Fuzzy Document Clustering Approach using WordNet Lexical Categories

NASA Astrophysics Data System (ADS)

Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.

Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.

Ellipsoidal fuzzy learning for smart car platoons

NASA Astrophysics Data System (ADS)

Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Information Clustering Based on Fuzzy Multisets.

ERIC Educational Resources Information Center

Miyamoto, Sadaaki

2003-01-01

Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

Dynamic Fuzzy Model Development for a Drum-type Boiler-turbine Plant Through GK Clustering

NASA Astrophysics Data System (ADS)

Habbi, Ahcène; Zelmat, Mimoun

2008-10-01

This paper discusses a TS fuzzy model identification method for an industrial drum-type boiler plant using the GK fuzzy clustering approach. The fuzzy model is constructed from a set of input-output data that covers a wide operating range of the physical plant. The reference data is generated using a complex first-principle-based mathematical model that describes the key dynamical properties of the boiler-turbine dynamics. The proposed fuzzy model is derived by means of fuzzy clustering method with particular attention on structure flexibility and model interpretability issues. This may provide a basement of a new way to design model based control and diagnosis mechanisms for the complex nonlinear plant.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

PubMed

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

PubMed

Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

2017-01-01

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering

NASA Astrophysics Data System (ADS)

Onishi, Masaki; Yoda, Ikushi

In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

NASA Astrophysics Data System (ADS)

Ebrahimi, A.; Pahlavani, P.; Masoumi, Z.

2017-09-01

Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

An effective fuzzy kernel clustering analysis approach for gene expression data.

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

Mitra, Sunanda

1993-01-01

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

NASA Technical Reports Server (NTRS)

Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

NASA Astrophysics Data System (ADS)

Dzung Nguyen, Sy; Choi, Seung-Bok

2012-08-01

This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.

Fuzzy forecasting based on fuzzy-trend logical relationship groups.

PubMed

Chen, Shyi-Ming; Wang, Nai-Yi

2010-10-01

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

PubMed

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

Forecasting Jakarta composite index (IHSG) based on chen fuzzy time series and firefly clustering algorithm

NASA Astrophysics Data System (ADS)

Ningrum, R. W.; Surarso, B.; Farikhin; Safarudin, Y. M.

2018-03-01

This paper proposes the combination of Firefly Algorithm (FA) and Chen Fuzzy Time Series Forecasting. Most of the existing fuzzy forecasting methods based on fuzzy time series use the static length of intervals. Therefore, we apply an artificial intelligence, i.e., Firefly Algorithm (FA) to set non-stationary length of intervals for each cluster on Chen Method. The method is evaluated by applying on the Jakarta Composite Index (IHSG) and compare with classical Chen Fuzzy Time Series Forecasting. Its performance verified through simulation using Matlab.

QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm.

PubMed

Bao, Ying; Lei, Weimin; Zhang, Wei; Zhan, Yuzhuo

2016-01-01

At present, to realize or improve the quality of experience (QoE) is a major goal for network media transmission service, and QoE evaluation is the basis for adjusting the transmission control mechanism. Therefore, a kind of QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm is proposed in this paper, which is concentrated on service score calculation at the server side. The server side collects network transmission quality of service (QoS) parameter, node location data, and user expectation value from client feedback information. Then it manages the historical data in database through the "big data" process mode, and predicts user score according to heuristic rules. On this basis, it completes fuzzy clustering analysis, and generates service QoE score and management message, which will be finally fed back to clients. Besides, this paper mainly discussed service evaluation generative rules, heuristic evaluation rules and fuzzy clustering analysis methods, and presents service-based QoE evaluation processes. The simulation experiments have verified the effectiveness of QoE collaborative evaluation method based on fuzzy clustering heuristic rules.

Fuzzy jets

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

Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variablesmore » in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less

An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

PubMed Central

Salcedo-Sanz, S.; Del Ser, J.; Geem, Z. W.

2014-01-01

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases. PMID:24977235

Fuzzy jets

DOE PAGES

Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; ...

2016-06-01

Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variablesmore » in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less

Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis

NASA Astrophysics Data System (ADS)

Fu, Pei-hua; Yin, Hong-bo

In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.

Improved fuzzy clustering algorithms in segmentation of DC-enhanced breast MRI.

PubMed

Kannan, S R; Ramathilagam, S; Devi, Pandiyarajan; Sathya, A

2012-02-01

Segmentation of medical images is a difficult and challenging problem due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. The objective of this work is to develop some robust fuzzy clustering segmentation systems for effective segmentation of DCE - breast MRI. This paper obtains the robust fuzzy clustering algorithms by incorporating kernel methods, penalty terms, tolerance of the neighborhood attraction, additional entropy term and fuzzy parameters. The initial centers are obtained using initialization algorithm to reduce the computation complexity and running time of proposed algorithms. Experimental works on breast images show that the proposed algorithms are effective to improve the similarity measurement, to handle large amount of noise, to have better results in dealing the data corrupted by noise, and other artifacts. The clustering results of proposed methods are validated using Silhouette Method.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.

PubMed

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-06-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

PubMed Central

Zhang, Zhaoyang; Wang, Honggang

2016-01-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering is more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services. PMID:27126063

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

PubMed Central

Wang, Zhihao; Yi, Jing

2016-01-01

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

Introduction to Fuzzy Set Theory

NASA Technical Reports Server (NTRS)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

NASA Astrophysics Data System (ADS)

Santiago Girola Schneider, Rafael

2015-08-01

The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with “imprecise” information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic’s techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

NASA Astrophysics Data System (ADS)

Girola Schneider, R.

2017-07-01

The fuzzy logic is a branch of the artificial intelligence founded on the concept that everything is a matter of degree. It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters. Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

A fuzzy automated object classification by infrared laser camera

NASA Astrophysics Data System (ADS)

Kanazawa, Seigo; Taniguchi, Kazuhiko; Asari, Kazunari; Kuramoto, Kei; Kobashi, Syoji; Hata, Yutaka

2011-06-01

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.

Image segmentation using fuzzy LVQ clustering networks

NASA Technical Reports Server (NTRS)

Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

1992-01-01

In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

NASA Astrophysics Data System (ADS)

Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-01-01

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift

NASA Astrophysics Data System (ADS)

Arfan Jaffar, M.

2017-01-01

In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.

Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

NASA Astrophysics Data System (ADS)

Rustam, Z.; Talita, A. S.

2017-07-01

Intrusion Detection System (IDS) is an essential part of security systems to strengthen the security of information systems. IDS can be used to detect the abuse by intruders who try to get into the network system in order to access and utilize the available data sources in the system. There are two approaches of IDS, Misuse Detection and Anomaly Detection (behavior-based intrusion detection). Fuzzy clustering-based methods have been widely used to solve Anomaly Detection problems. Other than using fuzzy membership concept to determine the object to a cluster, other approaches as in combining fuzzy and possibilistic membership or feature-weighted based methods are also used. We propose Fuzzy Kernel k-Medoids that combining fuzzy and possibilistic membership as a powerful method to solve anomaly detection problem since on numerical experiment it is able to classify IDS benchmark data into five different classes simultaneously. We classify IDS benchmark data KDDCup'99 data set into five different classes simultaneously with the best performance was achieved by using 30 % of training data with clustering accuracy reached 90.28 percent.

A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

NASA Astrophysics Data System (ADS)

Błaszczuk, Artur; Krzywański, Jarosław

2017-03-01

The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB) has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131-1156 K and 1.93-6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109-241 W/(m2K) and 111-240 W/(m2K), for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.

Determination System Of Food Vouchers For the Poor Based On Fuzzy C-Means Method

NASA Astrophysics Data System (ADS)

Anamisa, D. R.; Yusuf, M.; Syakur, M. A.

2018-01-01

Food vouchers are government programs to tackle the poverty of rural communities. This program aims to help the poor group in getting enough food and nutrients from carbohydrates. There are several factors that influence to receive the food voucher, such as: job, monthly income, Taxes, electricity bill, size of house, number of family member, education certificate and amount of rice consumption every week. In the execution for the distribution of vouchers is often a lot of problems, such as: the distribution of food vouchers has been misdirected and someone who receives is still subjective. Some of the solutions to decision making have not been done. The research aims to calculating the change of each partition matrix and each cluster using Fuzzy C-Means method. Hopefully this research makes contribution by providing higher result using Fuzzy C-Means comparing to other method for this case study. In this research, decision making is done by using Fuzzy C-Means method. The Fuzzy C-Means method is a clustering method that has an organized and scattered cluster structure with regular patterns on two-dimensional datasets. Furthermore, Fuzzy C-Means method used for calculates the change of each partition matrix. Each cluster will be sorted by the proximity of the data element to the centroid of the cluster to get the ranking. Various trials were conducted for grouping and ranking of proposed data that received food vouchers based on the quota of each village. This testing by Fuzzy C-Means method, is developed and abled for determining the recipient of the food voucher with satisfaction results. Fulfillment of the recipient of the food voucher is 80% to 90% and this testing using data of 115 Family Card from 6 Villages. The quality of success affected, has been using the number of iteration factors is 20 and the number of clusters is 3

Health state evaluation of shield tunnel SHM using fuzzy cluster method

NASA Astrophysics Data System (ADS)

Zhou, Fa; Zhang, Wei; Sun, Ke; Shi, Bin

2015-04-01

Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

PubMed

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Identification of different geologic units using fuzzy constrained resistivity tomography

NASA Astrophysics Data System (ADS)

Singh, Anand; Sharma, S. P.

2018-01-01

Different geophysical inversion strategies are utilized as a component of an interpretation process that tries to separate geologic units based on the resistivity distribution. In the present study, we present the results of separating different geologic units using fuzzy constrained resistivity tomography. This was accomplished using fuzzy c means, a clustering procedure to improve the 2D resistivity image and geologic separation within the iterative minimization through inversion. First, we developed a Matlab-based inversion technique to obtain a reliable resistivity image using different geophysical data sets (electrical resistivity and electromagnetic data). Following this, the recovered resistivity model was converted into a fuzzy constrained resistivity model by assigning the highest probability value of each model cell to the cluster utilizing fuzzy c means clustering procedure during the iterative process. The efficacy of the algorithm is demonstrated using three synthetic plane wave electromagnetic data sets and one electrical resistivity field dataset. The presented approach shows improvement on the conventional inversion approach to differentiate between different geologic units if the correct number of geologic units will be identified. Further, fuzzy constrained resistivity tomography was performed to examine the augmentation of uranium mineralization in the Beldih open cast mine as a case study. We also compared geologic units identified by fuzzy constrained resistivity tomography with geologic units interpreted from the borehole information.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PubMed Central

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PubMed

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Collaborative filtering recommendation model based on fuzzy clustering algorithm

NASA Astrophysics Data System (ADS)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

NASA Astrophysics Data System (ADS)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

PubMed Central

Vianney Kinani, Jean Marie; Gallegos Funes, Francisco; Mújica Vargas, Dante; Ramos Díaz, Eduardo; Arellano, Alfonso

2017-01-01

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time. PMID:29158887

Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

NASA Astrophysics Data System (ADS)

Rodríguez, Aida; Nieves, Juan Luis; Valero, Eva; Garrote, Estíbaliz; Hernández-Andrés, Javier; Romero, Javier

2012-01-01

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

NASA Technical Reports Server (NTRS)

Key, J. R.; Maslanik, J. A.; Barry, R. G.

1988-01-01

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

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.

Quantitative estimation of time-variable earthquake hazard by using fuzzy set theory

NASA Astrophysics Data System (ADS)

Deyi, Feng; Ichikawa, M.

1989-11-01

In this paper, the various methods of fuzzy set theory, called fuzzy mathematics, have been applied to the quantitative estimation of the time-variable earthquake hazard. The results obtained consist of the following. (1) Quantitative estimation of the earthquake hazard on the basis of seismicity data. By using some methods of fuzzy mathematics, seismicity patterns before large earthquakes can be studied more clearly and more quantitatively, highly active periods in a given region and quiet periods of seismic activity before large earthquakes can be recognized, similarities in temporal variation of seismic activity and seismic gaps can be examined and, on the other hand, the time-variable earthquake hazard can be assessed directly on the basis of a series of statistical indices of seismicity. Two methods of fuzzy clustering analysis, the method of fuzzy similarity, and the direct method of fuzzy pattern recognition, have been studied is particular. One method of fuzzy clustering analysis is based on fuzzy netting, and another is based on the fuzzy equivalent relation. (2) Quantitative estimation of the earthquake hazard on the basis of observational data for different precursors. The direct method of fuzzy pattern recognition has been applied to research on earthquake precursors of different kinds. On the basis of the temporal and spatial characteristics of recognized precursors, earthquake hazards in different terms can be estimated. This paper mainly deals with medium-short-term precursors observed in Japan and China.

Development of a fuzzy logic expert system for pile selection. Master's thesis

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

Ulshafer, M.L.

1989-01-01

This thesis documents the development of prototype expert system for pile selection for use on microcomputers. It concerns the initial selection of a pile foundation taking into account the parameters such as soil condition, pile length, loading scenario, material availability, contractor experience, and noise or vibration constraints. The prototype expert system called Pile Selection, version 1 (PS1) was developed using an expert system shell FLOPS. FLOPS is a shell based on the AI language OPS5 with many unique features. The system PS1 utilizes all of these unique features. Among the features used are approximate reasoning with fuzzy set theory, themore » blackboard architecture, and the emulated parallel processing of fuzzy production rules. A comprehensive review of the parameters used in selecting a pile was made, and the effects of the uncertainties associated with the vagueness of these parameters was examined in detail. Fuzzy set theory was utilized to deal with such uncertainties and provides the basis for developing a method for determining the best possible choice of piles for a given situation. Details of the development of PS1, including documenting and collating pile information for use in the expert knowledge data bases, are discussed.« less

Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer

NASA Astrophysics Data System (ADS)

Brandl, Miriam B.; Beck, Dominik; Pham, Tuan D.

2011-06-01

The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

What Fuzzy HOS May Mean

DTIC Science & Technology

1978-11-01

a fuzzy set of real numbers clustered around m, or as a possibility distribution on the value of some ill-known quantity. A fuzzy relation R on the...distribution of . va.- nossibly clustered around some mean value. STho -Ict.n uf F to X is f. Moreover, It should be noticed that the image of a *uz!y L...10) 1(•)(y)= sup Rjin (i(x),• ()(y)) xcX One may verify that 11(-())(z)= sup min (V(x),N)( 0)(×)(z)) . Gmx ) xCX •) ’ . This shows that the extension

Fuzzy Clustering Analysis in Environmental Impact Assessment--A Complement Tool to Environmental Quality Index.

ERIC Educational Resources Information Center

Kung, Hsiang-Te; And Others

1993-01-01

In spite of rapid progress achieved in the methodological research underlying environmental impact assessment (EIA), the problem of weighting various parameters has not yet been solved. This paper presents a new approach, fuzzy clustering analysis, which is illustrated with an EIA case study on Baoshan-Wusong District in Shanghai, China. (Author)

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

PubMed

Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

2017-07-03

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising

NASA Astrophysics Data System (ADS)

Cui, Xin; Li, Shipeng; Yan, Xiutian; He, Xinhua

2018-04-01

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

PubMed

Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

2014-12-01

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

A fuzzy clustering algorithm to detect planar and quadric shapes

NASA Technical Reports Server (NTRS)

Krishnapuram, Raghu; Frigui, Hichem; Nasraoui, Olfa

1992-01-01

In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications.

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

NASA Astrophysics Data System (ADS)

El-Sebakhy, Emad A.

2009-09-01

Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

Fast divide-and-conquer algorithm for evaluating polarization in classical force fields

NASA Astrophysics Data System (ADS)

Nocito, Dominique; Beran, Gregory J. O.

2017-03-01

Evaluation of the self-consistent polarization energy forms a major computational bottleneck in polarizable force fields. In large systems, the linear polarization equations are typically solved iteratively with techniques based on Jacobi iterations (JI) or preconditioned conjugate gradients (PCG). Two new variants of JI are proposed here that exploit domain decomposition to accelerate the convergence of the induced dipoles. The first, divide-and-conquer JI (DC-JI), is a block Jacobi algorithm which solves the polarization equations within non-overlapping sub-clusters of atoms directly via Cholesky decomposition, and iterates to capture interactions between sub-clusters. The second, fuzzy DC-JI, achieves further acceleration by employing overlapping blocks. Fuzzy DC-JI is analogous to an additive Schwarz method, but with distance-based weighting when averaging the fuzzy dipoles from different blocks. Key to the success of these algorithms is the use of K-means clustering to identify natural atomic sub-clusters automatically for both algorithms and to determine the appropriate weights in fuzzy DC-JI. The algorithm employs knowledge of the 3-D spatial interactions to group important elements in the 2-D polarization matrix. When coupled with direct inversion in the iterative subspace (DIIS) extrapolation, fuzzy DC-JI/DIIS in particular converges in a comparable number of iterations as PCG, but with lower computational cost per iteration. In the end, the new algorithms demonstrated here accelerate the evaluation of the polarization energy by 2-3 fold compared to existing implementations of PCG or JI/DIIS.

A new type of simplified fuzzy rule-based system

NASA Astrophysics Data System (ADS)

Angelov, Plamen; Yager, Ronald

2012-02-01

Over the last quarter of a century, two types of fuzzy rule-based (FRB) systems dominated, namely Mamdani and Takagi-Sugeno type. They use the same type of scalar fuzzy sets defined per input variable in their antecedent part which are aggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed. Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.

SAR image segmentation using skeleton-based fuzzy clustering

NASA Astrophysics Data System (ADS)

Cao, Yun Yi; Chen, Yan Qiu

2003-06-01

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

Inflation data clustering of some cities in Indonesia

NASA Astrophysics Data System (ADS)

Setiawan, Adi; Susanto, Bambang; Mahatma, Tundjung

2017-06-01

In this paper, it is presented how to cluster inflation data of cities in Indonesia by using k-means cluster method and fuzzy c-means method. The data that are used is limited to the monthly inflation data from 15 cities across Indonesia which have highest weight of donations and is supplemented with 5 cities used in the calculation of inflation in Indonesia. When they are applied into two clusters with k = 2 for k-means cluster method and c = 2, w = 1.25 for fuzzy c-means cluster method, Ambon, Manado and Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). However, if they are applied into two clusters with c=2, w=1.5, Surabaya, Medan, Makasar, Samarinda, Makasar, Manado, Ambon dan Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). Furthermore, when we use two clusters with k=3 for k-means cluster method and c=3, w = 1.25 for fuzzy c-means cluster method, Ambon tends to become member of first cluster (high inflation), Manado and Jayapura tend to become member of second cluster (moderate inflation), other cities tend to become members of third cluster (low inflation). If it is applied c=3, w = 1.5, Ambon, Manado and Jayapura tend to become member of first cluster (high inflation), Surabaya, Bandung, Medan, Makasar, Banyuwangi, Denpasar, Samarinda dan Mataram tend to become members of second cluster (moderate inflation), meanwhile other cities tend to become members of third cluster (low inflation). Similarly, interpretation can be made to the results of applying 5 clusters.

Applications of Some Artificial Intelligence Methods to Satellite Soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.

1985-01-01

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

Fuzzy set methods for object recognition in space applications

NASA Technical Reports Server (NTRS)

Keller, James M.

1992-01-01

Progress on the following tasks is reported: feature calculation; membership calculation; clustering methods (including initial experiments on pose estimation); and acquisition of images (including camera calibration information for digitization of model). The report consists of 'stand alone' sections, describing the activities in each task. We would like to highlight the fact that during this quarter, we believe that we have made a major breakthrough in the area of fuzzy clustering. We have discovered a method to remove the probabilistic constraints that the sum of the memberships across all classes must add up to 1 (as in the fuzzy c-means). A paper, describing this approach, is included.

Clustering-based spot segmentation of cDNA microarray images.

PubMed

Uslan, Volkan; Bucak, Ihsan Ömür

2010-01-01

Microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarray image processing has been implemented. Clustering-based methods, fuzzy c-means and k-means, have been applied for the segmentation step that separates the spots from the background. The experiments show that fuzzy c-means have segmented spots of the microarray image more accurately than the k-means.

Fuzzy cluster analysis of air quality in Beijing district

NASA Astrophysics Data System (ADS)

Liu, Hongkai

2018-02-01

The principle of fuzzy clustering analysis is applied in this article, by using the method of transitive closure, the main air pollutants in 17 districts of Beijing from 2014 to 2016 were classified. The results of the analysis reflects the nearly three year’s changes of the main air pollutants in Beijing. This can provide the scientific for atmospheric governance in the Beijing area and digital support.

Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment

NASA Astrophysics Data System (ADS)

Chen, Xiao; Li, Yaan; Yu, Jing; Li, Yuxing

2018-01-01

For fast and more effective implementation of tracking multiple targets in a cluttered environment, we propose a multiple targets tracking (MTT) algorithm called maximum entropy fuzzy c-means clustering joint probabilistic data association that combines fuzzy c-means clustering and the joint probabilistic data association (PDA) algorithm. The algorithm uses the membership value to express the probability of the target originating from measurement. The membership value is obtained through fuzzy c-means clustering objective function optimized by the maximum entropy principle. When considering the effect of the public measurement, we use a correction factor to adjust the association probability matrix to estimate the state of the target. As this algorithm avoids confirmation matrix splitting, it can solve the high computational load problem of the joint PDA algorithm. The results of simulations and analysis conducted for tracking neighbor parallel targets and cross targets in a different density cluttered environment show that the proposed algorithm can realize MTT quickly and efficiently in a cluttered environment. Further, the performance of the proposed algorithm remains constant with increasing process noise variance. The proposed algorithm has the advantages of efficiency and low computational load, which can ensure optimum performance when tracking multiple targets in a dense cluttered environment.

Fuzzy cluster analysis of simple physicochemical properties of amino acids for recognizing secondary structure in proteins.

PubMed Central

Mocz, G.

1995-01-01

Fuzzy cluster analysis has been applied to the 20 amino acids by using 65 physicochemical properties as a basis for classification. The clustering products, the fuzzy sets (i.e., classical sets with associated membership functions), have provided a new measure of amino acid similarities for use in protein folding studies. This work demonstrates that fuzzy sets of simple molecular attributes, when assigned to amino acid residues in a protein's sequence, can predict the secondary structure of the sequence with reasonable accuracy. An approach is presented for discriminating standard folding states, using near-optimum information splitting in half-overlapping segments of the sequence of assigned membership functions. The method is applied to a nonredundant set of 252 proteins and yields approximately 73% matching for correctly predicted and correctly rejected residues with approximately 60% overall success rate for the correctly recognized ones in three folding states: alpha-helix, beta-strand, and coil. The most useful attributes for discriminating these states appear to be related to size, polarity, and thermodynamic factors. Van der Waals volume, apparent average thickness of surrounding molecular free volume, and a measure of dimensionless surface electron density can explain approximately 95% of prediction results. hydrogen bonding and hydrophobicity induces do not yet enable clear clustering and prediction. PMID:7549882

Effects of cluster-shell competition and BCS-like pairing in 12C

NASA Astrophysics Data System (ADS)

Matsuno, H.; Itagaki, N.

2017-12-01

The antisymmetrized quasi-cluster model (AQCM) was proposed to describe α-cluster and jj-coupling shell models on the same footing. In this model, the cluster-shell transition is characterized by two parameters, R representing the distance between α clusters and Λ describing the breaking of α clusters, and the contribution of the spin-orbit interaction, very important in the jj-coupling shell model, can be taken into account starting with the α-cluster model wave function. Not only the closure configurations of the major shells but also the subclosure configurations of the jj-coupling shell model can be described starting with the α-cluster model wave functions; however, the particle-hole excitations of single particles have not been fully established yet. In this study we show that the framework of AQCM can be extended even to the states with the character of single-particle excitations. For ^{12}C, two-particle-two-hole (2p2h) excitations from the subclosure configuration of 0p_{3/2} corresponding to a BCS-like pairing are described, and these shell model states are coupled with the three α-cluster model wave functions. The correlation energy from the optimal configuration can be estimated not only in the cluster part but also in the shell model part. We try to pave the way to establish a generalized description of the nuclear structure.

A decentralized fuzzy C-means-based energy-efficient routing protocol for wireless sensor networks.

PubMed

Alia, Osama Moh'd

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

PubMed Central

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols. PMID:25162060

A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

PubMed Central

Kwong, C. K.; Fung, K. Y.; Jiang, Huimin; Chan, K. Y.

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort. PMID:24385884

A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

PubMed

Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

PubMed

Rajab, Maher I

2011-11-01

Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

NASA Astrophysics Data System (ADS)

Zhou, Shuguang; Zhou, Kefa; Wang, Jinlin; Yang, Genfang; Wang, Shanshan

2017-12-01

Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms (the hierarchical, k-means, and fuzzy c-means algorithms) and six data transformation methods (the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT) are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of column- or variable-based (R-type) cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based (Q-type) cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy c-means clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

PubMed Central

Sabit, Hakilo; Al-Anbuky, Adnan

2014-01-01

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering.

PubMed

Sansone, Mario; Zeni, Olga; Esposito, Giovanni

2012-05-01

Comet assay is one of the most popular tests for the detection of DNA damage at single cell level. In this study, an algorithm for comet assay analysis has been proposed, aiming to minimize user interaction and providing reproducible measurements. The algorithm comprises two-steps: (a) comet identification via Gaussian pre-filtering and morphological operators; (b) comet segmentation via fuzzy clustering. The algorithm has been evaluated using comet images from human leukocytes treated with a commonly used DNA damaging agent. A comparison of the proposed approach with a commercial system has been performed. Results show that fuzzy segmentation can increase overall sensitivity, giving benefits in bio-monitoring studies where weak genotoxic effects are expected.

Fuzzy Set Methods for Object Recognition in Space Applications

NASA Technical Reports Server (NTRS)

Keller, James M. (Editor)

1992-01-01

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

PubMed Central

Cuenca-Jara, Jesus; Valdes-Vela, Mercedes; Skarmeta, Antonio F.

2017-01-01

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. PMID:28837120

A robust fuzzy local Information c-means clustering algorithm with noise detection

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Huang, Junwei

2018-04-01

Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

PubMed

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

PubMed

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-11-11

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Lane detection based on color probability model and fuzzy clustering

NASA Astrophysics Data System (ADS)

Yu, Yang; Jo, Kang-Hyun

2018-04-01

In the vehicle driver assistance systems, the accuracy and speed of lane line detection are the most important. This paper is based on color probability model and Fuzzy Local Information C-Means (FLICM) clustering algorithm. The Hough transform and the constraints of structural road are used to detect the lane line accurately. The global map of the lane line is drawn by the lane curve fitting equation. The experimental results show that the algorithm has good robustness.

Systematic methods for the design of a class of fuzzy logic controllers

NASA Astrophysics Data System (ADS)

Yasin, Saad Yaser

2002-09-01

Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental data, and a conversion algorithm, to develop a fuzzy-based control algorithm. Results were similar to those obtained by recently published conventional control based studies. The influence of the fuzzy inference operators and parameters on performance and stability of the fuzzy logic controller was studied Results indicated that, the selections of certain parameters or combinations of parameters, affect greatly the performance and stability of the fuzzy controller. Diagnostic guidelines used to tune or change certain factors or parameters to improve controller performance were developed based on knowledge gained from conventional control methods and knowledge gained from the experimental and the simulation results of this study.

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

ERIC Educational Resources Information Center

Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

Fuzzy cluster analysis of high-field functional MRI data.

PubMed

Windischberger, Christian; Barth, Markus; Lamm, Claus; Schroeder, Lee; Bauer, Herbert; Gur, Ruben C; Moser, Ewald

2003-11-01

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

Clustering behavior of hermit crabs (Decapoda, Anomura) in an intertidal rocky shore at São Sebastião, southeastern Brazil.

PubMed

Turra, A; Leite, F P

2000-02-01

The clustering behavior and cluster composition of hermit crabs as well as the patterns of shell utilization of clustered and scattered individuals were studied. This study was conducted in the intertidal region of Grande Beach, São Sebastião, southeastern Brazil. Samples were taken both in randomized transects and 1 m2 quadrats during low tide periods. Crabs were counted, measured (shield length), and sexed. Shells were identified and had their adequacy and condition (physical damage and incrustation) recorded. Clusters occurred mainly in air exposed areas and were dominated or composed only by Clibanarius antillensis. Other species like Paguristes tortugae, Pagurus criniticornis, and Calcinus tibicen were also present in these clusters, but in small numbers. Only one monospecific aggregation composed by individuals of P. criniticornis was recorded in tide pools. Almost all crabs were inactive, despite some that were submerged in tide pools. Most of the individuals of C. antillensis were clustered (70.88%). Scattered individuals were larger than clustered ones and occupied mainly shells of Tegula viridula, which seemed to be the most adequate shell to the crabs. Clustered individuals used less incrusted shells than isolated ones. In general, clustering in Grande Beach presented the same patterns of size and sex distribution, and shell utilization than others already studied, with the exception of the smaller cluster size registered in this area.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

PubMed

Blessy, S A Praylin Selva; Sulochana, C Helen

2015-01-01

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

Design of double fuzzy clustering-driven context neural networks.

PubMed

Kim, Eun-Hu; Oh, Sung-Kwun; Pedrycz, Witold

2018-08-01

In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively. Cluster space refers to the local space positioned in the input space whereas context space concerns a local space formed in the output space. Through partitioning the output space into several local spaces, each context space is used as the desired (target) local output to construct local models. To complete this, the proposed network includes a new context layer for reasoning about context space in the output space. In this sense, Fuzzy C-Means (FCM) clustering is useful to form local spaces in both input and output spaces. The first one is used in order to form clusters and train weights positioned between the input and hidden layer, whereas the other one is applied to the output space to form context spaces. The key features of the proposed DFCCNNs can be enumerated as follows: (i) the parameters between the input layer and hidden layer are built through FCM clustering. The connections (weights) are specified as constant terms being in fact the centers of the clusters. The membership functions (represented through the partition matrix) produced by the FCM are used as activation functions located at the hidden layer of the "conventional" neural networks. (ii) Following the hidden layer, a context layer is formed to approximate the context space of the output variable and each node in context layer means individual local model. The outputs of the context layer are specified as a combination of both weights formed as linear function and the outputs of the hidden layer. The weights are updated using the least square estimation (LSE)-based method. (iii) At the output layer, the outputs of context layer are decoded to produce the corresponding numeric output. At this time, the weighted average is used and the weights are also adjusted with the use of the LSE scheme. From the viewpoint of performance improvement, the proposed design methodologies are discussed and experimented with the aid of benchmark machine learning datasets. Through the experiments, it is shown that the generalization abilities of the proposed DFCCNNs are better than those of the conventional FCNNs reported in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants

PubMed Central

Castro, Alfonso; Boveda, Carmen; Arcay, Bernardino; Sanjurjo, Pedro

2016-01-01

The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium). PMID:27517049

Adding-point strategy for reduced-order hypersonic aerothermodynamics modeling based on fuzzy clustering

NASA Astrophysics Data System (ADS)

Chen, Xin; Liu, Li; Zhou, Sida; Yue, Zhenjiang

2016-09-01

Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

Use of an Electronic Tongue System and Fuzzy Logic to Analyze Water Samples

NASA Astrophysics Data System (ADS)

Braga, Guilherme S.; Paterno, Leonardo G.; Fonseca, Fernando J.

2009-05-01

An electronic tongue (ET) system incorporating 8 chemical sensors was used in combination with two pattern recognition tools, namely principal component analysis (PCA) and Fuzzy logic for discriminating/classification of water samples from different sources (tap, distilled and three brands of mineral water). The Fuzzy program exhibited a higher accuracy than the PCA and allowed the ET to classify correctly 4 in 5 types of water. Exception was made for one brand of mineral water which was sometimes misclassified as tap water. On the other hand, the PCA grouped water samples in three clusters, one with the distilled water; a second with tap water and one brand of mineral water, and the third with the other two other brands of mineral water. Samples in the second and third clusters could not be distinguished. Nevertheless, close grouping between repeated tests indicated that the ET system response is reproducible. The potential use of the Fuzzy logic as the data processing tool in combination with an electronic tongue system is discussed.

Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

NASA Astrophysics Data System (ADS)

Ward, W. O. C.; Wilkinson, P. B.; Chambers, J. E.; Oxby, L. S.; Bai, L.

2014-04-01

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

Detection of Anomalies in Hydrometric Data Using Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Lauzon, N.; Lence, B. J.

2002-12-01

This work focuses on the detection of anomalies in hydrometric data sequences, such as 1) outliers, which are individual data having statistical properties that differ from those of the overall population; 2) shifts, which are sudden changes over time in the statistical properties of the historical records of data; and 3) trends, which are systematic changes over time in the statistical properties. For the purpose of the design and management of water resources systems, it is important to be aware of these anomalies in hydrometric data, for they can induce a bias in the estimation of water quantity and quality parameters. These anomalies may be viewed as specific patterns affecting the data, and therefore pattern recognition techniques can be used for identifying them. However, the number of possible patterns is very large for each type of anomaly and consequently large computing capacities are required to account for all possibilities using the standard statistical techniques, such as cluster analysis. Artificial intelligence techniques, such as the Kohonen neural network and fuzzy c-means, are clustering techniques commonly used for pattern recognition in several areas of engineering and have recently begun to be used for the analysis of natural systems. They require much less computing capacity than the standard statistical techniques, and therefore are well suited for the identification of outliers, shifts and trends in hydrometric data. This work constitutes a preliminary study, using synthetic data representing hydrometric data that can be found in Canada. The analysis of the results obtained shows that the Kohonen neural network and fuzzy c-means are reasonably successful in identifying anomalies. This work also addresses the problem of uncertainties inherent to the calibration procedures that fit the clusters to the possible patterns for both the Kohonen neural network and fuzzy c-means. Indeed, for the same database, different sets of clusters can be established with these calibration procedures. A simple method for analyzing uncertainties associated with the Kohonen neural network and fuzzy c-means is developed here. The method combines the results from several sets of clusters, either from the Kohonen neural network or fuzzy c-means, so as to provide an overall diagnosis as to the identification of outliers, shifts and trends. The results indicate an improvement in the performance for identifying anomalies when the method of combining cluster sets is used, compared with when only one cluster set is used.

Core–shell interaction and its impact on the optical absorption of pure and doped core-shell CdSe/ZnSe nanoclusters

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

Wang, Xinqin; Cui, Yingqi; Zeng, Qun

The structural, electronic, and optical properties of core-shell nanoclusters, (CdSe){sub x}@(CdSe){sub y} and their Zn-substituted complexes of x = 2–4 and y = 16–28, were studied with density functional theory calculations. The substitution was applied in the cores, the shells, and/or the whole clusters. All these clusters are characterized by their core-shell structures in which the core-shell interaction was found different from those in core or in shell, as reflected by their bondlengths, volumes, and binding energies. Moreover, the core and shell combine together to compose a new cluster with electronic and optical properties different from those of separated individuals,more » as reflected by their HOMO-LUMO gaps and optical absorptions. With the substitution of Cd by Zn, the structural, electronic, and optical properties of clusters change regularly. The binding energy increases with Zn content, attributed to the strong Zn–Se bonding. For the same core/shell, the structure with a CdSe shell/core has a narrower gap than that with a ZnSe shell/core. The optical absorption spectra also change accordingly with Zn substitution. The peaks blueshift with increasing Zn concentration, accompanying with shape variations in case large number of Cd atoms are substituted. Our calculations reveal the core-shell interaction and its influence on the electronic and optical properties of the core-shell clusters, suggesting a composition–structure–property relationship for the design of core-shell CdSe and ZnSe nanoclusters.« less

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil

PubMed Central

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-01-01

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters. PMID:26569243

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.

PubMed

Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

2013-05-01

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

PubMed

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Approximation Of Multi-Valued Inverse Functions Using Clustering And Sugeno Fuzzy Inference

NASA Technical Reports Server (NTRS)

Walden, Maria A.; Bikdash, Marwan; Homaifar, Abdollah

1998-01-01

Finding the inverse of a continuous function can be challenging and computationally expensive when the inverse function is multi-valued. Difficulties may be compounded when the function itself is difficult to evaluate. We show that we can use fuzzy-logic approximators such as Sugeno inference systems to compute the inverse on-line. To do so, a fuzzy clustering algorithm can be used in conjunction with a discriminating function to split the function data into branches for the different values of the forward function. These data sets are then fed into a recursive least-squares learning algorithm that finds the proper coefficients of the Sugeno approximators; each Sugeno approximator finds one value of the inverse function. Discussions about the accuracy of the approximation will be included.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

NASA Technical Reports Server (NTRS)

Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Application of fuzzy c-means clustering to PRTR chemicals uncovering their release and toxicity characteristics.

PubMed

Xue, Mianqiang; Zhou, Liang; Kojima, Naoya; Dos Muchangos, Leticia Sarmento; Machimura, Takashi; Tokai, Akihiro

2018-05-01

Increasing manufacture and usage of chemicals have not been matched by the increase in our understanding of their risks. Pollutant release and transfer register (PRTR) is becoming a popular measure for collecting chemical data and enhancing the public right to know. However, these data are usually in high dimensionality which restricts their wider use. The present study partitions Japanese PRTR chemicals into five fuzzy clusters by fuzzy c-mean clustering (FCM) to explore the implicit information. Each chemical with membership degrees belongs to each cluster. Cluster I features high releases from non-listed industries and the household sector and high environmental toxicity. Cluster II is characterized by high reported releases and transfers from 24 listed industries above the threshold, mutagenicity, and high environmental toxicity. Chemicals in cluster III have characteristics of high releases from non-listed industries and low toxicity. Cluster IV is characterized by high reported releases and transfers from 24 listed industries above the threshold and extremely high environmental toxicity. Cluster V is characterized by low releases yet mutagenicity and high carcinogenicity. Chemicals with the highest membership degree were identified as representatives for each cluster. For the highest membership degree, half of the chemicals have a value higher than 0.74. If we look at both the highest and the second highest membership degrees simultaneously, about 94% of the chemicals have a value higher than 0.5. FCM can serve as an approach to uncover the implicit information of highly complex chemical dataset, which subsequently supports the strategy development for efficient and effective chemical management. Copyright © 2017 Elsevier B.V. All rights reserved.

Cluster Differences Scaling with a Within-Clusters Loss Component and a Fuzzy Successive Approximation Strategy To Avoid Local Minima.

ERIC Educational Resources Information Center

Heiser, Willem J.; And Others

1997-01-01

The least squares loss function of cluster differences scaling, originally defined only on residuals of pairs allocated to different clusters, is extended with a loss component for pairs allocated to the same cluster. Findings show that this makes the method equivalent to multidimensional scaling with cluster constraints on the coordinates. (SLD)

Intelligent Traffic Quantification System

NASA Astrophysics Data System (ADS)

Mohanty, Anita; Bhanja, Urmila; Mahapatra, Sudipta

2017-08-01

Currently, city traffic monitoring and controlling is a big issue in almost all cities worldwide. Vehicular ad-hoc Network (VANET) technique is an efficient tool to minimize this problem. Usually, different types of on board sensors are installed in vehicles to generate messages characterized by different vehicle parameters. In this work, an intelligent system based on fuzzy clustering technique is developed to reduce the number of individual messages by extracting important features from the messages of a vehicle. Therefore, the proposed fuzzy clustering technique reduces the traffic load of the network. The technique also reduces congestion and quantifies congestion.

Feature extraction using molecular planes for fuzzy relational clustering of a flexible dopamine reuptake inhibitor.

PubMed

Banerjee, Amit; Misra, Milind; Pai, Deepa; Shih, Liang-Yu; Woodley, Rohan; Lu, Xiang-Jun; Srinivasan, A R; Olson, Wilma K; Davé, Rajesh N; Venanzi, Carol A

2007-01-01

Six rigid-body parameters (Shift, Slide, Rise, Tilt, Roll, Twist) are commonly used to describe the relative displacement and orientation of successive base pairs in a nucleic acid structure. The present work adapts this approach to describe the relative displacement and orientation of any two planes in an arbitrary molecule-specifically, planes which contain important pharmacophore elements. Relevant code from the 3DNA software package (Nucleic Acids Res. 2003, 31, 5108-5121) was generalized to treat molecular fragments other than DNA bases as input for the calculation of the corresponding rigid-body (or "planes") parameters. These parameters were used to construct feature vectors for a fuzzy relational clustering study of over 700 conformations of a flexible analogue of the dopamine reuptake inhibitor, GBR 12909. Several cluster validity measures were used to determine the optimal number of clusters. Translational (Shift, Slide, Rise) rather than rotational (Tilt, Roll, Twist) features dominate clustering based on planes that are relatively far apart, whereas both types of features are important to clustering when the pair of planes are close by. This approach was able to classify the data set of molecular conformations into groups and to identify representative conformers for use as template conformers in future Comparative Molecular Field Analysis studies of GBR 12909 analogues. The advantage of using the planes parameters, rather than the combination of atomic coordinates and angles between molecular planes used in our previous fuzzy relational clustering of the same data set (J. Chem. Inf. Model. 2005, 45, 610-623), is that the present clustering results are independent of molecular superposition and the technique is able to identify clusters in the molecule considered as a whole. This approach is easily generalizable to any two planes in any molecule.

Crossover from disordered to core-shell structures of nano-oxide Y{sub 2}O{sub 3} dispersed particles in Fe

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

Higgins, M. P.; Wang, L. M.; Gao, F., E-mail: gaofeium@umich.edu

Molecular dynamic simulations of Y{sub 2}O{sub 3} in bcc Fe and transmission electron microscopy (TEM) observations were used to understand the structure of Y{sub 2}O{sub 3} nano-clusters in an oxide dispersion strengthened steel matrix. The study showed that Y{sub 2}O{sub 3} nano-clusters below 2 nm were completely disordered. Y{sub 2}O{sub 3} nano-clusters above 2 nm, however, form a core-shell structure, with a shell thickness of 0.5–0.7 nm that is independent of nano-cluster size. Y{sub 2}O{sub 3} nano-clusters were surrounded by off-lattice Fe atoms, further increasing the stability of these nano-clusters. TEM was used to corroborate our simulation results and showed a crossover frommore » a disordered nano-cluster to a core-shell structure.« less

Spatial pattern recognition of seismic events in South West Colombia

NASA Astrophysics Data System (ADS)

Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

2013-09-01

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

Using Fuzzy Clustering for Real-time Space Flight Safety

NASA Technical Reports Server (NTRS)

Lee, Charles; Haskell, Richard E.; Hanna, Darrin; Alena, Richard L.

2004-01-01

To ensure space flight safety, it is necessary to monitor myriad sensor readings on the ground and in flight. Since a space shuttle has many sensors, monitoring data and drawing conclusions from information contained within the data in real time is challenging. The nature of the information can be critical to the success of the mission and safety of the crew and therefore, must be processed with minimal data-processing time. Data analysis algorithms could be used to synthesize sensor readings and compare data associated with normal operation with the data obtained that contain fault patterns to draw conclusions. Detecting abnormal operation during early stages in the transition from safe to unsafe operation requires a large amount of historical data that can be categorized into different classes (non-risk, risk). Even though the 40 years of shuttle flight program has accumulated volumes of historical data, these data don t comprehensively represent all possible fault patterns since fault patterns are usually unknown before the fault occurs. This paper presents a method that uses a similarity measure between fuzzy clusters to detect possible faults in real time. A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This method is applied to downlink data from Columbia flights. The results show that this technique can detect an unexpected fault that has not been present in the training data set.

Singlet-paired coupled cluster theory for open shells

NASA Astrophysics Data System (ADS)

Gomez, John A.; Henderson, Thomas M.; Scuseria, Gustavo E.

2016-06-01

Restricted single-reference coupled cluster theory truncated to single and double excitations accurately describes weakly correlated systems, but often breaks down in the presence of static or strong correlation. Good coupled cluster energies in the presence of degeneracies can be obtained by using a symmetry-broken reference, such as unrestricted Hartree-Fock, but at the cost of good quantum numbers. A large body of work has shown that modifying the coupled cluster ansatz allows for the treatment of strong correlation within a single-reference, symmetry-adapted framework. The recently introduced singlet-paired coupled cluster doubles (CCD0) method is one such model, which recovers correct behavior for strong correlation without requiring symmetry breaking in the reference. Here, we extend singlet-paired coupled cluster for application to open shells via restricted open-shell singlet-paired coupled cluster singles and doubles (ROCCSD0). The ROCCSD0 approach retains the benefits of standard coupled cluster theory and recovers correct behavior for strongly correlated, open-shell systems using a spin-preserving ROHF reference.

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

PubMed Central

Castañón–Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-01-01

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi–Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information. PMID:26633417

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals.

PubMed

Castañón-Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-12-02

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

Supergiants and their shells in young globular clusters

NASA Astrophysics Data System (ADS)

Szécsi, Dorottya; Mackey, Jonathan; Langer, Norbert

2018-04-01

Context. Anomalous surface abundances are observed in a fraction of the low-mass stars of Galactic globular clusters, that may originate from hot-hydrogen-burning products ejected by a previous generation of massive stars. Aims: We aim to present and investigate a scenario in which the second generation of polluted low-mass stars can form in shells around cool supergiant stars within a young globular cluster. Methods: Simulations of low-metallicity massive stars (Mi 150-600 M⊙) show that both core-hydrogen-burning cool supergiants and hot ionizing stellar sources are expected to be present simulaneously in young globular clusters. Under these conditions, photoionization-confined shells form around the supergiants. We have simulated such a shell, investigated its stability and analysed its composition. Results: We find that the shell is gravitationally unstable on a timescale that is shorter than the lifetime of the supergiant, and the Bonnor-Ebert mass of the overdense regions is low enough to allow star formation. Since the low-mass stellar generation formed in this shell is made up of the material lost from the supergiant, its composition necessarily reflects the composition of the supergiant wind. We show that the wind contains hot-hydrogen-burning products, and that the shell-stars therefore have very similar abundance anomalies that are observed in the second generation stars of globular clusters. Considering the mass-budget required for the second generation star-formation, we offer two solutions. Either a top-heavy initial mass function is needed with an index of -1.71 to -2.07. Alternatively, we suggest the shell-stars to have a truncated mass distribution, and solve the mass budget problem by justifiably accounting for only a fraction of the first generation. Conclusions: Star-forming shells around cool supergiants could form the second generation of low-mass stars in Galactic globular clusters. Even without forming a photoionizaton-confined shell, the cool supergiant stars predicted at low-metallicity could contribute to the pollution of the interstellar medium of the cluster from which the second generation was born. Thus, the cool supergiant stars should be regarded as important contributors to the evolution of globular clusters.

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

NASA Astrophysics Data System (ADS)

Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata

2014-12-01

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.

Implementation of Automatic Clustering Algorithm and Fuzzy Time Series in Motorcycle Sales Forecasting

NASA Astrophysics Data System (ADS)

Rasim; Junaeti, E.; Wirantika, R.

2018-01-01

Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

PubMed

Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J C

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

A Data Analytics Approach to Discovering Unique Microstructural Configurations Susceptible to Fatigue

NASA Astrophysics Data System (ADS)

Jha, S. K.; Brockman, R. A.; Hoffman, R. M.; Sinha, V.; Pilchak, A. L.; Porter, W. J.; Buchanan, D. J.; Larsen, J. M.; John, R.

2018-05-01

Principal component analysis and fuzzy c-means clustering algorithms were applied to slip-induced strain and geometric metric data in an attempt to discover unique microstructural configurations and their frequencies of occurrence in statistically representative instantiations of a titanium alloy microstructure. Grain-averaged fatigue indicator parameters were calculated for the same instantiation. The fatigue indicator parameters strongly correlated with the spatial location of the microstructural configurations in the principal components space. The fuzzy c-means clustering method identified clusters of data that varied in terms of their average fatigue indicator parameters. Furthermore, the number of points in each cluster was inversely correlated to the average fatigue indicator parameter. This analysis demonstrates that data-driven methods have significant potential for providing unbiased determination of unique microstructural configurations and their frequencies of occurrence in a given volume from the point of view of strain localization and fatigue crack initiation.

Estimating the concrete compressive strength using hard clustering and fuzzy clustering based regression techniques.

PubMed

Nagwani, Naresh Kumar; Deo, Shirish V

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

PubMed Central

Nagwani, Naresh Kumar; Deo, Shirish V.

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939

Benzoate-Induced High-Nuclearity Silver Thiolate Clusters.

PubMed

Su, Yan-Min; Liu, Wei; Wang, Zhi; Wang, Shu-Ao; Li, Yan-An; Yu, Fei; Zhao, Quan-Qin; Wang, Xing-Po; Tung, Chen-Ho; Sun, Di

2018-04-03

Compared with the well-known anion-templated effects in shaping silver thiolate clusters, the influence from the organic ligands in the outer shell is still poorly understood. Herein, three new benzoate-functionalized high-nuclearity silver(I) thiolate clusters are isolated and characterized for the first time in the presence of diverse anion templates such as S 2- , α-[Mo 5 O 18 ] 6- , and MoO 4 2- . Single-crystal X-ray analysis reveals that the nuclearities of the three silver clusters (SD/Ag28, SD/Ag29, SD/Ag30) vary from 32 to 38 to 78 with co-capped tBuS - and benzoate ligands on the surface. SD/Ag28 is a turtle-like cluster comprising a Ag 29 shell caging a Ag 3 S 3 trigon in the center, whereas SD/Ag29 is a prolate Ag 38 sphere templated by the α-[Mo 5 O 18 ] 6- anion. Upon changing from benzoate to methoxyl-substituted benzoate, SD/Ag30 is isolated as a very complicated core-shell spherical cluster composed of a Ag 57 shell and a vase-like Ag 21 S 13 core. Four MoO 4 2- anions are arranged in a supertetrahedron and located in the interstice between the core and shell. Introduction of the bulky benzoate changes elaborately the nuclearity and arrangements of silver polygons on the shell of silver clusters, which is exemplified by comparing SD/Ag28 and a known similar silver thiolate cluster. The three new clusters emit luminescence in the near-infrared (NIR) region and show different thermochromic luminescence properties. This work presents a flexible approach to synthetic studies of high-nuclearity silver clusters decorated by different benzoates, and structural modulations are also achieved. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Robust traffic sign detection using fuzzy shape recognizer

NASA Astrophysics Data System (ADS)

Li, Lunbo; Li, Jun; Sun, Jianhong

2009-10-01

A novel fuzzy approach for the detection of traffic signs in natural environments is presented. More than 3000 road images were collected under different weather conditions by a digital camera, and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by the hue and saturation thresholds. A symmetrical detector was used to extract the local features of the regions of interest (ROI), and the shape of ROI was determined by a fuzzy shape recognizer which invoked a set of fuzzy rules. The experimental results show that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur.

Dynamic-thresholding level set: a novel computer-aided volumetry method for liver tumors in hepatic CT images

NASA Astrophysics Data System (ADS)

Cai, Wenli; Yoshida, Hiroyuki; Harris, Gordon J.

2007-03-01

Measurement of the volume of focal liver tumors, called liver tumor volumetry, is indispensable for assessing the growth of tumors and for monitoring the response of tumors to oncology treatments. Traditional edge models, such as the maximum gradient and zero-crossing methods, often fail to detect the accurate boundary of a fuzzy object such as a liver tumor. As a result, the computerized volumetry based on these edge models tends to differ from manual segmentation results performed by physicians. In this study, we developed a novel computerized volumetry method for fuzzy objects, called dynamic-thresholding level set (DT level set). An optimal threshold value computed from a histogram tends to shift, relative to the theoretical threshold value obtained from a normal distribution model, toward a smaller region in the histogram. We thus designed a mobile shell structure, called a propagating shell, which is a thick region encompassing the level set front. The optimal threshold calculated from the histogram of the shell drives the level set front toward the boundary of a liver tumor. When the volume ratio between the object and the background in the shell approaches one, the optimal threshold value best fits the theoretical threshold value and the shell stops propagating. Application of the DT level set to 26 hepatic CT cases with 63 biopsy-confirmed hepatocellular carcinomas (HCCs) and metastases showed that the computer measured volumes were highly correlated with those of tumors measured manually by physicians. Our preliminary results showed that DT level set was effective and accurate in estimating the volumes of liver tumors detected in hepatic CT images.

Joint inversion of multiple geophysical and petrophysical data using generalized fuzzy clustering algorithms

NASA Astrophysics Data System (ADS)

Sun, Jiajia; Li, Yaoguo

2017-02-01

Joint inversion that simultaneously inverts multiple geophysical data sets to recover a common Earth model is increasingly being applied to exploration problems. Petrophysical data can serve as an effective constraint to link different physical property models in such inversions. There are two challenges, among others, associated with the petrophysical approach to joint inversion. One is related to the multimodality of petrophysical data because there often exist more than one relationship between different physical properties in a region of study. The other challenge arises from the fact that petrophysical relationships have different characteristics and can exhibit point, linear, quadratic, or exponential forms in a crossplot. The fuzzy c-means (FCM) clustering technique is effective in tackling the first challenge and has been applied successfully. We focus on the second challenge in this paper and develop a joint inversion method based on variations of the FCM clustering technique. To account for the specific shapes of petrophysical relationships, we introduce several different fuzzy clustering algorithms that are capable of handling different shapes of petrophysical relationships. We present two synthetic and one field data examples and demonstrate that, by choosing appropriate distance measures for the clustering component in the joint inversion algorithm, the proposed joint inversion method provides an effective means of handling common petrophysical situations we encounter in practice. The jointly inverted models have both enhanced structural similarity and increased petrophysical correlation, and better represent the subsurface in the spatial domain and the parameter domain of physical properties.

a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering

NASA Astrophysics Data System (ADS)

Toori, S.; Esmaeily, A.

2017-09-01

Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.

Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods

NASA Astrophysics Data System (ADS)

RazaviToosi, S. L.; Samani, J. M. V.

2016-03-01

Watersheds are considered as hydrological units. Their other important aspects such as economic, social and environmental functions play crucial roles in sustainable development. The objective of this work is to develop methodologies to prioritize watersheds by considering different development strategies in environmental, social and economic sectors. This ranking could play a significant role in management to assign the most critical watersheds where by employing water management strategies, best condition changes are expected to be accomplished. Due to complex relations among different criteria, two new hybrid fuzzy ANP (Analytical Network Process) algorithms, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and fuzzy max-min set methods are used to provide more flexible and accurate decision model. Five watersheds in Iran named Oroomeyeh, Atrak, Sefidrood, Namak and Zayandehrood are considered as alternatives. Based on long term development goals, 38 water management strategies are defined as subcriteria in 10 clusters. The main advantage of the proposed methods is its ability to overcome uncertainty. This task is accomplished by using fuzzy numbers in all steps of the algorithms. To validate the proposed method, the final results were compared with those obtained from the ANP algorithm and the Spearman rank correlation coefficient is applied to find the similarity in the different ranking methods. Finally, the sensitivity analysis was conducted to investigate the influence of cluster weights on the final ranking.

Vote Stuffing Control in IPTV-based Recommender Systems

NASA Astrophysics Data System (ADS)

Bhatt, Rajen

Vote stuffing is a general problem in the functioning of the content rating-based recommender systems. Currently IPTV viewers browse various contents based on the program ratings. In this paper, we propose a fuzzy clustering-based approach to remove the effects of vote stuffing and consider only the genuine ratings for the programs over multiple genres. The approach requires only one authentic rating, which is generally available from recommendation system administrators or program broadcasters. The entire process is automated using fuzzy c-means clustering. Computational experiments performed over one real-world program rating database shows that the proposed approach is very efficient for controlling vote stuffing.

Detecting subject-specific activations using fuzzy clustering

PubMed Central

Seghier, Mohamed L.; Friston, Karl J.; Price, Cathy J.

2007-01-01

Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner. PMID:17478103

A Scalable Framework For Segmenting Magnetic Resonance Images

PubMed Central

Hore, Prodip; Goldgof, Dmitry B.; Gu, Yuhua; Maudsley, Andrew A.; Darkazanli, Ammar

2009-01-01

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data. PMID:20046893

Reference set design for relational modeling of fuzzy systems

NASA Astrophysics Data System (ADS)

Lapohos, Tibor; Buchal, Ralph O.

1994-10-01

One of the keys to the successful relational modeling of fuzzy systems is the proper design of fuzzy reference sets. This has been discussed throughout the literature. In the frame of modeling a stochastic system, we analyze the problem numerically. First, we briefly describe the relational model and present the performance of the modeling in the most trivial case: the reference sets are triangle shaped. Next, we present a known fuzzy reference set generator algorithm (FRSGA) which is based on the fuzzy c-means (Fc-M) clustering algorithm. In the second section of this chapter we improve the previous FRSGA by adding a constraint to the Fc-M algorithm (modified Fc-M or MFc-M): two cluster centers are forced to coincide with the domain limits. This is needed to obtain properly shaped extreme linguistic reference values. We apply this algorithm to uniformly discretized domains of the variables involved. The fuzziness of the reference sets produced by both Fc-M and MFc-M is determined by a parameter, which in our experiments is modified iteratively. Each time, a new model is created and its performance analyzed. For certain algorithm parameter values both of these two algorithms have shortcomings. To eliminate the drawbacks of these two approaches, we develop a completely new generator algorithm for reference sets which we call Polyline. This algorithm and its performance are described in the last section. In all three cases, the modeling is performed for a variety of operators used in the inference engine and two defuzzification methods. Therefore our results depend neither on the system model order nor the experimental setup.

Two generalizations of Kohonen clustering

NASA Technical Reports Server (NTRS)

Bezdek, James C.; Pal, Nikhil R.; Tsao, Eric C. K.

1993-01-01

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ.

Refining Linear Fuzzy Rules by Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap S.; Malkani, Anil

1996-01-01

Linear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.

Fuzzy approach for improved recognition of citric acid induced piglet coughing from continuous registration

NASA Astrophysics Data System (ADS)

Van Hirtum, A.; Berckmans, D.

2003-09-01

A natural acoustic indicator of animal welfare is the appearance (or absence) of coughing in the animal habitat. A sound-database of 5319 individual sounds including 2034 coughs was collected on six healthy piglets containing both animal vocalizations and background noises. Each of the test animals was repeatedly placed in a laboratory installation where coughing was induced by nebulization of citric acid. A two-class classification into 'cough' or 'other' was performed by the application of a distance function to a fast Fourier spectral sound analysis. This resulted in a positive cough recognition of 92%. For the whole sound-database however there was a misclassification of 21%. As spectral information up to 10000 Hz is available, an improved overall classification on the same database is obtained by applying the distance function to nine frequency ranges and combining the achieved distance-values in fuzzy rules. For each frequency range clustering threshold is determined by fuzzy c-means clustering.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

PubMed

Liu, Zhi; Xu, Shuqiong; Zhang, Yun; Chen, Chun Lung Philip

2014-11-01

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

Cluster Analysis of Rat Olfactory Bulb Responses to Diverse Odorants

PubMed Central

Falasconi, Matteo; Leon, Michael; Johnson, Brett A.; Marco, Santiago

2012-01-01

In an effort to deepen our understanding of mammalian olfactory coding, we have used an objective method to analyze a large set of odorant-evoked activity maps collected systematically across the rat olfactory bulb to determine whether such an approach could identify specific glomerular regions that are activated by related odorants. To that end, we combined fuzzy c-means clustering methods with a novel validity approach based on cluster stability to evaluate the significance of the fuzzy partitions on a data set of glomerular layer responses to a large diverse group of odorants. Our results confirm the existence of glomerular response clusters to similar odorants. They further indicate a partial hierarchical chemotopic organization wherein larger glomerular regions can be subdivided into smaller areas that are rather specific in their responses to particular functional groups of odorants. These clusters bear many similarities to, as well as some differences from, response domains previously proposed for the glomerular layer of the bulb. These data also provide additional support for the concept of an identity code in the mammalian olfactory system. PMID:22459165

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

Model of cholera dissemination using geographic information systems and fuzzy clustering means: case study, Chabahar, Iran.

PubMed

Pezeshki, Z; Tafazzoli-Shadpour, M; Mansourian, A; Eshrati, B; Omidi, E; Nejadqoli, I

2012-10-01

Cholera is spread by drinking water or eating food that is contaminated by bacteria, and is related to climate changes. Several epidemics have occurred in Iran, the most recent of which was in 2005 with 1133 cases and 12 deaths. This study investigated the incidence of cholera over a 10-year period in Chabahar district, a region with one of the highest incidence rates of cholera in Iran. Descriptive retrospective study on data of patients with Eltor and NAG cholera reported to the Iranian Centre of Disease Control between 1997 and 2006. Data on the prevalence of cholera were gathered through a surveillance system, and a spatial database was developed using geographic information systems (GIS) to describe the relation of spatial and climate variables to cholera incidences. Fuzzy clustering (fuzzy C) method and statistical analysis based on logistic regression were used to develop a model of cholera dissemination. The variables were demographic characteristics, specifications of cholera infection, climate conditions and some geographical parameters. The incidence of cholera was found to be significantly related to higher temperature and humidity, lower precipitation, shorter distance to the eastern border of Iran and local health centres, and longer distance to the district health centre. The fuzzy C means algorithm showed that clusters were geographically distributed in distinct regions. In order to plan, manage and monitor any public health programme, GIS provide ideal platforms for the convergence of disease-specific information, analysis and computation of new data for statistical analysis. Copyright © 2012 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems

PubMed Central

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data. PMID:28806754

Potential impacts of robust surface roughness indexes on DTM-based segmentation

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano; Rocca, Michele

2017-04-01

In this study, we explore the impact of robust surface texture indexes based on MAD (median absolute differences), implemented by Trevisani and Rocca (2015), in the unsupervised morphological segmentation of an alpine basin. The area was already object of a geomorphometric analysis, consisting in the roughness-based segmentation of the landscape (Trevisani et al. 2012); the roughness indexes were calculated on a high resolution DTM derived by means of airborne Lidar using the variogram as estimator. The calculated roughness indexes have been then used for the fuzzy clustering (Odeh et al., 1992; Burrough et al., 2000) of the basin, revealing the high informative geomorphometric content of the roughness-based indexes. However, the fuzzy clustering revealed a high fuzziness and a high degree of mixing between textural classes; this was ascribed both to the morphological complexity of the basin and to the high sensitivity of variogram to non-stationarity and signal-noise. Accordingly, we explore how the new implemented roughness indexes based on MAD affect the morphological segmentation of the studied basin. References Burrough, P.A., Van Gaans, P.F.M., MacMillan, R.A., 2000. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems 113, 37-52. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., 1992. Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Sciences Society of America Journal 56, 505-516. Trevisani, S., Cavalli, M. & Marchi, L. 2012, "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani, S. & Rocca, M. 2015, "MAD: Robust image texture analysis for applications in high resolution geomorphometry", Computers and Geosciences, vol. 81, pp. 78-92.

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

PubMed

Almaraashi, Majid

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.

Magnetically Recoverable Pd/Fe 3O 4 Core-Shell Nanowire Clusters with Increased Hydrogenation Activity

DOE PAGES

Watt, John; Kotula, Paul G.; Huber, Dale L.

2017-02-06

Core-shell nanostructures are promising candidates for the next generation of catalysts due to synergistic effects which can arise from having two active species in close contact, leading to increased activity. Likewise, catalysts displaying added functionality, such as a magnetic response, can increase their scientific and industrial potential. Here, we synthesize Pd/Fe 3O 4 core-shell nanowire clusters and apply them as hydrogenation catalysts for an industrially important hydrogenation reaction; the conversion of acetophenone to 1-phenylethanol. During synthesis, the palladium nanowires self-assemble into clusters which act as a high surface area framework for the growth of a magnetic iron oxide shell. Wemore » demonstrate excellent catalytic activity due to the presence of palladium while the strong magnetic properties provided by the iron oxide shell enable facile catalyst recovery.« less

Fuzzy recurrence plots

NASA Astrophysics Data System (ADS)

Pham, T. D.

2016-12-01

Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.

Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

NASA Astrophysics Data System (ADS)

Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

2012-01-01

The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers

USGS Publications Warehouse

Hong, Y.-S.; Rosen, Michael R.; Reeves, R.R.

2002-01-01

In an urban fractured-rock aquifer in the Mt. Eden area of Auckland, New Zealand, disposal of storm water is via "soakholes" drilled directly into the top of the fractured basalt rock. The dynamic response of the groundwater level due to the storm water infiltration shows characteristics of a strongly time-varying system. A dynamic fuzzy modeling approach, which is based on multiple local models that are weighted using fuzzy membership functions, has been developed to identify and predict groundwater level fluctuations caused by storm water infiltration. The dynamic fuzzy model is initialized by the fuzzy clustering algorithm and optimized by the gradient-descent algorithm in order to effectively derive the multiple local models-each of which is associated with a locally valid model that represents the groundwater level state as a response to different intensities of rainfall events. The results have shown that even if the number of fuzzy local models derived is small, the fuzzy modeling approach developed provides good prediction results despite the highly time-varying nature of this urban fractured-rock aquifer system. Further, it allows interpretable representations of the dynamic behavior of the groundwater system due to storm water infiltration.

HF in clusters of molecular hydrogen. I. Size evolution of quantum solvation by parahydrogen molecules.

PubMed

Jiang, Hao; Bacić, Zlatko

2005-06-22

We present a theoretical study of the quantum solvation of the HF molecule by a small number of parahydrogen molecules, having n = 1-13 solvent particles. The minimum-energy cluster structures determined for n = 1-12 have all of the H(2) molecules in the first solvent shell. The first solvent shell closes at n = 12 and its geometry is icosahedral, with the HF molecule at the center. The quantum-mechanical ground-state properties of the clusters are calculated exactly using the diffusion Monte Carlo method. The zero-point energy of (p-H(2))(n)HF clusters is unusually large, amounting to 86% of the potential well depth for n > 7. The radial probability distribution functions (PDFs) confirm that the first solvent shell is complete for n = 12, and that the 13th p-H(2) molecule begins to fill the second solvent shell. The p-H(2) molecules execute large-amplitude motions and are highly mobile, making the solvent cage exceptionally fluxional. The anisotropy of the solvent, very pronounced for small clusters, decreases rapidly with increasing n, so that for n approximately 8-9 the solvent environment is practically isotropic. The analysis of the pair angular PDF reveals that for a given n, the parahydrogen solvent density around the HF is modulated in a pattern which clearly reflects the lowest-energy cluster configuration. The rigidity of the solvent clusters displays an interesting size dependence, increasing from n = 6 to 9, becoming floppier for n = 10, and increasing again up to n = 12, as the solvent shell is filled. The rigidity of the solvent cage appears to reach its maximum for n = 12, the point at which the first solvent shell is closed.

Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

NASA Astrophysics Data System (ADS)

Meng, Xiaocheng; Che, Renfei; Gao, Shi; He, Juntao

2018-04-01

With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods

NASA Astrophysics Data System (ADS)

Juniati, D.; Khotimah, C.; Wardani, D. E. K.; Budayasa, K.

2018-01-01

The heart abnormalities can be detected from heart sound. A heart sound can be heard directly with a stethoscope or indirectly by a phonocardiograph, a machine of the heart sound recording. This paper presents the implementation of fractal dimension theory to make a classification of phonocardiograms into a normal heart sound, a murmur, or an extrasystole. The main algorithm used to calculate the fractal dimension was Higuchi’s Algorithm. There were two steps to make a classification of phonocardiograms, feature extraction, and classification. For feature extraction, we used Discrete Wavelet Transform to decompose the signal of heart sound into several sub-bands depending on the selected level. After the decomposition process, the signal was processed using Fast Fourier Transform (FFT) to determine the spectral frequency. The fractal dimension of the FFT output was calculated using Higuchi Algorithm. The classification of fractal dimension of all phonocardiograms was done with KNN and Fuzzy c-mean clustering methods. Based on the research results, the best accuracy obtained was 86.17%, the feature extraction by DWT decomposition level 3 with the value of kmax 50, using 5-fold cross validation and the number of neighbors was 5 at K-NN algorithm. Meanwhile, for fuzzy c-mean clustering, the accuracy was 78.56%.

BP network identification technology of infrared polarization based on fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Zeng, Haifang; Gu, Guohua; He, Weiji; Chen, Qian; Yang, Wei

2011-08-01

Infrared detection system is frequently employed on surveillance operations and reconnaissance mission to detect particular targets of interest in both civilian and military communities. By incorporating the polarization of light as supplementary information, the target discrimination performance could be enhanced. So this paper proposed an infrared target identification method which is based on fuzzy theory and neural network with polarization properties of targets. The paper utilizes polarization degree and light intensity to advance the unsupervised KFCM (kernel fuzzy C-Means) clustering method. And establish different material pol1arization properties database. In the built network, the system can feedback output corresponding material types of probability distribution toward any input polarized degree such as 10° 15°, 20°, 25°, 30°. KFCM, which has stronger robustness and accuracy than FCM, introduces kernel idea and gives the noise points and invalid value different but intuitively reasonable weights. Because of differences in characterization of material properties, there will be some conflicts in classification results. And D - S evidence theory was used in the combination of the polarization and intensity information. Related results show KFCM clustering precision and operation rate are higher than that of the FCM clustering method. The artificial neural network method realizes material identification, which reasonable solved the problems of complexity in environmental information of infrared polarization, and improperness of background knowledge and inference rule. This method of polarization identification is fast in speed, good in self-adaption and high in resolution.

Shell effect on the electron and hole reorganization energy of core-shell II-VI nanoclusters

NASA Astrophysics Data System (ADS)

Cui, Xianhui; Wang, Xinqin; Yang, Fang; Cui, Yingqi; Yang, Mingli

2017-09-01

Density functional theory calculations were performed to study the effect of shell encapsulation on the geometrical and electronic properties of pure and hybrid core-shell CdSe nanoclusters. The CdSe cores are distorted by the shells, and the shells exhibit distinct surface activity from the cores, which leads to remarkable changes in their electron transition behaviors. Although the electron and hole reorganization energies, which are related to the formation and recombination of electron-hole pairs, vary in a complicated way, their itemized contributions, potentials of electron extraction, ionization and affinity, and hole extraction (HEP), are dependent on the cluster size, shell composition and/or solvent. Our calculations suggest that the behaviors of charge carriers, free electrons and holes, in the semiconductor core-shell nanoclusters can be modulated by selecting appropriate cluster size and controlling the chemical composition of the shells.

Communication: Spin densities within a unitary group based spin-adapted open-shell coupled-cluster theory: Analytic evaluation of isotropic hyperfine-coupling constants for the combinatoric open-shell coupled-cluster scheme

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

Datta, Dipayan, E-mail: datta.dipayan@gmail.com; Gauss, Jürgen, E-mail: gauss@uni-mainz.de

We report analytical calculations of isotropic hyperfine-coupling constants in radicals using a spin-adapted open-shell coupled-cluster theory, namely, the unitary group based combinatoric open-shell coupled-cluster (COSCC) approach within the singles and doubles approximation. A scheme for the evaluation of the one-particle spin-density matrix required in these calculations is outlined within the spin-free formulation of the COSCC approach. In this scheme, the one-particle spin-density matrix for an open-shell state with spin S and M{sub S} = + S is expressed in terms of the one- and two-particle spin-free (charge) density matrices obtained from the Lagrangian formulation that is used for calculating themore » analytic first derivatives of the energy. Benchmark calculations are presented for NO, NCO, CH{sub 2}CN, and two conjugated π-radicals, viz., allyl and 1-pyrrolyl in order to demonstrate the performance of the proposed scheme.« less

Pattern Classification of Tropical Cyclone Tracks over the Western North Pacific using a Fuzzy Clustering Method

NASA Astrophysics Data System (ADS)

Kim, H.; Ho, C.; Kim, J.

2008-12-01

This study presents the pattern classification of tropical cyclone (TC) tracks over the western North Pacific (WNP) basin during the typhoon season (June through October) for 1965-2006 (total 42 years) using a fuzzy clustering method. After the fuzzy c-mean clustering algorithm to the TC trajectory interpolated into 20 segments of equivalent length, we divided the whole tracks into 7 patterns. The optimal number of the fuzzy cluster is determined by several validity measures. The classified TC track patterns represent quite different features in the recurving latitudes, genesis locations, and geographical pathways: TCs mainly forming in east-northern part of the WNP and striking Korean and Japan (C1); mainly forming in west-southern part of the WNP, traveling long pathway, and partly striking Japan (C2); mainly striking Taiwan and East China (C3); traveling near the east coast of Japan (C4); traveling the distant ocean east of Japan (C5); moving toward South China and Vietnam straightly (C6); and forming in the South China Sea (C7). Atmospheric environments related to each cluster show physically consistent with each TC track patterns. The straight track pattern is closely linked to a developed anticyclonic circulation to the north of the TC. It implies that this ridge acts as a steering flow forcing TCs to move to the northwest with a more west-oriented track. By contrast, recurving patterns occur commonly under the influence of the strong anomalous westerlies over the TC pathway but there definitely exist characteristic anomalous circulations over the mid- latitudes by pattern. Some clusters are closely related to the well-known large-scale phenomena. The C1 and C2 are highly related to the ENSO phase: The TCs in the C1 (C2) is more active during La Niña (El Niño). The TC activity in the C3 is associated with the WNP summer monsoon. The TCs in the C4 is more (less) vigorous during the easterly (westerly) phase of the stratospheric quasi-biennial oscillation. This study may be applied to the statistical-dynamic long-range forecast model of TC activity as well as the diagnostic study of TC activity.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

PubMed

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-10-09

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

PubMed Central

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-01-01

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method. PMID:26450665

The search for structure - Object classification in large data sets. [for astronomers

NASA Technical Reports Server (NTRS)

Kurtz, Michael J.

1988-01-01

Research concerning object classifications schemes are reviewed, focusing on large data sets. Classification techniques are discussed, including syntactic, decision theoretic methods, fuzzy techniques, and stochastic and fuzzy grammars. Consideration is given to the automation of MK classification (Morgan and Keenan, 1973) and other problems associated with the classification of spectra. In addition, the classification of galaxies is examined, including the problems of systematic errors, blended objects, galaxy types, and galaxy clusters.

Effects of stomata clustering on leaf gas exchange.

PubMed

Lehmann, Peter; Or, Dani

2015-09-01

A general theoretical framework for quantifying the stomatal clustering effects on leaf gaseous diffusive conductance was developed and tested. The theory accounts for stomatal spacing and interactions among 'gaseous concentration shells'. The theory was tested using the unique measurements of Dow et al. (2014) that have shown lower leaf diffusive conductance for a genotype of Arabidopsis thaliana with clustered stomata relative to uniformly distributed stomata of similar size and density. The model accounts for gaseous diffusion: through stomatal pores; via concentration shells forming at pore apertures that vary with stomata spacing and are thus altered by clustering; and across the adjacent air boundary layer. Analytical approximations were derived and validated using a numerical model for 3D diffusion equation. Stomata clustering increases the interactions among concentration shells resulting in larger diffusive resistance that may reduce fluxes by 5-15%. A similar reduction in conductance was found for clusters formed by networks of veins. The study resolves ambiguities found in the literature concerning stomata end-corrections and stomatal shape, and provides a new stomata density threshold for diffusive interactions of overlapping vapor shells. The predicted reduction in gaseous exchange due to clustering, suggests that guard cell function is impaired, limiting stomatal aperture opening. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Classification of posture maintenance data with fuzzy clustering algorithms

NASA Technical Reports Server (NTRS)

Bezdek, James C.

1992-01-01

Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various sensory organization test (SOT) conditions were collected in conjunction with Johnson Space Center postural control studies using a tilt-translation device (TTD). The University of West Florida applied the fuzzy c-meams (FCM) clustering algorithms to this data with a view towards identifying various states and stages of subjects experiencing such changes. Feature analysis, time step analysis, pooling data, response of the subjects, and the algorithms used are discussed.

CdSe/AsS core-shell quantum dots: preparation and two-photon fluorescence.

PubMed

Wang, Junzhong; Lin, Ming; Yan, Yongli; Wang, Zhe; Ho, Paul C; Loh, Kian Ping

2009-08-19

Arsenic(II) sulfide (AsS)-coated CdSe core-shell nanocrystals can be prepared by a cluster-complex deposition approach under mild conditions. At 60 degrees C, growth of an AsS shell onto a CdSe nanocrystal can be realized through the crystallization of a cluster complex of AsS/butylamine in a mixed solvent of isopropanol/chloroform. The new, type I core-shell nanocrystal exhibits markedly enhanced one-photon fluorescence as well two-photon upconversion fluorescence. The nanocrystals can be used for infrared-excited upconversion cellular labeling.

Structures of small Pd Pt bimetallic clusters by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Cheng, Daojian; Huang, Shiping; Wang, Wenchuan

2006-11-01

Segregation phenomena of Pd-Pt bimetallic clusters with icosahedral and decahedral structures are investigated by using Monte Carlo method based on the second-moment approximation of the tight-binding (TB-SMA) potentials. The simulation results indicate that the Pd atoms generally lie on the surface of the smaller clusters. The three-shell onion-like structures are observed in 55-atom Pd-Pt bimetallic clusters, in which a single Pd atom is located in the center, and the Pt atoms are in the middle shell, while the Pd atoms are enriched on the surface. With the increase of Pd mole fraction in 55-atom Pd-Pt bimetallic clusters, the Pd atoms occupy the vertices of clusters first, then edge and center sites, and finally the interior shell. It is noticed that some decahedral structures can be transformed into the icosahedron-like structure at 300 and 500 K. Comparisons are made with previous experiments and theoretical studies of Pd-Pt bimetallic clusters.

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

Gomez, John A.; Henderson, Thomas M.; Scuseria, Gustavo E.

Restricted single-reference coupled cluster theory truncated to single and double excitations accurately describes weakly correlated systems, but often breaks down in the presence of static or strong correlation. Good coupled cluster energies in the presence of degeneracies can be obtained by using a symmetry-broken reference, such as unrestricted Hartree-Fock, but at the cost of good quantum numbers. A large body of work has shown that modifying the coupled cluster ansatz allows for the treatment of strong correlation within a single-reference, symmetry-adapted framework. The recently introduced singlet-paired coupled cluster doubles (CCD0) method is one such model, which recovers correct behavior formore » strong correlation without requiring symmetry breaking in the reference. Here, we extend singlet-paired coupled cluster for application to open shells via restricted open-shell singlet-paired coupled cluster singles and doubles (ROCCSD0). The ROCCSD0 approach retains the benefits of standard coupled cluster theory and recovers correct behavior for strongly correlated, open-shell systems using a spin-preserving ROHF reference.« less

Analysis of Intergrade Variables In The Fuzzy C-Means And Improved Algorithm Cat Swarm Optimization(FCM-ISO) In Search Segmentation

NASA Astrophysics Data System (ADS)

Saragih, Jepronel; Salim Sitompul, Opim; Situmorang, Zakaria

2017-12-01

One of the techniques known in Data Mining namely clustering. Image segmentation process does not always represent the actual image which is caused by a combination of algorithms as long as it has not been able to obtain optimal cluster centers. In this research will search for the smallest error with the counting result of a Fuzzy C Means process optimized with Cat swam Algorithm Optimization that has been developed by adding the weight of the energy in the process of Tracing Mode.So with the parameter can be determined the most optimal cluster centers and most closely with the data will be made the cluster. Weigh inertia in this research, namely: (0.1), (0.2), (0.3), (0.4), (0.5), (0.6), (0.7), (0.8) and (0.9). Then compare the results of each variable values inersia (W) which is different and taken the smallest results. Of this weighting analysis process can acquire the right produce inertia variable cost function the smallest.

A Distributed Fuzzy Associative Classifier for Big Data.

PubMed

Segatori, Armando; Bechini, Alessio; Ducange, Pietro; Marcelloni, Francesco

2017-09-19

Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11,000,000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

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.

A recurrent self-organizing neural fuzzy inference network.

PubMed

Juang, C F; Lin, C T

1999-01-01

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

PubMed

El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

NASA Astrophysics Data System (ADS)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

Role of shell corrections in the phenomenon of cluster radioactivity

NASA Astrophysics Data System (ADS)

Kaur, Mandeep; Singh, Bir Bikram; Sharma, Manoj K.

2018-05-01

The detailed investigation has been carried out to explore the role of shell corrections in the decay of various radioactive parent nuclei in trans-lead region, specifically, which lead to doubly magic 208Pb daughter nucleus through emission of clusters such as 14C, 18,20O, 22,24,26Ne, 28,30 Mg and 34S i. The fragmentation potential comprises of binding energies (BE), Coulomb potential (Vc) and nuclear or proximity potential (VP) of the decaying fragments (or clusters). It is relevant to mention here that the contributions of VLDM (T=0) and δU (T=0) in the BE have been analysed within the Strutinsky renormanlization procedure. In the framework of quantum mechanical fragmentation theory (QMFT), we have investigated the above mentioned cluster decays with and without inclusion of shell corrections in the fragmentation potential for spherical as well as non-compact oriented nuclei. We find that the experimentally observed clusters 14C, 18,20O, 22,24,26 Ne, 28,30 Mg and 34Si having doubly magic 208 Pb daughter nucleus are not strongly minimized, they do so only after the inclusion of shell corrections in the fragmentation potential. The nuclear structure information carried by the shell corrections have been explored via these calculations, within the collective clusterisation process of QMFT, in the study of ground state decay of radioactive nuclei. The role of different parts of fragmentation potentials such as VLDM, δU, Vc and Vp is dually analysed for better understanding of radioactive cluster decay.

A coupled-cluster study of photodetachment cross sections of closed-shell anions

NASA Astrophysics Data System (ADS)

Cukras, Janusz; Decleva, Piero; Coriani, Sonia

2014-11-01

We investigate the performance of Stieltjes Imaging applied to Lanczos pseudo-spectra generated at the coupled cluster singles and doubles, coupled cluster singles and approximate iterative doubles and coupled cluster singles levels of theory in modeling the photodetachment cross sections of the closed shell anions H-, Li-, Na-, F-, Cl-, and OH-. The accurate description of double excitations is found to play a much more important role than in the case of photoionization of neutral species.

A coupled-cluster study of photodetachment cross sections of closed-shell anions.

PubMed

Cukras, Janusz; Decleva, Piero; Coriani, Sonia

2014-11-07

We investigate the performance of Stieltjes Imaging applied to Lanczos pseudo-spectra generated at the coupled cluster singles and doubles, coupled cluster singles and approximate iterative doubles and coupled cluster singles levels of theory in modeling the photodetachment cross sections of the closed shell anions H(-), Li(-), Na(-), F(-), Cl(-), and OH(-). The accurate description of double excitations is found to play a much more important role than in the case of photoionization of neutral species.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

NASA Astrophysics Data System (ADS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-09-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

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

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlapmore » with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.« less

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

PubMed Central

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space. PMID:25240340

Neural fuzzy modelization of copper removal from water by biosorption in fixed-bed columns using olive stone and pinion shell.

PubMed

Calero, M; Iáñez-Rodríguez, I; Pérez, A; Martín-Lara, M A; Blázquez, G

2018-03-01

Continuous copper biosorption in fixed-bed column by olive stone and pinion shell was studied. The effect of three operational parameters was analyzed: feed flow rate (2-6 ml/min), inlet copper concentration (40-100 mg/L) and bed-height (4.4-13.4 cm). Artificial Neural-Fuzzy Inference System (ANFIS) was used in order to optimize the percentage of copper removal and the retention capacity in the column. The highest percentage of copper retained was achieved at 2 ml/min, 40 mg/L and 4.4 cm. However, the optimum biosorption capacity was obtained at 6 ml/min, 100 mg/L and 13.4 cm. Finally, breakthrough curves were simulated with mathematical traditional models and ANFIS model. The calculated results obtained with each model were compared with experimental data. The best results were given by ANFIS modelling that predicted copper biosorption with high accuracy. Breakthrough curves surfaces, which enable the visualization of the behavior of the system in different process conditions, were represented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Motion estimation in the frequency domain using fuzzy c-planes clustering.

PubMed

Erdem, C E; Karabulut, G Z; Yanmaz, E; Anarim, E

2001-01-01

A recent work explicitly models the discontinuous motion estimation problem in the frequency domain where the motion parameters are estimated using a harmonic retrieval approach. The vertical and horizontal components of the motion are independently estimated from the locations of the peaks of respective periodogram analyses and they are paired to obtain the motion vectors using a procedure proposed. In this paper, we present a more efficient method that replaces the motion component pairing task and hence eliminates the problems of the pairing method described. The method described in this paper uses the fuzzy c-planes (FCP) clustering approach to fit planes to three-dimensional (3-D) frequency domain data obtained from the peaks of the periodograms. Experimental results are provided to demonstrate the effectiveness of the proposed method.

Identification of a Unique Fe-S Cluster Binding Site in a Glycyl-Radical Type Microcompartment Shell Protein

PubMed Central

Thompson, Michael C.; Wheatley, Nicole M.; Jorda, Julien; Sawaya, Michael R.; Gidaniyan, Soheil D.; Ahmed, Hoda; Yang, Zhongyu; McCarty, Krystal N.; Whitelegge, Julian P.; Yeates, Todd O.

2014-01-01

Recently, progress has been made toward understanding the functional diversity of bacterial microcompartment (MCP) systems, which serve as protein-based metabolic organelles in diverse microbes. New types of MCPs have been identified, including the glycyl-radical propanediol (Grp) MCP. Within these elaborate protein complexes, BMC-domain shell proteins assemble to form a polyhedral barrier that encapsulates the enzymatic contents of the MCP. Interestingly, the Grp MCP contains a number of shell proteins with unusual sequence features. GrpU is one such shell protein, whose amino acid sequence is particularly divergent from other members of the BMC-domain superfamily of proteins that effectively defines all MCPs. Expression, purification, and subsequent characterization of the protein showed, unexpectedly, that it binds an iron-sulfur cluster. We determined X-ray crystal structures of two GrpU orthologs, providing the first structural insight into the homohexameric BMC-domain shell proteins of the Grp system. The X-ray structures of GrpU, both obtained in the apo form, combined with spectroscopic analyses and computational modeling, show that the metal cluster resides in the central pore of the BMC shell protein at a position of broken 6-fold symmetry. The result is a structurally polymorphic iron-sulfur cluster binding site that appears to be unique among metalloproteins studied to date. PMID:25102080

Fuzzy spaces topology change as a possible solution to the black hole information loss paradox

NASA Astrophysics Data System (ADS)

Silva, C. A. S.

2009-06-01

The black hole information loss paradox is one of the most intricate problems in modern theoretical physics. A proposal to solve this is one related with topology change. However it has found some obstacles related to unitarity and cluster decomposition (locality). In this Letter we argue that modelling the black hole's event horizon as a noncommutative manifold - the fuzzy sphere - we can solve the problems with topology change, getting a possible solution to the black hole information loss paradox.

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

NASA Astrophysics Data System (ADS)

Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

MRI brain tumor segmentation based on improved fuzzy c-means method

NASA Astrophysics Data System (ADS)

Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

2009-10-01

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

Possibility-based robust design optimization for the structural-acoustic system with fuzzy parameters

NASA Astrophysics Data System (ADS)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2018-03-01

The conventional engineering optimization problems considering uncertainties are based on the probabilistic model. However, the probabilistic model may be unavailable because of the lack of sufficient objective information to construct the precise probability distribution of uncertainties. This paper proposes a possibility-based robust design optimization (PBRDO) framework for the uncertain structural-acoustic system based on the fuzzy set model, which can be constructed by expert opinions. The objective of robust design is to optimize the expectation and variability of system performance with respect to uncertainties simultaneously. In the proposed PBRDO, the entropy of the fuzzy system response is used as the variability index; the weighted sum of the entropy and expectation of the fuzzy response is used as the objective function, and the constraints are established in the possibility context. The computations for the constraints and objective function of PBRDO are a triple-loop and a double-loop nested problem, respectively, whose computational costs are considerable. To improve the computational efficiency, the target performance approach is introduced to transform the calculation of the constraints into a double-loop nested problem. To further improve the computational efficiency, a Chebyshev fuzzy method (CFM) based on the Chebyshev polynomials is proposed to estimate the objective function, and the Chebyshev interval method (CIM) is introduced to estimate the constraints, thereby the optimization problem is transformed into a single-loop one. Numerical results on a shell structural-acoustic system verify the effectiveness and feasibility of the proposed methods.

Identification of piecewise affine systems based on fuzzy PCA-guided robust clustering technique

NASA Astrophysics Data System (ADS)

Khanmirza, Esmaeel; Nazarahari, Milad; Mousavi, Alireza

2016-12-01

Hybrid systems are a class of dynamical systems whose behaviors are based on the interaction between discrete and continuous dynamical behaviors. Since a general method for the analysis of hybrid systems is not available, some researchers have focused on specific types of hybrid systems. Piecewise affine (PWA) systems are one of the subsets of hybrid systems. The identification of PWA systems includes the estimation of the parameters of affine subsystems and the coefficients of the hyperplanes defining the partition of the state-input domain. In this paper, we have proposed a PWA identification approach based on a modified clustering technique. By using a fuzzy PCA-guided robust k-means clustering algorithm along with neighborhood outlier detection, the two main drawbacks of the well-known clustering algorithms, i.e., the poor initialization and the presence of outliers, are eliminated. Furthermore, this modified clustering technique enables us to determine the number of subsystems without any prior knowledge about system. In addition, applying the structure of the state-input domain, that is, considering the time sequence of input-output pairs, provides a more efficient clustering algorithm, which is the other novelty of this work. Finally, the proposed algorithm has been evaluated by parameter identification of an IGV servo actuator. Simulation together with experiment analysis has proved the effectiveness of the proposed method.

An explicitly spin-free compact open-shell coupled cluster theory using a multireference combinatoric exponential ansatz: formal development and pilot applications.

PubMed

Datta, Dipayan; Mukherjee, Debashis

2009-07-28

In this paper, we present a comprehensive account of an explicitly spin-free compact state-universal multireference coupled cluster (CC) formalism for computing the state energies of simple open-shell systems, e.g., doublets and biradicals, where the target open-shell states can be described by a few configuration state functions spanning a model space. The cluster operators in this formalism are defined in terms of the spin-free unitary generators with respect to the common closed-shell component of all model functions (core) as vacuum. The spin-free cluster operators are either closed-shell-like n hole-n particle excitations (denoted by T(mu)) or involve excitations from the doubly occupied (nonvalence) orbitals to the singly occupied (valence) orbitals (denoted by S(e)(mu)). In addition, there are cluster operators with exchange spectator scatterings involving the valence orbitals (denoted by S(re)(mu)). We propose a new multireference cluster expansion ansatz for the wave operator with the above generally noncommuting cluster operators which essentially has the same physical content as the Jeziorski-Monkhorst ansatz with the commuting cluster operators defined in the spin-orbital basis. The T(mu) operators in our ansatz are taken to commute with all other operators, while the S(e)(mu) and S(re)(mu) operators are allowed to contract among themselves through the spectator valence orbitals. An important innovation of this ansatz is the choice of an appropriate automorphic factor accompanying each contracted composite of cluster operators in order to ensure that each distinct excitation generated by this composite appears only once in the wave operator. The resulting CC equations consist of two types of terms: a "direct" term and a "normalization" term containing the effective Hamiltonian operator. It is emphasized that the direct term is almost quartic in the cluster amplitudes, barring only a handful of terms and termination of the normalization term depends on the valence rank of the effective Hamiltonian operator and the excitation rank of the cluster operators at which the theory is truncated. Illustrative applications are presented by computing the state energies of neutral doublet radicals and doublet molecular cations and ionization energies of neutral molecules and comparing our results with the other open-shell CC theories, benchmark full CI results (when available) in the same basis, and the experimental results. Highly encouraging results show the efficacy of the method.

Query by example video based on fuzzy c-means initialized by fixed clustering center

NASA Astrophysics Data System (ADS)

Hou, Sujuan; Zhou, Shangbo; Siddique, Muhammad Abubakar

2012-04-01

Currently, the high complexity of video contents has posed the following major challenges for fast retrieval: (1) efficient similarity measurements, and (2) efficient indexing on the compact representations. A video-retrieval strategy based on fuzzy c-means (FCM) is presented for querying by example. Initially, the query video is segmented and represented by a set of shots, each shot can be represented by a key frame, and then we used video processing techniques to find visual cues to represent the key frame. Next, because the FCM algorithm is sensitive to the initializations, here we initialized the cluster center by the shots of query video so that users could achieve appropriate convergence. After an FCM cluster was initialized by the query video, each shot of query video was considered a benchmark point in the aforesaid cluster, and each shot in the database possessed a class label. The similarity between the shots in the database with the same class label and benchmark point can be transformed into the distance between them. Finally, the similarity between the query video and the video in database was transformed into the number of similar shots. Our experimental results demonstrated the performance of this proposed approach.

Precipitation, pH and metal load in AMD river basins: an application of fuzzy clustering algorithms to the process characterization.

PubMed

Grande, J A; Andújar, J M; Aroba, J; de la Torre, M L; Beltrán, R

2005-04-01

In the present work, Acid Mine Drainage (AMD) processes in the Chorrito Stream, which flows into the Cobica River (Iberian Pyrite Belt, Southwest Spain) are characterized by means of clustering techniques based on fuzzy logic. Also, pH behavior in contrast to precipitation is clearly explained, proving that the influence of rainfall inputs on the acidity and, as a result, on the metal load of a riverbed undergoing AMD processes highly depends on the moment when it occurs. In general, the riverbed dynamic behavior is the response to the sum of instant stimuli produced by isolated rainfall, the seasonal memory depending on the moment of the target hydrological year and, finally, the own inertia of the river basin, as a result of an accumulation process caused by age-long mining activity.

Boosted ARTMAP: modifications to fuzzy ARTMAP motivated by boosting theory.

PubMed

Verzi, Stephen J; Heileman, Gregory L; Georgiopoulos, Michael

2006-05-01

In this paper, several modifications to the Fuzzy ARTMAP neural network architecture are proposed for conducting classification in complex, possibly noisy, environments. The goal of these modifications is to improve upon the generalization performance of Fuzzy ART-based neural networks, such as Fuzzy ARTMAP, in these situations. One of the major difficulties of employing Fuzzy ARTMAP on such learning problems involves over-fitting of the training data. Structural risk minimization is a machine-learning framework that addresses the issue of over-fitting by providing a backbone for analysis as well as an impetus for the design of better learning algorithms. The theory of structural risk minimization reveals a trade-off between training error and classifier complexity in reducing generalization error, which will be exploited in the learning algorithms proposed in this paper. Boosted ART extends Fuzzy ART by allowing the spatial extent of each cluster formed to be adjusted independently. Boosted ARTMAP generalizes upon Fuzzy ARTMAP by allowing non-zero training error in an effort to reduce the hypothesis complexity and hence improve overall generalization performance. Although Boosted ARTMAP is strictly speaking not a boosting algorithm, the changes it encompasses were motivated by the goals that one strives to achieve when employing boosting. Boosted ARTMAP is an on-line learner, it does not require excessive parameter tuning to operate, and it reduces precisely to Fuzzy ARTMAP for particular parameter values. Another architecture described in this paper is Structural Boosted ARTMAP, which uses both Boosted ART and Boosted ARTMAP to perform structural risk minimization learning. Structural Boosted ARTMAP will allow comparison of the capabilities of off-line versus on-line learning as well as empirical risk minimization versus structural risk minimization using Fuzzy ARTMAP-based neural network architectures. Both empirical and theoretical results are presented to enhance the understanding of these architectures.

An algebraic cluster model based on the harmonic oscillator basis

NASA Technical Reports Server (NTRS)

Levai, Geza; Cseh, J.

1995-01-01

We discuss the semimicroscopic algebraic cluster model introduced recently, in which the internal structure of the nuclear clusters is described by the harmonic oscillator shell model, while their relative motion is accounted for by the Vibron model. The algebraic formulation of the model makes extensive use of techniques associated with harmonic oscillators and their symmetry group, SU(3). The model is applied to some cluster systems and is found to reproduce important characteristics of nuclei in the sd-shell region. An approximate SU(3) dynamical symmetry is also found to hold for the C-12 + C-12 system.

Shell Corrections Stabilizing Superheavy Nuclei and Semi-spheroidal Atomic Clusters

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

Poenaru, Dorin N.

2008-01-24

The macroscopic-microscopic method is used to illustrate the shell effect stabilizing superheavy nuclei and to study the stability of semi-spheroidal clusters deposited on planar surfaces. The alpha decay of superheavy nuclei is calculated using three models: the analytical superasymmetric fission model; the universal curve, and the semiempirical formula taking into account the shell effects. Analytical relationships are obtained for the energy levels of the new semi-spheroidal harmonic oscillator (SSHO) single-particle model and for the surface and curvature energies of the semi-spheroidal clusters. The maximum degeneracy of the SSHO is reached at a super-deformed prolate shape for which the minimum ofmore » the liquid drop model energy is also attained.« less

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

Formation of Core-Shell Ethane-Silver Clusters in He Droplets.

PubMed

Loginov, Evgeny; Gomez, Luis F; Sartakov, Boris G; Vilesov, Andrey F

2017-08-17

Ethane core-silver shell clusters consisting of several thousand particles have been assembled in helium droplets upon capture of ethane molecules followed by Ag atoms. The composite clusters were studied via infrared laser spectroscopy in the range of the C-H stretching vibrations of ethane. The spectra reveal a splitting of the vibrational bands, which is ascribed to interaction with Ag. A rigorous analysis of band intensities for a varying number of trapped ethane molecules and Ag atoms indicates that the composite clusters consist of a core of ethane that is covered by relatively small Ag clusters. This metastable structure is stabilized due to fast dissipation in superfluid helium droplets of the cohesion energy of the clusters.

Superclustering in the explosion scenario. II - Prolate spheroidal shells from superconducting cosmic strings

NASA Technical Reports Server (NTRS)

Borden, David; Ostriker, Jeremiah P.; Weinberg, David H.

1989-01-01

If galaxies form on shells, then clusters of galaxies should form at the vertices where three shells intersect. Weinberg, Ostriker, and Dekel (WOD, 1989) studied this picture quantitatively and found that an intersecting spherical shell model reproduces many of the properties of the observed distribution of galaxy clusters, but that too much superclustering is produced. In this paper, the WOD analysis is repeated with prolate spheroids that could be created by superconducting cosmic strings. It is found that most of the attractive features of the WOD model are maintained in the more general case and there is slight improvement in some aspects, but that the overall problem of excessive superclustering is not really alleviated.

Normed kernel function-based fuzzy possibilistic C-means (NKFPCM) algorithm for high-dimensional breast cancer database classification with feature selection is based on Laplacian Score

NASA Astrophysics Data System (ADS)

Lestari, A. W.; Rustam, Z.

2017-07-01

In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

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

2010-10-01

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

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Finding Semirigid Domains in Biomolecules by Clustering Pair-Distance Variations

PubMed Central

Schreiner, Wolfgang

2014-01-01

Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction. PMID:24959586

Fuzzy C-Means Algorithm for Segmentation of Aerial Photography Data Obtained Using Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Akinin, M. V.; Akinina, N. V.; Klochkov, A. Y.; Nikiforov, M. B.; Sokolova, A. V.

2015-05-01

The report reviewed the algorithm fuzzy c-means, performs image segmentation, give an estimate of the quality of his work on the criterion of Xie-Beni, contain the results of experimental studies of the algorithm in the context of solving the problem of drawing up detailed two-dimensional maps with the use of unmanned aerial vehicles. According to the results of the experiment concluded that the possibility of applying the algorithm in problems of decoding images obtained as a result of aerial photography. The considered algorithm can significantly break the original image into a plurality of segments (clusters) in a relatively short period of time, which is achieved by modification of the original k-means algorithm to work in a fuzzy task.

Structural transition of (InSb)n clusters at n = 6-10

NASA Astrophysics Data System (ADS)

Lu, Qi Liang; Luo, Qi Quan; Huang, Shou Guo; Li, Yi De

2016-10-01

An optimization strategy combining global semi-empirical quantum mechanical search with all-electron density functional theory was adopted to determine the lowest energy structure of (InSb)n clusters with n = 6-10. A new structural growth pattern of the clusters was observed. The lowest energy structures of (InSb)6 and (InSb)8 were different from that of previously reported results. Competition existed between core-shell and cage-like structures of (InSb)8. The structural transition of (InSb)n clusters occurred at size n = 8-9. For (InSb)9 and (InSb)10 clusters, core-shell structure were more energetically favorable than the cage. The corresponding electronic properties were investigated.

Carbon-shell-constrained silicon cluster derived from Al-Si alloy as long-cycling life lithium ion batteries anode

NASA Astrophysics Data System (ADS)

Su, Junming; Zhang, Congcong; Chen, Xiang; Liu, Siyang; Huang, Tao; Yu, Aishui

2018-03-01

Although silicon is the most promising anode material for Li-ion batteries, large volume expansion during lithiation and delithiation is the main obstacle limiting the commercial application of silicon anodes. There are two ways to alleviate volume expansion and prevent further pulverization of a Si anode: fabrication of a rational nanostructure possessing void spaces and uniform distribution of the conducting sites, without a good balance effect in mitigating the limiting factors and enhancing battery performance. In this paper, we propose a novel nanostructure - a carbon-shell-constrained Si cluster (Si/C shell) with both adequate void space and good distribution of electrical contact sites to guarantee homogeneous lithiation in the initial cycle. Benefiting from the ability to maintain electrical conductivity of the outer carbon shell, even after cluster fragmentation, the Si/C shell synthesized from low-cost commercial Al-Si alloy spheres can deliver 0.03% capacity loss from 100th to 1000th cycles at a current density of 1 A g-1. The Si/C shell sample with the dual functional structure mentioned above can also maintain its own nanostructure during cycling and deliver excellent rate performance. It is a concise and scalable strategy which can simplify the preparation of other alloy anode materials for Li-ion batteries.

Cooperative inversion of magnetotelluric and seismic data sets

NASA Astrophysics Data System (ADS)

Markovic, M.; Santos, F.

2012-04-01

Cooperative inversion of magnetotelluric and seismic data sets Milenko Markovic,Fernando Monteiro Santos IDL, Faculdade de Ciências da Universidade de Lisboa 1749-016 Lisboa Inversion of single geophysical data has well-known limitations due to the non-linearity of the fields and non-uniqueness of the model. There is growing need, both in academy and industry to use two or more different data sets and thus obtain subsurface property distribution. In our case ,we are dealing with magnetotelluric and seismic data sets. In our approach,we are developing algorithm based on fuzzy-c means clustering technique, for pattern recognition of geophysical data. Separate inversion is performed on every step, information exchanged for model integration. Interrelationships between parameters from different models is not required in analytical form. We are investigating how different number of clusters, affects zonation and spatial distribution of parameters. In our study optimization in fuzzy c-means clustering (for magnetotelluric and seismic data) is compared for two cases, firstly alternating optimization and then hybrid method (alternating optimization+ Quasi-Newton method). Acknowledgment: This work is supported by FCT Portugal

A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data

PubMed Central

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-01-01

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data. PMID:27482473

Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

NASA Astrophysics Data System (ADS)

Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2018-03-01

Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing algorithms in accuracy and efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

NASA Astrophysics Data System (ADS)

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

Mapping Emission from Clusters of CdSe/ZnS Nanoparticles

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

Ryan, Duncan P.; Goodwin, Peter M.; Sheehan, Chris J.

In this paper, we have carried out correlated super-resolution and SEM imaging studies of clusters of CdSe/ZnS nanoparticles containing up to ten particles to explore how the fluorescence behavior of these clusters depends on the number of particles, the specific cluster geometry, the shell thickness, and the technique used to produce the clusters. The total emission yield was less than proportional to the number of particles in the clusters for both thick and thin shells. With super-resolution imaging, the emission center of the cluster could be spatially resolved at distance scales on the order of the cluster size. The intrinsicmore » fluorescence intermittency of the nanoparticles altered the emission distribution across the cluster, which enabled the identification of relative emission intensities of individual particles or small groups of particles within the cluster. Finally, for clusters undergoing interparticle energy transfer, donor/acceptor pairs and regions where energy was funneled could be identified.« less

Mapping Emission from Clusters of CdSe/ZnS Nanoparticles

DOE PAGES

Ryan, Duncan P.; Goodwin, Peter M.; Sheehan, Chris J.; ...

2018-01-24

In this paper, we have carried out correlated super-resolution and SEM imaging studies of clusters of CdSe/ZnS nanoparticles containing up to ten particles to explore how the fluorescence behavior of these clusters depends on the number of particles, the specific cluster geometry, the shell thickness, and the technique used to produce the clusters. The total emission yield was less than proportional to the number of particles in the clusters for both thick and thin shells. With super-resolution imaging, the emission center of the cluster could be spatially resolved at distance scales on the order of the cluster size. The intrinsicmore » fluorescence intermittency of the nanoparticles altered the emission distribution across the cluster, which enabled the identification of relative emission intensities of individual particles or small groups of particles within the cluster. Finally, for clusters undergoing interparticle energy transfer, donor/acceptor pairs and regions where energy was funneled could be identified.« less

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

NASA Astrophysics Data System (ADS)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

Mn@Si14+: a singlet fullerene-like endohedrally doped silicon cluster.

PubMed

Ngan, Vu Thi; Pierloot, Kristine; Nguyen, Minh Tho

2013-04-21

The electronic structure of Mn@Si14(+) is determined using DFT and CASPT2/CASSCF(14,15) computations with large basis sets. The endohedrally Mn-doped Si cationic cluster has a D3h fullerene-like structure featuring a closed-shell singlet ground state with a singlet-triplet gap of ~1 eV. A strong stabilizing interaction occurs between the 3d(Mn) and the 2D-shell(Si14) orbitals, and a large amount of charge is transferred from the Si14 cage to the Mn dopant. The 3d(Mn) orbitals are filled by encapsulation, and the magnetic moment of Mn is completely quenched. Full occupation of [2S, 2P, 2D] shell orbitals by 18 delocalized electrons confers the doped Mn@Si14(+) cluster a spherically aromatic character.

Application of fuzzy C-Means Algorithm for Determining Field of Interest in Information System Study STTH Medan

NASA Astrophysics Data System (ADS)

Rahman Syahputra, Edy; Agustina Dalimunthe, Yulia; Irvan

2017-12-01

Many students are confused in choosing their own field of specialization, ultimately choosing areas of specialization that are incompatible with a variety of reasons such as just following a friend or because of the area of interest of many choices without knowing whether they have Competencies in the chosen field of interest. This research aims to apply Clustering method with Fuzzy C-means algorithm to classify students in the chosen interest field. The Fuzzy C-Means algorithm is one of the easiest and often used algorithms in data grouping techniques because it makes efficient estimates and does not require many parameters. Several studies have led to the conclusion that the Fuzzy C-Means algorithm can be used to group data based on certain attributes. In this research will be used Fuzzy C-Means algorithm to classify student data based on the value of core subjects in the selection of specialization field. This study also tested the accuracy of the Fuzzy C-Means algorithm in the determination of interest area. The study was conducted on the STT-Harapan Medan Information System Study program, and the object of research is the value of all students of STT-Harapan Medan Information System Study Program 2012. From this research, it is expected to get the specialization field, according to the students' ability based on the prerequisite principal value.

The stabilities and electron structures of Al-Mg clusters with 18 and 20 valence electrons

NASA Astrophysics Data System (ADS)

Yang, Huihui; Chen, Hongshan

2017-07-01

The spherical jellium model predicts that metal clusters having 18 and 20 valence electrons correspond to the magic numbers and will show specific stabilities. We explore in detail the geometric structures, stabilities and electronic structures of Al-Mg clusters containing 18 and 20 valence electrons by using genetic algorithm combined with density functional theories. The stabilities of the clusters are governed by the electronic configurations and Mg/Al ratios. The clusters with lower Mg/Al ratios are more stable. The molecular orbitals accord with the shell structures predicted by the jellium model but the 2S level interweaves with the 1D levels and the 2S and 1D orbitals form a subgroup. The clusters having 20 valence electrons form closed 1S21P61D102S2 shells and show enhanced stability. The Al-Mg clusters with a valence electron count of 18 do not form closed shells because one 1D orbital is unoccupied. The ionization potential and electron affinity are closely related to the electronic configurations; their values are determined by the subgroups the HOMO or LUMO belong to. Supplementary material in the form of one pdf file available from the Journal web page at http://https://doi.org/10.1140/epjd/e2017-80042-9

Constituency and origins of cyclic growth layers in pelecypod shells, part 1

NASA Technical Reports Server (NTRS)

Berry, W. B. N.

1972-01-01

Growth layers occurring in shells of 98 species of pelecypods were examined microscopically in thin section and as natural and etched surfaces. Study began with shells of eleven species known from life history investigations to have annual cycles of growth. Internal microstructural features of the annual layers in these shells provided criteria for recognition of similar, apparently annual shell increments in eighty-six of eighty-seven other species. All of the specimens feature growth laminae, commonly on the order of 50 microns in thickness. The specimens from shallow marine environments show either a clustering of growth laminae related to the formation of concentric ridges or minor growth bands on the external shell surface. Based on observations of the number of growth laminae and clusters per annual-growth layer, it was hypothesised that the subannual increments may be related to daily and fortnightly (and in some cases monthly) cycles in the environment. Possible applications of the paleogrowth method in the fields of paleoecology and paleoclimatology are discussed.

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

2015-05-01

Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering

NASA Astrophysics Data System (ADS)

Fu, Xiaowei; Xiang, Yuhan; Chen, Li; Xu, Xin; Li, Xi

2015-12-01

High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively estimate the energy function in the fuzzy system based on Markov Random Filed (MRF). Before defuzzification, a quantum-inspired probability distribution based on distance and gray correction is proposed, which can adaptively adjust the inaccurate probability estimation of uncertain points caused by noises and edge points. In this study, the proposed method improves accuracy and effectiveness of three-phase identification on the micro-investigation. It provides firm foundation to investigate the microstructural evolution and its related properties.

Biclustering Models for Two-Mode Ordinal Data.

PubMed

Matechou, Eleni; Liu, Ivy; Fernández, Daniel; Farias, Miguel; Gjelsvik, Bergljot

2016-09-01

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets.

Nanosized (mu12-Pt)Pd164-xPtx(CO)72(PPh3)20 (x approximately 7) containing Pt-centered four-shell 165-atom Pd-Pt core with unprecedented intershell bridging carbonyl ligands: comparative analysis of icosahedral shell-growth patterns with geometrically related Pd145(CO)x(PEt3)30 (x approximately 60) containing capped three-shell Pd145 core.

PubMed

Mednikov, Evgueni G; Jewell, Matthew C; Dahl, Lawrence F

2007-09-19

Presented herein are the preparation and crystallographic/microanalytical/magnetic/spectroscopic characterization of the Pt-centered four-shell 165-atom Pd-Pt cluster, (mu(12)-Pt)Pd(164-x)Pt(x)(CO)(72)(PPh(3))(20) (x approximately 7), 1, that replaces the geometrically related capped three-shell icosahedral Pd(145) cluster, Pd(145)(CO)(x)(PEt(3))(30) (x approximately 60), 2, as the largest crystallographically determined discrete transition metal cluster with direct metal-metal bonding. A detailed comparison of their shell-growth patterns gives rise to important stereochemical implications concerning completely unexpected structural dissimilarities as well as similarities and provides new insight concerning possible synthetic approaches for generation of multi-shell metal clusters. 1 was reproducibly prepared in small yields (<10%) from the reaction of Pd(10)(CO)(12)(PPh(3))(6) with Pt(CO)(2)(PPh(3))(2). Its 165-atom metal-core geometry and 20 PPh(3) and 72 CO ligands were established from a low-temperature (100 K) CCD X-ray diffraction study. The well-determined crystal structure is attributed largely to 1 possessing cubic T(h) (2/m3) site symmetry, which is the highest crystallographic subgroup of the noncrystallographic pseudo-icosahedral I(h) (2/m35) symmetry. The "full" four-shell Pd-Pt anatomy of 1 consists of: (a) shell 1 with the centered (mu(12)-Pt) atom encapsulated by the 12-atom icosahedral Pt(x)Pd(12-x) cage, x = 1.2(3); (b) shell 2 with the 42-atom nu(2) icosahedral Pt(x)Pd(42-x) cage, x = 3.5(5); (c) shell 3 with the anti-Mackay 60-atom semi-regular rhombicosidodecahedral Pt(x)Pd(60-x) cage, x = 2.2(6); (d) shell 4 with the 50-atom nu(2) pentagonal dodecahedral Pd(50) cage. The total number of crystallographically estimated Pt atoms, 8 +/- 3, which was obtained from least-squares (Pt(x)/Pd(1-x))-occupancy analysis of the X-ray data that conclusively revealed the central atom to be pure Pt (occupancy factor, x = 1.00(3)), is fortuitously in agreement with that of 7.6(7) found from an X-ray Pt/Pd microanalysis (WDS spectrometer) on three crystals of 1. Our utilization of this site-occupancy (Pt(x)Pd(1-x))-analysis for shells 1-3 originated from the microanalytical results; otherwise, the presumed metal-core composition would have been (mu(12)-Pt)Pd(164). [Alternatively, the (mu(12)-Pt)M(164) core-geometry of 1 may be viewed as a pseudo-Ih Pt-centered six-shell successive nu(1) polyhedral system, each with radially equivalent vertex atoms: Pt@M(12)(icosahedron)@M(30)(icosidodecahedron)@M(12)(icosahedron)@M(60)(rhombicosidodecahedron)@M(30)(icosidodecahedron)@M(20)(pentagonal dodecahedron)]. Completely surprising structural dissimilarities between 1 and 2 are: (1) to date 1 is only reproducibly isolated as a heterometallic Pd-Pt cluster with a central Pt instead of Pd atom; (2) the 50 atoms comprising the outer fourth nu(2) pentagonal dodecahedral shell in 1 are less than the 60 atoms of the inner third shell in 1, in contradistinction to shell-by-shell growth processes in all other known shell-based structures; (3) the 10 fewer PR3 ligands in 1 necessitate larger bulky PPh(3) ligands to protect the Pd-Pt core-geometry; (4) the 72 CO ligands consist of six bridging COs within each of the 12 pentagons in shell 4 that are coordinated to intershell metal atoms. SQUID magnetometry measurements showed a single-crystal sample of 1 to be diamagnetic over the entire temperature range of 10-300 K.

Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators.

PubMed

Karayiannis, N B

2000-01-01

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

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

Dumidu Wijayasekara; Ondrej Linda; Milos Manic

Building Energy Management Systems (BEMSs) are essential components of modern buildings that utilize digital control technologies to minimize energy consumption while maintaining high levels of occupant comfort. However, BEMSs can only achieve these energy savings when properly tuned and controlled. Since indoor environment is dependent on uncertain criteria such as weather, occupancy, and thermal state, performance of BEMS can be sub-optimal at times. Unfortunately, the complexity of BEMS control mechanism, the large amount of data available and inter-relations between the data can make identifying these sub-optimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)more » based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm based BEMS. In six different scenarios, the Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more), that the alarm based BEMS. In addition, the Fuzzy-ADLD method identified cases that were missed by the alarm based system, demonstrating potential for increased state-awareness of abnormal building behavior.« less

Efficient fuzzy C-means architecture for image segmentation.

PubMed

Li, Hui-Ya; Hwang, Wen-Jyi; Chang, Chia-Yen

2011-01-01

This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate.

Microsolvation of the potassium ion with aromatic rings: comparison between hexafluorobenzene and benzene.

PubMed

Marques, J M C; Llanio-Trujillo, J L; Albertí, M; Aguilar, A; Pirani, F

2013-08-22

We employ a recently developed methodology to study structural and energetic properties of the first solvation shells of the potassium ion in nonpolar environments due to aromatic rings, which is important to understand the selectivity of several biochemical phenomena. Our evolutionary algorithm is used in the global optimization study of clusters formed of K(+) solvated with hexafluorobenzene (HFBz) molecules. The global intermolecular interaction for these clusters has been decomposed in HFBz-HFBz and in K(+)-HFBz contributions, using a potential model based on different decompositions of the molecular polarizability of hexafluorobenzene. Putative global minimum structures of microsolvation clusters up to 21 hexafluorobenzene molecules were obtained and compared with the analogous K(+)-benzene clusters reported in our previous work (J. Phys. Chem. A 2012, 116, 4947-4956). We have found that both K(+)-(Bz)n and K(+)-(HFBz)n clusters show a strong magic number around the closure of the first solvation shell. Nonetheless, all K(+)-benzene clusters have essentially the same first solvation shell geometry with four solvent molecules around the ion, whereas the corresponding one for K(+)-(HFBz)n is completed with nine HFBz species, and its structural motif varies as n increases. This is attributed to the ion-solvent interaction that has a larger magnitude for K(+)-Bz than in the case of K(+)-HFBz. In addition, the ability of having more HFBz than Bz molecules around K(+) in the first solvation shell is intimately related to the inversion in the sign of the quadrupole moment of the two solvent species, which leads to a distinct ion-solvent geometry of approach.

Antiferromagnetic exchange coupling measurements on single Co clusters

NASA Astrophysics Data System (ADS)

Wernsdorfer, W.; Leroy, D.; Portemont, C.; Brenac, A.; Morel, R.; Notin, L.; Mailly, D.

2009-03-01

We report on single-cluster measurements of the angular dependence of the low-temperature ferromagnetic core magnetization switching field in exchange-coupled Co/CoO core-shell clusters (4 nm) using a micro-bridge DC superconducting quantum interference device (μ-SQUID). It is observed that the coupling with the antiferromagnetic shell induces modification in the switching field for clusters with intrinsic uniaxial anisotropy depending on the direction of the magnetic field applied during the cooling. Using a modified Stoner-Wohlfarth model, it is shown that the core interacts with two weakly coupled and asymmetrical antiferromagnetic sublattices. Ref.: C. Portemont, R. Morel, W. Wernsdorfer, D. Mailly, A. Brenac, and L. Notin, Phys. Rev. B 78, 144415 (2008)

Secondary ion mass spectra of gold super clusters up to 140000 Dalton

NASA Astrophysics Data System (ADS)

Feld, H.; Leute, A.; Rading, D.; Benninghoven, A.; Schmid, G.

1990-03-01

The bombardment of a two-shell gold complex (Au55(PPh3)12Cl6) with 10 keV Xe+-ions results in the formation of secondary ion masses up to 140000 u. These are by far the largest secondary ions observed under primary particle bombardment. The detection and identification of these ions with a Time-Of-Flight Secondary Ion Mass Spectrometer (TOF-SIMS) gives important information about the behavior of naked full-shell clusters. Au13 particles, generated from the Au55 cluster, serve as building blocks for a series of super-clusters up to (Au13)55. The results for keV-ion bombardment are compared to those for MeV-ion bombardment.

Cluster shell model: I. Structure of 9Be, 9B

NASA Astrophysics Data System (ADS)

Della Rocca, V.; Iachello, F.

2018-05-01

We calculate energy spectra, electromagnetic transition rates, longitudinal and transverse electron scattering form factors and log ft values for beta decay in 9Be, 9B, within the framework of a cluster shell model. By comparing with experimental data, we find strong evidence for the structure of these nuclei to be two α-particles in a dumbbell configuration with Z2 symmetry, plus an additional nucleon.

Study of clusters using negative ion photodetachment spectroscopy

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

Zhao, Yuexing

1995-12-01

The weak van der Waals interaction between an open-shell halogen atom and a closed-shell atom or molecule has been investigated using zero electron kinetic energy (ZEKE) spectroscopy. This technique is also applied to study the low-lying electronic states in GaAs and GaAs -. In addition, the spectroscopy and electron detachment dynamics of several small carbon cluster anions are studied using resonant multiphoton detachment spectroscopy.

Nanoclusters as a new family of high temperature superconductors (Conference Presentation)

NASA Astrophysics Data System (ADS)

Halder, Avik; Kresin, Vitaly V.

2017-03-01

Electrons in metal clusters organize into quantum shells, akin to atomic shells in the periodic table. Such nanoparticles are referred to as "superatoms". The electronic shell levels are highly degenerate giving rise to sharp peaks in the density of states, which can enable exceptionally strong electron pairing in certain clusters containing tens to hundreds of atoms. A spectroscopic investigation of size - resolved aluminum nanoclusters has revealed a sharp rise in the density of states near the Fermi level as the temperature decreases towards 100 K. The effect is especially prominent in the closed-shell "magic" cluster Al66 [1, 2]. The characteristics of this behavior are fully consistent with a pairing transition, implying a high temperature superconducting state with Tc < 100K. This value exceeds that of bulk aluminum by two orders of magnitude. As a new class of high-temperature superconductors, such metal nanocluster particles are promising building blocks for high-Tc materials, devices, and networks. ---------- 1. Halder, A., Liang, A., Kresin, V. V. A novel feature in aluminum cluster photoionization spectra and possibility of electron pairing at T 100K. Nano Lett 15, 1410 - 1413 (2015) 2. Halder, A., Kresin, V. V. A transition in the density of states of metal "superatom" nanoclusters and evidence for superconducting pairing at T 100K. Phys. Rev. B 92, 214506 (2015).

Block clustering based on difference of convex functions (DC) programming and DC algorithms.

PubMed

Le, Hoai Minh; Le Thi, Hoai An; Dinh, Tao Pham; Huynh, Van Ngai

2013-10-01

We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.

Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

PubMed

Pasquier, M; Quek, C; Toh, M

2001-10-01

This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

PubMed Central

Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

2014-01-01

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

USSR and Eastern Europe Scientific Abstracts, Physics and Mathematics, Number 38

DTIC Science & Technology

1977-12-23

used to optimize the parameters of ultrashort pulse lasers , particularly in the single- pulse mode. Figures 1; references 5: 3 Russian, 2 Western. USSR...reflection of intense laser emission from dense clusters of relativistic electrons is severely re- stricted by fuzziness of the interface for real clusters ...The most widely used method of forming ultrashort pulses of elec- tromagnetic radiation at the present time is self-mode locking by means of

Studies of Copper, Silver, and Gold Cluster Anions: Evidence of Electronic Shell Structure.

NASA Astrophysics Data System (ADS)

Pettiette, Claire Lynn

A new Ultraviolet Magnetic Time-of-Flight Photoelectron Spectrometer (MTOFPES) has been developed for the study of the electronic structure of clusters produced in a pulsed supersonic molecular beam. This is the first technique which has been successful in probing the valence electronic states of metal clusters. The ultraviolet photoelectron spectra of negative cluster ions of the noble metals have been taken at several different photon energies. These are presented along with the electron affinity and HOMO-LUMO gap measurements for Cu_6^- to Cu_ {41}^-, using 4.66 eV and 6.42 eV detachment energies; Ag_3^- to Ag_{21}^-, using 6.42 eV detachment energy; and Au_3^ - to Au_{21}^-, using 6.42 eV and 7.89 eV detachment energies. The spectra provide the first detailed probes of the s valence electrons of the noble metal clusters. In addition, the 6.42 eV and 7.89 eV spectra probe the first one to two electron volts of the molecular orbitals of the d valence electrons of copper and gold clusters. The electron affinity and HOMO-LUMO gap measurements of the noble metal clusters agree with the predictions of the ellipsoidal shell model for mono-valent metal clusters. In particular, cluster numbers 8, 20, and 40--which correspond to the spherical shell closings of this model--have low electron affinities and large HOMO-LUMO gaps. The spectra of the gold cluster ions indicate that the molecular orbital energies of the cluster valence electrons are more widely spaced for gold than for copper or silver. This is to be expected for the heavy atom clusters when relativistic effects are taken into account.

Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches.

PubMed

Butaciu, Sinziana; Senila, Marin; Sarbu, Costel; Ponta, Michaela; Tanaselia, Claudiu; Cadar, Oana; Roman, Marius; Radu, Emil; Sima, Mihaela; Frentiu, Tiberiu

2017-04-01

The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na + , K + , Ca 2+ , Mg 2+ , HCO 3 - , Cl - , F - , SO 4 2- , PO 4 3- , NO 3 - ), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F - species and the anthropogenic one for NO 3 - , SO 4 2- , PO 4 3- and K + and (ii) classification of groundwater based on content of arsenic species. The HCO 3 - type of local groundwater and alkaline pH (8.31-8.49) were found to be responsible for the enrichment of arsenic species and occurrence of F - but by different paths. The PO 4 3- -AsO 4 3- ion exchange, water-rock interaction (silicates hydrolysis and desorption from clay) were associated to arsenate enrichment in the oxidizing aquifer. Fuzzy Hierarchical Cross-Clustering was the strongest tool for the rapid simultaneous classification of groundwaters as a function of arsenic content and hydrogeochemical characteristics. The approach indicated the Na + -F - -pH cluster as marker for groundwater with naturally elevated As and highlighted which parameters need to be monitored. A chemical conceptual model illustrating the natural and anthropogenic paths and enrichment of As and co-occurring species in the local groundwater supported by mineralogical analysis of rocks was established. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stabilizing ultrasmall Au clusters for enhanced photoredox catalysis.

PubMed

Weng, Bo; Lu, Kang-Qiang; Tang, Zichao; Chen, Hao Ming; Xu, Yi-Jun

2018-04-18

Recently, loading ligand-protected gold (Au) clusters as visible light photosensitizers onto various supports for photoredox catalysis has attracted considerable attention. However, the efficient control of long-term photostability of Au clusters on the metal-support interface remains challenging. Herein, we report a simple and efficient method for enhancing the photostability of glutathione-protected Au clusters (Au GSH clusters) loaded on the surface of SiO 2 sphere by utilizing multifunctional branched poly-ethylenimine (BPEI) as a surface charge modifying, reducing and stabilizing agent. The sequential coating of thickness controlled TiO 2 shells can further significantly improve the photocatalytic efficiency, while such structurally designed core-shell SiO 2 -Au GSH clusters-BPEI@TiO 2 composites maintain high photostability during longtime light illumination conditions. This joint strategy via interfacial modification and composition engineering provides a facile guideline for stabilizing ultrasmall Au clusters and rational design of Au clusters-based composites with improved activity toward targeting applications in photoredox catalysis.

Are Pericentric Inversions Reorganizing Wedge Shell Genomes?

PubMed Central

García-Souto, Daniel; Pérez-García, Concepción

2017-01-01

Wedge shells belonging to the Donacidae family are the dominant bivalves in exposed beaches in almost all areas of the world. Typically, two or more sympatric species of wedge shells differentially occupy intertidal, sublittoral, and offshore coastal waters in any given locality. A molecular cytogenetic analysis of two sympatric and closely related wedge shell species, Donax trunculus and Donax vittatus, was performed. Results showed that the karyotypes of these two species were both strikingly different and closely alike; whilst metacentric and submetacentric chromosome pairs were the main components of the karyotype of D. trunculus, 10–11 of the 19 chromosome pairs were telocentric in D. vittatus, most likely as a result of different pericentric inversions. GC-rich heterochromatic bands were present in both species. Furthermore, they showed coincidental 45S ribosomal RNA (rRNA), 5S rRNA and H3 histone gene clusters at conserved chromosomal locations, although D. trunculus had an additional 45S rDNA cluster. Intraspecific pericentric inversions were also detected in both D. trunculus and D. vittatus. The close genetic similarity of these two species together with the high degree of conservation of the 45S rRNA, 5S rRNA and H3 histone gene clusters, and GC-rich heterochromatic bands indicate that pericentric inversions contribute to the karyotype divergence in wedge shells. PMID:29215567

Fuzzy C-mean clustering on kinetic parameter estimation with generalized linear least square algorithm in SPECT

NASA Astrophysics Data System (ADS)

Choi, Hon-Chit; Wen, Lingfeng; Eberl, Stefan; Feng, Dagan

2006-03-01

Dynamic Single Photon Emission Computed Tomography (SPECT) has the potential to quantitatively estimate physiological parameters by fitting compartment models to the tracer kinetics. The generalized linear least square method (GLLS) is an efficient method to estimate unbiased kinetic parameters and parametric images. However, due to the low sensitivity of SPECT, noisy data can cause voxel-wise parameter estimation by GLLS to fail. Fuzzy C-Mean (FCM) clustering and modified FCM, which also utilizes information from the immediate neighboring voxels, are proposed to improve the voxel-wise parameter estimation of GLLS. Monte Carlo simulations were performed to generate dynamic SPECT data with different noise levels and processed by general and modified FCM clustering. Parametric images were estimated by Logan and Yokoi graphical analysis and GLLS. The influx rate (K I), volume of distribution (V d) were estimated for the cerebellum, thalamus and frontal cortex. Our results show that (1) FCM reduces the bias and improves the reliability of parameter estimates for noisy data, (2) GLLS provides estimates of micro parameters (K I-k 4) as well as macro parameters, such as volume of distribution (Vd) and binding potential (BP I & BP II) and (3) FCM clustering incorporating neighboring voxel information does not improve the parameter estimates, but improves noise in the parametric images. These findings indicated that it is desirable for pre-segmentation with traditional FCM clustering to generate voxel-wise parametric images with GLLS from dynamic SPECT data.

Rheological Characterization and Cluster Classification of Iranian Commercial Foods, Drinks and Desserts to Recommend for Esophageal Dysphagia Diets

PubMed Central

ZARGARAAN, Azizollaah; OMARAEE, Yasaman; RASTMANESH, Reza; TAHERI, Negin; FADAVI, Ghasem; FADAEI, Morteza; MOHAMMADIFAR, Mohammad Amin

2013-01-01

Abstract Background In the absence of dysphagia-oriented food products, rheological characterization of available food items is of importance for safe swallowing and adequate nutrient intake of dysphagic patients. In this way, introducing alternative items (with similar ease of swallow) is helpful to improve quality of life and nutritional intake of esophageal cancer dysphagia patients. The present study aimed at rheological characterization and cluster classification of potentially suitable foodstuffs marketed in Iran for their possible use in dysphagia diets. Methods In this descriptive study, rheological data were obtained during January and February 2012 in Rheology Lab of National Nutrition and Food Technology Research Institute Tehran, Iran. Steady state and oscillatory shear parameters of 39 commercial samples were obtained using a Physica MCR 301 rheometer (Anton-Paar, GmbH, Graz, Austria). Matlab Fuzzy Logic Toolbox (R2012 a) was utilized for cluster classification of the samples. Results Using an extended list of rheological parameters and fuzzy logic methods, 39 commercial samples (drinks, main courses and desserts) were divided to 5 clusters and degree of membership to each cluster was stated by a number between 0 and 0.99. Conclusion Considering apparent viscosity of foodstuffs as a single criterion for classification of dysphagia-oriented food products is shortcoming of current guidelines in dysphagia diets. Authors proposed to some revisions in classification of dysphagia-oriented food products and including more rheological parameters (especially, viscoelastic parameters) in the classification. PMID:26060647

Rheological Characterization and Cluster Classification of Iranian Commercial Foods, Drinks and Desserts to Recommend for Esophageal Dysphagia Diets.

PubMed

Zargaraan, Azizollaah; Omaraee, Yasaman; Rastmanesh, Reza; Taheri, Negin; Fadavi, Ghasem; Fadaei, Morteza; Mohammadifar, Mohammad Amin

2013-12-01

In the absence of dysphagia-oriented food products, rheological characterization of available food items is of importance for safe swallowing and adequate nutrient intake of dysphagic patients. In this way, introducing alternative items (with similar ease of swallow) is helpful to improve quality of life and nutritional intake of esophageal cancer dysphagia patients. The present study aimed at rheological characterization and cluster classification of potentially suitable foodstuffs marketed in Iran for their possible use in dysphagia diets. In this descriptive study, rheological data were obtained during January and February 2012 in Rheology Lab of National Nutrition and Food Technology Research Institute Tehran, Iran. Steady state and oscillatory shear parameters of 39 commercial samples were obtained using a Physica MCR 301 rheometer (Anton-Paar, GmbH, Graz, Austria). Matlab Fuzzy Logic Toolbox (R2012 a) was utilized for cluster classification of the samples. Using an extended list of rheological parameters and fuzzy logic methods, 39 commercial samples (drinks, main courses and desserts) were divided to 5 clusters and degree of membership to each cluster was stated by a number between 0 and 0.99. Considering apparent viscosity of foodstuffs as a single criterion for classification of dysphagia-oriented food products is shortcoming of current guidelines in dysphagia diets. Authors proposed to some revisions in classification of dysphagia-oriented food products and including more rheological parameters (especially, viscoelastic parameters) in the classification.

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

HOW SIGNIFICANT IS RADIATION PRESSURE IN THE DYNAMICS OF THE GAS AROUND YOUNG STELLAR CLUSTERS?

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

Silich, Sergiy; Tenorio-Tagle, Guillermo, E-mail: silich@inaoep.mx

2013-03-01

The impact of radiation pressure on the dynamics of the gas in the vicinity of young stellar clusters is thoroughly discussed. The radiation over the thermal/ram pressure ratio time evolution is calculated explicitly and the crucial roles of the cluster mechanical power, the strong time evolution of the ionizing photon flux, and the bolometric luminosity of the exciting cluster are stressed. It is shown that radiation has only a narrow window of opportunity to dominate the wind-driven shell dynamics. This may occur only at early stages of the bubble evolution and if the shell expands into a dusty and/or amore » very dense proto-cluster medium. The impact of radiation pressure on the wind-driven shell always becomes negligible after about 3 Myr. Finally, the wind-driven model results allow one to compare the model predictions with the distribution of thermal pressure derived from X-ray observations. The shape of the thermal pressure profile then allows us to distinguish between the energy and the momentum-dominated regimes of expansion and thus conclude whether radiative losses of energy or the leakage of hot gas from the bubble interior have been significant during bubble evolution.« less

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

PubMed Central

Li, Zhao-Liang

2018-01-01

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. PMID:29373548

A new method for generating an invariant iris private key based on the fuzzy vault system.

PubMed

Lee, Youn Joo; Park, Kang Ryoung; Lee, Sung Joo; Bae, Kwanghyuk; Kim, Jaihie

2008-10-01

Cryptographic systems have been widely used in many information security applications. One main challenge that these systems have faced has been how to protect private keys from attackers. Recently, biometric cryptosystems have been introduced as a reliable way of concealing private keys by using biometric data. A fuzzy vault refers to a biometric cryptosystem that can be used to effectively protect private keys and to release them only when legitimate users enter their biometric data. In biometric systems, a critical problem is storing biometric templates in a database. However, fuzzy vault systems do not need to directly store these templates since they are combined with private keys by using cryptography. Previous fuzzy vault systems were designed by using fingerprint, face, and so on. However, there has been no attempt to implement a fuzzy vault system that used an iris. In biometric applications, it is widely known that an iris can discriminate between persons better than other biometric modalities. In this paper, we propose a reliable fuzzy vault system based on local iris features. We extracted multiple iris features from multiple local regions in a given iris image, and the exact values of the unordered set were then produced using the clustering method. To align the iris templates with the new input iris data, a shift-matching technique was applied. Experimental results showed that 128-bit private keys were securely and robustly generated by using any given iris data without requiring prealignment.

Parallel fuzzy connected image segmentation on GPU

PubMed Central

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

2011-01-01

Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037

Parallel fuzzy connected image segmentation on GPU.

PubMed

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

2011-07-01

Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

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

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

NASA Astrophysics Data System (ADS)

Velthuizen, Robert P.; Hall, Lawrence O.; Clarke, Laurence P.; Bensaid, Amine M.; Arrington, J. A.; Silbiger, Martin L.

1993-07-01

Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.

Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images.

PubMed

Ngan, Tran Thi; Tuan, Tran Manh; Son, Le Hoang; Minh, Nguyen Hai; Dey, Nilanjan

2016-12-01

Medical diagnosis is considered as an important step in dentistry treatment which assists clinicians to give their decision about diseases of a patient. It has been affirmed that the accuracy of medical diagnosis, which is much influenced by the clinicians' experience and knowledge, plays an important role to effective treatment therapies. In this paper, we propose a novel decision making method based on fuzzy aggregation operators for medical diagnosis from dental X-Ray images. It firstly divides a dental X-Ray image into some segments and identified equivalent diseases by a classification method called Affinity Propagation Clustering (APC+). Lastly, the most potential disease is found using fuzzy aggregation operators. The experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of accuracy.

Ab initio Bogoliubov coupled cluster theory for open-shell nuclei

DOE PAGES

Signoracci, Angelo J.; Duguet, Thomas; Hagen, Gaute; ...

2015-06-29

Background: Ab initio many-body methods have been developed over the past 10 yr to address closed-shell nuclei up to mass A≈130 on the basis of realistic two- and three-nucleon interactions. A current frontier relates to the extension of those many-body methods to the description of open-shell nuclei. Several routes to address open-shell nuclei are currently under investigation, including ideas that exploit spontaneous symmetry breaking. Purpose: Singly open-shell nuclei can be efficiently described via the sole breaking of U(1) gauge symmetry associated with particle-number conservation as a way to account for their superfluid character. While this route was recently followed withinmore » the framework of self-consistent Green's function theory, the goal of the present work is to formulate a similar extension within the framework of coupled cluster theory. Methods: We formulate and apply Bogoliubov coupled cluster (BCC) theory, which consists of representing the exact ground-state wave function of the system as the exponential of a quasiparticle excitation cluster operator acting on a Bogoliubov reference state. Equations for the ground-state energy and the cluster amplitudes are derived at the singles and doubles level (BCCSD) both algebraically and diagrammatically. The formalism includes three-nucleon forces at the normal-ordered two-body level. The first BCC code is implemented in m scheme, which will permit the treatment of doubly open-shell nuclei via the further breaking of SU(2) symmetry associated with angular momentum conservation. Results: Proof-of-principle calculations in an N max=6 spherical harmonic oscillator basis for 16,18O and 18Ne in the BCCD approximation are in good agreement with standard coupled cluster results with the same chiral two-nucleon interaction, while 20O and 20Mg display underbinding relative to experiment. The breaking of U(1) symmetry, monitored by computing the variance associated with the particle-number operator, is relatively constant for all five nuclei, in both the Hartree-Fock-Bogoliubov and BCCD approximations. Conclusions: The newly developed many-body formalism increases the potential span of ab initio calculations based on single-reference coupled cluster techniques tremendously, i.e., potentially to reach several hundred additional midmass nuclei. The new formalism offers a wealth of potential applications and further extensions dedicated to the description of ground and excited states of open-shell nuclei. Short-term goals include the implementation of three-nucleon forces at the normal-ordered two-body level. Midterm extensions include the approximate treatment of triples corrections and the development of the equation-of-motion methodology to treat both excited states and odd nuclei. Long-term extensions include exact restoration of U(1) and SU(2) symmetries.« less

Fabrication of Silica-Coated Hollow Carbon Nanospheres Encapsulating Fe3O4 Cluster for Magnetical and MR Imaging Guided NIR Light Triggering Hyperthermia and Ultrasound Imaging.

PubMed

Huang, Yun-Kai; Su, Chia-Hao; Chen, Jiu-Jeng; Chang, Chun-Ting; Tsai, Yu-Hsin; Syu, Sheng-Fu; Tseng, Tsu-Ting; Yeh, Chen-Sheng

2016-06-15

Iron oxide nanoparticles (IONPs)-carbon (C) hybrid zero-dimensional nanostructures normally can be categorized into core-shell and yolk-shell architectures. Although IONP-C is a promising theranostic nanoagent, the in vivo study has surprisingly been less described. In addition, little effort has strived toward the fabrication of yolk-shell compared to the core-shell structures. In this context, we synthesized a yolk-shell type of the silica-coated hollow carbon nanospheres encapsulating IONPs cluster, which can be dispersed in aqueous solution for systemic studies in vivo, via the preparation involving the mixed micellization, polymerization/hollowing, sol-gel (hydration-condensation), and pyrolysis processes. Through a surface modification of the polyethylenimine followed by the sol-gel process, the silica shell coating was able to escape from condensing and sintering courses resulting in aggregation, due to the annealing. Not limited to the well-known functionalities in magnetical targeting and magnetic resonance (MR) imaging for IONP-C hybrid structures, we expanded this yolk-shell NPs as a near-infrared (NIR) light-responsive echogenic nanoagent giving an enhanced ultrasound imaging. Overall, we fabricated the NIR sensitive yolk-shell IONP-C to activate ultrasound imaging and photothermal ablation under magnetically and MR imaging guided therapy.

Effect of Deep Cryogenic treatment on AISI A8 Tool steel & Development of Wear Mechanism maps using Fuzzy Clustering

NASA Astrophysics Data System (ADS)

Pillai, Nandakumar; Karthikeyan, R., Dr.

2018-04-01

Tool steels are widely classified according to their constituents and type of thermal treatments carried out to obtain its properties. Viking a special purpose tool steel coming under AISI A8 cold working steel classification is widely used for heavy duty blanking and forming operations. The optimum combination of wear resistance and toughness as well as ease of machinability in pre-treated condition makes this material accepted in heavy cutting and non cutting tool manufacture. Air or vacuum hardening is recommended as the normal treatment procedure to obtain the desired mechanical and tribological properties for steels under this category. In this study, we are incorporating a deep cryogenic phase within the conventional treatment cycle both before and after tempering. The thermal treatments at sub zero temperatures up to -195°C using cryogenic chamber with liquid nitrogen as medium was conducted. Micro structural changes in its microstructure and the corresponding improvement in the tribological and physical properties are analyzed. The cryogenic treatment leads to more conversion of retained austenite to martensite and also formation of fine secondary carbides. The microstructure is studied using the micrographs taken using optical microscopy. The wear tests are conducted on DUCOM tribometer for different combinations of speed and load under normal temperature. The wear rates and coefficient of friction obtained from these experiments are used to developed wear mechanism maps with the help of fuzzy c means clustering and probabilistic neural network models. Fuzzy C means clustering is an effective algorithm to group data of similar patterns. The wear mechanisms obtained from the computationally developed maps are then compared with the SEM photographs taken and the improvement in properties due to this additional cryogenic treatment is validated.

Fast detection of the fuzzy communities based on leader-driven algorithm

NASA Astrophysics Data System (ADS)

Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

2018-03-01

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data

NASA Astrophysics Data System (ADS)

Husein, A. M.; Harahap, M.; Aisyah, S.; Purba, W.; Muhazir, A.

2018-03-01

Medication planning aim to get types, amount of medicine according to needs, and avoid the emptiness medicine based on patterns of disease. In making the medicine planning is still rely on ability and leadership experience, this is due to take a long time, skill, difficult to obtain a definite disease data, need a good record keeping and reporting, and the dependence of the budget resulted in planning is not going well, and lead to frequent lack and excess of medicines. In this research, we propose Adaptive Neuro Fuzzy Inference System (ANFIS) method to predict medication needs in 2016 and 2017 based on medical data in 2015 and 2016 from two source of hospital. The framework of analysis using two approaches. The first phase is implementing ANFIS to a data source, while the second approach we keep using ANFIS, but after the process of clustering from K-Means algorithm, both approaches are calculated values of Root Mean Square Error (RMSE) for training and testing. From the testing result, the proposed method with better prediction rates based on the evaluation analysis of quantitative and qualitative compared with existing systems, however the implementation of K-Means Algorithm against ANFIS have an effect on the timing of the training process and provide a classification accuracy significantly better without clustering.

Pandora Cluster Seen by Spitzer

NASA Image and Video Library

2016-09-28

This image of galaxy cluster Abell 2744, also called Pandora's Cluster, was taken by the Spitzer Space Telescope. The gravity of this galaxy cluster is strong enough that it acts as a lens to magnify images of more distant background galaxies. This technique is called gravitational lensing. The fuzzy blobs in this Spitzer image are the massive galaxies at the core of this cluster, but astronomers will be poring over the images in search of the faint streaks of light created where the cluster magnifies a distant background galaxy. The cluster is also being studied by NASA's Hubble Space Telescope and Chandra X-Ray Observatory in a collaboration called the Frontier Fields project. In this image, light from Spitzer's infrared channels is colored blue at 3.6 microns and green at 4.5 microns. http://photojournal.jpl.nasa.gov/catalog/PIA20920

A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION

EPA Science Inventory

A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with ...

Multirate parallel distributed compensation of a cluster in wireless sensor and actor networks

NASA Astrophysics Data System (ADS)

Yang, Chun-xi; Huang, Ling-yun; Zhang, Hao; Hua, Wang

2016-01-01

The stabilisation problem for one of the clusters with bounded multiple random time delays and packet dropouts in wireless sensor and actor networks is investigated in this paper. A new multirate switching model is constructed to describe the feature of this single input multiple output linear system. According to the difficulty of controller design under multi-constraints in multirate switching model, this model can be converted to a Takagi-Sugeno fuzzy model. By designing a multirate parallel distributed compensation, a sufficient condition is established to ensure this closed-loop fuzzy control system to be globally exponentially stable. The solution of the multirate parallel distributed compensation gains can be obtained by solving an auxiliary convex optimisation problem. Finally, two numerical examples are given to show, compared with solving switching controller, multirate parallel distributed compensation can be obtained easily. Furthermore, it has stronger robust stability than arbitrary switching controller and single-rate parallel distributed compensation under the same conditions.

An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin

2018-04-01

Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function.

PubMed

Yin, Jiandong; Sun, Hongzan; Yang, Jiawen; Guo, Qiyong

2014-01-01

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection.

Comparison of K-Means and Fuzzy c-Means Algorithm Performance for Automated Determination of the Arterial Input Function

PubMed Central

Yin, Jiandong; Sun, Hongzan; Yang, Jiawen; Guo, Qiyong

2014-01-01

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection. PMID:24503700

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

PubMed

Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

PubMed Central

Yang, Zhang; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

Efficient red luminescence from organic-soluble Au25 clusters by ligand structure modification

NASA Astrophysics Data System (ADS)

Mathew, Ammu; Varghese, Elizabeth; Choudhury, Susobhan; Pal, Samir Kumar; Pradeep, T.

2015-08-01

An efficient method to enhance visible luminescence in a visibly non-luminescent organic-soluble 4-(tert butyl)benzyl mercaptan (SBB)-stabilized Au25 cluster has been developed. This method relies mainly on enhancing the surface charge density on the cluster by creating an additional shell of thiolate on the cluster surface, which enhances visible luminescence. The viability of this method has been demonstrated by imparting red luminescence to various ligand-protected quantum clusters (QCs), observable to the naked eye. The bright red luminescent material derived from Au25SBB18 clusters was characterized using UV-vis and luminescence spectroscopy, TEM, SEM/EDS, XPS, TG, ESI and MALDI mass spectrometry, which collectively proposed an uncommon molecular formula of Au29SBB24S, suggested to be due to different stapler motifs protecting the Au25 core. The critical role of temperature on the emergence of luminescence in QCs has been studied. The restoration of the surface ligand shell on the Au25 cluster and subsequent physicochemical modification to the cluster were probed by various mass spectral and spectroscopic techniques. Our results provide fundamental insights into the ligand characteristics determining luminescence in QCs.An efficient method to enhance visible luminescence in a visibly non-luminescent organic-soluble 4-(tert butyl)benzyl mercaptan (SBB)-stabilized Au25 cluster has been developed. This method relies mainly on enhancing the surface charge density on the cluster by creating an additional shell of thiolate on the cluster surface, which enhances visible luminescence. The viability of this method has been demonstrated by imparting red luminescence to various ligand-protected quantum clusters (QCs), observable to the naked eye. The bright red luminescent material derived from Au25SBB18 clusters was characterized using UV-vis and luminescence spectroscopy, TEM, SEM/EDS, XPS, TG, ESI and MALDI mass spectrometry, which collectively proposed an uncommon molecular formula of Au29SBB24S, suggested to be due to different stapler motifs protecting the Au25 core. The critical role of temperature on the emergence of luminescence in QCs has been studied. The restoration of the surface ligand shell on the Au25 cluster and subsequent physicochemical modification to the cluster were probed by various mass spectral and spectroscopic techniques. Our results provide fundamental insights into the ligand characteristics determining luminescence in QCs. Electronic supplementary information (ESI) available: Additional data on characterization of red luminescent Au29 QC and comparison with parent Au25SBB18 are given. See DOI: 10.1039/c5nr03457d

A Weight-Adaptive Laplacian Embedding for Graph-Based Clustering.

PubMed

Cheng, De; Nie, Feiping; Sun, Jiande; Gong, Yihong

2017-07-01

Graph-based clustering methods perform clustering on a fixed input data graph. Thus such clustering results are sensitive to the particular graph construction. If this initial construction is of low quality, the resulting clustering may also be of low quality. We address this drawback by allowing the data graph itself to be adaptively adjusted in the clustering procedure. In particular, our proposed weight adaptive Laplacian (WAL) method learns a new data similarity matrix that can adaptively adjust the initial graph according to the similarity weight in the input data graph. We develop three versions of these methods based on the L2-norm, fuzzy entropy regularizer, and another exponential-based weight strategy, that yield three new graph-based clustering objectives. We derive optimization algorithms to solve these objectives. Experimental results on synthetic data sets and real-world benchmark data sets exhibit the effectiveness of these new graph-based clustering methods.

Possibility of Exciton Mediated Superconductivity in Nano-Sized Sn/Si Core-Shell Clusters: A Process Technology towards Heterogeneous Material in Nano-Scale

NASA Astrophysics Data System (ADS)

Kurokawa, Yuichiro; Hihara, Takehiko; Ichinose, Ikuo; Sumiyama, Kenji

2012-07-01

We have produced Sn/Si core-shell cluster assemblies by a plasma-gas-condensation cluster beam deposition apparatus. For the sample with Si content = 12 at. %, the temperature dependence of electrical resistivity exhibits a metallic behavior above 10 K and the onset of superconducting transition below 6.1 K. With decreasing temperature, the thermomagnetic curve for the sample with Si content = 8 at. % begins to decrease steadily toward negative value below 7.7 K, indicating the Meissner effect. An increase in the transition temperature, TC is attributable to exciton-type superconductivity.

Nanoscale alloys and core-shell materials: Model predictions of the nanostructure and mechanical properties

NASA Astrophysics Data System (ADS)

Zhurkin, E. E.; van Hoof, T.; Hou, M.

2007-06-01

Atomic scale modeling methods are used to investigate the relationship between the properties of clusters of nanometer size and the materials that can be synthesized by assembling them. The examples of very different bimetallic systems are used. The first one is the Ni3Al ordered alloy and the second is the AgCo core-shell system. While the Ni3Al cluster assembled materials modeling is already reported in our previous work, here we focus on the prediction of new materials synthesized by low energy deposition and accumulation of AgCo clusters. It is found that the core-shell structure is preserved by deposition with energies typical of low energy cluster beam deposition, although deposition may induce substantial cluster deformation. In contrast with Ni3Al deposited cluster assemblies, no grain boundary between clusters survives deposition and the silver shells merge into a noncrystalline system with a layered structure, in which the fcc Co grains are embedded. To our knowledge, such a material has not yet been synthesized experimentally. Mechanical properties are discussed by confronting the behaviors of Ni3Al and AgCo under the effect of a uniaxial load. To this end, a molecular dynamics scheme is established in view of circumventing rate effects inherent to short term modeling and thereby allowing to examine large plastic deformation mechanisms. Although the mechanisms are different, large plastic deformations are found to improve the elastic properties of both the Ni3Al and AgCo systems by stabilizing their nanostructure. Beyond this improvement, when the load is further increased, the Ni3Al system displays reduced ductility while the AgCo system is superplastic. The superplasticity is explained by the fact that the layered structure of the Ag system is not modified by the deformation. Some coalescence of the Co grains is identified as a geometrical effect and is suggested to be a limiting factor to superplasticity.

Strong Lensing Analysis of the Galaxy Cluster MACS J1319.9+7003 and the Discovery of a Shell Galaxy

NASA Astrophysics Data System (ADS)

Zitrin, Adi

2017-01-01

We present a strong-lensing (SL) analysis of the galaxy cluster MACS J1319.9+7003 (z = 0.33, also known as Abell 1722), as part of our ongoing effort to analyze massive clusters with archival Hubble Space Telescope (HST) imaging. We spectroscopically measured with Keck/Multi-Object Spectrometer For Infra-Red Exploration (MOSFIRE) two galaxies multiply imaged by the cluster. Our analysis reveals a modest lens, with an effective Einstein radius of {θ }e(z=2)=12+/- 1\\prime\\prime , enclosing 2.1+/- 0.3× {10}13 M⊙. We briefly discuss the SL properties of the cluster, using two different modeling techniques (see the text for details), and make the mass models publicly available (ftp://wise-ftp.tau.ac.il/pub/adiz/MACS1319/). Independently, we identified a noteworthy, young shell galaxy (SG) system forming around two likely interacting cluster members, 20″ north of the brightest cluster galaxy. SGs are rare in galaxy clusters, and indeed, a simple estimate reveals that they are only expected in roughly one in several dozen, to several hundred, massive galaxy clusters (the estimate can easily change by an order of magnitude within a reasonable range of characteristic values relevant for the calculation). Taking advantage of our lens model best-fit, mass-to-light scaling relation for cluster members, we infer that the total mass of the SG system is ˜ 1.3× {10}11 {M}⊙ , with a host-to-companion mass ratio of about 10:1. Despite being rare in high density environments, the SG constitutes an example to how stars of cluster galaxies are efficiently redistributed to the intra-cluster medium. Dedicated numerical simulations for the observed shell configuration, perhaps aided by the mass model, might cast interesting light on the interaction history and properties of the two galaxies. An archival HST search in galaxy cluster images can reveal more such systems.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

Electronic shell structure in Ga12 icosahedra and the relation to the bulk forms of gallium.

PubMed

Schebarchov, D; Gaston, N

2012-07-28

The electronic structure of known cluster compounds with a cage-like icosahedral Ga(12) centre is studied by first-principles theoretical methods, based on density functional theory. We consider these hollow metalloid nanostructures in the context of the polymorphism of the bulk, and identify a close relation to the α phase of gallium. This previously unrecognised connection is established using the electron localisation function, which reveals the ubiquitous presence of radially-pointing covalent bonds around the Ga(12) centre--analogous to the covalent bonds between buckled deltahedral planes in α-Ga. Furthermore, we find prominent superatom shell structure in these clusters, despite their hollow icosahedral motif and the presence of covalent bonds. The exact nature of the electronic shell structure is contrasted with simple electron shell models based on jellium, and we demonstrate how the interplay between gallium dimerisation, ligand- and crystal-field effects can alter the splitting of the partially filled 1F shell. Finally, in the unique compound where the Ga(12) centre is bridged by six phosphorus ligands, the electronic structure most closely resembles that of δ-Ga and there are no well-defined superatom orbitals. The results of this comprehensive study bring new insights into the nature of chemical bonding in metalloid gallium compounds and the relation to bulk gallium metal, and they may also guide the development of more general models for ligand-protected clusters.

Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory.

PubMed

Qin, Jiahu; Fu, Weiming; Gao, Huijun; Zheng, Wei Xing

2016-03-03

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

Fast parallel algorithms that compute transitive closure of a fuzzy relation

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik YA.

1993-01-01

The notion of a transitive closure of a fuzzy relation is very useful for clustering in pattern recognition, for fuzzy databases, etc. The original algorithm proposed by L. Zadeh (1971) requires the computation time O(n(sup 4)), where n is the number of elements in the relation. In 1974, J. C. Dunn proposed a O(n(sup 2)) algorithm. Since we must compute n(n-1)/2 different values s(a, b) (a not equal to b) that represent the fuzzy relation, and we need at least one computational step to compute each of these values, we cannot compute all of them in less than O(n(sup 2)) steps. So, Dunn's algorithm is in this sense optimal. For small n, it is ok. However, for big n (e.g., for big databases), it is still a lot, so it would be desirable to decrease the computation time (this problem was formulated by J. Bezdek). Since this decrease cannot be done on a sequential computer, the only way to do it is to use a computer with several processors working in parallel. We show that on a parallel computer, transitive closure can be computed in time O((log(sub 2)(n))2).

Enhancing dissolved oxygen control using an on-line hybrid fuzzy-neural soft-sensing model-based control system in an anaerobic/anoxic/oxic process.

PubMed

Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Ma, Yongwen; Wang, Yan

2013-12-01

An on-line hybrid fuzzy-neural soft-sensing model-based control system was developed to optimize dissolved oxygen concentration in a bench-scale anaerobic/anoxic/oxic (A(2)/O) process. In order to improve the performance of the control system, a self-adapted fuzzy c-means clustering algorithm and adaptive network-based fuzzy inference system (ANFIS) models were employed. The proposed control system permits the on-line implementation of every operating strategy of the experimental system. A set of experiments involving variable hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO), and mixed-liquid return ratio (r) was carried out. Using the proposed system, the amount of COD in the effluent stabilized at the set-point and below. The improvement was achieved with optimum dissolved oxygen concentration because the performance of the treatment process was optimized using operating rules implemented in real time. The system allows various expert operational approaches to be deployed with the goal of minimizing organic substances in the outlet while using the minimum amount of energy.

Fuzzy rule-based forecast of meteorological drought in western Niger

NASA Astrophysics Data System (ADS)

Abdourahamane, Zakari Seybou; Acar, Reşat

2018-01-01

Understanding the causes of rainfall anomalies in the West African Sahel to effectively predict drought events remains a challenge. The physical mechanisms that influence precipitation in this region are complex, uncertain, and imprecise in nature. Fuzzy logic techniques are renowned to be highly efficient in modeling such dynamics. This paper attempts to forecast meteorological drought in Western Niger using fuzzy rule-based modeling techniques. The 3-month scale standardized precipitation index (SPI-3) of four rainfall stations was used as predictand. Monthly data of southern oscillation index (SOI), South Atlantic sea surface temperature (SST), relative humidity (RH), and Atlantic sea level pressure (SLP), sourced from the National Oceanic and Atmosphere Administration (NOAA), were used as predictors. Fuzzy rules and membership functions were generated using fuzzy c-means clustering approach, expert decision, and literature review. For a minimum lead time of 1 month, the model has a coefficient of determination R 2 between 0.80 and 0.88, mean square error (MSE) below 0.17, and Nash-Sutcliffe efficiency (NSE) ranging between 0.79 and 0.87. The empirical frequency distributions of the predicted and the observed drought classes are equal at the 99% of confidence level based on two-sample t test. Results also revealed the discrepancy in the influence of SOI and SLP on drought occurrence at the four stations while the effect of SST and RH are space independent, being both significantly correlated (at α < 0.05 level) to the SPI-3. Moreover, the implemented fuzzy model compared to decision tree-based forecast model shows better forecast skills.

Hidden Charge States in Soft-X-Ray Laser-Produced Nanoplasmas Revealed by Fluorescence Spectroscopy

NASA Astrophysics Data System (ADS)

Schroedter, L.; Müller, M.; Kickermann, A.; Przystawik, A.; Toleikis, S.; Adolph, M.; Flückiger, L.; Gorkhover, T.; Nösel, L.; Krikunova, M.; Oelze, T.; Ovcharenko, Y.; Rupp, D.; Sauppe, M.; Wolter, D.; Schorb, S.; Bostedt, C.; Möller, T.; Laarmann, T.

2014-05-01

Highly charged ions are formed in the center of composite clusters by strong free-electron laser pulses and they emit fluorescence on a femtosecond time scale before competing recombination leads to neutralization of the nanoplasma core. In contrast to mass spectrometry that detects remnants of the interaction, fluorescence in the extreme ultraviolet spectral range provides fingerprints of transient states of high energy density matter. Spectra from clusters consisting of a xenon core and a surrounding argon shell show that a small fraction of the fluorescence signal comes from multiply charged xenon ions in the cluster core. Initially, these ions are as highly charged as the ions in the outer shells of pure xenon clusters with charge states up to at least 11+.

Dynamic stabilities of icosahedral-like clusters and their ability to form quasicrystals

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

Liang, Xiaogang; Hamid, Ilyar; Duan, Haiming, E-mail: dhm@xju.edu.cn

2016-06-15

The dynamic stabilities of the icosahedral-like clusters containing up to 2200 atoms are investigated for 15 metal elements. The clusters originate from five different initial structures (icosahedron, truncated decahedron, octahedron, closed-shell fragment of an HCP structure, and non-closed-shell fragment of an HCP structure). The obtained order of the dynamic stabilities of the icosahedral-like clusters can be assigned to three groups, from stronger to weaker, according to the size ranges involved: (Zr, Al, Ti) > (Cu, Fe, Co, Ni, Mg, Ag) > (Pb, Au, Pd, Pt, Rh, Ir), which correspond to the predicted formation ability of the quasicrystals. The differences ofmore » the sequences can be explained by analyzing the parameters of the Gupta-type many-body inter-atomic potentials.« less

Two-dimensional and three-dimensional Coulomb clusters in parabolic traps

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

D'yachkov, L. G., E-mail: dyachk@mail.ru; Myasnikov, M. I., E-mail: miasnikovmi@mail.ru; Petrov, O. F.

2014-09-15

We consider the shell structure of Coulomb clusters in an axially symmetric parabolic trap exhibiting a confining potential U{sub c}(ρ,z)=(mω{sup 2}/2)(ρ{sup 2}+αz{sup 2}). Assuming an anisotropic parameter α = 4 (corresponding to experiments employing a cusp magnetic trap under microgravity conditions), we have calculated cluster configurations for particle numbers N = 3 to 30. We have shown that clusters with N ≤ 12 initially remain flat, transitioning to three-dimensional configurations as N increases. For N = 8, we have calculated the configurations of minimal potential energy for all values of α and found the points of configuration transitions. For N = 13 and 23, we discuss the influence of bothmore » the shielding and anisotropic parameter on potential energy, cluster size, and shell structure.« less

Hund’s rule in superatoms with transition metal impurities

PubMed Central

Medel, Victor M.; Reveles, Jose Ulises; Khanna, Shiv N.; Chauhan, Vikas; Sen, Prasenjit; Castleman, A. Welford

2011-01-01

The quantum states in metal clusters bunch into supershells with associated orbitals having shapes resembling those in atoms, giving rise to the concept that selected clusters could mimic the characteristics of atoms and be classified as superatoms. Unlike atoms, the superatom orbitals span over multiple atoms and the filling of orbitals does not usually exhibit Hund’s rule seen in atoms. Here, we demonstrate the possibility of enhancing exchange splitting in superatom shells via a composite cluster of a central transition metal and surrounding nearly free electron metal atoms. The transition metal d states hybridize with superatom D states and result in enhanced splitting between the majority and minority sets where the moment and the splitting can be controlled by the nature of the central atom. We demonstrate these findings through studies on TMMgn clusters where TM is a 3d atom. The clusters exhibit Hund’s filling, opening the pathway to superatoms with magnetic shells. PMID:21646542

Hund's rule in superatoms with transition metal impurities.

PubMed

Medel, Victor M; Reveles, Jose Ulises; Khanna, Shiv N; Chauhan, Vikas; Sen, Prasenjit; Castleman, A Welford

2011-06-21

The quantum states in metal clusters bunch into supershells with associated orbitals having shapes resembling those in atoms, giving rise to the concept that selected clusters could mimic the characteristics of atoms and be classified as superatoms. Unlike atoms, the superatom orbitals span over multiple atoms and the filling of orbitals does not usually exhibit Hund's rule seen in atoms. Here, we demonstrate the possibility of enhancing exchange splitting in superatom shells via a composite cluster of a central transition metal and surrounding nearly free electron metal atoms. The transition metal d states hybridize with superatom D states and result in enhanced splitting between the majority and minority sets where the moment and the splitting can be controlled by the nature of the central atom. We demonstrate these findings through studies on TMMg(n) clusters where TM is a 3d atom. The clusters exhibit Hund's filling, opening the pathway to superatoms with magnetic shells.

Untangling Magmatic Processes and Hydrothermal Alteration of in situ Superfast Spreading Ocean Crust at ODP/IODP Site 1256 with Fuzzy c-means Cluster Analysis of Rock Magnetic Properties

NASA Astrophysics Data System (ADS)

Dekkers, M. J.; Heslop, D.; Herrero-Bervera, E.; Acton, G.; Krasa, D.

2014-12-01

Ocean Drilling Program (ODP)/Integrated ODP (IODP) Hole 1256D (6.44.1' N, 91.56.1' W) on the Cocos Plate occurs in 15.2 Ma oceanic crust generated by superfast seafloor spreading. Presently, it is the only drill hole that has sampled all three oceanic crust layers in a tectonically undisturbed setting. Here we interpret down-hole trends in several rock-magnetic parameters with fuzzy c-means cluster analysis, a multivariate statistical technique. The parameters include the magnetization ratio, the coercivity ratio, the coercive force, the low-field susceptibility, and the Curie temperature. By their combined, multivariate, analysis the effects of magmatic and hydrothermal processes can be evaluated. The optimal number of clusters - a key point in the analysis because there is no a priori information on this - was determined through a combination of approaches: by calculation of several cluster validity indices, by testing for coherent cluster distributions on non-linear-map plots, and importantly by testing for stability of the cluster solution from all possible starting points. Here, we consider a solution robust if the cluster allocation is independent of the starting configuration. The five-cluster solution appeared to be robust. Three clusters are distinguished in the extrusive segment of the Hole that express increasing hydrothermal alteration of the lavas. The sheeted dike and gabbro portions are characterized by two clusters, both with higher coercivities than in lava samples. Extensive alteration, however, can obliterate magnetic property differences between lavas, dikes, and gabbros. The imprint of thermochemical alteration on the iron-titanium oxides is only partially related to the porosity of the rocks. All clusters display rock magnetic characteristics in line with a stable NRM. This implies that the entire sampled sequence of ocean crust can contribute to marine magnetic anomalies. Determination of the absolute paleointensity with thermal techniques is not straightforward because of the propensity of oxyexsolution during laboratory heating and/or the presence of intergrowths. The upper part of the extrusive sequence, the granoblastic portion of the dikes, and moderately altered gabbros may contain a comparatively uncontaminated thermoremanent magnetization.

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods.

PubMed

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2010-07-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

Composition Formulas of Inorganic Compounds in Terms of Cluster Plus Glue Atom Model.

PubMed

Ma, Yanping; Dong, Dandan; Wu, Aimin; Dong, Chuang

2018-01-16

The present paper attempts to identify the molecule-like structural units in inorganic compounds, by applying the so-called "cluster plus glue atom model". This model, originating from metallic glasses and quasi-crystals, describes any structure in terms of a nearest-neighbor cluster and a few outer-shell glue atoms, expressed in the cluster formula [cluster](glue atoms). Similar to the case for normal molecules where the charge transfer occurs within the molecule to meet the commonly known octet electron rule, the octet state is reached after matching the nearest-neighbor cluster with certain outer-shell glue atoms. These kinds of structural units contain information on local atomic configuration, chemical composition, and electron numbers, just as for normal molecules. It is shown that the formulas of typical inorganic compounds, such as fluorides, oxides, and nitrides, satisfy a similar octet electron rule, with the total number of valence electrons per unit formula being multiples of eight.

Explicitly Representing the Solvation Shell in Continuum Solvent Calculations

PubMed Central

Svendsen, Hallvard F.; Merz, Kenneth M.

2009-01-01

A method is presented to explicitly represent the first solvation shell in continuum solvation calculations. Initial solvation shell geometries were generated with classical molecular dynamics simulations. Clusters consisting of solute and 5 solvent molecules were fully relaxed in quantum mechanical calculations. The free energy of solvation of the solute was calculated from the free energy of formation of the cluster and the solvation free energy of the cluster calculated with continuum solvation models. The method has been implemented with two continuum solvation models, a Poisson-Boltzmann model and the IEF-PCM model. Calculations were carried out for a set of 60 ionic species. Implemented with the Poisson-Boltzmann model the method gave an unsigned average error of 2.1 kcal/mol and a RMSD of 2.6 kcal/mol for anions, for cations the unsigned average error was 2.8 kcal/mol and the RMSD 3.9 kcal/mol. Similar results were obtained with the IEF-PCM model. PMID:19425558

Single-step generation of metal-plasma polymer multicore@shell nanoparticles from the gas phase.

PubMed

Solař, Pavel; Polonskyi, Oleksandr; Olbricht, Ansgar; Hinz, Alexander; Shelemin, Artem; Kylián, Ondřej; Choukourov, Andrei; Faupel, Franz; Biederman, Hynek

2017-08-17

Nanoparticles composed of multiple silver cores and a plasma polymer shell (multicore@shell) were prepared in a single step with a gas aggregation cluster source operating with Ar/hexamethyldisiloxane mixtures and optionally oxygen. The size distribution of the metal inclusions as well as the chemical composition and the thickness of the shells were found to be controlled by the composition of the working gas mixture. Shell matrices ranging from organosilicon plasma polymer to nearly stoichiometric SiO 2 were obtained. The method allows facile fabrication of multicore@shell nanoparticles with tailored functional properties, as demonstrated here with the optical response.

Dual shell-like magnetic clusters containing Ni(II) and Ln(III) (Ln = La, Pr, and Nd) ions.

PubMed

Kong, Xiang-Jian; Ren, Yan-Ping; Long, La-Sheng; Zheng, Zhiping; Nichol, Gary; Huang, Rong-Bin; Zheng, Lan-Sun

2008-04-07

Dual shell-like nanoscopic magnetic clusters featuring a polynuclear nickel(II) framework encapsulating that of lanthanide ions (Ln = La, Pr, and Nd) were synthesized using Ni(NO3)(2).6H2O, Ln(NO3)(3).6H2O, and iminodiacetic acid (IDA) under hydrothermal conditions. Structurally established by crystallographic studies, these clusters are [La20Ni30(IDA)30(CO3)6(NO3)6(OH)30(H2O)12](CO3)(6).72H2O (1), [Ln20Ni21(C4H5NO4)21(OH)24(C2H2O3)6(C2O4)3(NO3)9(H2O)12](NO3)9.nH2O [C2H2O3 is the alkoxide form of glycolate; Ln = Pr (2), n = 42; Nd (3), n = 50], and {[La4Ni5Na(IDA)5(CO3)(NO3)4(OH)5(H2O)5][CO3].10H2O} infinity (4). Carbonate, oxalate, and glycolate are products of hydrothermal decomposition of IDA. Compositions of these compounds were confirmed by satisfactory elemental analyses. It has been found that the cluster structure is dependent on the identity of the lanthanide ion as well as the starting Ln/Ni/IDA ratio. The cationic cluster of 1 features a core of the Keplerate type with an outer icosidodecahedron of Ni(II) ions encaging a dodecahedral kernel of La(III). Clusters 2 and 3, distinctly different from 1, are isostructural, possessing a core of an outer shell of 21 Ni(II) ions encapsulating an inner shell of 20 Ln(III) ions. Complex 4 is a three-dimensional assembly of cluster building blocks connected by units of Na(NO3)/La(NO3)3; the structure of the building block resembles closely that of 1, with a hydrated La(III) ion internalized in the decanuclear cage being an extra feature. Magnetic studies indicated ferromagnetic interactions in 1, while overall antiferromagnetic interactions were revealed for 2 and 3. The polymeric, three-dimensional cluster network 4 displayed interesting ferrimagnetic interactions.

Classification of posture maintenance data with fuzzy clustering algorithms

NASA Technical Reports Server (NTRS)

Bezdek, James C.

1991-01-01

Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various conditions were collected in conjunction with JSC postural control studies using a Tilt-Translation Device (TTD). The University of West Florida proposed applying the Fuzzy C-Means Clustering (FCM) Algorithms to this data with a view towards identifying various states and stages. Data supplied by NASA/JSC were submitted to the FCM algorithms in an attempt to identify and characterize cluster substructure in a mixed ensemble of pre- and post-adaptational TTD data. Following several unsuccessful trials with FCM using a full 11 dimensional data set, a set of two channels (features) were found to enable FCM to separate pre- from post-adaptational TTD data. The main conclusions are that: (1) FCM seems able to separate pre- from post-TTD subject no. 2 on the one trial that was used, but only in certain subintervals of time; and (2) Channels 2 (right rear transducer force) and 8 (hip sway bar) contain better discrimination information than other supersets and combinations of the data that were tried so far.

Solvation of carbonaceous molecules by para-H2 and ortho-D2 clusters. II. Fullerenes.

PubMed

Calvo, F; Yurtsever, E

2016-08-28

The coating of various fullerenes by para-hydrogen and ortho-deuterium molecules has been computationally studied as a function of the solvent amount. Rotationally averaged interaction potentials for structureless hydrogen molecules are employed to model their interaction with neutral or charged carbonaceous dopants containing between 20 and 240 atoms, occasionally comparing different fullerenes having the same size but different shapes. The solvation energy and the size of the first solvation shell obtained from path-integral molecular dynamics simulations at 2 K show only minor influence on the dopant charge and on the possible deuteration of the solvent, although the shell size is largest for ortho-D2 coating cationic fullerenes. Nontrivial finite size effects have been found with the shell size varying non-monotonically close to its completion limit. For fullerenes embedded in large hydrogen clusters, the shell size and solvation energy both follow linear scaling with the fullerene size. The shell sizes obtained for C60 (+) and C70 (+) are close to 49 and 51, respectively, and agree with mass spectrometry experiments.

Solvation of carbonaceous molecules by para-H2 and ortho-D2 clusters. II. Fullerenes

NASA Astrophysics Data System (ADS)

Calvo, F.; Yurtsever, E.

2016-08-01

The coating of various fullerenes by para-hydrogen and ortho-deuterium molecules has been computationally studied as a function of the solvent amount. Rotationally averaged interaction potentials for structureless hydrogen molecules are employed to model their interaction with neutral or charged carbonaceous dopants containing between 20 and 240 atoms, occasionally comparing different fullerenes having the same size but different shapes. The solvation energy and the size of the first solvation shell obtained from path-integral molecular dynamics simulations at 2 K show only minor influence on the dopant charge and on the possible deuteration of the solvent, although the shell size is largest for ortho-D2 coating cationic fullerenes. Nontrivial finite size effects have been found with the shell size varying non-monotonically close to its completion limit. For fullerenes embedded in large hydrogen clusters, the shell size and solvation energy both follow linear scaling with the fullerene size. The shell sizes obtained for C 60+ and C 70+ are close to 49 and 51, respectively, and agree with mass spectrometry experiments.

Dynamic Segmentation Of Behavior Patterns Based On Quantity Value Movement Using Fuzzy Subtractive Clustering Method

NASA Astrophysics Data System (ADS)

Sangadji, Iriansyah; Arvio, Yozika; Indrianto

2018-03-01

to understand by analyzing the pattern of changes in value movements that can dynamically vary over a given period with relative accuracy, an equipment is required based on the utilization of technical working principles or specific analytical method. This will affect the level of validity of the output that will occur from this system. Subtractive clustering is based on the density (potential) size of data points in a space (variable). The basic concept of subtractive clustering is to determine the regions in a variable that has high potential for the surrounding points. In this paper result is segmentation of behavior pattern based on quantity value movement. It shows the number of clusters is formed and that has many members.

Making sense of the conflicting magic numbers in WSi{sub n} clusters

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

Abreu, Marissa Baddick; Reber, Arthur C.; Khanna, Shiv N.

2015-08-21

First principles studies on the geometric structure, stability, and electronic structure of WSi{sub n} clusters, n = 6-16, have been carried out to show that the observed differing “magic sizes” for WSi{sub n} clusters are associated with the nature of the growth processes. The WSi{sub 12} cluster, observed as a magic species in experiments reacting transition metal ions with silane, is not stable due to a filled shell of 18 electrons, as previously proposed, but due to its atomic structure that arrests further growth because of an endohedral transition metal site. In fact, it is found that all of thesemore » clusters, n = 6-16, have filled 5d shells except for WSi{sub 12}, which has a 5d{sup 8} configuration that is caused by crystal field splitting. The stability of WSi{sub 15}{sup +}, observed as highly stable in clusters generated by vaporizing silicon and metal carbonyls, is shown to be associated with a combination of geometric and electronic features. The findings are compared with previous results on CrSi{sub n} clusters.« less

Brain vascular image segmentation based on fuzzy local information C-means clustering

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

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.

Combined approach of shell and shear-warp rendering for efficient volume visualization

NASA Astrophysics Data System (ADS)

Falcao, Alexandre X.; Rocha, Leonardo M.; Udupa, Jayaram K.

2003-05-01

In Medical Imaging, shell rendering (SR) and shear-warp rendering (SWR) are two ultra-fast and effective methods for volume visualization. We have previously shown that, typically, SWR can be on the average 1.38 times faster than SR, but it requires from 2 to 8 times more memory space than SR. In this paper, we propose an extension of the compact shell data structure utilized in SR to allow shear-warp factorization of the viewing matrix in order to obtain speed up gains for SR, without paying the high storage price of SWR. The new approach is called shear-warp shell rendering (SWSR). The paper describes the methods, points out their major differences in the computational aspects, and presents a comparative analysis of them in terms of speed, storage, and image quality. The experiments involve hard and fuzzy boundaries of 10 different objects of various sizes, shapes, and topologies, rendered on a 1GHz Pentium-III PC with 512MB RAM, utilizing surface and volume rendering strategies. The results indicate that SWSR offers the best speed and storage characteristics compromise among these methods. We also show that SWSR improves the rendition quality over SR, and provides renditions similar to those produced by SWR.

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

Eriksen, Janus J., E-mail: janusje@chem.au.dk; Jørgensen, Poul; Matthews, Devin A.

The accuracy at which total energies of open-shell atoms and organic radicals may be calculated is assessed for selected coupled cluster perturbative triples expansions, all of which augment the coupled cluster singles and doubles (CCSD) energy by a non-iterative correction for the effect of triple excitations. Namely, the second- through sixth-order models of the recently proposed CCSD(T–n) triples series [J. J. Eriksen et al., J. Chem. Phys. 140, 064108 (2014)] are compared to the acclaimed CCSD(T) model for both unrestricted as well as restricted open-shell Hartree-Fock (UHF/ROHF) reference determinants. By comparing UHF- and ROHF-based statistical results for a test setmore » of 18 modest-sized open-shell species with comparable RHF-based results, no behavioral differences are observed for the higher-order models of the CCSD(T–n) series in their correlated descriptions of closed- and open-shell species. In particular, we find that the convergence rate throughout the series towards the coupled cluster singles, doubles, and triples (CCSDT) solution is identical for the two cases. For the CCSD(T) model, on the other hand, not only its numerical consistency, but also its established, yet fortuitous cancellation of errors breaks down in the transition from closed- to open-shell systems. The higher-order CCSD(T–n) models (orders n > 3) thus offer a consistent and significant improvement in accuracy relative to CCSDT over the CCSD(T) model, equally for RHF, UHF, and ROHF reference determinants, albeit at an increased computational cost.« less

Laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF): performance, reference spectra and classification of atmospheric samples

NASA Astrophysics Data System (ADS)

Shen, Xiaoli; Ramisetty, Ramakrishna; Mohr, Claudia; Huang, Wei; Leisner, Thomas; Saathoff, Harald

2018-04-01

The laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF, AeroMegt GmbH) is able to identify the chemical composition and mixing state of individual aerosol particles, and thus is a tool for elucidating their impacts on human health, visibility, ecosystem, and climate. The overall detection efficiency (ODE) of the instrument we use was determined to range from ˜ (0.01 ± 0.01) to ˜ (4.23 ± 2.36) % for polystyrene latex (PSL) in the size range of 200 to 2000 nm, ˜ (0.44 ± 0.19) to ˜ (6.57 ± 2.38) % for ammonium nitrate (NH4NO3), and ˜ (0.14 ± 0.02) to ˜ (1.46 ± 0.08) % for sodium chloride (NaCl) particles in the size range of 300 to 1000 nm. Reference mass spectra of 32 different particle types relevant for atmospheric aerosol (e.g. pure compounds NH4NO3, K2SO4, NaCl, oxalic acid, pinic acid, and pinonic acid; internal mixtures of e.g. salts, secondary organic aerosol, and metallic core-organic shell particles; more complex particles such as soot and dust particles) were determined. Our results show that internally mixed aerosol particles can result in spectra with new clusters of ions, rather than simply a combination of the spectra from the single components. An exemplary 1-day ambient data set was analysed by both classical fuzzy clustering and a reference-spectra-based classification method. Resulting identified particle types were generally well correlated. We show how a combination of both methods can greatly improve the interpretation of single-particle data in field measurements.

Sound wave generation by a spherically symmetric outburst and AGN feedback in galaxy clusters II: impact of thermal conduction.

NASA Astrophysics Data System (ADS)

Tang, Xiaping; Churazov, Eugene

2018-04-01

We analyze the impact of thermal conduction on the appearance of a shock-heated gas shell which is produced when a spherically symmetric outburst of a supermassive black hole inflates bubbles of relativistic plasma at the center of a galaxy cluster. The presence of the hot and low-density shell can be used as an ancillary indicator for a high rate of energy release during the outburst, which is required to drive strong shocks into the gas. Here we show that conduction can effectively erase such shell, unless the diffusion of electrons is heavily suppressed. We conclude that a more robust proxy to the energy release rate is the ratio between the shock radius and bubble radius. We also revisited the issue of sound waves dissipation induced by thermal conduction in a scenario, where characteristic wavelength of the sound wave is set by the total energy of the outburst. For a fiducial short outburst model, the dissipation length does not exceed the cooling radius in a typical cluster, provided that the conduction is suppressed by a factor not larger than ˜100. For quasi-continuous energy injection neither the shock-heated shell nor the outgoing sound wave are important and the role of conduction is subdominant.

A Novel Approach to Constrain the Mass Ratio of Minor Mergers in Elliptical Galaxies: Application to NGC 4889, the Brightest Cluster Galaxy in Coma

NASA Astrophysics Data System (ADS)

Gu, Meng; Ho, Luis C.; Peng, Chien Y.; Huang, Song

2013-08-01

Minor mergers are thought to be important for the buildup and structural evolution of massive elliptical galaxies. In this work, we report the discovery of a system of four shell features in NGC 4889, one of the brightest members of the Coma cluster, using optical images taken with the Hubble Space Telescope and the Sloan Digital Sky Survey. The shells are well aligned with the major axis of the host and are likely to have been formed by the accretion of a small satellite galaxy. We have performed a detailed two-dimensional photometric decomposition of NGC 4889 and of the many overlapping nearby galaxies in its vicinity. This comprehensive model allows us not only to firmly detect the low-surface brightness shells, but, crucially, also to accurately measure their luminosities and colors. The shells are bluer than the underlying stars at the same radius in the main galaxy. We make use of the colors of the shells and the color-magnitude relation of the Coma cluster to infer the luminosity (or mass) of the progenitor galaxy. The shells in NGC 4889 appear to have been produced by the minor merger of a moderate-luminosity (MI ≈ -18.7 mag) disk (S0 or spiral) galaxy with a luminosity (mass) ratio of ~90:1 with respect to the primary galaxy. The novel methodology presented in this work can be exploited to decode the fossil record imprinted in the photometric substructure of other nearby early-type galaxies. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the Data Archive at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under NASA contract NAS5-26555.

X-ray and Neutron Scattering Study of the Formation of Core–Shell-Type Polyoxometalates

DOE PAGES

Yin, Panchao; Wu, Bin; Mamontov, Eugene; ...

2016-02-05

A typical type of core-shell polyoxometalates can be obtained through the Keggin-type polyoxometalate-templated growth of a layer of spherical shell structure of {Mo 72Fe 30}. Small angle X-ray scattering is used to study the structural features and stability of the core-shell structures in aqueous solutions. Time-resolved small angle X-ray scattering is applied to monitor the synthetic reactions and a three-stage formation mechanism is proposed to describe the synthesis of the core-shell polyoxometalates based on the monitoring results. Quasi-elastic and inelastic neutron scattering are used to probe the dynamics of water molecules in the core-shell structures and two different types ofmore » water molecules, the confined and structured water, are observed. These water molecules play an important role in bridging core and shell structures and stabilizing the cluster structures. A typical type of core shell polyoxometalates can be obtained through the Keggin-type polyoxometalate-templated growth of a layer of spherical shell structure of {Mo 72Fe 30}. Small-angle X-ray scattering is used to study the structural features and stability of the core shell structures in aqueous solutions. Time-resolved small-angle X-ray scattering is applied to monitor the synthetic reactions, and a three-stage formation mechanism is proposed to describe the synthesis of the core shell polyoxometalates based on the monitoring results. New protocols have been developed by fitting the X-ray data with custom physical models, which provide more convincing, objective, and completed data interpretation. Quasi-elastic and inelastic neutron scattering are used to probe the dynamics of water molecules in the core shell structures, and two different types of water molecules, the confined and structured water, are observed. These water molecules play an important role in bridging core and shell structures and stabilizing the cluster structures.« less

Delivery of Learning Knowledge Objects Using Fuzzy Clustering

ERIC Educational Resources Information Center

Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay

2016-01-01

e-Learning industry is rapidly changing and the current learning trends are based on personalized, social and mobile learning, content reusability, cloud-based and talent management. The learning systems have attained a significant growth catering to the needs of a wide range of learners, having different approaches and styles of learning. Objects…

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed

K, Jalal Deen; R, Ganesan; A, Merline

2017-07-27

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed Central

K, Jalal Deen; R, Ganesan; A, Merline

2017-01-01

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

PubMed Central

Seghier, Mohamed L.; Ramlackhansingh, Anil; Crinion, Jenny; Leff, Alexander P.; Price, Cathy J.

2008-01-01

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes. PMID:18482850

A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation

NASA Astrophysics Data System (ADS)

Vadiveloo, Mogana; Abdullah, Rosni; Rajeswari, Mandava

2015-12-01

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

Multi-Patches IRIS Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

NASA Astrophysics Data System (ADS)

Shekar, B. H.; Bhat, S. S.

2017-05-01

Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

Meta-analyses of microarrays of Arabidopsis asymmetric leaves1 (as1), as2 and their modifying mutants reveal a critical role for the ETT pathway in stabilization of adaxial-abaxial patterning and cell division during leaf development.

PubMed

Takahashi, Hiro; Iwakawa, Hidekazu; Ishibashi, Nanako; Kojima, Shoko; Matsumura, Yoko; Prananingrum, Pratiwi; Iwasaki, Mayumi; Takahashi, Anna; Ikezaki, Masaya; Luo, Lilan; Kobayashi, Takeshi; Machida, Yasunori; Machida, Chiyoko

2013-03-01

It is necessary to use algorithms to analyze gene expression data from DNA microarrays, such as in clustering and machine learning. Previously, we developed the knowledge-based fuzzy adaptive resonance theory (KB-FuzzyART), a clustering algorithm suitable for analyzing gene expression data, to find clues for identifying gene networks. Leaf primordia form around the shoot apical meristem (SAM), which consists of indeterminate stem cells. Upon initiation of leaf development, adaxial-abaxial patterning is crucial for lateral expansion, via cellular proliferation, and the formation of flat symmetric leaves. Many regulatory genes that specify such patterning have been identified. Analysis by the KB-FuzzyART and subsequent molecular and genetic analyses previously showed that ASYMMETRIC LEAVES1 (AS1) and AS2 repress the expression of some abaxial-determinant genes, such as AUXIN RESPONSE FACTOR3 (ARF3)/ETTIN (ETT) and ARF4, which are responsible for defects in leaf adaxial-abaxial polarity in as1 and as2. In the present study, genetic analysis revealed that ARF3/ETT and ARF4 were regulated by modifier genes, BOBBER1 (BOB1) and ELONGATA3 (ELO3), together with AS1-AS2. We analyzed expression arrays with as2 elo3 and as2 bob1, and extracted genes downstream of ARF3/ETT by using KB-FuzzyART and molecular analyses. The results showed that expression of Kip-related protein (KRP) (for inhibitors of cyclin-dependent protein kinases) and Isopentenyltransferase (IPT) (for biosynthesis of cytokinin) genes were controlled by AS1-AS2 through ARF3/ETT and ARF4 functions, which suggests that the AS1-AS2-ETT pathway plays a critical role in controlling the cell division cycle and the biosynthesis of cytokinin around SAM to stabilize leaf development in Arabidopsis thaliana.

Lithology identification of aquifers from geophysical well logs and fuzzy logic analysis: Shui-Lin Area, Taiwan

NASA Astrophysics Data System (ADS)

Hsieh, Bieng-Zih; Lewis, Charles; Lin, Zsay-Shing

2005-04-01

The purpose of this study is to construct a fuzzy lithology system from well logs to identify formation lithology of a groundwater aquifer system in order to better apply conventional well logging interpretation in hydro-geologic studies because well log responses of aquifers are sometimes different from those of conventional oil and gas reservoirs. The input variables for this system are the gamma-ray log reading, the separation between the spherically focused resistivity and the deep very-enhanced resistivity curves, and the borehole compensated sonic log reading. The output variable is groundwater formation lithology. All linguistic variables are based on five linguistic terms with a trapezoidal membership function. In this study, 50 data sets are clustered into 40 training sets and 10 testing sets for constructing the fuzzy lithology system and validating the ability of system prediction, respectively. The rule-based database containing 12 fuzzy lithology rules is developed from the training data sets, and the rule strength is weighted. A Madani inference system and the bisector of area defuzzification method are used for fuzzy inference and defuzzification. The success of training performance and the prediction ability were both 90%, with the calculated correlation of training and testing equal to 0.925 and 0.928, respectively. Well logs and core data from a clastic aquifer (depths 100-198 m) in the Shui-Lin area of west-central Taiwan are used for testing the system's construction. Comparison of results from core analysis, well logging and the fuzzy lithology system indicates that even though the well logging method can easily define a permeable sand formation, distinguishing between silts and sands and determining grain size variation in sands is more subjective. These shortcomings can be improved by a fuzzy lithology system that is able to yield more objective decisions than some conventional methods of log interpretation.

B80 and B101-103 clusters: Remarkable stability of the core-shell structures established by validated density functionalsa)

NASA Astrophysics Data System (ADS)

Li, Fengyu; Jin, Peng; Jiang, De-en; Wang, Lu; Zhang, Shengbai B.; Zhao, Jijun; Chen, Zhongfang

2012-02-01

Prompted by the very recent claim that the volleyball-shaped B80 fullerene [X. Wang, Phys. Rev. B 82, 153409 (2010), 10.1103/PhysRevB.82.153409] is lower in energy than the B80 buckyball [N. G. Szwacki, A. Sadrzadeh, and B. I. Yakobson, Phys. Rev. Lett. 98, 166804 (2007), 10.1103/PhysRevLett.98.166804] and core-shell structure [J. Zhao, L. Wang, F. Li, and Z. Chen, J. Phys. Chem. A 114, 9969 (2010), 10.1021/jp1018873], and inspired by the most recent finding of another core-shell isomer as the lowest energy B80 isomer [S. De, A. Willand, M. Amsler, P. Pochet, L. Genovese, and S. Goedecher, Phys. Rev. Lett. 106, 225502 (2011), 10.1103/PhysRevLett.106.225502], we carefully evaluated the performance of the density functional methods in the energetics of boron clusters and confirmed that the core-shell construction (stuffed fullerene) is thermodynamically the most favorable structural pattern for B80. Our global minimum search showed that both B101 and B103 also prefer a core-shell structure and that B103 can reach the complete core-shell configuration. We called for great attention to the theoretical community when using density functionals to investigate boron-related nanomaterials.

Lifetime of inner-shell hole states of Ar (2p) and Kr (3d) using equation-of-motion coupled cluster method

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

Ghosh, Aryya; Vaval, Nayana, E-mail: np.vaval@ncl.res.in; Pal, Sourav

2015-07-14

Auger decay is an efficient ultrafast relaxation process of core-shell or inner-shell excited atom or molecule. Generally, it occurs in femto-second or even atto-second time domain. Direct measurement of lifetimes of Auger process of single ionized and double ionized inner-shell state of an atom or molecule is an extremely difficult task. In this paper, we have applied the highly correlated complex absorbing potential-equation-of-motion coupled cluster (CAP-EOMCC) approach which is a combination of CAP and EOMCC approach to calculate the lifetime of the states arising from 2p inner-shell ionization of an Ar atom and 3d inner-shell ionization of Kr atom. Wemore » have also calculated the lifetime of Ar{sup 2+}(2p{sup −1}3p{sup −1}) {sup 1}D, Ar{sup 2+}(2p{sup −1}3p{sup −1}) {sup 1}S, and Ar{sup 2+}(2p{sup −1}3s{sup −1}) {sup 1}P double ionized states. The predicted results are compared with the other theoretical results as well as experimental results available in the literature.« less

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yi, J.; Choi, C.

2014-12-01

Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

Charge-doping and chemical composition-driven magnetocrystalline anisotropy in CoPt core-shell alloy clusters

NASA Astrophysics Data System (ADS)

Ruiz-Díaz, P.; Muñoz-Navia, M.; Dorantes-Dávila, J.

2018-03-01

Charge-doping together with 3 d-4 d alloying emerges as promising mechanisms for tailoring the magnetic properties of low-dimensional systems. Here, throughout ab initio calculations, we present a systematic overview regarding the impact of both electron(hole) charge-doping and chemical composition on the magnetocrystalline anisotropy (MA) of CoPt core-shell alloy clusters. By taking medium-sized Co n Pt m ( N = n + m = 85) octahedral-like alloy nanoparticles for some illustrative core-sizes as examples, we found enhanced MA energies and large induced spin(orbital) moments in Pt-rich clusters. Moreover, depending on the Pt-core-size, both in-plane and off-plane directions of magnetization are observed. In general, the MA of these binary compounds further stabilizes upon charge-doping. In addition, in the clusters with small MA, the doping promotes magnetization switching. Insights into the microscopical origins of the MA behavior are associated to changes in the electronic structure of the clusters. [Figure not available: see fulltext.

Unusual behavior in magnesium-copper cluster matter produced by helium droplet mediated deposition.

PubMed

Emery, S B; Xin, Y; Ridge, C J; Buszek, R J; Boatz, J A; Boyle, J M; Little, B K; Lindsay, C M

2015-02-28

We demonstrate the ability to produce core-shell nanoclusters of materials that typically undergo intermetallic reactions using helium droplet mediated deposition. Composite structures of magnesium and copper were produced by sequential condensation of metal vapors inside the 0.4 K helium droplet baths and then gently deposited onto a substrate for analysis. Upon deposition, the individual clusters, with diameters ∼5 nm, form a cluster material which was subsequently characterized using scanning and transmission electron microscopies. Results of this analysis reveal the following about the deposited cluster material: it is in the un-alloyed chemical state, it maintains a stable core-shell 5 nm structure at sub-monolayer quantities, and it aggregates into unreacted structures of ∼75 nm during further deposition. Surprisingly, high angle annular dark field scanning transmission electron microscopy images revealed that the copper appears to displace the magnesium at the core of the composite cluster despite magnesium being the initially condensed species within the droplet. This phenomenon was studied further using preliminary density functional theory which revealed that copper atoms, when added sequentially to magnesium clusters, penetrate into the magnesium cores.

Size-Dependent Specific Surface Area of Nanoporous Film Assembled by Core-Shell Iron Nanoclusters

DOE PAGES

Antony, Jiji; Nutting, Joseph; Baer, Donald R.; ...

2006-01-01

Nmore » anoporous films of core-shell iron nanoclusters have improved possibilities for remediation, chemical reactivity rate, and environmentally favorable reaction pathways. Conventional methods often have difficulties to yield stable monodispersed core-shell nanoparticles. We produced core-shell nanoclusters by a cluster source that utilizes combination of Fe target sputtering along with gas aggregations in an inert atmosphere at 7 ∘ C . Sizes of core-shell iron-iron oxide nanoclusters are observed with transmission electron microscopy (TEM). The specific surface areas of the porous films obtained from Brunauer-Emmett-Teller (BET) process are size-dependent and compared with the calculated data.« less

Adaptive density trajectory cluster based on time and space distance

NASA Astrophysics Data System (ADS)

Liu, Fagui; Zhang, Zhijie

2017-10-01

There are some hotspot problems remaining in trajectory cluster for discovering mobile behavior regularity, such as the computation of distance between sub trajectories, the setting of parameter values in cluster algorithm and the uncertainty/boundary problem of data set. As a result, based on the time and space, this paper tries to define the calculation method of distance between sub trajectories. The significance of distance calculation for sub trajectories is to clearly reveal the differences in moving trajectories and to promote the accuracy of cluster algorithm. Besides, a novel adaptive density trajectory cluster algorithm is proposed, in which cluster radius is computed through using the density of data distribution. In addition, cluster centers and number are selected by a certain strategy automatically, and uncertainty/boundary problem of data set is solved by designed weighted rough c-means. Experimental results demonstrate that the proposed algorithm can perform the fuzzy trajectory cluster effectively on the basis of the time and space distance, and obtain the optimal cluster centers and rich cluster results information adaptably for excavating the features of mobile behavior in mobile and sociology network.

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Understanding the interface of six-shell cuboctahedral and icosahedral palladium clusters on reduced graphene oxide: experimental and theoretical study.

PubMed

Gracia-Espino, Eduardo; Hu, Guangzhi; Shchukarev, Andrey; Wågberg, Thomas

2014-05-07

Studies on noble-metal-decorated carbon nanostructures are reported almost on a daily basis, but detailed studies on the nanoscale interactions for well-defined systems are very rare. Here we report a study of reduced graphene oxide (rGOx) homogeneously decorated with palladium (Pd) nanoclusters with well-defined shape and size (2.3 ± 0.3 nm). The rGOx was modified with benzyl mercaptan (BnSH) to improve the interaction with Pd clusters, and N,N-dimethylformamide was used as solvent and capping agent during the decoration process. The resulting Pd nanoparticles anchored to the rGOx-surface exhibit high crystallinity and are fully consistent with six-shell cuboctahedral and icosahedral clusters containing ~600 Pd atoms, where 45% of these are located at the surface. According to X-ray photoelectron spectroscopy analysis, the Pd clusters exhibit an oxidized surface forming a PdO(x) shell. Given the well-defined experimental system, as verified by electron microscopy data and theoretical simulations, we performed ab initio simulations using 10 functionalized graphenes (with vacancies or pyridine, amine, hydroxyl, carboxyl, or epoxy groups) to understand the adsorption process of BnSH, their further role in the Pd cluster formation, and the electronic properties of the graphene-nanoparticle hybrid system. Both the experimental and theoretical results suggest that Pd clusters interact with functionalized graphene by a sulfur bridge while the remaining Pd surface is oxidized. Our study is of significant importance for all work related to anchoring of nanoparticles on nanocarbon-based supports, which are used in a variety of applications.

Theoretical study of the H2 reaction with a Pt4 (111) cluster

NASA Astrophysics Data System (ADS)

Cruz, A.; Bertin, V.; Poulain, E.; Benitez, J. I.; Castillo, S.

2004-04-01

The Cs symmetry reaction of the H2 molecule on a Pt4 (111) clusters, has been studied using ab initio multiconfiguration self-consistent field plus extensive multireference configuration interaction variational and perturbative calculations. The H2 interaction by the vertex and by the base of a tetrahedral Pt4 cluster were studied in ground and excited triplet and singlet states (closed and open shells), where the reaction curves are obtained through many avoided crossings. The Pt4 cluster captures and activates the hydrogen molecule; it shows a similar behavior compared with other Ptn (n=1,2,3) systems. The Pt4 cluster in their lowest five open and closed shell electronic states: 3B2, 1B2, 1A1 3A1, 1A1, respectively, may capture and dissociate the H2 molecule without activation barriers for the hydrogen molecule vertex approach. For the threefolded site reaction, i.e., by the base, the situation is different, the hydrogen adsorption presents some barriers. The potential energy minima occur outside and inside the cluster, with strong activation of the H-H bond. In all cases studied, the Pt4 cluster does not absorb the hydrogen molecule.

Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

NASA Astrophysics Data System (ADS)

Jahangoshai Rezaee, Mustafa; Jozmaleki, Mehrdad; Valipour, Mahsa

2018-01-01

One of the main features to invest in stock exchange companies is their financial performance. On the other hand, conventional evaluation methods such as data envelopment analysis are not only a retrospective process, but are also a process, which are incomplete and ineffective approaches to evaluate the companies in the future. To remove this problem, it is required to plan an expert system for evaluating organizations when the online data are received from stock exchange market. This paper deals with an approach for predicting the online financial performance of companies when data are received in different time's intervals. The proposed approach is based on integrating fuzzy C-means (FCM), data envelopment analysis (DEA) and artificial neural network (ANN). The classical FCM method is unable to update the number of clusters and their members when the data are changed or the new data are received. Hence, this method is developed in order to make dynamic features for the number of clusters and clusters members in classical FCM. Then, DEA is used to evaluate DMUs by using financial ratios to provide targets in neural network. Finally, the designed network is trained and prepared for predicting companies' future performance. The data on Tehran Stock Market companies for six consecutive years (2007-2012) are used to show the abilities of the proposed approach.

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

PubMed

Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

A new paradigm of oral cancer detection using digital infrared thermal imaging

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Mukhopadhyay, S.; Dasgupta, A.; Banerjee, S.; Mukhopadhyay, S.; Patsa, S.; Ray, J. G.; Chaudhuri, K.

2016-03-01

Histopathology is considered the gold standard for oral cancer detection. But a major fraction of patient pop- ulation is incapable of accessing such healthcare facilities due to poverty. Moreover, such analysis may report false negatives when test tissue is not collected from exact cancerous location. The proposed work introduces a pioneering computer aided paradigm of fast, non-invasive and non-ionizing modality for oral cancer detection us- ing Digital Infrared Thermal Imaging (DITI). Due to aberrant metabolic activities in carcinogenic facial regions, heat signatures of patients are different from that of normal subjects. The proposed work utilizes asymmetry of temperature distribution of facial regions as principle cue for cancer detection. Three views of a subject, viz. front, left and right are acquired using long infrared (7:5 - 13μm) camera for analysing distribution of temperature. We study asymmetry of facial temperature distribution between: a) left and right profile faces and b) left and right half of frontal face. Comparison of temperature distribution suggests that patients manifest greater asymmetry compared to normal subjects. For classification, we initially use k-means and fuzzy k-means for unsupervised clustering followed by cluster class prototype assignment based on majority voting. Average classification accuracy of 91:5% and 92:8% are achieved by k-mean and fuzzy k-mean framework for frontal face. The corresponding metrics for profile face are 93:4% and 95%. Combining features of frontal and profile faces, average accuracies are increased to 96:2% and 97:6% respectively for k-means and fuzzy k-means framework.

Landscape of α preformation probability for even-even nuclei in medium mass region

NASA Astrophysics Data System (ADS)

Qian, Yibin; Ren, Zhongzhou

2018-03-01

The behavior of α cluster preformation probability, in α decay, is a rich source of the structural information, such as the clustering, pairing, and shell evolution in heavy nuclei. Meanwhile, the experimental α decay data have been very recently compiled in the newest table NUBASE2016. Through a least square fit to the available experimental data of nuclear charge radii plus the neutron skin thickness, we obtain a new set of parameters for the two-parameter Fermi nucleon density distributions in target nuclei. Subsequently, we make use of these refreshed inputs, involved in the density-dependent cluster model, to extract α preformation factor ({P}α ) for a large range of medium α emitters with N < 126 from the newest data table. Besides checking the supposed smooth pattern of P α in the open-shell region, the special attention has been paid to those exotic α-decaying nuclei around the Z = 50 and N = 82 shell closures. Moreover, the correlation between the α preformation factor and the microscopic correction of nuclear mass, corresponding to the effect of shell and pairing plus deformation, is in particular investigated, to pursue the valuable knowledge of the P α pattern over the nuclide chart. The feature of α preformation factor along with the neutron-proton asymmetry is then detected and discussed to some extent.

Structure of overheated metal clusters: MD simulation study

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

Vorontsov, Alexander

2015-08-17

The structure of overheated metal clusters appeared in condensation process was studied by computer simulation techniques. It was found that clusters with size larger than several tens of atoms have three layers: core part, intermediate dense packing layer and a gas- like shell with low density. The change of the size and structure of these layers with the variation of internal energy and the size of cluster is discussed.

RFM-based eco-efficiency analysis using Takagi-Sugeno fuzzy and AHP approach

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

Chen Ruiyang

2009-04-15

Eco-design is crucial to take environmental aspects into account in the phases of design. Few literature review that green product must meet market value. The development of models to predict market value is thus very useful because it can provide early eco-efficiency to the eco-design. For the eco-design engineer, when he tries to solve a customer feedback problem, he usually faces the eco-efficiency. There are, however, often other types of fuzziness uncertainty present, which are related to the quantity of eco-design conditions. In this paper, it is derived that analysis of eco-efficiency can be identified by using the RFM valuemore » for quantifying eco-design with Takagi-Sugeno fuzzy system on customer feedback problem. It clusters eco-efficiency into segments according to green product usages value expressed in terms of weighted RFM. This experiment examined the weighted RFM effect of overall average normalized, AHP and non-weighted for F1 metric. The experimental results show that the proposed methodology indeed can yield identification of higher quality.« less

Coordinated control system modelling of ultra-supercritical unit based on a new T-S fuzzy structure.

PubMed

Hou, Guolian; Du, Huan; Yang, Yu; Huang, Congzhi; Zhang, Jianhua

2018-03-01

The thermal power plant, especially the ultra-supercritical unit is featured with severe nonlinearity, strong multivariable coupling. In order to deal with these difficulties, it is of great importance to build an accurate and simple model of the coordinated control system (CCS) in the ultra-supercritical unit. In this paper, an improved T-S fuzzy model identification approach is proposed. First of all, the k-means++ algorithm is employed to identify the premise parameters so as to guarantee the number of fuzzy rules. Then, the local linearized models are determined by using the incremental historical data around the cluster centers, which are obtained via the stochastic gradient descent algorithm with momentum and variable learning rate. Finally, with the proposed method, the CCS model of a 1000 MW USC unit in Tai Zhou power plant is developed. The effectiveness of the proposed approach is validated by the given extensive simulation results, and it can be further employed to design the overall advanced controllers for the CCS in an USC unit. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

Water Quality Evaluation of the Yellow River Basin Based on Gray Clustering Method

NASA Astrophysics Data System (ADS)

Fu, X. Q.; Zou, Z. H.

2018-03-01

Evaluating the water quality of 12 monitoring sections in the Yellow River Basin comprehensively by grey clustering method based on the water quality monitoring data from the Ministry of environmental protection of China in May 2016 and the environmental quality standard of surface water. The results can reflect the water quality of the Yellow River Basin objectively. Furthermore, the evaluation results are basically the same when compared with the fuzzy comprehensive evaluation method. The results also show that the overall water quality of the Yellow River Basin is good and coincident with the actual situation of the Yellow River basin. Overall, gray clustering method for water quality evaluation is reasonable and feasible and it is also convenient to calculate.

Covalent Binding with Neutrons on the Femto-scale

NASA Astrophysics Data System (ADS)

von Oertzen, W.; Kanada-En'yo, Y.; Kimura, M.

2017-06-01

In light nuclei we have well defined clusters, nuclei with closed shells, which serve as centers for binary molecules with covalent binding by valence neutrons. Single neutron orbitals in light neutron-excess nuclei have well defined shell model quantum numbers. With the combination of two clusters and their neutron valence states, molecular two-center orbitals are defined; in the two-center shell model we can place valence neutrons in a large variety of molecular two-center states, and the formation of Dimers becomes possible. The corresponding rotational bands point with their large moments of inertia and the Coriolis decoupling effect (for K = 1/2 bands) to the internal molecular orbital structure in these states. On the basis of these the neutron rich isotopes allow the formation of a large variety molecular structures on the nuclear scale. An extended Ikeda diagram can be drawn for these cases. Molecular bands in Be and Ne-isotopes are discussed as text-book examples.

Green Synthesis of Ag-Cu Nanoalloys Using Opuntia ficus- indica

NASA Astrophysics Data System (ADS)

Rocha-Rocha, O.; Cortez-Valadez, M.; Hernández-Martínez, A. R.; Gámez-Corrales, R.; Alvarez, Ramón A. B.; Britto-Hurtado, R.; Delgado-Beleño, Y.; Martinez-Nuñez, C. E.; Pérez-Rodríguez, A.; Arizpe-Chávez, H.; Flores-Acosta, M.

2017-02-01

Bimetallic Ag/Cu nanoparticles have been obtained by green synthesis using Opuntia ficus- indica plant extract. Two synthesis methods were applied to obtain nanoparticles with core-shell and Janus morphologies by reversing the order of precursors. Transmission electronic microscopy revealed size of 10 nm and 20 nm for the core-shell and Janus nanoparticles, respectively. Other small particles with size of up to 2 nm were also observed. Absorption bands attributed to surface plasmon resonance were detected at 440 nm and 500 nm for the core-shell and Janus nanoparticles, respectively. Density functional theory predicted a breathing mode type (BMT) located at low wavenumber due to small, low-energy clusters of (AgCu) n with n = 2 to 9, showing a certain correlation with the experimental one (at 220 cm-1). The dependence of the BMT on the number of atoms constituting the cluster is also studied.

Unusual behavior in magnesium-copper cluster matter produced by helium droplet mediated deposition

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

Emery, S. B., E-mail: samuel.emery@navy.mil; Little, B. K.; Air Force Research Laboratory, Munitions Directorate, 2306 Perimeter Rd., Eglin AFB, Florida 32542

2015-02-28

We demonstrate the ability to produce core-shell nanoclusters of materials that typically undergo intermetallic reactions using helium droplet mediated deposition. Composite structures of magnesium and copper were produced by sequential condensation of metal vapors inside the 0.4 K helium droplet baths and then gently deposited onto a substrate for analysis. Upon deposition, the individual clusters, with diameters ∼5 nm, form a cluster material which was subsequently characterized using scanning and transmission electron microscopies. Results of this analysis reveal the following about the deposited cluster material: it is in the un-alloyed chemical state, it maintains a stable core-shell 5 nm structuremore » at sub-monolayer quantities, and it aggregates into unreacted structures of ∼75 nm during further deposition. Surprisingly, high angle annular dark field scanning transmission electron microscopy images revealed that the copper appears to displace the magnesium at the core of the composite cluster despite magnesium being the initially condensed species within the droplet. This phenomenon was studied further using preliminary density functional theory which revealed that copper atoms, when added sequentially to magnesium clusters, penetrate into the magnesium cores.« less

Simulating Self-Assembly with Simple Models

NASA Astrophysics Data System (ADS)

Rapaport, D. C.

Results from recent molecular dynamics simulations of virus capsid self-assembly are described. The model is based on rigid trapezoidal particles designed to form polyhedral shells of size 60, together with an atomistic solvent. The underlying bonding process is fully reversible. More extensive computations are required than in previous work on icosahedral shells built from triangular particles, but the outcome is a high yield of closed shells. Intermediate clusters have a variety of forms, and bond counts provide a useful classification scheme

Hydrogen sorption in Pd-decorated Mg-MgO core-shell nanoparticles

NASA Astrophysics Data System (ADS)

Callini, E.; Pasquini, L.; Piscopiello, E.; Montone, A.; Antisari, M. Vittori; Bonetti, E.

2009-06-01

Mg nanoparticles with metal-oxide core-shell morphology were synthesized by inert-gas condensation and decorated by in situ Pd deposition. Transmission electron microscopy and x-ray diffraction underline the formation of a noncontinuous layer with Pd clusters on top of the MgO shell. Even in the presence of a thick MgO interlayer, a modest (2 at. %) Pd decoration deeply enhances the hydrogen sorption properties: previously inert nanoparticles exhibit metal-hydride transformation with fast kinetics and gravimetric capacity above 5 wt %.

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

Kurt Derr; Milos Manic

Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. In addition, pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A person/object in proximity to certain areas for specific durations of time may pose a risk hazard either to themselves, others, or the environment. This paper presents a novel fuzzy based spatio-temporal risk calculationmore » DSTiPE method that an object with wireless communications presents to the environment. The presented Matlab based application for fuzzy spatio-temporal risk cluster extraction is verified on a diagonal vehicle movement example.« less

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Fuzzy Clustering-Based Modeling of Surface Interactions and Emulsions of Selected Whey Protein Concentrate Combined to i-Carrageenan and Gum Arabic Solutions

USDA-ARS?s Scientific Manuscript database

Gums and proteins are valuable ingredients with a wide spectrum of applications. Surface properties (surface tension, interfacial tension, emulsion activity index “EAI” and emulsion stability index “ESI”) of 4% whey protein concentrate (WPC) in a combination with '- carrageenan (0.05%, 0.1%, and 0.5...

A research of road centerline extraction algorithm from high resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yushan; Xu, Tingfa

2017-09-01

Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

NASA Astrophysics Data System (ADS)

B. Shokouhi, Shahriar; Fooladivanda, Aida; Ahmadinejad, Nasrin

2017-12-01

A computer-aided detection (CAD) system is introduced in this paper for detection of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed CAD system firstly compensates motion artifacts and segments the breast region. Then, the potential lesion voxels are detected and used as the initial seed points for the seeded region-growing algorithm. A new and robust region-growing algorithm incorporating with Fuzzy C-means (FCM) clustering and vesselness filter is proposed to segment any potential lesion regions. Subsequently, the false positive detections are reduced by applying a discrimination step. This is based on 3D morphological characteristics of the potential lesion regions and kinetic features which are fed to the support vector machine (SVM) classifier. The performance of the proposed CAD system is evaluated using the free-response operating characteristic (FROC) curve. We introduce our collected dataset that includes 76 DCE-MRI studies, 63 malignant and 107 benign lesions. The prepared dataset has been used to verify the accuracy of the proposed CAD system. At 5.29 false positives per case, the CAD system accurately detects 94% of the breast lesions.

Identification of food spoilage in the smart home based on neural and fuzzy processing of odour sensor responses.

PubMed

Green, Geoffrey C; Chan, Adrian D C; Goubran, Rafik A

2009-01-01

Adopting the use of real-time odour monitoring in the smart home has the potential to alert the occupant of unsafe or unsanitary conditions. In this paper, we measured (with a commercial metal-oxide sensor-based electronic nose) the odours of five household foods that had been left out at room temperature for a week to spoil. A multilayer perceptron (MLP) neural network was trained to recognize the age of the samples (a quantity related to the degree of spoilage). For four of these foods, median correlation coefficients (between target values and MLP outputs) of R > 0.97 were observed. Fuzzy C-means clustering (FCM) was applied to the evolving odour patterns of spoiling milk, which had been sampled more frequently (4h intervals for 7 days). The FCM results showed that both the freshest and oldest milk samples had a high degree of membership in "fresh" and "spoiled" clusters, respectively. In the future, as advancements in electronic nose development remove the present barriers to acceptance, signal processing methods like those explored in this paper can be incorporated into odour monitoring systems used in the smart home.

Detached dust shell around Wolf-Rayet star WR60-6 in the young stellar cluster VVV CL036

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

Borissova, J.; Amigo, P.; Kurtev, R.

The discovery of a detached dust shell around the Wolf-Rayet (WR) star WR60-6 in the young stellar cluster VVV CL036 is reported. This shell is uncovered through the Spitzer-MIPS 24 μm image, where it appears brightest, and it is invisible at shorter wavelengths. Using new APEX observations and other data available from the literature, we have estimated some of the shell parameters: the inner and outer radii of 0.15 and 0.90 pc, respectively; the overall systemic velocity of the molecular {sup 12}CO(3 → 2) emission of –45.7 ± 2.3 km s{sup –1}; an expansion velocity of the gas of 16.3more » ± 1 km s{sup –1}; the dust temperature and opacity of 122 ± 12 K and 1.04, respectively; and an age of 2.8 × 10{sup 4} yr. The WR star displays some cyclic variability. The mass computed for the WR60-6 nebula indicates that the material was probably ejected during its previous stages of evolution. In addition, we have identified a bright spot very close to the shell, which can be associated with the Midcourse Space Experiment source G312.13+00.20.« less

Global optimization of additive potential energy functions: Predicting binary Lennard-Jones clusters

NASA Astrophysics Data System (ADS)

Kolossváry, István; Bowers, Kevin J.

2010-11-01

We present a method for minimizing additive potential-energy functions. Our hidden-force algorithm can be described as an intricate multiplayer tug-of-war game in which teams try to break an impasse by randomly assigning some players to drop their ropes while the others are still tugging until a partial impasse is reached, then, instructing the dropouts to resume tugging, for all teams to come to a new overall impasse. Utilizing our algorithm in a non-Markovian parallel Monte Carlo search, we found 17 new putative global minima for binary Lennard-Jones clusters in the size range of 90-100 particles. The method is efficient enough that an unbiased search was possible; no potential-energy surface symmetries were exploited. All new minima are comprised of three nested polyicosahedral or polytetrahedral shells when viewed as a nested set of Connolly surfaces (though the shell structure has previously gone unscrutinized, known minima are often qualitatively similar). Unlike known minima, in which the outer and inner shells are comprised of the larger and smaller atoms, respectively, in 13 of the new minima, the atoms are not as clearly separated by size. Furthermore, while some known minima have inner shells stabilized by larger atoms, four of the new minima have outer shells stabilized by smaller atoms.

Three-cluster dynamics within an ab initio framework

DOE PAGES

Quaglioni, Sofia; Romero-Redondo, Carolina; Navratil, Petr

2013-09-26

In this study, we introduce a fully antisymmetrized treatment of three-cluster dynamics within the ab initio framework of the no-core shell model/resonating-group method. Energy-independent nonlocal interactions among the three nuclear fragments are obtained from realistic nucleon-nucleon interactions and consistent ab initio many-body wave functions of the clusters. The three-cluster Schrödinger equation is solved with bound-state boundary conditions by means of the hyperspherical-harmonic method on a Lagrange mesh. We discuss the formalism in detail and give algebraic expressions for systems of two single nucleons plus a nucleus. Using a soft similarity-renormalization-group evolved chiral nucleon-nucleon potential, we apply the method to amore » 4He+n+n description of 6He and compare the results to experiment and to a six-body diagonalization of the Hamiltonian performed within the harmonic-oscillator expansions of the no-core shell model. Differences between the two calculations provide a measure of core ( 4He) polarization effects.« less

Structural evolution of atomically precise thiolated bimetallic [Au(12+n)Cu₃₂(SR)(30+n)]⁴⁻ (n = 0, 2, 4, 6) nanoclusters.

PubMed

Yang, Huayan; Wang, Yu; Yan, Juanzhu; Chen, Xi; Zhang, Xin; Häkkinen, Hannu; Zheng, Nanfeng

2014-05-21

A series of all-thiol stabilized bimetallic Au-Cu nanoclusters, [Au(12+n)Cu32(SR)(30+n)](4-) (n = 0, 2, 4, 6 and SR = SPhCF3), are successfully synthesized and characterized by X-ray single-crystal analysis and density functional theory (DFT) calculations. Each cluster consists of a Keplerate two-shell Au12@Cu20 core protected by (6 - n) units of Cu2(SR)5 and n units of Cu2Au(SR)6 (n = 0, 2, 4, 6) motifs on its surface. The size and structural evolution of the clusters is atomically controlled by the Au precursors and countercations used in the syntheses. The clusters exhibit similar optical absorption properties that are not dependent on the number of surface Cu2Au(SR)6 units. Although DFT suggests an electronic structure with an 18-electron superatom shell closure, the clusters display different thermal stabilities. [Au(12+n)Cu32(SR)(30+n)](4-) clusters with n = 0 and 2 are more stable than those with n = 4 and 6. Moreover, an oxidation product of the clusters, [Au13Cu12(SR)20](4-), is structurally identified to gain insight into how the clusters are oxidized.

Synthesis and characterization of Pd(0), PdS, and Pd-PdO core-shell nanoparticles by solventless thermolysis of a Pd-thiolate cluster

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

Jose, Deepa; Jagirdar, Balaji R., E-mail: jagirdar@ipc.iisc.ernet.i

2010-09-15

Colloids of palladium nanoparticles have been prepared by the solvated metal atom dispersion (SMAD) method. The as-prepared Pd colloid consists of particles with an average diameter of 2.8{+-}0.1 nm. Digestive ripening of the as-prepared Pd colloid, a process involving refluxing the as-prepared colloid at or near the boiling point of the solvent in the presence of a passivating agent, dodecanethiol resulted in a previously reported Pd-thiolate cluster, [Pd(SC{sub 12}H{sub 25}){sub 2}]{sub 6} but did not render the expected narrowing down of the particle size distribution. Solventless thermolysis of the Pd-thiolate complex resulted in various Pd systems such as Pd(0), PdS,more » and Pd-PdO core-shell nanoparticles thus demonstrating its versatility. These Pd nanostructures have been characterized using high-resolution electron microscopy and powder X-ray diffraction methods. - Graphical abstract: Solventless thermolysis of a single palladium-thiolate cluster affords various Pd systems such as Pd(0), Pd-PdO core-shell, and PdS nanoparticles demonstrating the versatility of the precursor and the methodology.« less

On the shape and orientation control of an orbiting shallow spherical shell structure

NASA Technical Reports Server (NTRS)

Bainum, P. M.; Reddy, A. S. S. R.

1982-01-01

The dynamics of orbiting shallow flexible spherical shell structures under the influence of control actuators was studied. Control laws are developed to provide both attitude and shape control of the structure. The elastic modal frequencies for the fundamental and lower modes are closely grouped due to the effect of the shell curvature. The shell is gravity stabilized by a spring loaded dumbbell type damper attached at its apex. Control laws are developed based on the pole clustering techniques. Savings in fuel consumption can be realized by using the hybrid shell dumbbell system together with point actuators. It is indicated that instability may result by not including the orbital and first order gravity gradient effects in the plant prior to control law design.

Large-scale Map of Millimeter-wavelength Hydrogen Radio Recombination Lines around a Young Massive Star Cluster

NASA Astrophysics Data System (ADS)

Nguyen-Luong, Q.; Anderson, L. D.; Motte, F.; Kim, Kee-Tae; Schilke, P.; Carlhoff, P.; Beuther, H.; Schneider, N.; Didelon, P.; Kramer, C.; Louvet, F.; Nony, T.; Bihr, S.; Rugel, M.; Soler, J.; Wang, Y.; Bronfman, L.; Simon, R.; Menten, K. M.; Wyrowski, F.; Walmsley, C. M.

2017-08-01

We report the first map of large-scale (10 pc in length) emission of millimeter-wavelength hydrogen recombination lines (mm-RRLs) toward the giant H II region around the W43-Main young massive star cluster (YMC). Our mm-RRL data come from the IRAM 30 m telescope and are analyzed together with radio continuum and cm-RRL data from the Karl G. Jansky Very Large Array and HCO+ 1-0 line emission data from the IRAM 30 m. The mm-RRLs reveal an expanding wind-blown ionized gas shell with an electron density ˜70-1500 cm-3 driven by the WR/OB cluster, which produces a total Lyα photon flux of 1.5× {10}50 s-1. This shell is interacting with the dense neutral molecular gas in the W43-Main dense cloud. Combining the high spectral and angular resolution mm-RRL and cm-RRL cubes, we derive the two-dimensional relative distributions of dynamical and pressure broadening of the ionized gas emission and find that the RRL line shapes are dominated by pressure broadening (4-55 {km} {{{s}}}-1) near the YMC and by dynamical broadening (8-36 {km} {{{s}}}-1) near the shell’s edge. Ionized gas clumps hosting ultra-compact H II regions found at the edge of the shell suggest that large-scale ionized gas motion triggers the formation of new star generation near the periphery of the shell.

Dissociating functional brain networks by decoding the between-subject variability

PubMed Central

Seghier, Mohamed L.; Price, Cathy J.

2009-01-01

In this study we illustrate how the functional networks involved in a single task (e.g. the sensory, cognitive and motor components) can be segregated without cognitive subtractions at the second-level. The method used is based on meaningful variability in the patterns of activation between subjects with the assumption that regions belonging to the same network will have comparable variations from subject to subject. fMRI data were collected from thirty nine healthy volunteers who were asked to indicate with a button press if visually presented words were semantically related or not. Voxels were classified according to the similarity in their patterns of between-subject variance using a second-level unsupervised fuzzy clustering algorithm. The results were compared to those identified by cognitive subtractions of multiple conditions tested in the same set of subjects. This illustrated that the second-level clustering approach (on activation for a single task) was able to identify the functional networks observed using cognitive subtractions (e.g. those associated with vision, semantic associations or motor processing). In addition the fuzzy clustering approach revealed other networks that were not dissociated by the cognitive subtraction approach (e.g. those associated with high- and low-level visual processing and oculomotor movements). We discuss the potential applications of our method which include the identification of “hidden” or unpredicted networks as well as the identification of systems level signatures for different subgroupings of clinical and healthy populations. PMID:19150501

Signal-Noise Identification of Magnetotelluric Signals Using Fractal-Entropy and Clustering Algorithm for Targeted De-Noising

NASA Astrophysics Data System (ADS)

Li, Jin; Zhang, Xian; Gong, Jinzhe; Tang, Jingtian; Ren, Zhengyong; Li, Guang; Deng, Yanli; Cai, Jin

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

FISim: A new similarity measure between transcription factor binding sites based on the fuzzy integral

PubMed Central

Garcia, Fernando; Lopez, Francisco J; Cano, Carlos; Blanco, Armando

2009-01-01

Background Regulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in computational biology which includes the issue of comparing putative motifs with one another and with motifs that are already known. The relative importance of each nucleotide within a given position in the PFMs should be considered in order to compute PFM similarities. Furthermore, biological data are inherently noisy and imprecise. Fuzzy set theory is particularly suitable for modeling imprecise data, whereas fuzzy integrals are highly appropriate for representing the interaction among different information sources. Results We propose FISim, a new similarity measure between PFMs, based on the fuzzy integral of the distance of the nucleotides with respect to the information content of the positions. Unlike existing methods, FISim is designed to consider the higher contribution of better conserved positions to the binding affinity. FISim provides excellent results when dealing with sets of randomly generated motifs, and outperforms the remaining methods when handling real datasets of related motifs. Furthermore, we propose a new cluster methodology based on kernel theory together with FISim to obtain groups of related motifs potentially bound by the same TFs, providing more robust results than existing approaches. Conclusion FISim corrects a design flaw of the most popular methods, whose measures favour similarity of low information content positions. We use our measure to successfully identify motifs that describe binding sites for the same TF and to solve real-life problems. In this study the reliability of fuzzy technology for motif comparison tasks is proven. PMID:19615102

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

PubMed Central

2012-01-01

Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms. Conclusions This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces. PMID:22871125

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

PubMed

Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

2012-08-08

Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms. This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

Fullerene-like Polyoxotitanium Cage with High Solution Stability.

PubMed

Gao, Mei-Yan; Wang, Fei; Gu, Zhi-Gang; Zhang, De-Xiang; Zhang, Lei; Zhang, Jian

2016-03-02

We present the formation of the largest titanium-oxo cluster, [Ti42(μ3-O)60(OiPr)42(OH)12)](6-), with the first fullerene-like Ti-O shell structure. The {Ti42O60} core of this compound exemplifies the same icosahedral (Ih) symmetry as C60, the highest possible symmetry for molecules. According to the coordination environments, the Ti centers in this cluster can be arranged into a Platonic {Ti12} icosahedron and an Archimedean {Ti30} icosidodecahedron. The solution stability of this cluster was confirmed by electrospray ionization mass spectrometry. The spherical body of the {Ti42O60} core has an inside diameter of 1.05 nm and an outside diameter of 1.53 nm, which could be directly visualized by high-resolution transmission electron microscopy. Our results demonstrate that titanium oxide can also form fullerene-like shell structures.

Classification of arrhythmia using hybrid networks.

PubMed

Haseena, Hassan H; Joseph, Paul K; Mathew, Abraham T

2011-12-01

Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.

Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

USGS Publications Warehouse

Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

2007-01-01

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering

NASA Astrophysics Data System (ADS)

Liu, Ti C.; Mitra, Sunanda

1996-06-01

Image compression plays a crucial role in many important and diverse applications requiring efficient storage and transmission. This work mainly focuses on a wavelet transform (WT) based compression of fingerprint images and the subsequent classification of the reconstructed images. The algorithm developed involves multiresolution wavelet decomposition, uniform scalar quantization, entropy and run- length encoder/decoder and K-means clustering of the invariant moments as fingerprint features. The performance of the WT-based compression algorithm has been compared with JPEG current image compression standard. Simulation results show that WT outperforms JPEG in high compression ratio region and the reconstructed fingerprint image yields proper classification.

Categorizing document by fuzzy C-Means and K-nearest neighbors approach

NASA Astrophysics Data System (ADS)

Priandini, Novita; Zaman, Badrus; Purwanti, Endah

2017-08-01

Increasing of technology had made categorizing documents become important. It caused by increasing of number of documents itself. Managing some documents by categorizing is one of Information Retrieval application, because it involve text mining on its process. Whereas, categorization technique could be done both Fuzzy C-Means (FCM) and K-Nearest Neighbors (KNN) method. This experiment would consolidate both methods. The aim of the experiment is increasing performance of document categorize. First, FCM is in order to clustering training documents. Second, KNN is in order to categorize testing document until the output of categorization is shown. Result of the experiment is 14 testing documents retrieve relevantly to its category. Meanwhile 6 of 20 testing documents retrieve irrelevant to its category. Result of system evaluation shows that both precision and recall are 0,7.

Data driven model generation based on computational intelligence

NASA Astrophysics Data System (ADS)

Gemmar, Peter; Gronz, Oliver; Faust, Christophe; Casper, Markus

2010-05-01

The simulation of discharges at a local gauge or the modeling of large scale river catchments are effectively involved in estimation and decision tasks of hydrological research and practical applications like flood prediction or water resource management. However, modeling such processes using analytical or conceptual approaches is made difficult by both complexity of process relations and heterogeneity of processes. It was shown manifold that unknown or assumed process relations can principally be described by computational methods, and that system models can automatically be derived from observed behavior or measured process data. This study describes the development of hydrological process models using computational methods including Fuzzy logic and artificial neural networks (ANN) in a comprehensive and automated manner. Methods We consider a closed concept for data driven development of hydrological models based on measured (experimental) data. The concept is centered on a Fuzzy system using rules of Takagi-Sugeno-Kang type which formulate the input-output relation in a generic structure like Ri : IFq(t) = lowAND...THENq(t+Δt) = ai0 +ai1q(t)+ai2p(t-Δti1)+ai3p(t+Δti2)+.... The rule's premise part (IF) describes process states involving available process information, e.g. actual outlet q(t) is low where low is one of several Fuzzy sets defined over variable q(t). The rule's conclusion (THEN) estimates expected outlet q(t + Δt) by a linear function over selected system variables, e.g. actual outlet q(t), previous and/or forecasted precipitation p(t ?Δtik). In case of river catchment modeling we use head gauges, tributary and upriver gauges in the conclusion part as well. In addition, we consider temperature and temporal (season) information in the premise part. By creating a set of rules R = {Ri|(i = 1,...,N)} the space of process states can be covered as concise as necessary. Model adaptation is achieved by finding on optimal set A = (aij) of conclusion parameters with respect to a defined rating function and experimental data. To find A, we use for example a linear equation solver and RMSE-function. In practical process models, the number of Fuzzy sets and the according number of rules is fairly low. Nevertheless, creating the optimal model requires some experience. Therefore, we improved this development step by methods for automatic generation of Fuzzy sets, rules, and conclusions. Basically, the model achievement depends to a great extend on the selection of the conclusion variables. It is the aim that variables having most influence on the system reaction being considered and superfluous ones being neglected. At first, we use Kohonen maps, a specialized ANN, to identify relevant input variables from the large set of available system variables. A greedy algorithm selects a comprehensive set of dominant and uncorrelated variables. Next, the premise variables are analyzed with clustering methods (e.g. Fuzzy-C-means) and Fuzzy sets are then derived from cluster centers and outlines. The rule base is automatically constructed by permutation of the Fuzzy sets of the premise variables. Finally, the conclusion parameters are calculated and the total coverage of the input space is iteratively tested with experimental data, rarely firing rules are combined and coarse coverage of sensitive process states results in refined Fuzzy sets and rules. Results The described methods were implemented and integrated in a development system for process models. A series of models has already been built e.g. for rainfall-runoff modeling or for flood prediction (up to 72 hours) in river catchments. The models required significantly less development effort and showed advanced simulation results compared to conventional models. The models can be used operationally and simulation takes only some minutes on a standard PC e.g. for a gauge forecast (up to 72 hours) for the whole Mosel (Germany) river catchment.

Fuzzy C-means classification for corrosion evolution of steel images

NASA Astrophysics Data System (ADS)

Trujillo, Maite; Sadki, Mustapha

2004-05-01

An unavoidable problem of metal structures is their exposure to rust degradation during their operational life. Thus, the surfaces need to be assessed in order to avoid potential catastrophes. There is considerable interest in the use of patch repair strategies which minimize the project costs. However, to operate such strategies with confidence in the long useful life of the repair, it is essential that the condition of the existing coatings and the steel substrate can be accurately quantified and classified. This paper describes the application of fuzzy set theory for steel surfaces classification according to the steel rust time. We propose a semi-automatic technique to obtain image clustering using the Fuzzy C-means (FCM) algorithm and we analyze two kinds of data to study the classification performance. Firstly, we investigate the use of raw images" pixels without any pre-processing methods and neighborhood pixels. Secondly, we apply Gaussian noise to the images with different standard deviation to study the FCM method tolerance to Gaussian noise. The noisy images simulate the possible perturbations of the images due to the weather or rust deposits in the steel surfaces during typical on-site acquisition procedures

Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.

PubMed

Nguyen, M N

2010-04-01

Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.

On fuzzy semantic similarity measure for DNA coding.

PubMed

Ahmad, Muneer; Jung, Low Tang; Bhuiyan, Md Al-Amin

2016-02-01

A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. Copyright © 2015 Elsevier Ltd. All rights reserved.

A fuzzy decision tree for fault classification.

PubMed

Zio, Enrico; Baraldi, Piero; Popescu, Irina C

2008-02-01

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.

Development of New Open-Shell Perturbation and Coupled-Cluster Theories Based on Symmetric Spin Orbitals

NASA Technical Reports Server (NTRS)

Lee, Timothy J.; Arnold, James O. (Technical Monitor)

1994-01-01

A new spin orbital basis is employed in the development of efficient open-shell coupled-cluster and perturbation theories that are based on a restricted Hartree-Fock (RHF) reference function. The spin orbital basis differs from the standard one in the spin functions that are associated with the singly occupied spatial orbital. The occupied orbital (in the spin orbital basis) is assigned the delta(+) = 1/square root of 2(alpha+Beta) spin function while the unoccupied orbital is assigned the delta(-) = 1/square root of 2(alpha-Beta) spin function. The doubly occupied and unoccupied orbitals (in the reference function) are assigned the standard alpha and Beta spin functions. The coupled-cluster and perturbation theory wave functions based on this set of "symmetric spin orbitals" exhibit much more symmetry than those based on the standard spin orbital basis. This, together with interacting space arguments, leads to a dramatic reduction in the computational cost for both coupled-cluster and perturbation theory. Additionally, perturbation theory based on "symmetric spin orbitals" obeys Brillouin's theorem provided that spin and spatial excitations are both considered. Other properties of the coupled-cluster and perturbation theory wave functions and models will be discussed.

Equation-of-motion coupled-cluster method for ionised states with spin-orbit coupling using open-shell reference wavefunction

NASA Astrophysics Data System (ADS)

Wang, Zhifan; Wang, Fan

2018-04-01

The equation-of-motion coupled-cluster method for ionised states at the singles and doubles level (EOM-IP-CCSD) with spin-orbit coupling (SOC) included in post-Hartree-Fock (HF) steps is extended to spatially non-degenerate open-shell systems such as high spin states of s1, p3, σ1 or π2 configuration in this work. Pseudopotentials are employed to treat relativistic effects and spin-unrestricted scalar relativistic HF determinant is adopted as reference in calculations. Symmetry is not exploited in the implementation since both time-reversal and spatial symmetry is broken due to SOC. IPs with the EOM-IP-CCSD approach are those from the 3Σ1- states for high spin state of π2 configuration, while the ground state is the 3Σ0- state. When removing an electron from the high spin state of p3 configuration, only the 3P2 state can be reached. The open-shell EOM-IP-CCSD approach with SOC was employed in calculating IPs of some open-shell atoms with s1 configuration, diatomic molecules with π2 configuration and SOC splitting of the ionised π1 state, as well as IPs of VA atoms with p3 configuration. Our results demonstrate that this approach can be applied to ionised states of spatially non-degenerate open-shell states containing heavy elements with reasonable accuracy.

ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.

PubMed

Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine

2012-04-01

Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem.

MAPS OF MASSIVE CLUMPS IN THE EARLY STAGE OF CLUSTER FORMATION: TWO MODES OF CLUSTER FORMATION, COEVAL OR NON-COEVAL?

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

Higuchi, Aya E.; Saito, Masao; Mauersberger, Rainer

2013-03-10

We present maps of seven young massive molecular clumps within five target regions in C{sup 18}O (J = 1-0) line emission, using the Nobeyama 45 m telescope. These clumps, which are not associated with clusters, lie at distances between 0.7 and 2.1 kpc. We find C{sup 18}O clumps with radii of 0.5-1.7 pc, masses of 470-4200 M{sub Sun }, and velocity widths of 1.4-3.3 km s{sup -1}. All of the clumps are massive and approximately in virial equilibrium, suggesting they will potentially form clusters. Three of our target regions are associated with H II regions (CWHRs), while the other twomore » are unassociated with H II regions (CWOHRs). The C{sup 18}O clumps can be classified into two morphological types: CWHRs with a filamentary or shell-like structure and spherical CWOHRs. The two CWOHRs have systematic velocity gradients. Using the publicly released WISE database, Class I and Class II protostellar candidates are identified within the C{sup 18}O clumps. The fraction of Class I candidates among all YSO candidates (Class I+Class II) is {>=}50% in CWHRs and {<=}50% in CWOHRs. We conclude that effects from the H II regions can be seen in (1) the spatial distributions of the clumps: filamentary or shell-like structure running along the H II regions; (2) the velocity structures of the clumps: large velocity dispersion along shells; and (3) the small age spreads of YSOs. The small spreads in age of the YSOs show that the presence of H II regions tends to trigger coeval cluster formation.« less

A FORMATION SCENARIO OF YOUNG STELLAR GROUPS IN THE REGION OF THE SCORPIO CENTAURUS OB ASSOCIATION

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

Ortega, V. G.; Jilinski, E.; De la Reza, R.

2009-04-15

The main objective of this work is to investigate the role played by Lower Centaurus Crux (LCC) and Upper Centaurus Lupus (UCL), both subcomponents of the Scorpio Centaurus OB association (Sco-Cen), in the formation of the groups {beta} Pictoris, TW Hydrae, and the {eta} Chamaeleontis cluster. The dynamical evolution of all the stellar groups involved and of the bubbles and shells blown by LCC and UCL are calculated, and followed from the past to the present. This leads to a formation scenario in which (1) the groups {beta} Pictoris, TW Hydrae were formed in the wake of the shells createdmore » by LCC and UCL, (2) the young cluster {eta} Chamaeleontis was born as a consequence of the collision of the shells of LCC and UCL, and (3) the formation of Upper Scorpius (US), the other main subcomponent of the Sco-Cen association, may have been started by the same process that created {eta} Chamaeleontis.« less

Machine learning approaches for estimation of prediction interval for the model output.

PubMed

Shrestha, Durga L; Solomatine, Dimitri P

2006-03-01

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

Electrical Load Profile Analysis Using Clustering Techniques

NASA Astrophysics Data System (ADS)

Damayanti, R.; Abdullah, A. G.; Purnama, W.; Nandiyanto, A. B. D.

2017-03-01

Data mining is one of the data processing techniques to collect information from a set of stored data. Every day the consumption of electricity load is recorded by Electrical Company, usually at intervals of 15 or 30 minutes. This paper uses a clustering technique, which is one of data mining techniques to analyse the electrical load profiles during 2014. The three methods of clustering techniques were compared, namely K-Means (KM), Fuzzy C-Means (FCM), and K-Means Harmonics (KHM). The result shows that KHM is the most appropriate method to classify the electrical load profile. The optimum number of clusters is determined using the Davies-Bouldin Index. By grouping the load profile, the demand of variation analysis and estimation of energy loss from the group of load profile with similar pattern can be done. From the group of electric load profile, it can be known cluster load factor and a range of cluster loss factor that can help to find the range of values of coefficients for the estimated loss of energy without performing load flow studies.

Systematic study of α decay of nuclei around the Z =82 , N =126 shell closures within the cluster-formation model and proximity potential 1977 formalism

NASA Astrophysics Data System (ADS)

Deng, Jun-Gang; Zhao, Jie-Cheng; Chu, Peng-Cheng; Li, Xiao-Hua

2018-04-01

In the present work, we systematically study the α decay preformation factors Pα within the cluster-formation model and α decay half-lives by the proximity potential 1977 formalism for nuclei around Z =82 ,N =126 closed shells. The calculations show that the realistic Pα is linearly dependent on the product of valance protons (holes) and valance neutrons (holes) NpNn . It is consistent with our previous works [Sun et al., Phys. Rev. C 94, 024338 (2016), 10.1103/PhysRevC.94.024338; Deng et al., Phys. Rev. C 96, 024318 (2017), 10.1103/PhysRevC.96.024318], in which Pα are model dependent and extracted from the ratios of calculated α half-lives to experimental data. Combining with our previous works, we confirm that the valance proton-neutron interaction plays a key role in the α preformation for nuclei around Z =82 ,N =126 shell closures whether the Pα is model dependent or microcosmic. In addition, our calculated α decay half-lives by using the proximity potential 1977 formalism taking Pα evaluated by the cluster-formation model can well reproduce the experimental data and significantly reduce the errors.

Detection of Another Molecular Bubble in the Galactic Center

NASA Astrophysics Data System (ADS)

Tsujimoto, Shiho; Oka, Tomoharu; Takekawa, Shunya; Yamada, Masaya; Tokuyama, Sekito; Iwata, Yuhei; Roll, Justin A.

2018-04-01

The l=-1\\buildrel{\\circ}\\over{.} 2 region in the Galactic center has a high CO J = 3–2/J = 1–0 intensity ratio and extremely broad velocity width. This paper reports the detection of five expanding shells in the l=-1\\buildrel{\\circ}\\over{.} 2 region based on the CO J = 1–0, 13CO J = 1–0, CO J = 3–2, and SiO J = 8–7 line data sets obtained with the Nobeyama Radio Observatory 45 m telescope and James Clerk Maxwell Telescope. The kinetic energy and expansion time of the expanding shells are estimated to be {10}48.3{--50.8} erg and {10}4.7{--5.0} yr, respectively. The origin of these expanding shells is discussed. The total kinetic energy of 1051 erg and the typical expansion time of ∼105 yr correspond to multiple supernova explosions at a rate of 10‑5–10‑4 yr‑1. This indicates that the l=-1\\buildrel{\\circ}\\over{.} 2 region may be a molecular bubble associated with an embedded massive star cluster, although the absence of an infrared counterpart makes this interpretation somewhat controversial. The expansion time of the shells increases as the Galactic longitude decreases, suggesting that the massive star cluster is moving from Galactic west to east with respect to the interacting molecular gas. We propose a model wherein the cluster is moving along the innermost x 1 orbit and the interacting gas collides with it from the Galactic eastern side.

Vitellogenesis in Archigetes sieboldi Leuckart, 1878 (Cestoda, Caryophyllidea, Caryophyllaeidae), an intestinal parasite of carp (Cyprinus carpio L.).

PubMed

Brunanská, M; Mackiewicz, J S; Nebesárová, J

2012-12-01

Vitellogenesis in the caryophyllidean tapeworm Archigetes sieboldi Leuckart, 1878, from carp Cyprinus carpio L. in Slovakia, has been examined using transmission electron microscopy and cytochemical staining with periodic acid-thiosemicarbazide-silver proteinate (PA-TSC-SP) for glycogen. Vitelline follicles extend in two lateral bands in the medullary parenchyma along both sides of the monozoic body. They are surrounded by an external basal lamina and contain vitellocytes and an interstitial tissue. The general pattern of vitellogenesis is essentially like that of other caryophyllideans. It involves four stages: immature, early maturing, advanced maturing cells and mature vitellocytes. During vitellogenesis, a continuous increase in cell volume is accompanied by an extensive development of cell components engaged in shell globule formation, e.g. granular endoplasmic reticulum and Golgi. Shell globule clusters are membrane-bound. Nuclear and nucleolar transformation are associated with formation and storage of large amounts of intranuclear glycogen, a very specific feature of the Caryophyllidea. For the first time, (a) additional vitelline material in Archigetes is represented by lamellar bodies and (b) lipid droplets are described in the mature vitellocytes from vitelline follicles and vitelloduct of the Caryophyllidea. Our results indicate that there may be a double origin of lamellar bodies: either from the endoplasmic reticulum or through transformation of shell globule/shell globule clusters. Lamellar body clusters and some single lamellar bodies appear to have a membrane. Other ultrastructural features of vitellogenesis and/or vitellocyte in A. sieboldi from its vertebrate (fish) and invertebrate (oligochaete) hosts are briefly compared and contrasted with those in other caryophyllideans and/or Neodermata.

A platonic solid templating Archimedean solid: an unprecedented nanometre-sized Ag37 cluster

NASA Astrophysics Data System (ADS)

Li, Xiao-Yu; Su, Hai-Feng; Yu, Kai; Tan, Yuan-Zhi; Wang, Xing-Po; Zhao, Ya-Qin; Sun, Di; Zheng, Lan-Sun

2015-04-01

The spontaneous formation of discrete spherical nanosized molecules is prevalent in nature, but the authentic structural mimicry of such highly symmetric polyhedra from edge sharing of regular polygons has remained elusive. Here we present a novel ball-shaped {(HNEt3)[Ag37S4(SC6H4tBu)24(CF3COO)6(H2O)12]} cluster (1) that is assembled via a one-pot process from polymeric {(HNEt3)2[Ag10(SC6H4tBu)12]}n and CF3COOAg. Single crystal X-ray analysis confirmed that 1 is a Td symmetric spherical molecule with a [Ag36(SC6H4tBu)24] anion shell enwrapping a AgS4 tetrahedron. The shell topology of 1 belongs to one of 13 Archimedean solids, a truncated tetrahedron with four edge-shared hexagons and trigons, which are supported by a AgS4 Platonic solid in the core. Interestingly, the cluster emits green luminescence centered at 515 nm at room temperature. Our investigations have provided a promising synthetic protocol for a high-nuclearity silver cluster based on underlying geometrical principles.The spontaneous formation of discrete spherical nanosized molecules is prevalent in nature, but the authentic structural mimicry of such highly symmetric polyhedra from edge sharing of regular polygons has remained elusive. Here we present a novel ball-shaped {(HNEt3)[Ag37S4(SC6H4tBu)24(CF3COO)6(H2O)12]} cluster (1) that is assembled via a one-pot process from polymeric {(HNEt3)2[Ag10(SC6H4tBu)12]}n and CF3COOAg. Single crystal X-ray analysis confirmed that 1 is a Td symmetric spherical molecule with a [Ag36(SC6H4tBu)24] anion shell enwrapping a AgS4 tetrahedron. The shell topology of 1 belongs to one of 13 Archimedean solids, a truncated tetrahedron with four edge-shared hexagons and trigons, which are supported by a AgS4 Platonic solid in the core. Interestingly, the cluster emits green luminescence centered at 515 nm at room temperature. Our investigations have provided a promising synthetic protocol for a high-nuclearity silver cluster based on underlying geometrical principles. Electronic supplementary information (ESI) available: detailed synthesis procedure, tables, crystal data in CIF files, IR data, TGA results and powder X-ray diffractogram for 1. CCDC 1042228. See DOI: 10.1039/c5nr01222h

7TH International Workshop on Laser Physics (LPHYS󈨦) Berlin, Germany July 6-10, 1998 Program and Book of Abstracts: Volume 1.

DTIC Science & Technology

1998-07-01

to a hydrodynamic expansion of the so-called " nanoplasma " into the vacuum. The relative weight of each of these two explosion mechanisms depends on... nanoplasma . In particular, we observe L-shell emission in the case of Krypton and Xenon clusters and K-shell emission for Argon. Our results concern the

Ligand-protected gold clusters: the structure, synthesis and applications

NASA Astrophysics Data System (ADS)

Pichugina, D. A.; Kuz'menko, N. E.; Shestakov, A. F.

2015-11-01

Modern concepts of the structure and properties of atomic gold clusters protected by thiolate, selenolate, phosphine and phenylacetylene ligands are analyzed. Within the framework of the superatom theory, the 'divide and protect' approach and the structure rule, the stability and composition of a cluster are determined by the structure of the cluster core, the type of ligands and the total number of valence electrons. Methods of selective synthesis of gold clusters in solution and on the surface of inorganic composites based, in particular, on the reaction of Aun with RS, RSe, PhC≡C, Hal ligands or functional groups of proteins, on stabilization of clusters in cavities of the α-, β and γ-cyclodextrin molecules (Au15 and Au25) and on anchorage to a support surface (Au25/SiO2, Au20/C, Au10/FeOx) are reviewed. Problems in this field are also discussed. Among the methods for cluster structure prediction, particular attention is given to the theoretical approaches based on the density functional theory (DFT). The structures of a number of synthesized clusters are described using the results obtained by X-ray diffraction analysis and DFT calculations. A possible mechanism of formation of the SR(AuSR)n 'staple' units in the cluster shell is proposed. The structure and properties of bimetallic clusters MxAunLm (M=Pd, Pt, Ag, Cu) are discussed. The Pd or Pt atom is located at the centre of the cluster, whereas Ag and Cu atoms form bimetallic compounds in which the heteroatom is located on the surface of the cluster core or in the 'staple' units. The optical properties, fluorescence and luminescence of ligand-protected gold clusters originate from the quantum effects of the Au atoms in the cluster core and in the oligomeric SR(AuSR)x units in the cluster shell. Homogeneous and heterogeneous reactions catalyzed by atomic gold clusters are discussed in the context of the reaction mechanism and the nature of the active sites. The bibliography includes 345 references.

Statistical and Clustering Based Rules Extraction Approaches for Fuzzy Model to Estimate Academic Performance in Distance Education

ERIC Educational Resources Information Center

Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc

2015-01-01

The demand for distance education has been increasing at a rapid pace all around the world. This, in turn, places a special importance on the need for the development of more distance education systems. However, there is an alarming rise in the number of distance education students that drop out of the system without asking for any help. The…

The heterogeneous ice shell thickness of Enceladus

NASA Astrophysics Data System (ADS)

Lucchetti, Alice; Pozzobon, Riccardo; Mazzarini, Francesco; Cremonese, Gabriele; Massironi, Matteo

2016-10-01

Saturn's moon Enceladus is the smallest Solar System body that presents an intense geologic activity on its surface. Plumes erupting from Enceladus' South Polar terrain (SPT) provide direct evidence of a reservoir of liquid below the surface. Previous analysis of gravity data determined that the ice shell above the liquid ocean must be 30-40 km thick from the South Pole up to 50° S latitude (Iess et al., 2014), however, understand the global or regional nature of the ocean beneath the ice crust is still challenging. To infer the thickness of the outer ice shell and prove the global extent of the ocean, we used the self-similar clustering method (Bonnet et al., 2001; Bour et al., 2002) to analyze the widespread fractures of the Enceladus's surface. The spatial distribution of fractures has been analyzed in terms of their self-similar clustering and a two-point correlation method was used to measure the fractal dimension of the fractures population (Mazzarini, 2004, 2010). A self-similar clustering of fractures is characterized by a correlation coefficient with a size range defined by a lower and upper cut-off, that represent a mechanical discontinuity and the thickness of the fractured icy crust, thus connected to the liquid reservoir. Hence, this method allowed us to estimate the icy shell thickness values in different regions of Enceladus from SPT up to northern regions.We mapped fractures in ESRI ArcGis environment in different regions of the satellite improving the recently published geological map (Crow-Willard and Pappalardo, 2015). On these regions we have taken into account the fractures, such as wide troughs and narrow troughs, located in well-defined geological units. Firstly, we analyzed the distribution of South Polar Region fracture patterns finding an ice shell thickness of ~ 31 km, in agreement with gravity measurements (Iess et al., 2014). Then, we applied the same approach to other four regions of the satellite inferring an increasing of the ice shell thickness from 31 to 70 km from the South Pole to northern regions. By these findings, we prove the global extent of the ocean underneath the ice crust of the satellite.

A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

PubMed

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2015-12-01

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Komasi, Mehdi

2013-05-01

This paper demonstrates the potential use of Artificial Intelligence (AI) techniques for predicting daily runoff at multiple gauging stations. Uncertainty and complexity of the rainfall-runoff process due to its variability in space and time in one hand and lack of historical data on the other hand, cause difficulties in the spatiotemporal modeling of the process. In this paper, an Integrated Geomorphological Adaptive Neuro-Fuzzy Inference System (IGANFIS) model conjugated with C-means clustering algorithm was used for rainfall-runoff modeling at multiple stations of the Eel River watershed, California. The proposed model could be used for predicting runoff in the stations with lack of data or any sub-basin within the watershed because of employing the spatial and temporal variables of the sub-basins as the model inputs. This ability of the integrated model for spatiotemporal modeling of the process was examined through the cross validation technique for a station. In this way, different ANFIS structures were trained using Sugeno algorithm in order to estimate daily discharge values at different stations. In order to improve the model efficiency, the input data were then classified into some clusters by the means of fuzzy C-means (FCMs) method. The goodness-of-fit measures support the gainful use of the IGANFIS and FCM methods in spatiotemporal modeling of hydrological processes.

Provenance study of ancient Chinese Yaozhou porcelain by neutron activation analysis

NASA Astrophysics Data System (ADS)

Li, G. X.; Y Gao, Z.; Li, R. W.; Zhao, W. J.; Xie, J. Z.; Feng, S. L.; Zhuo, Z. X.; Y Fan, D.; Zhang, Y.; Cai, Z. F.; Liu, H.

2003-09-01

This paper reports our study of the provenance of ancient Chinese Yaozhou porcelain. The content of 29 elements in the Yaozhou porcelain samples was measured by neutron activation analysis (NAA). The NAA data were further analysed using fuzzy cluster analysis to obtain the trend fuzzy cluster diagrams. These samples with different glaze colour, ranging over more than 700 years, were fired in different kilns. Our analysis indicates the relatively concentrated distribution of the sources of the raw material for the Yaozhou porcelain body samples. They can be classified into two independent periods, i.e. the Tang (AD 618-907) and the Five Dynasties (AD 907-960) period, and the Song (AD 960-1279) and Jin (AD 1115-1234) period. Our analysis also indicates that the sources of the raw material for the ancient Yaozhou porcelain glaze samples are quite scattered and those for the black glaze in the Tang Dynasty are very concentrated. The sources of the raw material for the celadon glaze and the white glaze in the Tang Dynasty are widely distributed and those for the celadon glaze in the Song Dynasty are close to those of the bluish white glaze in the Jin Dynasty, and they are very concentrated. The sources of the raw material for the porcelain glazes cover those of the porcelain bodies.

Determination of the ultrasound power effects on flavonoid compounds from Psidium guajava L. using ANFIS

NASA Astrophysics Data System (ADS)

Ratu Ayu, Humairoh; Suryono, Suryono; Endro Suseno, Jatmiko; Kurniawati, Ratna

2018-05-01

The Adaptive Neural Fuzzy Inference System (ANFIS) model was used to predict and optimize the content of flavonoid compounds in guava leaves (Psidium Guajava L.). The extraction process was carried out by using ultrasound assisted extraction (UAE) with the variable parameters: temperature ranging from 25°C to 35°C, ultrasonic frequency (30 - 40 kHz) and extraction time (20 - 40 minutes). ANFIS learning procedure began by providing the input variable data set (temperature, frequency and time) and the output of the flavonoid compounds from the experiments that had been done. Subtractive clustering methods was used in the manufacture of FIS (fuzzy inference system) structures by varying the range of influence parameters to generate the ANFIS system. The ANFIS trainingsconducted wereaimed at minimum error value. The results showed that the best ANFIS models used a subtractive clustering method, in which the ranges of influence 0.1 were 0.70 x 10-4 for training RMSE, 8.11 for testing RMSE, 2.7 % MAPE, and 7.72 MAE. The optimum condition was obtained at a temperature of 35°C and frequency of 40 kHz, for 30 minutes. This result proves that the ANFIS model can be used to predict the content of flavonoid compounds in guava leaves.

Studies of the evolution of the x ray emission of clusters of galaxies

NASA Technical Reports Server (NTRS)

Henry, J. Patrick

1990-01-01

The x ray luminosity function of clusters of galaxies was determined at different cosmic epoches using data from the Einstein Observatory Extended Medium Survey. The sample consisted of 67 x ray selected clusters that were grouped into three redshift shells. Evolution was detected in the x ray properties of clusters. The present volume density of high luminosity clusters was found to be greater than it was in the past. This result is the first convincing evidence for evolution in the x ray properties of clusters. Investigations into the constraints provided by these data on various Cold Dark Matter models are underway.

Interactive visual exploration and refinement of cluster assignments.

PubMed

Kern, Michael; Lex, Alexander; Gehlenborg, Nils; Johnson, Chris R

2017-09-12

With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

Capturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems research.

PubMed

Liu, Shiyong; Triantis, Konstantinos P; Zhao, Li; Wang, Youfa

2018-01-01

In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.

Automated segmentation of MS lesions in FLAIR, DIR and T2-w MR images via an information theoretic approach

NASA Astrophysics Data System (ADS)

Hill, Jason E.; Matlock, Kevin; Pal, Ranadip; Nutter, Brian; Mitra, Sunanda

2016-03-01

Magnetic Resonance Imaging (MRI) is a vital tool in the diagnosis and characterization of multiple sclerosis (MS). MS lesions can be imaged with relatively high contrast using either Fluid Attenuated Inversion Recovery (FLAIR) or Double Inversion Recovery (DIR). Automated segmentation and accurate tracking of MS lesions from MRI remains a challenging problem. Here, an information theoretic approach to cluster the voxels in pseudo-colorized multispectral MR data (FLAIR, DIR, T2-weighted) is utilized to automatically segment MS lesions of various sizes and noise levels. The Improved Jump Method (IJM) clustering, assisted by edge suppression, is applied to the segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions, if present, into a subset of slices determined to be the best MS lesion candidates via Otsu's method. From this preliminary clustering, the modal data values for the tissues can be determined. A Euclidean distance is then used to estimate the fuzzy memberships of each brain voxel for all tissue types and their 50/50 partial volumes. From these estimates, binary discrete and fuzzy MS lesion masks are constructed. Validation is provided by using three synthetic MS lesions brains (mild, moderate and severe) with labeled ground truths. The MS lesions of mild, moderate and severe designations were detected with a sensitivity of 83.2%, and 88.5%, and 94.5%, and with the corresponding Dice similarity coefficient (DSC) of 0.7098, 0.8739, and 0.8266, respectively. The effect of MRI noise is also examined by simulated noise and the application of a bilateral filter in preprocessing.

Eigenspace-based fuzzy c-means for sensing trending topics in Twitter

NASA Astrophysics Data System (ADS)

Muliawati, T.; Murfi, H.

2017-07-01

As the information and communication technology are developed, the fulfillment of information can be obtained through social media, like Twitter. The enormous number of internet users has triggered fast and large data flow, thus making the manual analysis is difficult or even impossible. An automated methods for data analysis is needed, one of which is the topic detection and tracking. An alternative method other than latent Dirichlet allocation (LDA) is a soft clustering approach using Fuzzy C-Means (FCM). FCM meets the assumption that a document may consist of several topics. However, FCM works well in low-dimensional data but fails in high-dimensional data. Therefore, we propose an approach where FCM works on low-dimensional data by reducing the data using singular value decomposition (SVD). Our simulations show that this approach gives better accuracies in term of topic recall than LDA for sensing trending topic in Twitter about an event.

Optimization of Sinter Plant Operating Conditions Using Advanced Multivariate Statistics: Intelligent Data Processing

NASA Astrophysics Data System (ADS)

Fernández-González, Daniel; Martín-Duarte, Ramón; Ruiz-Bustinza, Íñigo; Mochón, Javier; González-Gasca, Carmen; Verdeja, Luis Felipe

2016-08-01

Blast furnace operators expect to get sinter with homogenous and regular properties (chemical and mechanical), necessary to ensure regular blast furnace operation. Blends for sintering also include several iron by-products and other wastes that are obtained in different processes inside the steelworks. Due to their source, the availability of such materials is not always consistent, but their total production should be consumed in the sintering process, to both save money and recycle wastes. The main scope of this paper is to obtain the least expensive iron ore blend for the sintering process, which will provide suitable chemical and mechanical features for the homogeneous and regular operation of the blast furnace. The systematic use of statistical tools was employed to analyze historical data, including linear and partial correlations applied to the data and fuzzy clustering based on the Sugeno Fuzzy Inference System to establish relationships among the available variables.

Inhomogeneity compensation for MR brain image segmentation using a multi-stage FCM-based approach.

PubMed

Szilágyi, László; Szilágyi, Sándor M; Dávid, László; Benyó, Zoltán

2008-01-01

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of the slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

Is the Eagle Nebula powered by a hidden supernova remnant ?

NASA Astrophysics Data System (ADS)

Boulanger, Francois

2008-10-01

Spitzer observations of the Eagle nebula (M16) reveal the presence of a large (8 pc diameter) shell of dust heated to anomalously high temperatures. Modeling of dust excitation shows that the shell emission cannot be powered by the cluster UV radiation but that it can be accounted for by collisionally heated dust in a young (a few 1000 yrs) supernova remnant. We have re-analyzed deep Chandra observations that show diffuse emission consistent with this hypothesis, but also with galactic ridge emission. We propose a 50 ksec XMM observation to probe the spatial extent of the diffuse X-ray emission beyond the Spitzer shell. Absence of emission outside of this shell will strongly support the supernova remnant interpretation

Certain and possible rules for decision making using rough set theory extended to fuzzy sets

NASA Technical Reports Server (NTRS)

Dekorvin, Andre; Shipley, Margaret F.

1993-01-01

Uncertainty may be caused by the ambiguity in the terms used to describe a specific situation. It may also be caused by skepticism of rules used to describe a course of action or by missing and/or erroneous data. To deal with uncertainty, techniques other than classical logic need to be developed. Although, statistics may be the best tool available for handling likelihood, it is not always adequate for dealing with knowledge acquisition under uncertainty. Inadequacies caused by estimating probabilities in statistical processes can be alleviated through use of the Dempster-Shafer theory of evidence. Fuzzy set theory is another tool used to deal with uncertainty where ambiguous terms are present. Other methods include rough sets, the theory of endorsements and nonmonotonic logic. J. Grzymala-Busse has defined the concept of lower and upper approximation of a (crisp) set and has used that concept to extract rules from a set of examples. We will define the fuzzy analogs of lower and upper approximations and use these to obtain certain and possible rules from a set of examples where the data is fuzzy. Central to these concepts will be the idea of the degree to which a fuzzy set A is contained in another fuzzy set B, and the degree of intersection of a set A with set B. These concepts will also give meaning to the statement; A implies B. The two meanings will be: (1) if x is certainly in A then it is certainly in B, and (2) if x is possibly in A then it is possibly in B. Next, classification will be looked at and it will be shown that if a classification will be looked at and it will be shown that if a classification is well externally definable then it is well internally definable, and if it is poorly externally definable then it is poorly internally definable, thus generalizing a result of Grzymala-Busse. Finally, some ideas of how to define consensus and group options to form clusters of rules will be given.

Planar CoB18- Cluster: a New Motif for - and Metallo-Borophenes

NASA Astrophysics Data System (ADS)

Chen, Teng-Teng; Jian, Tian; Lopez, Gary; Li, Wan-Lu; Chen, Xin; Li, Jun; Wang, Lai-Sheng

2016-06-01

Combined Photoelectron Spectroscopy (PES) and theoretical calculations have found that anion boron clusters (Bn-) are planar and quasi-planar up to B25-. Recent works show that anion pure boron clusters continued to be planar at B27-,B30-,B35- and B36-. B35- and B36- provide the first experimental evidence for the viability of the two-dimensional (2D) boron sheets (Borophene). The 2D to three-dimensional (3D) transitions are shown to happen at B40-,B39- and B28-, which possess cage-like structures. These fullerene-like boron cage clusters are named as Borospherene. Recently, borophenes or similar structures are claimed to be synthesized by several groups. Following an electronic design principle, a series of transition-metal-doped boron clusters (M©Bn-, n=8-10) are found to possess the monocyclic wheel structures. Meanwhile, CoB12- and RhB12- are revealed to adopt half-sandwich-type structures with the quasi-planar B12 moiety similar to the B12- cluster. Very lately, we show that the CoB16- cluster possesses a highly symmetric Cobalt-centered drum-like structure, with a new record of coordination number at 16. Here we report the CoB18- cluster to possess a unique planar structure, in which the Co atom is doped into the network of a planar boron cluster. PES reveals that the CoB18- cluster is a highly stable electronic system with the first adiabatic detachment energy (ADE) at 4.0 eV. Global minimum searches along with high-level quantum calculations show the global minimum for CoB18- is perfectly planar and closed shell (1A1) with C2v symmetry. The Co atom is bonded with 7 boron atoms in the closest coordination shell and the other 11 boron atoms in the outer coordination shell. The calculated vertical detachment energy (VDE) values match quite well with our experimental results. Chemical bonding analysis by the Adaptive Natural Density Partitioning (AdNDP) method shows the CoB18- cluster is π-aromatic with four 4-centered-2-electron (4c-2e) π bonds and one 19-centered-2-electron (19c-2e) π bond, 10 π electrons in total. This perfectly planar structure reveals the viability of creating a new class of hetero-borophenes and metallo-borophenes by doping metal atoms into the plane of monolayer boron atoms. This gives a new approach to design perspective hetero-borophenes and metallo-borophenes materials with tunable chemical, magnetic and optical properties.

Reprint of: Negative carbon cluster ion beams: New evidence for the special nature of C60

NASA Astrophysics Data System (ADS)

Liu, Y.; O'brien, S. C.; Zhang, Q.; Heath, J. R.; Tittel, F. K.; Curl, R. F.; Kroto, H. W.; Smalley, R. E.

2013-12-01

Cold carbon cluster negative ions are formed by supersonic expansion of a plasma created at the nozzle of a supersonic cluster beam source by an excimer laser pulse. The observed distribution of mass peaks for the Cn- ions for n > 40 demonstrates that the evidence previously given for the special stability of neutral C60 and the existence of spheroidal carbon shells cannot be an artifact of the ionization conditions.

Observations of different core water cluster ions Y-(H2O)n (Y = O2, HOx, NOx, COx) and magic number in atmospheric pressure negative corona discharge mass spectrometry.

PubMed

Sekimoto, Kanako; Takayama, Mitsuo

2011-01-01

Reliable mass spectrometry data from large water clusters Y(-)(H(2)O)(n) with various negative core ions Y(-) such as O(2)(-), HO(-), HO(2)(-), NO(2)(-), NO(3)(-), NO(3)(-)(HNO(3))(2), CO(3)(-) and HCO(4)(-) have been obtained using atmospheric pressure negative corona discharge mass spectrometry. All the core Y(-) ions observed were ionic species that play a central role in tropospheric ion chemistry. These mass spectra exhibited discontinuities in ion peak intensity at certain size clusters Y(-)(H(2)O)(m) indicating specific thermochemical stability. Thus, Y(-)(H(2)O)(m) may correspond to the magic number or first hydrated shell in the cluster series Y(-)(H(2)O)(n). The high intensity discontinuity at HO(-)(H(2)O)(3) observed was the first mass spectrometric evidence for the specific stability of HO(-)(H(2)O)(3) as the first hydrated shell which Eigen postulated in 1964. The negative ion water clusters Y(-)(H(2)O)(n) observed in the mass spectra are most likely to be formed via core ion formation in the ambient discharge area (760 torr) and the growth of water clusters by adiabatic expansion in the vacuum region of the mass spectrometers (≈1 torr). The detailed mechanism of the formation of the different core water cluster ions Y(-)(H(2)O)(n) is described. Copyright © 2010 John Wiley & Sons, Ltd.

Tuning optical properties of magic number cluster (SiO2)4O2H4 by substitutional bonding with gold atoms.

PubMed

Cai, Xiulong; Zhang, Peng; Ma, Liuxue; Zhang, Wenxian; Ning, Xijing; Zhao, Li; Zhuang, Jun

2009-04-30

By bonding gold atoms to the magic number cluster (SiO(2))(4)O(2)H(4), two groups of Au-adsorbed shell-like clusters Au(n)(SiO(2))(4)O(2)H(4-n) (n = 1-4) and Au(n)(SiO(2))(4)O(2) (n = 5-8) were obtained, and their spectral properties were studied. The ground-state structures of these clusters were optimized by density functional theory, and the results show that in despite of the different numbers and types of the adsorbed Au atoms, the cluster core (SiO(2))(4)O(2) of T(d) point-group symmetry keeps almost unchanged. The absorption spectra were obtained by time-dependent density functional theory. From one group to the other, an extension of absorption wavelength from the UV-visible to the NIR region was observed, and in each group the absorption strengths vary linearly with the number of Au atoms. These features indicate their advantages for exploring novel materials with easily controlled tunable optical properties. Furthermore, due to the weak electronic charge transfer between the Au atoms, the clusters containing Au(2) dimers, especially Au(8)(SiO(2))(4)O(2), absorb strongly NIR light at 900 approximately 1200 nm. Such strong absorption suggests potential applications of these shell-like clusters in tumor cells thermal therapy, like the gold-coated silica nanoshells with larger sizes.

Assembly of Robust Bacterial Microcompartment Shells Using Building Blocks from an Organelle of Unknown Function

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

Lassila, JK; Bernstein, SL; Kinney, JN

Bacterial microconnpartnnents (BMCs) sequester enzymes from the cytoplasmic environment by encapsulation inside a selectively permeable protein shell. Bioinformatic analyses indicate that many bacteria encode BMC clusters of unknown function and with diverse combinations of shell proteins. The genome of the halophilic myxobacterium Haliangium ochraceum encodes one of the most atypical sets of shell proteins in terms of composition and primary structure. We found that microconnpartnnent shells could be purified in high yield when all seven H. ochraceum BMC shell genes were expressed from a synthetic operon in Escherichia coll. These shells differ substantially from previously isolated shell systems in thatmore » they are considerably smaller and more homogeneous, with measured diameters of 39 2 nm. The size and nearly uniform geometry allowed the development of a structural model for the shells composed of 260 hexagonal units and 13 hexagons per icosahedral face. We found that new proteins could be recruited to the shells by fusion to a predicted targeting peptide sequence, setting the stage for the use of these remarkably homogeneous shells for applications such as three-dimensional scaffolding and the construction of synthetic BMCs. Our results demonstrate the value of selecting from the diversity of BMC shell building blocks found in genomic sequence data for the construction of novel compartments. (C) 2014 Elsevier Ltd. All rights reserved.« less

Bonding properties of FCC-like Au 44 (SR) 28 clusters from X-ray absorption spectroscopy

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

Yang, Rui; Chevrier, Daniel M.; Zeng, Chenjie

Thiolate-protected gold clusters with precisely controlled atomic composition have recently emerged as promising candidates for a variety of applications because of their unique optical, electronic, and catalytic properties. The recent discovery of the Au44(SR)28 total structure is considered as an interesting finding in terms of the face-centered cubic (FCC)-like core structure in small gold-thiolate clusters. Herein, the unique bonding properties of Au44(SR)28 is analyzed using temperature-dependent X-ray absorption spectroscopy (XAS) measurements at the Au L3-edge and compared with other FCC-like clusters such as Au36(SR)24 and Au28(SR)20. A negative thermal expansion was detected for the Au–Au bonds of the metal coremore » (the first Au–Au shell) and was interpreted based on the unique Au core structure consisting of the Au4 units. EXAFS fitting results from Au28(SR)20, Au36(SR)24, and Au44(SR)28 show a size-dependent negative thermal expansion behavior in the first Au–Au shell, further highlighting the importance of the Au4 units in determining the Au core bonding properties and shedding light on the growth mechanism of these FCC-like Au clusters.« less

Study of lithium cation in water clusters: based on atom-bond electronegativity equalization method fused into molecular mechanics.

PubMed

Li, Xin; Yang, Zhong-Zhi

2005-05-12

We present a potential model for Li(+)-water clusters based on a combination of the atom-bond electronegativity equalization and molecular mechanics (ABEEM/MM) that is to take ABEEM charges of the cation and all atoms, bonds, and lone pairs of water molecules into the intermolecular electrostatic interaction term in molecular mechanics. The model allows point charges on cationic site and seven sites of an ABEEM-7P water molecule to fluctuate responding to the cluster geometry. The water molecules in the first sphere of Li(+) are strongly structured and there is obvious charge transfer between the cation and the water molecules; therefore, the charge constraint on the ionic cluster includes the charged constraint on the Li(+) and the first-shell water molecules and the charge neutrality constraint on each water molecule in the external hydration shells. The newly constructed potential model based on ABEEM/MM is first applied to ionic clusters and reproduces gas-phase state properties of Li(+)(H(2)O)(n) (n = 1-6 and 8) including optimized geometries, ABEEM charges, binding energies, frequencies, and so on, which are in fair agreement with those measured by available experiments and calculated by ab initio methods. Prospects and benefits introduced by this potential model are pointed out.

Stabilizing subnanometer Ag(0) nanoclusters by thiolate and diphosphine ligands and their crystal structures

NASA Astrophysics Data System (ADS)

Yang, Huayan; Wang, Yu; Zheng, Nanfeng

2013-03-01

The combined use of thiolate and diphosphine as surface ligands helps to stabilize subnanometer Ag(0) nanoclusters, resulting in the successful crystallization of two Ag(0)-containing nanoclusters (Ag16 and Ag32) for X-ray single crystal analysis. Both clusters have core-shell structures with Ag86+ and Ag2212+ as their cores, which are not simply either fragments of face-centered cubic metals or their five-fold twinned counterparts. The clusters display UV-Vis absorption spectra consisting of molecule-like optical transitions.The combined use of thiolate and diphosphine as surface ligands helps to stabilize subnanometer Ag(0) nanoclusters, resulting in the successful crystallization of two Ag(0)-containing nanoclusters (Ag16 and Ag32) for X-ray single crystal analysis. Both clusters have core-shell structures with Ag86+ and Ag2212+ as their cores, which are not simply either fragments of face-centered cubic metals or their five-fold twinned counterparts. The clusters display UV-Vis absorption spectra consisting of molecule-like optical transitions. Electronic supplementary information (ESI) available: Experimental details, more pictures of the structure and XPS spectra of the clusters. CCDC 916463 and 916464. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/c3nr34328f

Deterministic annealing for density estimation by multivariate normal mixtures

NASA Astrophysics Data System (ADS)

Kloppenburg, Martin; Tavan, Paul

1997-03-01

An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

Selection of Atmospheric Environmental Monitoring Sites based on Geographic Parameters Extraction of GIS and Fuzzy Matter-Element Analysis.

PubMed

Wu, Jianfa; Peng, Dahao; Ma, Jianhao; Zhao, Li; Sun, Ce; Ling, Huanzhang

2015-01-01

To effectively monitor the atmospheric quality of small-scale areas, it is necessary to optimize the locations of the monitoring sites. This study combined geographic parameters extraction by GIS with fuzzy matter-element analysis. Geographic coordinates were extracted by GIS and transformed into rectangular coordinates. These coordinates were input into the Gaussian plume model to calculate the pollutant concentration at each site. Fuzzy matter-element analysis, which is used to solve incompatible problems, was used to select the locations of sites. The matter element matrices were established according to the concentration parameters. The comprehensive correlation functions KA (xj) and KB (xj), which reflect the degree of correlation among monitoring indices, were solved for each site, and a scatter diagram of the sites was drawn to determine the final positions of the sites based on the functions. The sites could be classified and ultimately selected by the scatter diagram. An actual case was tested, and the results showed that 5 positions can be used for monitoring, and the locations conformed to the technical standard. In the results of this paper, the hierarchical clustering method was used to improve the methods. The sites were classified into 5 types, and 7 locations were selected. Five of the 7 locations were completely identical to the sites determined by fuzzy matter-element analysis. The selections according to these two methods are similar, and these methods can be used in combination. In contrast to traditional methods, this study monitors the isolated point pollutant source within a small range, which can reduce the cost of monitoring.

Molecular phylogenetic analysis of Chinese indigenous blue-shelled chickens inferred from whole genomic region of the SLCO1B3 gene.

PubMed

Dalirsefat, Seyed Benyamin; Dong, Xianggui; Deng, Xuemei

2015-08-01

In total, 246 individuals from 8 Chinese indigenous blue- and brown-shelled chicken populations (Yimeng Blue, Wulong Blue, Lindian Blue, Dongxiang Blue, Lushi Blue, Jingmen Blue, Dongxiang Brown, and Lushi Brown) were genotyped for 21 SNP markers from the SLCO1B3 gene to evaluate phylogenetic relationships. As a representative of nonblue-shelled breeds, White Leghorn was included in the study for reference. A high proportion of SNP polymorphism was observed in Chinese chicken populations, ranging from 89% in Jingmen Blue to 100% in most populations, with a mean of 95% across all populations. The White Leghorn breed showed the lowest polymorphism, accounting for 43% of total SNPs. The mean expected heterozygosity varied from 0.11 in Dongxiang Blue to 0.46 in Yimeng Blue. Analysis of molecular variation (AMOVA) for 2 groups of Chinese chickens based on eggshell color type revealed 52% within-group and 43% between-group variations of the total genetic variation. As expected, FST and Reynolds' genetic distance were greatest between White Leghorn and Chinese chicken populations, with average values of 0.40 and 0.55, respectively. The first and second principal coordinates explained approximately 92% of the total variation and supported the clustering of the populations according to their eggshell color type and historical origins. STRUCTURE analysis showed a considerable source of variation among populations for the clustering into blue-shelled and nonblue-shelled chicken populations. The low estimation of genetic differentiation (FST) between Chinese chicken populations is possibly due to a common historical origin and high gene flow. Remarkably similar population classifications were obtained with all methods used in the study. Aligning endogenous avian retroviral (EAV)-HP insertion sequences showed no difference among the blue-shelled chickens. © 2015 Poultry Science Association Inc.

Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

PubMed

Orsi, Rebecca

2017-02-01

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Properties of small Ar sub N-1 K/+/ ionic clusters

NASA Technical Reports Server (NTRS)

Etters, R. D.; Danilowicz, R.; Dugan, J.

1977-01-01

A self-consistent formalism is developed that, based upon a many-body potential, dynamically determines the thermodynamic properties of ionic clusters without an a priori designation of the equilibrium structures. Aggregates consisting of a single closed shell K(+) ion and N-1 isoelectronic argon atoms were studied. The clusters form crystallites at low temperatures, and melting transitions and spontaneous dissociations are indicated. The results confirm experimental evidence that shows that ionic clusters become less stable with increasing N. The crystallite structures formed by four different clusters are isosceles triangle, skewed form, octahedron with ion in the middle, and icosahedron with the ion in the middle.

GOClonto: an ontological clustering approach for conceptualizing PubMed abstracts.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Kim, Hong-Gee

2010-02-01

Concurrent with progress in biomedical sciences, an overwhelming of textual knowledge is accumulating in the biomedical literature. PubMed is the most comprehensive database collecting and managing biomedical literature. To help researchers easily understand collections of PubMed abstracts, numerous clustering methods have been proposed to group similar abstracts based on their shared features. However, most of these methods do not explore the semantic relationships among groupings of documents, which could help better illuminate the groupings of PubMed abstracts. To address this issue, we proposed an ontological clustering method called GOClonto for conceptualizing PubMed abstracts. GOClonto uses latent semantic analysis (LSA) and gene ontology (GO) to identify key gene-related concepts and their relationships as well as allocate PubMed abstracts based on these key gene-related concepts. Based on two PubMed abstract collections, the experimental results show that GOClonto is able to identify key gene-related concepts and outperforms the STC (suffix tree clustering) algorithm, the Lingo algorithm, the Fuzzy Ants algorithm, and the clustering based TRS (tolerance rough set) algorithm. Moreover, the two ontologies generated by GOClonto show significant informative conceptual structures.

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

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

Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu, E-mail: hatt@univ-brest.fr

Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basismore » was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.« less

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

PubMed

Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

2015-09-01

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

PubMed

Zhang, Jian-Hua; Peng, Xiao-Di; Liu, Hua; Raisch, Jörg; Wang, Ru-Bin

2013-12-01

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

Color image analysis technique for measuring of fat in meat: an application for the meat industry

NASA Astrophysics Data System (ADS)

Ballerini, Lucia; Hogberg, Anders; Lundstrom, Kerstin; Borgefors, Gunilla

2001-04-01

Intramuscular fat content in meat influences some important meat quality characteristics. The aim of the present study was to develop and apply image processing techniques to quantify intramuscular fat content in beefs together with the visual appearance of fat in meat (marbling). Color images of M. longissimus dorsi meat samples with a variability of intramuscular fat content and marbling were captured. Image analysis software was specially developed for the interpretation of these images. In particular, a segmentation algorithm (i.e. classification of different substances: fat, muscle and connective tissue) was optimized in order to obtain a proper classification and perform subsequent analysis. Segmentation of muscle from fat was achieved based on their characteristics in the 3D color space, and on the intrinsic fuzzy nature of these structures. The method is fully automatic and it combines a fuzzy clustering algorithm, the Fuzzy c-Means Algorithm, with a Genetic Algorithm. The percentages of various colors (i.e. substances) within the sample are then determined; the number, size distribution, and spatial distributions of the extracted fat flecks are measured. Measurements are correlated with chemical and sensory properties. Results so far show that advanced image analysis is useful for quantify the visual appearance of meat.

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the Estimation of Depth of Anaesthesia.

PubMed

Benzy, V K; Jasmin, E A; Koshy, Rachel Cherian; Amal, Frank; Indiradevi, K P

2018-01-01

The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available on electroencephalogram signals during anaesthesia are drawn by extracting relative wave energy features from the anaesthetic electroencephalogram signals. Discrete wavelet transform is used to decomposes the electroencephalogram signals into four levels and then relative wave energy is computed from approximate and detail coefficients of sub-band signals. Relative wave energy is extracted to find out the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases awake, induction, maintenance and recovery. The Kruskal-Wallis statistical test is applied on the relative wave energy features to check the discriminating capability of relative wave energy features as awake, light anaesthesia, moderate anaesthesia and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing a Adaptive neuro-fuzzy inference system based fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor Bispectral index.

Thermodynamic Theory of Spherically Trapped Coulomb Clusters

NASA Astrophysics Data System (ADS)

Wrighton, Jeffrey; Dufty, James; Bonitz, Michael; K"{A}Hlert, Hanno

2009-11-01

The radial density profile of a finite number of identical charged particles confined in a harmonic trap is computed over a wide ranges of temperatures (Coulomb coupling) and particle numbers. At low temperatures these systems form a Coulomb crystal with spherical shell structure which has been observed in ultracold trapped ions and in dusty plasmas. The shell structure is readily reproduced in simulations. However, analytical theories which used a mean field approachfootnotetext[1]C. Henning et al., Phys. Rev. E 74, 056403 (2006) or a local density approximationfootnotetext[2]C. Henning et al., Phys. Rev. E 76, 036404 (2007) have, so far, only been able to reproduce the average density profile. Here we present an approach to Coulomb correlations based on the hypernetted chain approximation with additional bridge diagrams. It is demonstrated that this model reproduces the correct shell structure within a few percent and provides the basis for a thermodynamic theory of Coulomb clusters in the strongly coupled fluid state.footnotetext[3]J. Wrighton, J.W. Dufty, H. K"ahlert and M. Bonitz, J. Phys. A 42, 214052 (2009) and Phys. Rev. E (2009) (to be submitted)

Recurrent-neural-network-based Boolean factor analysis and its application to word clustering.

PubMed

Frolov, Alexander A; Husek, Dusan; Polyakov, Pavel Yu

2009-07-01

The objective of this paper is to introduce a neural-network-based algorithm for word clustering as an extension of the neural-network-based Boolean factor analysis algorithm (Frolov , 2007). It is shown that this extended algorithm supports even the more complex model of signals that are supposed to be related to textual documents. It is hypothesized that every topic in textual data is characterized by a set of words which coherently appear in documents dedicated to a given topic. The appearance of each word in a document is coded by the activity of a particular neuron. In accordance with the Hebbian learning rule implemented in the network, sets of coherently appearing words (treated as factors) create tightly connected groups of neurons, hence, revealing them as attractors of the network dynamics. The found factors are eliminated from the network memory by the Hebbian unlearning rule facilitating the search of other factors. Topics related to the found sets of words can be identified based on the words' semantics. To make the method complete, a special technique based on a Bayesian procedure has been developed for the following purposes: first, to provide a complete description of factors in terms of component probability, and second, to enhance the accuracy of classification of signals to determine whether it contains the factor. Since it is assumed that every word may possibly contribute to several topics, the proposed method might be related to the method of fuzzy clustering. In this paper, we show that the results of Boolean factor analysis and fuzzy clustering are not contradictory, but complementary. To demonstrate the capabilities of this attempt, the method is applied to two types of textual data on neural networks in two different languages. The obtained topics and corresponding words are at a good level of agreement despite the fact that identical topics in Russian and English conferences contain different sets of keywords.

Switching Plasmons: Gold Nanorod-Copper Chalcogenide Core-Shell Nanoparticle Clusters with Selectable Metal/Semiconductor NIR Plasmon Resonances.

PubMed

Muhammed, Madathumpady Abubaker Habeeb; Döblinger, Markus; Rodríguez-Fernández, Jessica

2015-09-16

Exerting control over the near-infrared (NIR) plasmonic response of nanosized metals and semiconductors can facilitate access to unexplored phenomena and applications. Here we combine electrostatic self-assembly and Cd(2+)/Cu(+) cation exchange to obtain an anisotropic core-shell nanoparticle cluster (NPC) whose optical properties stem from two dissimilar plasmonic materials: a gold nanorod (AuNR) core and a copper selenide (Cu(2-x)Se, x ≥ 0) supraparticle shell. The spectral response of the AuNR@Cu2Se NPCs is governed by the transverse and longitudinal plasmon bands (LPB) of the anisotropic metallic core, since the Cu2Se shell is nonplasmonic. Under aerobic conditions the shell undergoes vacancy doping (x > 0), leading to the plasmon-rich NIR spectrum of the AuNR@Cu(2-x)Se NPCs. For low vacancy doping levels the NIR optical properties of the dually plasmonic NPCs are determined by the LPBs of the semiconductor shell (along its major longitudinal axis) and of the metal core. Conversely, for high vacancy doping levels their NIR optical response is dominated by the two most intense plasmon modes from the shell: the transverse (along the shortest transversal axis) and longitudinal (along the major longitudinal axis) modes. The optical properties of the NPCs can be reversibly switched back to a purely metallic plasmonic character upon reversible conversion of AuNR@Cu(2-x)Se into AuNR@Cu2Se. Such well-defined nanosized colloidal assemblies feature the unique ability of holding an all-metallic, a metallic/semiconductor, or an all-semiconductor plasmonic response in the NIR. Therefore, they can serve as an ideal platform to evaluate the crosstalk between plasmonic metals and plasmonic semiconductors at the nanoscale. Furthermore, their versatility to display plasmon modes in the first, second, or both NIR windows is particularly advantageous for bioapplications, especially considering their strong absorbing and near-field enhancing properties.

Efficient architecture for spike sorting in reconfigurable hardware.

PubMed

Hwang, Wen-Jyi; Lee, Wei-Hao; Lin, Shiow-Jyu; Lai, Sheng-Ying

2013-11-01

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

Comparison of mineral dust and droplet residuals measured with two single particle aerosol mass spectrometers

NASA Astrophysics Data System (ADS)

Wonaschütz, Anna; Ludwig, Wolfgang; Zawadowicz, Maria; Hiranuma, Naruki; Hitzenberger, Regina; Cziczo, Daniel; DeMott, Paul; Möhler, Ottmar

2017-04-01

Single Particle mass spectrometers are used to gain information on the chemical composition of individual aerosol particles, aerosol mixing state, and other valuable aerosol characteristics. During the Mass Spectrometry Intercomparison at the Fifth Ice Nucleation (FIN-01) Workshop, the new LAAPTOF single particle aerosol mass spectrometer (AeroMegt GmbH) was conducting simultaneous measurements together with the PALMS (Particle Analysis by Laser Mass Spectrometry) instrument. The aerosol particles were sampled from the AIDA chamber during ice cloud expansion experiments. Samples of mineral dust and ice droplet residuals were measured simultaneously. In this work, three expansion experiments are chosen for a comparison between the two mass spectrometers. A fuzzy clustering routine is used to group the spectra. Cluster centers describing the ensemble of particles are compared. First results show that while differences in the peak heights are likely due to the use of an amplifier in PALMS, cluster centers are comparable.

Reconstruction of sediment transport pathways in modern microtidal sand flat by multiple classification analysis

NASA Astrophysics Data System (ADS)

Yamashita, S.; Nakajo, T.; Naruse, H.

2009-12-01

In this study, we statistically classified the grain size distribution of the bottom surface sediment on a microtidal sand flat to analyze the depositional processes of the sediment. Multiple classification analysis revealed that two types of sediment populations exist in the bottom surface sediment. Then, we employed the sediment trend model developed by Gao and Collins (1992) for the estimation of sediment transport pathways. As a result, we found that statistical discrimination of the bottom surface sediment provides useful information for the sediment trend model while dealing with various types of sediment transport processes. The microtidal sand flat along the Kushida River estuary, Ise Bay, central Japan, was investigated, and 102 bottom surface sediment samples were obtained. Then, their grain size distribution patterns were measured by the settling tube method, and each grain size distribution parameter (mud and gravel contents, mean grain size, coefficient of variance (CV), skewness, kurtosis, 5, 25, 50, 75, and 95 percentile) was calculated. Here, CV is the normalized sorting value divided by the mean grain size. Two classical statistical methods—principal component analysis (PCA) and fuzzy cluster analysis—were applied. The results of PCA showed that the bottom surface sediment of the study area is mainly characterized by grain size (mean grain size and 5-95 percentile) and the CV value, indicating predominantly large absolute values of factor loadings in primal component (PC) 1. PC1 is interpreted as being indicative of the grain-size trend, in which a finer grain-size distribution indicates better size sorting. The frequency distribution of PC1 has a bimodal shape and suggests the existence of two types of sediment populations. Therefore, we applied fuzzy cluster analysis, the results of which revealed two groupings of the sediment (Cluster 1 and Cluster 2). Cluster 1 shows a lower value of PC1, indicating coarse and poorly sorted sediments. Cluster 1 sediments are distributed around the branched channel from Kushida River and show an expanding distribution from the river mouth toward the northeast direction. Cluster 2 shows a higher value of PC1, indicating fine and well-sorted sediments; this cluster is distributed in a distant area from the river mouth, including the offshore region. Therefore, Cluster 1 and Cluster 2 are interpreted as being deposited by fluvial and wave processes, respectively. Finally, on the basis of this distribution pattern, the sediment trend model was applied in areas dominated separately by fluvial and wave processes. Resultant sediment transport patterns showed good agreement with those obtained by field observations. The results of this study provide an important insight into the numerical models of sediment transport.

Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis

PubMed Central

Sato, João Ricardo; Balardin, Joana; Vidal, Maciel Calebe; Fujita, André

2016-01-01

Background Several neuroimaging studies support the model of abnormal development of brain connectivity in patients with autism-spectrum disorders (ASD). In this study, we aimed to test the hypothesis of reduced functional network segregation in autistic patients compared with controls. Methods Functional MRI data from children acquired under a resting-state protocol (Autism Brain Imaging Data Exchange [ABIDE]) were submitted to both fuzzy spectral clustering (FSC) with entropy analysis and graph modularity analysis. Results We included data from 814 children in our analysis. We identified 5 regions of interest comprising the motor, temporal and occipito-temporal cortices with increased entropy (p < 0.05) in the clustering structure (i.e., more segregation in the controls). Moreover, we noticed a statistically reduced modularity (p < 0.001) in the autistic patients compared with the controls. Significantly reduced eigenvector centrality values (p < 0.05) in the patients were observed in the same regions that were identified in the FSC analysis. Limitations There is considerable heterogeneity in the fMRI acquisition protocols among the sites that contributed to the ABIDE data set (e.g., scanner type, pulse sequence, duration of scan and resting-state protocol). Moreover, the sites differed in many variables related to sample characterization (e.g., age, IQ and ASD diagnostic criteria). Therefore, we cannot rule out the possibility that additional differences in functional network organization would be found in a more homogeneous data sample of individuals with ASD. Conclusion Our results suggest that the organization of the whole-brain functional network in patients with ASD is different from that observed in controls, which implies a reduced modularity of the brain functional networks involved in sensorimotor, social, affective and cognitive processing. PMID:26505141

Fuzzy Clustering of Multiple Instance Data

DTIC Science & Technology

2015-11-30

depth is not. To illustrate this data, in figure 1 we display the GPR signatures of the same mine buried at 3 in deep in two geographically different...target signature depends on the soil properties of the site. The same mine type is buried at 3in deep in both sites. Since its formal introduction...drug design [15], and the problem of handwritten digit recognition [16]. To the best of our knowledge, Diet - terich, et. al [1] were the first to

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

H{sub 2} MOLECULAR CLUSTERS WITH EMBEDDED MOLECULES AND ATOMS AS THE SOURCE OF THE DIFFUSE INTERSTELLAR BANDS

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

Bernstein, L. S.; Clark, F. O.; Lynch, D. K., E-mail: larry@spectral.com, E-mail: dave@thulescientific.com

2013-05-01

We suggest that the diffuse interstellar bands (DIBs) arise from absorption lines of electronic transitions in molecular clusters primarily composed of a single molecule, atom, or ion ({sup s}eed{sup )}, embedded in a single-layer shell of H{sub 2} molecules. Less abundant variants of the cluster, including two seed molecules and/or a two-layer shell of H{sub 2} molecules, may also occur. The lines are broadened, blended, and wavelength-shifted by interactions between the seed and surrounding H{sub 2} shell. We refer to these clusters as contaminated H{sub 2} clusters (CHCs). We show that CHC spectroscopy matches the diversity of observed DIB spectralmore » profiles and provides good fits to several DIB profiles based on a rotational temperature of 10 K. CHCs arise from {approx}centimeter-sized, dirty H{sub 2} ice balls, called contaminated H{sub 2} ice macro-particles (CHIMPs), formed in cold, dense, giant molecular clouds (GMCs), and later released into the interstellar medium (ISM) upon GMC disruption. Attractive interactions, arising from Van der Waals and ion-induced dipole potentials, between the seeds and H{sub 2} molecules enable CHIMPs to attain centimeter-sized dimensions. When an ultraviolet (UV) photon is absorbed in the outer layer of a CHIMP, it heats the icy matrix and expels CHCs into the ISM. While CHCs are quickly destroyed by absorbing UV photons, they are replenished by the slowly eroding CHIMPs. Since CHCs require UV photons for their release, they are most abundant at, but not limited to, the edges of UV-opaque molecular clouds, consistent with the observed, preferred location of DIBs. An inherent property of CHCs, which can be characterized as nanometer size, spinning, dipolar dust grains, is that they emit in the radio-frequency region. We also show that the CHCs offer a natural explanation for the anomalous microwave emission feature in the {approx}10-100 GHz spectral region.« less

Implementation of spectral clustering on microarray data of carcinoma using k-means algorithm

NASA Astrophysics Data System (ADS)

Frisca, Bustamam, Alhadi; Siswantining, Titin

2017-03-01

Clustering is one of data analysis methods that aims to classify data which have similar characteristics in the same group. Spectral clustering is one of the most popular modern clustering algorithms. As an effective clustering technique, spectral clustering method emerged from the concepts of spectral graph theory. Spectral clustering method needs partitioning algorithm. There are some partitioning methods including PAM, SOM, Fuzzy c-means, and k-means. Based on the research that has been done by Capital and Choudhury in 2013, when using Euclidian distance k-means algorithm provide better accuracy than PAM algorithm. So in this paper we use k-means as our partition algorithm. The major advantage of spectral clustering is in reducing data dimension, especially in this case to reduce the dimension of large microarray dataset. Microarray data is a small-sized chip made of a glass plate containing thousands and even tens of thousands kinds of genes in the DNA fragments derived from doubling cDNA. Application of microarray data is widely used to detect cancer, for the example is carcinoma, in which cancer cells express the abnormalities in his genes. The purpose of this research is to classify the data that have high similarity in the same group and the data that have low similarity in the others. In this research, Carcinoma microarray data using 7457 genes. The result of partitioning using k-means algorithm is two clusters.

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.

Identification of RAPD and SCAR markers associated with yield traits in the Indian tropical tasar silkworm Antheraea mylitta drury

PubMed Central

Dutta, Suhrid R.; Kar, Prasanta K.; Srivastava, Ashok K.; Sinha, Manoj K.; Shankar, Jai; Ghosh, Ananta K.

2012-01-01

The tropical tasar silkworm, Antheraea mylitta, is a semi-domesticated vanya silk-producing insect of high economic importance. To date, no molecular marker associated with cocoon and shell weights has been identified in this species. In this report, we identified a randomly amplified polymorphic DNA (RAPD) marker and examined its inheritance, and also developed a stable diagnostic sequence-characterized amplified region (SCAR) marker. Silkworms were divided into groups with high (HCSW) and low (LCSW) cocoon and shell weights, and the F2 progeny of a cross between these two groups were obtained. DNA from these silkworms was screened by PCR using 34 random primers and the resulting RAPD fragments were used for cluster analysis and discriminant function analysis (DFA). The clustering pattern in a UPGMA-based dendogram and DFA clearly distinguished the HCSW and LCSW groups. Multiple regression analysis identified five markers associated with cocoon and shell weights. The marker OPW16905 bp showed the most significant association with cocoon and shell weights, and its inheritance was confirmed in F2 progeny. Cloning and sequencing of this 905 bp fragment showed 88% identity between its 134 nucleotides and the Bmc-1/Yamato-like retroposon of A. mylitta. This marker was further converted into a diagnostic SCAR marker (SCOPW 16826 bp). The SCAR marker developed here may be useful in identifying the right parental stock of tasar silk-worms for high cocoon and shell weights in breeding programs designed to enhance the productivity of tasar silk. PMID:23271934

Spatial analysis of biomineralization associated gene expression from the mantle organ of the pearl oyster Pinctada maxima

PubMed Central

2011-01-01

Background Biomineralization is a process encompassing all mineral containing tissues produced within an organism. One of the most dynamic examples of this process is the formation of the mollusk shell, comprising a variety of crystal phases and microstructures. The organic component incorporated within the shell is said to dictate this architecture. However general understanding of how this process is achieved remains ambiguous. The mantle is a conserved organ involved in shell formation throughout molluscs. Specifically the mantle is thought to be responsible for secreting the protein component of the shell. This study employs molecular approaches to determine the spatial expression of genes within the mantle tissue to further the elucidation of the shell biomineralization. Results A microarray platform was custom generated (PmaxArray 1.0) from the pearl oyster Pinctada maxima. PmaxArray 1.0 consists of 4992 expressed sequence tags (ESTs) originating from mantle tissue. This microarray was used to analyze the spatial expression of ESTs throughout the mantle organ. The mantle was dissected into five discrete regions and analyzed for differential gene expression with PmaxArray 1.0. Over 2000 ESTs were determined to be differentially expressed among the tissue sections, identifying five major expression regions. In situ hybridization validated and further localized the expression for a subset of these ESTs. Comparative sequence similarity analysis of these ESTs revealed a number of the transcripts were novel while others showed significant sequence similarities to previously characterized shell related genes. Conclusions This investigation has mapped the spatial distribution for over 2000 ESTs present on PmaxArray 1.0 with reference to specific locations of the mantle. Expression profile clusters have indicated at least five unique functioning zones in the mantle. Three of these zones are likely involved in shell related activities including formation of nacre, periostracum and calcitic prismatic microstructure. A number of novel and known transcripts have been identified from these clusters. The development of PmaxArray 1.0, and the spatial map of its ESTs expression in the mantle has begun characterizing the molecular mechanisms linking the organics and inorganics of the molluscan shell. PMID:21936921

Photoabsorption in sodium clusters: first principles configuration interaction calculations

NASA Astrophysics Data System (ADS)

Priya, Pradip Kumar; Rai, Deepak Kumar; Shukla, Alok

2017-05-01

We present systematic and comprehensive correlated-electron calculations of the linear photoabsorption spectra of small neutral closed- and open-shell sodium clusters (Nan, n = 2 - 6), as well as closed-shell cation clusters (Nan+, n = 3, 5). We have employed the configuration interaction (CI) methodology at the full CI (FCI) and quadruple CI (QCI) levels to compute the ground, and the low-lying excited states of the clusters. For most clusters, besides the minimum energy structures, we also consider their energetically close isomers. The photoabsorption spectra were computed under the electric-dipole approximation, employing the dipole-matrix elements connecting the ground state with the excited states of each isomer. Our calculations were tested rigorously for convergence with respect to the basis set, as well as with respect to the size of the active orbital space employed in the CI calculations. These calculations reveal that as far as electron-correlation effects are concerned, core excitations play an important role in determining the optimized ground state geometries of various clusters, thereby requiring all-electron correlated calculations. But, when it comes to low-lying optical excitations, only valence electron correlation effects play an important role, and excellent agreement with the experimental results is obtained within the frozen-core approximation. For the case of Na6, the largest cluster studied in this work, we also discuss the possibility of occurrence of plasmonic resonance in the optical absorption spectrum. Supplementary material in the form of one pdf file available from the Journal web page at http://https://doi.org/10.1140/epjd/e2017-70728-3

Changes in cluster magnetism and suppression of local superconductivity in amorphous FeCrB alloy irradiated by Ar+ ions

NASA Astrophysics Data System (ADS)

Okunev, V. D.; Samoilenko, Z. A.; Szymczak, H.; Szewczyk, A.; Szymczak, R.; Lewandowski, S. J.; Aleshkevych, P.; Malinowski, A.; Gierłowski, P.; Więckowski, J.; Wolny-Marszałek, M.; Jeżabek, M.; Varyukhin, V. N.; Antoshina, I. A.

2016-02-01

We show that сluster magnetism in ferromagnetic amorphous Fe67Cr18B15 alloy is related to the presence of large, D=150-250 Å, α-(Fe Cr) clusters responsible for basic changes in cluster magnetism, small, D=30-100 Å, α-(Fe, Cr) and Fe3B clusters and subcluster atomic α-(Fe, Cr, B) groupings, D=10-20 Å, in disordered intercluster medium. For initial sample and irradiated one (Φ=1.5×1018 ions/cm2) superconductivity exists in the cluster shells of metallic α-(Fe, Cr) phase where ferromagnetism of iron is counterbalanced by antiferromagnetism of chromium. At Φ=3×1018 ions/cm2, the internal stresses intensify and the process of iron and chromium phase separation, favorable for mesoscopic superconductivity, changes for inverse one promoting more homogeneous distribution of iron and chromium in the clusters as well as gigantic (twice as much) increase in density of the samples. As a result, in the cluster shells ferromagnetism is restored leading to the increase in magnetization of the sample and suppression of local superconductivity. For initial samples, the temperature dependence of resistivity ρ(T) T2 is determined by the electron scattering on quantum defects. In strongly inhomogeneous samples, after irradiation by fluence Φ=1.5×1018 ions/cm2, the transition to a dependence ρ(T) T1/2 is caused by the effects of weak localization. In more homogeneous samples, at Φ=3×1018 ions/cm2, a return to the dependence ρ(T) T2 is observed.

Stabilities of protonated water-ammonia clusters

NASA Astrophysics Data System (ADS)

Sundén, A. E. K.; Støchkel, K.; Hvelplund, P.; Brøndsted Nielsen, S.; Dynefors, B.; Hansen, K.

2018-05-01

Branching ratios of water and ammonia evaporation have been measured for spontaneous evaporation from protonated mixed clusters H+(H2O)n(NH3)m in the size range 0 ≤ n ≤ 11 and 0 ≤ m ≤ 7. Mixed clusters evaporate water except for clusters containing six or more ammonia molecules, indicating the formation of a stable core of one ammonium ion surrounded by four ammonia molecules and a second shell consisting predominantly of water. We relate evaporative branching ratios to free energy differences between the products of competing channels and determine the free energy differences for clusters with up to seven ammonia molecules. Clusters containing up to five ammonia molecules show a very strong scaling of these free energy differences.

Structural and electronic properties for atomic clusters

NASA Astrophysics Data System (ADS)

Sun, Yan

We have studied the structural and electronic properties for different groups of atomic clusters by doing a global search on the potential energy surface using the Taboo Search in Descriptors Space (TSDS) method and calculating the energies with Kohn-Sham Density Functional Theory (KS-DFT). Our goal was to find the structural and electronic principles for predicting the structure and stability of clusters. For Ben (n = 3--20), we have found that the evolution of geometric and electronic properties with size reflects a change in the nature of the bonding from van der Waals to metallic and then bulk-like. The cluster sizes with extra stability agree well with the predictions of the jellium model. In the 4d series of transition metal (TM) clusters, as the d-type bonding becomes more important, the preferred geometric structure changes from icosahedral (Y, Zr), to distorted compact structures (Nb, Mo), and FCC or simple cubic crystal fragments (Tc, Ru, Rh) due to the localized nature of the d-type orbital. Analysis of relative isomer energies and their electronic density of states suggest that these clusters tend to follow a maximum hardness principle (MHP). For A4B12 clusters (A is divalent, B is monovalent), we found unusually large (on average 1.95 eV) HOMO-LUMO gap values. This shows the extra stability at an electronic closed shell (20 electrons) predicted by the jellium model. The importance of symmetry, closed electronic and ionic shells in stability is shown by the relative stability of homotops of Mg4Ag12 which also provides support for the hypothesis that clusters that satisfy more than one stability criterion ("double magic") should be particularly stable.

Star formation in shells of colliding multi-SNe bubbles

NASA Astrophysics Data System (ADS)

Vasiliev, Evgenii O.; Shchekinov, Yuri A.

2017-12-01

It is believed that when bubbles formed by multiple supernovae explosions interact with one another, they stimulate star formation in overlapping shells. We consider the evolution of a shocked layer formed by the collision of two identical bubbles each of which originated from OB clusters of ˜ 50 members and ˜ 50 pc. The clusters are separated by 200-400 pc.We found that depending on evolutionary status of colliding bubbles the shocked layer can either be destroyed into diffuse lumps, or be fragmented into dense clumps: the former occurs in collisions of young bubbles with continuing supernovae explosions, and the latter occurs in older bubble interactions.We argue that fragmentation efficiency in shells depends on external heating: for a heating rate <˜ 1.7×10-24 erg s-1 the number of fragments formed in a collision of two old bubbles reaches several tens at t ˜ 4 Myr, while a heating rate >˜ 7 × 10-24 erg s-1 prevents fragmentation. The clumps formed in freely expanding parts of bubbles are gradually destroyed and disappear on t <˜ 1 Myr,whereas those formed in the overlapping shells survive much longer. Because of this the number of fragments in an isolated bubble begins to decrease after reaching a maximum, while in collision of two old bubbles it fluctuates around 60-70 until longer than t ˜ 5 Myr.

Synthesis and Catalytic Properties of Au Pd Nanoflowers

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

Xu, Jianguang; Wilson, Adria; Howe, Jane Y

2011-01-01

Reduction of Pd ions by hydroquinone in the presence of gold nanoparticles and polyvinylpyrrolidone resulted in the formation of nanoflowers with a Au core and Pd petals. Addition of HCl to the synthesis halted the reduction by hydroquinone and enabled the acquisition of snapshots of the nanoflowers at different stages of growth. TEM images of the reaction after 10 s show that the nanoflower morphology resulted from the homogeneous nucleation of Pd clusters in solution and their subsequent attachment to gold seeds coated with a thin (0.8 0.1 nm) shell of Pd. UV visible spectra also indicate Pd clusters formedmore » in the early stages of the reaction and disappeared as the nanoflowers grew. The speed at which this reaction can be halted is useful not only for producing a variety of bimetallic nanostructures with precisely controlled dimensions and morphologies but also for understanding the growth mechanism of these structures. The ability of the AuPd core shell structure to catalyze the Suzuki coupling reaction of iodobenzene to phenylboronic acid was probed and compared against the activity of Pd nanocubes and thin-shelled AuPd core shell nanoparticles. The results of this study suggest that Suzuki coupling was not affected by the surface structure or subsurface composition of the nanoparticles, but instead was primarily catalyzed by molecular Pd species that leached from the nanostructures.« less

Synthesis of Au-Pd Nanoflowers Through Nanocluster Assembly

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

Xu, Jianguang; Howe, Jane Y; Chi, Miaofang

2011-01-01

Reduction of Pd ions by hydroquinone in the presence of gold nanoparticles and polyvinylpyrrolidone resulted in the formation of nanoflowers with a Au core and Pd petals. Addition of HCl to the synthesis halted the reduction by hydroquinone and enabled the acquisition of snapshots of the nanoflowers at different stages of growth. TEM images of the reaction after 10 s show that the nanoflower morphology resulted from the homogeneous nucleation of Pd clusters in solution and their subsequent attachment to gold seeds coated with a thin (0.8 {+-} 0.1 nm) shell of Pd. UV-visible spectra also indicate Pd clusters formedmore » in the early stages of the reaction and disappeared as the nanoflowers grew. The speed at which this reaction can be halted is useful not only for producing a variety of bimetallic nanostructures with precisely controlled dimensions and morphologies but also for understanding the growth mechanism of these structures. The ability of the AuPd core-shell structure to catalyze the Suzuki coupling reaction of iodobenzene to phenylboronic acid was probed and compared against the activity of Pd nanocubes and thin-shelled AuPd core-shell nanoparticles. The results of this study suggest that Suzuki coupling was not affected by the surface structure or subsurface composition of the nanoparticles, but instead was primarily catalyzed by molecular Pd species that leached from the nanostructures.« less

Hemoglobin–Albumin Cluster Incorporating a Pt Nanoparticle: Artificial O2 Carrier with Antioxidant Activities

PubMed Central

Hosaka, Hitomi; Haruki, Risa; Yamada, Kana; Böttcher, Christoph; Komatsu, Teruyuki

2014-01-01

A covalent core–shell structured protein cluster composed of hemoglobin (Hb) at the center and human serum albumins (HSA) at the periphery, Hb-HSAm, is an artificial O2 carrier that can function as a red blood cell substitute. Here we described the preparation of a novel Hb-HSA3 cluster with antioxidant activities and its O2 complex stable in aqueous H2O2 solution. We used an approach of incorporating a Pt nanoparticle (PtNP) into the exterior HSA unit of the cluster. A citrate reduced PtNP (1.8 nm diameter) was bound tightly within the cleft of free HSA with a binding constant (K) of 1.1×107 M−1, generating a stable HSA-PtNP complex. This platinated protein showed high catalytic activities for dismutations of superoxide radical anions (O2 •–) and hydrogen peroxide (H2O2), i.e., superoxide dismutase and catalase activities. Also, Hb-HSA3 captured PtNP into the external albumin unit (K = 1.1×107 M−1), yielding an Hb-HSA3(PtNP) cluster. The association of PtNP caused no alteration of the protein surface net charge and O2 binding affinity. The peripheral HSA-PtNP shell prevents oxidation of the core Hb, which enables the formation of an extremely stable O2 complex, even in H2O2 solution. PMID:25310133

Structure determination in 55-atom Li-Na and Na-K nanoalloys.

PubMed

Aguado, Andrés; López, José M

2010-09-07

The structure of 55-atom Li-Na and Na-K nanoalloys is determined through combined empirical potential (EP) and density functional theory (DFT) calculations. The potential energy surface generated by the EP model is extensively sampled by using the basin hopping technique, and a wide diversity of structural motifs is reoptimized at the DFT level. A composition comparison technique is applied at the DFT level in order to make a final refinement of the global minimum structures. For dilute concentrations of one of the alkali atoms, the structure of the pure metal cluster, namely, a perfect Mackay icosahedron, remains stable, with the minority component atoms entering the host cluster as substitutional impurities. At intermediate concentrations, the nanoalloys adopt instead a core-shell polyicosahedral (p-Ih) packing, where the element with smaller atomic size and larger cohesive energy segregates to the cluster core. The p-Ih structures show a marked prolate deformation, in agreement with the predictions of jelliumlike models. The electronic preference for a prolate cluster shape, which is frustrated in the 55-atom pure clusters due to the icosahedral geometrical shell closing, is therefore realized only in the 55-atom nanoalloys. An analysis of the electronic densities of states suggests that photoelectron spectroscopy would be a sufficiently sensitive technique to assess the structures of nanoalloys with fixed size and varying compositions.

Magnetic/NIR-thermally responsive hybrid nanogels for optical temperature sensing, tumor cell imaging and triggered drug release

NASA Astrophysics Data System (ADS)

Wang, Hui; Yi, Jinhui; Mukherjee, Sumit; Banerjee, Probal; Zhou, Shuiqin

2014-10-01

The paper demonstrates a class of multifunctional core-shell hybrid nanogels with fluorescent and magnetic properties, which have been successfully developed for simultaneous optical temperature sensing, tumor cell imaging and magnetic/NIR-thermally responsive drug carriers. The as-synthesized hybrid nanogels were designed by coating bifunctional nanoparticles (BFNPs, fluorescent carbon dots embedded in the porous carbon shell and superparamagnetic iron oxide nanocrystals clustered in the core) with a thermo-responsive poly(N-isopropylacrylamide-co-acrylamide) [poly(NIPAM-AAm)]-based hydrogel as the shell. The BFNPs in hybrid nanogels not only demonstrate excellent photoluminescence (PL) and photostability due to the fluorescent carbon dots embedded in the porous carbon shell, but also has targeted drug accumulation potential and a magnetic-thermal conversion ability due to the superparamagnetic iron oxide nanocrystals clustered in the core. The thermo-responsive poly(NIPAM-AAm)-based gel shell can not only modify the physicochemical environment of the BFNPs core to manipulate the fluorescence intensity for sensing the variation of the environmental temperature, but also regulate the release rate of the loaded anticancer drug (curcumin) by varying the local temperature of environmental media. In addition, the carbon layer of BFNPs can adsorb and convert the NIR light to heat, leading to a promoted drug release under NIR irradiation and improving the therapeutic efficacy of drug-loaded hybrid nanogels. Furthermore, the superparamagnetic iron oxide nanocrystals in the core of BFNPs can trigger localized heating using an alternating magnetic field, leading to a phase change in the polymer gel to trigger the release of loaded drugs. Finally, the multifunctional hybrid nanogels can overcome cellular barriers to enter the intracellular region and light up the mouse melanoma B16F10 cells. The demonstrated hybrid nanogels would be an ideal system for the biomedical applications due to their excellent optical properties, magnetic properties, high drug loading capacity and responsive drug release behavior.The paper demonstrates a class of multifunctional core-shell hybrid nanogels with fluorescent and magnetic properties, which have been successfully developed for simultaneous optical temperature sensing, tumor cell imaging and magnetic/NIR-thermally responsive drug carriers. The as-synthesized hybrid nanogels were designed by coating bifunctional nanoparticles (BFNPs, fluorescent carbon dots embedded in the porous carbon shell and superparamagnetic iron oxide nanocrystals clustered in the core) with a thermo-responsive poly(N-isopropylacrylamide-co-acrylamide) [poly(NIPAM-AAm)]-based hydrogel as the shell. The BFNPs in hybrid nanogels not only demonstrate excellent photoluminescence (PL) and photostability due to the fluorescent carbon dots embedded in the porous carbon shell, but also has targeted drug accumulation potential and a magnetic-thermal conversion ability due to the superparamagnetic iron oxide nanocrystals clustered in the core. The thermo-responsive poly(NIPAM-AAm)-based gel shell can not only modify the physicochemical environment of the BFNPs core to manipulate the fluorescence intensity for sensing the variation of the environmental temperature, but also regulate the release rate of the loaded anticancer drug (curcumin) by varying the local temperature of environmental media. In addition, the carbon layer of BFNPs can adsorb and convert the NIR light to heat, leading to a promoted drug release under NIR irradiation and improving the therapeutic efficacy of drug-loaded hybrid nanogels. Furthermore, the superparamagnetic iron oxide nanocrystals in the core of BFNPs can trigger localized heating using an alternating magnetic field, leading to a phase change in the polymer gel to trigger the release of loaded drugs. Finally, the multifunctional hybrid nanogels can overcome cellular barriers to enter the intracellular region and light up the mouse melanoma B16F10 cells. The demonstrated hybrid nanogels would be an ideal system for the biomedical applications due to their excellent optical properties, magnetic properties, high drug loading capacity and responsive drug release behavior. Electronic supplementary information (ESI) available: Fig. S1-S12. See DOI: 10.1039/c4nr03748k

Implementation of hybrid clustering based on partitioning around medoids algorithm and divisive analysis on human Papillomavirus DNA

NASA Astrophysics Data System (ADS)

Arimbi, Mentari Dian; Bustamam, Alhadi; Lestari, Dian

2017-03-01

Data clustering can be executed through partition or hierarchical method for many types of data including DNA sequences. Both clustering methods can be combined by processing partition algorithm in the first level and hierarchical in the second level, called hybrid clustering. In the partition phase some popular methods such as PAM, K-means, or Fuzzy c-means methods could be applied. In this study we selected partitioning around medoids (PAM) in our partition stage. Furthermore, following the partition algorithm, in hierarchical stage we applied divisive analysis algorithm (DIANA) in order to have more specific clusters and sub clusters structures. The number of main clusters is determined using Davies Bouldin Index (DBI) value. We choose the optimal number of clusters if the results minimize the DBI value. In this work, we conduct the clustering on 1252 HPV DNA sequences data from GenBank. The characteristic extraction is initially performed, followed by normalizing and genetic distance calculation using Euclidean distance. In our implementation, we used the hybrid PAM and DIANA using the R open source programming tool. In our results, we obtained 3 main clusters with average DBI value is 0.979, using PAM in the first stage. After executing DIANA in the second stage, we obtained 4 sub clusters for Cluster-1, 9 sub clusters for Cluster-2 and 2 sub clusters in Cluster-3, with the BDI value 0.972, 0.771, and 0.768 for each main cluster respectively. Since the second stage produce lower DBI value compare to the DBI value in the first stage, we conclude that this hybrid approach can improve the accuracy of our clustering results.

Application of a Fuzzy Neural Network Model in Predicting Polycyclic Aromatic Hydrocarbon- Mediated Perturbations of the Cyp1b1 Transcriptional Regulatory Network in Mouse Skin

PubMed Central

Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

2013-01-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave one out cross-validation. Predictions were within 1 log2 fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. PMID:23274566

Excitation energy shift and size difference of low-energy levels in p -shell Λ hypernuclei

NASA Astrophysics Data System (ADS)

Kanada-En'yo, Yoshiko

2018-02-01

Structures of low-lying 0 s -orbit Λ states in p -shell Λ hypernuclei (ZAΛ) are investigated by applying microscopic cluster models for nuclear structure and a single-channel folding potential model for a Λ particle. For A >10 systems, the size reduction of core nuclei is small, and the core polarization effect is regarded as a higher-order perturbation in the Λ binding. The present calculation qualitatively describes the systematic trend of experimental data for excitation energy change from Z-1A to ZAΛ, in A >10 systems. The energy change shows a clear correlation with the nuclear size difference between the ground and excited states. In Li7Λ and Be9Λ, the significant shrinkage of cluster structures occurs consistently with the prediction of other calculations.

The equation-of-motion coupled cluster method for triple electron attached states

NASA Astrophysics Data System (ADS)

Musiał, Monika; Olszówka, Marta; Lyakh, Dmitry I.; Bartlett, Rodney J.

2012-11-01

The initial implementation of the triple electron attachment (TEA) equation-of-motion (EOM) coupled cluster (CC) method is presented, aiming at the description of electronic states with three open shell electrons outside a suitably chosen closed shell vacuum. In particular, such an approach can be used for describing dissociation of chemical bonds predominantly formed by three valence electrons, for example, in LiC and NaC molecules. Both ground and excited states are considered while rigorously maintaining the correct spin value. The preliminary results show a correct asymptotic behavior of the dissociation curves. At the same time, we emphasize that a chemically accurate description will require an extension of the minimal TEA-EOM-CC model introduced here, analogous to those already used in the double ionization potential and double electron attachment methods.

Transport of Light Ions in Matter

NASA Technical Reports Server (NTRS)

Wilson, J. W.; Cucinotta, F. A.; Tai, H.; Shinn, J. L.; Chun, S. Y.; Tripathi, R. K.; Sihver, L.

1998-01-01

A recent set of light ion experiments are analyzed using the Green's function method of solving the Boltzmann equation for ions of high charge and energy (the GRNTRN transport code) and the NUCFRG2 fragmentation database generator code. Although the NUCFRG2 code reasonably represents the fragmentation of heavy ions, the effects of light ion fragmentation requires a more detailed nuclear model including shell structure and short range correlations appearing as tightly bound clusters in the light ion nucleus. The most recent NTJCFRG2 code is augmented with a quasielastic alpha knockout model and semiempirical adjustments (up to 30 percent in charge removal) in the fragmentation process allowing reasonable agreement with the experiments to be obtained. A final resolution of the appropriate cross sections must await the full development of a coupled channel reaction model in which shell structure and clustering can be accurately evaluated.

The hydration structure of the heavy-alkalines Rb+ and Cs+ through molecular dynamics and X-ray absorption spectroscopy: surface clusters and eccentricity.

PubMed

Caralampio, Daniel Z; Martínez, José M; Pappalardo, Rafael R; Marcos, Enrique Sánchez

2017-11-01

Physicochemical properties of the two heaviest stable alkaline cations, Rb + and Cs + , in water have been examined from classical molecular dynamics (MD) simulations. Alkaline cation-water intermolecular potentials have been built from ab initio interaction energies of [M(H 2 O) n ] + clusters. Unlike in the case of other monatomic metal cations, the sampling needed the inclusion of surface clusters to properly describe the interactions. The first coordination shell is found at an average M-O distance of 2.87 Å and 3.12 Å for Rb + and Cs + , respectively, with coordination numbers of 8 and 10. Structural, dynamical and energetic properties are discussed on the basis of the delicate compromise among the ion-water and water-water interactions which contribute almost on the same foot to the definition of the solvent structure around the ions. A significant asymmetry is detected in the Rb + and Cs + first hydration shell. Reorientational times of first-shell water molecules for Cs + support a clear structure-breaking nature for this cation, whereas the Rb + values do not differ from pure water behavior. Experimental EXAFS and XANES spectra have been compared to simulated ones, obtained by means of application of the FEFF code to a set of statistically significant structures taken from the MD simulations. Due to the presence of multi-excitations in the absorption spectra, theoretical-experimental agreement for the EXAFS spectra is reached when the multi-excitations are removed from the experimental spectra.

Alkali-ion microsolvation with benzene molecules.

PubMed

Marques, J M C; Llanio-Trujillo, J L; Albertí, M; Aguilar, A; Pirani, F

2012-05-24

The target of this investigation is to characterize by a recently developed methodology, the main features of the first solvation shells of alkaline ions in nonpolar environments due to aromatic rings, which is of crucial relevance to understand the selectivity of several biochemical phenomena. We employ an evolutionary algorithm to obtain putative global minima of clusters formed with alkali-ions (M(+)) solvated with n benzene (Bz) molecules, i.e., M(+)-(Bz)(n). The global intermolecular interaction has been decomposed in Bz-Bz and in M(+)-Bz contributions, using a potential model based on different decompositions of the molecular polarizability of benzene. Specifically, we have studied the microsolvation of Na(+), K(+), and Cs(+) with benzene molecules. Microsolvation clusters up to n = 21 benzene molecules are involved in this work and the achieved global minimum structures are reported and discussed in detail. We observe that the number of benzene molecules allocated in the first solvation shell increases with the size of the cation, showing three molecules for Na(+) and four for both K(+) and Cs(+). The structure of this solvation shell keeps approximately unchanged as more benzene molecules are added to the cluster, which is independent of the ion. Particularly stable structures, so-called "magic numbers", arise for various nuclearities of the three alkali-ions. Strong "magic numbers" appear at n = 2, 3, and 4 for Na(+), K(+), and Cs(+), respectively. In addition, another set of weaker "magic numbers" (three per alkali-ion) are reported for larger nuclearities.

Ab initio calculation of one-nucleon halo states

NASA Astrophysics Data System (ADS)

Rodkin, D. M.; Tchuvil'sky, Yu M.

2018-02-01

We develop an approach to microscopic and ab initio description of clustered systems, states with halo nucleon and one-nucleon resonances. For these purposes a basis combining ordinary shell-model components and cluster-channel terms is built up. The transformation of clustered wave functions to the uniform Slater-determinant type is performed using the concept of cluster coefficients. The resulting basis of orthonormalized wave functions is used for calculating the eigenvalues and the eigenvectors of Hamiltonians built in the framework of ab initio approaches. Calculations of resonance and halo states of 5He, 9Be and 9B nuclei demonstrate that the approach is workable and labor-saving.

Presence of glassy state and large exchange bias in nanocrystalline BiFeO3

NASA Astrophysics Data System (ADS)

Srivastav, Simant Kumar; Johari, Anima; Patel, S. K. S.; Gajbhiye, N. S.

2017-11-01

We investigated the static and dynamic aspects of the magnetic properties for single phase nanocrystalline BiFeO3 with average crystallite size of 35 nm. The frequency dependence of the peak is observed in the real part of ac susceptibility χ‧ac vs T measurement and described well by the Vogel-Fulcher law as well as the power law. These analyses indicated the existence of cluster glass state with significant interaction among the spin clusters and results in cluster-glass like cooperative freezing at low temperature. The influence of temperature and magnetic field cooling on the exchange bias effect is investigated. A training effect is also observed. We have reported a significantly high ZFC & FC exchange bias of 200 Oe & 450 Oe at 300 K and 900 Oe & 2100 Oe at 5 K. The obtained results are interpreted in the framework of core-shell model, where the core of the BFO nanoparticles shows antiferromagnetic behavior and surrounded by CG-like ferromagnetic (FM) shell associated to uncompensated surface spins.

N(2)O in small para-hydrogen clusters: Structures and energetics.

PubMed

Zhu, Hua; Xie, Daiqian

2009-04-30

We present the minimum-energy structures and energetics of clusters of the linear N(2)O molecule with small numbers of para-hydrogen molecules with pairwise additive potentials. Interaction energies of (p-H(2))-N(2)O and (p-H(2))-(p-H(2)) complexes were calculated by averaging the corresponding full-dimensional potentials over the H(2) angular coordinates. The averaged (p-H(2))-N(2)O potential has three minima corresponding to the T-shaped and the linear (p-H(2))-ONN and (p-H(2))-NNO structures. Optimization of the minimum-energy structures was performed using a Genetic Algorithm. It was found that p-H(2) molecules fill three solvation rings around the N(2)O axis, each of them containing up to five p-H(2) molecules, followed by accumulation of two p-H(2) molecules at the oxygen and nitrogen ends. The first solvation shell is completed at N = 17. The calculated chemical potential oscillates with cluster size up to the completed first solvation shell. These results are consistent with the available experimental measurements. (c) 2009 Wiley Periodicals, Inc.

EM Transition Sum Rules Within the Framework of sdg Proton-Neutron Interacting Boson Model, Nuclear Pair Shell Model and Fermion Dynamical Symmetry Model

NASA Astrophysics Data System (ADS)

Zhao, Yumin

1997-07-01

By the techniques of the Wick theorem for coupled clusters, the no-energy-weighted electromagnetic sum-rule calculations are presented in the sdg neutron-proton interacting boson model, the nuclear pair shell model and the fermion-dynamical symmetry model. The project supported by Development Project Foundation of China, National Natural Science Foundation of China, Doctoral Education Fund of National Education Committee, Fundamental Research Fund of Southeast University

A new method based on Dempster-Shafer theory and fuzzy c-means for brain MRI segmentation

NASA Astrophysics Data System (ADS)

Liu, Jie; Lu, Xi; Li, Yunpeng; Chen, Xiaowu; Deng, Yong

2015-10-01

In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster-Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel’s BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

NASA Astrophysics Data System (ADS)

Niknam, Taher; Kavousifard, Abdollah; Tabatabaei, Sajad; Aghaei, Jamshid

2011-10-01

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the 'best' compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.

Simulation study of sulfonate cluster swelling in ionomers

NASA Astrophysics Data System (ADS)

Allahyarov, Elshad; Taylor, Philip L.; Löwen, Hartmut

2009-12-01

We have performed simulations to study how increasing humidity affects the structure of Nafion-like ionomers under conditions of low sulfonate concentration and low humidity. At the onset of membrane hydration, the clusters split into smaller parts. These subsequently swell, but then maintain constant the number of sulfonates per cluster. We find that the distribution of water in low-sulfonate membranes depends strongly on the sulfonate concentration. For a relatively low sulfonate concentration, nearly all the side-chain terminal groups are within cluster formations, and the average water loading per cluster matches the water content of membrane. However, for a relatively higher sulfonate concentration the water-to-sulfonate ratio becomes nonuniform. The clusters become wetter, while the intercluster bridges become drier. We note the formation of unusual shells of water-rich material that surround the sulfonate clusters.

Nucleon localization and fragment formation in nuclear fission

DOE PAGES

Zhang, C. L.; Schuetrumpf, B.; Nazarewicz, W.

2016-12-27

An electron localization measure was originally introduced to characterize chemical bond structures in molecules. Recently, a nucleon localization based on Hartree-Fock densities has been introduced to investigate α-cluster structures in light nuclei. Compared to the local nucleonic densities, the nucleon localization function has been shown to be an excellent indicator of shell effects and cluster correlations. In this work, using the spatial nucleon localization measure, we investigated the emergence of fragments in fissioning heavy nuclei using the self-consistent energy density functional method with a quantified energy density functional optimized for fission studies. We studied the particle densities and spatial nucleonmore » localization distributions along the fission pathways of 264Fm, 232Th, and 240Pu. We demonstrated that the fission fragments were formed fairly early in the evolution, well before scission. To illustrate the usefulness of the localization measure, we showed how the hyperdeformed state of 232Th could be understood in terms of a quasimolecular state made of 132Sn and 100Zr fragments. Compared to nucleonic distributions, the nucleon localization function more effectively quantifies nucleonic clustering: its characteristic oscillating pattern, traced back to shell effects, is a clear fingerprint of cluster/fragment configurations. This is of particular interest for studies of fragment formation and fragment identification in fissioning nuclei.« less

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

Zhang, C. L.; Schuetrumpf, B.; Nazarewicz, W.

An electron localization measure was originally introduced to characterize chemical bond structures in molecules. Recently, a nucleon localization based on Hartree-Fock densities has been introduced to investigate α-cluster structures in light nuclei. Compared to the local nucleonic densities, the nucleon localization function has been shown to be an excellent indicator of shell effects and cluster correlations. In this work, using the spatial nucleon localization measure, we investigated the emergence of fragments in fissioning heavy nuclei using the self-consistent energy density functional method with a quantified energy density functional optimized for fission studies. We studied the particle densities and spatial nucleonmore » localization distributions along the fission pathways of 264Fm, 232Th, and 240Pu. We demonstrated that the fission fragments were formed fairly early in the evolution, well before scission. To illustrate the usefulness of the localization measure, we showed how the hyperdeformed state of 232Th could be understood in terms of a quasimolecular state made of 132Sn and 100Zr fragments. Compared to nucleonic distributions, the nucleon localization function more effectively quantifies nucleonic clustering: its characteristic oscillating pattern, traced back to shell effects, is a clear fingerprint of cluster/fragment configurations. This is of particular interest for studies of fragment formation and fragment identification in fissioning nuclei.« less

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

High-temperature investigation on morphology, phase and size of iron/iron-oxide core–shell nanoclusters for radiation nanodetector

NASA Astrophysics Data System (ADS)

Khanal, Lokendra Raj; Williams, Thomas; Qiang, You

2018-06-01

Iron/iron-oxide (Fe–Fe3O4) core–shell nanoclusters (NCs) synthesized by a cluster deposition technique were subjected to a study of their high temperature structural and morphological behavior. Annealing effects have been investigated up to 800 °C in vacuum, oxygen and argon environments. The ~18 nm average size of the as-prepared NCs increases slowly in temperatures up to 500 °C in all three environments. The size increases abruptly in the argon environment but slowly in vacuum and oxygen when annealed at 800 °C. The x-ray diffraction (XRD) studies have shown that the iron core remains in the core–shell NCs only when they were annealed in the vacuum. A dramatic change in the surface morphology, an island like structure and/or a network like pattern, was observed at the elevated temperature. The as-prepared and annealed samples were analyzed using XRD, scanning electron microscopy and imageJ software for a close inspection of the temperature aroused properties. This work presents the temperature induced size growth mechanism, oxidation kinetics and phase transformation of the NCs accompanied by cluster aggregation, particle coalescence, and diffusion.

Mechanochemical mechanism for reaction of aluminium nano- and micrometre-scale particles.

PubMed

Levitas, Valery I

2013-11-28

A recently suggested melt-dispersion mechanism (MDM) for fast reaction of aluminium (Al) nano- and a few micrometre-scale particles during fast heating is reviewed. Volume expansion of 6% during Al melting produces pressure of several GPa in a core and tensile hoop stresses of 10 GPa in an oxide shell. Such stresses cause dynamic fracture and spallation of the shell. After spallation, an unloading wave propagates to the centre of the particle and creates a tensile pressure of 3-8 GPa. Such a tensile pressure exceeds the cavitation strength of liquid Al and disperses the melt into small, bare clusters (fragments) that fly at a high velocity. Reaction of the clusters is not limited by diffusion through a pre-existing oxide shell. Some theoretical and experimental results related to the MDM are presented. Various theoretical predictions based on the MDM are in good qualitative and quantitative agreement with experiments, which resolves some basic puzzles in combustion of Al particles. Methods to control and improve reactivity of Al particles are formulated, which are exactly opposite to the current trends based on diffusion mechanism. Some of these suggestions have experimental confirmation.

Laser-induced transformation of supramolecular complexes: approach to controlled formation of hybrid multi-yolk-shell Au-Ag@a-C:H nanostructures

PubMed Central

Manshina, A. A.; Grachova, E. V.; Povolotskiy, A. V.; Povolotckaia, A. V.; Petrov, Y. V.; Koshevoy, I. O.; Makarova, A. A.; Vyalikh, D. V.; Tunik, S. P.

2015-01-01

In the present work an efficient approach of the controlled formation of hybrid Au–Ag–C nanostructures based on laser-induced transformation of organometallic supramolecular cluster compound is suggested. Herein the one-step process of the laser-induced synthesis of hybrid multi-yolk-shell Au-Ag@a-C:H nanoparticles which are bimetallic gold-silver subnanoclusters dispersed in nanospheres of amorphous hydrogenated a-C:H carbon is reported in details. It has been demonstrated that variation of the experimental parameters such as type of the organometallic precursor, solvent, deposition geometry and duration of laser irradiation allows directed control of nanoparticles’ dimension and morphology. The mechanism of Au-Ag@a-C:H nanoparticles formation is suggested: the photo-excitation of the precursor molecule through metal-to-ligand charge transfer followed by rupture of metallophilic bonds, transformation of the cluster core including red-ox intramolecular reaction and aggregation of heterometallic species that results in the hybrid metal/carbon nanoparticles with multi-yolk-shell architecture formation. It has been found that the nanoparticles obtained can be efficiently used for the Surface-Enhanced Raman Spectroscopy label-free detection of human serum albumin in low concentration solution. PMID:26153347

A new near-linear scaling, efficient and accurate, open-shell domain-based local pair natural orbital coupled cluster singles and doubles theory.

PubMed

Saitow, Masaaki; Becker, Ute; Riplinger, Christoph; Valeev, Edward F; Neese, Frank

2017-04-28

The Coupled-Cluster expansion, truncated after single and double excitations (CCSD), provides accurate and reliable molecular electronic wave functions and energies for many molecular systems around their equilibrium geometries. However, the high computational cost, which is well-known to scale as O(N 6 ) with system size N, has limited its practical application to small systems consisting of not more than approximately 20-30 atoms. To overcome these limitations, low-order scaling approximations to CCSD have been intensively investigated over the past few years. In our previous work, we have shown that by combining the pair natural orbital (PNO) approach and the concept of orbital domains it is possible to achieve fully linear scaling CC implementations (DLPNO-CCSD and DLPNO-CCSD(T)) that recover around 99.9% of the total correlation energy [C. Riplinger et al., J. Chem. Phys. 144, 024109 (2016)]. The production level implementations of the DLPNO-CCSD and DLPNO-CCSD(T) methods were shown to be applicable to realistic systems composed of a few hundred atoms in a routine, black-box fashion on relatively modest hardware. In 2011, a reduced-scaling CCSD approach for high-spin open-shell unrestricted Hartree-Fock reference wave functions was proposed (UHF-LPNO-CCSD) [A. Hansen et al., J. Chem. Phys. 135, 214102 (2011)]. After a few years of experience with this method, a few shortcomings of UHF-LPNO-CCSD were noticed that required a redesign of the method, which is the subject of this paper. To this end, we employ the high-spin open-shell variant of the N-electron valence perturbation theory formalism to define the initial guess wave function, and consequently also the open-shell PNOs. The new PNO ansatz properly converges to the closed-shell limit since all truncations and approximations have been made in strict analogy to the closed-shell case. Furthermore, given the fact that the formalism uses a single set of orbitals, only a single PNO integral transformation is necessary, which offers large computational savings. We show that, with the default PNO truncation parameters, approximately 99.9% of the total CCSD correlation energy is recovered for open-shell species, which is comparable to the performance of the method for closed-shells. UHF-DLPNO-CCSD shows a linear scaling behavior for closed-shell systems, while linear to quadratic scaling is obtained for open-shell systems. The largest systems we have considered contain more than 500 atoms and feature more than 10 000 basis functions with a triple-ζ quality basis set.

A new near-linear scaling, efficient and accurate, open-shell domain-based local pair natural orbital coupled cluster singles and doubles theory

NASA Astrophysics Data System (ADS)

Saitow, Masaaki; Becker, Ute; Riplinger, Christoph; Valeev, Edward F.; Neese, Frank

2017-04-01

The Coupled-Cluster expansion, truncated after single and double excitations (CCSD), provides accurate and reliable molecular electronic wave functions and energies for many molecular systems around their equilibrium geometries. However, the high computational cost, which is well-known to scale as O(N6) with system size N, has limited its practical application to small systems consisting of not more than approximately 20-30 atoms. To overcome these limitations, low-order scaling approximations to CCSD have been intensively investigated over the past few years. In our previous work, we have shown that by combining the pair natural orbital (PNO) approach and the concept of orbital domains it is possible to achieve fully linear scaling CC implementations (DLPNO-CCSD and DLPNO-CCSD(T)) that recover around 99.9% of the total correlation energy [C. Riplinger et al., J. Chem. Phys. 144, 024109 (2016)]. The production level implementations of the DLPNO-CCSD and DLPNO-CCSD(T) methods were shown to be applicable to realistic systems composed of a few hundred atoms in a routine, black-box fashion on relatively modest hardware. In 2011, a reduced-scaling CCSD approach for high-spin open-shell unrestricted Hartree-Fock reference wave functions was proposed (UHF-LPNO-CCSD) [A. Hansen et al., J. Chem. Phys. 135, 214102 (2011)]. After a few years of experience with this method, a few shortcomings of UHF-LPNO-CCSD were noticed that required a redesign of the method, which is the subject of this paper. To this end, we employ the high-spin open-shell variant of the N-electron valence perturbation theory formalism to define the initial guess wave function, and consequently also the open-shell PNOs. The new PNO ansatz properly converges to the closed-shell limit since all truncations and approximations have been made in strict analogy to the closed-shell case. Furthermore, given the fact that the formalism uses a single set of orbitals, only a single PNO integral transformation is necessary, which offers large computational savings. We show that, with the default PNO truncation parameters, approximately 99.9% of the total CCSD correlation energy is recovered for open-shell species, which is comparable to the performance of the method for closed-shells. UHF-DLPNO-CCSD shows a linear scaling behavior for closed-shell systems, while linear to quadratic scaling is obtained for open-shell systems. The largest systems we have considered contain more than 500 atoms and feature more than 10 000 basis functions with a triple-ζ quality basis set.

A Fuzzy Query Mechanism for Human Resource Websites

NASA Astrophysics Data System (ADS)

Lai, Lien-Fu; Wu, Chao-Chin; Huang, Liang-Tsung; Kuo, Jung-Chih

Users' preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. User's fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to user's preference.

Quantitative properties of clustering within modern microscopic nuclear models

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

Volya, A.; Tchuvil’sky, Yu. M., E-mail: tchuvl@nucl-th.sinp.msu.ru

2016-09-15

A method for studying cluster spectroscopic properties of nuclear fragmentation, such as spectroscopic amplitudes, cluster form factors, and spectroscopic factors, is developed on the basis of modern precision nuclear models that take into account the mixing of large-scale shell-model configurations. Alpha-cluster channels are considered as an example. A mathematical proof of the need for taking into account the channel-wave-function renormalization generated by exchange terms of the antisymmetrization operator (Fliessbach effect) is given. Examples where this effect is confirmed by a high quality of the description of experimental data are presented. By and large, the method in question extends substantially themore » possibilities for studying clustering phenomena in nuclei and for improving the quality of their description.« less

Adsorption of volatile organic compounds by pecan shell- and almond shell-based granular activated carbons.

PubMed

Bansode, R R; Losso, J N; Marshall, W E; Rao, R M; Portier, R J

2003-11-01

The objective of this research was to determine the effectiveness of using pecan and almond shell-based granular activated carbons (GACs) in the adsorption of volatile organic compounds (VOCs) of health concern and known toxic compounds (such as bromo-dichloromethane, benzene, carbon tetrachloride, 1,1,1-trichloromethane, chloroform, and 1,1-dichloromethane) compared to the adsorption efficiency of commercially used carbons (such as Filtrasorb 200, Calgon GRC-20, and Waterlinks 206C AW) in simulated test medium. The pecan shell-based GACs were activated using steam, carbon dioxide or phosphoric acid. An almond shell-based GAC was activated with phosphoric acid. Our results indicated that steam- or carbon dioxide-activated pecan shell carbons were superior in total VOC adsorption to phosphoric acid-activated pecan shell or almond shell carbons, inferring that the method of activation selected for the preparation of activated carbons affected the adsorption of VOCs and hence are factors to be considered in any adsorption process. The steam-activated, pecan shell carbon adsorbed more total VOCs than the other experimental carbons and had an adsorption profile similar to the two coconut shell-based commercial carbons, but had greater adsorption than the coal-based commercial carbon. All the carbons studied adsorbed benzene more effectively than the other organics. Pecan shell, steam-activated and acid-activated GACs showed higher adsorption of 1,1,1-trichloroethane than the other carbons studied. Multivariate analysis was conducted to group experimental carbons and commercial carbons based on their physical, chemical, and adsorptive properties. The results of the analysis conclude that steam-activated and acid-activated pecan shell carbons clustered together with coal-based and coconut shell-based commercial carbons, thus inferring that these experimental carbons could potentially be used as alternative sources for VOC adsorption in an aqueous environment.

He-Ion Microscopy as a High-Resolution Probe for Complex Quantum Heterostructures in Core-Shell Nanowires.

PubMed

Pöpsel, Christian; Becker, Jonathan; Jeon, Nari; Döblinger, Markus; Stettner, Thomas; Gottschalk, Yeanitza Trujillo; Loitsch, Bernhard; Matich, Sonja; Altzschner, Marcus; Holleitner, Alexander W; Finley, Jonathan J; Lauhon, Lincoln J; Koblmüller, Gregor

2018-06-13

Core-shell semiconductor nanowires (NW) with internal quantum heterostructures are amongst the most complex nanostructured materials to be explored for assessing the ultimate capabilities of diverse ultrahigh-resolution imaging techniques. To probe the structure and composition of these materials in their native environment with minimal damage and sample preparation calls for high-resolution electron or ion microscopy methods, which have not yet been tested on such classes of ultrasmall quantum nanostructures. Here, we demonstrate that scanning helium ion microscopy (SHeIM) provides a powerful and straightforward method to map quantum heterostructures embedded in complex III-V semiconductor NWs with unique material contrast at ∼1 nm resolution. By probing the cross sections of GaAs-Al(Ga)As core-shell NWs with coaxial GaAs quantum wells as well as short-period GaAs/AlAs superlattice (SL) structures in the shell, the Al-rich and Ga-rich layers are accurately discriminated by their image contrast in excellent agreement with correlated, yet destructive, scanning transmission electron microscopy and atom probe tomography analysis. Most interestingly, quantitative He-ion dose-dependent SHeIM analysis of the ternary AlGaAs shell layers and of compositionally nonuniform GaAs/AlAs SLs reveals distinct alloy composition fluctuations in the form of Al-rich clusters with size distributions between ∼1-10 nm. In the GaAs/AlAs SLs the alloy clustering vanishes with increasing SL-period (>5 nm-GaAs/4 nm-AlAs), providing insights into critical size dimensions for atomic intermixing effects in short-period SLs within a NW geometry. The straightforward SHeIM technique therefore provides unique benefits in imaging the tiniest nanoscale features in topography, structure and composition of a multitude of diverse complex semiconductor nanostructures.

Discovery of a new Wolf-Rayet star and a candidate star cluster in the Large Magellanic Cloud with Spitzer

NASA Astrophysics Data System (ADS)

Gvaramadze, V. V.; Chené, A.-N.; Kniazev, A. Y.; Schnurr, O.; Shenar, T.; Sander, A.; Hainich, R.; Langer, N.; Hamann, W.-R.; Chu, Y.-H.; Gruendl, R. A.

2014-08-01

We report the first-ever discovery of a Wolf-Rayet (WR) star in the Large Magellanic Cloud via detection of a circular shell with the Spitzer Space Telescope. Follow-up observations with Gemini-South resolved the central star of the shell into two components separated from each other by ≈2 arcsec (or ≈0.5 pc in projection). One of these components turns out to be a WN3 star with H and He lines both in emission and absorption (we named it BAT99 3a using the numbering system based on extending the Breysacher et al. catalogue). Spectroscopy of the second component showed that it is a B0 V star. Subsequent spectroscopic observations of BAT99 3a with the du Pont 2.5-m telescope and the Southern African Large Telescope revealed that it is a close, eccentric binary system, and that the absorption lines are associated with an O companion star. We analysed the spectrum of the binary system using the non-LTE Potsdam WR (POWR) code, confirming that the WR component is a very hot (≈90 kK) WN star. For this star, we derived a luminosity of log L/ L⊙ = 5.45 and a mass-loss rate of 10- 5.8 M⊙ yr- 1, and found that the stellar wind composition is dominated by helium with 20 per cent of hydrogen. Spectroscopy of the shell revealed an He III region centred on BAT99 3a and having the same angular radius (≈15 arcsec) as the shell. We thereby add a new example to a rare class of high-excitation nebulae photoionized by WR stars. Analysis of the nebular spectrum showed that the shell is composed of unprocessed material, implying that the shell was swept-up from the local interstellar medium. We discuss the physical relationship between the newly identified massive stars and their possible membership of a previously unrecognized star cluster.

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.

Temperature-Dependent Evolution of the Oxidation States of Cobalt and Platinum in Co 1–xPt x Clusters under H 2 and CO + H 2 Atmospheres

DOE PAGES

Yang, Bing; Khadra, Ghassan; Tuaillon-Combes, Juliette; ...

2016-08-25

In this study, Co 1–xPt x clusters of 2.9-nm size with a range of atomically precise Pt/Co atomic ratios (x = 0, 0.25, 0.5, 0.75, 1) were synthesized using the mass-selected low-energy cluster beam deposition (LECBD) technique and soft-landed onto an amorphous alumina thin film prepared by atomic layer deposition (ALD). Utilizing ex situ X-ray photoemission spectroscopy (XPS), the oxidation state of the as-made clusters supported on Al 2O 3 was determined after both a 1-h-long exposure to air and aging for several weeks while exposed to air. Next, the aged cluster samples were characterized by grazing-incidence X-ray absorption spectroscopymore » (GIXAS) and then pretreated with diluted hydrogen and further exposed to the mixture of diluted CO and H 2 up to 225°C at atmospheric pressure, and the temperature-dependent evolutions of the particle size/shape and the oxidation states of the individual metal components within the clusters were monitored using in situ grazing-incidence small-angle X-ray scattering and X-ray absorption spectroscopy (GISAXS/GIXAS). The changes in the oxidation states of Co and Pt exhibited a nonlinear dependence on the Pt/Co atomic ratio of the clusters. For example, a low Pt/Co ratio (x ≤ 0.5) facilitates the formation of Co(OH) 2, whereas a high Pt/Co ratio (x = 0.75) stabilizes the Co 3O 4 composition instead through the formation of a Co–Pt core–shell structure where the platinum shell inhibits the reduction of cobalt in the core of the Co 1–xPt x alloy clusters. Finally, the obtained results indicate methods for optimizing the composition and structure of binary alloy clusters for catalysis.« less

Estimation of multiple accelerated motions using chirp-Fourier transform and clustering.

PubMed

Alexiadis, Dimitrios S; Sergiadis, George D

2007-01-01

Motion estimation in the spatiotemporal domain has been extensively studied and many methodologies have been proposed, which, however, cannot handle both time-varying and multiple motions. Extending previously published ideas, we present an efficient method for estimating multiple, linearly time-varying motions. It is shown that the estimation of accelerated motions is equivalent to the parameter estimation of superpositioned chirp signals. From this viewpoint, one can exploit established signal processing tools such as the chirp-Fourier transform. It is shown that accelerated motion results in energy concentration along planes in the 4-D space: spatial frequencies-temporal frequency-chirp rate. Using fuzzy c-planes clustering, we estimate the plane/motion parameters. The effectiveness of our method is verified on both synthetic as well as real sequences and its advantages are highlighted.

Research on potential user identification model for electric energy substitution

NASA Astrophysics Data System (ADS)

Xia, Huaijian; Chen, Meiling; Lin, Haiying; Yang, Shuo; Miao, Bo; Zhu, Xinzhi

2018-01-01

The implementation of energy substitution plays an important role in promoting the development of energy conservation and emission reduction in china. Energy service management platform of alternative energy users based on the data in the enterprise production value, product output, coal and other energy consumption as a potential evaluation index, using principal component analysis model to simplify the formation of characteristic index, comprehensive index contains the original variables, and using fuzzy clustering model for the same industry user’s flexible classification. The comprehensive index number and user clustering classification based on constructed particle optimization neural network classification model based on the user, user can replace electric potential prediction. The results of an example show that the model can effectively predict the potential of users’ energy potential.

A Novel Clustering Method Curbing the Number of States in Reinforcement Learning

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Nunobiki, Masayuki; Taniguchi, Kenji

We propose an efficient state-space construction method for a reinforcement learning. Our method controls the number of categories with improving the clustering method of Fuzzy ART which is an autonomous state-space construction method. The proposed method represents weight vector as the mean value of input vectors in order to curb the number of new categories and eliminates categories whose state values are low to curb the total number of categories. As the state value is updated, the size of category becomes small to learn policy strictly. We verified the effectiveness of the proposed method with simulations of a reaching problem for a two-link robot arm. We confirmed that the number of categories was reduced and the agent achieved the complex task quickly.

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

PubMed

de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic

NASA Astrophysics Data System (ADS)

Al-Mousa, Amjed A.

Thin films are essential constituents of modern electronic devices and have a multitude of applications in such devices. The impact of the surface morphology of thin films on the device characteristics where these films are used has generated substantial attention to advanced film characterization techniques. In this work, we present a new approach to characterize surface nanostructures of thin films by focusing on isolating nanostructures and extracting quantitative information, such as the shape and size of the structures. This methodology is applicable to any Scanning Probe Microscopy (SPM) data, such as Atomic Force Microscopy (AFM) data which we are presenting here. The methodology starts by compensating the AFM data for some specific classes of measurement artifacts. After that, the methodology employs two distinct techniques. The first, which we call the overlay technique, proceeds by systematically processing the raster data that constitute the scanning probe image in both vertical and horizontal directions. It then proceeds by classifying points in each direction separately. Finally, the results from both the horizontal and the vertical subsets are overlaid, where a final decision on each surface point is made. The second technique, based on fuzzy logic, relies on a Fuzzy Inference Engine (FIE) to classify the surface points. Once classified, these points are clustered into surface structures. The latter technique also includes a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and then tune the fuzzy technique system uniquely for that surface. Both techniques have been applied to characterize organic semiconductor thin films of pentacene on different substrates. Also, we present a case study to demonstrate the effectiveness of our methodology to identify quantitatively particle sizes of two specimens of gold nanoparticles of different nominal dimensions dispersed on a mica surface. A comparison with other techniques like: thresholding, watershed and edge detection is presented next. Finally, we present a systematic study of the fuzzy logic technique by experimenting with synthetic data. These results are discussed and compared along with the challenges of the two techniques.

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

NASA Astrophysics Data System (ADS)

Mehmanpazir, Farhad; Asadi, Shahrokh

2017-03-01

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a "data mining-based evolutionary fuzzy expert system" (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.

Structures of p -shell double-Λ hypernuclei studied with microscopic cluster models

NASA Astrophysics Data System (ADS)

Kanada-En'yo, Yoshiko

2018-03-01

0 s -orbit Λ states in p -shell double-Λ hypernuclei (Z Λ Λ A ), Li Λ Λ 8 , Li Λ Λ 9 , Be Λ Λ 10 ,11 ,12 , B Λ Λ 12 ,13 , and C Λ Λ 14 are investigated. Microscopic cluster models are applied to core nuclear part and a potential model is adopted for Λ particles. The Λ -core potential is a folding potential obtained with effective G -matrix Λ -N interactions, which reasonably reproduce energy spectra of Z Λ A -1 . System dependence of the Λ -Λ binding energies is understood by the core polarization energy from nuclear size reduction. Reductions of nuclear sizes and E 2 transition strengths by Λ particles are also discussed.

Intelligent Predictor of Energy Expenditure with the Use of Patch-Type Sensor Module

PubMed Central

Li, Meina; Kwak, Keun-Chang; Kim, Youn-Tae

2012-01-01

This paper is concerned with an intelligent predictor of energy expenditure (EE) using a developed patch-type sensor module for wireless monitoring of heart rate (HR) and movement index (MI). For this purpose, an intelligent predictor is designed by an advanced linguistic model (LM) with interval prediction based on fuzzy granulation that can be realized by context-based fuzzy c-means (CFCM) clustering. The system components consist of a sensor board, the rubber case, and the communication module with built-in analysis algorithm. This sensor is patched onto the user's chest to obtain physiological data in indoor and outdoor environments. The prediction performance was demonstrated by root mean square error (RMSE). The prediction performance was obtained as the number of contexts and clusters increased from 2 to 6, respectively. Thirty participants were recruited from Chosun University to take part in this study. The data sets were recorded during normal walking, brisk walking, slow running, and jogging in an outdoor environment and treadmill running in an indoor environment, respectively. We randomly divided the data set into training (60%) and test data set (40%) in the normalized space during 10 iterations. The training data set is used for model construction, while the test set is used for model validation. The experimental results revealed that the prediction error on treadmill running simulation was improved by about 51% and 12% in comparison to conventional LM for training and checking data set, respectively. PMID:23202166

Detection of pre-symptomatic rose powdery-mildew and gray-mold diseases based on thermal vision

NASA Astrophysics Data System (ADS)

Jafari, M.; Minaei, S.; Safaie, N.

2017-09-01

Roses are the most important plants in ornamental horticulture. Roses are susceptible to a number of phytopathogenic diseases. Among the most serious diseases of rose, powdery mildew (Podosphaera pannosa var. rosae) and gray mold (Botrytis cinerea) are widespread which require considerable attention. In this study, the potential of implementing thermal imaging to detect the pre-symptomatic appearance of these fungal diseases was investigated. Effects of powdery mildew and gray mold diseases on rose plants (Rosa hybrida L.) were examined by two experiments conducted in a growth chamber. To classify the healthy and infected plants, feature selection was carried out and the best extracted thermal features with the largest linguistic hedge values were chosen. Two neuro-fuzzy classifiers were trained to distinguish between the healthy and infected plants. Best estimation rates of 92.55% and 92.3% were achieved in training and testing the classifier with 8 clusters in order to identify the leaves infected with powdery mildew. In addition, the best estimation rates of 97.5% and 92.59% were achieved in training and testing the classifier with 4 clusters to identify the gray mold disease on flowers. Performance of the designed neuro-fuzzy classifiers were evaluated with the thermal images captured using an automatic imaging setup. Best correct estimation rates of 69% and 80% were achieved (on the second day post-inoculation) for pre-symptomatic appearance detection of powdery mildew and gray mold diseases, respectively.

Self-consistent calculations for the electronic structure of a vacancy in copper. A solution of the embedding problem

NASA Astrophysics Data System (ADS)

Zeller, R.; Braspenning, P. J.

1982-06-01

The charge density and the local density of states for a vacancy in Cu and for the first shell of Cu neighbours are calculated by the KKR-Green's function technique. The muffin-tin potentials for the vacancy and the neighbour shell atoms are determined self-consistently in the local density approximation of density functional theory. By the use of the proper host Green's function the embedding of this cluster of 13 perturbed muffin-tins into the infinite array of bulk Cu muffin-tin potentials is described rigorously, thus representing a solution of the embedding problem. The calculations demonstrate a rather large charge transfer of 1.1 electrons from the first neighbour shell to the vacancy.

Hierarchically assembled theranostic nanostructures for siRNA delivery and imaging applications.

PubMed

Shrestha, Ritu; Elsabahy, Mahmoud; Luehmann, Hannah; Samarajeewa, Sandani; Florez-Malaver, Stephanie; Lee, Nam S; Welch, Michael J; Liu, Yongjian; Wooley, Karen L

2012-10-24

Dual functional hierarchically assembled nanostructures, with two unique functions of carrying therapeutic cargo electrostatically and maintaining radiolabeled imaging agents covalently within separate component building blocks, have been developed via the supramolecular assembly of several spherical cationic shell cross-linked nanoparticles clustered around a central anionic shell cross-linked cylinder. The shells of the cationic nanoparticles and the hydrophobic core domain of the anionic central cylindrical nanostructure of the assemblies were utilized to complex negatively charged nucleic acids (siRNA) and to undergo radiolabeling, respectively, for potential theranostic applications. The assemblies exhibited exceptional cell transfection and radiolabeling efficiencies, providing an overall advantage over the individual components, which could each facilitate only one or the other of the functions.

Open sd-shell nuclei from first principles

DOE PAGES

Jansen, Gustav R.; Signoracci, Angelo J.; Hagen, Gaute; ...

2016-07-05

We extend the ab initio coupled-cluster effective interaction (CCEI) method to open-shell nuclei with protons and neutrons in the valence space, and compute binding energies and excited states of isotopes of neon and magnesium. We employ a nucleon-nucleon and three-nucleon interaction from chiral effective field theory evolved to a lower cutoff via a similarity renormalization group transformation. We find good agreement with experiment for binding energies and spectra, while charge radii of neon isotopes are underestimated. For the deformed nuclei 20Ne and 24Mg we reproduce rotational bands and electric quadrupole transitions within uncertainties estimated from an effective field theory formore » deformed nuclei, thereby demonstrating that collective phenomena in sd-shell nuclei emerge from complex ab initio calculations.« less

Open sd-shell nuclei from first principles

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

Jansen, Gustav R.; Signoracci, Angelo J.; Hagen, Gaute

We extend the ab initio coupled-cluster effective interaction (CCEI) method to open-shell nuclei with protons and neutrons in the valence space, and compute binding energies and excited states of isotopes of neon and magnesium. We employ a nucleon-nucleon and three-nucleon interaction from chiral effective field theory evolved to a lower cutoff via a similarity renormalization group transformation. We find good agreement with experiment for binding energies and spectra, while charge radii of neon isotopes are underestimated. For the deformed nuclei 20Ne and 24Mg we reproduce rotational bands and electric quadrupole transitions within uncertainties estimated from an effective field theory formore » deformed nuclei, thereby demonstrating that collective phenomena in sd-shell nuclei emerge from complex ab initio calculations.« less

Dynamical mass estimates in M13

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

Leonard, P.J.T.; Richer, H.B.; Fahlman, G.G.

We have used the proper motion data of Cudworth Monet to make mass estimates in the globular cluster M13 by solving the spherical Jeans equation. We find a mass inside a spherical shell centered on the cluster with a radius corresponding to 390 arcsec on the sky of 5.5 or 7.6 {times} 10{sup 5} M{circle dot}, depending on the adopted cluster distance. This large dynamical mass estimate together with the observed fact that the mass function of M13 is rising steeply at the low-mass end suggest that much of the cluster mass may be in the form of low-mass starsmore » and brown dwarfs.« less

Dynamical mass estimates in M13

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

Leonard, P.J.T.; Richer, H.B.; Fahlman, G.G.

We have used the proper motion data of Cudworth Monet to make mass estimates in the globular cluster M13 by solving the spherical Jeans equation. We find a mass inside a spherical shell centered on the cluster with a radius corresponding to 390 arcsec on the sky of 5.5 or 7.6 {times} 10{sup 5} M{circle_dot}, depending on the adopted cluster distance. This large dynamical mass estimate together with the observed fact that the mass function of M13 is rising steeply at the low-mass end suggest that much of the cluster mass may be in the form of low-mass stars andmore » brown dwarfs.« less

Clustering analysis strategies for electron energy loss spectroscopy (EELS).

PubMed

Torruella, Pau; Estrader, Marta; López-Ortega, Alberto; Baró, Maria Dolors; Varela, Maria; Peiró, Francesca; Estradé, Sònia

2018-02-01

In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyze electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyze spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis of the Structures and Properties of (GaSb)n (n = 4-9) Clusters through Density Functional Theory.

PubMed

Lu, Qi Liang; Luo, Qi Quan; Huang, Shou Guo; Li, Yi De; Wan, Jian Guo

2016-07-07

An optimization strategy combining global semiempirical quantum mechanical search with all-electron density functional theory was adopted to determine the lowest energy structure of (GaSb)n clusters up to n = 9. The growth pattern of the clusters differed from those of previously reported group III-V binary clusters. A cagelike configuration was found for cluster sizes n ≤ 7. The structure of (GaSb)6 deviated from that of other III-V clusters. Competition existed between core-shell and hollow cage structures of (GaSb)7. Novel noncagelike structures were energetically preferred over the cages for the (GaSb)8 and (GaSb)9 clusters. Electronic properties, such as vertical ionization potential, adiabatic electron affinities, HOMO-LUMO gaps, and average on-site charges on Ga or Sb atoms, as well as binding energies, were computed.

Optimal solution of full fuzzy transportation problems using total integral ranking

NASA Astrophysics Data System (ADS)

Sam’an, M.; Farikhin; Hariyanto, S.; Surarso, B.

2018-03-01

Full fuzzy transportation problem (FFTP) is a transportation problem where transport costs, demand, supply and decision variables are expressed in form of fuzzy numbers. To solve fuzzy transportation problem, fuzzy number parameter must be converted to a crisp number called defuzzyfication method. In this new total integral ranking method with fuzzy numbers from conversion of trapezoidal fuzzy numbers to hexagonal fuzzy numbers obtained result of consistency defuzzyfication on symmetrical fuzzy hexagonal and non symmetrical type 2 numbers with fuzzy triangular numbers. To calculate of optimum solution FTP used fuzzy transportation algorithm with least cost method. From this optimum solution, it is found that use of fuzzy number form total integral ranking with index of optimism gives different optimum value. In addition, total integral ranking value using hexagonal fuzzy numbers has an optimal value better than the total integral ranking value using trapezoidal fuzzy numbers.

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

Goudfrooij, Paul, E-mail: goudfroo@stsci.edu

We study mass functions of globular clusters derived from Hubble Space Telescope/Advanced Camera for Surveys images of the early-type merger remnant galaxy NGC 1316, which hosts a significant population of metal-rich globular clusters of intermediate age ({approx}3 Gyr). For the old, metal-poor ({sup b}lue{sup )} clusters, the peak mass of the mass function M{sub p} increases with internal half-mass density {rho}{sub h} as M{sub p}{proportional_to}{rho}{sub h}{sup 0.44}, whereas it stays approximately constant with galactocentric distance R{sub gal}. The mass functions of these clusters are consistent with a simple scenario in which they formed with a Schechter initial mass function andmore » evolved subsequently by internal two-body relaxation. For the intermediate-age population of metal-rich ({sup r}ed{sup )} clusters, the faint end of the previously reported power-law luminosity function of the clusters with R{sub gal} > 9 kpc is due to many of those clusters having radii larger than the theoretical maximum value imposed by the tidal field of NGC 1316 at their R{sub gal}. This renders disruption by two-body relaxation ineffective. Only a few such diffuse clusters are found in the inner regions of NGC 1316. Completeness tests indicate that this is a physical effect. Using comparisons with star clusters in other galaxies and cluster disruption calculations using published models, we hypothesize that most red clusters in the low-{rho}{sub h} tail of the initial distribution have already been destroyed in the inner regions of NGC 1316 by tidal shocking, and that several remaining low-{rho}{sub h} clusters will evolve dynamically to become similar to 'faint fuzzies' that exist in several lenticular galaxies. Finally, we discuss the nature of diffuse red clusters in early-type galaxies.« less

Commercial applications

NASA Technical Reports Server (NTRS)

Togai, Masaki

1990-01-01

Viewgraphs on commercial applications of fuzzy logic in Japan are presented. Topics covered include: suitable application area of fuzzy theory; characteristics of fuzzy control; fuzzy closed-loop controller; Mitsubishi heavy air conditioner; predictive fuzzy control; the Sendai subway system; automatic transmission; fuzzy logic-based command system for antilock braking system; fuzzy feed-forward controller; and fuzzy auto-tuning system.

Computational studies of the 2D self-assembly of bacterial microcompartment shell proteins

NASA Astrophysics Data System (ADS)

Mahalik, Jyoti; Brown, Kirsten; Cheng, Xiaolin; Fuentes-Cabrera, Miguel

Bacterial microcomartments (BMCs) are subcellular organelles that exist within wide variety of bacteria and function like nano-reactors. Among the different types of BMCs known, the carboxysome has been studied the most. The carboxysomes plays an important role in the transport of metabolites across its outer proteinaceous shell. Plenty of studies have investigated the structure of this shell, yet little is known about its self-assembly . Understanding the self-assembly process of BMCs' shell might allow disrupting their functioning and designing new synthetic nano-reactors. We have investigated the self-assembly process of a major protein component of the carboxysome's shell using a Monte Carlo technique that employed a coarse-grained protein model that was calibrated with the all-atomistic potential of mean force. The simulations reveal that this protein self-assembles into clusters that resemble what were seen experimentally in 2D layers. Further analysis of the simulation results suggests that the 2D self-assembly of carboxysome's facets is driven by nucleation-growth process, which in turn could play an important role in the hierarchical self-assembly of BMCs' shell in general. 1. Science Undergraduate Laboratory Internships, ORNL 2. Oak Ridge Leadership Computing Facility, ORNL.

Isostructural solid-solid phase transition in monolayers of soft core-shell particles at fluid interfaces: structure and mechanics.

PubMed

Rey, Marcel; Fernández-Rodríguez, Miguel Ángel; Steinacher, Mathias; Scheidegger, Laura; Geisel, Karen; Richtering, Walter; Squires, Todd M; Isa, Lucio

2016-04-21

We have studied the complete two-dimensional phase diagram of a core-shell microgel-laden fluid interface by synchronizing its compression with the deposition of the interfacial monolayer. Applying a new protocol, different positions on the substrate correspond to different values of the monolayer surface pressure and specific area. Analyzing the microstructure of the deposited monolayers, we discovered an isostructural solid-solid phase transition between two crystalline phases with the same hexagonal symmetry, but with two different lattice constants. The two phases corresponded to shell-shell and core-core inter-particle contacts, respectively; with increasing surface pressure the former mechanically failed enabling the particle cores to come into contact. In the phase-transition region, clusters of particles in core-core contacts nucleate, melting the surrounding shell-shell crystal, until the whole monolayer moves into the second phase. We furthermore measured the interfacial rheology of the monolayers as a function of the surface pressure using an interfacial microdisk rheometer. The interfaces always showed a strong elastic response, with a dip in the shear elastic modulus in correspondence with the melting of the shell-shell phase, followed by a steep increase upon the formation of a percolating network of the core-core contacts. These results demonstrate that the core-shell nature of the particles leads to a rich mechanical and structural behavior that can be externally tuned by compressing the interface, indicating new routes for applications, e.g. in surface patterning or emulsion stabilization.