive, scattered landslides were induced at the Jou-Jou Mountain area of the Wu-Chi basin in Nantou County. We evaluated three methods for assessing landslide hazard and vegetation recovery conditions. (1) Self-organizing map (SOM) neural network coupled with fuzzy technique was used to quickly extract the landslide. (2) The NDVI-based vegetation recovery index derived from multi-temporal SPOT satellite images was used to evaluate vegetation recovery rate in the denudation sites. (3) The spatial distribution index (SDI) based on land-cover topographic location was employed to analyze vegetation recovery patterns, including the invading, surviving and mixed patterns at the Jou-Jou Mountain area. On September 27, 1999, there were 849.20 ha of landslide area extracted using the self-organizing map and fuzzy technique combined model. After six years of natural vegetation succession, the landslide has gradually restored, and vegetation recovery rate reached up to 86%. On-site observation shows that many native pioneer plants have invaded onto the denudation sites even if disturbed by several typhoons. Two native surviving plants, Arundo formosana Hack and Pinus taiwanensis Hayata, play a vital role in natural vegetation succession in this area, especially for the sites on ridgeline and steep slopes.

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.

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.

PubMed

Castaño, Fernando; Beruvides, Gerardo; Haber, Rodolfo E; Artuñedo, Antonio

2017-09-14

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

DOE PAGES

Kaleita, Amy L.; Schott, Linda R.; Hargreaves, Sarah K.; ...

2017-07-07

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using sixmore » landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.« less

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

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

Kaleita, Amy L.; Schott, Linda R.; Hargreaves, Sarah K.

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using sixmore » landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.« less

A Verification Method for MASOES.

PubMed

Perozo, N; Aguilar Perozo, J; Terán, O; Molina, H

2013-02-01

MASOES is a 3agent architecture for designing and modeling self-organizing and emergent systems. This architecture describes the elements, relationships, and mechanisms, both at the individual and the collective levels, that favor the analysis of the self-organizing and emergent phenomenon without mathematically modeling the system. In this paper, a method is proposed for verifying MASOES from the point of view of design in order to study the self-organizing and emergent behaviors of the modeled systems. The verification criteria are set according to what is proposed in MASOES for modeling self-organizing and emerging systems and the principles of the wisdom of crowd paradigm and the fuzzy cognitive map (FCM) theory. The verification method for MASOES has been implemented in a tool called FCM Designer and has been tested to model a community of free software developers that works under the bazaar style as well as a Wikipedia community in order to study their behavior and determine their self-organizing and emergent capacities.

Using E-Maps to Organize and Navigate Online Content

ERIC Educational Resources Information Center

Ruffini, Michael F.

2008-01-01

Computer-generated mind maps, or e-maps, provide an outstanding e-learning tool for organizing and navigating web-based content and files. Considerable research indicates the effectiveness of using graphic organizers such as mind maps to facilitate meaningful learning. Tony Buzan and Barry Buzan argue that mind maps better harness the way the…

An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps. Designing the outlier analysis software package for the next Gaia survey

NASA Astrophysics Data System (ADS)

Fustes, D.; Manteiga, M.; Dafonte, C.; Arcay, B.; Ulla, A.; Smith, K.; Borrachero, R.; Sordo, R.

2013-11-01

Aims: A new method applied to the segmentation and further analysis of the outliers resulting from the classification of astronomical objects in large databases is discussed. The method is being used in the framework of the Gaia satellite Data Processing and Analysis Consortium (DPAC) activities to prepare automated software tools that will be used to derive basic astrophysical information that is to be included in final Gaia archive. Methods: Our algorithm has been tested by means of simulated Gaia spectrophotometry, which is based on SDSS observations and theoretical spectral libraries covering a wide sample of astronomical objects. Self-organizing maps networks 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. Results: We demonstrate the usefulness of the method by analyzing the spectra that were rejected by the SDSS spectroscopic classification pipeline and thus classified as "UNKNOWN". First, our method can help distinguish between astrophysical objects and instrumental artifacts. Additionally, the application of our algorithm to SDSS objects of unknown nature has allowed us to identify classes of objects with similar astrophysical natures. In addition, the method allows for the potential discovery of hundreds of new objects, such as white dwarfs and quasars. Therefore, the proposed method is shown to be very promising for data exploration and knowledge discovery in very large astronomical databases, such as the archive from the upcoming Gaia mission.

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.

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.

Self-localization of wireless sensor networks using self-organizing maps

NASA Astrophysics Data System (ADS)

Ertin, Emre; Priddy, Kevin L.

2005-03-01

Recently there has been a renewed interest in the notion of deploying large numbers of networked sensors for applications ranging from environmental monitoring to surveillance. In a typical scenario a number of sensors are distributed in a region of interest. Each sensor is equipped with sensing, processing and communication capabilities. The information gathered from the sensors can be used to detect, track and classify objects of interest. For a number of locations the sensors location is crucial in interpreting the data collected from those sensors. Scalability requirements dictate sensor nodes that are inexpensive devices without a dedicated localization hardware such as GPS. Therefore the network has to rely on information collected within the network to self-localize. In the literature a number of algorithms has been proposed for network localization which uses measurements informative of range, angle, proximity between nodes. Recent work by Patwari and Hero relies on sensor data without explicit range estimates. The assumption is that the correlation structure in the data is a monotone function of the intersensor distances. In this paper we propose a new method based on unsupervised learning techniques to extract location information from the sensor data itself. We consider a grid consisting of virtual nodes and try to fit grid in the actual sensor network data using the method of self organizing maps. Then known sensor network geometry can be used to rotate and scale the grid to a global coordinate system. Finally, we illustrate how the virtual nodes location information can be used to track a target.

Adding dynamic rules to self-organizing fuzzy systems

NASA Technical Reports Server (NTRS)

Buhusi, Catalin V.

1992-01-01

This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

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.

The Self-Organized Archive: SPASE, PDS and Archive Cooperatives

NASA Astrophysics Data System (ADS)

King, T. A.; Hughes, J. S.; Roberts, D. A.; Walker, R. J.; Joy, S. P.

2005-05-01

Information systems with high quality metadata enable uses and services which often go beyond the original purpose. There are two types of metadata: annotations which are items that comment on or describe the content of a resource and identification attributes which describe the external properties of the resource itself. For example, annotations may indicate which columns are present in a table of data, whereas an identification attribute would indicate source of the table, such as the observatory, instrument, organization, and data type. When the identification attributes are collected and used as the basis of a search engine, a user can constrain on an attribute, the archive can then self-organize around the constraint, presenting the user with a particular view of the archive. In an archive cooperative where each participating data system or archive may have its own metadata standards, providing a multi-system search engine requires that individual archive metadata be mapped to a broad based standard. To explore how cooperative archives can form a larger self-organized archive we will show how the Space Physics Archive Search and Extract (SPASE) data model will allow different systems to create a cooperative and will use Planetary Data System (PDS) plus existing space physics activities as a demonstration.

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

Psychosocial experiences associated with confirmed and self-identified dyslexia: a participant-driven concept map of adult perspectives.

PubMed

Nalavany, Blace Arthur; Carawan, Lena Williams; Rennick, Robyn A

2011-01-01

Concept mapping (a mixed qualitative-quantitative methodology) was used to describe and understand the psychosocial experiences of adults with confirmed and self-identified dyslexia. Using innovative processes of art and photography, Phase 1 of the study included 15 adults who participated in focus groups and in-depth interviews and were asked to elucidate their experiences with dyslexia. On index cards, 75 statements and experiences with dyslexia were recorded. The second phase of the study included 39 participants who sorted these statements into self-defined categories and rated each statement to reflect their personal experiences to produce a visual representation, or concept map, of their experience. The final concept map generated nine distinct cluster themes: Organization Skills for Success; Finding Success; A Good Support System Makes the Difference; On Being Overwhelmed; Emotional Downside; Why Can't They See It?; Pain, Hurt, and Embarrassment From Past to Present; Fear of Disclosure; and Moving Forward. Implications of these findings are discussed.

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.

A proto-architecture for innate directionally selective visual maps.

PubMed

Adams, Samantha V; Harris, Chris M

2014-01-01

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.

The Motivation and Pleasure Scale-Self-Report (MAP-SR): reliability and validity of a self-report measure of negative symptoms.

PubMed

Llerena, Katiah; Park, Stephanie G; McCarthy, Julie M; Couture, Shannon M; Bennett, Melanie E; Blanchard, Jack J

2013-07-01

The Clinical Assessment Interview for Negative Symptoms (CAINS) is an empirically developed interview measure of negative symptoms. Building on prior work, this study examined the reliability and validity of a self-report measure based on the CAINS-the Motivation and Pleasure Scale-Self-Report (MAP-SR)-that assesses the motivation and pleasure domain of negative symptoms. Thirty-seven participants with schizophrenia or schizoaffective disorder completed the 18-item MAP-SR, the CAINS, and other measures of functional outcome. Item analyses revealed three items that performed poorly. The revised 15-item MAP-SR demonstrated good internal consistency and convergent validity with the clinician-rated Motivation and Pleasure scale of the CAINS, as well as good discriminant validity, with little association with psychotic symptoms or depression/anxiety. MAP-SR scores were related to social anhedonia, social closeness, and clinician-rated social functioning. The MAP-SR is a promising self-report measure of severity of negative symptoms. Copyright © 2013 Elsevier Inc. All rights reserved.

Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.).

PubMed

Samecka-Cymerman, A; Stankiewicz, A; Kolon, K; Kempers, A J

2009-07-01

Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends.

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

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.

High and low density development in Puerto Rico

Treesearch

William A. Gould; Sebastian Martinuzzi; Olga M. Ramos Gonzalez

2008-01-01

This map shows the distribution of high and low density developed lands in Puerto Rico (Martinuzzi et al. 2007). The map was created using a mosaic of Landsat ETM+ images that range from the years 2000 to 2003. The developed land cover was classified using the Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification (ERDAS 2003)....

Self-Organization of an Artificial Neural Network Subjected to Attention Shift Impairments and Familiarity Preference, Characteristics Studied in Autism

ERIC Educational Resources Information Center

Gustafsson, Lennart; Paplinski, Andrew

2004-01-01

Autism is a developmental disorder with possibly multiple pathophysiologies. It has been theorized that cortical feature maps in individuals with autism are inadequate for forming abstract codes and representations. Cortical feature maps make it possible to classify stimuli, such as phonemes of speech, disregarding incidental detail. Hierarchies…

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).

Modeling the human mental lexicon with self-organizing feature maps

NASA Astrophysics Data System (ADS)

Wittenburg, Peter; Frauenfelder, Uli H.

1992-10-01

Recent efforts to model the remarkable ability of humans to recognize speech and words are described. Different techniques including the use of neural nets for representing phonological similarity between words in the lexicon with self organizing algorithms are discussed. Simulations using the standard Kohonen algorithm are presented to illustrate some problems confronted with this technique in modeling similarity relations of form in the human mental lexicon. Alternative approaches that can potentially deal with some of these limitations are sketched.

Clustering Similarity Digest Bloom Filters in Self-Organizing Maps

DTIC Science & Technology

2012-12-01

www.sciencedirect.com/science/article/ pii/S1742287610000368 [4] M. Rogers, J . Goldman, R. Mislan, T. Wedge, and S. Debrota, “Computer forensics field triage...1990. [9] T. Kohonen, S. Kaski, K. Lagus, J . Salojarvi, J . Honkela, V. Paatero, and A. Saarela, “Self organization of a massive document collection...the IEEE-INNS-ENNS Interna- tional Joint Conference on, vol. 6, 2000, pp. 15 –19 vol.6. [12] G. Salton , A. Wong, and C. Yang, “A vector space model for

SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

PubMed

Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

2014-01-01

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

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 nanobiosensor for dynamic single cell analysis during microvascular self-organization.

PubMed

Wang, S; Sun, J; Zhang, D D; Wong, P K

2016-10-14

The formation of microvascular networks plays essential roles in regenerative medicine and tissue engineering. Nevertheless, the self-organization mechanisms underlying the dynamic morphogenic process are poorly understood due to a paucity of effective tools for mapping the spatiotemporal dynamics of single cell behaviors. By establishing a single cell nanobiosensor along with live cell imaging, we perform dynamic single cell analysis of the morphology, displacement, and gene expression during microvascular self-organization. Dynamic single cell analysis reveals that endothelial cells self-organize into subpopulations with specialized phenotypes to form microvascular networks and identifies the involvement of Notch1-Dll4 signaling in regulating the cell subpopulations. The cell phenotype correlates with the initial Dll4 mRNA expression level and each subpopulation displays a unique dynamic Dll4 mRNA expression profile. Pharmacological perturbations and RNA interference of Notch1-Dll4 signaling modulate the cell subpopulations and modify the morphology of the microvascular network. Taken together, a nanobiosensor enables a dynamic single cell analysis approach underscoring the importance of Notch1-Dll4 signaling in microvascular self-organization.

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.

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.

CrowdMapping: A Crowdsourcing-Based Terminology Mapping Method for Medical Data Standardization.

PubMed

Mao, Huajian; Chi, Chenyang; Huang, Boyu; Meng, Haibin; Yu, Jinghui; Zhao, Dongsheng

2017-01-01

Standardized terminology is the prerequisite of data exchange in analysis of clinical processes. However, data from different electronic health record systems are based on idiosyncratic terminology systems, especially when the data is from different hospitals and healthcare organizations. Terminology standardization is necessary for the medical data analysis. We propose a crowdsourcing-based terminology mapping method, CrowdMapping, to standardize the terminology in medical data. CrowdMapping uses a confidential model to determine how terminologies are mapped to a standard system, like ICD-10. The model uses mappings from different health care organizations and evaluates the diversity of the mapping to determine a more sophisticated mapping rule. Further, the CrowdMapping model enables users to rate the mapping result and interact with the model evaluation. CrowdMapping is a work-in-progress system, we present initial results mapping terminologies.

Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps

NASA Astrophysics Data System (ADS)

Obermayer, K.; Blasdel, G. G.; Schulten, K.

1992-05-01

We report a detailed analytical and numerical model study of pattern formation during the development of visual maps, namely, the formation of topographic maps and orientation and ocular dominance columns in the striate cortex. Pattern formation is described by a stimulus-driven Markovian process, the self-organizing feature map. This algorithm generates topologically correct maps between a space of (visual) input signals and an array of formal ``neurons,'' which in our model represents the cortex. We define order parameters that are a function of the set of visual stimuli an animal perceives, and we demonstrate that the formation of orientation and ocular dominance columns is the result of a global instability of the retinoptic projection above a critical value of these order parameters. We characterize the spatial structure of the emerging patterns by power spectra, correlation functions, and Gabor transforms, and we compare model predictions with experimental data obtained from the striate cortex of the macaque monkey with optical imaging. Above the critical value of the order parameters the model predicts a lateral segregation of the striate cortex into (i) binocular regions with linear changes in orientation preference, where iso-orientation slabs run perpendicular to the ocular dominance bands, and (ii) monocular regions with low orientation specificity, which contain the singularities of the orientation map. Some of these predictions have already been verified by experiments.

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.

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.

Classification of a set of vectors using self-organizing map- and rule-based technique

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Okaniwa, Kaishirou; Nosaka, Kenzaburou

2005-02-01

There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence. Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of "Tour".

Energy Spectral Behaviors of Communication Networks of Open-Source Communities

PubMed Central

Yang, Jianmei; Yang, Huijie; Liao, Hao; Wang, Jiangtao; Zeng, Jinqun

2015-01-01

Large-scale online collaborative production activities in open-source communities must be accompanied by large-scale communication activities. Nowadays, the production activities of open-source communities, especially their communication activities, have been more and more concerned. Take CodePlex C # community for example, this paper constructs the complex network models of 12 periods of communication structures of the community based on real data; then discusses the basic concepts of quantum mapping of complex networks, and points out that the purpose of the mapping is to study the structures of complex networks according to the idea of quantum mechanism in studying the structures of large molecules; finally, according to this idea, analyzes and compares the fractal features of the spectra in different quantum mappings of the networks, and concludes that there are multiple self-similarity and criticality in the communication structures of the community. In addition, this paper discusses the insights and application conditions of different quantum mappings in revealing the characteristics of the structures. The proposed quantum mapping method can also be applied to the structural studies of other large-scale organizations. PMID:26047331

Using a Metro Map Metaphor for Organizing Web-Based Learning Resources.

ERIC Educational Resources Information Center

Bang, Tove; Gronbaek, Kaj; Hansen, Per Steen

This paper briefly describes the WebNize system and how it applies a Metro Map metaphor for organizing guided tours in Web based resources. Then, experiences in using the Metro Map based tours in a Knowledge Sharing project at the library at Aarhus School of Business (ASB) in Denmark, are discussed. The Library has been involved in establishing a…

Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

NASA Astrophysics Data System (ADS)

Hortos, William S.

2006-05-01

A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the algorithms from flat topologies to two-tier hierarchies of sensor nodes are presented. Results from a few simulations of the proposed algorithms are compared to the published results of other approaches to sensor network self-organization in common scenarios. The estimated network lifetime and extent under static resource allocations are computed.

Adaptive self-assembly and induced-fit transformations of anion-binding metal-organic macrocycles

NASA Astrophysics Data System (ADS)

Zhang, Ting; Zhou, Li-Peng; Guo, Xiao-Qing; Cai, Li-Xuan; Sun, Qing-Fu

2017-06-01

Container-molecules are attractive to chemists due to their unique structural characteristics comparable to enzymes and receptors in nature. We report here a family of artificial self-assembled macrocyclic containers that feature induced-fit transformations in response to different anionic guests. Five metal-organic macrocycles with empirical formula of MnL2n (M=Metal L=Ligand n=3, 4, 5, 6, 7) are selectively obtained starting from one simple benzimidazole-based ligand and square-planar palladium(II) ions, either by direct anion-adaptive self-assembly or induced-fit transformations. Hydrogen-bonding interactions between the inner surface of the macrocycles and the anionic guests dictate the shape and size of the product. A comprehensive induced-fit transformation map across all the MnL2n species is drawn, with a representative reconstitution process from Pd7L14 to Pd3L6 traced in detail, revealing a gradual ring-shrinking mechanism. We envisage that these macrocyclic molecules with adjustable well-defined hydrogen-bonding pockets will find wide applications in molecular sensing or catalysis.

76 FR 31384 - Self-Regulatory Organizations; Notice of Filing and Immediate Effectiveness of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-31

... participate for that calendar month. The Exchange pays a per- contract MAP Subsidy to any Exchange member... Provider Subsidy is a per contract fee payable by the Exchange to Eligible Market Access Providers \\3\\ for... currently pays a monthly subsidy of $0.10 (the ``Subsidy Rate'') to Eligible MAPs for each Eligible Contract...

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

PubMed

Bocci, Giovanni; Carosati, Emanuele; Vayer, Philippe; Arrault, Alban; Lozano, Sylvain; Cruciani, Gabriele

2017-07-25

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Mapping the nomological network of employee self-determined safety motivation: A preliminary measure in China.

PubMed

Jiang, Li; Tetrick, Lois E

2016-09-01

The present study introduced a preliminary measure of employee safety motivation based on the definition of self-determination theory from Fleming (2012) research and validated the structure of self-determined safety motivation (SDSM) by surveying 375 employees in a Chinese high-risk organization. First, confirmatory factor analysis (CFA) was used to examine the factor structure of SDSM, and indices of five-factor model CFA met the requirements. Second, a nomological network was examined to provide evidence of the construct validity of SDSM. Beyond construct validity, the analysis also produced some interesting results concerning the relationship between leadership antecedents and safety motivation, and between safety motivation and safety behavior. Autonomous motivation was positively related to transformational leadership, negatively related to abusive supervision, and positively related to safety behavior. Controlled motivation with the exception of introjected regulation was negatively related to transformational leadership, positively related to abusive supervision, and negatively related to safety behavior. The unique role of introjected regulation and future research based on self-determination theory were discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

25 CFR 900.122 - What does an Indian tribe or tribal organization do if it wants to secure a construction contract?

Code of Federal Regulations, 2010 CFR

2010-04-01

... UNDER THE INDIAN SELF-DETERMINATION AND EDUCATION ASSISTANCE ACT Construction § 900.122 What does an... information available about the construction project, including construction drawings, maps, engineering... tribal organization to review all plans, specifications, engineering reports, cost estimates, and other...

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.

Characterization of Wind Patterns over Texas Using Self-Organizing Maps: Impact on Dallas-Fort Worth Long Term Ozone Trends

NASA Astrophysics Data System (ADS)

Kotsakis, A.; Choi, Y.; Souri, A.; Jeon, W.; Flynn, J. H., III

2017-12-01

From the years 2000 to 2014, Dallas-Fort Worth (DFW) has seen a decrease in ozone exceedances due to decreased emissions of ozone precursors. In this study, a wind pattern analysis was done to gain a better understanding of the meteorological patterns that have historically contributed to ozone exceedances over the DFW area. Long-term trends in ozone and the seasonal distribution of ozone exceedances were analyzed using surface monitoring data. Using a clustering algorithm called self-organizing maps, characteristic regional wind patterns from 2000-2014 were determined. For each of the wind pattern clusters, the frequency over the last 15 years and average ozone from monitors across DFW was analyzed. Finally, model simulations were performed to determine if pollution transported out of Houston affected incoming background ozone into DFW.

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.

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.

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.

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.

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

Self-organizing map classifier for stressed speech recognition

NASA Astrophysics Data System (ADS)

Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

2016-05-01

This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

Competing Effects Between Screen Media Time and Physical Activity in Adolescent Girls: Clustering a Self-Organizing Maps Analysis.

PubMed

Valencia-Peris, Alexandra; Devís-Devís, José; García-Massó, Xavier; Lizandra, Jorge; Pérez-Gimeno, Esther; Peiró-Velert, Carmen

2016-06-01

Previous research shows contradictory findings on potential competing effects between sedentary screen media usage (SMU) and physical activity (PA). This study examined these effects on adolescent girls via self-organizing maps analysis focusing on 3 target profiles. A sample of 1,516 girls aged 12 to 18 years self-reported daily time engagement in PA (moderate and vigorous intensity) and in screen media activities (TV/video/DVD, computer, and videogames), separately and combined. Topological interrelationships from the 13 emerging maps indicated a moderate competing effect between physically active and sedentary SMU patterns. Higher SES and overweight status were linked to either active or inactive behaviors. Three target clusters were explored in more detail. Cluster 1, named temperate-media actives, showed capabilities of being active while engaging in a moderate level of SMU (TV/video/DVD mainly). In Cluster 2, named prudent-media inactives, and Cluster 3, compulsive-media inactives, a competing effect between SMU and PA emerged, being sedentary SMU behaviors responsible for a low involvement in active pursuits. SMU and PA emerge as both related and independent behaviors in girls, resulting in a moderate competing effect. Findings support the case for recommending the timing of PA and SMU for recreational purposes considering different profiles, sociodemographic factors and types of SMU.

A High Density Consensus Map of Rye (Secale cereale L.) Based on DArT Markers

PubMed Central

Myśków, Beata; Stojałowski, Stefan; Heller-Uszyńska, Katarzyna; Góralska, Magdalena; Brągoszewski, Piotr; Uszyński, Grzegorz; Kilian, Andrzej; Rakoczy-Trojanowska, Monika

2011-01-01

Background Rye (Secale cereale L.) is an economically important crop, exhibiting unique features such as outstanding resistance to biotic and abiotic stresses and high nutrient use efficiency. This species presents a challenge to geneticists and breeders due to its large genome containing a high proportion of repetitive sequences, self incompatibility, severe inbreeding depression and tissue culture recalcitrance. The genomic resources currently available for rye are underdeveloped in comparison with other crops of similar economic importance. The aim of this study was to create a highly saturated, multilocus linkage map of rye via consensus mapping, based on Diversity Arrays Technology (DArT) markers. Methodology/Principal Findings Recombinant inbred lines (RILs) from 5 populations (564 in total) were genotyped using DArT markers and subjected to linkage analysis using Join Map 4.0 and Multipoint Consensus 2.2 software. A consensus map was constructed using a total of 9703 segregating markers. The average chromosome map length ranged from 199.9 cM (2R) to 251.4 cM (4R) and the average map density was 1.1 cM. The integrated map comprised 4048 loci with the number of markers per chromosome ranging from 454 for 7R to 805 for 4R. In comparison with previously published studies on rye, this represents an eight-fold increase in the number of loci placed on a consensus map and a more than two-fold increase in the number of genetically mapped DArT markers. Conclusions/Significance Through the careful choice of marker type, mapping populations and the use of software packages implementing powerful algorithms for map order optimization, we produced a valuable resource for rye and triticale genomics and breeding, which provides an excellent starting point for more in-depth studies on rye genome organization. PMID:22163026

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

Using Concept Mapping to Develop a Strategy for Self-Management Support for Underserved Populations Living With Chronic Conditions, British Columbia, August 2013-June 2014.

PubMed

Mills, Susan L; Bergeron, Kim; Pérez, Guillermina

2015-10-08

Self-management support (SMS) is an essential component of public health approaches to chronic conditions. Given increasing concerns about health equity, the needs of diverse populations must be considered. This study examined potential solutions for addressing the gaps in self-management support initiatives for underserved populations. Stakeholders representing government, nongovernment organizations, Aboriginal communities, health authorities, medical practices, and research institutions generated, sorted, and rated ideas on what could be done to improve self-management support for underserved populations. Concept mapping was used to facilitate the collection and organization of the data and to generate conceptual maps. Participants generated 92 ideas that were sorted into 11 clusters (foster partnerships, promote integrated community care, enhance health care provider training, shift government policy, support community development, increase community education, enable client engagement, incorporate client support systems, recognize client capacity, tailor self-management support programs, and develop client skills, training, and tools) and grouped into system, community, and individual levels within a partnership framework. The strategy can stimulate public health dialogue and be a roadmap for developing SMS initiatives. It has the potential to address SMS and chronic condition inequities in underserved populations in several ways: 1) by targeting populations that have greater inequities, 2) by advocating for shifts in government policies that create and perpetuate inequities, 3) by promoting partnerships that may increase the number of SMS initiatives for underserved groups, and 4) by promoting training and engagement that increase the relevance, uptake, and overall effectiveness of SMS.

Mapping visual cortex in monkeys and humans using surface-based atlases

NASA Technical Reports Server (NTRS)

Van Essen, D. C.; Lewis, J. W.; Drury, H. A.; Hadjikhani, N.; Tootell, R. B.; Bakircioglu, M.; Miller, M. I.

2001-01-01

We have used surface-based atlases of the cerebral cortex to analyze the functional organization of visual cortex in humans and macaque monkeys. The macaque atlas contains multiple partitioning schemes for visual cortex, including a probabilistic atlas of visual areas derived from a recent architectonic study, plus summary schemes that reflect a combination of physiological and anatomical evidence. The human atlas includes a probabilistic map of eight topographically organized visual areas recently mapped using functional MRI. To facilitate comparisons between species, we used surface-based warping to bring functional and geographic landmarks on the macaque map into register with corresponding landmarks on the human map. The results suggest that extrastriate visual cortex outside the known topographically organized areas is dramatically expanded in human compared to macaque cortex, particularly in the parietal lobe.

The Topic Analysis of Hospice Care Research Using Co-word Analysis and GHSOM

NASA Astrophysics Data System (ADS)

Yang, Yu-Hsiang; Bhikshu, Huimin; Tsaih, Rua-Huan

The purpose of this study was to propose a multi-layer topic map analysis of palliative care research using co-word analysis of informetrics with Growing Hierarchical Self-Organizing Map (GHSOM). The topic map illustrated the delicate intertwining of subject areas and provided a more explicit illustration of the concepts within each subject area. We applied GHSOM, a text-mining Neural Networks tool, to obtain a hierarchical topic map. The result of the topic map may indicate that the subject area of health care science and service played an importance role in multidiscipline within the research related to palliative care.

Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm.

PubMed

Morizawa, Seiichiro; Shimoyama, Koji; Obayashi, Shigeru; Funamoto, Kenichi; Hayase, Toshiyuki

2011-12-01

This study was performed to determine the relations between the features of wall shear stress and aneurysm rupture. For this purpose, visual data mining was performed in unsteady blood flow simulation data for an aortic aneurysm. The time-series data of wall shear stress given at each grid point were converted to spatial and temporal indices, and the grid points were sorted using a self-organizing map based on the similarity of these indices. Next, the results of cluster analysis were mapped onto the real space of the aortic aneurysm to specify the regions that may lead to aneurysm rupture. With reference to previous reports regarding aneurysm rupture, the visual data mining suggested specific hemodynamic features that cause aneurysm rupture. GRAPHICAL ABSTRACT:

Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data

NASA Astrophysics Data System (ADS)

Nanda, Trushnamayee; Sahoo, Bhabagrahi; Chatterjee, Chandranath

2017-06-01

The Kohonen Self-Organizing Map (KSOM) estimator is prescribed as a useful tool for infilling the missing data in hydrometeorology. However, in this study, when the performance of the KSOM estimator is tested for gap-filling in the streamflow, rainfall, evapotranspiration (ET), and temperature timeseries data, collected from 30 gauging stations in India under missing data situations, it is felt that the KSOM modeling performance could be further improved. Consequently, this study tries to answer the research questions as to whether the length of record of the historical data and its variability has any effect on the performance of the KSOM? Whether inclusion of temporal distribution of timeseries data and the nature of outliers in the KSOM framework enhances its performance further? Subsequently, it is established that the KSOM framework should include the coefficient of variation of the datasets for determination of the number of map units, without considering it as a single value function of the sample data size. This could help to upscale and generalize the applicability of KSOM for varied hydrometeorological data types.

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.

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.

Constructing a Graphic Organizer in the Classroom: Introductory Students' Perception of Achievement Using a Decision Map to Solve Aqueous Acid-Base Equilibria Problems

ERIC Educational Resources Information Center

DeMeo, Stephen

2007-01-01

Common examples of graphic organizers include flow diagrams, concept maps, and decision trees. The author has created a novel type of graphic organizer called a decision map. A decision map is a directional heuristic that helps learners solve problems within a generic framework. It incorporates questions that the user must answer and contains…

Critical space-time networks and geometric phase transitions from frustrated edge antiferromagnetism

NASA Astrophysics Data System (ADS)

Trugenberger, Carlo A.

2015-12-01

Recently I proposed a simple dynamical network model for discrete space-time that self-organizes as a graph with Hausdorff dimension dH=4 . The model has a geometric quantum phase transition with disorder parameter (dH-ds) , where ds is the spectral dimension of the dynamical graph. Self-organization in this network model is based on a competition between a ferromagnetic Ising model for vertices and an antiferromagnetic Ising model for edges. In this paper I solve a toy version of this model defined on a bipartite graph in the mean-field approximation. I show that the geometric phase transition corresponds exactly to the antiferromagnetic transition for edges, the dimensional disorder parameter of the former being mapped to the staggered magnetization order parameter of the latter. The model has a critical point with long-range correlations between edges, where a continuum random geometry can be defined, exactly as in Kazakov's famed 2D random lattice Ising model but now in any number of dimensions.

Can rodents conceive hyperbolic spaces?

PubMed Central

Urdapilleta, Eugenio; Troiani, Francesca; Stella, Federico; Treves, Alessandro

2015-01-01

The grid cells discovered in the rodent medial entorhinal cortex have been proposed to provide a metric for Euclidean space, possibly even hardwired in the embryo. Yet, one class of models describing the formation of grid unit selectivity is entirely based on developmental self-organization, and as such it predicts that the metric it expresses should reflect the environment to which the animal has adapted. We show that, according to self-organizing models, if raised in a non-Euclidean hyperbolic cage rats should be able to form hyperbolic grids. For a given range of grid spacing relative to the radius of negative curvature of the hyperbolic surface, such grids are predicted to appear as multi-peaked firing maps, in which each peak has seven neighbours instead of the Euclidean six, a prediction that can be tested in experiments. We thus demonstrate that a useful universal neuronal metric, in the sense of a multi-scale ruler and compass that remain unaltered when changing environments, can be extended to other than the standard Euclidean plane. PMID:25948611

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.

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.

Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

PubMed

Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

2000-01-01

Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

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.

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Lin; Gupta, Hoshin V.; Gao, Xiaogang; Sorooshian, Soroosh; Imam, Bisher

2002-12-01

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.

Vector representation of user's view using self-organizing map

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Yamaguchi, Tomohisa; Monden, Eri; Kawabata, Shunji; Kamitani, Motoki

2004-05-01

There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. Therefore, we propose a method which acquires a view as a vector, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc.. These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. We demonstrate a city-sequence generation system which reflects user's intension as an application of sequence generation containing user's view. We apply the self-organizing map to this system to represent user's view.

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams.

PubMed

Wu, Yingcai; Chen, Zhutian; Sun, Guodao; Xie, Xiao; Cao, Nan; Liu, Shixia; Cui, Weiwei

2017-10-18

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.

New Approaches to Tsunami Hazard Mitigation Demonstrated in Oregon

NASA Astrophysics Data System (ADS)

Priest, G. R.; Rizzo, A.; Madin, I.; Lyles Smith, R.; Stimely, L.

2012-12-01

Oregon Department of Geology and Mineral Industries and Oregon Emergency Management collaborated over the last four years to increase tsunami preparedness for residents and visitors to the Oregon coast. Utilizing support from the National Tsunami Hazards Mitigation Program (NTHMP), new approaches to outreach and tsunami hazard assessment were developed and then applied. Hazard assessment was approached by first doing two pilot studies aimed at calibrating theoretical models to direct observations of tsunami inundation gleaned from the historical and prehistoric (paleoseismic/paleotsunami) data. The results of these studies were then submitted to peer-reviewed journals and translated into 1:10,000-12,000-scale inundation maps. The inundation maps utilize a powerful new tsunami model, SELFE, developed by Joseph Zhang at the Oregon Health & Science University. SELFE uses unstructured computational grids and parallel processing technique to achieve fast accurate simulation of tsunami interactions with fine-scale coastal morphology. The inundation maps were simplified into tsunami evacuation zones accessed as map brochures and an interactive mapping portal at http://www.oregongeology.org/tsuclearinghouse/. Unique in the world are new evacuation maps that show separate evacuation zones for distant versus locally generated tsunamis. The brochure maps explain that evacuation time is four hours or more for distant tsunamis but 15-20 minutes for local tsunamis that are invariably accompanied by strong ground shaking. Since distant tsunamis occur much more frequently than local tsunamis, the two-zone maps avoid needless over evacuation (and expense) caused by one-zone maps. Inundation mapping for the entire Oregon coast will be complete by ~2014. Educational outreach was accomplished first by doing a pilot study to measure effectiveness of various approaches using before and after polling and then applying the most effective methods. In descending order, the most effective methods were: (1) door-to-door (person-to-person) education, (2) evacuation drills, (3) outreach to K-12 schools, (4) media events, and (5) workshops targeted to key audiences (lodging facilities, teachers, and local officials). Community organizers were hired to apply these five methods to clusters of small communities, measuring performance by before and after polling. Organizers were encouraged to approach the top priority, person-to-person education, by developing Community Emergency Response Teams (CERT) or CERT-like organizations in each community, thereby leaving behind a functioning volunteer-based group that will continue the outreach program and build long term resiliency. One of the most effective person-to-person educational tools was the Map Your Neighborhood program that brings people together so they can sketch the basic layout of their neighborhoods to depict key earthquake and tsunami hazards and mitigation solutions. The various person-to-person volunteer efforts and supporting outreach activities are knitting communities together and creating a permanent culture of tsunami and earthquake preparedness. All major Oregon coastal population centers will have been covered by this intensive outreach program by ~2014.

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.

Development of the Social Network-Based Intervention “Powerful Together with Diabetes” Using Intervention Mapping

PubMed Central

Vissenberg, Charlotte; Nierkens, Vera; Uitewaal, Paul J. M.; Middelkoop, Barend J. C.; Nijpels, Giel; Stronks, Karien

2017-01-01

This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM) among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and diminishing social influences hindering DSM (e.g., peer pressure and social norms). The intervention was specifically developed for patients with Dutch, Turkish, Moroccan, and Surinamese backgrounds. The intervention was developed according to Intervention Mapping. This article describes the first four steps of Intervention Mapping: (1) the needs assessment; (2) development of performance and change objectives; (3) selection of theory-based methods and strategies; and (4) the translation of these into an organized program. These four steps resulted in Powerful Together with Diabetes, a 10-month group-based intervention consisting of 24 meetings, 6 meetings for significant others, and 2 meetings for participants and their spouses. The IM method resulted in a tailored approach with a specific focus on the social networks of its participants. This article concludes that the IM method helped our planning team to tailor the intervention to the needs of our target population and facilitated our evaluation design. However, in hindsight, the intervention could have been improved by investing more in participatory planning and community involvement. PMID:29326916

Development of the Social Network-Based Intervention "Powerful Together with Diabetes" Using Intervention Mapping.

PubMed

Vissenberg, Charlotte; Nierkens, Vera; Uitewaal, Paul J M; Middelkoop, Barend J C; Nijpels, Giel; Stronks, Karien

2017-01-01

This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM) among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and diminishing social influences hindering DSM (e.g., peer pressure and social norms). The intervention was specifically developed for patients with Dutch, Turkish, Moroccan, and Surinamese backgrounds. The intervention was developed according to Intervention Mapping. This article describes the first four steps of Intervention Mapping: (1) the needs assessment; (2) development of performance and change objectives; (3) selection of theory-based methods and strategies; and (4) the translation of these into an organized program. These four steps resulted in Powerful Together with Diabetes , a 10-month group-based intervention consisting of 24 meetings, 6 meetings for significant others, and 2 meetings for participants and their spouses. The IM method resulted in a tailored approach with a specific focus on the social networks of its participants. This article concludes that the IM method helped our planning team to tailor the intervention to the needs of our target population and facilitated our evaluation design. However, in hindsight, the intervention could have been improved by investing more in participatory planning and community involvement.

Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits.

PubMed

Ihmaid, Saleh K; Ahmed, Hany E A; Zayed, Mohamed F; Abadleh, Mohammed M

2016-01-30

The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool 'Emergent Self-Organizing Maps' for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.

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.

Evidence of Water Quality Degradation in Lower Mekong Basin Revealed by Self-Organizing Map

PubMed Central

Chea, Ratha; Grenouillet, Gaël; Lek, Sovan

2016-01-01

To reach a better understanding of the spatial variability of water quality in the Lower Mekong Basin (LMB), the Self-Organizing Map (SOM) was used to classify 117 monitoring sites and hotspots of pollution within the basin identified according to water quality indicators and US-EPA guidelines. Four different clusters were identified based on their similar physicochemical characteristics. The majority of sites in upper (Laos and Thailand) and middle part (Cambodia) of the basin were grouped in two clusters, considered as good quality water with high DO and low nutrient levels. The other two clusters were mostly composed of sites in Mekong delta (Vietnam) and few sites in upstream tributaries (i.e., northwestern Thailand, Tonle Sap Lake, and swamps close to Vientiane), known for moderate to poor quality of water and characterized by high nutrient and dissolved solid levels. Overall, we found that the water in the mainstream was less polluted than its tributaries; eutrophication and salinity could be key factors affecting water quality in LMB. Moreover, the seasonal variation of water quality seemed to be less marked than spatial variation occurring along the longitudinal gradient of Mekong River. Significant degradations were mainly associated with human disturbance and particularly apparent in sites distributed along the man-made canals in Vietnam delta where population growth and agricultural development are intensive. PMID:26731522

Identification of homogeneous regions for regionalization of watersheds by two-level self-organizing feature maps

NASA Astrophysics Data System (ADS)

Farsadnia, F.; Rostami Kamrood, M.; Moghaddam Nia, A.; Modarres, R.; Bray, M. T.; Han, D.; Sadatinejad, J.

2014-02-01

One of the several methods in estimating flood quantiles in ungauged or data-scarce watersheds is regional frequency analysis. Amongst the approaches to regional frequency analysis, different clustering techniques have been proposed to determine hydrologically homogeneous regions in the literature. Recently, Self-Organization feature Map (SOM), a modern hydroinformatic tool, has been applied in several studies for clustering watersheds. However, further studies are still needed with SOM on the interpretation of SOM output map for identifying hydrologically homogeneous regions. In this study, two-level SOM and three clustering methods (fuzzy c-mean, K-mean, and Ward's Agglomerative hierarchical clustering) are applied in an effort to identify hydrologically homogeneous regions in Mazandaran province watersheds in the north of Iran, and their results are compared with each other. Firstly the SOM is used to form a two-dimensional feature map. Next, the output nodes of the SOM are clustered by using unified distance matrix algorithm and three clustering methods to form regions for flood frequency analysis. The heterogeneity test indicates the four regions achieved by the two-level SOM and Ward approach after adjustments are sufficiently homogeneous. The results suggest that the combination of SOM and Ward is much better than the combination of either SOM and FCM or SOM and K-mean.

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

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.

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.

Probabilistic self-localisation on a qualitative map based on occlusions

NASA Astrophysics Data System (ADS)

Santos, Paulo E.; Martins, Murilo F.; Fenelon, Valquiria; Cozman, Fabio G.; Dee, Hannah M.

2016-09-01

Spatial knowledge plays an essential role in human reasoning, permitting tasks such as locating objects in the world (including oneself), reasoning about everyday actions and describing perceptual information. This is also the case in the field of mobile robotics, where one of the most basic (and essential) tasks is the autonomous determination of the pose of a robot with respect to a map, given its perception of the environment. This is the problem of robot self-localisation (or simply the localisation problem). This paper presents a probabilistic algorithm for robot self-localisation that is based on a topological map constructed from the observation of spatial occlusion. Distinct locations on the map are defined by means of a classical formalism for qualitative spatial reasoning, whose base definitions are closer to the human categorisation of space than traditional, numerical, localisation procedures. The approach herein proposed was systematically evaluated through experiments using a mobile robot equipped with a RGB-D sensor. The results obtained show that the localisation algorithm is successful in locating the robot in qualitatively distinct regions.

Successful Teaching, Learning, and Use of Digital Mapping Technology in Mazvihwa, Rural Zimbabwe

NASA Astrophysics Data System (ADS)

Eitzel Solera, M. V.; Madzoro, S.; Solera, J.; Mhike Hove, E.; Changarara, A.; Ndlovu, D.; Chirindira, A.; Ndlovu, A.; Gwatipedza, S.; Mhizha, M.; Ndlovu, M.

2016-12-01

Participatory mapping is now a staple of community-based work around the world. Particularly for indigenous and rural peoples, it can represent a new avenue for environmental justice and can be a tool for culturally appropriate management of local ecosystems. We present a successful example of teaching and learning digital mapping technology in rural Zimbabwe. Our digital mapping project is part of the long-term community-based participatory research of The Muonde Trust in Mazvihwa, Zimbabwe. By gathering and distributing local knowledge and also bringing in visitors to share knowledge, Muonde has been able to spread relevant information among rural farmers. The authors were all members of Muonde or were Muonde's visitors, and were mentors and learners of digital mapping technologies at different times. Key successful characteristics of participants included patience, compassion, openness, perseverance, respect, and humility. Important mentoring strategies included: 1) instruction in Shona and in English, 2) locally relevant examples, assignments, and analogies motivated by real needs, 3) using a variety of teaching methods for different learning modalities, 4) building on and modifying familiar teaching methods, and 5) paying attention to the social and relational aspects of teaching and learning. The Muonde mapping team has used their new skills for a wide variety of purposes, including: identifying, discussing, and acting on emerging needs; using digital mapping for land-use and agropastoral planning; and using mapping as a tool for recording and telling important historical and cultural stories. Digital mapping has built self-confidence as well as providing employable skills and giving Muonde more visibility to other local and national non-governmental organizations, utility companies, and educational institutions. Digital mapping, as taught in a bottom-up, collaborative way, has proven to be both accessible and of enormous practical use to rural Zimbabweans.

Self-organizing sensing and actuation for automatic control

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

Cheng, George Shu-Xing

A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

Self-Regulated Learning Study Strategies and Academic Performance in Undergraduate Organic Chemistry: An Investigation Examining Ethnically Diverse Students

ERIC Educational Resources Information Center

Lopez, Enrique J.; Nandagopal, Kiruthiga; Shavelson, Richard J.; Szu, Evan; Penn, John

2013-01-01

This study sought to identify ethnically diverse students' study strategies in organic chemistry and their relationships to course outcomes. Study diaries, concept maps, and problem sets were used to assess study outcomes. Findings show that students engage in four commonly used reviewing-type strategies, regardless of ethnic group affiliation.…

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

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

Speeding up the learning of robot kinematics through function decomposition.

PubMed

Ruiz de Angulo, Vicente; Torras, Carme

2005-11-01

The main drawback of using neural networks or other example-based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i.e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off-line and on-line training schemes to learn these component functions are also proposed. Experimental results obtained by using nearest neighbors and parameterized self-organizing map, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.

Principal semantic components of language and the measurement of meaning.

PubMed

Samsonovich, Alexei V; Samsonovic, Alexei V; Ascoli, Giorgio A

2010-06-11

Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of "good/bad" (valence), "calm/excited" (arousal), and "open/closed" (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number ( approximately 4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a universal metric system for semantics of human experiences, which is necessary for developing a rigorous science of the mind.

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

The Effectiveness of Web-Based Asthma Self-Management System, My Asthma Portal (MAP): A Pilot Randomized Controlled Trial

PubMed Central

Ernst, Pierre; Bartlett, Susan J; Valois, Marie-France; Zaihra, Tasneem; Paré, Guy; Grad, Roland; Eilayyan, Owis; Perreault, Robert; Tamblyn, Robyn

2016-01-01

Background Whether Web-based technologies can improve disease self-management is uncertain. My Asthma Portal (MAP) is a Web-based self-management support system that couples evidence-based behavioral change components (self-monitoring of symptoms, physical activity, and medication adherence) with real-time monitoring, feedback, and support from a nurse case manager. Objective The aim of this study was to compare the impact of access to a Web-based asthma self-management patient portal linked to a case-management system (MAP) over 6 months compared with usual care on asthma control and quality of life. Methods A multicenter, parallel, 2-arm, pilot, randomized controlled trial was conducted with 100 adults with confirmed diagnosis of asthma from 2 specialty clinics. Asthma control was measured using an algorithm based on overuse of fast-acting bronchodilators and emergency department visits, and asthma-related quality of life was assessed using the Mini-Asthma Quality of Life Questionnaire (MAQLQ). Secondary mediating outcomes included asthma symptoms, depressive symptoms, self-efficacy, and beliefs about medication. Process evaluations were also included. Results A total of 49 individuals were randomized to MAP and 51 to usual care. Compared with usual care, participants in the intervention group reported significantly higher asthma quality of life (mean change 0.61, 95% CI 0.03 to 1.19), and the change in asthma quality of life for the intervention group between baseline and 3 months (mean change 0.66, 95% CI 0.35 to 0.98) was not seen in the control group. No significant differences in asthma quality of life were found between the intervention and control groups at 6 (mean change 0.46, 95% CI –0.12 to 1.05) and 9 months (mean change 0.39, 95% CI –0.2 to 0.98). For poor control status, there was no significant effect of group, time, or group by time. For all self-reported measures, the intervention group had a significantly higher proportion of individuals, demonstrating a minimal clinically meaningful improvement compared with the usual care group. Conclusions This study supported the use of MAP to enhance asthma quality of life but not asthma control as measured by an administrative database. Implementation of MAP beyond 6 months with tailored protocols for monitoring symptoms and health behaviors as individuals’ knowledge and self-management skills improve may result in long-term gains in asthma control. ClinicalTrial International Standard Randomized Controlled Trial Number (ISRCTN): 34326236; http://www.isrctn.com/ISRCTN34326236 (Archived by Webcite at http://www.webcitation.org/6mGxoI1R7). PMID:27908846

Motivation and Pleasure Scale-Self-Report (MAP-SR): Validation of the German version of a self-report measure for screening negative symptoms in schizophrenia.

PubMed

Engel, Maike; Lincoln, Tania Marie

2016-02-01

Validated self-report instruments could provide a time efficient screening method for negative symptoms in people with schizophrenia. The aim of this study was to examine the psychometric properties of a German version of the Motivation and Pleasure Scale-Self-Report (MAP-SR) which is based on the Clinical Assessment Interview for Negative Symptoms (CAINS). In- and outpatients (N=50) with schizophrenia or schizoaffective disorder were assessed with standardized interviews and questionnaires on negative and positive symptoms and general psychopathology in schizophrenia, depression, and global functioning. The German version of the MAP-SR showed high internal consistency. Convergent validity was supported by significant correlations between the MAP-SR with the experience sub-scale of the CAINS and the negative symptom sub-scale of the Positive and Negative Syndrome Scale. The MAP-SR also exhibited discriminant validity indicated by its non-significant correlations with positive symptoms and general psychopathology, which is in line with the findings for the original version of the MAP-SR. However, the MAP-SR correlated moderately with depression. The German MAP-SR appears to be a valid and suitable diagnostic tool for the identification of negative symptoms in schizophrenia. Copyright © 2015 Elsevier Inc. All rights reserved.

Distributed memory approaches for robotic neural controllers

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1990-01-01

The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

USING SELF-ORGANIZING MAPS TO EXPLORE PATTERNS IN SPECIES RICHNESS AND PROTECTION

EPA Science Inventory

The combination of species distributions with abiotic and landscape variables using Geographic Information Systems can be used to help prioritize areas for biodiversity protection, although the number of variables and complexity of the relationships between them can prove difficu...

Machine-learned analysis of quantitative sensory testing responses to noxious cold stimulation in healthy subjects.

PubMed

Weyer-Menkhoff, I; Thrun, M C; Lötsch, J

2018-05-01

Pain in response to noxious cold has a complex molecular background probably involving several types of sensors. A recent observation has been the multimodal distribution of human cold pain thresholds. This study aimed at analysing reproducibility and stability of this observation and further exploration of data patterns supporting a complex background. Pain thresholds to noxious cold stimuli (range 32-0 °C, tonic: temperature decrease -1 °C/s, phasic: temperature decrease -8 °C/s) were acquired in 148 healthy volunteers. The probability density distribution was analysed using machine-learning derived methods implemented as Gaussian mixture modeling (GMM), emergent self-organizing maps and self-organizing swarms of data agents. The probability density function of pain responses was trimodal (mean thresholds at 25.9, 18.4 and 8.0 °C for tonic and 24.5, 18.1 and 7.5 °C for phasic stimuli). Subjects' association with Gaussian modes was consistent between both types of stimuli (weighted Cohen's κ = 0.91). Patterns emerging in self-organizing neuronal maps and swarms could be associated with different trends towards decreasing cold pain sensitivity in different Gaussian modes. On self-organizing maps, the third Gaussian mode emerged as particularly distinct. Thresholds at, roughly, 25 and 18 °C agree with known working temperatures of TRPM8 and TRPA1 ion channels, respectively, and hint at relative local dominance of either channel in respective subjects. Data patterns suggest involvement of further distinct mechanisms in cold pain perception at lower temperatures. Findings support data science approaches to identify biologically plausible hints at complex molecular mechanisms underlying human pain phenotypes. Sensitivity to pain is heterogeneous. Data-driven computational research approaches allow the identification of subgroups of subjects with a distinct pattern of sensitivity to cold stimuli. The subgroups are reproducible with different types of noxious cold stimuli. Subgroups show pattern that hints at distinct and inter-individually different types of the underlying molecular background. © 2018 European Pain Federation - EFIC®.

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.

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin, northeastern China, from 2011 to 2015.

PubMed

Jiang, Mingcen; Wang, Yeyao; Yang, Qi; Meng, Fansheng; Yao, Zhipeng; Cheng, Peixuan

2018-03-30

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH 3 -N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD 5 ) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.

Analysis of short single rest/activation epoch fMRI by self-organizing map neural network

NASA Astrophysics Data System (ADS)

Erberich, Stephan G.; Dietrich, Thomas; Kemeny, Stefan; Krings, Timo; Willmes, Klaus; Thron, Armin; Oberschelp, Walter

2000-04-01

Functional magnet resonance imaging (fMRI) has become a standard non invasive brain imaging technique delivering high spatial resolution. Brain activation is determined by magnetic susceptibility of the blood oxygen level (BOLD effect) during an activation task, e.g. motor, auditory and visual tasks. Usually box-car paradigms have 2 - 4 rest/activation epochs with at least an overall of 50 volumes per scan in the time domain. Statistical test based analysis methods need a large amount of repetitively acquired brain volumes to gain statistical power, like Student's t-test. The introduced technique based on a self-organizing neural network (SOM) makes use of the intrinsic features of the condition change between rest and activation epoch and demonstrated to differentiate between the conditions with less time points having only one rest and one activation epoch. The method reduces scan and analysis time and the probability of possible motion artifacts from the relaxation of the patients head. Functional magnet resonance imaging (fMRI) of patients for pre-surgical evaluation and volunteers were acquired with motor (hand clenching and finger tapping), sensory (ice application), auditory (phonological and semantic word recognition task) and visual paradigms (mental rotation). For imaging we used different BOLD contrast sensitive Gradient Echo Planar Imaging (GE-EPI) single-shot pulse sequences (TR 2000 and 4000, 64 X 64 and 128 X 128, 15 - 40 slices) on a Philips Gyroscan NT 1.5 Tesla MR imager. All paradigms were RARARA (R equals rest, A equals activation) with an epoch width of 11 time points each. We used the self-organizing neural network implementation described by T. Kohonen with a 4 X 2 2D neuron map. The presented time course vectors were clustered by similar features in the 2D neuron map. Three neural networks were trained and used for labeling with the time course vectors of one, two and all three on/off epochs. The results were also compared by using a Kolmogorov-Smirnov statistical test of all 66 time points. To remove non- periodical time courses from training an auto-correlation function and bandwidth limiting Fourier filtering in combination with Gauss temporal smoothing was used. None of the trained maps, with one, two and three epochs, were significantly different which indicates that the feature space of only one on/off epoch is sufficient to differentiate between the rest and task condition. We found, that without pre-processing of the data no meaningful results can be achieved because of the huge amount of the non-activated and background voxels represents the majority of the features and is therefore learned by the SOM. Thus it is crucial to remove unnecessary capacity load of the neural network by selection of the training input, using auto-correlation function and/or Fourier spectrum analysis. However by reducing the time points to one rest and one activation epoch either strong auto- correlation or a precise periodical frequency is vanishing. Self-organizing maps can be used to separate rest and activation epochs of with only a 1/3 of the usually acquired time points. Because of the nature of the SOM technique, the pattern or feature separation, only the presence of a state change between the conditions is necessary for differentiation. Also the variance of the individual hemodynamic response function (HRF) and the variance of the spatial different regional cerebral blood flow (rCBF) is learned from the subject and not compared with a fixed model done by statistical evaluation. We found that reducing the information to only a few time points around the BOLD effect was not successful due to delays of rCBF and the insufficient extension of the BOLD feature in the time space. Especially for patient routine observation and pre-surgical planing a reduced scan time is of interest.

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

Teacher-Directed and Student-Mediated Textbook Comprehension Strategies.

ERIC Educational Resources Information Center

Reynolds, Catharine J.; Salend, Spencer J.

1990-01-01

The article describes teacher-directed and student-mediated comprehension strategies to improve the text comprehension skills of mainstreamed students with mild disabilities. Techniques include advance organizers, study guides, color coding, oral reading, critical thinking maps, and self-questioning techniques. Guidelines are offered for assessing…

Identifying individual sperm whales acoustically using self-organizing maps

NASA Astrophysics Data System (ADS)

Ioup, Juliette W.; Ioup, George E.

2005-09-01

The Littoral Acoustic Demonstration Center (LADC) is a consortium at Stennis Space Center comprising the University of New Orleans, the University of Southern Mississippi, the Naval Research Laboratory, and the University of Louisiana at Lafayette. LADC deployed three Environmental Acoustic Recording System (EARS) buoys in the northern Gulf of Mexico during the summer of 2001 to study ambient noise and marine mammals. Each LADC EARS was an autonomous, self-recording buoy capable of 36 days of continuous recording of a single channel at an 11.7-kHz sampling rate (bandwidth to 5859 Hz). The hydrophone selected for this analysis was approximately 50 m from the bottom in a water depth of 800 m on the continental slope off the Mississippi River delta. This paper contains recent analysis results for sperm whale codas recorded during a 3-min period. Results are presented for the identification of individual sperm whales from their codas, using the acoustic properties of the clicks within each coda. The recorded time series, the Fourier transform magnitude, and the wavelet transform coefficients are each used separately with a self-organizing map procedure for 43 codas. All show the codas as coming from four or five individual whales. [Research supported by ONR.

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.

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.

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

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.

System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

DOEpatents

Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.

2014-06-17

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

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.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

PubMed

Oyama, Katsunori; Sakatani, Kaoru

2016-01-01

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

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.

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.

Self-Organization Activities of College Students: Challenges and Opportunities

ERIC Educational Resources Information Center

Shmurygina, Natalia; Bazhenova, Natalia; Bazhenov, Ruslan; Nikolaeva, Natalia; Tcytcarev, Andrey

2016-01-01

The article provides the analysis of self-organization activities of college students related to their participation in youth associations activities. The purpose of research is to disclose a degree of students' activities demonstration based on self-organization processes, assessment of existing self-organization practices of the youth,…

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.

A Self-Organizing Map Based Evaluation of the Antarctic Mesoscale Prediction System Using Observations from a 30-m Instrumented Tower on the Ross Ice Shelf, Antarctica

NASA Astrophysics Data System (ADS)

Nigro, M. A.; Cassano, J. J.; Wille, J.; Bromwich, D. H.; Lazzara, M. A.

2015-12-01

An accurate representation of the atmospheric boundary layer in numerical weather prediction models is important for predicting turbulence and energy exchange in the atmosphere. This study uses two years of observations from a 30-m automatic weather station (AWS) installed on the Ross Ice Shelf, Antarctica to evaluate forecasts from the Antarctic Mesoscale Prediction System (AMPS), a numerical weather prediction system based on the polar version of the Weather Research and Forecasting (Polar WRF) model that uses the MYJ planetary boundary layer scheme and that primarily supports the extensive aircraft operations of the U.S. Antarctic Program. The 30-m AWS has six levels of instrumentation, providing vertical profiles of temperature, wind speed, and wind direction. The observations show the atmospheric boundary layer over the Ross Ice Shelf is stable approximately 80% of the time, indicating the influence of the permanent ice surface in this region. The observations from the AWS are further analyzed using the method of self-organizing maps (SOM) to identify the range of potential temperature profiles that occur over the Ross Ice Shelf. The SOM analysis identified 30 patterns, which range from strong inversions to slightly unstable profiles. The corresponding AMPS forecasts were evaluated for each of the 30 patterns to understand the accuracy of the AMPS near surface layer under different atmospheric conditions. The results indicate that under stable conditions AMPS with MYJ under predicts the inversion strength by as much as 7.4 K over the 30-m depth of the tower and over predicts the near surface wind speed by as much as 3.8 m s-1. Conversely, under slightly unstable conditions, AMPS predicts both the inversion strength and near surface wind speeds with reasonable accuracy.

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.

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.

Complementary and conventional medicine: a concept map

PubMed Central

Baldwin, Carol M; Kroesen, Kendall; Trochim, William M; Bell, Iris R

2004-01-01

Background Despite the substantive literature from survey research that has accumulated on complementary and alternative medicine (CAM) in the United States and elsewhere, very little research has been done to assess conceptual domains that CAM and conventional providers would emphasize in CAM survey studies. The objective of this study is to describe and interpret the results of concept mapping with conventional and CAM practitioners from a variety of backgrounds on the topic of CAM. Methods Concept mapping, including free sorts, ratings, and multidimensional scaling was used to organize conceptual domains relevant to CAM into a visual "cluster map." The panel consisted of CAM providers, conventional providers, and university faculty, and was convened to help formulate conceptual domains to guide the development of a CAM survey for use with United States military veterans. Results Eight conceptual clusters were identified: 1) Self-assessment, Self-care, and Quality of Life; 2) Health Status, Health Behaviors; 3) Self-assessment of Health; 4) Practical/Economic/ Environmental Concerns; 5) Needs Assessment; 6) CAM vs. Conventional Medicine; 7) Knowledge of CAM; and 8) Experience with CAM. The clusters suggest panelists saw interactions between CAM and conventional medicine as a critical component of the current medical landscape. Conclusions Concept mapping provided insight into how CAM and conventional providers view the domain of health care, and was shown to be a useful tool in the formulation of CAM-related conceptual domains. PMID:15018623

Nanometer-Scale Chemistry of a Calcite Biomineralization Template: Implications for Skeletal Composition and Nucleation

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

Branson, Oscar; Bonnin, Elisa A.; Perea, Daniel E.

Biomineralizing organisms exhibit exquisite control over skeletal morphology and composition. The promise of understanding and harnessing this feat of natural engineering has motivated an intense search for the mechanisms that direct in vivo mineral self-assembly. We used atom probe tomography, a sub-nanometer 3D chemical mapping technique, to examine the chemistry of a buried organic-mineral interface in biomineral calcite from a marine foraminifer. The chemical patterns at this interface capture the processes of early biomineralization, when the shape, mineralogy, and orientation of skeletal growth are initially established. Sodium is enriched by a factor of nine on the organic side of themore » interface. Based on this pattern, we suggest that sodium plays an integral role in early biomineralization, potentially altering interfacial energy to promote crystal nucleation, and that interactions between organic surfaces and electrolytes other than calcium or carbonate could be a crucial aspect of CaCO3 biomineralization.« less

Nanometer-Scale Chemistry of a Calcite Biomineralization Template: Implications for Skeletal Composition and Nucleation

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

Branson, Oscar; Bonnin, Elisa A.; Perea, Daniel E.

2016-10-28

Biomineralizing organisms exhibit exquisite control over skeletal morphology and composition. The promise of understanding and harnessing this feat of natural engineering has motivated an intense search for the mechanisms that direct in vivo mineral self-assembly. We used atom probe tomography, a sub-nanometer 3D chemical mapping technique, to examine the chemistry of a buried organic-mineral interface in biomineral calcite from a marine foraminifer. The chemical patterns at this interface capture the processes of early biomineralization, when the shape, mineralogy, and orientation of skeletal growth are initially established. Sodium is enriched by a factor of nine on the organic side of themore » interface. Based on this pattern, we suggest that sodium plays an integral role in early biomineralization, potentially altering interfacial energy to promote crystal nucleation, and that interactions between organic surfaces and electrolytes other than calcium or carbonate could be a crucial aspect of CaCO3 biomineralization.« less

Nanometer-Scale Chemistry of a Calcite Biomineralization Template: Implications for Skeletal Composition and Nucleation

DOE PAGES

Branson, Oscar; Bonnin, Elisa A.; Perea, Daniel E.; ...

2016-10-28

Biomineralizing organisms exhibit exquisite control over skeletal morphology and composition. The promise of understanding and harnessing this feat of natural engineering has motivated an intense search for the mechanisms that direct in vivo mineral self-assembly. We used atom probe tomography, a sub-nanometer 3D chemical mapping technique, to examine the chemistry of a buried organic-mineral interface in biomineral calcite from a marine foraminifer. Here, the chemical patterns at this interface capture the processes of early biomineralization, when the shape, mineralogy, and orientation of skeletal growth are initially established. Sodium is enriched by a factor of nine on the organic side ofmore » the interface. Based on this pattern, we suggest that sodium plays an integral role in early biomineralization, potentially altering interfacial energy to promote crystal nucleation, and that interactions between organic surfaces and electrolytes other than calcium or carbonate could be a crucial aspect of CaCO 3 biomineralization.« less

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

PubMed

Kamimura, Ryotaro

2011-05-01

In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

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

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.

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.

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.

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.

Portraits of self-organization in fish schools interacting with robots

NASA Astrophysics Data System (ADS)

Aureli, M.; Fiorilli, F.; Porfiri, M.

2012-05-01

In this paper, we propose an enabling computational and theoretical framework for the analysis of experimental instances of collective behavior in response to external stimuli. In particular, this work addresses the characterization of aggregation and interaction phenomena in robot-animal groups through the exemplary analysis of fish schooling in the vicinity of a biomimetic robot. We adapt global observables from statistical mechanics to capture the main features of the shoal collective motion and its response to the robot from experimental observations. We investigate the shoal behavior by using a diffusion mapping analysis performed on these global observables that also informs the definition of relevant portraits of self-organization.

Understanding Complexity and Self-Organization in a Defense Program Management Organization (Experimental Design)

DTIC Science & Technology

2016-03-18

SPONSORED REPORT SERIES Understanding Complexity and Self - Organization in a Defense Program Management Organization (Experimental Design...experiment will examine the decision-making process within the program office and the self - organization of key program office personnel based upon formal...and informal communications links. Additionally, we are interested in the effects of this self - organizing process on the organization’s shared

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

Stop Think: a simple approach to encourage the self-assessment of learning.

PubMed

Guy, Richard; Byrne, Bruce; Dobos, Marian

2017-03-01

A simple "stop think" approach was developed to encourage the self-assessment of learning. A key element was the requirement for students to rate their feeling of difficulty before [FOD (pre) ] and after [FOD (post) ] completing each of three authentic anatomy and physiology concept map exercises. The cohort was divided into low- (group L) and high-performing (group H) groups (based on final subject marks). Both FOD (pre) (group L) and FOD (post) (groups L and H) were significantly negatively correlated with score for some maps. A comparison of FOD (pre) and FOD (post) showed that students changed their mind about difficulty in 58-70% of the completed maps. Students who changed their estimation were asked to provide explanatory comments, and an increase in difficulty was related to problems with map link generation. For students who found the maps easier, 40% of comments indicated that map generation prompted recall of information from memory. Both difficulty estimations and comments supported the contention that students were self-assessing their interaction with the concept maps. Group H was significantly older than group L, had significantly higher levels of deep strategic and deep motivational learning, and had significantly higher marks in two of three concept map exercises. Notwithstanding these differences, the results from the "stop think" approach were similar between groups, indicating that it may be appropriate for students of varying academic ability. It is suggested that "stop think" may be a useful approach to encourage student self-assessment, an important step in assisting self-regulated learning development. Copyright © 2017 the American Physiological Society.

The Effectiveness of Web-Based Asthma Self-Management System, My Asthma Portal (MAP): A Pilot Randomized Controlled Trial.

PubMed

Ahmed, Sara; Ernst, Pierre; Bartlett, Susan J; Valois, Marie-France; Zaihra, Tasneem; Paré, Guy; Grad, Roland; Eilayyan, Owis; Perreault, Robert; Tamblyn, Robyn

2016-12-01

Whether Web-based technologies can improve disease self-management is uncertain. My Asthma Portal (MAP) is a Web-based self-management support system that couples evidence-based behavioral change components (self-monitoring of symptoms, physical activity, and medication adherence) with real-time monitoring, feedback, and support from a nurse case manager. The aim of this study was to compare the impact of access to a Web-based asthma self-management patient portal linked to a case-management system (MAP) over 6 months compared with usual care on asthma control and quality of life. A multicenter, parallel, 2-arm, pilot, randomized controlled trial was conducted with 100 adults with confirmed diagnosis of asthma from 2 specialty clinics. Asthma control was measured using an algorithm based on overuse of fast-acting bronchodilators and emergency department visits, and asthma-related quality of life was assessed using the Mini-Asthma Quality of Life Questionnaire (MAQLQ). Secondary mediating outcomes included asthma symptoms, depressive symptoms, self-efficacy, and beliefs about medication. Process evaluations were also included. A total of 49 individuals were randomized to MAP and 51 to usual care. Compared with usual care, participants in the intervention group reported significantly higher asthma quality of life (mean change 0.61, 95% CI 0.03 to 1.19), and the change in asthma quality of life for the intervention group between baseline and 3 months (mean change 0.66, 95% CI 0.35 to 0.98) was not seen in the control group. No significant differences in asthma quality of life were found between the intervention and control groups at 6 (mean change 0.46, 95% CI -0.12 to 1.05) and 9 months (mean change 0.39, 95% CI -0.2 to 0.98). For poor control status, there was no significant effect of group, time, or group by time. For all self-reported measures, the intervention group had a significantly higher proportion of individuals, demonstrating a minimal clinically meaningful improvement compared with the usual care group. This study supported the use of MAP to enhance asthma quality of life but not asthma control as measured by an administrative database. Implementation of MAP beyond 6 months with tailored protocols for monitoring symptoms and health behaviors as individuals' knowledge and self-management skills improve may result in long-term gains in asthma control. International Standard Randomized Controlled Trial Number (ISRCTN): 34326236; http://www.isrctn.com/ISRCTN34326236 (Archived by Webcite at http://www.webcitation.org/6mGxoI1R7). ©Sara Ahmed, Pierre Ernst, Susan J Bartlett, Marie-France Valois, Tasneem Zaihra, Guy Paré, Roland Grad, Owis Eilayyan, Robert Perreault, Robyn Tamblyn. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 01.12.2016.

Investigating the Link between Self Directed Learning Readiness and Project-Based Learning Outcomes: The Case of International Masters Students in an Engineering Management Course

ERIC Educational Resources Information Center

Stewart, Rodney A.

2007-01-01

Modern learning approaches increasingly have fewer structured learning activities and more self-directed learning tasks guided through consultation with academics. Such tasks are predominately project-/problem-based where the student is required to follow a freely guided road map to self discovery while simultaneously achieving desired learning…

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%

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.

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.

Computer-aided diagnosis of HIE based on segmentation of MRI

NASA Astrophysics Data System (ADS)

Sun, Ziguang; Li, Chungui; Wang, Qin

2009-10-01

Computer-aided diagnosis has become one of the major research subjects in medical imaging and diagnostic radiology. Hypoxic-ischemic encephalopathy (HIE), remains a serious condition that causes significant mortality and long-term morbidity to neonates. We adopt self-organizing feature maps to segment the tissues, such as white matter and grey matter in the magnetic resonance images. The borderline between white matter and grey matter can be found and the doubtful regions along with the borderline can be localized, then the feature in doubtful regions can be quantified. The method can assist doctors to easily diagnose whether a neonate is ill with mild HIE.

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.

HelpfulMed: Intelligent Searching for Medical Information over the Internet.

ERIC Educational Resources Information Center

Chen, Hsinchun; Lally, Ann M.; Zhu, Bin; Chau, Michael

2003-01-01

Discussion of the information needs of medical professionals and researchers focuses on the architecture of a Web portal designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, and self-organizing map technologies to provide searchers with fine-grained results. Reports results of evaluation of spider algorithms…

Kohonen Self-Organizing Maps in Validity Maintenance for Automated Scoring of Constructed Response.

ERIC Educational Resources Information Center

Williamson, David M.; Bejar, Isaac I.

As the automated scoring of constructed responses reaches operational status, monitoring the scoring process becomes a primary concern, particularly if automated scoring is intended to operate completely unassisted by humans. Using actual candidate selections from the Architectural Registration Examination (n=326), this study uses Kohonen…

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.

Monitoring developmental force distributions in reconstituted embryonic epithelia.

PubMed

Przybyla, L; Lakins, J N; Sunyer, R; Trepat, X; Weaver, V M

2016-02-01

The way cells are organized within a tissue dictates how they sense and respond to extracellular signals, as cues are received and interpreted based on expression and organization of receptors, downstream signaling proteins, and transcription factors. Part of this microenvironmental context is the result of forces acting on the cell, including forces from other cells or from the cellular substrate or basement membrane. However, measuring forces exerted on and by cells is difficult, particularly in an in vivo context, and interpreting how forces affect downstream cellular processes poses an even greater challenge. Here, we present a simple method for monitoring and analyzing forces generated from cell collectives. We demonstrate the ability to generate traction force data from human embryonic stem cells grown in large organized epithelial sheets to determine the magnitude and organization of cell-ECM and cell-cell forces within a self-renewing colony. We show that this method can be used to measure forces in a dynamic hESC system and demonstrate the ability to map intracolony protein localization to force organization. Copyright © 2015 Elsevier Inc. All rights reserved.

Global Consensus Theorem and Self-Organized Criticality: Unifying Principles for Understanding Self-Organization, Swarm Intelligence and Mechanisms of Carcinogenesis

PubMed Central

Rosenfeld, Simon

2013-01-01

Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. Generally, each level in a biological hierarchy possesses its own systemic properties and requires its own way of observation, conceptualization, and modeling. In this work, an attempt is made to outline general guiding principles in exploration of a wide range of seemingly dissimilar phenomena observed in large communities of individuals devoid of any personal intelligence and interacting with each other through simple stimulus-response rules. Mathematically, these guiding principles are well captured by the Global Consensus Theorem (GCT) equally applicable to neural networks and to Lotka-Volterra population dynamics. Universality of the mechanistic principles outlined by GCT allows for a unified approach to such diverse systems as biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, social networks and many other systems. Another cluster of universal laws governing the self-organization in large communities of locally interacting individuals is built around the principle of self-organized criticality (SOC). The GCT and SOC, separately or in combination, provide a conceptual basis for understanding the phenomena of self-organization occurring in large communities without involvement of a supervisory authority, without system-wide informational infrastructure, and without mapping of general plan of action onto cognitive/behavioral faculties of its individual members. Cancer onset and proliferation serves as an important example of application of these conceptual approaches. In this paper, the point of view is put forward that apparently irreconcilable contradictions between two opposing theories of carcinogenesis, that is, the Somatic Mutation Theory and the Tissue Organization Field Theory, may be resolved using the systemic approaches provided by GST and SOC. PMID:23471309

Hillslope chemical weathering across Paraná, Brazil: a data mining-GIS hybrid approach

USGS Publications Warehouse

Iwashita, Fabio; Friedel, Michael J.; Filho, Carlos Roberto de Souza; Fraser, Stephen J.

2011-01-01

Self-organizing map (SOM) and geographic information system (GIS) models were used to investigate the nonlinear relationships associated with geochemical weathering processes at local (~100 km2) and regional (~50,000 km2) scales. The data set consisted of 1) 22 B-horizon soil variables: P, C, pH, Al, total acidity, Ca, Mg, K, total cation exchange capacity, sum of exchangeable bases, base saturation, Cu, Zn, Fe, B, S, Mn, gammaspectrometry (total count, potassium, thorium, and uranium) and magnetic susceptibility measures; and 2) six topographic variables: elevation, slope, aspect, hydrological accumulated flux, horizontal curvature and vertical curvature. It is characterized at 304 locations from a quasi-regular grid spaced about 24 km across the state of Paraná. This data base was split into two subsets: one for analysis and modeling (274 samples) and the other for validation (30 samples) purposes. The self-organizing map and clustering methods were used to identify and classify the relations among solid-phase chemical element concentrations and GIS derived topographic models. The correlation between elevation and k-means clusters related the relative position inside hydrologic macro basins, which was interpreted as an expression of the weathering process reaching a steady-state condition at the regional scale. Locally, the chemical element concentrations were related to the vertical curvature representing concave–convex hillslope features, where concave hillslopes with convergent flux tends to be a reducing environment and convex hillslopes with divergent flux, oxidizing environments. Stochastic cross validation demonstrated that the SOM produced unbiased classifications and quantified the relative amount of uncertainty in predictions. This work strengthens the hypothesis that, at B-horizon steady-state conditions, the terrain morphometry were linked with the soil geochemical weathering in a two-way dependent process: the topographic relief was a factor on environmental geochemistry while chemical weathering was for terrain feature delineation.

Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

2013-12-01

It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more serious before occurring.

Hillslope chemical weathering across Paraná, Brazil: A data mining-GIS hybrid approach

NASA Astrophysics Data System (ADS)

Iwashita, Fabio; Friedel, Michael J.; Filho, Carlos Roberto de Souza; Fraser, Stephen J.

2011-09-01

Self-organizing map (SOM) and geographic information system (GIS) models were used to investigate the nonlinear relationships associated with geochemical weathering processes at local (~100 km 2) and regional (~50,000 km 2) scales. The data set consisted of 1) 22 B-horizon soil variables: P, C, pH, Al, total acidity, Ca, Mg, K, total cation exchange capacity, sum of exchangeable bases, base saturation, Cu, Zn, Fe, B, S, Mn, gammaspectrometry (total count, potassium, thorium, and uranium) and magnetic susceptibility measures; and 2) six topographic variables: elevation, slope, aspect, hydrological accumulated flux, horizontal curvature and vertical curvature. It is characterized at 304 locations from a quasi-regular grid spaced about 24 km across the state of Paraná. This data base was split into two subsets: one for analysis and modeling (274 samples) and the other for validation (30 samples) purposes. The self-organizing map and clustering methods were used to identify and classify the relations among solid-phase chemical element concentrations and GIS derived topographic models. The correlation between elevation and k-means clusters related the relative position inside hydrologic macro basins, which was interpreted as an expression of the weathering process reaching a steady-state condition at the regional scale. Locally, the chemical element concentrations were related to the vertical curvature representing concave-convex hillslope features, where concave hillslopes with convergent flux tends to be a reducing environment and convex hillslopes with divergent flux, oxidizing environments. Stochastic cross validation demonstrated that the SOM produced unbiased classifications and quantified the relative amount of uncertainty in predictions. This work strengthens the hypothesis that, at B-horizon steady-state conditions, the terrain morphometry were linked with the soil geochemical weathering in a two-way dependent process: the topographic relief was a factor on environmental geochemistry while chemical weathering was for terrain feature delineation.

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.

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

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.

Analysis of mass incident diffusion in Weibo based on self-organization theory

NASA Astrophysics Data System (ADS)

Pan, Jun; Shen, Huizhang

2018-02-01

This study introduces some theories and methods of self-organization system to the research of the diffusion mechanism of mass incidents in Weibo (Chinese Twitter). Based on the analysis on massive Weibo data from Songjiang battery factory incident happened in 2013 and Jiiangsu Qidong OJI PAPER incident happened in 2012, we find out that diffusion system of mass incident in Weibo satisfies Power Law, Zipf's Law, 1/f noise and Self-similarity. It means this system is the self-organization criticality system and dissemination bursts can be understood as one kind of Self-organization behavior. As the consequence, self-organized criticality (SOC) theory can be used to explain the evolution of mass incident diffusion and people may come up with the right strategy to control such kind of diffusion if they can handle the key ingredients of Self-organization well. Such a study is of practical importance which can offer opportunities for policy makers to have good management on these events.

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

USGS Publications Warehouse

Rover, Jennifer R.; Wylie, Bruce K.; Ji, Lei

2010-01-01

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

Competition in high dimensional spaces using a sparse approximation of neural fields.

PubMed

Quinton, Jean-Charles; Girau, Bernard; Lefort, Mathieu

2011-01-01

The Continuum Neural Field Theory implements competition within topologically organized neural networks with lateral inhibitory connections. However, due to the polynomial complexity of matrix-based implementations, updating dense representations of the activity becomes computationally intractable when an adaptive resolution or an arbitrary number of input dimensions is required. This paper proposes an alternative to self-organizing maps with a sparse implementation based on Gaussian mixture models, promoting a trade-off in redundancy for higher computational efficiency and alleviating constraints on the underlying substrate.This version reproduces the emergent attentional properties of the original equations, by directly applying them within a continuous approximation of a high dimensional neural field. The model is compatible with preprocessed sensory flows but can also be interfaced with artificial systems. This is particularly important for sensorimotor systems, where decisions and motor actions must be taken and updated in real-time. Preliminary tests are performed on a reactive color tracking application, using spatially distributed color features.

Large scale analysis of protein-binding cavities using self-organizing maps and wavelet-based surface patches to describe functional properties, selectivity discrimination, and putative cross-reactivity.

PubMed

Kupas, Katrin; Ultsch, Alfred; Klebe, Gerhard

2008-05-15

A new method to discover similar substructures in protein binding pockets, independently of sequence and folding patterns or secondary structure elements, is introduced. The solvent-accessible surface of a binding pocket, automatically detected as a depression on the protein surface, is divided into a set of surface patches. Each surface patch is characterized by its shape as well as by its physicochemical characteristics. Wavelets defined on surfaces are used for the description of the shape, as they have the great advantage of allowing a comparison at different resolutions. The number of coefficients to describe the wavelets can be chosen with respect to the size of the considered data set. The physicochemical characteristics of the patches are described by the assignment of the exposed amino acid residues to one or more of five different properties determinant for molecular recognition. A self-organizing neural network is used to project the high-dimensional feature vectors onto a two-dimensional layer of neurons, called a map. To find similarities between the binding pockets, in both geometrical and physicochemical features, a clustering of the projected feature vector is performed using an automatic distance- and density-based clustering algorithm. The method was validated with a small training data set of 109 binding cavities originating from a set of enzymes covering 12 different EC numbers. A second test data set of 1378 binding cavities, extracted from enzymes of 13 different EC numbers, was then used to prove the discriminating power of the algorithm and to demonstrate its applicability to large scale analyses. In all cases, members of the data set with the same EC number were placed into coherent regions on the map, with small distances between them. Different EC numbers are separated by large distances between the feature vectors. A third data set comprising three subfamilies of endopeptidases is used to demonstrate the ability of the algorithm to detect similar substructures between functionally related active sites. The algorithm can also be used to predict the function of novel proteins not considered in training data set. 2007 Wiley-Liss, Inc.

Many-body physics in two-component Bose–Einstein condensates in a cavity: fragmented superradiance and polarization

NASA Astrophysics Data System (ADS)

Lode, Axel U. J.; Diorico, Fritz S.; Wu, RuGway; Molignini, Paolo; Papariello, Luca; Lin, Rui; Lévêque, Camille; Exl, Lukas; Tsatsos, Marios C.; Chitra, R.; Mauser, Norbert J.

2018-05-01

We consider laser-pumped one-dimensional two-component bosons in a parabolic trap embedded in a high-finesse optical cavity. Above a threshold pump power, the photons that populate the cavity modify the effective atom trap and mediate a coupling between the two components of the Bose–Einstein condensate. We calculate the ground state of the laser-pumped system and find different stages of self-organization depending on the power of the laser. The modified potential and the laser-mediated coupling between the atomic components give rise to rich many-body physics: an increase of the pump power triggers a self-organization of the atoms while an even larger pump power causes correlations between the self-organized atoms—the BEC becomes fragmented and the reduced density matrix acquires multiple macroscopic eigenvalues. In this fragmented superradiant state, the atoms can no longer be described as two-level systems and the mapping of the system to the Dicke model breaks down.

Self-Organized Defects of Half-Metallic Nanowires in MgO-Based Magnetic Tunnel Junctions

NASA Astrophysics Data System (ADS)

Seike, Masayoshi; Fukushima, Tetsuya; Sato, Kazunori; Katayama-Yoshida, Hiroshi

2013-03-01

The purpose of this study is to examine the possibility of self-organization of defects and defect-induced properties in MgO-based magnetic tunnel junctions (MTJs). Using the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional, first-principles calculations were performed to estimate the electronic structures and total energies of MgO with various defects. From our thorough evaluation of the calculated results and previously reported experimental data, we propose that self-organized half-metallic nanowires of magnesium vacancies can be formed in MgO-based MTJs. This self-organization may provide the foundation for a comprehensive understanding of the conductivity, tunnel barriers and quantum oscillations of MgO-based MTJs. Further experimental verification is needed before firm conclusions can be drawn.

A Biologically Plausible Architecture of the Striatum to Solve Context-Dependent Reinforcement Learning Tasks.

PubMed

Shivkumar, Sabyasachi; Muralidharan, Vignesh; Chakravarthy, V Srinivasa

2017-01-01

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks.

A Biologically Plausible Architecture of the Striatum to Solve Context-Dependent Reinforcement Learning Tasks

PubMed Central

Shivkumar, Sabyasachi; Muralidharan, Vignesh; Chakravarthy, V. Srinivasa

2017-01-01

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks. PMID:28680395

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.

Computational approach for elucidating interactions of cross-species miRNAs and their targets in Flaviviruses.

PubMed

Shinde, Santosh P; Banerjee, Amit Kumar; Arora, Neelima; Murty, U S N; Sripathi, Venkateswara Rao; Pal-Bhadra, Manika; Bhadra, Utpal

2015-03-01

Combating viral diseases has been a challenging task since time immemorial. Available molecular approaches are limited and not much effective for this daunting task. MicroRNA based therapies have shown promise in recent times. MicroRNAs are tiny non-coding RNAs that regulate translational repression of target mRNA in highly specific manner. In this study, we have determined the target regions for human and viral microRNAs in the conserved genomic regions of selected viruses of Flaviviridae family using miRanda and performed a comparative target selectivity analysis among them. Specific target regions were determined and they were compared extensively among themselves by exploring their position to determine the vicinity. Based on the multiplicity and cooperativity analysis, interaction maps were developed manually to represent the interactions between top-ranking miRNAs and genomes of the viruses considered in this study. Self-organizing map (SOM) was used to cluster the best-ranked microRNAs based on the vital physicochemical properties. This study will provide deep insight into the interrelation of the viral and human microRNAs interactions with the selected Flaviviridae genomes and will help to identify cross-species microRNA targets on the viral genome.

The Lifetime Career Manager.

ERIC Educational Resources Information Center

Cabrera, James C.; Albrecht, Charles F., Jr.

This book provides a road map for readers to plan and develop their careers. It suggests self-assessment, setting short- and long-term goals, and working toward identified goals in short increments. Beyond careers, attention is paid to lifestyles and personal values. The book is organized in 17 chapters that cover the following topics: (1) the…

Analysis of large-scale gene expression data.

PubMed

Sherlock, G

2000-04-01

The advent of cDNA and oligonucleotide microarray technologies has led to a paradigm shift in biological investigation, such that the bottleneck in research is shifting from data generation to data analysis. Hierarchical clustering, divisive clustering, self-organizing maps and k-means clustering have all been recently used to make sense of this mass of data.

Physical Activity, Physical Fitness and Academic Achievement in Adolescents: A Self-Organizing Maps Approach

ERIC Educational Resources Information Center

Pellicer-Chenoll, Maite; Garcia-Massó, Xavier; Morales, Jose; Serra-Añó, Pilar; Solana-Tramunt, Mònica; González, Luis-Millán; Toca-Herrera, José-Luis

2015-01-01

The relationship among physical activity, physical fitness and academic achievement in adolescents has been widely studied; however, controversy concerning this topic persists. The methods used thus far to analyse the relationship between these variables have included mostly traditional lineal analysis according to the available literature. The…

Dysphonia Detected by Pattern Recognition of Spectral Composition.

ERIC Educational Resources Information Center

Leinonen, Lea; And Others

1992-01-01

This study analyzed production of a long vowel sound within Finnish words by normal or dysphonic voices, using the Self-Organizing Map, the artificial neural network algorithm of T. Kohonen which produces two-dimensional representations of speech. The method was found to be both sensitive and specific in the detection of dysphonia. (Author/JDD)

Kohonen Self-Organizing Feature Maps as a Means to Benchmark College and University Websites

ERIC Educational Resources Information Center

Cooper, Cameron; Burns, Andrew

2007-01-01

Websites for colleges and universities have become the primary means for students to obtain information in the college search process. Consequently, institutions of higher education should target their websites toward prospective and current students' needs, interests, and tastes. Numerous parameters must be determined in creating a school website…

The Environmental Self-Audit for Campus-Based Organizations: A Quick and Easy Guide to Environmental Compliance.

ERIC Educational Resources Information Center

New York State Dept. of Environmental Conservation, Albany.

This guide is intended to help public and not-for-profit campus-based organizations in New York State to comply with local, state, and federal environmental regulations. The environmental self-audit serves as a basic diagnostic tool for campus-based organizations (centralized schools, colleges/universities, correctional facilities, mental health…

Application of a self-organizing map and positive matrix factorization to investigate the spatial distributions and sources of polycyclic aromatic hydrocarbons in soils from Xiangfen County, northern China.

PubMed

Tao, Shi-Yang; Zhong, Bu-Qing; Lin, Yan; Ma, Jin; Zhou, Yongzhang; Hou, Hong; Zhao, Long; Sun, Zaijin; Qin, Xiaopeng; Shi, Huading

2017-07-01

The concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs) were measured in 128 surface soil samples from Xiangfen County, northern China. The total mass concentration of these PAHs ranged from 52 to 10,524ng/g, with a mean of 723ng/g. Four-ring PAHs contributed almost 50% of the total PAH burden. A self-organizing map and positive matrix factorization were applied to investigate the spatial distribution and source apportionment of PAHs. Three emission sources of PAHs were identified, namely, coking ovens (21.9%), coal/biomass combustion (60.1%), and anthracene oil (18.0%). High concentrations of low-molecular-weight PAHs were particularly apparent in the coking plant zone in the region around Gucheng Town. High-molecular-weight PAHs mainly originated from coal/biomass combustion around Gucheng Town, Xincheng Town, and Taosi Town. PAHs in the soil of Xiangfen County are unlikely to pose a significant cancer risk for the population. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparative transcriptomics with self-organizing map reveals cryptic photosynthetic differences between two accessions of North American Lake cress.

PubMed

Nakayama, Hokuto; Sakamoto, Tomoaki; Okegawa, Yuki; Kaminoyama, Kaori; Fujie, Manabu; Ichihashi, Yasunori; Kurata, Tetsuya; Motohashi, Ken; Al-Shehbaz, Ihsan; Sinha, Neelima; Kimura, Seisuke

2018-02-19

Because natural variation in wild species is likely the result of local adaptation, it provides a valuable resource for understanding plant-environmental interactions. Rorippa aquatica (Brassicaceae) is a semi-aquatic North American plant with morphological differences between several accessions, but little information available on any physiological differences. Here, we surveyed the transcriptomes of two R. aquatica accessions and identified cryptic physiological differences between them. We first reconstructed a Rorippa phylogeny to confirm relationships between the accessions. We performed large-scale RNA-seq and de novo assembly; the resulting 87,754 unigenes were then annotated via comparisons to different databases. Between-accession physiological variation was identified with transcriptomes from both accessions. Transcriptome data were analyzed with principal component analysis and self-organizing map. Results of analyses suggested that photosynthetic capability differs between the accessions. Indeed, physiological experiments revealed between-accession variation in electron transport rate and the redox state of the plastoquinone pool. These results indicated that one accession may have adapted to differences in temperature or length of the growing season.

Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

2015-04-01

The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

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.

Exploring the combined effects of the Arctic Oscillation and ENSO on the wintertime climate over East Asia using self-organizing maps

NASA Astrophysics Data System (ADS)

Huang, Wenyu; Chen, Ruyan; Yang, Zifan; Wang, Bin; Ma, Wenqian

2017-09-01

To examine the combined effects of the different spatial patterns of the Arctic Oscillation (AO)-related sea level pressure (SLP) anomalies and the El Niño-Southern Oscillation (ENSO)-related sea surface temperature (SST) anomalies on the wintertime surface temperature anomalies over East Asia, a nonlinear method based on self-organizing maps is employed. Investigation of identified regimes reveals that the AO can affect East Asian temperature anomalies when there are significant SLP anomalies over the Arctic Ocean and northern parts of Eurasian continent. Analogously, ENSO is found to affect East Asian temperature anomalies when significant SST anomalies are present over the tropical central Pacific. The regimes with the warmest and coldest temperature anomalies over East Asia are both associated with the negative phase of the AO. The ENSO-activated, Pacific-East Asian teleconnection pattern could affect the higher latitude continental regions when the impact of the AO is switched off. When the spatial patterns of the AO and ENSO have significant, but opposite, impacts on the coastal winds, no obvious temperature anomalies can be observed over south China. Further, the circulation state with nearly the same AO and Niño3 indices may drive rather different responses in surface temperature over East Asia. The well-known continuous weakening (recovery) of the East Asian winter monsoon that occurred around 1988 (2009) can be attributed to the transitions of the spatial patterns of the SLP anomalies over the Arctic Ocean and Eurasian continent, through their modulation on the occurrences of the Ural and central Siberian blocking events.

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.

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.

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.

SOTXTSTREAM: Density-based self-organizing clustering of text streams.

PubMed

Bryant, Avory C; Cios, Krzysztof J

2017-01-01

A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets.

Portraying the Expression Landscapes of B-Cell Lymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes

PubMed Central

Hopp, Lydia; Lembcke, Kathrin; Binder, Hans; Wirth, Henry

2013-01-01

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics. PMID:24833231

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.

Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries.

PubMed

Lee, M L; Schneider, G

2001-01-01

Natural products were analyzed to determine whether they contain appealing novel scaffold architectures for potential use in combinatorial chemistry. Ring systems were extracted and clustered on the basis of structural similarity. Several such potential scaffolds for combinatorial chemistry were identified that are not present in current trade drugs. For one of these scaffolds a virtual combinatorial library was generated. Pharmacophoric properties of natural products, trade drugs, and the virtual combinatorial library were assessed using a self-organizing map. Obviously, current trade drugs and natural products have several topological pharmacophore patterns in common. These features can be systematically explored with selected combinatorial libraries based on a combination of natural product-derived and synthetic molecular building blocks.

Self-Organization in Coordination-Driven Self-Assembly

PubMed Central

Northrop, Brian H.; Zheng, Yao-Rong; Chi, Ki-Whan; Stang, Peter J.

2009-01-01

Conspectus Self-assembly allows for the preparation of highly complex molecular and supramolecular systems from relatively simple starting materials. Typically, self-assembled supramolecules are constructed by combining complementary pairs of two highly symmetric molecular components, thus limiting the chances of forming unwanted side products. Combining asymmetric molecular components or multiple complementary sets of molecules in one complex mixture can produce myriad different ordered and disordered supramolecular assemblies. Alternatively, spontaneous self-organization phenomena can promote the formation of specific product(s) out of a collection of multiple possibilities. Self-organization processes are common throughout much of nature and are especially common in biological systems. Recently, researchers have studied self-organized self-assembly in purely synthetic systems. This Account describes our investigations of self-organization in the coordination-driven self-assembly of platinum(II)-based metallosupramolecules. The modularity of the coordination-driven approach to self-assembly has allowed us to systematically study a wide variety of different factors that can control the extent of supramolecular self-organization. In particular, we have evaluated the effects of the symmetry and polarity of ambidentate donor subunits, differences in geometrical parameters (e.g. the size, angularity, and dimensionality) of Pt(II)-based acceptors and organic donors, the influence of temperature and solvent, and the effects of intermolecular steric interactions and hydrophobic interactions on self-organization. Our studies have shown that the extent of self-organization in the coordination-driven self-assembly of both 2D polygons and 3D polyhedra ranges from no organization (a statistical mixture of multiple products), to amplified organization (wherein a particular product or products are favored over others), and all the way to the absolute self-organization of discrete supramolecular assemblies. In many cases, inputs such as dipolar interactions, steric interactions, and differences in the geometric parameters of subunits—used either alone or as multiple factors simultaneously—can achieve absolute self-organization of discrete supramolecules. We have also observed instances where self-organization is not absolute and varies in its deviation from statistical results. Steric interactions are particularly useful control factors for driving such amplified self-organization because they can be subtly tuned through small structural variations. Having the ability to fully understand and control the self-organization of complex mixtures into specific synthetic supramolecules can provide a better understanding of analogous processes in biological systems. Furthermore, self-organization may allow for the facile synthesis of complex multifunctional, multicomponent systems from simply mixing a collection of much simpler, judiciously designed individual molecular components. PMID:19555073

A pseudoinverse deformation vector field generator and its applications

PubMed Central

Yan, C.; Zhong, H.; Murphy, M.; Weiss, E.; Siebers, J. V.

2010-01-01

Purpose: To present, implement, and test a self-consistent pseudoinverse displacement vector field (PIDVF) generator, which preserves the location of information mapped back-and-forth between image sets. Methods: The algorithm is an iterative scheme based on nearest neighbor interpolation and a subsequent iterative search. Performance of the algorithm is benchmarked using a lung 4DCT data set with six CT images from different breathing phases and eight CT images for a single prostrate patient acquired on different days. A diffeomorphic deformable image registration is used to validate our PIDVFs. Additionally, the PIDVF is used to measure the self-consistency of two nondiffeomorphic algorithms which do not use a self-consistency constraint: The ITK Demons algorithm for the lung patient images and an in-house B-Spline algorithm for the prostate patient images. Both Demons and B-Spline have been QAed through contour comparison. Self-consistency is determined by using a DIR to generate a displacement vector field (DVF) between reference image R and study image S (DVFR–S). The same DIR is used to generate DVFS–R. Additionally, our PIDVF generator is used to create PIDVFS–R. Back-and-forth mapping of a set of points (used as surrogates of contours) using DVFR–S and DVFS–R is compared to back-and-forth mapping performed with DVFR–S and PIDVFS–R. The Euclidean distances between the original unmapped points and the mapped points are used as a self-consistency measure. Results: Test results demonstrate that the consistency error observed in back-and-forth mappings can be reduced two to nine times in point mapping and 1.5 to three times in dose mapping when the PIDVF is used in place of the B-Spline algorithm. These self-consistency improvements are not affected by the exchanging of R and S. It is also demonstrated that differences between DVFS–R and PIDVFS–R can be used as a criteria to check the quality of the DVF. Conclusions: Use of DVF and its PIDVF will improve the self-consistency of points, contour, and dose mappings in image guided adaptive therapy. PMID:20384247

Using Academy Standards of Excellence in Nutrition and Dietetics for organization self-assessment and quality improvement.

PubMed

Price, Joyce A; Kent, Sue; Cox, Sharon A; McCauley, Sharon M; Parekh, Janki; Klein, Catherine J

2014-08-01

Standards of Excellence in Nutrition and Dietetics for an Organization is a self-assessment tool to measure and evaluate an organization's program, services, and initiatives that identify and distinguish the Registered Dietitian Nutritionist (RDN) brand as the professional expert in food and nutrition. The Standards of Excellence will serve as a road map to recognize RDNs as leaders and collaborators. Standards of Excellence criteria apply to all practice segments of nutrition and dietetics: health care, education and research, business and industry, and community nutrition and public health. Given the membership's call to action to be recognized for their professional expertise, the Academy of Nutrition and Dietetics Quality Management Committee developed four Standards of Excellence in Nutrition and Dietetics for Organizations: Quality of Leadership, Quality of Organization, Quality of Practice, and Quality of Outcomes. Within each standard, specific indicators provide strategies for an organization to demonstrate excellence. The Academy will develop a self-evaluation scoring tool to assist the organization in applying and implementing one or more of the strategies in the Standards of Excellence indicators. The organization can use the self-assessment tool to establish itself as a Center of Excellence in Nutrition and Dietetics. The role examples illustrate initiatives RDNs and organizations can take to identify themselves as a Center of Excellence in Nutrition and Dietetics. Achieving the Excellence level is an important collaborative initiative between nutrition and dietetics organizations and the Academy to provide increased autonomy, supportive management, respect within peers and community, opportunities for professional development, support for further education, and compensation for the RDN. For purposes of the Standards, "organization" means workplace or practice setting. Copyright © 2014 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Mapping publication status and exploring hotspots in a research field: chronic disease self-management.

PubMed

Lu, Yang; Li, Zheng; Arthur, David

2014-08-01

To provide insight into the characteristics of chronic disease self-management by mapping publication status and exploring hotspots. Chronic disease is becoming a major public health issue worldwide, highlighting the importance of self-management in this area. Despite the volume and variety of publications, little is known about how 'chronic disease self-management' has developed, since the first publication 40 years ago. Such is the number of publications in the area, that there is a need for a systematic bibliographic examination to enable clinicians and researchers to navigate this literature. A bibliometric analysis of publications was used. Publication status was achieved using BICOMB software, whereas hotspots were identified with Ucinet software. A search of PubMed was conducted for papers published between 1971-2012. By 2011, the number of publications reached 696, a fourfold increase from the previous 10 years, of which 75% came from the USA and UK. There were 1284 journals, which published chronic disease self-management research, involving various disciplines. The research hotspots highlighted various self-management strategies for the following: diabetes; cardiac vascular and pulmonary chronic disease; pain relief for neoplasms; and obesity. Psychological adjustment was a permeating theme in self-management processes as was using internet-based interventions. Self-management in chronic disease publication has been most evident in developed countries. The bibliographic mapping and identification of publication hotspots provides scholars and practitioners with key target journals, as well as a rigorous overview of the field for use in further research, evidence-based practice and health policy development. © 2014 John Wiley & Sons Ltd.

Training Aids for Basic Combat Skills: Developing Map-Reading Skills

DTIC Science & Technology

2011-03-01

types of training aids were developed for determining grid coordinates. Each training aid consisted of a set of self-study flashcards . One set of... flashcards contained scaled sections of topological maps and asked the Soldiers to find map features based on grid coordinates or to provide the grid...coordinates for a given map feature. The reverse of each flashcard provided feedback (i.e., correct answers and rationale to help the Soldier

The efficacy of the 'mind map' study technique.

PubMed

Farrand, Paul; Hussain, Fearzana; Hennessy, Enid

2002-05-01

To examine the effectiveness of using the 'mind map' study technique to improve factual recall from written information. To obtain baseline data, subjects completed a short test based on a 600-word passage of text prior to being randomly allocated to form two groups: 'self-selected study technique' and 'mind map'. After a 30-minute interval the self-selected study technique group were exposed to the same passage of text previously seen and told to apply existing study techniques. Subjects in the mind map group were trained in the mind map technique and told to apply it to the passage of text. Recall was measured after an interfering task and a week later. Measures of motivation were taken. Barts and the London School of Medicine and Dentistry, University of London. 50 second- and third-year medical students. Recall of factual material improved for both the mind map and self-selected study technique groups at immediate test compared with baseline. However this improvement was only robust after a week for those in the mind map group. At 1 week, the factual knowledge in the mind map group was greater by 10% (adjusting for baseline) (95% CI -1% to 22%). However motivation for the technique used was lower in the mind map group; if motivation could have been made equal in the groups, the improvement with mind mapping would have been 15% (95% CI 3% to 27%). Mind maps provide an effective study technique when applied to written material. However before mind maps are generally adopted as a study technique, consideration has to be given towards ways of improving motivation amongst users.

Concept mapping as an approach for expert-guided model building: The example of health literacy.

PubMed

Soellner, Renate; Lenartz, Norbert; Rudinger, Georg

2017-02-01

Concept mapping served as the starting point for the aim of capturing the comprehensive structure of the construct of 'health literacy.' Ideas about health literacy were generated by 99 experts and resulted in 105 statements that were subsequently organized by 27 experts in an unstructured card sorting. Multidimensional scaling was applied to the sorting data and a two and three-dimensional solution was computed. The three dimensional solution was used in subsequent cluster analysis and resulted in a concept map of nine "clusters": (1) self-regulation, (2) self-perception, (3) proactive approach to health, (4) basic literacy and numeracy skills, (5) information appraisal, (6) information search, (7) health care system knowledge and acting, (8) communication and cooperation, and (9) beneficial personality traits. Subsequently, this concept map served as a starting point for developing a "qualitative" structural model of health literacy and a questionnaire for the measurement of health literacy. On the basis of questionnaire data, a "quantitative" structural model was created by first applying exploratory factor analyses (EFA) and then cross-validating the model with confirmatory factor analyses (CFA). Concept mapping proved to be a highly valuable tool for the process of model building up to translational research in the "real world". Copyright © 2016 Elsevier Ltd. All rights reserved.

State Recognition and Visualization of Hoisting Motor of Quayside Container Crane Based on SOFM

NASA Astrophysics Data System (ADS)

Yang, Z. Q.; He, P.; Tang, G.; Hu, X.

2017-07-01

The neural network structure and algorithm of self-organizing feature map (SOFM) are researched and analysed. The method is applied to state recognition and visualization of the quayside container crane hoisting motor. By using SOFM, the clustering and visualization of attribute reduction of data are carried out, and three kinds motor states are obtained with Root Mean Square(RMS), Impulse Index and Margin Index, and the simulation visualization interface is realized by MATLAB. Through the processing of the sample data, it can realize the accurate identification of the motor state, thus provide better monitoring of the quayside container crane hoisting motor and a new way for the mechanical state recognition.

Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

PubMed Central

Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V.; Alvarez-Santos, Victor; Pardo, Xose Manuel

2013-01-01

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal. PMID:23271604

Self-organized multi-camera network for a fast and easy deployment of ubiquitous robots in unknown environments.

PubMed

Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V; Alvarez-Santos, Victor; Pardo, Xose Manuel

2012-12-27

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.

Mapping the Natchez Trace Parkway

USGS Publications Warehouse

Rangoonwala, Amina; Bannister, Terri; Ramsey, Elijah W.

2011-01-01

Based on a National Park Service (NPS) landcover classification, a landcover map of the 715-km (444-mile) NPS Natchez Trace Parkway (hereafter referred to as the "Parkway") was created. The NPS landcover classification followed National Vegetation Classification (NVC) protocols. The landcover map, which extended the initial landcover classification to the entire Parkway, was based on color-infrared photography converted to 1-m raster-based digital orthophoto quarter quadrangles, according to U.S. Geological Survey mapping standards. Our goal was to include as many alliance classes as possible in the Parkway landcover map. To reach this goal while maintaining a consistent and quantifiable map product throughout the Parkway extent, a mapping strategy was implemented based on the migration of class-based spectral textural signatures and the congruent progressive refinement of those class signatures along the Parkway. Progressive refinement provided consistent mapping by evaluating the spectral textural distinctiveness of the alliance-association classes, and where necessary, introducing new map classes along the Parkway. By following this mapping strategy, the use of raster-based image processing and geographic information system analyses for the map production provided a quantitative and reproducible product. Although field-site classification data were severely limited, the combination of spectral migration of class membership along the Parkway and the progressive classification strategy produced an organization of alliances that was internally highly consistent. The organization resulted from the natural patterns or alignments of spectral variance and the determination of those spectral patterns that were compositionally similar in the dominant species as NVC alliances. Overall, the mapped landcovers represented the existent spectral textural patterns that defined and encompassed the complex variety of compositional alliances and associations of the Parkway. Based on that mapped representation, forests dominate the Parkway landscape. Grass is the second largest Parkway land cover, followed by scrub-shrub and shrubland classes and pine plantations. The map provides a good representation of the landcover patterns and their changes over the extent of the Parkway, south to north.

Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy

NASA Astrophysics Data System (ADS)

Acosta, Oscar; Dowling, Jason; Cazoulat, Guillaume; Simon, Antoine; Salvado, Olivier; de Crevoisier, Renaud; Haigron, Pascal

The prediction of toxicity is crucial to managing prostate cancer radiotherapy (RT). This prediction is classically organ wise and based on the dose volume histograms (DVH) computed during the planning step, and using for example the mathematical Lyman Normal Tissue Complication Probability (NTCP) model. However, these models lack spatial accuracy, do not take into account deformations and may be inappropiate to explain toxicity events related with the distribution of the delivered dose. Producing voxel wise statistical models of toxicity might help to explain the risks linked to the dose spatial distribution but is challenging due to the difficulties lying on the mapping of organs and dose in a common template. In this paper we investigate the use of atlas based methods to perform the non-rigid mapping and segmentation of the individuals' organs at risk (OAR) from CT scans. To build a labeled atlas, 19 CT scans were selected from a population of patients treated for prostate cancer by radiotherapy. The prostate and the OAR (Rectum, Bladder, Bones) were then manually delineated by an expert and constituted the training data. After a number of affine and non rigid registration iterations, an average image (template) representing the whole population was obtained. The amount of consensus between labels was used to generate probabilistic maps for each organ. We validated the accuracy of the approach by segmenting the organs using the training data in a leave one out scheme. The agreement between the volumes after deformable registration and the manually segmented organs was on average above 60% for the organs at risk. The proposed methodology provides a way to map the organs from a whole population on a single template and sets the stage to perform further voxel wise analysis. With this method new and accurate predictive models of toxicity will be built.

Comparison of peanut gentics and physical maps provided insights on collinearity, reversions and translocations

USDA-ARS?s Scientific Manuscript database

Genetic and physical maps are the valuable resources for peanut research community in understanding genome organization and serving as the basis for map-based cloning and marker-assisted selection. Physical maps of two diploid wild peanut progenitor species, Arachis duranensis (A genome) and A. ipae...

Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.

PubMed

Reibnegger, G; Wachter, H

1996-04-15

Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.

Typha latifolia (broadleaf cattail) as bioindicator of different types of pollution in aquatic ecosystems-application of self-organizing feature map (neural network).

PubMed

Klink, Agnieszka; Polechońska, Ludmiła; Cegłowska, Aurelia; Stankiewicz, Andrzej

2016-07-01

The contents of Cd, Cu, Fe, Mn, Ni, Pb, and Zn in leaves of Typha latifolia (broadleaf cattail), water and bottom sediment from 72 study sites designated in different regions of Poland were determined using atomic absorption spectrometry. The aim of the study was to evaluate potential use of T. latifolia in biomonitoring of trace metal pollution. The self-organizing feature map (SOFM) identifying groups of sampling sites with similar concentrations of metals in cattail leaves was able to classify study sites according to similar use and potential sources of pollution. Maps prepared for water and bottom sediment showed corresponding groups of sampling sites which suggested similarity of samples features. High concentrations of Fe, Cd, Cu, and Ni were characteristic for industrial areas. Elevated Pb concentrations were noted in regions with intensive vehicle traffic, while high Mn and Zn contents were reported in leaves from the agricultural area. Manganese content in leaves of T. latifolia was high irrespectively of the concentrations in bottom sediments and water so cattail can be considered the leaf accumulator of Mn. Once trained, SOFMs can be applied in ecological investigations and could form a future basis for recognizing the type of pollution in aquatic environments by analyzing the concentrations of elements in T. latifolia.

Quantitative characterization of all single amino acid variants of a viral capsid-based drug delivery vehicle.

PubMed

Hartman, Emily C; Jakobson, Christopher M; Favor, Andrew H; Lobba, Marco J; Álvarez-Benedicto, Ester; Francis, Matthew B; Tullman-Ercek, Danielle

2018-04-11

Self-assembling proteins are critical to biological systems and industrial technologies, but predicting how mutations affect self-assembly remains a significant challenge. Here, we report a technique, termed SyMAPS (Systematic Mutation and Assembled Particle Selection), that can be used to characterize the assembly competency of all single amino acid variants of a self-assembling viral structural protein. SyMAPS studies on the MS2 bacteriophage coat protein revealed a high-resolution fitness landscape that challenges some conventional assumptions of protein engineering. An additional round of selection identified a previously unknown variant (CP[T71H]) that is stable at neutral pH but less tolerant to acidic conditions than the wild-type coat protein. The capsids formed by this variant could be more amenable to disassembly in late endosomes or early lysosomes-a feature that is advantageous for delivery applications. In addition to providing a mutability blueprint for virus-like particles, SyMAPS can be readily applied to other self-assembling proteins.

Computerized mappings of the cerebral cortex: a multiresolution flattening method and a surface-based coordinate system

NASA Technical Reports Server (NTRS)

Drury, H. A.; Van Essen, D. C.; Anderson, C. H.; Lee, C. W.; Coogan, T. A.; Lewis, J. W.

1996-01-01

We present a new method for generating two-dimensional maps of the cerebral cortex. Our computerized, two-stage flattening method takes as its input any well-defined representation of a surface within the three-dimensional cortex. The first stage rapidly converts this surface to a topologically correct two-dimensional map, without regard for the amount of distortion introduced. The second stage reduces distortions using a multiresolution strategy that makes gross shape changes on a coarsely sampled map and further shape refinements on progressively finer resolution maps. We demonstrate the utility of this approach by creating flat maps of the entire cerebral cortex in the macaque monkey and by displaying various types of experimental data on such maps. We also introduce a surface-based coordinate system that has advantages over conventional stereotaxic coordinates and is relevant to studies of cortical organization in humans as well as non-human primates. Together, these methods provide an improved basis for quantitative studies of individual variability in cortical organization.

The Identity Mapping Project: Demographic differences in patterns of distributed identity.

PubMed

Gilbert, Richard L; Dionisio, John David N; Forney, Andrew; Dorin, Philip

2015-01-01

The advent of cloud computing and a multi-platform digital environment is giving rise to a new phase of human identity called "The Distributed Self." In this conception, aspects of the self are distributed into a variety of 2D and 3D digital personas with the capacity to reflect any number of combinations of now malleable personality traits. In this way, the source of human identity remains internal and embodied, but the expression or enactment of the self becomes increasingly external, disembodied, and distributed on demand. The Identity Mapping Project (IMP) is an interdisciplinary collaboration between psychology and computer Science designed to empirically investigate the development of distributed forms of identity. Methodologically, it collects a large database of "identity maps" - computerized graphical representations of how active someone is online and how their identity is expressed and distributed across 7 core digital domains: email, blogs/personal websites, social networks, online forums, online dating sites, character based digital games, and virtual worlds. The current paper reports on gender and age differences in online identity based on an initial database of distributed identity profiles.

Using cryoEM Reconstruction and Phase Extension to Determine Crystal Structure of Bacteriophage $${\\Phi}$$6 Major Capsid Protein

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

Nemecek, Daniel; Plevka, Pavel; Boura, Evzen

2013-11-29

Bacteriophagemore » $${\\Phi}$$6 is a double-stranded RNA virus that has been extensively studied as a model organism. In this paper we describe structure determination of $${\\Phi}$$6 major capsid protein P1. The protein crystallized in base centered orthorhombic space group C2221. Matthews’s coefficient indicated that the crystals contain from four to seven P1 subunits in the crystallographic asymmetric unit. The self-rotation function had shown presence of fivefold axes of non-crystallographic symmetry in the crystals. Thus, electron density map corresponding to a P1 pentamer was excised from a previously determined cryoEM reconstruction of the $${\\Phi}$$6 procapsid at 7 Å resolution and used as a model for molecular replacement. The phases for reflections at higher than 7 Å resolution were obtained by phase extension employing the fivefold non-crystallographic symmetry present in the crystal. Lastly, the averaged 3.6 Å-resolution electron density map was of sufficient quality to allow model building.« less

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...

Confinement of surface state electrons in self-organized Co islands on Au(111)

NASA Astrophysics Data System (ADS)

Schouteden, Koen; Lijnen, Erwin; Janssens, Ewald; Ceulemans, Arnout; Chibotaru, Liviu F.; Lievens, Peter; Van Haesendonck, Chris

2008-04-01

We report on detailed low temperature scanning tunneling spectroscopy measurements performed on nanoscale Co islands on Au(111) films. At low coverages, Co islands self-organize in arrays of mono- and bilayer nanoscale structures that often have an hexagonal shape. The process of self-organization is induced by the Au(111) 'herringbone' reconstruction. By means of mapping of the local density of states with lock-in detection, electron standing wave patterns are resolved on top of the atomically flat Co islands. The surface state electrons are observed to be strongly confined laterally inside the Co nanosized islands, with their wavefunctions reflecting the symmetry of the islands. To complement the experimental work, particle-in-a-box calculations were performed. The calculations are based on a newly developed variational method that can be applied to '2D boxes' of arbitrary polygonal shape. The experimental patterns are found to fit nicely to the calculated wavefunctions for a box having a symmetry corresponding to the experimental island symmetry. The small size of the Co islands under study (down to 7.7 nm2) is observed to induce a strong discretization of the energy levels, with very large energy separations between the eigenstates up to several 100 meV. The observed standing wave patterns are identified either as individual eigenstates or as a 'mixture' of two or more energetically close-lying eigenstates of the cobalt island. Additionally, the Co surface state appears not to be limited to mono- and bilayer islands, but this state remains observable for multilayered islands up to five monolayers of Co.

Women's clitoris, vagina and cervix mapped on the sensory cortex: fMRI evidence

PubMed Central

Komisaruk, Barry R.; Wise, Nan; Frangos, Eleni; Liu, Wen-Ching; Allen, Kachina; Brody, Stuart

2011-01-01

Introduction The projection of vagina, uterine cervix, and nipple to the sensory cortex in humans has not been reported. Aims To map the sensory cortical fields of the clitoris, vagina, cervix and nipple, toward an elucidation of the neural systems underlying sexual response. Methods Using functional Magnetic Resonance Imaging (fMRI) we mapped sensory cortical responses to clitoral, vaginal, cervical, and nipple self-stimulation. For points of reference on the homunculus, we also mapped responses to the thumb and great toe (hallux) stimulation. Main Outcome Measures fMRI of brain regions activated by the various sensory stimuli. Results Clitoral, vaginal, and cervical self-stimulation activate differentiable sensory cortical regions, all clustered in the medial cortex (medial paracentral lobule). Nipple self-stimulation activated the genital sensory cortex (as well as the thoracic) region of the homuncular map. Conclusion The genital sensory cortex, identified in the classical Penfield homunculus based on electrical stimulation of the brain only in men, was confirmed for the first time in the literature by the present study in women, applying clitoral, vaginal, and cervical self-stimulation, and observing their regional brain responses using fMRI. Vaginal, clitoral, and cervical regions of activation were differentiable, consistent with innervation by different afferent nerves and different behavioral correlates. Activation of the genital sensory cortex by nipple self-stimulation was unexpected, but suggests a neurological basis for women’s reports of its erotogenic quality. PMID:21797981

Work organization in hospital wards and nurses' emotional exhaustion: A multi-method study of observation-based assessment and nurses' self-reports.

PubMed

Stab, Nicole; Hacker, Winfried; Weigl, Matthias

2016-09-01

Ward organization is a major determinant for nurses' well-being on the job. The majority of previous research on this relationship is based on single source methods, which have been criticized as skewed estimations mainly due to subjectivity of the ratings and due to common source bias. To investigate the association of ward organization characteristics and nurses' exhaustion by combining observation-based assessments with nurses' self-reports. Cross-sectional study on 25 wards of four hospitals and 245 nurses. Our multi-method approach to evaluate hospital ward organization consisted of on-site observations with a standardized assessment tool and of questionnaires to evaluate nurses' self-reports and exhaustion. After establishing the reliability of our measures, we applied multi-level regression analyses to determine associations between determinant and outcome variables. We found substantial convergence in ward organization between the observation-based assessments and nurses' self-reports, which supports the validity of our external assessments. Furthermore, two observation-based characteristics, namely participation and patient-focused care, were significantly associated with lower emotional exhaustion among the nurses. Our results suggest that observation-based assessments are a valid and feasible way to assess ward organization in hospitals. Nurses' self-reported as well as observation-based ratings on ward organization were associated with nurses' emotional exhaustion. This is of interest mainly for identifying alternative measures in evaluating nurses' work environments, to inform health promotion activities and to evaluate job redesign intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mapping patent classifications: portfolio and statistical analysis, and the comparison of strengths and weaknesses.

PubMed

Leydesdorff, Loet; Kogler, Dieter Franz; Yan, Bowen

2017-01-01

The Cooperative Patent Classifications (CPC) recently developed cooperatively by the European and US Patent Offices provide a new basis for mapping patents and portfolio analysis. CPC replaces International Patent Classifications (IPC) of the World Intellectual Property Organization. In this study, we update our routines previously based on IPC for CPC and use the occasion for rethinking various parameter choices. The new maps are significantly different from the previous ones, although this may not always be obvious on visual inspection. We provide nested maps online and a routine for generating portfolio overlays on the maps; a new tool is provided for "difference maps" between patent portfolios of organizations or firms. This is illustrated by comparing the portfolios of patents granted to two competing firms-Novartis and MSD-in 2016. Furthermore, the data is organized for the purpose of statistical analysis.

Stochastic associative memory

NASA Astrophysics Data System (ADS)

Baumann, Erwin W.; Williams, David L.

1993-08-01

Artificial neural networks capable of learning and recalling stochastic associations between non-deterministic quantities have received relatively little attention to date. One potential application of such stochastic associative networks is the generation of sensory 'expectations' based on arbitrary subsets of sensor inputs to support anticipatory and investigate behavior in sensor-based robots. Another application of this type of associative memory is the prediction of how a scene will look in one spectral band, including noise, based upon its appearance in several other wavebands. This paper describes a semi-supervised neural network architecture composed of self-organizing maps associated through stochastic inter-layer connections. This 'Stochastic Associative Memory' (SAM) can learn and recall non-deterministic associations between multi-dimensional probability density functions. The stochastic nature of the network also enables it to represent noise distributions that are inherent in any true sensing process. The SAM architecture, training process, and initial application to sensor image prediction are described. Relationships to Fuzzy Associative Memory (FAM) are discussed.

Temporal abstraction for the analysis of intensive care information

NASA Astrophysics Data System (ADS)

Hadad, Alejandro J.; Evin, Diego A.; Drozdowicz, Bartolomé; Chiotti, Omar

2007-11-01

This paper proposes a scheme for the analysis of time-stamped series data from multiple monitoring devices of intensive care units, using Temporal Abstraction concepts. This scheme is oriented to obtain a description of the patient state evolution in an unsupervised way. The case of study is based on a dataset clinically classified with Pulmonary Edema. For this dataset a trends based Temporal Abstraction mechanism is proposed, by means of a Behaviours Base of time-stamped series and then used in a classification step. Combining this approach with the introduction of expert knowledge, using Fuzzy Logic, and multivariate analysis by means of Self-Organizing Maps, a states characterization model is obtained. This model is feasible of being extended to different patients groups and states. The proposed scheme allows to obtain intermediate states descriptions through which it is passing the patient and that could be used to anticipate alert situations.

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.

Modality-based organization of ascending somatosensory axons in the direct dorsal column pathway.

PubMed

Niu, Jingwen; Ding, Long; Li, Jian J; Kim, Hyukmin; Liu, Jiakun; Li, Haipeng; Moberly, Andrew; Badea, Tudor C; Duncan, Ian D; Son, Young-Jin; Scherer, Steven S; Luo, Wenqin

2013-11-06

The long-standing doctrine regarding the functional organization of the direct dorsal column (DDC) pathway is the "somatotopic map" model, which suggests that somatosensory afferents are primarily organized by receptive field instead of modality. Using modality-specific genetic tracing, here we show that ascending mechanosensory and proprioceptive axons, two main types of the DDC afferents, are largely segregated into a medial-lateral pattern in the mouse dorsal column and medulla. In addition, we found that this modality-based organization is likely to be conserved in other mammalian species, including human. Furthermore, we identified key morphological differences between these two types of afferents, which explains how modality segregation is formed and why a rough "somatotopic map" was previously detected. Collectively, our results establish a new functional organization model for the mammalian direct dorsal column pathway and provide insight into how somatotopic and modality-based organization coexist in the central somatosensory pathway.

Application of diffusion maps to identify human factors of self-reported anomalies in aviation.

PubMed

Andrzejczak, Chris; Karwowski, Waldemar; Mikusinski, Piotr

2012-01-01

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. Diffusion Maps (DM) were selected as the method of choice for performing dimensionality reduction on text records for this study. Diffusion Maps have seen successful use in other domains such as image classification and pattern recognition. High-dimensionality data in the form of narrative text reports from the NASA Aviation Safety Reporting System (ASRS) were clustered and categorized by way of dimensionality reduction. Supervised analyses were performed to create a baseline document clustering system. Dimensionality reduction techniques identified concepts or keywords within records, and allowed the creation of a framework for an unsupervised document classification system. Results from the unsupervised clustering algorithm performed similarly to the supervised methods outlined in the study. The dimensionality reduction was performed on 100 of the most commonly occurring words within 126,000 text records describing commercial aviation incidents. This study demonstrates that unsupervised machine clustering and organization of incident reports is possible based on unbiased inputs. Findings from this study reinforced traditional views on what factors contribute to civil aviation anomalies, however, new associations between previously unrelated factors and conditions were also found.

The Impact of Concept Mapping on the Process of Problem-Based Learning

ERIC Educational Resources Information Center

Zwaal, Wichard; Otting, Hans

2012-01-01

A concept map is a graphical tool to activate and elaborate on prior knowledge, to support problem solving, promote conceptual thinking and understanding, and to organize and memorize knowledge. The aim of this study is to determine if the use of concept mapping (CM) in a problem-based learning (PBL) curriculum enhances the PBL process. The paper…

Fast IR laser mapping ellipsometry for the study of functional organic thin films.

PubMed

Furchner, Andreas; Sun, Guoguang; Ketelsen, Helge; Rappich, Jörg; Hinrichs, Karsten

2015-03-21

Fast infrared mapping with sub-millimeter lateral resolution as well as time-resolved infrared studies of kinetic processes of functional organic thin films require a new generation of infrared ellipsometers. We present a novel laboratory-based infrared (IR) laser mapping ellipsometer, in which a laser is coupled to a variable-angle rotating analyzer ellipsometer. Compared to conventional Fourier-transform infrared (FT-IR) ellipsometers, the IR laser ellipsometer provides ten- to hundredfold shorter measurement times down to 80 ms per measured spot, as well as about tenfold increased lateral resolution of 120 μm, thus enabling mapping of small sample areas with thin-film sensitivity. The ellipsometer, equipped with a HeNe laser emitting at about 2949 cm(-1), was applied for the optical characterization of inhomogeneous poly(3-hexylthiophene) [P3HT] and poly(N-isopropylacrylamide) [PNIPAAm] organic thin films used for opto-electronics and bioapplications. With the constant development of tunable IR laser sources, laser-based infrared ellipsometry is a promising technique for fast in-depth mapping characterization of thin films and blends.

Apprehensions and Expectations of the Adoption of Systematically Planned, Outcome-Oriented Practice

ERIC Educational Resources Information Center

Savaya, Riki; Altschuler, Dorit; Melamed, Sharon

2013-01-01

Objectives: The study examined social workers' apprehensions and expectations of the impending adoption of systematically planned, empirically based, outcome-oriented practice (SEOP). Method: Employing a mixed methods longitudinal design, the study used concept mapping to identify and map workers' apprehensions and expectations and a self-reported…

Using Cognitive Maps to Promote Self-Managed Learning in Online Communities of Inquiry

ERIC Educational Resources Information Center

Peacock, Susi; Cowan, John

2016-01-01

As online learners become more diverse and less well-prepared individually, particular help is required when transitioning into new, online learning environments, requiring engagement in collaborative, community-based educational activities. Cognitive maps provide one tool for tutors to support individuals in navigating the unfamiliar maze of…

Self-organizing control strategy for asteroid intelligent detection swarm based on attraction and repulsion

NASA Astrophysics Data System (ADS)

An, Meiyan; Wang, Zhaokui; Zhang, Yulin

2017-01-01

The self-organizing control strategy for asteroid intelligent detection swarm, which is considered as a space application instance of intelligent swarm, is developed. The leader-follower model for the asteroid intelligent detection swarm is established, and the further analysis is conducted for massive asteroid and small asteroid. For a massive asteroid, the leader spacecraft flies under the gravity field of the asteroid. For a small asteroid, the asteroid gravity is negligible, and a trajectory planning method is proposed based on elliptic cavity virtual potential field. The self-organizing control strategy for the follower spacecraft is developed based on a mechanism of velocity planning and velocity tracking. The simulation results show that the self-organizing control strategy is valid for both massive asteroid and small asteroid, and the exploration swarm forms a stable configuration.

Construction of a high-density high-resolution genetic map and its integration with BAC-based physical map in channel catfish

USDA-ARS?s Scientific Manuscript database

Construction of genetic linkage map is essential for genetic and genomic studies. Recent advances in sequencing and genotyping technologies made it possible to generate high-density and high-resolution genetic linkage maps, especially for the organisms lacking extensive genomic resources. In the pre...

Preliminary Mapping of the Western Corn Rootworm (Diabrotica virgifera virgifera) Genome and Quantitative Trait Locus (QTL) Interval Mapping for Growth

USDA-ARS?s Scientific Manuscript database

Preliminary investigations into the organization of the western corn rootworm (Diabrotica virgifera virgifera; WCR) genome have resulted in low to moderate density gender-specific maps constructed from progeny of a backcrossed, short-diapause WCR family. Maps were based upon variation at microsatel...

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.

Mentoring and Organizational Citizenship Behavior: Estimating the Mediating Effects of Organization-Based Self-Esteem and Affective Commitment

ERIC Educational Resources Information Center

Ghosh, Rajashi; Reio, Thomas G., Jr.; Haynes, Ray K.

2012-01-01

This study explored how perceptions of reciprocal support in mentoring influence mentors' and proteges' intent to extend work-related help to coworkers in organizations. Our findings shed light on the role that organization-based self-esteem (OBSE) and affective organizational commitment (AOC) play as mediators in transmitting the effect of…

Studies on Manfred Eigen's model for the self-organization of information processing.

PubMed

Ebeling, W; Feistel, R

2018-05-01

In 1971, Manfred Eigen extended the principles of Darwinian evolution to chemical processes, from catalytic networks to the emergence of information processing at the molecular level, leading to the emergence of life. In this paper, we investigate some very general characteristics of this scenario, such as the valuation process of phenotypic traits in a high-dimensional fitness landscape, the effect of spatial compartmentation on the valuation, and the self-organized transition from structural to symbolic genetic information of replicating chain molecules. In the first part, we perform an analysis of typical dynamical properties of continuous dynamical models of evolutionary processes. In particular, we study the mapping of genotype to continuous phenotype spaces following the ideas of Wright and Conrad. We investigate typical features of a Schrödinger-like dynamics, the consequences of the high dimensionality, the leading role of saddle points, and Conrad's extra-dimensional bypass. In the last part, we discuss in brief the valuation of compartment models and the self-organized emergence of molecular symbols at the beginning of life.

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.

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.

EnviroAtlas - Agricultural Water Demand by 12-Digit HUC for the Conterminous United States

EPA Pesticide Factsheets

The national agricultural water demand metric provides insight into the amount of water currently used for agricultural irrigation in the contiguous United States. The values are based on 2005 irrigation water use; combined 2010 crop, 2006 land use, and 2001 remotely sensed irrigation location estimates; and have been summarized by watershed or 12-digit hydrologic unit code (HUC). Agricultural irrigation water use, as defined in this case, meets a variety of needs before, during, and after growing seasons (e.g., dust suppression, field preparation, chemical application, weed control, salt removal from root zones, frost protection, crop cooling, and harvesting). Estimates include self-supplied surface and groundwater, as well as supplies from irrigation-specific organizations (e.g., companies, districts, cooperatives, government). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The practice of neogeography in community-based organizations

NASA Astrophysics Data System (ADS)

Oberle, Patrick

Neogeography and Volunteered Geographic Information (VGI) are two terms that have emerged recently to describe the practice of geography by those not formally trained in it as a discipline and spatial data provided by individuals through social media and other Web-based tools. Both neogeography and VGI can be directly linked to the growth of various online mapping websites and applications that allow for the creation of electronic maps that are interactive, adaptable, and easily shared via the Internet and Web. As recent phenomena, the practice of neogeography and VGI is not well understood, nor are the links these new fields have to previously established knowledge on Geographic Information Systems and its associated practices. This thesis attempts to fill this knowledge gap through a participatory study of neogeographic practice. Using a participatory workshop format, I observed and documented representatives of community-based organizations in Syracuse, NY as they encountered online mapping tools for the first time. I followed up with two of those organizations in longer case studies to better understand how organizations with no obvious geographic focus come to see geography as a way of communicating complex ideas about space. This study revealed that while the technical complexity of the online mapping software continues to prove to be a hindrance to its use, there remains space for professional geographers to interact with laypeople who make maps. Furthermore, such engagement is necessary to begin to understand the issues involved with location-based information and privacy, access to data, and ability to use and communicate geographic concepts and knowledge.

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.

A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES/spl I.bar/DECOMPOSE.

PubMed

Aras, N; Altinel, I K; Oommen, J

2003-01-01

In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. We introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.

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.

Self-organization of a tetrasubstituted tetrathiafulvalene (TTF) in a silica based hybrid organic-inorganic material.

PubMed

Cerveau, Geneviève; Corriu, Robert J P; Lerouge, Frédéric; Bellec, Nathalie; Lorcy, Dominique; Nobili, Maurizio

2004-02-21

A hybrid organic inorganic nanostructured material containing a TTF core substituted by four arms exhibited a high level of both condensation at silicon (96%) and self-organization as evidenced by X-ray diffraction and an unprecedented birefringent behaviour.

Data selection techniques in the interpretation of MAGSAT data over Australia

NASA Technical Reports Server (NTRS)

Johnson, B. D.; Dampney, C. N. G.

1983-01-01

The MAGSAT data require critical selection in order to produce a self-consistent data set suitable for map construction and subsequent interpretation. Interactive data selection techniques are described which involve the use of a special-purpose profile-oriented data base and a colour graphics display. The careful application of these data selection techniques permits validation every data value and ensures that the best possible self-consistent data set is being used to construct the maps of the magnetic field measured at satellite altitudes over Australia.

Development of an electronic emergency department-based geo-information injury surveillance system in Hong Kong.

PubMed

Chow, C B; Leung, M; Lai, Adela; Chow, Y H; Chung, Joanne; Tong, K M; Lit, Albert

2012-06-01

To describe the experience in the development of an electronic emergency department (ED)-based injury surveillance (IS) system in Hong Kong using data-mining and geo-spatial information technology (IT) for a Safe Community setup. This paper described the phased development of an emergency department-based IS system based on World Health Organization (WHO) injury surveillance Guideline to support safety promotion and injury prevention in a Safe Community in Hong Kong starting 2002. The initial ED data-based only collected data on name, sex, age, address, eight general categories of injury types (traffic, domestic, common assault, indecent assault, batter, industrial, self-harm and sports) and disposal from ED. Phase 1--manual data collection on International Classification of External Causes of Injury pre-event data; Phase 2--manual form was converted to electronic format using web-based data mining technology with built in data quality monitoring mechanism; Phase 3--integration of injury surveillance-data with in-patient hospital information; and Phase 4--geo-spatial information and body mapping were introduced to geo-code exact place of injury in an electronic map and site of injury on body map. It was feasible to develop a geo-spatial IS system at busy ED to collect valuable information for safety promotion and injury prevention at Safe Community setting. The keys for successful development and implementation involves engagement of all stakeholders at design and implementation of the system with injury prevention as ultimate goal, detail workflow planning at front end, support from the management, building on exiting system and appropriate utilisation of modern technology. Copyright © 2011 Elsevier Ltd. All rights reserved.

Methodical approaches to providing sustainable development of the transport industry management system based on self-organization

NASA Astrophysics Data System (ADS)

Belyantseva, Oksana; Panenkov, Andrey; Safonova, Nataliya

2017-10-01

Current conditions of the cognitive economy formation demand to take into account the leading role of information, knowledge and human capital in the development of the transport industry management system. The article substantiates the conceptual approach to the self-organization of a management system on the basis of innovative changes. Human capital is the key aspect of self-organization, so the directions of improving the workforce quality are justified. Basing on the information-innovative genesis of the process of self-organization, the authors justified the necessity of preventing asymmetric information. For this pupose the actions against the resistance to innovations were proposed. The implementation of certain measures contributes to the effective development of the transport management system.

Management, leadership, and user control in self-advocacy: an english case study.

PubMed

Tilley, Elizabeth

2013-12-01

This paper presents findings from a qualitative research project on an English self-advocacy organization. In light of recent political and economic developments that have threatened the sustainability of a number of self-advocacy groups for people with intellectual disability, I seek to explore how one particular organization managed to survive and grow. In particular, the paper explores themes of management, leadership, and user control, linking these to external perceptions about self-advocacy organizations. The organization in my study developed an "interdependent" governance model based on key organizational roles for nondisabled advisors and self-advocates, which proved popular with external funders. Despite the organization's notable achievements, its success raises questions for the wider self-advocacy movement, notably how leadership capacity can be developed among self-advocates.

Effects of a Question Prompt-Based Concept Mapping Approach on Students' Learning Achievements, Attitudes and 5C Competences in Project-Based Computer Course Activities

ERIC Educational Resources Information Center

Wang, Hsiu-Ying; Huang, Iwen; Hwang, Gwo-Jen

2016-01-01

Concept mapping has been widely used in various fields to facilitate students' organization of knowledge. Previous studies have, however, pointed out that it is difficult for students to construct concept maps from the abundant searched data without appropriate scaffolding. Thus, researchers have suggested that students could produce high quality…

SOMBI: Bayesian identification of parameter relations in unstructured cosmological data

NASA Astrophysics Data System (ADS)

Frank, Philipp; Jasche, Jens; Enßlin, Torsten A.

2016-11-01

This work describes the implementation and application of a correlation determination method based on self organizing maps and Bayesian inference (SOMBI). SOMBI aims to automatically identify relations between different observed parameters in unstructured cosmological or astrophysical surveys by automatically identifying data clusters in high-dimensional datasets via the self organizing map neural network algorithm. Parameter relations are then revealed by means of a Bayesian inference within respective identified data clusters. Specifically such relations are assumed to be parametrized as a polynomial of unknown order. The Bayesian approach results in a posterior probability distribution function for respective polynomial coefficients. To decide which polynomial order suffices to describe correlation structures in data, we include a method for model selection, the Bayesian information criterion, to the analysis. The performance of the SOMBI algorithm is tested with mock data. As illustration we also provide applications of our method to cosmological data. In particular, we present results of a correlation analysis between galaxy and active galactic nucleus (AGN) properties provided by the SDSS catalog with the cosmic large-scale-structure (LSS). The results indicate that the combined galaxy and LSS dataset indeed is clustered into several sub-samples of data with different average properties (for example different stellar masses or web-type classifications). The majority of data clusters appear to have a similar correlation structure between galaxy properties and the LSS. In particular we revealed a positive and linear dependency between the stellar mass, the absolute magnitude and the color of a galaxy with the corresponding cosmic density field. A remaining subset of data shows inverted correlations, which might be an artifact of non-linear redshift distortions.

An adaptable neuromorphic model of orientation selectivity based on floating gate dynamics

PubMed Central

Gupta, Priti; Markan, C. M.

2014-01-01

The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps in silicon. PMID:24765062

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.

Defining functional groups based on running kinematics using Self-Organizing Maps and Support Vector Machines.

PubMed

Hoerzer, Stefan; von Tscharner, Vinzenz; Jacob, Christian; Nigg, Benno M

2015-07-16

A functional group is a collection of individuals who react in a similar way to a specific intervention/product such as a sport shoe. Matching footwear features to a functional group can possibly enhance footwear-related comfort, improve running performance, and decrease the risk of movement-related injuries. To match footwear features to a functional group, one has to first define the different groups using their distinctive movement patterns. Therefore, the main objective of this study was to propose and apply a methodological approach to define functional groups with different movement patterns using Self-Organizing Maps and Support Vector Machines. Further study objectives were to identify differences in age, gender and footwear-related comfort preferences between the functional groups. Kinematic data and subjective comfort preferences of 88 subjects (16-76 years; 45 m/43 f) were analysed. Eight functional groups with distinctive movement patterns were defined. The findings revealed that most of the groups differed in age or gender. Certain functional groups differed in their comfort preferences and, therefore, had group-specific footwear requirements to enhance footwear-related comfort. Some of the groups, which had group-specific footwear requirements, did not show any differences in age or gender. This is important because when defining functional groups simply using common grouping criteria like age or gender, certain functional groups with group-specific movement patterns and footwear requirements might not be detected. This emphasises the power of the proposed pattern recognition approach to automatically define groups by their distinctive movement patterns in order to be able to address their group-specific product requirements. Copyright © 2015 Elsevier Ltd. All rights reserved.

17 CFR 249.819 - Form 19b-4, for electronic filings with respect to proposed rule changes, advance notices and...

Code of Federal Regulations, 2013 CFR

2013-04-01

... self-regulatory organizations. 249.819 Section 249.819 Commodity and Securities Exchanges SECURITIES... changes, advance notices and security-based swap submissions by all self-regulatory organizations. This form shall be used by all self-regulatory organizations, as defined in Section 3(a)(26) of the...

17 CFR 249.819 - Form 19b-4, for electronic filings with respect to proposed rule changes, advance notices and...

Code of Federal Regulations, 2014 CFR

2014-04-01

... self-regulatory organizations. 249.819 Section 249.819 Commodity and Securities Exchanges SECURITIES... changes, advance notices and security-based swap submissions by all self-regulatory organizations. This form shall be used by all self-regulatory organizations, as defined in Section 3(a)(26) of the...

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.

Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology

NASA Astrophysics Data System (ADS)

Parard, G.; Charantonis, A. A.; Rutgerson, A.

2015-06-01

Studies of coastal seas in Europe have noted the high variability of the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes, complicates the accurate estimation of these mechanisms. This is particularly pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been characterized in as much detail as in the open oceans. In addition, the joint availability of in situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology, which combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO2 (pCO2) in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and mixed-layer depth. The outputs of this research have a horizontal resolution of 4 km and cover the 1998-2011 period. These outputs give a monthly map of the Baltic Sea at a very fine spatial resolution. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in situ measurements and a root mean square error of 36 μatm. Removing any of the satellite parameters degraded this reconstructed CO2 flux, so we chose to supply any missing data using statistical imputation. The pCO2 maps produced using this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data, and we expect to be able to produce even more accurate reconstructions in coming years, given the predicted acquisition of new data.

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.

Compass: a hybrid method for clinical and biobank data mining.

PubMed

Krysiak-Baltyn, K; Nordahl Petersen, T; Audouze, K; Jørgensen, Niels; Angquist, L; Brunak, S

2014-02-01

We describe a new method for identification of confident associations within large clinical data sets. The method is a hybrid of two existing methods; Self-Organizing Maps and Association Mining. We utilize Self-Organizing Maps as the initial step to reduce the search space, and then apply Association Mining in order to find association rules. We demonstrate that this procedure has a number of advantages compared to traditional Association Mining; it allows for handling numerical variables without a priori binning and is able to generate variable groups which act as "hotspots" for statistically significant associations. We showcase the method on infertility-related data from Danish military conscripts. The clinical data we analyzed contained both categorical type questionnaire data and continuous variables generated from biological measurements, including missing values. From this data set, we successfully generated a number of interesting association rules, which relate an observation with a specific consequence and the p-value for that finding. Additionally, we demonstrate that the method can be used on non-clinical data containing chemical-disease associations in order to find associations between different phenotypes, such as prostate cancer and breast cancer. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory.

PubMed

Chen, Qiuwen; Rui, Han; Li, Weifeng; Zhang, Yanhui

2014-06-01

Algal blooms are a serious problem in waters, which damage aquatic ecosystems and threaten drinking water safety. However, the outbreak mechanism of algal blooms is very complex with great uncertainty, especially for large water bodies where environmental conditions have obvious variation in both space and time. This study developed an innovative method which integrated a self-organizing map (SOM) and fuzzy information diffusion theory to comprehensively analyze algal bloom risks with uncertainties. The Lake Taihu was taken as study case and the long-term (2004-2010) on-site monitoring data were used. The results showed that algal blooms in Taihu Lake were classified into four categories and exhibited obvious spatial-temporal patterns. The lake was mainly characterized by moderate bloom but had high uncertainty, whereas severe blooms with low uncertainty were observed in the northwest part of the lake. The study gives insight on the spatial-temporal dynamics of algal blooms, and should help government and decision-makers outline policies and practices on bloom monitoring and prevention. The developed method provides a promising approach to estimate algal bloom risks under uncertainties. Copyright © 2014 Elsevier B.V. All rights reserved.

Reducing breast biopsies by ultrasonographic analysis and a modified self-organizing map

NASA Astrophysics Data System (ADS)

Zheng, Yi; Greenleaf, James F.; Gisvold, John J.

1997-05-01

Recent studies suggest that visual evaluation of ultrasound images could decrease negative biopsies of breast cancer diagnosis. However, visual evaluation requires highly experienced breast sonographers. The objective of this study is to develop computerized radiologist assistant to reduce breast biopsies needed for evaluating suspected breast cancer. The approach of this study utilizes a neural network and tissue features extracted from digital sonographic breast images. The features include texture parameters of breast images: characteristics of echoes within and around breast lesions, and geometrical information of breast tumors. Clusters containing only benign lesions in the feature space are then identified by a modified self- organizing map. This newly developed neural network objectively segments population distributions of lesions and accurately establishes benign and equivocal regions.t eh method was applied to high quality breast sonograms of a large number of patients collected with a controlled procedure at Mayo Clinic. The study showed that the number of biopsies in this group of women could be decreased by 40 percent to 59 percent with high confidence and that no malignancies would have been included in the nonbiopsied group. The advantages of this approach are that it is robust, simple, and effective and does not require highly experienced sonographers.

Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography.

PubMed

Kim, Kwang Baek; Kim, Chang Won

2015-01-01

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

PubMed

Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

2014-03-01

The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

PubMed Central

Kim, Kwang Baek

2015-01-01

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future. PMID:26247023

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

PubMed Central

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Self-Organized Dynamic Flocking Behavior from a Simple Deterministic Map

NASA Astrophysics Data System (ADS)

Krueger, Wesley

2007-10-01

Coherent motion exhibiting large-scale order, such as flocking, swarming, and schooling behavior in animals, can arise from simple rules applied to an initial random array of self-driven particles. We present a completely deterministic dynamic map that exhibits emergent, collective, complex motion for a group of particles. Each individual particle is driven with a constant speed in two dimensions adopting the average direction of a fixed set of non-spatially related partners. In addition, the particle changes direction by π as it reaches a circular boundary. The dynamical patterns arising from these rules range from simple circular-type convective motion to highly sophisticated, complex, collective behavior which can be easily interpreted as flocking, schooling, or swarming depending on the chosen parameters. We present the results as a series of short movies and we also explore possible order parameters and correlation functions capable of quantifying the resulting coherence.

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.

Not all memories are the same: Situational context influences spatial recall within one's city of residency.

PubMed

Meilinger, Tobias; Frankenstein, Julia; Simon, Nadine; Bülthoff, Heinrich H; Bresciani, Jean-Pierre

2016-02-01

Reference frames in spatial memory encoding have been examined intensively in recent years. However, their importance for recall has received considerably less attention. In the present study, passersby used tags to arrange a configuration map of prominent city center landmarks. It has been shown that such configurational knowledge is memorized within a north-up reference frame. However, participants adjusted their maps according to their body orientations. For example, when participants faced south, the maps were likely to face south-up. Participants also constructed maps along their location perspective-that is, the self-target direction. If, for instance, they were east of the represented area, their maps were oriented west-up. If the location perspective and body orientation were in opposite directions (i.e., if participants faced away from the city center), participants relied on location perspective. The results indicate that reference frames in spatial recall depend on the current situation rather than on the organization in long-term memory. These results cannot be explained by activation spread within a view graph, which had been used to explain similar results in the recall of city plazas. However, the results are consistent with forming and transforming a spatial image of nonvisible city locations from the current location. Furthermore, prior research has almost exclusively focused on body- and environment-based reference frames. The strong influence of location perspective in an everyday navigational context indicates that such a reference frame should be considered more often when examining human spatial cognition.

Chemical and Morphological Control of Interfacial Self-Doping for Efficient Organic Electronics.

PubMed

Liu, Yao; Cole, Marcus D; Jiang, Yufeng; Kim, Paul Y; Nordlund, Dennis; Emrick, Todd; Russell, Thomas P

2018-04-01

Solution-based processing of materials for electrical doping of organic semiconductor interfaces is attractive for boosting the efficiency of organic electronic devices with multilayer structures. To simplify this process, self-doping perylene diimide (PDI)-based ionene polymers are synthesized, in which the semiconductor PDI components are embedded together with electrolyte dopants in the polymer backbone. Functionality contained within the PDI monomers suppresses their aggregation, affording self-doping interlayers with controllable thickness when processed from solution into organic photovoltaic devices (OPVs). Optimal results for interfacial self-doping lead to increased power conversion efficiencies (PCEs) of the fullerene-based OPVs, from 2.62% to 10.64%, and of the nonfullerene-based OPVs, from 3.34% to 10.59%. These PDI-ionene interlayers enable chemical and morphological control of interfacial doping and conductivity, demonstrating that the conductive channels are crucial for charge transport in doped organic semiconductor films. Using these novel interlayers with efficient doping and high conductivity, both fullerene- and nonfullerene-based OPVs are achieved with PCEs exceeding 9% over interlayer thicknesses ranging from ≈3 to 40 nm. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-mapping in treating suicide ideation: a case study.

PubMed

Robertson, Lloyd Hawkeye

2011-03-01

This case study traces the development and use of a self-mapping exercise in the treatment of a youth who had been at risk for re-attempting suicide. A life skills exercise was modified to identify units of culture called memes from which a map of the youth's self was prepared. A successful treatment plan followed the mapping exercise. The process of self-map construction is presented along with an interpretive analysis. It is suggested that therapists from a range of perspectives could use this technique in assessment and treatment.

MAPPING THE GALAXY COLOR–REDSHIFT RELATION: OPTIMAL PHOTOMETRIC REDSHIFT CALIBRATION STRATEGIES FOR COSMOLOGY SURVEYS

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

Masters, Daniel; Steinhardt, Charles; Faisst, Andreas

2015-11-01

Calibrating the photometric redshifts of ≳10{sup 9} galaxies for upcoming weak lensing cosmology experiments is a major challenge for the astrophysics community. The path to obtaining the required spectroscopic redshifts for training and calibration is daunting, given the anticipated depths of the surveys and the difficulty in obtaining secure redshifts for some faint galaxy populations. Here we present an analysis of the problem based on the self-organizing map, a method of mapping the distribution of data in a high-dimensional space and projecting it onto a lower-dimensional representation. We apply this method to existing photometric data from the COSMOS survey selectedmore » to approximate the anticipated Euclid weak lensing sample, enabling us to robustly map the empirical distribution of galaxies in the multidimensional color space defined by the expected Euclid filters. Mapping this multicolor distribution lets us determine where—in galaxy color space—redshifts from current spectroscopic surveys exist and where they are systematically missing. Crucially, the method lets us determine whether a spectroscopic training sample is representative of the full photometric space occupied by the galaxies in a survey. We explore optimal sampling techniques and estimate the additional spectroscopy needed to map out the color–redshift relation, finding that sampling the galaxy distribution in color space in a systematic way can efficiently meet the calibration requirements. While the analysis presented here focuses on the Euclid survey, similar analysis can be applied to other surveys facing the same calibration challenge, such as DES, LSST, and WFIRST.« less

A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin.

PubMed

Akay, Altug; Dragomir, Andrei; Erlandsson, Björn-Erik

2015-01-01

A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.

Meteor tracking via local pattern clustering in spatio-temporal domain

NASA Astrophysics Data System (ADS)

Kukal, Jaromír.; Klimt, Martin; Švihlík, Jan; Fliegel, Karel

2016-09-01

Reliable meteor detection is one of the crucial disciplines in astronomy. A variety of imaging systems is used for meteor path reconstruction. The traditional approach is based on analysis of 2D image sequences obtained from a double station video observation system. Precise localization of meteor path is difficult due to atmospheric turbulence and other factors causing spatio-temporal fluctuations of the image background. The proposed technique performs non-linear preprocessing of image intensity using Box-Cox transform as recommended in our previous work. Both symmetric and asymmetric spatio-temporal differences are designed to be robust in the statistical sense. Resulting local patterns are processed by data whitening technique and obtained vectors are classified via cluster analysis and Self-Organized Map (SOM).

Broadband-Emitting 2 D Hybrid Organic-Inorganic Perovskite Based on Cyclohexane-bis(methylamonium) Cation.

PubMed

Neogi, Ishita; Bruno, Annalisa; Bahulayan, Damodaran; Goh, Teck Wee; Ghosh, Biplab; Ganguly, Rakesh; Cortecchia, Daniele; Sum, Tze Chien; Soci, Cesare; Mathews, Nripan; Mhaisalkar, Subodh Gautam

2017-10-09

A new broadband-emitting 2 D hybrid organic-inorganic perovskite (CyBMA)PbBr 4 based on highly flexible cis-1,3-bis(methylaminohydrobromide)cyclohexane (CyBMABr) core has been designed, synthesized, and investigated, highlighting the effects of stereoisomerism of the templating cation on the formation and properties of the resulting perovskite. The new 2 D material has high exciton binding energy of 340 meV and a broad emission spanning from 380 to 750 nm, incorporating a prominent excitonic band and a less intense broad peak at room temperature. Significant changes in the photoluminescence (PL) spectrum were observed at lower temperatures, showing remarkable enhancement in the intensity of the broadband at the cost of excitonic emission. Temperature-dependent PL mapping indicates the effective role of only a narrow band of excitonic absorption in the generation of the active channel for emission. Based on the evidences obtained from the photophysical investigations, we attributed the evolution of the broad B-band of (CyBMA)PbBr 4 to excitonic self-trapped states. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Waveguide electro-optic modulators based on intrinsically polar self-assembled superlattices (SASs)

NASA Astrophysics Data System (ADS)

Liu, Zhifu; Ho, Seng Tiong; Chang, Seongsik; Zhao, Yiguang; Marks, Tobin J.; Kang, Hu; van der Boom, Milko E.; Zhu, Peiwang

2002-12-01

In this paper we describe methods of fabricating and characterizing organic electro-optic modulators based on intrinsically polar self-assembled superlattices. These structures are intrinsically acentric, and exhibit large second harmonic generation and electro-optic responses without the requirement of poling by an external electric field. A novel wet chemical protection-deprotection approach for the growth of self-assembled superlattices have been developed, and the refractive indices of self-assembled organic electro-optic superlattices may be tuned during the self-assembly process. Prototype electro-optic modulators based on chromophoric self-assembled superlattices have been designed and fabricated. The effective electro-optic coefficient of the self-assembled superlattice film in a phase modulator is estimated as about 20 pm/V at a wavelength of 1064 nm.

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.

2013-03-01

This study produced 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 values were estimated by using a self-organizing map neural network technique to explain the non-linear relationships between observed pCO2sea data and four oceanic parameters: sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS). The observed pCO2sea data was obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies. The reconstructed pCO2sea values agreed rather well with the pCO2sea measurements, the root mean square error being 17.6 μatm. The pCO2sea estimates were 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 stations in the North Pacific. The distributions of pCO2sea revealed by seven-year averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology and more precisely reflected oceanic 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.

I Move: systematic development of a web-based computer tailored physical activity intervention, based on motivational interviewing and self-determination theory

PubMed Central

2014-01-01

Background This article describes the systematic development of the I Move intervention: a web-based computer tailored physical activity promotion intervention, aimed at increasing and maintaining physical activity among adults. This intervention is based on the theoretical insights and practical applications of self-determination theory and motivational interviewing. Methods/design Since developing interventions in a systemically planned way increases the likelihood of effectiveness, we used the Intervention Mapping protocol to develop the I Move intervention. In this article, we first describe how we proceeded through each of the six steps of the Intervention Mapping protocol. After that, we describe the content of the I Move intervention and elaborate on the planned randomized controlled trial. Discussion By integrating self-determination theory and motivational interviewing in web-based computer tailoring, the I Move intervention introduces a more participant-centered approach than traditional tailored interventions. Adopting this approach might enhance computer tailored physical activity interventions both in terms of intervention effectiveness and user appreciation. We will evaluate this in an randomized controlled trial, by comparing the I Move intervention to a more traditional web-based computer tailored intervention. Trial registration NTR4129 PMID:24580802

The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease.

PubMed

Buscema, Massimo; Grossi, Enzo

2008-01-01

We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weights matrix of the AutoCM represents the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re-design the overall structure of factors related to complex and specific diseases description.

Evidence-Based Concept Mapping for the Athletic Training Student

ERIC Educational Resources Information Center

Speicher, Timothy E.; Martin, Malissa; Zigmont, Jason

2013-01-01

Context: A concept map is a graphical and cognitive tool that enables learners to link together interrelated concepts using propositions or statements that answer a posed problem. As an assessment tool, concept mapping reveals a learner's research skill proficiency and cognitive processing. Background: The identification and organization of the…

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.

Characteristics of aperiodic sequence of slip events caused by interaction between seismic patches and that caused be self-organized stress heterogeneity

NASA Astrophysics Data System (ADS)

Kato, N.

2017-12-01

Numerical simulations of earthquake cycles are conducted to investigate the origin of complexity of earthquake recurrence. There are two main causes of the complexity. One is self-organized stress heterogeneity due to dynamical effect. The other is the effect of interaction between some fault patches. In the model, friction on the fault is assumed to obey a rate- and state-dependent friction law. Circular patches of velocity-weakening frictional property are assumed on the fault. On the remaining areas of the fault, velocity-strengthening friction is assumed. We consider three models: Single patch model, two-patch model, and three-patch model. In the first model, the dynamical effect is mainly examined. The latter two models take into consideration the effect of interaction as well as the dynamical effect. Complex multiperiodic or aperiodic sequences of slip events occur when slip behavior changes from the seismic to aseismic, and when the degree of interaction between seismic patches is intermediate. The former is observed in all the models, and the latter is observed in the two-patch model and the three-patch model. Evolution of spatial distribution of shear stress on the fault suggests that aperiodicity at the transition from seismic to aseismic slip is caused by self-organized stress heterogeneity. The iteration maps of recurrence intervals of slip events in aperiodic sequences are examined, and they are approximately expressed by simple curves for aperiodicity at the transition from seismic to aseismic slip. In contrast, the iteration maps for aperiodic sequences caused by interaction between seismic patches are scattered and they are not expressed by simple curves. This result suggests that complex sequences caused by different mechanisms may be distinguished.

Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison.

PubMed

Kireeva, N; Baskin, I I; Gaspar, H A; Horvath, D; Marcou, G; Varnek, A

2012-04-01

Here, the utility of Generative Topographic Maps (GTM) for data visualization, structure-activity modeling and database comparison is evaluated, on hand of subsets of the Database of Useful Decoys (DUD). Unlike other popular dimensionality reduction approaches like Principal Component Analysis, Sammon Mapping or Self-Organizing Maps, the great advantage of GTMs is providing data probability distribution functions (PDF), both in the high-dimensional space defined by molecular descriptors and in 2D latent space. PDFs for the molecules of different activity classes were successfully used to build classification models in the framework of the Bayesian approach. Because PDFs are represented by a mixture of Gaussian functions, the Bhattacharyya kernel has been proposed as a measure of the overlap of datasets, which leads to an elegant method of global comparison of chemical libraries. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estimation of intrinsic and extrinsic capacitances of graphene self-switching diode using conformal mapping technique

NASA Astrophysics Data System (ADS)

Singh, Arun K.; Auton, Gregory; Hill, Ernie; Song, Aimin

2018-07-01

Due to a very high carrier concentration and low band gap, graphene based self-switching diodes do not demonstrate a very high rectification ratio. Despite that, it takes the advantage of graphene’s high carrier mobility and has been shown to work at very high microwave frequencies. However, the AC component of these devices is hidden in the very linear current–voltage characteristics. Here, we extract and quantitatively study the device capacitance that determines the device nonlinearity by implementing a conformal mapping technique. The estimated value of the nonlinear component or curvature coefficient from DC results based on Shichman–Hodges model predicts the rectified output voltage, which is in good agreement with the experimental RF results.

Thinking Maps: Research-Based Instructional Strategy in a PDS

ERIC Educational Resources Information Center

Kessler, Cristy; Zuercher, Deborah K.; Wong, Caroline S.

2013-01-01

An exploratory action research case study was conducted at Moanalua Middle School from 2006-2009 to examine the impact of Thinking Maps on student achievement. Thinking Maps are not just another set of graphic organizers but a set of eight of unique visual mind maps with each linked to a specific higher-order thinking pattern. This study tells the…

Mapping ecological risks with a portfolio-based technique: incorporating uncertainty and decision-making preferences

Treesearch

Denys Yemshanov; Frank H. Koch; Mark Ducey; Klaus Koehler

2013-01-01

Geographic mapping of risks is a useful analytical step in ecological risk assessments and in particular, in analyses aimed to estimate risks associated with introductions of invasive organisms. In this paper, we approach invasive species risk mapping as a portfolio allocation problem and apply techniques from decision theory to build an invasion risk map that combines...

A two-stage flow-based intrusion detection model for next-generation networks.

PubMed

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

A two-stage flow-based intrusion detection model for next-generation networks

PubMed Central

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results. PMID:29329294

A structured policy review of the principles of professional self-regulation.

PubMed

Benton, D C; González-Jurado, M A; Beneit-Montesinos, J V

2013-03-01

The International Council of Nurses (ICN) has, for many years, based its work on professional self-regulation on a set of 12 principles. These principles are research based and were identified nearly three decades ago. ICN has conducted a number of reviews of the principles; however, changes have been minimal. In the past 5-10 years, a number of authors and governments, often as part of the review of regulatory systems, have started to propose principles to guide the way regulatory frameworks are designed and implemented. These principles vary in number and content. This study examines the current policy literature on principle-based regulation and compares this with the set of principles advocated by the ICN. A systematic search of the literature on principle-based regulation is used as the basis for a qualitative thematic analysis to compare and contrast the 12 principles of self-regulation with more recently published work. A mapping of terms based on a detailed description of the principles used in the various research and policy documents was generated. This mapping forms the basis of a critique of the current ICN principles. A professional self-regulation advocated by the ICN were identified. A revised and extended set of 13 principles is needed if contemporary developments in the field of regulatory frameworks are to be accommodated. These revised principles should be considered for adoption by the ICN to underpin their advocacy work on professional self-regulation. © 2013 The Authors. International Nursing Review © 2013 International Council of Nurses.

Self-organized ferromagnetic nanowires in MgO-based magnetic tunnel junctions

NASA Astrophysics Data System (ADS)

Seike, Masayoshi; Fukushima, Tetsuya; Sato, Kazunori; Katayama-Yoshida, Hiroshi

2013-08-01

The focus of this study is to examine the distribution of defects and defect-induced properties in MgO-based magnetic tunnel junctions (MTJs). To this end, first-principles calculations were performed to estimate the electronic structures and total energies of MgO with various defects by using the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional. From connections drawn between the calculated results and previously reported experimental data, we propose that self-organized ferromagnetic nanowires of magnesium vacancies can be formed in MgO-based MTJs. This self-organization may provide the foundation for a comprehensive understanding of the conductivity, tunnel barriers and quantum oscillations of MgO-based MTJs. Further experimental verification is needed before firm conclusions can be drawn.

Application of Generative Topographic Mapping to Gear Failures Monitoring

NASA Astrophysics Data System (ADS)

Liao, Guanglan; Li, Weihua; Shi, Tielin; Rao, Raj B. K. N.

2002-07-01

The Generative Topographic Mapping (GTM) model is introduced as a probabilistic re-formation of the self-organizing map and has already been used in a variety of applications. This paper presents a study of the GTM in industrial gear failures monitoring. Vibration signals are analyzed using the GTM model, and the results show that gear feature data sets can be projected into a two-dimensional space and clustered in different areas according to their conditions, which can classify and identify clearly a gear work condition with cracked or broken tooth compared with the normal condition. With the trace of the image points in the two-dimensional space, the variation of gear work conditions can be observed visually, therefore, the occurrence and varying trend of gear failures can be monitored in time.

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

Self-generated morphology in lagoon reefs

PubMed Central

Hamblin, Michael G.

2015-01-01

The three-dimensional form of a coral reef develops through interactions and feedbacks between its constituent organisms and their environment. Reef morphology therefore contains a potential wealth of ecological information, accessible if the relationships between morphology and ecology can be decoded. Traditionally, reef morphology has been attributed to external controls such as substrate topography or hydrodynamic influences. Little is known about inherent reef morphology in the absence of external control. Here we use reef growth simulations, based on observations in the cellular reefs of Western Australia’s Houtman Abrolhos Islands, to show that reef morphology is fundamentally determined by the mechanical behaviour of the reef-building organisms themselves—specifically their tendency to either remain in place or to collapse. Reef-building organisms that tend to remain in place, such as massive and encrusting corals or coralline algae, produce nodular reefs, whereas those that tend to collapse, such as branching Acropora, produce cellular reefs. The purest reef growth forms arise in sheltered lagoons dominated by a single type of reef builder, as in the branching Acropora-dominated lagoons of the Abrolhos. In these situations reef morphology can be considered a phenotype of the predominant reef building organism. The capacity to infer coral type from reef morphology can potentially be used to identify and map specific coral habitat in remotely sensed images. More generally, identifying ecological mechanisms underlying other examples of self-generated reef morphology can potentially improve our understanding of present-day reef ecology, because any ecological process capable of shaping a reef will almost invariably be an important process in real time on the living reef. PMID:26175962

Self-Mapping in Treating Suicide Ideation: A Case Study

ERIC Educational Resources Information Center

Robertson, Lloyd Hawkeye

2011-01-01

This case study traces the development and use of a self-mapping exercise in the treatment of a youth who had been at risk for re-attempting suicide. A life skills exercise was modified to identify units of culture called "memes" from which a map of the youth's self was prepared. A successful treatment plan followed the mapping exercise. The…

The development of collective moral leadership among parents through education organizing.

PubMed

Evans, Michael P; Shirley, Dennis

2008-01-01

It is often assumed that parent participation in schools is primarily based on self-interest and that this is a frequent source of contention between parents and teachers. This article examines the experiences of members of the Jamaica Plain Parent Organizing Project (JP-POP), a community-based organization in Boston, and reveals that some parents have learned to act beyond their individual self-interest and to organize on behalf of the entire community as a result of their participation. The authors present qualitative data from interviews with JP-POP members to ascertain the motivations behind their initial decisions to become involved in education, the benefits they derive from their participation, and the gradual evolution of narrow definitions of self-interest to more communal understandings. Finally, they draw out implications for the potential capacity enhancement of community-based organizations in education at both the institutional and district levels.

Coupling Self-Organizing Maps with a Naïve Bayesian classifier: A case study for classifying Vermont streams using geomorphic, habitat and biological assessment data

NASA Astrophysics Data System (ADS)

Fytilis, N.; Rizzo, D. M.

2012-12-01

Environmental managers are increasingly required to forecast the long-term effects and the resilience or vulnerability of biophysical systems to human-generated stresses. Mitigation strategies for hydrological and environmental systems need to be assessed in the presence of uncertainty. An important aspect of such complex systems is the assessment of variable uncertainty on the model response outputs. We develop a new classification tool that couples a Naïve Bayesian Classifier with a modified Kohonen Self-Organizing Map to tackle this challenge. For proof-of-concept, we use rapid geomorphic and reach-scale habitat assessments data from over 2500 Vermont stream reaches (~1371 stream miles) assessed by the Vermont Agency of Natural Resources (VTANR). In addition, the Vermont Department of Environmental Conservation (VTDEC) estimates stream habitat biodiversity indices (macro-invertebrates and fish) and a variety of water quality data. Our approach fully utilizes the existing VTANR and VTDEC data sets to improve classification of stream-reach habitat and biological integrity. The combined SOM-Naïve Bayesian architecture is sufficiently flexible to allow for continual updates and increased accuracy associated with acquiring new data. The Kohonen Self-Organizing Map (SOM) is an unsupervised artificial neural network that autonomously analyzes properties inherent in a given a set of data. It is typically used to cluster data vectors into similar categories when a priori classes do not exist. The ability of the SOM to convert nonlinear, high dimensional data to some user-defined lower dimension and mine large amounts of data types (i.e., discrete or continuous, biological or geomorphic data) makes it ideal for characterizing the sensitivity of river networks in a variety of contexts. The procedure is data-driven, and therefore does not require the development of site-specific, process-based classification stream models, or sets of if-then-else rules associated with expert systems. This has the potential to save time and resources, while enabling a truly adaptive management approach using existing knowledge (expressed as prior probabilities) and new information (expressed as likelihood functions) to update estimates (i.e., in this case, improved stream classifications expressed as posterior probabilities). The distribution parameters of these posterior probabilities are used to quantify uncertainty associated with environmental data. Since classification plays a leading role in the future development of data-enabled science and engineering, such a computational tool is applicable to a variety of engineering applications. The ability of the new classification neural network to characterize streams with high environmental risk is essential for a proactive adaptive watershed management approach.

Molecular and genetic characterization of the S locus in Hordeum bulbosum L., a wild self-incompatible species related to cultivated barley.

PubMed

Kakeda, Katsuyuki; Ibuki, Toshiro; Suzuki, Junko; Tadano, Hidetaka; Kurita, Yuko; Hanai, Yosuke; Kowyama, Yasuo

2008-12-01

Gametophytic self-incompatibility (GSI) in the grasses is controlled by a distinct two-locus genetic system governed by the multiallelic loci S and Z. We have employed diploid Hordeum bulbosum as a model species for identifying the self-incompatibility (SI) genes and for elucidating the molecular mechanisms of the two-locus SI system in the grasses. In this study, we attempted to identify S haplotype-specific cDNAs expressed in pistils and anthers at the flowering stage in H. bulbosum, using the AFLP-based mRNA fingerprinting (AMF, also called cDNA-AFLP) technique. We used the AMF-derived DNA clones as markers for fine mapping of the S locus, and found that the locus resided in a chromosomal region displaying remarkable suppression of recombination, encompassing a large physical region. Furthermore, we identified three AMF-derived markers displaying complete linkage to the S locus, although they showed no significant homology with genes of known functions. Two of these markers showed expression patterns that were specific to the reproductive organs (pistil or anther), suggesting that they could be potential candidates for the S gene.

Quantized Self-Assembly of Discotic Rings in a Liquid Crystal Confined in Nanopores

NASA Astrophysics Data System (ADS)

Sentker, Kathrin; Zantop, Arne W.; Lippmann, Milena; Hofmann, Tommy; Seeck, Oliver H.; Kityk, Andriy V.; Yildirim, Arda; Schönhals, Andreas; Mazza, Marco G.; Huber, Patrick

2018-02-01

Disklike molecules with aromatic cores spontaneously stack up in linear columns with high, one-dimensional charge carrier mobilities along the columnar axes, making them prominent model systems for functional, self-organized matter. We show by high-resolution optical birefringence and synchrotron-based x-ray diffraction that confining a thermotropic discotic liquid crystal in cylindrical nanopores induces a quantized formation of annular layers consisting of concentric circular bent columns, unknown in the bulk state. Starting from the walls this ring self-assembly propagates layer by layer towards the pore center in the supercooled domain of the bulk isotropic-columnar transition and thus allows one to switch on and off reversibly single, nanosized rings through small temperature variations. By establishing a Gibbs free energy phase diagram we trace the phase transition quantization to the discreteness of the layers' excess bend deformation energies in comparison to the thermal energy, even for this near room-temperature system. Monte Carlo simulations yielding spatially resolved nematic order parameters, density maps, and bond-orientational order parameters corroborate the universality and robustness of the confinement-induced columnar ring formation as well as its quantized nature.

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

Wołek, Karol; Cieplak, Marek, E-mail: mc@ifpan.edu.pl

In structure-based models of proteins, one often assumes that folding is accomplished when all contacts are established. This assumption may frequently lead to a conceptual problem that folding takes place in a temperature region of very low thermodynamic stability, especially when the contact map used is too sparse. We consider six different structure-based models and show that allowing for a small, but model-dependent, percentage of the native contacts not being established boosts the folding temperature substantially while affecting the time scales of folding only in a minor way. We also compare other properties of the six models. We show thatmore » the choice of the description of the backbone stiffness has a substantial effect on the values of characteristic temperatures that relate both to equilibrium and kinetic properties. Models without any backbone stiffness (like the self-organized polymer) are found to perform similar to those with the stiffness, including in the studies of stretching.« less

Characterization of DOM adsorption of CNTs by using excitation-emission matrix fluorescence spectroscopy and multiway analysis.

PubMed

Peng, Mingguo; Li, Huajie; Li, Dongdong; Du, Erdeng; Li, Zhihong

2017-06-01

Carbon nanotubes (CNTs) were utilized to adsorb DOM in micro-polluted water. The characteristics of DOM adsorption on CNTs were investigated based on UV 254 , TOC, and fluorescence spectrum measurements. Based on PARAFAC (parallel factor) analysis, four fluorescent components were extracted, including one protein-like component (C4) and three humic acid-like components (C1, C2, and C3). The adsorption isotherms, kinetics, and thermodynamics of DOM adsorption on CNTs were further investigated. A Freundlich isotherm model fit the adsorption data well with high values of correlation. As a type of macro-porous and meso-porous adsorbent, CNTs preferably adsorb humic acid-like substances rather than protein-like substances. The increasing temperature will speed up the adsorption process. The self-organizing map (SOM) analysis further explains the fluorescent properties of water samples. The results provide a new insight into the adsorption behaviour of DOM fluorescent components on CNTs.

A neural approach for improving the measurement capability of an electronic nose

NASA Astrophysics Data System (ADS)

Chimenti, M.; DeRossi, D.; Di Francesco, F.; Domenici, C.; Pieri, G.; Pioggia, G.; Salvetti, O.

2003-06-01

Electronic noses, instruments for automatic recognition of odours, are typically composed of an array of partially selective sensors, a sampling system, a data acquisition device and a data processing system. For the purpose of evaluating the quality of olive oil, an electronic nose based on an array of conducting polymer sensors capable of discriminating olive oil aromas was developed. The selection of suitable pattern recognition techniques for a particular application can enhance the performance of electronic noses. Therefore, an advanced neural recognition algorithm for improving the measurement capability of the device was designed and implemented. This method combines multivariate statistical analysis and a hierarchical neural-network architecture based on self-organizing maps and error back-propagation. The complete system was tested using samples composed of characteristic olive oil aromatic components in refined olive oil. The results obtained have shown that this approach is effective in grouping aromas into different categories representative of their chemical structure.

Comparison of SOM point densities based on different criteria.

PubMed

Kohonen, T

1999-11-15

Point densities of model (codebook) vectors in self-organizing maps (SOMs) are evaluated in this article. For a few one-dimensional SOMs with finite grid lengths and a given probability density function of the input, the numerically exact point densities have been computed. The point density derived from the SOM algorithm turned out to be different from that minimizing the SOM distortion measure, showing that the model vectors produced by the basic SOM algorithm in general do not exactly coincide with the optimum of the distortion measure. A new computing technique based on the calculus of variations has been introduced. It was applied to the computation of point densities derived from the distortion measure for both the classical vector quantization and the SOM with general but equal dimensionality of the input vectors and the grid, respectively. The power laws in the continuum limit obtained in these cases were found to be identical.

Augmenting Satellite Precipitation Estimation with Lightning Information

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

Mahrooghy, Majid; Anantharaj, Valentine G; Younan, Nicolas H.

2013-01-01

We have used lightning information to augment the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network - Cloud Classification System (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from GOES-12 infrared data, into either electrified (EL) or non-electrified (NEL) patches. A set of features is extracted separately for the EL and NEL cloud patches. The features for the EL cloud patches include new features based on the lightning information. The cloud patches are classified and clustered using self-organizing maps (SOM). Then brightness temperature and rain rate (T-R) relationships are derived for the different clusters.more » Rain rates are estimated for the cloud patches based on their representative T-R relationship. The Equitable Threat Score (ETS) for daily precipitation estimates is improved by almost 12% for the winter season. In the summer, no significant improvements in ETS are noted.« less

Procedure for extraction of disparate data from maps into computerized data bases

NASA Technical Reports Server (NTRS)

Junkin, B. G.

1979-01-01

A procedure is presented for extracting disparate sources of data from geographic maps and for the conversion of these data into a suitable format for processing on a computer-oriented information system. Several graphic digitizing considerations are included and related to the NASA Earth Resources Laboratory's Digitizer System. Current operating procedures for the Digitizer System are given in a simplified and logical manner. The report serves as a guide to those organizations interested in converting map-based data by using a comparable map digitizing system.

Physical mapping of a pollen modifier locus controlling self-incompatibility in apricot and synteny analysis within the Rosaceae.

PubMed

Zuriaga, Elena; Molina, Laura; Badenes, María Luisa; Romero, Carlos

2012-06-01

S-locus products (S-RNase and F-box proteins) are essential for the gametophytic self-incompatibility (GSI) specific recognition in Prunus. However, accumulated genetic evidence suggests that other S-locus unlinked factors are also required for GSI. For instance, GSI breakdown was associated with a pollen-part mutation unlinked to the S-locus in the apricot (Prunus armeniaca L.) cv. 'Canino'. Fine-mapping of this mutated modifier gene (M-locus) and the synteny analysis of the M-locus within the Rosaceae are here reported. A segregation distortion loci mapping strategy, based on a selectively genotyped population, was used to map the M-locus. In addition, a bacterial artificial chromosome (BAC) contig was constructed for this region using overlapping oligonucleotides probes, and BAC-end sequences (BES) were blasted against Rosaceae genomes to perform micro-synteny analysis. The M-locus was mapped to the distal part of chr.3 flanked by two SSR markers within an interval of 1.8 cM corresponding to ~364 Kb in the peach (Prunus persica L. Batsch) genome. In the integrated genetic-physical map of this region, BES were mapped against the peach scaffold_3 and BACs were anchored to the apricot map. Micro-syntenic blocks were detected in apple (Malus × domestica Borkh.) LG17/9 and strawberry (Fragaria vesca L.) FG6 chromosomes. The M-locus fine-scale mapping provides a solid basis for self-compatibility marker-assisted selection and for positional cloning of the underlying gene, a necessary goal to elucidate the pollen rejection mechanism in Prunus. In a wider context, the syntenic regions identified in peach, apple and strawberry might be useful to interpret GSI evolution in Rosaceae.

Sugar-Based Polyamides: Self-Organization in Strong Polar Organic Solvents.

PubMed

Rosu, Cornelia; Russo, Paul S; Daly, William H; Cueto, Rafael; Pople, John A; Laine, Roger A; Negulescu, Ioan I

2015-09-14

Periodic patterns resembling spirals were observed to form spontaneously upon unassisted cooling of d-glucaric acid- and d-galactaric acid-based polyamide solutions in N-methyl-N-morpholine oxide (NMMO) monohydrate. Similar observations were made in d-galactaric acid-based polyamide/ionic liquid (IL) solutions. The morphologies were investigated by optical, polarized light and confocal microscopy assays to reveal pattern details. Differential scanning calorimetry was used to monitor solution thermal behavior. Small- and wide-angle X-ray scattering data reflected the complex and heterogeneous nature of the self-organized patterns. Factors such as concentration and temperature were found to influence spiral dimensions and geometry. The distance between rings followed a first-order exponential decay as a function of polymer concentration. Fourier-Transform Infrared Microspectroscopy analysis of spirals pointed to H-bonding between the solvent and the pendant hydroxyl groups of the glucose units from the polymer backbone. Tests on self-organization into spirals of ketal-protected d-galactaric acid polyamides in NMMO monohydrate confirmed the importance of the monosaccharide's pendant free hydroxyl groups on the formation of these patterns. Rheology performed on d-galactaric-based polyamides at high concentration in NMMO monohydrate solution revealed the optimum conditions necessary to process these materials as fibers by spinning. The self-organization of these sugar-based polyamides mimics certain biological materials.

Perceptual Mapping Software as a Tool for Facilitating School-Based Consultation

ERIC Educational Resources Information Center

Rush, S. Craig; Kalish, Ashley; Wheeler, Joanna

2013-01-01

Perceptual mapping is a systematic method for collecting, analyzing, and presenting group perceptions that is potentially useful in consultation. With input and feedback from a consultee group, perceptual mapping allows the consultant to capture the group's collective perceptions and display them as an organized image that may foster…

Using concept maps in a modified team-based learning exercise.

PubMed

Knollmann-Ritschel, Barbara E C; Durning, Steven J

2015-04-01

Medical school education has traditionally been driven by single discipline teaching and assessment. Newer medical school curricula often implement an organ-based approach that fosters integration of basic science and clinical disciplines. Concept maps are widely used in education. Through diagrammatic depiction of a variety of concepts and their specific connections with other ideas, concept maps provide a unique perspective into learning and performance that can complement other assessment methods commonly used in medical schools. In this innovation, we describe using concepts maps as a vehicle for a modified a classic Team-Based Learning (TBL) exercise. Modifications to traditional TBL in our innovation included replacing an individual assessment using multiple-choice questions with concept maps as well as combining the group assessment and application exercise whereby teams created concept maps. These modifications were made to further assess understanding of content across the Fundamentals module (the introductory module of the preclerkship curriculum). While preliminary, student performance and feedback from faculty and students support the use of concept maps in TBL. Our findings suggest concept maps can provide a unique means of determining assessment of learning and generating feedback to students. Concept maps can also demonstrate knowledge acquisition, organization of prior and new knowledge, and synthesis of that knowledge across disciplines in a unique way providing an additional means of assessment in addition to traditional multiple-choice questions. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Pre-Scheduled and Self Organized Sleep-Scheduling Algorithms for Efficient K-Coverage in Wireless Sensor Networks

PubMed Central

Hwang, I-Shyan

2017-01-01

The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized. PMID:29257078

Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family.

PubMed

Scotti, Marcus T; Emerenciano, Vicente; Ferreira, Marcelo J P; Scotti, Luciana; Stefani, Ricardo; da Silva, Marcelo S; Mendonça Junior, Francisco Jaime B

2012-04-20

The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

Spatial analysis of agro-ecological data: Detection of spatial patterns combining three different methodical approaches

NASA Astrophysics Data System (ADS)

Heuer, A.; Casper, M. C.; Vohland, M.

2009-04-01

Processes in natural systems and the resulting patterns occur in ecological space and time. To study natural structures and to understand the functional processes it is necessary to identify the relevant spatial and temporal space at which these all occur; or with other words to isolate spatial and temporal patterns. In this contribution we will concentrate on the spatial aspects of agro-ecological data analysis. Data were derived from two agricultural plots, each of about 5 hectares, in the area of Newel, located in Western Palatinate, Germany. The plots had been conventionally cultivated with a crop rotation of winter rape, winter wheat and spring barley. Data about physical and chemical soil properties, vegetation and topography were i) collected by measurements in the field during three vegetation periods (2005-2008) and/or ii) derived from hyperspectral image data, acquired by a HyMap airborne imaging sensor (2005). To detect spatial variability within the plots, we applied three different approaches that examine and describe relationships among data. First, we used variography to get an overview of the data. A comparison of the experimental variograms facilitated to distinguish variables, which seemed to occur in related or dissimilar spatial space. Second, based on data available in raster-format basic cell statistics were conducted, using a geographic information system. Here we could make advantage of the powerful classification and visualization tool, which supported the spatial distribution of patterns. Third, we used an approach that is being used for visualization of complex highly dimensional environmental data, the Kohonen self-organizing map. The self-organizing map (SOM) uses multidimensional data that gets further reduced in dimensionality (2-D) to detect similarities in data sets and correlation between single variables. One of SOM's advantages is its powerful visualization capability. The combination of the three approaches leads to comprehensive and reasonable results, which will be presented in detail. It can be concluded, that the chosen strategy made it possible to complement preliminary findings, to validate the results of a single approach and to clearly delineate spatial patterns.

Contractive type non-self mappings on metric spaces of hyperbolic type

NASA Astrophysics Data System (ADS)

Ciric, Ljubomir B.

2006-05-01

Let (X,d) be a metric space of hyperbolic type and K a nonempty closed subset of X. In this paper we study a class of mappings from K into X (not necessarily self-mappings on K), which are defined by the contractive condition (2.1) below, and a class of pairs of mappings from K into X which satisfy the condition (2.28) below. We present fixed point and common fixed point theorems which are generalizations of the corresponding fixed point theorems of Ciric [L.B. Ciric, Quasi-contraction non-self mappings on Banach spaces, Bull. Acad. Serbe Sci. Arts 23 (1998) 25-31; L.B. Ciric, J.S. Ume, M.S. Khan, H.K.T. Pathak, On some non-self mappings, Math. Nachr. 251 (2003) 28-33], Rhoades [B.E. Rhoades, A fixed point theorem for some non-self mappings, Math. Japon. 23 (1978) 457-459] and many other authors. Some examples are presented to show that our results are genuine generalizations of known results from this area.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

PubMed

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Ruan, Wenqian; Wei, Xionghui

2018-06-01

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Improving the segmentation of therapy-induced leukoencephalopathy using apriori information and a gradient magnitude threshold

NASA Astrophysics Data System (ADS)

Glass, John O.; Reddick, Wilburn E.; Reeves, Cara; Pui, Ching-Hon

2004-05-01

Reliably quantifying therapy-induced leukoencephalopathy in children treated for cancer is a challenging task due to its varying MR properties and similarity to normal tissues and imaging artifacts. T1, T2, PD, and FLAIR images were analyzed for a subset of 15 children from an institutional protocol for the treatment of acute lymphoblastic leukemia. Three different analysis techniques were compared to examine improvements in the segmentation accuracy of leukoencephalopathy versus manual tracings by two expert observers. The first technique utilized no apriori information and a white matter mask based on the segmentation of the first serial examination of each patient. MR images were then segmented with a Kohonen Self-Organizing Map. The other two techniques combine apriori maps from the ICBM atlas spatially normalized to each patient and resliced using SPM99 software. The apriori maps were included as input and a gradient magnitude threshold calculated on the FLAIR images was also utilized. The second technique used a 2-dimensional threshold, while the third algorithm utilized a 3-dimensional threshold. Kappa values were compared for the three techniques to each observer, and improvements were seen with each addition to the original algorithm (Observer 1: 0.651, 0.653, 0.744; Observer 2: 0.603, 0.615, 0.699).

Self-organized neural maps of human protein sequences.

PubMed Central

Ferrán, E. A.; Pflugfelder, B.; Ferrara, P.

1994-01-01

We have recently described a method based on artificial neural networks to cluster protein sequences into families. The network was trained with Kohonen's unsupervised learning algorithm using, as inputs, the matrix patterns derived from the dipeptide composition of the proteins. We present here a large-scale application of that method to classify the 1,758 human protein sequences stored in the SwissProt database (release 19.0), whose lengths are greater than 50 amino acids. In the final 2-dimensional topologically ordered map of 15 x 15 neurons, proteins belonging to known families were associated with the same neuron or with neighboring ones. Also, as an attempt to reduce the time-consuming learning procedure, we compared 2 learning protocols: one of 500 epochs (100 SUN CPU-hours [CPU-h]), and another one of 30 epochs (6.7 CPU-h). A further reduction of learning-computing time, by a factor of about 3.3, with similar protein clustering results, was achieved using a matrix of 11 x 11 components to represent the sequences. Although network training is time consuming, the classification of a new protein in the final ordered map is very fast (14.6 CPU-seconds). We also show a comparison between the artificial neural network approach and conventional methods of biosequence analysis. PMID:8019421

Use of GIS Mapping as a Public Health Tool—From Cholera to Cancer

PubMed Central

Musa, George J.; Chiang, Po-Huang; Sylk, Tyler; Bavley, Rachel; Keating, William; Lakew, Bereketab; Tsou, Hui-Chen; Hoven, Christina W.

2013-01-01

The field of medical geographic information systems (Medical GIS) has become extremely useful in understanding the bigger picture of public health. The discipline holds a substantial capacity to understand not only differences, but also similarities in population health all over the world. The main goal of marrying the disciplines of medical geography, public health and informatics is to understand how countless health issues impact populations, and the trends by which these populations are affected. From the 1990s to today, this practical approach has become a valued and progressive system in analyzing medical and epidemiological phenomena ranging from cholera to cancer. The instruments supporting this field include geographic information systems (GIS), disease surveillance, big data, and analytical approaches like the Geographical Analysis Machine (GAM), Dynamic Continuous Area Space Time Analysis (DYCAST), cellular automata, agent-based modeling, spatial statistics and self-organizing maps. The positive effects on disease mapping have proven to be tremendous as these instruments continue to have a great impact on the mission to improve worldwide health care. While traditional uses of GIS in public health are static and lacking real-time components, implementing a space-time animation in these instruments will be monumental as technology and data continue to grow. PMID:25114567

Use of GIS Mapping as a Public Health Tool-From Cholera to Cancer.

PubMed

Musa, George J; Chiang, Po-Huang; Sylk, Tyler; Bavley, Rachel; Keating, William; Lakew, Bereketab; Tsou, Hui-Chen; Hoven, Christina W

2013-01-01

The field of medical geographic information systems (Medical GIS) has become extremely useful in understanding the bigger picture of public health. The discipline holds a substantial capacity to understand not only differences, but also similarities in population health all over the world. The main goal of marrying the disciplines of medical geography, public health and informatics is to understand how countless health issues impact populations, and the trends by which these populations are affected. From the 1990s to today, this practical approach has become a valued and progressive system in analyzing medical and epidemiological phenomena ranging from cholera to cancer. The instruments supporting this field include geographic information systems (GIS), disease surveillance, big data, and analytical approaches like the Geographical Analysis Machine (GAM), Dynamic Continuous Area Space Time Analysis (DYCAST), cellular automata, agent-based modeling, spatial statistics and self-organizing maps. The positive effects on disease mapping have proven to be tremendous as these instruments continue to have a great impact on the mission to improve worldwide health care. While traditional uses of GIS in public health are static and lacking real-time components, implementing a space-time animation in these instruments will be monumental as technology and data continue to grow.

The neural network classification of false killer whale (Pseudorca crassidens) vocalizations.

PubMed

Murray, S O; Mercado, E; Roitblat, H L

1998-12-01

This study reports the use of unsupervised, self-organizing neural network to categorize the repertoire of false killer whale vocalizations. Self-organizing networks are capable of detecting patterns in their input and partitioning those patterns into categories without requiring that the number or types of categories be predefined. The inputs for the neural networks were two-dimensional characterization of false killer whale vocalization, where each vocalization was characterized by a sequence of short-time measurements of duty cycle and peak frequency. The first neural network used competitive learning, where units in a competitive layer distributed themselves to recognize frequently presented input vectors. This network resulted in classes representing typical patterns in the vocalizations. The second network was a Kohonen feature map which organized the outputs topologically, providing a graphical organization of pattern relationships. The networks performed well as measured by (1) the average correlation between the input vectors and the weight vectors for each category, and (2) the ability of the networks to classify novel vocalizations. The techniques used in this study could easily be applied to other species and facilitate the development of objective, comprehensive repertoire models.

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

PubMed

Dutt-Mazumder, Aviroop; Button, Chris; Robins, Anthony; Bartlett, Roger

2011-12-01

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

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.

Positioning Genomics in Biology Education: Content Mapping of Undergraduate Biology Textbooks†

PubMed Central

Wernick, Naomi L. B.; Ndung’u, Eric; Haughton, Dominique; Ledley, Fred D.

2014-01-01

Biological thought increasingly recognizes the centrality of the genome in constituting and regulating processes ranging from cellular systems to ecology and evolution. In this paper, we ask whether genomics is similarly positioned as a core concept in the instructional sequence for undergraduate biology. Using quantitative methods, we analyzed the order in which core biological concepts were introduced in textbooks for first-year general and human biology. Statistical analysis was performed using self-organizing map algorithms and conventional methods to identify clusters of terms and their relative position in the books. General biology textbooks for both majors and nonmajors introduced genome-related content after text related to cell biology and biological chemistry, but before content describing higher-order biological processes. However, human biology textbooks most often introduced genomic content near the end of the books. These results suggest that genomics is not yet positioned as a core concept in commonly used textbooks for first-year biology and raises questions about whether such textbooks, or courses based on the outline of these textbooks, provide an appropriate foundation for understanding contemporary biological science. PMID:25574293

Positioning genomics in biology education: content mapping of undergraduate biology textbooks.

PubMed

Wernick, Naomi L B; Ndung'u, Eric; Haughton, Dominique; Ledley, Fred D

2014-12-01

Biological thought increasingly recognizes the centrality of the genome in constituting and regulating processes ranging from cellular systems to ecology and evolution. In this paper, we ask whether genomics is similarly positioned as a core concept in the instructional sequence for undergraduate biology. Using quantitative methods, we analyzed the order in which core biological concepts were introduced in textbooks for first-year general and human biology. Statistical analysis was performed using self-organizing map algorithms and conventional methods to identify clusters of terms and their relative position in the books. General biology textbooks for both majors and nonmajors introduced genome-related content after text related to cell biology and biological chemistry, but before content describing higher-order biological processes. However, human biology textbooks most often introduced genomic content near the end of the books. These results suggest that genomics is not yet positioned as a core concept in commonly used textbooks for first-year biology and raises questions about whether such textbooks, or courses based on the outline of these textbooks, provide an appropriate foundation for understanding contemporary biological science.

Geography Map Knowledge Acquisition by Solving a Jigsaw Map Compared to Self-Study: Investigating Game Based Learning

ERIC Educational Resources Information Center

Dang, Srishti; Ved, Arunima; Vemuri, Kavita

2018-01-01

Efficacy of games as learning medium is of interest to researchers and the gaming industry. A critical metric for learning is knowledge retention and very few studies have conducted in-depth comparisons of: a) game versus no-game learning, b) collaborative versus individual learning. Towards this, the study reported in this article will present…

Perspective: Organizational professionalism: relevant competencies and behaviors.

PubMed

Egener, Barry; McDonald, Walter; Rosof, Bernard; Gullen, David

2012-05-01

The professionalism behaviors of physicians have been extensively discussed and defined; however, the professionalism behaviors of health care organizations have not been systemically categorized or described. Defining organizational professionalism is important because the behaviors of a health care organization may substantially impact the behaviors of physicians and others within the organization as well as other institutions and the larger community. In this article, the authors discuss the following competencies of organizational professionalism, derived from ethical values: service, respect, fairness, integrity, accountability, mindfulness, and self-motivation. How nonprofit health care organizations can translate these competencies into behaviors is described. For example, incorporating metrics of population health into assessments of corporate success may increase collaboration among regional health care organizations while also benefiting the community. The unique responsibilities of leadership to model these competencies, promote them in the community, and develop relevant organizational strategies are clarified. These obligations elevate the importance of the executive leadership's capacity for self-reflection and the governing boards' responsibility for mapping operational activities to organizational mission. Lastly, the authors consider how medical organizations are currently addressing professionalism challenges. In an environment made turbulent by regulatory change and financial constraints, achieving proficiency in professionalism competencies can assist nonprofit health care organizations to promote population health and the well-being of their workforces.

Distribution, richness, quality, and thermal maturity of source rock units on the North Slope of Alaska

USGS Publications Warehouse

Peters, K.E.; Bird, K.J.; Keller, M.A.; Lillis, P.G.; Magoon, L.B.

2003-01-01

Four source rock units on the North Slope were identified, characterized, and mapped to better understand the origin of petroleum in the area: Hue-gamma ray zone (Hue-GRZ), pebble shale unit, Kingak Shale, and Shublik Formation. Rock-Eval pyrolysis, total organic carbon analysis, and well logs were used to map the present-day thickness, organic quantity (TOC), quality (hydrogen index, HI), and thermal maturity (Tmax) of each unit. To map these units, we screened all available geochemical data for wells in the study area and assumed that the top and bottom of the oil window occur at Tmax of ~440° and 470°C, respectively. Based on several assumptions related to carbon mass balance and regional distributions of TOC, the present-day source rock quantity and quality maps were used to determine the extent of fractional conversion of the kerogen to petroleum and to map the original organic richness prior to thermal maturation.

A mitotically inheritable unit containing a MAP kinase module

PubMed Central

Kicka, Sébastien; Bonnet, Crystel; Sobering, Andrew K.; Ganesan, Latha P.; Silar, Philippe

2006-01-01

Prions are novel kinds of hereditary units, relying solely on proteins, that are infectious and inherited in a non-Mendelian fashion. To date, they are either based on autocatalytic modification of a 3D conformation or on autocatalytic cleavage. Here, we provide further evidence that in the filamentous fungus Podospora anserina, a MAP kinase cascade is probably able to self-activate and generate C, a hereditary unit that bears many similarities to prions and triggers cell degeneration. We show that in addition to the MAPKKK gene, both the MAPKK and MAPK genes are necessary for the propagation of C, and that overexpression of MAPK as that of MAPKKK facilitates the appearance of C. We also show that a correlation exists between the presence of C and localization of the MAPK inside nuclei. These data emphasize the resemblance between prions and a self-positively regulated cascade in terms of their transmission. This thus further expands the concept of protein-base inheritance to regulatory networks that have the ability to self-activate. PMID:16938837

A mitotically inheritable unit containing a MAP kinase module.

PubMed

Kicka, Sébastien; Bonnet, Crystel; Sobering, Andrew K; Ganesan, Latha P; Silar, Philippe

2006-09-05

Prions are novel kinds of hereditary units, relying solely on proteins, that are infectious and inherited in a non-Mendelian fashion. To date, they are either based on autocatalytic modification of a 3D conformation or on autocatalytic cleavage. Here, we provide further evidence that in the filamentous fungus Podospora anserina, a MAP kinase cascade is probably able to self-activate and generate C, a hereditary unit that bears many similarities to prions and triggers cell degeneration. We show that in addition to the MAPKKK gene, both the MAPKK and MAPK genes are necessary for the propagation of C, and that overexpression of MAPK as that of MAPKKK facilitates the appearance of C. We also show that a correlation exists between the presence of C and localization of the MAPK inside nuclei. These data emphasize the resemblance between prions and a self-positively regulated cascade in terms of their transmission. This thus further expands the concept of protein-base inheritance to regulatory networks that have the ability to self-activate.

Application of Intervention Mapping to the Development of a Complex Physical Therapist Intervention.

PubMed

Jones, Taryn M; Dear, Blake F; Hush, Julia M; Titov, Nickolai; Dean, Catherine M

2016-12-01

Physical therapist interventions, such as those designed to change physical activity behavior, are often complex and multifaceted. In order to facilitate rigorous evaluation and implementation of these complex interventions into clinical practice, the development process must be comprehensive, systematic, and transparent, with a sound theoretical basis. Intervention Mapping is designed to guide an iterative and problem-focused approach to the development of complex interventions. The purpose of this case report is to demonstrate the application of an Intervention Mapping approach to the development of a complex physical therapist intervention, a remote self-management program aimed at increasing physical activity after acquired brain injury. Intervention Mapping consists of 6 steps to guide the development of complex interventions: (1) needs assessment; (2) identification of outcomes, performance objectives, and change objectives; (3) selection of theory-based intervention methods and practical applications; (4) organization of methods and applications into an intervention program; (5) creation of an implementation plan; and (6) generation of an evaluation plan. The rationale and detailed description of this process are presented using an example of the development of a novel and complex physical therapist intervention, myMoves-a program designed to help individuals with an acquired brain injury to change their physical activity behavior. The Intervention Mapping framework may be useful in the development of complex physical therapist interventions, ensuring the development is comprehensive, systematic, and thorough, with a sound theoretical basis. This process facilitates translation into clinical practice and allows for greater confidence and transparency when the program efficacy is investigated. © 2016 American Physical Therapy Association.

Using self-organizing maps to identify potential halo white dwarfs.

PubMed

García-Berro, Enrique; Torres, Santiago; Isern, Jordi

2003-01-01

We present the results of an unsupervised classification of the disk and halo white dwarf populations in the solar neighborhood. The classification is done by merging the results of detailed Monte Carlo (MC) simulations, which reproduce very well the characteristics of the white dwarf populations in the solar neighborhood, with a catalogue of real stars. The resulting composite catalogue is analyzed using a competitive learning algorithm. In particular we have used the so-called self-organized map. The MC simulated stars are used as tracers and help in identifying the resulting clusters. The results of such an strategy turn out to be quite satisfactory, suggesting that this approach can provide an useful framework for analyzing large databases of white dwarfs with well determined kinematical, spatial and photometric properties once they become available in the next decade. Moreover, the results are of astrophysical interest as well, since a straightforward interpretation of several recent astronomical observations, like the detected microlensing events in the direction of the Magellanic Clouds, the possible detection of high proper motion white dwarfs in the Hubble Deep Field and the discovery of high velocity white dwarfs in the solar neighborhood, suggests that a fraction of the baryonic dark matter component of our galaxy could be in the form of old and dim halo white dwarfs.

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