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Sample records for hybrid c-mean clustering

  1. Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms

    Directory of Open Access Journals (Sweden)

    Arindam Chaudhuri

    2015-01-01

    Full Text Available Intuitionistic fuzzy sets (IFSs provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM. The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.

  2. The implementation of hybrid clustering using fuzzy c-means and divisive algorithm for analyzing DNA human Papillomavirus cause of cervical cancer

    Science.gov (United States)

    Andryani, Diyah Septi; Bustamam, Alhadi; Lestari, Dian

    2017-03-01

    Clustering aims to classify the different patterns into groups called clusters. In this clustering method, we use n-mers frequency to calculate the distance matrix which is considered more accurate than using the DNA alignment. The clustering results could be used to discover biologically important sub-sections and groups of genes. Many clustering methods have been developed, while hard clustering methods considered less accurate than fuzzy clustering methods, especially if it is used for outliers data. Among fuzzy clustering methods, fuzzy c-means is one the best known for its accuracy and simplicity. Fuzzy c-means clustering uses membership function variable, which refers to how likely the data could be members into a cluster. Fuzzy c-means clustering works using the principle of minimizing the objective function. Parameters of membership function in fuzzy are used as a weighting factor which is also called the fuzzier. In this study we implement hybrid clustering using fuzzy c-means and divisive algorithm which could improve the accuracy of cluster membership compare to traditional partitional approach only. In this study fuzzy c-means is used in the first step to find partition results. Furthermore divisive algorithms will run on the second step to find sub-clusters and dendogram of phylogenetic tree. To find the best number of clusters is determined using the minimum value of Davies Bouldin Index (DBI) of the cluster results. In this research, the results show that the methods introduced in this paper is better than other partitioning methods. Finally, we found 3 clusters with DBI value of 1.126628 at first step of clustering. Moreover, DBI values after implementing the second step of clustering are always producing smaller IDB values compare to the results of using first step clustering only. This condition indicates that the hybrid approach in this study produce better performance of the cluster results, in term its DBI values.

  3. Fuzzy Clustering Using C-Means Method

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    Georgi Krastev

    2015-05-01

    Full Text Available The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described in this paper: the problem about the fuzzy clustering has been discussed and the general formal concept of the problem of the fuzzy clustering analysis has been presented. The formulation of the problem has been specified and the algorithm for solving it has been described.

  4. Student academic performance analysis using fuzzy C-means clustering

    Science.gov (United States)

    Rosadi, R.; Akamal; Sudrajat, R.; Kharismawan, B.; Hambali, Y. A.

    2017-01-01

    Grade Point Average (GPA) is commonly used as an indicator of academic performance. Academic performance evaluations is a basic way to evaluate the progression of student performance, when evaluating student’s academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering method, where one of the methods is the Fuzzy C-Means algorithm. Furthermore, this algorithm is then applied to a set of student data form the Faculty of Mathematics and Natural Sciences, Padjadjaran University.

  5. A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION

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    Mutasem K. Alsmadi

    2014-01-01

    Full Text Available Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI. A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA with Fuzzy C-Means algorithm (FCM. In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

  6. COMPARISON OF PURITY AND ENTROPY OF K-MEANS CLUSTERING AND FUZZY C MEANS CLUSTERING

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    Satya Chaitanya Sripada

    2011-06-01

    Full Text Available Clustering is one the main area in data mining literature. There are various algorithms for clustering. The evaluation of the performance isdone by validation measures. The external validation measures are used to measure the extent to which cluster labels affirm with theexternally given class labels. The aim of this paper is to compare the for K-means and Fuzzy C means clustering using the Purity andEntropy. The data used for evaluating the external measures is medical data.

  7. Multivariate image segmentation with cluster size insensitive Fuzzy C-means

    NARCIS (Netherlands)

    Noordam, J.C.; Broek, van den W.H.A.M.; Buydens, L.M.C.

    2002-01-01

    This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to larger adjacent clusters. In order to overcome

  8. A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars

    CERN Document Server

    Martin, A; Saranya, G; Gayathri, P; Venkatesan, Prasanna

    2011-01-01

    Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.

  9. A HYBRID MODEL FOR BANKRUPTCY PREDICTION USING GENETIC ALGORITHM, FUZZY C-MEANS AND MARS

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    A.Martin

    2011-05-01

    Full Text Available Bankruptcy prediction is very important for all the organization since it affects the economy and rise manysocial problems with high costs. There are large number of techniques have been developed to predict thebankruptcy, which helps the decision makers such as investors and financial analysts. One of thebankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which usesstatic ratios taken from the bank financial statements for prediction, which has its own theoreticaladvantages. The performance of existing bankruptcy model can be improved by selecting the best featuresdynamically depend on the nature of the firm. This dynamic selection can be accomplished by GeneticAlgorithm and it improves the performance of prediction model. .

  10. Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network

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    Bhatti, Dost Muhammad Saqib; Saeed, Nasir; Nam, Haewoon

    2016-01-01

    We propose a novel cluster based cooperative spectrum sensing algorithm to save the wastage of energy, in which clusters are formed using fuzzy c-means (FCM) clustering and a cluster head (CH) is selected based on a sensor’s location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. The sensing information of a single sensor is not reliable enough due to shadowing and fading. To overcome these issues, cooperative spectrum sensing schemes were proposed to take advantage of spatial diversity. For cooperative spectrum sensing, all sensors sense the spectrum and report the sensed energy to FC for the final decision. However, it increases the energy consumption of the network when a large number of sensors need to cooperate; in addition to that, the efficiency of the network is also reduced. The proposed algorithm makes the cluster and selects the CHs such that very little amount of network energy is consumed and the highest efficiency of the network is achieved. Using the proposed algorithm maximum probability of detection under an imperfect channel is accomplished with minimum energy consumption as compared to conventional clustering schemes. PMID:27618061

  11. Fuzzy c-Means and Cluster Ensemble with Random Projection for Big Data Clustering

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    Mao Ye

    2016-01-01

    Full Text Available Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzy c-means (FCM clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.

  12. Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier

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    Hind R.M Shaaban

    2015-12-01

    Full Text Available This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR and Mean Square Error (MSE and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.

  13. AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de creased as comparing with that by a conventional algorithm: that is , the classification accura cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.

  14. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

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    Jian Zhang

    2014-01-01

    Full Text Available To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM based on particle swarm optimization (PSO and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.

  15. Taste Identification of Tea Through a Fuzzy Neural Network Based on Fuzzy C-means Clustering

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yan; ZHOU Chun-guang

    2003-01-01

    In this paper, we present a fuzzy neural network model based on Fuzzy C-Means (FCM) clustering algorithm to realize the taste identification of tea. The proposed method can acquire the fuzzy subset and its membership function in an automatic way with the aid of FCM clustering algorithm. Moreover, we improve the fuzzy weighted inference approach. The proposed model is illustrated with the simulation of taste identification of tea.

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

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    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

  17. Risk analysis of dam based on artificial bee colony algorithm with fuzzy c-means clustering

    Energy Technology Data Exchange (ETDEWEB)

    Li, Haojin; Li, Junjie; Kang, Fei

    2011-05-15

    Risk analysis is a method which has been incorporated into infrastructure engineering. Fuzzy c-means clustering (FCM) is a simple and fast method utilized most of the time, but it can induce errors as it is sensitive to initialization. The aim of this paper was to propose a new method for risk analysis using an artificial bee colony algorithm (ABC) with FCM. This new technique is first explained and then applied on three experiments. Results demonstrated that the combination of artificial bee colony algorithm fuzzy c-means clustering (ABCFCM) is overcoming the FCM issue since it is not initialization sensitive and experiments showed that this algorithm is more accurate and than FCM. This paper provides a new tool for risk analysis which can be used for risk prioritizing and reinforcing dangerous dams in a more scientific way.

  18. An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster

    Institute of Scientific and Technical Information of China (English)

    Wei Zhang; Sheng-Lin Yu; Gong Zhang

    2007-01-01

    For an airborne lookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate (CFAR) schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean (FCM) cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.

  19. A simple and fast method to determine the parameters for fuzzy c-means cluster analysis

    DEFF Research Database (Denmark)

    Schwämmle, Veit; Jensen, Ole Nørregaard

    2010-01-01

    MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...... on the main properties of the dataset. Taking the dimension of the set and the number of objects as input values instead of evaluating the entire dataset allows us to propose a functional relationship determining the fuzzifier directly. This result speaks strongly against using a predefined fuzzifier...

  20. Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique

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    Sriparna Das

    2012-11-01

    Full Text Available Clustering is partitioning of data set into subsets (clusters, so that the data in each subset share some common trait. In this paper, an algorithm has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmium by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimum and maximum value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorithm, modified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorithm has been tested in prediction of adsorption of cadmium by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.

  1. Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

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    Sylvia Jane Annatje Sumarauw

    2007-06-01

    Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

  2. New two-dimensional fuzzy C-means clustering algorithm for image segmentation

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation,a novel two-dimensional FCM clustering algorithm for image segmentation was proposed.In this method,the image segmentation was converted into an optimization problem.The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixcls described by the improved two-dimensional histogram.By making use of the global searching ability of the predator-prey particle swarm optimization,the optimal cluster center could be obtained by iterative optimization,and the image segmentation could be accomplished.The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%.The proposed algorithm has strong anti-noise capability,high clustering accuracy and good segment effect,indicating that it is an effective algorithm for image segmentation.

  3. Application of Fuzzy C-Means Clustering Algorithm Based on Particle Swarm Optimization in Computer Forensics

    Science.gov (United States)

    Wang, Deguang; Han, Baochang; Huang, Ming

    Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.

  4. Disorder Speech Clustering For Clinical Data Using Fuzzy C-Means Clustering And Comparison With SVM Classification

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    C.R.Bharathi

    2012-11-01

    Full Text Available Speech is the most vital skill of communication. Stammering is speech which is hesitant, stumbling, tense or jerky to the extent that it causes anxiety to the speaker. In the existing system, there are many effective treatments for the problem of stammering. Speech-language therapy is the treatment for most kids with speech and/or language disorders. In this work, mild level of mental retardation (MR children speech samples were taken for consideration. The proposed work is, the acute spot must be identified for affording speech training to the speech disordered children. To begin with the proposed work, initially Clustering of speech is done using Fuzzy C-means Clustering Algorithm. Feature Extraction is implemented using Mel Frequency Cepstrum Coefficients (MFCC and dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA. Finally the features were clustered using Fuzzy C-Means algorithm and compared with SVM classifier output[13].

  5. Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    XU Xiang-hua; ZHU Jie; GUO Qiang

    2005-01-01

    A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02 % respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.

  6. A Robust Background Removal Algortihms Using Fuzzy C-Means Clustering

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    S.Lakshmi

    2013-04-01

    Full Text Available Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edgedetector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used forsubsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combinethe Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accuratebackground is identified.

  7. Soil-landscape modelling using fuzzy c-means clustering of attribute data derived from a Digital Elevation Model (DEM).

    NARCIS (Netherlands)

    Bruin, de S.; Stein, A.

    1998-01-01

    This study explores the use of fuzzy c-means clustering of attribute data derived from a digital elevation model to represent transition zones in the soil-landscape. The conventional geographic model used for soil-landscape description is not able to properly deal with these. Fuzzy c-means clusterin

  8. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering

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    Yao-Tien Chen

    2017-01-01

    Full Text Available Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.

  9. Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

    Energy Technology Data Exchange (ETDEWEB)

    Tsantis, Stavros [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Spiliopoulos, Stavros; Karnabatidis, Dimitrios [Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Skouroliakou, Aikaterini [Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210 (Greece); Hazle, John D. [Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Kagadis, George C., E-mail: gkagad@gmail.com, E-mail: George.Kagadis@med.upatras.gr, E-mail: GKagadis@mdanderson.org [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)

    2014-07-15

    Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A

  10. Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

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    Oliynyk Andriy

    2012-08-01

    Full Text Available Abstract 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

  11. Acoustic-Based Cutting Pattern Recognition for Shearer through Fuzzy C-Means and a Hybrid Optimization Algorithm

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    Jing Xu

    2016-10-01

    Full Text Available As the conventional cutting pattern recognition methods for shearer are huge in size, have low recognition reliability and an inconvenient contacting measurement method, a fast and reliable coal-rock cutting pattern recognition system is always a baffling problem worldwide. However, the recognition rate has a direct relation with the outputs of coal mining and the safety quality of staff. In this paper, a novel cutting pattern identification method through the cutting acoustic signal of the shearer is proposed. The signal is clustering by fuzzy C-means (FCM and a hybrid optimization algorithm, combining the fruit fly and genetic optimization algorithm (FGOA. Firstly, an industrial microphone is installed on the shearer and the acoustic signal is collected as the source signal due to its obvious advantages of compact size, non-contact measurement and ease of remote transmission. The original sound is decomposed by multi-resolution wavelet packet transform (WPT, and the normalized energy of each node is extracted as a feature vector. Then, FGOA, by introducing a genetic proportion coefficient into the basic fruit fly optimization algorithm (FOA, is applied to overcome the disadvantages of being time-consuming and sensitivity to initial centroids of the traditional FCM. A simulation example, with the accuracy of 95%, and some comparisons prove the effectiveness and superiority of the proposed scheme. Finally, an industrial test validates the practical effect.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  14. Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

    Directory of Open Access Journals (Sweden)

    E.A. Zanaty

    2012-03-01

    Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

  15. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

    OpenAIRE

    Akara Sopharak; Sarah Barman; Bunyarit Uyyanonvara

    2009-01-01

    Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse se...

  16. Fuzzy C-means clustering for chromatographic fingerprints analysis: A gas chromatography-mass spectrometry case study.

    Science.gov (United States)

    Parastar, Hadi; Bazrafshan, Alisina

    2016-03-18

    Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then, the resolved elution profiles are used to make a new data matrix based on peak areas of pure components to cluster by FCM. The FCM clustering parameters (i.e., fuzziness coefficient and number of cluster) are optimized by two different methods of partial least squares (PLS) as a conventional method and minimization of FCM objective function as our new idea. The results showed that minimization of FCM objective function is an easier and better way to optimize FCM clustering parameters. Then, the optimized FCM clustering algorithm is used to cluster samples and variables to figure out the similarities and dissimilarities among samples and to find discriminant secondary metabolites in each cluster (chemotype). Finally, the FCM clustering results are compared with those of principal component analysis (PCA), hierarchical cluster analysis (HCA) and Kohonon maps. The results confirmed the outperformance of FCM over the frequently used clustering algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. A HYBRID APPROACH USING C MEAN AND CART FOR CLASSIFICATION IN DATA MINING

    Directory of Open Access Journals (Sweden)

    Jasbir Malik

    2012-09-01

    Full Text Available Data Mining is a field of search and researches ofdata. Mining the data means fetching out a piece ofdata from a huge data block. The basic work in thedata mining can be categorized in two subsequentways. One is called classification and the other iscalled clustering. Although both refers to some kind ofsame region but still there are differences in both theterms. The classification of the data is only possible ifyou have modified and identified the clusters. In thepresented research paper, our aim is to find out themaximum number of clusters in a specified region byapplying the area searching algorithms. Classificationis always based on two things. aThe area which youchoose for the classification that is the cluster region.bThe kind of dataset which you are going to apply onthe selected region .To increase the accuracy of thesearching technique, any one would need to focus ontwo things . aWhether the data set has been cauterizedin proper manner or not .bIf the clusters are defined ,whether they fit into the appropriate classified area ornot .

  18. Effect of co-operative fuzzy c-means clustering on estimates of three parameters AVA inversion

    Indian Academy of Sciences (India)

    Rajesh R Nair; Suresh Ch Kandpal

    2010-04-01

    We determine the degree of variation of model fitness,to a true model based on amplitude variation with angle (AVA)methodology for a synthetic gas hydrate model,using co-operative fuzzy c-means clustering,constrained to a rock physics model.When a homogeneous starting model is used,with only traditional least squares optimization scheme for inversion,the variance of the parameters is found to be comparatively high.In this co-operative methodology,the output from the least squares inversion is fed as an input to the fuzzy scheme.Tests with co-operative inversion using fuzzy c-means with damped least squares technique and constraints derived from empirical relationship based on rock properties model show improved stability,model fitness and variance for all the three parameters in comparison with the standard inversion alone.

  19. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.

    Science.gov (United States)

    Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao

    2015-01-01

    An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  20. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

    Directory of Open Access Journals (Sweden)

    Ahmed Elazab

    2015-01-01

    Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  1. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

    Science.gov (United States)

    Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah

    2009-01-01

    Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

  2. Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network

    Directory of Open Access Journals (Sweden)

    He Huang

    2013-01-01

    Full Text Available Forecasting of urban traffic flow is important to intelligent transportation system (ITS developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzy c-mean clustering method (FCM and advanced neural network (NN was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.

  3. Hybrid cluster identification

    Science.gov (United States)

    Martín-Herrero, J.

    2004-10-01

    I present a hybrid method for the labelling of clusters in two-dimensional lattices, which combines the recursive approach with iterative scanning to reduce the stack size required by the pure recursive technique, while keeping its benefits: single pass and straightforward cluster characterization and percolation detection parallel to the labelling. While the capacity to hold the entire lattice in memory is usually regarded as the major constraint for the applicability of the recursive technique, the required stack size is the real limiting factor. Resorting to recursion only for the transverse direction greatly reduces the recursion depth and therefore the required stack. It also enhances the overall performance of the recursive technique, as is shown by results on a set of uniform random binary lattices and on a set of samples of the Ising model. I also show how this technique may replace the recursive technique in Wolff's cluster algorithm, decreasing the risk of stack overflow and increasing its speed, and the Hoshen-Kopelman algorithm in the Swendsen-Wang cluster algorithm, allowing effortless characterization during generation of the samples and increasing its speed.

  4. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2009-03-01

    Full Text Available Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists’ hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV, positive likelihood ratio (PLR and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

  5. Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

    Energy Technology Data Exchange (ETDEWEB)

    Abdulbaqi, Hayder Saad [School of Physics, Universiti Sains Malaysia, 11700, Penang (Malaysia); Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya (Iraq); Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin [School of Physics, Universiti Sains Malaysia, 11700, Penang (Malaysia); Abood, Loay Kadom [Department of Computer Science, College of Science, University of Baghdad, Baghdad (Iraq)

    2015-04-24

    Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

  6. An infared polarization image fusion method based on NSCT and fuzzy C-means clustering segmentation algorithms

    Science.gov (United States)

    Yu, Xuelian; Chen, Qian; Gu, Guohua; Qian, Weixian; Xu, Mengxi

    2014-11-01

    The integration between polarization and intensity images possessing complementary and discriminative information has emerged as a new and important research area. On the basis of the consideration that the resulting image has different clarity and layering requirement for the target and background, we propose a novel fusion method based on non-subsampled Contourlet transform (NSCT) and fuzzy C-means (FCM) segmentation for IR polarization and light intensity images. First, the polarization characteristic image is derived from fusion of the degree of polarization (DOP) and the angle of polarization (AOP) images using local standard variation and abrupt change degree (ACD) combined criteria. Then, the polarization characteristic image is segmented with FCM algorithm. Meanwhile, the two source images are respectively decomposed by NSCT. The regional energy-weighted and similarity measure are adopted to combine the low-frequency sub-band coefficients of the object. The high-frequency sub-band coefficients of the object boundaries are integrated through the maximum selection rule. In addition, the high-frequency sub-band coefficients of internal objects are integrated by utilizing local variation, matching measure and region feature weighting. The weighted average and maximum rules are employed independently in fusing the low-frequency and high-frequency components of the background. Finally, an inverse NSCT operation is accomplished and the final fused image is obtained. The experimental results illustrate that the proposed IR polarization image fusion algorithm can yield an improved performance in terms of the contrast between artificial target and cluttered background and a more detailed representation of the depicted scene.

  7. A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Guoying Liu

    2015-01-01

    Full Text Available This paper presents a variation of the fuzzy local information c-means clustering (FLICM algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.

  8. A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhi-tao Wang

    2015-01-01

    Full Text Available As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM clustering algorithm and support vector machine (SVM classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT.

  9. Field-scale prediction of soil moisture patterns by means of a fuzzy c-means clustering algorithm, digital elevation data, and sparse TDR measurements

    Science.gov (United States)

    Schröter, Ingmar; Paasche, Hendik; Dietrich, Peter; Wollschläger, Ute

    2014-05-01

    Soil moisture is a key variable of the hydrological cycle. For example, it controls partitioning of rainfall into a runoff and an infiltration component and modulating physical, chemical and biological processes within the soil. For a better understanding of these processes, knowledge about the spatio-temporal distribution of soil moisture is indispensable. For the field to the small catchment scale with survey areas up to a few square kilometres, there are numerous new and innovative ground-based and remote sensing technologies available which have great potential to provide temporal information about soil moisture patterns. The aim of this work is to design an optimal soil moisture monitoring program for a low-mountain catchment in central Germany. In a first step, the fuzzy c-means clustering technique (Paasche et al., 2006) was used to identify structure-relevant patterns in a set of different terrain attributes derived from a DEM. Based on these patterns optimal measurement locations were identified to conduct in-situ soil moisture measurements. To consider different wetting and drying states in the catchment, several TDR measurement campaigns were conducted from April to October 2013. The TDR measurements have been integrated with the structure-relevant patterns obtained by the fuzzy cluster analysis to regionally predict soil moisture. In this study, we outline the conceptual framework of this integrative approach and present first results from field measurements. The results of the project are expected to improve the monitoring and understanding of small catchment-scale hydrological processes and to contribute to a better representation of soil moisture dynamics in physically-based, hydrological models operating at the field to the small catchment scale. Reference: Paasche, H., J. Tronicke, K. Holliger, A.G. Green, and H. Maurer (2006): Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy

  10. Cluster Tree Based Hybrid Document Similarity Measure

    Directory of Open Access Journals (Sweden)

    M. Varshana Devi

    2015-10-01

    Full Text Available <Cluster tree based hybrid similarity measure is established to measure the hybrid similarity. In cluster tree, the hybrid similarity measure can be calculated for the random data even it may not be the co-occurred and generate different views. Different views of tree can be combined and choose the one which is significant in cost. A method is proposed to combine the multiple views. Multiple views are represented by different distance measures into a single cluster. Comparing the cluster tree based hybrid similarity with the traditional statistical methods it gives the better feasibility for intelligent based search. It helps in improving the dimensionality reduction and semantic analysis.

  11. Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm

    Directory of Open Access Journals (Sweden)

    Yachun Pang

    2012-01-01

    Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.

  12. Mapping Soil Texture of a Plain Area Using Fuzzy-c-Means Clustering Method Based on Land Surface Diurnal Temperature Difference

    Institute of Scientific and Technical Information of China (English)

    WANG De-Cai; ZHANG Gan-Lin; PAN Xian-Zhang; ZHAO Yu-Guo; ZHAO Ming-Song; WANG Gai-Fen

    2012-01-01

    The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.

  13. 基于Hadoop二阶段并行模糊c-Means聚类算法%HADOOP-BASED TWO-STAGE PARALLEL FUZZY C-MEANS CLUSTERING ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    胡吉朝; 黄红艳

    2016-01-01

    Aiming at the problem of too high occupancy of communication time and limited applying value of the algorithm under the mechanism of Mapreduce,we put forward a Hadoop-based two-stage parallel c-Means clustering algorithm to deal with the problem of extra-large data classification.First,we improved the MPI communication management method in Mapreduce mechanism,and used membership management protocol mode to realise the synchronisation of members management and Mapreduce reducing operation.Secondly, we implemented typical individuals group reducing operation instead of global individual reducing operation,and defined the two-stage buffer algorithm.Finally,through the buffer in first stage we further reduced the data amount of Mapreduce operation in second stage,and reduced the negative impact brought about by big data on the algorithm as much as possible.Based on this,we carried out the simulation by using artificial big data test set and KDD CUP 99 invasion test data.Experimental result showed that the algorithm could both guarantee the clustering precision requirement and speed up effectively the operation efficiency of algorithm.%针对Mapreduce机制下算法通信时间占用比过高,实际应用价值受限的情况,提出基于Hadoop二阶段并行c-Means聚类算法用来解决超大数据的分类问题。首先,改进Mapreduce机制下的MPI通信管理方法,采用成员管理协议方式实现成员管理与Mapreduce降低操作的同步化;其次,实行典型个体组降低操作代替全局个体降低操作,并定义二阶段缓冲算法;最后,通过第一阶段的缓冲进一步降低第二阶段Mapreduce操作的数据量,尽可能降低大数据带来的对算法负面影响。在此基础上,利用人造大数据测试集和KDD CUP 99入侵测试集进行仿真,实验结果表明,该算法既能保证聚类精度要求又可有效加快算法运行效率。

  14. Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data.

    Science.gov (United States)

    Yu, Zhiwen; Chen, Hantao; You, Jane; Han, Guoqiang; Li, Le

    2013-01-01

    Cancer class discovery using biomolecular data is one of the most important tasks for cancer diagnosis and treatment. Tumor clustering from gene expression data provides a new way to perform cancer class discovery. Most of the existing research works adopt single-clustering algorithms to perform tumor clustering is from biomolecular data that lack robustness, stability, and accuracy. To further improve the performance of tumor clustering from biomolecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from biomolecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks (HFCEF), named as HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV, respectively, to identify samples that belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way. HFCEFs adopt suitable consensus functions, such as the fuzzy c-means algorithm or the normalized cut algorithm (Ncut), to summarize generated fuzzy matrices, and obtain the final results. The experiments on real data sets from UCI machine learning repository and cancer gene expression profiles illustrate that 1) the proposed hybrid fuzzy cluster ensemble frameworks work well on real

  15. DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    Farjam, Reza; Tsien, Christina I.; Lawrence, Theodore S. [Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, SPC 5010, Ann Arbor, Michigan 48109-5010 (United States); Cao, Yue, E-mail: yuecao@umich.edu [Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, SPC 5010, Ann Arbor, Michigan 48109-5010 (United States); Department of Radiology, University of Michigan, 1500 East Medical Center Drive, Med Inn Building C478, Ann Arbor, Michigan 48109-5842 (United States); Department of Biomedical Engineering, University of Michigan, 2200 Bonisteel Boulevard, Ann Arbor, Michigan 48109-2099 (United States)

    2014-01-15

    Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC

  16. Cluster hybrid Monte Carlo simulation algorithms

    Science.gov (United States)

    Plascak, J. A.; Ferrenberg, Alan M.; Landau, D. P.

    2002-06-01

    We show that addition of Metropolis single spin flips to the Wolff cluster-flipping Monte Carlo procedure leads to a dramatic increase in performance for the spin-1/2 Ising model. We also show that adding Wolff cluster flipping to the Metropolis or heat bath algorithms in systems where just cluster flipping is not immediately obvious (such as the spin-3/2 Ising model) can substantially reduce the statistical errors of the simulations. A further advantage of these methods is that systematic errors introduced by the use of imperfect random-number generation may be largely healed by hybridizing single spin flips with cluster flipping.

  17. Scalable classification by clustering: Hybrid can be better than Pure

    Institute of Scientific and Technical Information of China (English)

    Deng Shengchun; He Zengyou; Xu Xiaofei

    2007-01-01

    The problem of scalable classification by clustering in large databases was discussed. Clustering based classification method first generates clusters using clustering algorithms . To classify new coming data points , it finds the k nearest clusters of the data point as neighbors , and assign each data point to the dominant class of these neighbors . Existing algorithms incorporated class information in making clustering decisions and produced pure clusters (each cluster associated with only one class) . We presented hybrid cluster based algorithms , which produce clusters by unsupervised clustering and allow each cluster associated with multiple classes . Experimental results show that hybrid cluster based algorithms outperform pure ones in both classification accuracy and training speed.

  18. Wavelet neural networks initialization using hybridized clustering and harmony search algorithm: Application in epileptic seizure detection

    Science.gov (United States)

    Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline

    2013-04-01

    Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.

  19. Customer Clustering and Pattern Identification Approach Based on Vague C-means%基于直觉模糊 C-均值的客户聚类和识别方法

    Institute of Scientific and Technical Information of China (English)

    耿秀丽; 尤星星; 吕文元

    2015-01-01

    客户聚类和识别是大规模客户化生产中产品/服务快速有效设计的基础。考虑客户需求信息的不确定性,提出了基于直觉模糊 C-均值的客户聚类算法。针对传统基于欧式距离的 C-均值聚类方法无法计算直觉模糊数组间距离的缺点,采用直觉模糊交叉熵方法处理算法中的距离计算问题。同时,直觉模糊交叉熵还用来计算新客户和各客户类间的偏好相似度,进行客户识别。最后以某工程机械企业服务开发中的客户聚类和识别为例,验证了所提方法的有效性。%In the mass customization production,customer clustering and identification are the basis of quick and effective product/service design.Considering the uncertainty of customer requirements,a customer clustering and pattern identification approach based on vague C-means was proposed.Aiming at the problem that the traditional fuzzy C-means based on Euclidean distance cannot deal with the distance between vague sets,a vague cross-entropy approach was adopted to deal with the distance calculating problem in the C-means clustering algorithm.At the same time, the vague cross-entropy was also applied in calculating the similarity between new customer and different customer groups,and then the customer identification was realized.Finally,a case study of customer clustering and identification in a mechanical company’s service development was presented to illustrate the effectiveness of the proposed approach.

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

    Science.gov (United States)

    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).

  1. 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

    Science.gov (United States)

    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

  2. Intelligent Control Scheme of Engineering Machinery of Cluster Hybrid System

    Institute of Scientific and Technical Information of China (English)

    GAO Qiang; WANG Hongli

    2005-01-01

    In a hybrid system, the subsystems with discrete dynamics play a central role in a hybrid system. In the course of engineering machinery of cluster construction, the discrete control law is hard to obtain because the construction environment is complex and there exist many affecting factors. In this paper, hierarchically intelligent control, expert control and fuzzy control are introduced into the discrete subsystems of engineering machinery of cluster hybrid system, so as to rebuild the hybrid system and make the discrete control law easily and effectively obtained. The structures, reasoning mechanism and arithmetic of intelligent control are replanted to discrete dynamic, conti-nuous process and the interface of the hybrid system. The structures of three types of intelligent hybrid system are presented and the human experiences summarized from engineering machinery of cluster are taken into account.

  3. Implementation of hybrid clustering based on partitioning around medoids algorithm and divisive analysis on human Papillomavirus DNA

    Science.gov (United States)

    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.

  4. Brain tumor segmentation based on a hybrid clustering technique

    Directory of Open Access Journals (Sweden)

    Eman Abdel-Maksoud

    2015-03-01

    This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

  5. A HYBRID HEURISTIC ALGORITHM FOR THE CLUSTERED TRAVELING SALESMAN PROBLEM

    Directory of Open Access Journals (Sweden)

    Mário Mestria

    2016-04-01

    Full Text Available ABSTRACT This paper proposes a hybrid heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedure, Iterated Local Search and Variable Neighborhood Descent, to solve the Clustered Traveling Salesman Problem (CTSP. Hybrid Heuristic algorithm uses several variable neighborhood structures combining the intensification (using local search operators and diversification (constructive heuristic and perturbation routine. In the CTSP, the vertices are partitioned into clusters and all vertices of each cluster have to be visited contiguously. The CTSP is -hard since it includes the well-known Traveling Salesman Problem (TSP as a special case. Our hybrid heuristic is compared with three heuristics from the literature and an exact method. Computational experiments are reported for different classes of instances. Experimental results show that the proposed hybrid heuristic obtains competitive results within reasonable computational time.

  6. A Soft Discretization Method of Celestial Spectrum Characteristic Line Based on Fuzzy C-Means Clustering%基于模糊C均值聚类的天文光谱特征线软离散化

    Institute of Scientific and Technical Information of China (English)

    张继福; 李鑫; 杨海峰

    2012-01-01

    连续数值属性离散化是天文光谱数据预处理中的主要研究内容之一.针对天文光谱特征线,提出了一种基于改进模糊C均值聚类的天文光谱特征线软离散化算法.该算法首先利用样本的密度值选取特征线的候选初始模糊聚类中心,有效地克服了对噪声数据敏感的缺陷;其次采用决策表中的相容性作为评判标准,动态的调节聚类参数,以达到优化的光谱特征线离散化效果;最后采用晚型星、类星体、高红移类星体SDSS天文光谱特征线数据集.实验验证了该算法具有较高的识别率,为天文光谱特征线数据预处理提供了一种新途径.%Discretization of continuous numerical attribute is one of the important research works in the preprocessing of celestial spectrum data. For characteristic line of celestial spectrum, a soft discretization algorithm is presented by using improved fuzzy C-means clustering. Firstly, candidate fuzzy clustering centers of characteristic line are chosen by using density values of sample data, so that its anti-noise ability is improved. Secondly, parameters in the fuzzy clustering are dynamically adjusted by taking compatibility of decision table as criteria, so that optimal discretization effect of the characteristic line is achieved. In the end, experimental results effectively validate that the algorithm has higher correct recognition rate of the algorithm by using three SDSS celestial spectrum data sets of high-redshift quasars, late-type star and quasars.

  7. Hybrid Self Organizing Map for Overlapping Clusters

    Directory of Open Access Journals (Sweden)

    M.N.M. Sap

    2008-12-01

    Full Text Available The Kohonen self organizing map is an excellent tool in exploratoryphase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcomethe uncertainty, a two-level clustering algorithm based on SOM which employs the rough set theory is proposed. The two-level stage Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage is found to perform well and more accurate compared with the proposed crisp clustering method (Incremental SOM and reduces the errors.

  8. A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Koffka Khan

    2012-12-01

    Full Text Available Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results.

  9. Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis

    Directory of Open Access Journals (Sweden)

    S. Muthurajkumar

    2014-05-01

    Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.

  10. A hybrid monkey search algorithm for clustering analysis.

    Science.gov (United States)

    Chen, Xin; Zhou, Yongquan; Luo, Qifang

    2014-01-01

    Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  11. A Hybrid Monkey Search Algorithm for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2014-01-01

    Full Text Available Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  12. Hybrid cluster state proposal for a quantum game

    CERN Document Server

    Paternostro, M; Kim, M S

    2005-01-01

    We propose an experimental implementation of a quantum game algorithm in a hybrid scheme combining the quantum circuit approach and the cluster state model. An economical cluster configuration is suggested to embody a quantum version of the Prisoners' Dilemma. Our proposal is shown to be within the experimental state-of-art and can be realized with existing technology. The effects of relevant experimental imperfections are also carefully examined.

  13. Study of the Artificial Fish Swarm Algorithm for Hybrid Clustering

    Directory of Open Access Journals (Sweden)

    Hongwei Zhao

    2015-06-01

    Full Text Available The basic Artificial Fish Swarm (AFS Algorithm is a new type of an heuristic swarm intelligence algorithm, but it is difficult to optimize to get high precision due to the randomness of the artificial fish behavior, which belongs to the intelligence algorithm. This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS, which significantly improves the original AFS in solving complex optimization problems. K-medoids clustering algorithm is being used to classify data, but the approach is sensitive to the initial selection of the centers with low quality of the divided cluster. A novel hybrid clustering method based on the CAFS and K-medoids could be used for solving clustering problems. In this work, first, CAFS algorithm is used for optimizing six widely-used benchmark functions, coming up with comparative results produced by AFS and CAFS, then Particle Swarm Optimization (PSO is studied. Second, the hybrid algorithm with K-medoids and CAFS algorithms is used for data clustering on several benchmark data sets. The performance of the hybrid algorithm based on K-medoids and CAFS is compared with AFS and CAFS algorithms on a clustering problem. The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy and robustness.

  14. Silver cluster-biomolecule hybrids: from basics towards sensors.

    Science.gov (United States)

    Bonačić-Koutecký, Vlasta; Kulesza, Alexander; Gell, Lars; Mitrić, Roland; Antoine, Rodolphe; Bertorelle, Franck; Hamouda, Ramzi; Rayane, Driss; Broyer, Michel; Tabarin, Thibault; Dugourd, Philippe

    2012-07-14

    We focus on the functional role of small silver clusters in model hybrid systems involving peptides in the context of a new generation of nanostructured materials for biosensing. The optical properties of hybrids in the gas phase and at support will be addressed with the aim to bridge fundamental and application aspects. We show that extension and enhancement of absorption of peptides can be achieved by small silver clusters due to the interaction of intense intracluster excitations with the π-π* excitations of chromophoric aminoacids. Moreover, we demonstrate that the binding of a peptide to a supported silver cluster can be detected by the optical fingerprint. This illustrates that supported silver clusters can serve as building blocks for biosensing materials. Moreover, the clusters can be used simultaneously to immobilize biomolecules and to increase the sensitivity of detection, thus replacing the standard use of organic dyes and providing label-free detection. Complementary to that, we show that protected silver clusters containing a cluster core and a shell liganded by thiolates exhibit absorption properties with intense transitions in the visible regime which are also suitable for biosensing applications.

  15. Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

    2012-01-01

    Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

  16. The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zakir Hussain Ahmed

    2014-01-01

    Full Text Available The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.

  17. The ordered clustered travelling salesman problem: a hybrid genetic algorithm.

    Science.gov (United States)

    Ahmed, Zakir Hussain

    2014-01-01

    The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.

  18. A new hybrid imperialist competitive algorithm on data clustering

    Indian Academy of Sciences (India)

    Taher Niknam; Elahe Taherian Fard; Shervin Ehrampoosh; Alireza Rousta

    2011-06-01

    Clustering is a process for partitioning datasets. This technique is very useful for optimum solution. -means is one of the simplest and the most famous methods that is based on square error criterion. This algorithm depends on initial states and converges to local optima. Some recent researches show that -means algorithm has been successfully applied to combinatorial optimization problems for clustering. In this paper, we purpose a novel algorithm that is based on combining two algorithms of clustering; -means and Modify Imperialist Competitive Algorithm. It is named hybrid K-MICA. In addition, we use a method called modified expectation maximization (EM) to determine number of clusters. The experimented results show that the new method carries out better results than the ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and -means.

  19. Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

    Science.gov (United States)

    Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian

    2017-03-01

    DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.

  20. EFFECT OF CLUSTERING IN DESIGNING A FUZZY BASED HYBRID INTRUSION DETECTION SYSTEM FOR MOBILE AD HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    D. Vydeki

    2013-01-01

    Full Text Available Intrusion Detection System (IDS provides additional security for the most vulnerable Mobile Adhoc Networks (MANET. Use of Fuzzy Inference System (FIS in the design of IDS is proven to be efficient in detecting routing attacks in MANETs. Clustering is a vital means in the detection process of FIS based hybrid IDS. This study describes the design of such a system to detect black hole attack in MANET that uses Adhoc On-Demand Distance Vector (AODV routing protocol. It analyses the effect of two clustering algorithms and also prescribes the suitable clustering algorithm for the above-mentioned IDS. MANETs with various traffic scenarios were simulated and the data set required for the IDS is extracted. A hybrid IDS is designed using Sugeno type-2 FIS to detect black hole attack. From the experimental results, it is derived that the subtractive clustering algorithm produces 97% efficient detection while FCM offers 91%. It has been found that the subtractive clustering algorithm is more fit and efficient than the Fuzzy C-Means clustering (FCM for the FIS based detection system.

  1. Hybrid Weighted-based Clustering Routing Protocol for Railway Communications

    Directory of Open Access Journals (Sweden)

    Jianli Xie

    2013-12-01

    Full Text Available In the paper, a hybrid clustering routing strategy is proposed for railway emergency ad hoc network, when GSM-R base stations are destroyed or some terminals (or nodes are far from the signal coverage. In this case, the cluster-head (CH election procedure is invoked on-demand, which takes into consideration the degree difference from the ideal degree, relative clustering stability, the sum of distance between the node and it’s one-hop neighbors, consumed power, node type and node mobility. For the clustering forming, the weights for the CH election parameters are allocated rationally by rough set theory. The hybrid weighted-based clustering routing (HWBCR strategy is designed for railway emergency communication scene, which aims to get a good trade-off between the computation costs and performances. The simulation platform is constructed to evaluate the performance of our strategy in terms of the average end-to-end delay, packet loss ratio, routing overhead and average throughput. The results, by comparing with the railway communication QoS index, reveal that our strategy is suitable for transmitting dispatching voice and data between train and ground, when the train speed is less than 220km/h

  2. Hybrid Parallel Bidirectional Sieve based on SMP Cluster

    CERN Document Server

    Liao, Gang; Liu, Lei

    2012-01-01

    In this article, hybrid parallel bidirectional sieve method is implemented by SMP Cluster, the individual computational units joined together by the communication network, are usually shared-memory systems with one or more multicore processor. To high-efficiency optimization, we propose average divide data into nodes, generating double-ended queues (deque) for sieve method that are able to exploit dual-cores simultaneously start sifting out primes from the head and tail.And each node create a FIFO queue as dynamic data buffer to ache temporary data from another nodes send to. The approach obtains huge speedup and efficiency on SMP Cluster.

  3. 广义核或混合核FLICM畜肉图像分割方法%Meat Image Segmentation Using Fuzzy Local Information C-Means Clustering for Generalized or Mixed Kernel Function

    Institute of Scientific and Technical Information of China (English)

    吴一全; 曹鹏祥; 王凯; 朱丽

    2015-01-01

    针对传统核模糊C均值聚类(KernelFuzzyC-Means,KFCM)畜肉图像分割方法对噪声适应能力不强的问题,提出基于广义核函数或混合核函数的模糊局部信息C均值聚奚(Fuzzy Local Information C-Means,FLICM)畜肉图像分割方法(KFLICM UG 方法和KFLICM_MG方法).首先利用广义核函数或混合核函数可以有效兼顾学习能力和泛化能力的优势,将图像的每一个像素映射到高维的特征空间,扩大像素有用特征的类间差异,使像素在高维特征空间中拥有更优的线性可聚性;然后结合像素的局部空间和灰度信息,确定其模糊隶属度,在高维的特征空间中依据图像特征对像素进行模糊局部信息C均值聚类,最终实现畜肉图像的分割.大量的实验结果表明,相比现有的模糊C均值(Fuzzy C-Means,FCM)分割方法、KFCM分割方法和FLICM分割方法,本文提出的KFLICM UG方法和KFLICM MG方法可以获得更好的分割效果,更低的分割错误率,且具有更强的噪声适应能力和鲁棒性.

  4. Study of Magnesium Diboride Clusters Using Hybrid Density Functional Theory

    Directory of Open Access Journals (Sweden)

    D. Rodríguez

    2007-12-01

    Full Text Available Using hybrid density functional theory and a relatively large basis set, the lowest energy equilibrium structure, vibrational spectrum, and natural orbital analysis were obtained for magnesium diboride clusters [(MgB2x for x=1,2, and 3]. For comparison, boron clusters [Bx for x=2,4, and 6] were also considered. The MgB2 and (MgB22 showed equilibrium structures with the boron atoms in arrangements similar to what was obtained for pure boron atoms, whereas, for (MgB23 a different arrangement of boron was obtained. From the population analysis, large electron density in the boron atoms forming the clusters was observed.

  5. Hybrid cloud and cluster computing paradigms for life science applications.

    Science.gov (United States)

    Qiu, Judy; Ekanayake, Jaliya; Gunarathne, Thilina; Choi, Jong Youl; Bae, Seung-Hee; Li, Hui; Zhang, Bingjing; Wu, Tak-Lon; Ruan, Yang; Ekanayake, Saliya; Hughes, Adam; Fox, Geoffrey

    2010-12-21

    Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister. Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications. The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications. We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.

  6. EARLY EXPERIENCE WITH A HYBRID PROCESSOR: K-MEANS CLUSTERING

    Energy Technology Data Exchange (ETDEWEB)

    M. GOKHALE; ET AL

    2001-02-01

    We discuss hardware/software coprocessing on a hybrid processor for a compute- and data-intensive hyper-spectral imaging algorithm, K-Means Clustering. The experiments are performed on the Altera Excalibur board using the soft IP core 32-bit NIOS RISC processor. In our experiments, we compare performance of the sequential algorithm with two different accelerated versions. We consider granularity and synchronization issues when mapping an algorithm to a hybrid processor. Our results show that on the Excalibur NIOS, a 15% speedup can be achieved over the sequential algorithm on images with 8 spectral bands where the pixels are divided into 8 categories. Speedup is limited by the communication cost of transferring data from external memory through the NIOS processor to the customized circuits. Our results indicate that future hybrid processors must either (1) have a clock rate 10X the speed of the configurable logic circuits or (2) include dual port memories that both the processor and configurable logic can access. If either of these conditions is met, the hybrid processor will show a factor of 10 speedup over the sequential algorithm. Such systems will combine the convenience of conventional processors with the speed of configurable logic.

  7. Novel Hybrid Intrusion Detection System For Clustered Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Hichem Sedjelmaci

    2011-08-01

    Full Text Available Wireless sensor network (WSN is regularly deployed in unattended and hostile environments. The WSN isvulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the mostefficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low falsealarm.

  8. A hybrid clustering approach to recognition of protein families in 114 microbial genomes

    Directory of Open Access Journals (Sweden)

    Gogarten J Peter

    2004-04-01

    Full Text Available Abstract Background Grouping proteins into sequence-based clusters is a fundamental step in many bioinformatic analyses (e.g., homology-based prediction of structure or function. Standard clustering methods such as single-linkage clustering capture a history of cluster topologies as a function of threshold, but in practice their usefulness is limited because unrelated sequences join clusters before biologically meaningful families are fully constituted, e.g. as the result of matches to so-called promiscuous domains. Use of the Markov Cluster algorithm avoids this non-specificity, but does not preserve topological or threshold information about protein families. Results We describe a hybrid approach to sequence-based clustering of proteins that combines the advantages of standard and Markov clustering. We have implemented this hybrid approach over a relational database environment, and describe its application to clustering a large subset of PDB, and to 328577 proteins from 114 fully sequenced microbial genomes. To demonstrate utility with difficult problems, we show that hybrid clustering allows us to constitute the paralogous family of ATP synthase F1 rotary motor subunits into a single, biologically interpretable hierarchical grouping that was not accessible using either single-linkage or Markov clustering alone. We describe validation of this method by hybrid clustering of PDB and mapping SCOP families and domains onto the resulting clusters. Conclusion Hybrid (Markov followed by single-linkage clustering combines the advantages of the Markov Cluster algorithm (avoidance of non-specific clusters resulting from matches to promiscuous domains and single-linkage clustering (preservation of topological information as a function of threshold. Within the individual Markov clusters, single-linkage clustering is a more-precise instrument, discerning sub-clusters of biological relevance. Our hybrid approach thus provides a computationally efficient

  9. 基于模糊C均值聚类的居民用户中期用电需求预测模型%Residents mid-term electricity demand forecasting model based on fuzzy C-means clustering

    Institute of Scientific and Technical Information of China (English)

    蔡秀雯; 杨加生

    2016-01-01

    随着智能电网的加快建设和居民阶梯电价机制的实施,居民用电需求预测变得更加复杂。从现行居民阶梯电价机制下用户动态需求的特征出发,提出了一个新的基于模糊C均值聚类的居民用电需求分类预测模型。通过模糊C均值聚类对某地区居民用电行为进行聚类分析、数据分类,建立基于自组织模糊神经网络的用电需求预测模型,提高了中期用电需求预测精度。%With speeding up of smart grid construction and the implementation of the residents step tariff mechanism,residen⁃tial electricity demand forecasting has become more complicated. From the current residents step tariff mechanism and according to the characteristics of dynamic demand, this paper has proposed a new fuzzy C⁃means clustering based on the classification of resi⁃dential electricity demand forecasting model. By fuzzy C⁃means clustering for residential electricity behavior in order to clustering analysis and data classification,this paper has established electrici⁃ty demand forecasting model based on the self⁃organizing fuzzy neural network ,and has improved the medium⁃term demand fore⁃cast accuracy.

  10. A hybrid distance measure for clustering expressed sequence tags originating from the same gene family.

    Directory of Open Access Journals (Sweden)

    Keng-Hoong Ng

    Full Text Available BACKGROUND: Clustering is a key step in the processing of Expressed Sequence Tags (ESTs. The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes. METHODOLOGY/PRINCIPAL FINDINGS: We propose a hybrid distance measure that combines the global and local features extracted from ESTs, with the aim to address the clustering problem faced by ESTs derived from the same gene family. The clustering process is implemented using the DBSCAN algorithm. We test the hybrid distance measure on the ten EST datasets, and the clustering results are compared with the two alignment-free EST clustering tools, i.e. wcd and PEACE. The clustering results indicate that the proposed hybrid distance measure performs relatively better (in terms of clustering accuracy than both EST clustering tools. CONCLUSIONS/SIGNIFICANCE: The clustering results provide support for the effectiveness of the proposed hybrid distance measure in solving the clustering problem for ESTs that originate from the same gene family. The improvement of clustering accuracies on the experimental datasets has supported the claim that the sensitivity of the hybrid distance measure is sufficient to solve the clustering problem.

  11. A Hybrid Trajectory Clustering for Predicting User Navigation

    CERN Document Server

    Munaga, Hazarath; Venkateswarlu, N B

    2011-01-01

    Wireless sensor networks (WSNs) suffers from the hot spot problem where the sensor nodes closest to the base station are need to relay more packet than the nodes farther away from the base station. Thus, lifetime of sensory network depends on these closest nodes. Clustering methods are used to extend the lifetime of a wireless sensor network. However, current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and lengthen the network lifetime. Most of the algorithms use random selection for selecting the cluster heads. Here, we propose a novel trajectory clustering technique for selecting the cluster heads in WSNs. Our algorithm selects the cluster heads based on traffic and rotates periodically. It provides the first trajectory based clustering technique for selecting the cluster heads and to extenuate the hot spot problem by prolonging the network lif...

  12. 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).

    Science.gov (United States)

    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.

  13. Composite Hybrid Cluster Built from the Integration of Polyoxometalate and a Metal Halide Cluster: Synthetic Strategy, Structure, and Properties.

    Science.gov (United States)

    Li, Xin-Xiong; Ma, Xiang; Zheng, Wen-Xu; Qi, Yan-Jie; Zheng, Shou-Tian; Yang, Guo-Yu

    2016-09-06

    A step-by-step synthetic strategy, setting up a bridge between the polyoxometalate (POM) and metal halide cluster (MHC) systems, is demonstrated to construct an unprecedented composite hybrid cluster built up from one high-nuclearity cationic MHC [Cu8I6](2+) and eight Anderson-type anionic POMs [HCrMo6O18(OH)6](2-) cross-linked by a tripodal alcohol derivative.

  14. Near-infrared silver cluster optically signaling oligonucleotide hybridization and assembling two DNA hosts.

    Science.gov (United States)

    Petty, Jeffrey T; Nicholson, David A; Sergev, Orlin O; Graham, Stuart K

    2014-09-16

    Silver clusters with ~10 atoms form within DNA strands, and the conjugates are chemical sensors. The DNA host hybridizes with short oligonucleotides, and the cluster moieties optically respond to these analytes. Our studies focus on how the cluster adducts perturb the structure of their DNA hosts. Our sensor is comprised of an oligonucleotide with two components: a 5'-cluster domain that complexes silver clusters and a 3'-recognition site that hybridizes with a target oligonucleotide. The single-stranded sensor encapsulates an ~11 silver atom cluster with violet absorption at 400 nm and with minimal emission. The recognition site hybridizes with complementary oligonucleotides, and the violet cluster converts to an emissive near-infrared cluster with absorption at 730 nm. Our key finding is that the near-infrared cluster coordinates two of its hybridized hosts. The resulting tertiary structure was investigated using intermolecular and intramolecular variants of the same dimer. The intermolecular dimer assembles in concentrated (~5 μM) DNA solutions. Strand stoichiometries and orientations were chromatographically determined using thymine-modified complements that increase the overall conjugate size. The intramolecular dimer develops within a DNA scaffold that is founded on three linked duplexes. The high local cluster concentrations and relative strand arrangements again favor the antiparallel dimer for the near-infrared cluster. When the two monomeric DNA/violet cluster conjugates transform to one dimeric DNA/near-infrared conjugate, the DNA strands accumulate silver. We propose that these correlated changes in DNA structure and silver stoichiometry underlie the violet to near-infrared cluster transformation.

  15. Fuzzy Activation and Clustering of Nodes in a Hybrid Fibre Network Roll-out

    NARCIS (Netherlands)

    Kraak, J.J.; Phillipson, F.

    2015-01-01

    To design a Hybrid Fibre network, a selection of nodes is provided with active equipment and connected with fibre. If there is a need for a ring structure for high reliability, the activated nodes need to be clustered. In this paper a fuzzy method is proposed for this activation and clustering probl

  16. Titanium oxo-clusters: precursors for a Lego-like construction of nanostructured hybrid materials.

    Science.gov (United States)

    Rozes, Laurence; Sanchez, Clément

    2011-02-01

    Titanium oxo-clusters, well-defined monodispersed nano-objects, are appropriate nano-building blocks for the preparation of organic-inorganic materials by a bottom up approach. This critical review proposes to present the different structures of titanium oxo-clusters referenced in the literature and the different strategies followed to build up hybrid materials with these versatile building units. In particular, this critical review cites and reports on the most important papers in the literature, concentrating on recent developments in the field of synthesis, characterization, and the use of titanium oxo-clusters for the construction of advanced hybrid materials (137 references).

  17. Cluster Based Hybrid Niche Mimetic and Genetic Algorithm for Text Document Categorization

    Directory of Open Access Journals (Sweden)

    A. K. Santra

    2011-09-01

    Full Text Available An efficient cluster based hybrid niche mimetic and genetic algorithm for text document categorization to improve the retrieval rate of relevant document fetching is addressed. The proposal minimizes the processing of structuring the document with better feature selection using hybrid algorithm. In addition restructuring of feature words to associated documents gets reduced, in turn increases document clustering rate. The performance of the proposed work is measured in terms of cluster objects accuracy, term weight, term frequency and inverse document frequency. Experimental results demonstrate that it achieves very good performance on both feature selection and text document categorization, compared to other classifier methods.

  18. Photoelectron imaging of small aluminum clusters: quantifying s-p hybridization.

    Science.gov (United States)

    Melko, Joshua J; Castleman, A W

    2013-03-07

    Photoelectron imaging experiments and detailed calculations are conducted on Al(n)(-) clusters (n = 3-6) and a calibration method is developed for connecting experimental observations of photoelectron angular distributions to theoretical predictions. It is shown that this method can be used to quantify the degree to which the molecular orbitals are built from s- or p-like atomic orbitals. The highest occupied molecular orbitals of these small aluminum clusters are found to contain varying degrees of s-p mixing, with Al(3)(-) containing the "most hybridized" orbital and Al(4)(-) containing the "least hybridized" orbital. It is shown experimentally that s-p hybridization is already present for the trimer species and, similar to other properties of small metal clusters, oscillates with cluster size.

  19. HYBRID APPROACH FOR OPTIMAL CLUSTER HEAD SELECTION IN WSN USING LEACH AND MONKEY SEARCH ALGORITHMS

    Directory of Open Access Journals (Sweden)

    T. SHANKAR

    2017-02-01

    Full Text Available Wireless Sensor Networks (WSNs are being widely used with low-cost, lowpower, multifunction sensors based on the development of wireless communication, which has enabled a wide variety of new applications. In WSN, the main concern is that it contains a limited power battery and is constrained in energy consumption hence energy and lifetime are of paramount importance. To achieve high energy efficiency and prolong network lifetime in WSNs, clustering techniques have been widely adopted. The proposed algorithm is hybridization of well-known Low-Energy Adaptive Clustering Hierarchy (LEACH algorithm with a distinctive Monkey Search (MS algorithm, which is an optimization algorithm used for optimal cluster head selection. The proposed hybrid algorithm exhibit high throughput, residual energy and improved lifetime. Comparison of the proposed hybrid algorithm is made with the well-known cluster-based protocols for WSNs, namely, LEACH and monkey search algorithm, individually.

  20. Exponential Fuzzy C-Means for Collaborative Filtering

    Institute of Scientific and Technical Information of China (English)

    Kiatichai Treerattanapitak; Chuleerat Jaruskulchai

    2012-01-01

    Collaborative filtering (CF) is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions. The most popular approaches used in CF research area are Matrix factorization methods such as SVD.However,many wellknown recommendation systems do not use this method but still stick with Neighborhood models because of simplicity and explainability.There are some concerns that limit neighborhood models to achieve higher prediction accuracy.To address these concerns,we propose a new exponential fuzzy clustering (XFCM) algorithm by reformulating the clustering's objective function with an exponential equation in order to improve the method for membership assignment.The proposed method assigns data to the clusters by aggressively excluding irrelevant data,which is better than other fuzzy C-means (FCM) variants.The experiments show that XFCM-based CF improved 6.9% over item-based method and 3.0% over SVD in terms of mean absolute error for 100 K and 1 M MovieLens dataset.

  1. Size-dependent photoabsorption and photoemission of supported silver clusters and silver cluster-biomolecule hybrid systems

    Energy Technology Data Exchange (ETDEWEB)

    Mitric, Roland; Buergel, Christian; Petersen, Jens; Kulesza, Alexander; Bonacic-Koutecky, Vlasta [Humboldt-Universitaet zu Berlin, Institut fuer Chemie, Brook-Taylor-Str. 2, D-12489 Berlin (Germany)

    2008-07-01

    Silver clusters interacting with different environments such as surfaces or biomolecules exhibit fascinating absorption and emissive properties which can be exploited for biosensing and optoelectronic applications. We address theoretically size dependent structural and optical properties of silver clusters Ag{sub n} (n=2,4,6,8) suppported on MgO surface as well as optical properties of silver-cluster tryptophan hybrid systems Trp-Ag{sub n}{sup +} (n=1-9). Our results on supported silver clusters provide insight into the mechanism responsible for absorption and emission patterns arising from interaction between the excitation within the cluster and the environment. We demonstrate that small clusters such as Ag{sub 4} are good candidates for fluorescence centers in the visible regime. Furthermore, in the Trp-Ag{sub n}{sup +} hybrid system we identified different types of charge transfer between the silver and biomolecule subunits. Remarkably, we observe a strong reduction of the photofragmentation yield in Trp-Ag{sub 9}{sup +} in comparison with free Ag{sub 9}{sup +} which may be attributed to energy dissipation by fluorescence. Thus, the unique optical properties of supported silver nanoclusters combined with the specific bio-recognition of biomolecules will provide fundamentals for the future development of fluorescent nanocluster-based biochips.

  2. KLASTERISASI DAN ANALISIS TRAFIK INTERNET MENGGUNAKAN FUZZY C MEAN DENGAN EKSTRAKSI FITUR DATA

    Directory of Open Access Journals (Sweden)

    Adi Suryaputra P.

    2014-01-01

    Full Text Available Internet facilities is one important part of the infrastructure of the campus at this time. Internet facility is a part of teaching and learning activities. Important part of the internet facility is the internet bandwidth, which is often deemed less bandwidth for certain majors at certain hours of lecture hours especially active. To overcome this there needs to be an analysis and clustering of the internet traffic at each point where the distribution of bandwidth is done so that in the end can provide information that can support decision granting bandwidth at each point there. One algorithm for clustering algorithms used are Fuzzy C-Mean, in which the clustering process before the beginning of the internet bandwidth usage data that exists in one period will be collected to be input to the Fuzzy C-Mean algorithm for the distribution of clusters on the use of existing bandwidth based applications that use the internet and network users. But the initial dataset that of the Fuzzy C Mean is not optimal, so we need some optimization dataset using feature extraction data so that the resulting clusters by Fuzzy C Mean algorithm has the accurate output. Results to be obtained from this study is the extraction of feature data that is most appropriate to perform clustering and analysis of Internet traffic based on user applications and the amount of capacity used by the user, which information the clustering results can be used to optimize internet bandwidth

  3. Coherent resonance of quantum plasmons in the graphene-gold cluster hybrid system.

    Science.gov (United States)

    Zhang, Kaibiao; Zhang, Hong; Li, Chikang

    2015-05-14

    Noble metal nanoparticles can modify the optical properties of graphene. Here we present a detailed theoretical analysis of the coherent resonance of quantum plasmons in the graphene-gold cluster hybrid system by using time dependent density functional theory (TDDFT). This plasmon coherent effect is mainly attributed to the electromagnetic field coupling between the graphene and the gold cluster. As a result, the optical response of the hybrid system exhibits a remarkably strong, selectable tuning and polarization dependent plasmon resonance enhanced in wide frequency regions. This investigation provides an improved understanding of the plasmon enhancement effect in a graphene-based photoelectric device.

  4. Superior hybrid hydrogels of polyacrylamide enhanced by bacterial cellulose nanofiber clusters.

    Science.gov (United States)

    Yuan, Ningxiao; Xu, Lu; Zhang, Lu; Ye, Haowen; Zhao, Jianhao; Liu, Zhong; Rong, Jianhua

    2016-10-01

    Hybrid polyacrylamide/bacterial cellulose nanofiber clusters (PAM/BC) hydrogels with high strength, toughness and recoverability were synthesized by in situ polymerization of acrylamide monomer in BC nanofiber clusters suspension. The hybrid gels exhibited an extremely large elongation at break of 2200%, and a high fracture stress of 1.35MPa. Additionally, the original length of hydrogels could be recovered after releasing the tensile force. Compressive results showed that the PAM/BC hybrid gels could reach a strain of about 99% without break, and was able to completely recover its original shape immediately after releasing the compression force. The compressive stress at 99% reached as high as 30MPa. Nearly no hysteresis in cyclic compressive tests was observed with these hybrid gels. The FT-IR, XRD and TGA analysis showed that hydrogen bonds between the PAM chains and BC nanofiber clusters mainly contributed to the superior mechanical properties of hybrid hydrogels. The cell viability results suggested that PAM/BC hybrid hydrogel was benign for biomedical application. These PAM/BC hydrogels offer a great promise as biomaterials such as bone and cartilage repair materials.

  5. An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

    Institute of Scientific and Technical Information of China (English)

    Taher NIKNAM; Babak AMIRI; Javad OLAMAEI; Ali AREFI

    2009-01-01

    The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Riplcy's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

  6. Exchange-correlation interaction and AO-hybridization of alkali-metal atomic clusters.

    Science.gov (United States)

    Liu, Xuan; Ito, Haruhiko; Torikai, Eiko

    2013-09-19

    The structure of alkali-metal atomic clusters is optimized with B3P86 hybrid functional for the highest spin state as well as with B3LYP hybrid functional for the lowest spin state. A dramatic change from plane to solid occurs in the highest spin state when the number of constituent atoms is four. The binding, exchange, and correlation energies are evaluated for both the highest and lowest spin states. Next, we explore the dependence of the exchange and correlation energies on the binding energy. The exchange energy contributes to the formation of the highest spin clusters, whereas the correlation energy contributes to the formation of the lowest spin clusters. The highest spin clusters are most stable when the exchange energy is a minimum. Then, to see why the ferromagnetic bond among spin-aligned identical atoms arises against Pauli exclusion principle, we estimate the mixing ratio of p orbitals in molecular orbitals. The s-p hybridization increases the binding energy in absolute value due to the extensive overlap of molecular orbitals and leads to generation of the highest spin clusters.

  7. Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis

    Directory of Open Access Journals (Sweden)

    Yevgeniy Bodyanskiy

    2016-08-01

    Full Text Available In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal ("supervised", and without one ("unsupervised". As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.

  8. Penerapan Fuzzy C-Means untuk Deteksi Dini Kemampuan Penalaran Matematis

    Directory of Open Access Journals (Sweden)

    Muh. Nurtanziz Sutoyo

    2016-02-01

    Full Text Available Penalaran matematis (mathematical reasoning merupakan suatu proses berpikir yang dilakukan dengan cara untuk menarik kesimpulan. Penerapan data mining dapat membantu menganalisa data yang diperoleh dari kondisi kemampuan penalaran matematis. Teknik data mining yang digunakan adalah dengan menggunakan teknik clustering. Salah satu metode clustering adalah algoritma Fuzzy C-Means. Fuzzy C-Means memiliki tingkat akurasi yang tinggi dan waktu komputasi yang cepat. Uji validitas hasil clustering untuk deteksi dini kemampuan penalaran matematis dengan menggunakan perhitungan Partition Coeffecient (PC diperoleh 0.840, ini berarti dapat dikatakan bahwa hasil clustering tergolong dalam kategori baik. Dari hasil perhitungan diperoleh 11 orang (25% yang memiliki kemampuan penalaran matematis baik, sebanyak 25 orang (57% memiliki kemampuan penalaran matematis cukup, dan sebanyak 8 (18% orang memiliki kemampuan penalaran matematis yang kurang. 

  9. Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation

    Directory of Open Access Journals (Sweden)

    P.Vasuda,

    2010-08-01

    Full Text Available Segmentation is an important aspect of medical image processing, where Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal Magnetic Resonance Images (MRI. Fuzzy clustering using Fuzzy C- Means (FCM algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. Theeffectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, convergence rate is compared between the conventional FCM and the Improved FCM.

  10. Genetic Diversity among Parents of Hybrid Rice Based on Cluster Analysis of Morphological Traits and Simple Sequence Repeat Markers

    Institute of Scientific and Technical Information of China (English)

    WANG Sheng-jun; LU Zuo-mei; WAN Jian-min

    2006-01-01

    The genetic diversity of 41 parental lines popularized in commercial hybrid rice production in China was studied by using cluster analysis of morphological traits and simple sequence repeat (SSR) markers. Forty-one entries were assigned into two clusters (I.e. Early or medium-maturing cluster; medium or late-maturing cluster) and further assigned into six sub-clusters based on morphological trait cluster analysis. The early or medium-maturing cluster was composed of 15 maintainer lines, four early-maturing restorer lines and two thermo-sensitive genic male sterile lines, and the medium or late-maturing cluster included 16 restorer lines and 4 medium or late-maturing maintainer lines. Moreover, the SSR cluster analysis classified 41 entries into two clusters (I.e. Maintainer line cluster and restorer line cluster) and seven sub-clusters. The maintainer line cluster consisted of all 19 maintainer lines, two thermo-sensitive genic male sterile lines, while the restorer line cluster was composed of all 20 restorer lines. The SSR analysis fitted better with the pedigree information. From the views on hybrid rice breeding, the results suggested that SSR analysis might be a better method to study the diversity of parental lines in indica hybrid rice.

  11. AN APPLICATION OF HYBRID CLUSTERING AND NEURAL BASED PREDICTION MODELLING FOR DELINEATION OF MANAGEMENT ZONES

    Directory of Open Access Journals (Sweden)

    Babankumar S. Bansod

    2011-02-01

    Full Text Available Starting from descriptive data on crop yield and various other properties, the aim of this study is to reveal the trends on soil behaviour, such as crop yield. This study has been carried out by developing web application that uses a well known technique- Cluster Analysis. The cluster analysis revealed linkages between soil classes for the same field as well as between different fields, which can be partly assigned to crops rotation and determination of variable soil input rates. A hybrid clustering algorithm has been developed taking into account the traits of two clustering technologies: i Hierarchical clustering, ii K-means clustering. This hybrid clustering algorithm is applied to sensor- gathered data about soil and analysed, resulting in the formation of well delineatedmanagement zones based on various properties of soil, such as, ECa , crop yield, etc. One of the purposes of the study was to identify the main factors affecting the crop yield and the results obtained were validated with existing techniques. To accomplish this purpose, geo-referenced soil information has been examined. Also, based on this data, statistical method has been used to classify and characterize the soil behaviour. This is done using a prediction model, developed to predict the unknown behaviour of clusters based on the known behaviour of other clusters. In predictive modeling, data has been collected for the relevant predictors, a statistical model has been formulated, predictions were made and the model can be validated (or revised as additional data becomes available. The model used in the web application has been formed taking into account neural network based minimum hamming distance criterion.

  12. Formation of Compact Clusters from High Resolution Hybrid Cosmological Simulations

    CERN Document Server

    Richardson, Mark L A; Gray, William J

    2013-01-01

    The early Universe hosted a large population of small dark matter `minihalos' that were too small to cool and form stars on their own. These existed as static objects around larger galaxies until acted upon by some outside influence. Outflows, which have been observed around a variety of galaxies, can provide this influence in such a way as to collapse, rather than disperse the minihalo gas. Gray & Scannapieco performed an investigation in which idealized spherically-symmetric minihalos were struck by enriched outflows. Here we perform high-resolution cosmological simulations that form realistic minihalos, which we then extract to perform a large suite of simulations of outflow-minihalo interactions including non-equilibrium chemical reactions. In all models, the shocked minihalo forms molecules through non-equilibrium reactions, and then cools to form dense chemically homogenous clumps of star-forming gas. The formation of these high-redshift clusters will be observable with the next generation of telesc...

  13. Patterns of hybrid loss of imprinting reveal tissue- and cluster-specific regulation.

    Directory of Open Access Journals (Sweden)

    Christopher D Wiley

    Full Text Available BACKGROUND: Crosses between natural populations of two species of deer mice, Peromyscus maniculatus (BW, and P. polionotus (PO, produce parent-of-origin effects on growth and development. BW females mated to PO males (bwxpo produce growth-retarded but otherwise healthy offspring. In contrast, PO females mated to BW males (POxBW produce overgrown and severely defective offspring. The hybrid phenotypes are pronounced in the placenta and include POxBW conceptuses which lack embryonic structures. Evidence to date links variation in control of genomic imprinting with the hybrid defects, particularly in the POxBW offspring. Establishment of genomic imprinting is typically mediated by gametic DNA methylation at sites known as gDMRs. However, imprinted gene clusters vary in their regulation by gDMR sequences. METHODOLOGY/PRINCIPAL FINDINGS: Here we further assess imprinted gene expression and DNA methylation at different cluster types in order to discern patterns. These data reveal POxBW misexpression at the Kcnq1ot1 and Peg3 clusters, both of which lose ICR methylation in placental tissues. In contrast, some embryonic transcripts (Peg10, Kcnq1ot1 reactivated the silenced allele with little or no loss of DNA methylation. Hybrid brains also display different patterns of imprinting perturbations. Several cluster pairs thought to use analogous regulatory mechanisms are differentially affected in the hybrids. CONCLUSIONS/SIGNIFICANCE: These data reinforce the hypothesis that placental and somatic gene regulation differs significantly, as does that between imprinted gene clusters and between species. That such epigenetic regulatory variation exists in recently diverged species suggests a role in reproductive isolation, and that this variation is likely to be adaptive.

  14. A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means

    Institute of Scientific and Technical Information of China (English)

    TRAN Dang Cong; WU Zhijian; WANG Zelin; DENG Changshou

    2015-01-01

    To improve the performance of K-means clustering algorithm, this paper presents a new hybrid ap-proach of Enhanced artificial bee colony algorithm and K-means (EABCK). In EABCK, the original artificial bee colony algorithm (called ABC) is enhanced by a new mu-tation operation and guided by the global best solution (called EABC). Then, the best solution is updated by K-means in each iteration for data clustering. In the experi-ments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK out-perform other comparative ABC variants and data clus-tering algorithms, respectively.

  15. Malleable Fuzzy Local Median C Means Algorithm for Effective Biomedical Image Segmentation

    Science.gov (United States)

    Rajendran, Arunkumar; Balakrishnan, Nagaraj; Varatharaj, Mithya

    2016-12-01

    The traditional way of clustering plays an effective role in the field of segmentation which was developed to be more effective and also in the recent development the extraction of contextual information can be processed with ease. This paper presents a modified Fuzzy C-Means (FCM) algorithm that provides the better segmentation in the contour grayscale regions of the biomedical images where effective cluster is needed. Malleable Fuzzy Local Median C-Means (M-FLMCM) is the proposed algorithm, proposed to overcome the disadvantage of the traditional FCM method in which the convergence time requirement is more, lack of ability to remove the noise, and the inability to cluster the contour region such as images. M-FLMCM shows promising results in the experiment with real-world biomedical images. The experiment results, with 96 % accuracy compared to the other algorithms.

  16. Hybrid QTAIM and electrostatic potential-based quantum topology phase diagrams for water clusters.

    Science.gov (United States)

    Kumar, Anmol; Gadre, Shridhar R; Chenxia, Xiao; Tianlv, Xu; Kirk, Steven Robert; Jenkins, Samantha

    2015-06-21

    The topological diversity of sets of isomers of water clusters (W = H2O)n, 7 ≤ n ≤ 10, is analyzed employing the scalar fields of total electronic charge density ρ(r) and the molecular electrostatic potential (MESP). The features uncovered by the MESP are shown to be complementary to those revealed by the theory of atoms in molecules (QTAIM) analysis. The MESP is known to exhibit the electron localizations such as lone pairs that are central to water cluster behavior. Therefore, a 'hybrid' QTAIM and MESP quantum topology phase diagram (QTPD) for Wn, 7 ≤ n ≤ 10, is introduced in addition to the QTPD. The 'spanning' QTPD with upper and lower bounds is constructed from the solutions of the Poincaré-Hopf relation involving the non-degenerate critical points. The changing subtle balance between the planar and three dimensional character of the growing water clusters Wn, 4 ≤ n ≤ 10, is revealed. Characterization of the structure of the QTPDs, possible with new tools, demonstrated the migration of the position of the global minimum on the spanning QTPD from the lower bound to upper bound as the Wn, 4 ≤ n ≤ 10, cluster grows in size. Differences in the structure of the QTPD are found between the clusters containing even versus odd monomers for Wn, n = 7-10. The energetic stability of the clusters which possess even number of monomers viz. n = 8, 10 is higher than that of the n = 7, 9 clusters due to relatively higher numbers of hydrogen-bond BCPs in the n = 8, 10 clusters, in agreement with energetic results reported in the literature. A 'hybrid' QTPD is created from a new chemical relation bHB + l ≥ 2n for Wn that relates the number of hydrogen-bond bond critical points (bHB) with the number of oxygen lone pairs exclusively specified by the negative valued MESP (3,+3) critical points (l). The topologies of the subset bHB + l = 2n for Wn, point the way to the discovery of unknown 'missing' lower energy isomers. A discussion of the relative merits and

  17. A hybrid SPH/N-body method for star cluster simulations

    CERN Document Server

    Hubber, D A; Smith, R; Goodwin, S P

    2013-01-01

    We present a new hybrid Smoothed Particle Hydrodynamics (SPH)/N-body method for modelling the collisional stellar dynamics of young clusters in a live gas background. By deriving the equations of motion from Lagrangian mechanics we obtain a formally conservative combined SPH/N-body scheme. The SPH gas particles are integrated with a 2nd order Leapfrog, and the stars with a 4th order Hermite scheme. Our new approach is intended to bridge the divide between the detailed, but expensive, full hydrodynamical simulations of star formation, and pure N-body simulations of gas-free star clusters. We have implemented this hybrid approach in the SPH code SEREN (Hubber et al. 2011) and perform a series of simple tests to demonstrate the fidelity of the algorithm and its conservation properties. We investigate and present resolution criteria to adequately resolve the density field and to prevent strong numerical scattering effects. Future developments will include a more sophisticated treatment of binaries.

  18. Hybrid organic-inorganic polyoxometalates : synthesis and characterisation of organoarsonate and organophosphonate functionalised polyoxovanadate clusters

    OpenAIRE

    Breen, John Michael

    2010-01-01

    This thesis presents a significant contribution of research to the field of hybrid inorganic- organic polyoxometalates. Herein the functionalisation of polyoxovanadate clusters with aryl arsonates and aryl phosphonates is described and the structural and physiochemical properties of the product materials are discussed. Chapter 1 introduces the reader to the field of research, highlights recent significant achievements and puts accomplishments into a broader context. TARA (Trinity’s Access ...

  19. Heptanuclear lanthanide [Ln7] clusters: from blue-emitting solution-stable complexes to hybrid clusters.

    Science.gov (United States)

    Canaj, Angelos B; Tsikalas, George K; Philippidis, Aggelos; Spyros, Apostolos; Milios, Constantinos J

    2014-09-07

    The use of LH3 (2-(β-naphthalideneamino)-2-hydroxymethyl-1-propanol) and aibH (2-amino-isobutyric acid) in 4f chemistry has led to the isolation of eight new isostructural lanthanide complexes. More specifically, the reaction of the corresponding lanthanide nitrate salt with LH3 and aibH in MeOH, under solvothermal conditions in the presence of NEt3, led to the isolation and characterization of seven complexes with the general formulae [Ln(III)7(OH)2(L')9(aib)]·4MeOH (Ln = Gd, ·4MeOH; Tb, ·4MeOH; Dy, ·4MeOH; Ho, ·4MeOH; Er, ·4MeOH; Tm, ·4MeOH; Yb, ·4MeOH L' = the dianion of the Schiff base between naphthalene aldehyde and 2-amino-isobutyric acid). Furthermore, the isostructural Y(III) analogue, cluster [Y(III)7(OH)2(L')9(aib)]·4MeOH (·4MeOH), was synthesized in a similar manner to . The structure of all eight clusters describes a distorted [M(III)6] octahedron which encapsulates a seventh M(III) ion in an off-centre fashion. Dc magnetic susceptibility studies in the 5-300 K range for complexes reveal the presence of dominant antiferromagnetic exchange interactions within the metallic clusters as evidenced by the negative Weiss constant, θ, while ac magnetic susceptibility measurements show temperature and frequency dependent out-of-phase signals for the [Dy(III)7] analogue (·4MeOH), suggesting potential single molecule magnetism character. Furthermore, for complex , simulation of its dc magnetic susceptibility data yielded very weak antiferromagnetic interactions within the metallic centres. Solid-state emission studies for all clusters display ligand-based emission, while extended 1D and 2D NMR studies for ·4MeOH reveal that the species retain their structural integrity in solution. In addition, TGA measurements for , and revealed excellent thermal stability up to 340 °C for the clusters.

  20. Comparative Analysis of K-Means and Fuzzy C-Means Algorithms

    Directory of Open Access Journals (Sweden)

    Soumi Ghosh

    2013-05-01

    Full Text Available In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. There are various algorithms which are used to solve this problem. In this research work two important clustering algorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency of clustering output. The numbers of data points as well as the number of clusters are the factors upon which the behaviour patterns of both the algorithms are analyzed. FCM produces close results to K-Means clustering but it still requires more computation time than K-Means clustering.

  1. ERBF network with immune clustering

    Institute of Scientific and Technical Information of China (English)

    宫新保; 臧小刚; 周希朗

    2004-01-01

    Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.

  2. An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

    Directory of Open Access Journals (Sweden)

    Ioannis Panapakidis

    2017-09-01

    Full Text Available Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.

  3. Observations of lower hybrid cavities in the inner magnetosphere by the Cluster and Viking satellites

    Directory of Open Access Journals (Sweden)

    A. Tjulin

    2004-09-01

    Full Text Available Observations by the Viking and Cluster satellites at altitudes up to 35000km show that Lower Hybrid Cavities (LHCs are common in the inner magnetosphere. LHCs are density depletions filled with waves in the lower hybrid frequency range. The LHCs have, until recently, only been found at altitudes up to 2000km. Statistics of the locations and general shape of the LHCs is performed to obtain an overview of some of their properties. In total, we have observed 166 LHCs on Viking during 27h of data, and 535 LHCs on Cluster during 87h of data. These LHCs are found at invariant latitudes from the auroral region to the plasmapause. A comparison with lower altitude observations shows that the LHC occurrence frequency does not scale with the flux tube radius, so that the LHCs are moderately rarer at high altitudes. This indicates that the individual LHCs do not reach from the ionosphere to 35000km altitude, which gives an upper bound for their length. The width of the LHCs perpendicular to the geomagnetic field at high altitudes is a few times the ion gyroradius, consistent with observations at low altitudes. The estimated depth of the density depletions vary with altitude, being larger at altitudes of 20000-35000km (Cluster, 10-20%, smaller around 1500-13000km (Viking and previous Freja results, a few percent and again larger around 1000km (previous sounding rocket observations, 10-20%. The LHCs in the inner magnetosphere are situated in regions with background electrostatic hiss in the lower hybrid frequency range, consistent with investigations at low altitudes. Individual LHCs observed at high altitudes are stable at least on time scales of 0.2s (about the ion gyro period, which is consistent with previous results at lower altitudes, and observations by the four Cluster satellites show that the occurrence of LHCs in a region in space is a stable phenomenon, at least on time scales of an hour.

  4. Land cover classification using reformed fuzzy C-means

    Indian Academy of Sciences (India)

    B Sowmya; B Sheelarani

    2011-04-01

    This paper explains the task of land cover classification using reformed fuzzy C means. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. The most basic attribute for clustering of an image is its luminance amplitude for a monochrome image and colour components for a colour image. Since there are more than 16 million colours available in any given image and it is difficult to analyse the image on all of its colours, the likely colours are grouped together by clustering techniques. For that purpose reformed fuzzy C means algorithm has been used. The segmented images are compared using image quality metrics. The image quality metrics used are peak signal to noise ratio (PSNR), error image and compression ratio. The time taken for image segmentation is also used as a comparison parameter. The techniques have been applied to classify the land cover.

  5. Facile synthesis of surfactant-free Au cluster/graphene hybrids for high-performance oxygen reduction reaction.

    Science.gov (United States)

    Yin, Huajie; Tang, Hongjie; Wang, Dan; Gao, Yan; Tang, Zhiyong

    2012-09-25

    Non-Pt noble metal clusters like Au clusters are believed to be promising high performance catalysts for the oxygen reduction reaction (ORR) at the cathode of fuel cells, but they still suffer big problems during the catalysis reactions, such as a large amount of the capping agents being on the surface and easy occurrence of dissolution and aggregation. To overcome these obstacles, here, we present a novel and general strategy to grow ultrafine Au clusters and other metal (Pt, Pd) clusters on the reduced graphene oxide (rGO) sheets without any additional protecting molecule or reductant. Compared with the currently generally adopted nanocatalysts, including commercial Pt/C, rGO sheets, Au nanoparticle/rGO hybrids, and thiol-capped Au clusters of the same sizes, the as-synthesized Au cluster/rGO hybrids display an impressive eletrocatalytic performance toward ORR, for instance, high onset potential, superior methanol tolerance, and excellent stability.

  6. Detection of Epilepsy from EEG Signal during Seizure Using Entropy-Based Fuzzy c-Means

    Directory of Open Access Journals (Sweden)

    Tahir Ahmad

    2012-09-01

    Full Text Available One of the major roles of Electrocephalography (EEG is an aid to diagnose epilepsy. Abnormal patterns such as spikes, sharp wave complexes can be seen. Our main interest is to extract information about the dynamics from a few observations of this record signal. In this study, the entropy-based fuzzy c-Means is used to cluster EEG signal of patients during an epileptic seizure.We obtained signatures of general epilepsy by superimposing results of the method.

  7. Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation

    OpenAIRE

    segmentation, Outlier rejection fuzzy c-means (ORFCM)

    2013-01-01

    This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qua...

  8. Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

    Institute of Scientific and Technical Information of China (English)

    解翔; 侍洪波

    2012-01-01

    For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

  9. A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller

    Directory of Open Access Journals (Sweden)

    Endra Joelianto

    2009-11-01

    Full Text Available The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system.

  10. Hybrid Decomposition Method in Parallel Molecular Dynamics Simulation Based on SMP Cluster Architecture

    Institute of Scientific and Technical Information of China (English)

    WANG Bing; SHU Jiwu; ZHENG Weimin; WANG Jinzhao; CHEN Min

    2005-01-01

    A hybrid decomposition method for molecular dynamics simulations was presented, using simultaneously spatial decomposition and force decomposition to fit the architecture of a cluster of symmetric multi-processor (SMP) nodes. The method distributes particles between nodes based on the spatial decomposition strategy to reduce inter-node communication costs. The method also partitions particle pairs within each node using the force decomposition strategy to improve the load balance for each node. Simulation results for a nucleation process with 4 000 000 particles show that the hybrid method achieves better parallel performance than either spatial or force decomposition alone, especially when applied to a large scale particle system with non-uniform spatial density.

  11. Realization of R-tree for GIS on hybrid clustering algorithm

    Institute of Scientific and Technical Information of China (English)

    HUANG Ji-xian; BAO Guang-shu; LI Qing-song

    2005-01-01

    The characteristic of geographic information system(GIS) spatial data operation is that query is much more frequent than insertion and deletion, and a new hybrid spatial clustering method used to build R-tree for GIS spatial data was proposed in this paper. According to the aggregation of clustering method, R-tree was used to construct rules and specialty of spatial data. HCR-tree was the R-tree built with HCR algorithm. To test the efficiency of HCR algorithm, it was applied not only to the data organization of static R-tree but also to the nodes splitting of dynamic R-tree. The results show that R-tree with HCR has some advantages such as higher searching efficiency, less disk accesses and so on.

  12. A HYBRID APPROACH FOR NODE CO-OPERATION BASED CLUSTERING IN MOBILE AD HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    C. Sathiyakumar

    2013-01-01

    Full Text Available A Mobile Ad-Hoc Network (MANET is termed as a set of wireless nodes which could be built with infrastructure less environment where network services are afforded by the nodes themselves. In such a situation, if a node refuses to co-operate with other nodes, then it will lead to a considerable diminution in throughput and the network operation decreases to low optimum value. Mobile Ad hoc Networks (MANETs rely on the collaboration of nodes for packet routing ahead. Nevertheless, much of the existing work in MANETs imagines that mobile nodes (probably possessed by selfish users will pursue prearranged protocols without variation. Therefore, implementing the co-operation between the nodes turn out to be an significant issue. The previous work described a secured key model for ad hoc network with efficient node clustering based on reputation and ranking model. But the downside is that the co-operation with the nodes is less results in a communication error. To enhance the security in MANET, in this work, we present a hybrid approach, build a node co-operation among the nodes in MANET by evaluating the weightage of cooperativeness of each node in MANET. With the estimation of normal co-operative nodes, nodes are restructured on its own (self. Then clustering is made with the reorganized nodes to form a secured communication among the nodes in the MANET environment. The Simulation of the proposed Hybrid Approach for Node Cooperation based Clustering (HANCC work is done for varying topology, node size, attack type and intensity with different pause time settings and the performance evaluations are carried over in terms of node cooperativeness, clustering efficiency, communication overhead and compared with an existing secured key model. Compared to an existing secured key model, the proposed HANCC performance is 80-90% high.

  13. Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

    Science.gov (United States)

    Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

    2017-09-01

    Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

  14. The Involvement of hybrid cluster protein 4, HCP4, in Anaerobic Metabolism in Chlamydomonas reinhardtii.

    Science.gov (United States)

    Olson, Adam C; Carter, Clay J

    2016-01-01

    The unicellular green algae Chlamydomonas reinhardtii has long been studied for its unique fermentation pathways and has been evaluated as a candidate organism for biofuel production. Fermentation in C. reinhardtii is facilitated by a network of three predominant pathways producing four major byproducts: formate, ethanol, acetate and hydrogen. Previous microarray studies identified many genes as being highly up-regulated during anaerobiosis. For example, hybrid cluster protein 4 (HCP4) was found to be one of the most highly up-regulated genes under anoxic conditions. Hybrid cluster proteins have long been studied for their unique spectroscopic properties, yet their biological functions remain largely unclear. To probe its role during anaerobiosis, HCP4 was silenced using artificial microRNAs (ami-hcp4) followed by extensive phenotypic analyses of cells grown under anoxic conditions. Both the expression of key fermentative enzymes and their respective metabolites were significantly altered in ami-hcp4, with nitrogen uptake from the media also being significantly different than wild-type cells. The results strongly suggest a role for HCP4 in regulating key fermentative and nitrogen utilization pathways.

  15. Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations

    Directory of Open Access Journals (Sweden)

    Yingfeng Zhang

    2014-01-01

    Full Text Available This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS and precision forging stage (PFS of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.

  16. The Involvement of hybrid cluster protein 4, HCP4, in Anaerobic Metabolism in Chlamydomonas reinhardtii.

    Directory of Open Access Journals (Sweden)

    Adam C Olson

    Full Text Available The unicellular green algae Chlamydomonas reinhardtii has long been studied for its unique fermentation pathways and has been evaluated as a candidate organism for biofuel production. Fermentation in C. reinhardtii is facilitated by a network of three predominant pathways producing four major byproducts: formate, ethanol, acetate and hydrogen. Previous microarray studies identified many genes as being highly up-regulated during anaerobiosis. For example, hybrid cluster protein 4 (HCP4 was found to be one of the most highly up-regulated genes under anoxic conditions. Hybrid cluster proteins have long been studied for their unique spectroscopic properties, yet their biological functions remain largely unclear. To probe its role during anaerobiosis, HCP4 was silenced using artificial microRNAs (ami-hcp4 followed by extensive phenotypic analyses of cells grown under anoxic conditions. Both the expression of key fermentative enzymes and their respective metabolites were significantly altered in ami-hcp4, with nitrogen uptake from the media also being significantly different than wild-type cells. The results strongly suggest a role for HCP4 in regulating key fermentative and nitrogen utilization pathways.

  17. Gold conjugate-based liposomes with hybrid cluster bomb structure for liver cancer therapy.

    Science.gov (United States)

    Zhang, Ning; Chen, Huan; Liu, Ai-Yun; Shen, Jia-Jia; Shah, Vishva; Zhang, Can; Hong, Jin; Ding, Ya

    2016-01-01

    Hybrid drug delivery system containing both organic and inorganic nanocarriers is expected to achieve its complementary advantages for the aim of improving the performance of antineoplastic drugs in tumor therapy. Here we report the use of liposomes and gold nanoparticles to construct a liposome with a hybrid Cluster Bomb structure and discuss its unique multi-order drug release property for liver tumor treatment. A very simple method is used for the hybrid liposome preparation and involves mixing two solutions containing liposomes loaded with either non-covalent or covalent Paclitaxel (PTX, namely free PTX or PTX-conjugated GNPs, respectively) by different ratio of volume (25:75, 50:50, 25:75, v/v). Various mixed liposomes were tested to determine the optimal conditions for maximum drug delivery. The optimized liposome was then tested using xenograft Heps tumor-bearing mice and showed the best efficacy for chemotherapeutic inhibition of tumor at PTX liposome: PTX-conjugated GNP liposome of 25:75 ratio (v/v). This system allows for simple and easy preparation while providing a more accurate site- and time-release mode for tumor treatment using antitumor drugs.

  18. Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rajeev Kumar

    2016-01-01

    Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

  19. HWM: a hybrid workload migration mechanism of metadata server cluster in data center

    Institute of Scientific and Technical Information of China (English)

    Jian LIU; Huanqing DONG; Junwei ZHANG; Zhenjun LIU; Lu XU

    2017-01-01

    In data center,applications of big data analytics pose a big challenge to massive storage systems.It is significant to achieve high availability,high performance and high scalability for PB-scale or EB-scale storage systems.Metadata server (MDS) cluster architecture is one of the most effective solutions to meet the requirements of applications in data center.Workload migration can achieve load balance and energy saving of cluster systems.In this paper,a hybrid workload migration mechanism of MDS cluster is proposed and named as HWM.In HWM,workload of MDS is classified into two categories:metadata service and state service,and they can be migrated rapidly from a source MDS to a target MDS in different ways.Firstly,in metadata service migration,all the dirty metadata of one sub file system is flushed to a shared storage pool by the source MDS,and then is loaded by the target MDS.Secondly,in state service migration,all the states of that sub file system are migrated from source MDS to target MDS through network at file granularity,and then all of the related structures of these states are reconstructed in target MDS.Thirdly,in the process of workload migration,instead of blocking client requests,the source MDS can decide which MDS will respond to each request according to the operation type and the migration stage.The proposed mechanism is implemented in the Blue Whale MDS cluster.The performance measurements show that the HWM mechanism is efficient to migrate the workload of a MDS cluster system and provides low-latency access to metadata and states.

  20. A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm

    CERN Document Server

    Gomathi, M

    2010-01-01

    Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy CMeans Clustering (FCM) ...

  1. The hybrid-cluster protein ('prismane protein') from Escherichia coli. Characterization of the hybrid-cluster protein, redox properties of the [2Fe-2S] and [4Fe-2S-2O] clusters and identification of an associated NADH oxidoreductase containing FAD and[2Fe-2S

    NARCIS (Netherlands)

    Berg, van den W.A.M.; Hagen, W.R.; Dongen, van W.M.A.M.

    2000-01-01

    Hybrid-cluster proteins ('prismane proteins') have previously been isolated and characterized from strictly anaerobic sulfate-reducing bacteria. These proteins contain two types of Fe/S clusters unique in biological systems: a [4Fe-4S] cubane cluster with spin-admixed S = 3/2 ground-state

  2. The hybrid-cluster protein ('prismane protein') from Escherichia coli. Characterization of the hybrid-cluster protein, redox properties of the [2Fe-2S] and [4Fe-2S-2O] clusters and identification of an associated NADH oxidoreductase containing FAD and[2Fe-2S

    NARCIS (Netherlands)

    Berg, van den W.A.M.; Hagen, W.R.; Dongen, van W.M.A.M.

    2000-01-01

    Hybrid-cluster proteins ('prismane proteins') have previously been isolated and characterized from strictly anaerobic sulfate-reducing bacteria. These proteins contain two types of Fe/S clusters unique in biological systems: a [4Fe-4S] cubane cluster with spin-admixed S = 3/2 ground-state paramagnet

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

    Science.gov (United States)

    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.

  4. A Generalized Automatic Hybrid Fuzzy-Based GA-PSO Clustering Approach

    Directory of Open Access Journals (Sweden)

    Amir Hooshang Mazinan, ,

    2014-09-01

    Full Text Available The main contribution of the present research arises from developing the traditional methods in the area of segmentation of brain magnetic resonance imaging (MRI. Contemporary research is now developing techniques to solve the whole considerable problems in this field, such as the fuzzy local information c-mean (FLICM approach that incorporate the local spatial and the gray level information. It should be noted that the present approach is robust against noise, although the high computational complexity is not truly ignored. A novel approach in segmentation of brain MRI has been investigated and presented through the proposed research. Because of so many noises embedded in the acquiring procedure, like eddy currents, the segmentation of the brain MR is now tangibly taken into account as a difficult task. Fuzzy-based clustering algorithm is one of the solutions in the same way. But, it is so sensitive to change through noise and other imaging artifacts. The idea of combining the genetic algorithm (GA and particle swarm optimization (PSO for the purpose of generalizing the FLICM is the ultimate goal in the present investigation, since the computational complexity could actually be reduced. The experiments with a number of simulated images as well as the clinical MRI data illustrate that the proposed approach is applicable and effective.

  5. Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

    Directory of Open Access Journals (Sweden)

    Łapczyński Mariusz

    2014-08-01

    Full Text Available Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models, customers who purchase additional products (cross- and up-sell models or customers intending to resign from the cooperation (churn models. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm and cluster analysis (k-means. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

  6. Performance Modeling of Hybrid MPI/OpenMP Scientific Applications on Large-scale Multicore Cluster Systems

    KAUST Repository

    Wu, Xingfu

    2011-08-01

    In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore clusters: IBM POWER4, POWER5+ and Blue Gene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore clusters because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyro kinetic Toroidal Code in magnetic fusion to validate our performance model of the hybrid application on these multicore clusters. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore clusters. © 2011 IEEE.

  7. Numerical approach for solving kinetic equations in two-dimensional case on hybrid computational clusters

    Science.gov (United States)

    Malkov, Ewgenij A.; Poleshkin, Sergey O.; Kudryavtsev, Alexey N.; Shershnev, Anton A.

    2016-10-01

    The paper presents the software implementation of the Boltzmann equation solver based on the deterministic finite-difference method. The solver allows one to carry out parallel computations of rarefied flows on a hybrid computational cluster with arbitrary number of central processor units (CPU) and graphical processor units (GPU). Employment of GPUs leads to a significant acceleration of the computations, which enables us to simulate two-dimensional flows with high resolution in a reasonable time. The developed numerical code was validated by comparing the obtained solutions with the Direct Simulation Monte Carlo (DSMC) data. For this purpose the supersonic flow past a flat plate at zero angle of attack is used as a test case.

  8. IMPLEMENTASI METODE FUZZY C-MEANS PADA SISTEM CLUSTERINGDATA VARIETAS PADI

    Directory of Open Access Journals (Sweden)

    Nurjanah Nurjanah

    2014-09-01

    Full Text Available Mutations with gamma rays conducted on five local rice varieties tidal South Kalimantan produce a lot of data availability. In order for these data not only become a graveyard of useless data required a method that could be used to probe the hidden information from the data. The method known as data mining. Data mining is a technique to gain knowledge from the data by looking for certain patterns or rules of a number of large amounts of data. One method of data mining is clustering, where clustering is usually used to group objects that are similar in the same class or segment. By utilizing the data of local rice varieties tidal South Kalimantan mutated by gamma rays, data mining process is done by grouping the data based on the harvest age, productive tillers, and weight of 1000 seeds into 4 groups using fuzzy c-means algorithm. From that cluster information, carried ranking using the Simple Additive Weighting method and acquired knowledge about improved varieties by harvest age, productive tillers, and a weight of 1000 is kuatek with a dose of 30 krad. Keywords : Data Mining, Cluster, Fuzzy C-Means, Local Rice Varieties Mutasi dengan sinar gamma yang dilakukan terhadap lima varietas padi lokal pasang surut kalimantan selatan enghasilkan tersedianya banyak data. Agar data-data tersebut tidak hanya menjadi kuburan data yang tidak berguna dibutuhkan sebuah metode yang bisa digunakan untuk menggali informasi–informasi tersembunyi dari data tersebut. Metode tersebut dikenal dengan data mining. Data mining merupakan suatu teknik untuk menggali pengetahuan dari data dengan mencari pola atau aturan tertentu dari sejumlah data dalam jumlah besar. Salah satu metode data mining adalah klastering, dimana klastering biasanya digunakan untuk mengelompokan objek-objek yang memiliki kemiripan dalam kelas atau segmen yang sama. Dengan memanfaatkan data varietas padi hasil mutasi dengan sinar gamma dilakukan proses penggalian data dengan cara

  9. HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    Dahlia Asyiqin Ahmad Zainaddin

    2013-01-01

    Full Text Available Intrusion Detection System (IDS is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies activities which degenerates from the normal behaviours. In recent years, data mining approach for intrusion detection have been advised and used. The approach such as Genetic Algorithms , Support Vector Machines, Neural Networks as well as clustering has resulted in high accuracy and good detection rates but with moderate false alarm on novel attacks. Many researchers also have proposed hybrid data mining techniques. The previous resechers has intoduced the combination of Fuzzy Clustering and Artificial Neural Network. However, it was tested only on randomn selection of KDDCup 1999 dataset. In this study the framework experiment introduced, has been used over the NSL dataset to test the stability and reliability of the technique. The result of precision, recall and f-value rate is compared with previous experiment. Both dataset covers four types of main attacks, which are Derial of Services (DoS, User to Root (U2R, Remote to Local (R2L and Probe. Results had guarenteed that the hybrid approach performed better detection especially for low frequent over NSL datataset compared to original KDD dataset, due to the removal of redundancy and uncomplete elements in the original dataset. This electronic document is a “live” template. The various components of your paper [title, text, tables, figures and references] are already defined on the style sheet, as illustrated by the portions given in this document.

  10. Fuzzy-hybrid land vehicle driveline modelling based on a moving window subtractive clustering approach

    Science.gov (United States)

    Economou, J. T.; Knowles, K.; Tsourdos, A.; White, B. A.

    2011-02-01

    In this article, the fuzzy-hybrid modelling (FHM) approach is used and compared to the input-output system Takagi-Sugeno (TS) modelling approach which correlates the drivetrain power flow equations with the vehicle dynamics. The output power relations were related to the drivetrain bounded efficiencies and also to the wheel slips. The model relates also to the wheel and ground interactions via suitable friction coefficient models relative to the wheel slip profiles. The wheel slip had a significant efficiency contribution to the overall driveline system efficiency. The peak friction slip and peak coefficient of friction values are known a priori during the analysis. Lastly, the rigid body dynamical power has been verified through both simulation and experimental results. The mathematical analysis has been supported throughout the paper via experimental data for a specific electric robotic vehicle. The identification of the localised and input-output TS models for the fuzzy hybrid and the experimental data were obtained utilising the subtractive clustering (SC) methodology. These results were also compared to a real-time TS SC approach operating on periodic time windows. This article concludes with the benefits of the real-time FHM method for the vehicle electric driveline due to the advantage of both the analytical TS sub-model and the physical system modelling for the remaining process which can be clearly utilised for control purposes.

  11. High Density Impulse Noise Detection using Fuzzy C-means Algorithm

    Directory of Open Access Journals (Sweden)

    Isha Singh

    2016-01-01

    Full Text Available A new technique for detecting the high density impulse noise from corrupted images using Fuzzy C-means algorithm is proposed. The algorithm is iterative in nature and preserves more image details in high noise environment. Fuzzy C-means is initially used to cluster the image data. The application of Fuzzy C-means algorithm in the detection phase provides an optimum classification of noisy data and uncorrupted image data so that the pictorial information remains well preserved. Experimental results show that the proposed algorithm significantly outperforms existing well-known techniques. Results show that with the increase in percentage of noise density, the performance of the algorithm is not degraded. Furthermore, the varying window size in the two detection stages provides more efficient results in terms of low false alarm rate and miss detection rate. The simple structure of the algorithm to detect impulse noise makes it useful for various applications like satellite imaging, remote sensing, medical imaging diagnosis and military survillance. After the efficient detection of noise, the existing filtering techniques can be used for the removal of noise.Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 30-36, DOI: http://dx.doi.org/10.14429/dsj.66.8722

  12. Cluster-cluster clustering

    Science.gov (United States)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.

  13. Cluster-cluster clustering

    Energy Technology Data Exchange (ETDEWEB)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.

    1985-08-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references.

  14. Balancing energy consumption with hybrid clustering and routing strategy in wireless sensor networks.

    Science.gov (United States)

    Xu, Zhezhuang; Chen, Liquan; Liu, Ting; Cao, Lianyang; Chen, Cailian

    2015-10-20

    Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in Sensors 2015, 15 26584 gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.

  15. Modeling Fuzzy RBF Neural Network to Predict of Mechanical Properties of Welding Joints Based on Fuzzy C-means Cluster%基于模糊C均值聚类的模糊RBF神经网络预测焊接接头力学性能建模

    Institute of Scientific and Technical Information of China (English)

    张永志; 董俊慧

    2014-01-01

    针对焊接过程的高度非线性,多种因素的复杂交互作用,难以预测焊接接头力学性能的问题和常用反馈(Backpropagation,BP)神经网络的不足,利用模糊C均值(Fuzzy C-means,FCM)聚类算法和伪逆法相结合,建立焊接接头力学性能模糊径向基(Radial basis function,RBF)神经网络预测模型.以TC4钛合金惰性气体钨极保护焊(Tungsten inert gas arcwelding,TIG焊)焊接工艺参数(焊接电流、焊接速度和氩气流量)作为模型的输入参数,以焊后力学性能(抗拉强度、抗弯强度、伸长率、焊缝硬度和热影响区硬度)作为模型的输出参数.利用27组试验数据对所建模型进行学习训练,用另外9组试验数据进行仿真.结果表明,利用该方法所建模型具有结构稳定、训练速度快、适应性强、鲁棒性好、预测精度高的特点,能够预测焊接接头力学性能.通过数学解析,用函数形式表达焊接工艺参数与接头力学性能之间的规律,可以优化焊接工艺参数,为调控焊接接头的质量提供依据.

  16. HyCFS, a high-resolution shock capturing code for numerical simulation on hybrid computational clusters

    Science.gov (United States)

    Shershnev, Anton A.; Kudryavtsev, Alexey N.; Kashkovsky, Alexander V.; Khotyanovsky, Dmitry V.

    2016-10-01

    The present paper describes HyCFS code, developed for numerical simulation of compressible high-speed flows on hybrid CPU/GPU (Central Processing Unit / Graphical Processing Unit) computational clusters on the basis of full unsteady Navier-Stokes equations, using modern shock capturing high-order TVD (Total Variation Diminishing) and WENO (Weighted Essentially Non-Oscillatory) schemes on general curvilinear structured grids. We discuss the specific features of hybrid architecture and details of program implementation and present the results of code verification.

  17. A robust cluster-based dynamic-super-node scheme for hybrid peer-to-peer network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Hybrid peer-to-peer (P2P) system can improve the performance of the entire system using super-peer. But it is difficult to measure a peer's capability exactly and ensure high reliability of the network. This paper proposes a scheme to solve these problems. Firstly, we present a hybrid P2P network in which the upper layer is Chord network and the lower layer is cluster. Then we provide a strategy to measure a peer's capability so that a cluster can be organized to be a sorting network in which peers are classified into three types: dynamic-super-node (DSN), backup-node (BN) and ordinary-node (ON). In a cluster, DSN and BNs are strongly connected. And based on this, we present an algorithm DSN flood min (DSNFM) to select DSN BN and maintain consensus of the cluster. Furthermore, we do a reliability analysis of the cluster based on churn rate of the network and gathered three rules of thumb from our simulations.

  18. A Hybrid Distributed Mutual Exclusion Algorithm for Cluster-Based Systems

    Directory of Open Access Journals (Sweden)

    Moharram Challenger

    2013-01-01

    Full Text Available Distributed mutual exclusion is a fundamental problem which arises in various systems such as grid computing, mobile ad hoc networks (MANETs, and distributed databases. Reducing key metrics like message count per any critical section (CS and delay between two CS entrances, which is known as synchronization delay, is a great challenge for this problem. Various algorithms use either permission-based or token-based protocols. Token-based algorithms offer better communication costs and synchronization delay. Raymond's and Suzuki-Kasami's algorithms are well-known token-based ones. Raymond's algorithm needs only O(log2(N messages per CS and Suzuki-Kasami's algorithm needs just one message delivery time between two CS entrances. Nevertheless, both algorithms are weak in the other metric, synchronization delay and message complexity correspondingly. In this work, a new hybrid algorithm is proposed which gains from powerful aspects of both algorithms. Raysuz's algorithm (the proposed algorithm uses a clustered graph and executes Suzuki-Kasami's algorithm intraclusters and Raymond's algorithm interclusters. This leads to have better message complexity than that of pure Suzuki-Kasami's algorithm and better synchronization delay than that of pure Raymond's algorithm, resulting in an overall efficient DMX algorithm pure algorithm.

  19. Effect of the size of the quantum region in a hybrid embedded-cluster scheme for zeolite systems

    Energy Technology Data Exchange (ETDEWEB)

    Shor, Alexei M., E-mail: as@icct.ru [Institute of Chemistry and Chemical Technology, Russian Academy of Sciences, 660049 Krasnoyarsk (Russian Federation); Shor, Elena A. Ivanova [Institute of Chemistry and Chemical Technology, Russian Academy of Sciences, 660049 Krasnoyarsk (Russian Federation)] [Siberian Federal University, 660041 Krasnoyarsk (Russian Federation); Laletina, Svetlana [Institute of Chemistry and Chemical Technology, Russian Academy of Sciences, 660049 Krasnoyarsk (Russian Federation); Nasluzov, Vladimir A. [Institute of Chemistry and Chemical Technology, Russian Academy of Sciences, 660049 Krasnoyarsk (Russian Federation)] [Siberian Federal University, 660041 Krasnoyarsk (Russian Federation); Vayssilov, Georgi N., E-mail: gnv@chem.uni-sofia.bg [Faculty of Chemistry, University of Sofia, 1126 Sofia (Bulgaria); Roesch, Notker, E-mail: roesch@mytum.de [Technische Universitaet Muenchen, Department Chemie and Catalysis Research Center, 85747 Garching (Germany)

    2009-09-18

    Recently we presented an improved scheme for constructing the border region within the covEPE hybrid quantum mechanics/molecular mechanics (QM/MM) embedded cluster approach for zeolites and covalent oxides in the framework of the elastic polarizable environment method. In the present study we explored how size and shape of the embedded QM cluster affect the results for structural features, energies, and characteristic vibrational frequencies of two model systems, adsorption complexes of H{sub 2}O and Rh{sub 6} in faujasite frameworks that contain Bronsted acid sites. Comparison of calculated characteristics of different QM cluster models suggests that the local structure and vibrational frequencies of acid sites in adsorbate-free zeolite are well reproduced with all embedded QM clusters, which contain from 5T to 14T atoms. A proper description of systems with an H{sub 2}O adsorbate requires larger QM clusters, with at least 8T atoms, whereas vibrational frequencies of OH groups participating in hydrogen bonds demand even larger quantum clusters, preferably with 12T or 14T atoms. The structure of the metal particle in adsorbed rhodium species is well reproduced with all QM clusters scrutinized, from 12T atoms. Larger QM models, with 18T or 24T atoms, are recommended when one aims at a high accuracy of Rh-O and Rh-H distances and characteristic energies.

  20. Domainwise Web Page Optimization Based On Clustered Query Sessions Using Hybrid Of Trust And ACO For Effective Information Retrieval

    Directory of Open Access Journals (Sweden)

    Dr. Suruchi Chawla

    2015-08-01

    Full Text Available Abstract In this paper hybrid of Ant Colony OptimizationACO and trust has been used for domainwise web page optimization in clustered query sessions for effective Information retrieval. The trust of the web page identifies its degree of relevance in satisfying specific information need of the user. The trusted web pages when optimized using pheromone updates in ACO will identify the trusted colonies of web pages which will be relevant to users information need in a given domain. Hence in this paper the hybrid of Trust and ACO has been used on clustered query sessions for identifying more and more relevant number of documents in a given domain in order to better satisfy the information need of the user. Experiment was conducted on the data set of web query sessions to test the effectiveness of the proposed approach in selected three domains Academics Entertainment and Sports and the results confirm the improvement in the precision of search results.

  1. Optical properties of polysiloxane hybrid thin films containing nano-sized Ag-As-Se chalcogenide clusters

    Science.gov (United States)

    Zha, Congji; Osvath, Peter; Wilson, Gerry; Launikonis, Anton

    2009-02-01

    Chalcogenide glasses are attractive for all-optical signal processing due to their outstanding optical properties, including large optical nonlinearity, a high refractive index and high photosensitivity. In device fabrication, a challenge lies in the difficulty of obtaining thin films with a high stability and good uniformity. In this paper, optical thin films containing nano-sized chalcogenide clusters in polysiloxane matrices are fabricated by a modified plasma deposition process. The optical absorption and luminescence emission properties of the hybrid thin films were characterized by UV-Vis-NIR and fluorescence spectroscopy. Luminescent emission from Ag-As-Se nano-sized clusters was observed for the first time in these nano-hybrid thin films, and the mechanism was discussed.

  2. Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering

    Directory of Open Access Journals (Sweden)

    Suraj

    2015-01-01

    Full Text Available Transferring the brain computer interface (BCI from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG signal reasons us to look toward evolutionary algorithm (EA. Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA and particle swarm optimization (PSO based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD and desynchronization (ERD feature vector is formed.

  3. Eigenspace-based fuzzy c-means for sensing trending topics in Twitter

    Science.gov (United States)

    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.

  4. Categorizing document by fuzzy C-Means and K-nearest neighbors approach

    Science.gov (United States)

    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.

  5. Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Yun-Hao Li

    2015-12-01

    Full Text Available This paper presents a flexible transmission network expansion planning (TNEP approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.

  6. A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

    Science.gov (United States)

    2014-01-01

    Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

  7. A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Chi-Jie Lu

    2014-01-01

    Full Text Available Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA with K-means clustering and support vector regression (SVR. The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

  8. Anomalous Hall effect in epitaxially grown ferromagnetic FeGa/Fe3Ga hybrid structure: Evidence of spin carrier polarized by clusters

    Science.gov (United States)

    Duc Dung, Dang; Cho, Sunglae

    2013-05-01

    The anomalous Hall resistance relative with magnetic anisotropy of clusters Fe3Ga in Fe3Ga/Fe-Ga hybrid structural epitaxial was reported. The out-of-plane magnetic anisotropy was obtained for Fe3Ga/Fe-Ga hybrid structure, while in-plane magnetic anisotropy is shown in the single Fe-Ga phase epitaxial on GaAs(001). The observation of trend of saturation Hall resistance in Fe3Ga/Fe-Ga hybrid structural is compared with the Fe-Ga single crystal, which is solid evidence for spin polarization by local magnetic clusters.

  9. Super-Resolution Imaging and Quantitative Analysis of Membrane Protein/Lipid Raft Clustering Mediated by Cell-Surface Self-Assembly of Hybrid Nanoconjugates.

    Science.gov (United States)

    Hartley, Jonathan M; Chu, Te-Wei; Peterson, Eric M; Zhang, Rui; Yang, Jiyuan; Harris, Joel; Kopeček, Jindřich

    2015-08-17

    Super-resolution imaging was used to quantify organizational changes in the plasma membrane after treatment with hybrid nanoconjugates. The nanoconjugates crosslinked CD20 on the surface of malignant B cells, thereby inducing apoptosis. Super-resolution images were analyzed by using pair-correlation analysis to determine cluster size and to count the average number of molecules in the clusters. The role of lipid rafts was investigated by pre-treating cells with a cholesterol chelator and actin destabilizer to prevent lipid raft formation. Lipid raft cluster size correlated with apoptosis induction after treatment with the nanoconjugates. Lipid raft clusters had radii of ∼ 200 nm in cells treated with the hybrid nanoconjugates. Super-resolution images provided precise molecule location coordinates that could be used to determine density of bound conjugates, cluster size, and number of molecules per cluster.

  10. Investigation of Genetic Distance among Parental Lines of Hybrid Rice Based on Cluster Analysis of Morphological Traits

    Directory of Open Access Journals (Sweden)

    A. Baluch-Zehi

    2013-06-01

    Full Text Available conditions. Thus, these varieties could be suitable option for yield increase and an effective step toward food security. Selection of parental lines has essential role in developing ideal combinations. Therefore, it is essential to study the relationship and genetic diversity among parental lines in hybrid rice. Sixteen hybrid rice parental lines including 6 restorer lines (Poya, Sepidrud, Pajohesh, R2, R9 and IR50 and 5 CMS lines (Neda, Nemat, Dasht, Champa and Amol 3 with their 5 maintainers were studied at Research Farm of Sari Agricultural Sciences and Natural Resources University during 2011. Analysis of variance showed significant variations for all of the studied traits, which shows great diversity among the genotypes. The number of fertile tillers and length to width ratio of grain showed positive and significant correlation with yield. But, grain width showed negative and significant correlation with yield. Results of principal component analysis revealed that 3 components explained 75.64% of the total variations. Cluster analysis at 15 genetic distance criteria grouped genotypes in 4 clusters. In exploration of heterosis phenomenon, parents must be far away from each other. So, the results of this study suggested crosses between CMS lines of Neda A, Nemat A and Champa A with each of restorer lines R9, R2, IR50 and Poya for experimental hybrid seed production.

  11. Carbon nanotubes randomly decorated with gold clusters: from nano{sup 2}hybrid atomic structures to gas sensing prototypes

    Energy Technology Data Exchange (ETDEWEB)

    Charlier, J-C; Zanolli, Z [Unite de Physico-Chimie et de Physique des Materiaux (PCPM), European Theoretical Spectroscopy Facility (ETSF), Universite Catholique de Louvain, Place Croix du Sud 1, B-1348 Louvain-la-Neuve (Belgium); Arnaud, L; Avilov, I V; Felten, A; Pireaux, J-J [Centre de Recherche en Physique de la Matiere et du Rayonnement (PMR-LISE), Facultes Universitaires Notre-Dame de la Paix, 61 Rue de Bruxelles, B-5000 Namur (Belgium); Delgado, M [Sensotran, s.l., Avenida Remolar 31, E-08820 El Prat de Llobregat, Barcelona (Spain); Demoisson, F; Reniers, F [Service de Chimie Analytique et Chimie des Interfaces (CHANI), Universite Libre de Bruxelles, Faculte des Sciences, CP255, Boulevard du Triomphe 2, B-1050 Bruxelles (Belgium); Espinosa, E H; Ionescu, R; Leghrib, R; Llobet, E [Department of Electronic Engineering, Universitat Rovira i Virgili, Avenida Paisos Catalans 26, E-43007 Tarragona (Spain); Ewels, C P; Suarez-Martinez, I [Institut des Materiaux Jean Rouxel (IMN), Universite de Nantes, 2 rue de la Houssiniere-BP 32229, F-44322 Nantes Cedex 3 (France); Guillot, J; Mansour, A; Migeon, H-N [Departement Science et Analyse des Materiaux, Centre de Recherche Public-Gabriel Lippmann, rue du Brill 41, L-4422 Belvaux (Luxembourg); Watson, G E, E-mail: jean-jacques.pireaux@fundp.ac.b [Vega Science Trust, Unit 118, Science Park SQ, Brighton, BN1 9SB (United Kingdom)

    2009-09-16

    Carbon nanotube surfaces, activated and randomly decorated with metal nanoclusters, have been studied in uniquely combined theoretical and experimental approaches as prototypes for molecular recognition. The key concept is to shape metallic clusters that donate or accept a fractional charge upon adsorption of a target molecule, and modify the electron transport in the nanotube. The present work focuses on a simple system, carbon nanotubes with gold clusters. The nature of the gold-nanotube interaction is studied using first-principles techniques. The numerical simulations predict the binding and diffusion energies of gold atoms at the tube surface, including realistic atomic models for defects potentially present at the nanotube surface. The atomic structure of the gold nanoclusters and their effect on the intrinsic electronic quantum transport properties of the nanotube are also predicted. Experimentally, multi-wall CNTs are decorated with gold clusters using (1) vacuum evaporation, after activation with an RF oxygen plasma and (2) colloid solution injected into an RF atmospheric plasma; the hybrid systems are accurately characterized using XPS and TEM techniques. The response of gas sensors based on these nano{sup 2}hybrids is quantified for the detection of toxic species like NO{sub 2}, CO, C{sub 2}H{sub 5}OH and C{sub 2}H{sub 4}.

  12. SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

    Energy Technology Data Exchange (ETDEWEB)

    Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu, E-mail: hatt@univ-brest.fr [LaTIM, INSERM, UMR 1101, Brest 29609 (France); Pradier, Olivier [LaTIM, INSERM, UMR 1101, Brest 29609, France and Radiotherapy Department, CHRU Morvan, Brest 29609 (France); Cheze Le Rest, Catherine [DACTIM University of Poitiers, Nuclear Medicine Department, CHU Milétrie, Poitiers 86021 (France)

    2015-10-15

    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 basis 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.

  13. Selection of Superior Genotypes of Coffea Canephora Pierre on ControlledHybrid Population Using Cluster Analysis Method

    Directory of Open Access Journals (Sweden)

    Ucu Sumirat

    2007-05-01

    Full Text Available Selection of superior genotypes of robusta coffee (Coffea canephora to improve its important agronomic characters should be conducted continuously to get better planting productivity. The aim of this research was to select superior genotypes of Robusta coffee for high yield and high proportion of large bean. Selection was conducted on controlled hybrid populations, developed from three crossing parental clones, i.e. BP 961 x Q 121 (A, BP 409 x Q 121 (B and BP 961 x BP 409 (C. Selection was done by applying cluster analysis with complete linkage and Euclidean distance as the clustering method. The result of the research showed that the selection was successful to identify superior genotypes of Robusta coffee for high yield and high proportion of large bean. The parameters used (cherries weight/tree, bean weight/tree, bean size percentage > 6.5 mm and 100 cherries weight were effective in clustering the superior genotypes, indicated by increased minimum and average value of population. Yield potential and percentage of bean size > 6.5 mm of those genotypes were having better performance than the control genotype and its parent. The selection code A 95, B 28, B 62, B 66, B 74 and C 38 were considered  as promising superior genotypes of Robusta coffee, respectively. Key words: Coffea canephora, selection, bean size, yield, cluster analysis

  14. Design and synthesis of "dumb-bell" and "triangular" inorganic-organic hybrid nanopolyoxometalate clusters and their characterisation through ESI-MS analyses.

    Science.gov (United States)

    Pradeep, Chullikkattil P; Li, Feng-Yan; Lydon, Claire; Miras, Haralampos N; Long, De-Liang; Xu, Lin; Cronin, Leroy

    2011-06-27

    A series of tris(hydroxymethyl)aminomethane (TRIS)-based linear (bis(TRIS)) and triangular (tris(TRIS)) ligands has been synthesised and were covalently attached to the Wells-Dawson type cluster [P(2)V(3)W(15)O(62)](9-) to generate a series of nanometer-sized inorganic-organic hybrid polyoxometalate clusters. These huge hybrids, with a molecular mass similar to that of small proteins in the range of ≈10-16 kDa, were unambiguously characterised by using high-resolution ESI-MS. The ESI-MS spectra of these compounds revealed, in negative ion mode, a characteristic pattern showing distinct groups of peaks corresponding to different anionic charge states ranging from 3(-) to 8(-) for the hybrids. Each peak in these individual groups could be unambiguously assigned to the corresponding hybrid cluster anion with varying combinations of tetrabutylammonium (TBA) and other cations. This study therefore highlights the prowess of the high-resolution ESI-MS for the unambiguous characterisation of large, nanoscale, inorganic-organic hybrid clusters that have huge mass, of the order of 10-16 kDa. Also, the designed synthesis of these compounds points to the fact that we were able to achieve a great deal of structural pre-design in the synthesis of these inorganic-organic hybrid polyoxometalates (POMs) by means of a ligand design route, which is often not possible in traditional "one-pot" POM synthesis.

  15. A Hybrid LBFGS-DE Algorithm for Global Optimization of the Lennard-Jones Cluster Problem

    Directory of Open Access Journals (Sweden)

    Ernesto Padernal Adorio

    2004-12-01

    Full Text Available The Lennard-Jones cluster conformation problem is to determine a configuration of n atoms in three-dimensional space where the sum of the nonlinear pairwise potential function is at a minimum. In this formula, ri,j is the distance between atoms i and j. This optimization problem is a severe test for global optimization algorithms due to its computational complexity: the number of local minima grows exponentially large as the number of atoms in the cluster is increased. As a specific test case, a better cluster configuration than the previously published putative minimum for the 38-atom case was found in the mid-1990s.

  16. Polymerizable Ionic Liquid Crystals Comprising Polyoxometalate Clusters toward Inorganic-Organic Hybrid Solid Electrolytes

    Directory of Open Access Journals (Sweden)

    Takeru Ito

    2017-07-01

    Full Text Available Solid electrolytes are crucial materials for lithium-ion or fuel-cell battery technology due to their structural stability and easiness for handling. Emergence of high conductivity in solid electrolytes requires precise control of the composition and structure. A promising strategy toward highly-conductive solid electrolytes is employing a thermally-stable inorganic component and a structurally-flexible organic moiety to construct inorganic-organic hybrid materials. Ionic liquids as the organic component will be advantageous for the emergence of high conductivity, and polyoxometalate, such as heteropolyacids, are well-known as inorganic proton conductors. Here, newly-designed ionic liquid imidazolium cations, having a polymerizable methacryl group (denoted as MAImC1, were successfully hybridized with heteropolyanions of [PW12O40]3− (PW12 to form inorganic-organic hybrid monomers of MAImC1-PW12. The synthetic procedure of MAImC1-PW12 was a simple ion-exchange reaction, being generally applicable to several polyoxometalates, in principle. MAImC1-PW12 was obtained as single crystals, and its molecular and crystal structures were clearly revealed. Additionally, the hybrid monomer of MAImC1-PW12 was polymerized by a radical polymerization using AIBN as an initiator. Some of the resulting inorganic-organic hybrid polymers exhibited conductivity of 10−4 S·cm−1 order under humidified conditions at 313 K.

  17. Decisive Interactions between the Heterocyclic Moiety and the Cluster Observed in Polyoxometalate-Surfactant Hybrid Crystals

    Directory of Open Access Journals (Sweden)

    Saki Otobe

    2015-04-01

    Full Text Available Inorganic-organic hybrid crystals were successfully obtained as single crystals by using polyoxotungstate anion and cationic dodecylpyridazinium (C12pda and dodecylpyridinium (C12py surfactants. The decatungstate (W10 anion was used as the inorganic component, and the crystal structures were compared. In the crystal comprising C12pda (C12pda-W10, the heterocyclic moiety directly interacted with W10, which contributed to a build-up of the crystal structure. On the other hand, the crystal consisting of C12py (C12py-W10 had similar crystal packing and molecular arrangement to those in the W10 crystal hybridized with other pyridinium surfactants. These results indicate the significance of the heterocyclic moiety of the surfactant to construct hybrid crystals with polyoxometalate anions.

  18. Decisive interactions between the heterocyclic moiety and the cluster observed in polyoxometalate-surfactant hybrid crystals.

    Science.gov (United States)

    Otobe, Saki; Fujioka, Natsumi; Hirano, Takuro; Ishikawa, Eri; Naruke, Haruo; Fujio, Katsuhiko; Ito, Takeru

    2015-04-16

    Inorganic-organic hybrid crystals were successfully obtained as single crystals by using polyoxotungstate anion and cationic dodecylpyridazinium (C12pda) and dodecylpyridinium (C12py) surfactants. The decatungstate (W10) anion was used as the inorganic component, and the crystal structures were compared. In the crystal comprising C12pda (C12pda-W10), the heterocyclic moiety directly interacted with W10, which contributed to a build-up of the crystal structure. On the other hand, the crystal consisting of C12py (C12py-W10) had similar crystal packing and molecular arrangement to those in the W10 crystal hybridized with other pyridinium surfactants. These results indicate the significance of the heterocyclic moiety of the surfactant to construct hybrid crystals with polyoxometalate anions.

  19. A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Barari, Amin

    2011-01-01

    for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...

  20. Using Quadtree Algorithm for Improving Fuzzy C-means Method in Image Segmentation

    OpenAIRE

    Zahra Ghorbanzad; Farshid Babapour

    2012-01-01

    Image segmentation is an essential processing step for much image application and there are a large number of segmentation techniques. A new algorithm for image segmentation called Quad tree fuzzy c-means (QFCM) is presented I this work. The key idea in our approach is a Quad tree function combined with fuzzy c-means algorithm. In this article we also discuss the advantages and disadvantages of other image segmenting methods like: k-means, c-means, and blocked fuzzy c-means. Different experim...

  1. Performance Characteristics of Hybrid MPI/OpenMP Implementations of NAS Parallel Benchmarks SP and BT on Large-Scale Multicore Clusters

    KAUST Repository

    Wu, X.

    2011-07-18

    The NAS Parallel Benchmarks (NPB) are well-known applications with fixed algorithms for evaluating parallel systems and tools. Multicore clusters provide a natural programming paradigm for hybrid programs, whereby OpenMP can be used with the data sharing with the multicores that comprise a node, and MPI can be used with the communication between nodes. In this paper, we use Scalar Pentadiagonal (SP) and Block Tridiagonal (BT) benchmarks of MPI NPB 3.3 as a basis for a comparative approach to implement hybrid MPI/OpenMP versions of SP and BT. In particular, we can compare the performance of the hybrid SP and BT with the MPI counterparts on large-scale multicore clusters, Intrepid (BlueGene/P) at Argonne National Laboratory and Jaguar (Cray XT4/5) at Oak Ridge National Laboratory. Our performance results indicate that the hybrid SP outperforms the MPI SP by up to 20.76 %, and the hybrid BT outperforms the MPI BT by up to 8.58 % on up to 10 000 cores on Intrepid and Jaguar. We also use performance tools and MPI trace libraries available on these clusters to further investigate the performance characteristics of the hybrid SP and BT. © 2011 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

  2. Reliable Radiation Hybrid Maps: An Efficient Scalable Clustering-based Approach

    Science.gov (United States)

    The process of mapping markers from radiation hybrid mapping (RHM) experiments is equivalent to the traveling salesman problem and, thereby, has combinatorial complexity. As an additional problem, experiments typically result in some unreliable markers that reduce the overall quality of the map. We ...

  3. A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

    Directory of Open Access Journals (Sweden)

    Farshad Faezy Razi

    2014-06-01

    Full Text Available Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

  4. STUDI SIMULASI MENGGUNAKAN FUZZY C-MEANS DALAM MENGKLASIFIKASI KONSTRUK TES

    Directory of Open Access Journals (Sweden)

    Rukli Rukli

    2013-01-01

    Full Text Available Tulisan ini memperkenalkan metode fuzzy c-means dalam mengklasifikasi konstruk tes. Untuk memverifikasi sifat unidimensional suatu tes biasanya menggunakan analisis faktor sebagai bagian dari statistik parametrik dengan beberapa persyaratan yang ketat sedangkan metode fuzzy c-means termasuk metode heuristik yang tidak memerlukan persyaratan yang ketat. Studi simulasi penelitian ini menggunakan dua metode yakni analisis faktor menggunakan program SPSS dan fuzzy c-means menggunakan program Matlab. Data simulasi menggunakan tipe data dikotomi dan politomi yang dibangkitkan lewat prog-ram Microsoft Office Excel dengan desain 2 kategori, yakni: tiga butir soal dengan banyak peserta tes 10, dan 30 butir soal dengan banyak peserta tes 100. Hasil simulasi menunjukkan bahwa metode fuzzy c-means lebih memberikan gambaran pengelompokan secara deskriptif dan dinamis pada semua desain yang telah dibuat dalam memverifikasi unidimensional pada suatu tes. Kata kunci: fuzzy c-means, analisis faktor, unidimensional _____________________________________________________________ SIMULATION STUDY USING FUZZY C-MEANS FOR CLASIFYING TEST CONSTRUCTION Abstract This paper introduces the fuzzy c-means method for classifying the test constructs. To verify the unidimensional a test typically uses factor analysis as part of parametric statistics with some strict requirements, while fuzzy c-means methods including method heuristic that do not require strict require-ments. Simulation comparison between the method of factor analysis using SPSS program and fuzzy c-means methods using Matlab. Simulation data using data type dichotomy and politomus generated through Microsoft Office Excel programs each with a number of 3 items using the number of participants 10 tests, while the number of 30 test items using the number as many as 100 participants. The simulation results show that the fuzzy c-means method provides a more descriptive and dyna-mic grouping of all the designs that

  5. Cluster Analysis of Comparative Genomic Hybridization (CGH Data Using Self-Organizing Maps: Application to Prostate Carcinomas

    Directory of Open Access Journals (Sweden)

    Torsten Mattfeldt

    2001-01-01

    Full Text Available Comparative genomic hybridization (CGH is a modern genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20–30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self‐organizing map (Genecluster as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data. We studied a group of 40 recent cases without follow‐up, an older group of 20 cases with follow‐up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance.

  6. Super-distant molecular hybridization of plant seeds by ion beam-mediated gene cluster

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The N beam-mediated distant molecular hybridization between Ginkgo biloba I and watermelon was studied. The results showed that the ester gene of Ginkgo biloba L was successfully expressed in two varieties of watermelon. 3-16 and SR2-14-2, in both of which the ester quantities were measured as 17.0756 μg/g and 45.9998 μg/g respectively. Meanwhile, superoxide dismutase (SOD) activity in leaves of the watennelon expressing ester gene was increased twofold as compared to that of the control, showing that ion beam could mediate distant and/or super-distant donor gene expression in the cells of a receptor. Furthermore, the molecular nechanism of distant hybridization was analyzed.

  7. Evaluation of the courtship and of the hybrid male sterility among Drosophila buzzatii cluster species (Diptera, Drosophilidae

    Directory of Open Access Journals (Sweden)

    MACHADO L. P. de B.

    2002-01-01

    Full Text Available In the Drosophila repleta group the establishment of subgroups and complexes made on the basis of morphological and cytological evidences is supported by tests of reproductive isolation. Among species in the repleta group, the buzzatii cluster, due to its polymorphism and polytipism, is an excellent material for ecological and speciation studies. Some interspecific crosses involving Drosophila seriema, Drosophila sp. B, D. koepferae and D. buzzatii strains were completely sterile while others involving strains from these species produced F1 hybrids that did not yield F2. In the present work, data on courtship duration and copula occurrence obtained in the analysis of flies from parental sterile crosses and on spermatozoon mobility observed in F1 hybrids that did not yield F2 are presented. Copula did not occur during one hour of observation and the spermatozoon also did not show mobility at any of the analyzed stages (3, 7, 9 and 10 days old. There was a high variation in courtship average duration and in the percentage of males that courted the females. The reproductive isolation mechanisms indicated by these observations were pre and post-zygotic, as supported by the absence of copula and male sterility. Data obtained also showed the occurrence of different degrees of reproductive compatibility among the strains classified as the same species but from distinct geographic localities.

  8. Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network

    OpenAIRE

    Deepak Rathore; Anurag Jain

    2012-01-01

    In current scenario of internet technology security is big challenge. Internet network threats by various cyber-attack and loss the system data and degrade the performance of host computer. In this sense intrusion detection are challenging field of research in concern of network security based on firewall and some rule based detection technique. In this paper we proposed an Ensemble Cluster Classification technique using som network for detection of mixed variable data generated by malicious ...

  9. A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images.

    Science.gov (United States)

    Rastgarpour, Maryam; Shanbehzadeh, Jamshid; Soltanian-Zadeh, Hamid

    2014-08-01

    medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

  10. {ital s} -{ital p} Hybridization and Electron Shell Structures in Aluminum Clusters: A Photoelectron Spectroscopy Study

    Energy Technology Data Exchange (ETDEWEB)

    Li, X.; Wu, H.; Wang, X.; Wang, L. [Department of Physics, Washington State University, 2710 University Drive, Richland, Washington 99352-1671 (United States)]|[W. R. Wiley Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, MS K8-88, P.O. Box 999, Richland, Washington 99352 (United States)

    1998-08-01

    Using photoelectron spectroscopy of size-selected Al{sub x}{sup {minus}} (x=1{endash}162) clusters, we studied the electronic structure evolution of Al{sub x} and observed that the Al 3s - and 3p -derived bands evolve and broaden with cluster size and begin to overlap at Al{sub 9} . Direct spectroscopic signatures were obtained for electron shell structures with spherical shell closings at Al{sub 11}{sup {minus}} , Al{sub 13}{sup {minus}} , Al{sub 19}{sup {minus}} , Al{sub 23}{sup {minus}} , Al{sub 35}{sup {minus}} , Al{sub 37}{sup {minus}} , Al{sub 46} , Al{sub 52} , Al{sub 55}{sup {minus}} , Al{sub 56} , Al{sub 66} , and Al{sub 73}{sup {minus}} . The electron shell effect diminishes above Al{sub 75} and new spectral features appearing in Al{sub x}{sup {minus}} (x{gt}100) suggest a possible geometrical packing effect in large clusters. {copyright} {ital 1998} {ital The American Physical Society}

  11. A Novel Cluster-head Selection Algorithm Based on Hybrid Genetic Optimization for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Lejiang Guo

    2011-05-01

    Full Text Available Wireless Sensor Networks (WSN represent a new dimension in the field of network research. The cluster algorithm can significantly reduce the energy consumption of wireless sensor networks and prolong the network lifetime. This paper uses neuron to describe the WSN node and constructs neural network model for WSN. The neural network model includes three aspects: WSN node neuron model, WSN node control model and WSN node connection model. Through learning the framework of cluster algorithm for wireless sensor networks, this paper presents a weighted average of cluster-head selection algorithm based on an improved Genetic Optimization which makes the node weights directly related to the decision-making predictions. The Algorithm consists of two stages: single-parent evolution and population evolution. The initial population is formed in the stage of single-parent evolution by using gene pool, then the algorithm continues to the next further evolution process, finally the best solution will be generated and saved in the population. The simulation results illustrate that the new algorithm has the high convergence speed and good global searching capacity. It is to effectively balance the network energy consumption, improve the network life-cycle, ensure the communication quality and provide a certain theoretical foundation for the applications of the neural networks.

  12. Fuzzy Clustering

    DEFF Research Database (Denmark)

    Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan

    2000-01-01

    A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...

  13. Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network

    Directory of Open Access Journals (Sweden)

    Deepak Rathore

    2012-09-01

    Full Text Available In current scenario of internet technology security is bigchallenge. Internet network threats by various cyber-attackand loss the system data and degrade the performance ofhost computer. In this sense intrusion detection arechallenging field of research in concern of networksecurity based on firewall and some rule based detectiontechnique. In this paper we proposed an Ensemble ClusterClassification technique using som network for detectionof mixed variable data generated by malicious software forattack purpose in host system. In our methodology SOMnetwork control the iteration of distance of differentparameters of ensembling our experimental result showthat better empirical evaluation on KDD data set 99 incomparison of existing ensemble classifier.

  14. A hybrid Jacobi-Davidson method for interior cluster eigenvalues with large null-space in three dimensional lossless Drude dispersive metallic photonic crystals

    Science.gov (United States)

    Huang, Tsung-Ming; Lin, Wen-Wei; Wang, Weichung

    2016-10-01

    We study how to efficiently solve the eigenvalue problems in computing band structure of three-dimensional dispersive metallic photonic crystals with face-centered cubic lattices based on the lossless Drude model. The discretized Maxwell equations result in large-scale standard eigenvalue problems whose spectrum contains many zero and cluster eigenvalues, both prevent existed eigenvalue solver from being efficient. To tackle this computational difficulties, we propose a hybrid Jacobi-Davidson method (hHybrid) that integrates harmonic Rayleigh-Ritz extraction, a new and hybrid way to compute the correction vectors, and a FFT-based preconditioner. Intensive numerical experiments show that the hHybrid outperforms existed eigenvalue solvers in terms of timing and convergence behaviors.

  15. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tao Ma

    2016-10-01

    Full Text Available The development of intrusion detection systems (IDS that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC and deep neural network (DNN algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN, support vector machine (SVM, random forest (RF and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  16. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

    Science.gov (United States)

    Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

    2016-10-13

    The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  17. SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction

    Directory of Open Access Journals (Sweden)

    Anna Saro Vijendran

    2014-12-01

    Full Text Available Data mining is a hype-word and its major goal is to extract the information from the dataset and convert it into readable format. Web mining is one of the applications of data mining which helps to extract the web page. Personalized image was retrieved in existing systems by using tag-annotation-demand ranking for image retrieval (TAD where image uploading, query searching, and page refreshing steps were taken place. In the proposed work, both the image and web page are retrieved by several techniques. Two major steps are followed in this work, where the primary step is server database upload. Herein, database for both image and content are stored using block acquiring page segmentation (BAPS. The subsequent step is to extract the image and content from the respective server database. The subsequent database is further applied into semantic annotation based clustering (SANB (for image and semantic based clustering (SEB (for content. The experimental results show that the proposed approach accurately retrieves both the images and relevant pages.

  18. Cluster SIMS with a hybrid quadrupole time-of-flight mass spectrometer

    Science.gov (United States)

    Carado, A.; Kozole, J.; Passarelli, M.; Winograd, N.; Loboda, A.; Wingate, J.

    2008-12-01

    The new physics associated with cluster SIMS, i.e. reduced chemical damage enabling 3D dynamic imaging, and increased ion yields from organics samples, suggests that cluster sources may be suitable for use on commercial MALDI/electrospray (ESI) instruments. In efforts to investigate this approach to secondary ion analysis, a 20 keV C 60+ primary ion source by Ionoptika Ltd. was fitted to a commercial LC/MS/MS instrument; the QSTAR ® XL system by Applied Biosystems/MDS Sciex. This instrument is capable of MS/MS, ion trapping, chemical imaging, and utilizes an orthogonal ToF, enabling use of a DC primary ion beam for imaging and data collection. The system employs high nitrogen pressure, typically several millitorr, in the sample region, as opposed to large extraction voltages, to facilitate the transmission of the secondary ions to the ToF region. In these initial experiments, it was demonstrated that ion signal generated by C 60+ bombardment can be enhanced by trapping in the collision cell and that secondary ions can fragment via collision induced dissociation (CID) to yield MS/MS information. In ToF-MS mode, efficiencies are comparable with pulsed primary beam ToF-SIMS instruments. Mass resolution of over 12,000 is routinely observed with mass accuracy in the 2 ppm range, which has important implications in accurate ion mapping in imaging mode.

  19. Cluster SIMS with a hybrid quadrupole time-of-flight mass spectrometer

    Energy Technology Data Exchange (ETDEWEB)

    Carado, A. [Pennsylvania State University, 104 Chemistry Building, University Park, PA 16802 (United States)], E-mail: ajc161@psu.edu; Kozole, J.; Passarelli, M.; Winograd, N. [Pennsylvania State University, 104 Chemistry Building, University Park, PA 16802 (United States); Loboda, A.; Wingate, J. [Applied Biosystems/MDS Sciex, 71 Four Valley Drive, Concord, Ontario, CA (United States)

    2008-12-15

    The new physics associated with cluster SIMS, i.e. reduced chemical damage enabling 3D dynamic imaging, and increased ion yields from organics samples, suggests that cluster sources may be suitable for use on commercial MALDI/electrospray (ESI) instruments. In efforts to investigate this approach to secondary ion analysis, a 20 keV C{sub 60}{sup +} primary ion source by Ionoptika Ltd. was fitted to a commercial LC/MS/MS instrument; the QSTAR XL system by Applied Biosystems/MDS Sciex. This instrument is capable of MS/MS, ion trapping, chemical imaging, and utilizes an orthogonal ToF, enabling use of a DC primary ion beam for imaging and data collection. The system employs high nitrogen pressure, typically several millitorr, in the sample region, as opposed to large extraction voltages, to facilitate the transmission of the secondary ions to the ToF region. In these initial experiments, it was demonstrated that ion signal generated by C{sub 60}{sup +} bombardment can be enhanced by trapping in the collision cell and that secondary ions can fragment via collision induced dissociation (CID) to yield MS/MS information. In ToF-MS mode, efficiencies are comparable with pulsed primary beam ToF-SIMS instruments. Mass resolution of over 12,000 is routinely observed with mass accuracy in the 2 ppm range, which has important implications in accurate ion mapping in imaging mode.

  20. Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network

    Directory of Open Access Journals (Sweden)

    Deepak Rathore

    2012-09-01

    Full Text Available In current scenario of internet technology security is big challenge. Internet network threats by various cyber-attack and loss the system data and degrade the performance of host computer. In this sense intrusion detection are challenging field of research in concern of network security based on firewall and some rule based detection technique. In this paper we proposed an Ensemble Cluster Classification technique using som network for detection of mixed variable data generated by malicious software for attack purpose in host system. In our methodology SOM network control the iteration of distance of different parameters of ensembling our experimental result show that better empirical evaluation on KDD data set 99 in comparison of existing ensemble classifier.

  1. HiSC: A Hybrid XML Index Composing Structure-Encoded with Cluster

    Institute of Scientific and Technical Information of China (English)

    YANG Jincai; ZHANG Lin

    2007-01-01

    A new way of indexing and processing twig patterns in an XML documents is proposed in this paper. Every path in XML document can be transformed into a sequence of labels by Structure-Encoded that constructs a one-to-one correspondence between XML tree and sequence. Base on identifying characteristics of nodes in XML tree, the elements are classified and clustered. During query proceeding, the twig pattern is also transformed into its Structure-Encoded. By performing subsequence matching on the set of sequences in XML documents, all the occurrences of path in the XML documents are refined. Using the index, the numbers of elements retrieved are minimized. The search results with pertinent format provide more structure information without any false dismissals or false alarms. The index also supports keyword search.Experiment results indicate the index has significantly efficiency with high precision.

  2. Radiation hybrid mapping of a cytokine gene cluster located in the proximal region of 5q

    Energy Technology Data Exchange (ETDEWEB)

    Segal, A.L.; McPherson, J.D.; Wasmuth, J.J. [Univ. of California, Irvine (United States)

    1994-09-01

    The long (q) arm of chromosome 5 has been shown to contain a large number of genes encoding growth factors, growth factor receptors, hormone receptors and neurotransmitter receptors. IL-3, IL-4, IL-5, IL-9, IL-13, GM-CSF and IRF-1 are located in the 5q22-31.1 interval, while three GABA receptors map to 5q33-34. A number of receptors, including the prolactin and growth hormone receptors, the IL-7 receptor and the leukemia inhibitory factor receptor, map to proximal 5p. Genes encoding three of the complement components, C6, C7 and C9, are also located in the same region. YAC data indicates that C6 and C7 lie within 170 kb of each other. We have used a panel of 180 Chinese hamster-human radiation hybrids possessing fragments of human chromosome 5 to construct a physical map of this region of 5q. Two-point and multi-point analyses were done on the data and significant LOD scores (from 3 to 30) were observed. LIFR, PRLR, GHR, IL-7R, C6, C7, C9, TARS, and a number of CEPH-Genethon dinucleotide repeat markers were ordered and mapped. Yeast artificial chromosomes and cosmids have been isolated and inter-Alu PCR products from them are being used to construct a contig and to improve the physical map. The long term goal of this work is to identify and characterize new genes in the region.

  3. A Hybrid Technique Based on Combining Fuzzy K-means Clustering and Region Growing for Improving Gray Matter and White Matter Segmentation

    Directory of Open Access Journals (Sweden)

    Ashraf Afifi

    2012-07-01

    Full Text Available In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM and white matter (WM tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI datasets. The experimental results show that the proposed technique produces accurate and stable results.

  4. Low-temperature electronic transport in one-dimensional hybrid systems: Metal cluster embedded carbon nanotubes

    Science.gov (United States)

    Soldano, Caterina

    The investigation of the electronic and magnetotransport properties at low temperature in individual MWNT with embedded clusters are here presented. The majority of studies of transport in MWNT reported in literature has been carried out on arc-discharge grown tubes, generally considered "clean" and defect-free. In this project, individual MWNT grown in alumina template are used; these tubes are highly disordered compared for example to arc-discharge ones, conditions that dramatically will impact the charge transport. As-fabricated devices are in general highly resistive. A large decrease in the value of the device resistance can be achieved through a controlled and fast high-bias sweep method (HBT) across the sample. Scanning electron microscopy analysis shows that this method induces a metal (platinum) decoration of the MWNT surface as a consequence of the large amount of Joule heating developed during the sweep. Temperature dependence study (5wires. DFT calculations show that the enhancement in conductance can be explained in term of enhanced density of states around the Fermi energy due to presence of platinum on the wall. Magneto-transport measurements carried out up to a value of magnetic field up to |5|T show a clear dependence from the energy (i.e. applied bias). A nearly symmetric and monotonically increasing positive magneto-conductance is observed in the range of the applied field, confirming the presence of weak localization in the system. A small but distinct Rashba spin-orbit scattering effect in the magneto-conductance in the low-field regime (|B|<.5T) is found and attributed to the surface decoration. Electronic and magnetotransport measurements independently confirm the 1D nature of the transport in the system. "Zero-field" measurements were performed on magnetic cluster-embedded MWNT-based devices (FM-MWNT). Temperature dependence of the conductance reveals a Luttinger liquid type of behavior in the range of investigated temperatures but no

  5. Complex Contagions and hybrid phase transitions in unclustered and clustered random networks

    CERN Document Server

    Miller, Joel C

    2015-01-01

    A complex contagion is an infectious process in which an individual may require multiple transmissions. We typically think of individuals beginning inactive and becoming active once they are contacted by sufficient numbers of active partners. These have been studied in a number of contexts, but the analytic models for dynamic spread of complex contagions are typically complex. Here we study the dynamics of a generalized Watts Threshold Model (gWTM). We first show that a wide range of other processes can be thought of as a special case of this gWTM. Then we adapt an "edge-based compartmental modeling" approach used for infectious diseases in networks to develop and analyze analytic models for the dynamics the gWTM in configuration model and a class of random clustered (triangle-based) networks. The resulting model is relatively simple and compact, and we use this model to gain insights into the dynamics. Under some conditions a cascade can happen with an arbitrarily small initial proportion active, and we deri...

  6. Novel Cluster Validity Index for FCM Algorithm

    Institute of Scientific and Technical Information of China (English)

    Jian Yu; Cui-Xia Li

    2006-01-01

    How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means (FCM). In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.

  7. A new method based on Dempster-Shafer theory and fuzzy c-means for brain MRI segmentation

    Science.gov (United States)

    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.

  8. A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm

    DEFF Research Database (Denmark)

    Nadernejad, E; Barari, Amin

    2011-01-01

    for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm......-segmentation and produced better experimental results than those obtained with other pixon-based algorithms....

  9. A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

    In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    P. Akhavan

    2014-10-01

    Full Text Available 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.

  12. Model of spatial analysis of electric power market using the Fuzzy C-Means technical classification; Modelo de analise espacial de mercado de energia eletrica utilizando a tecnica de classificacao Fuzzy C-Means

    Energy Technology Data Exchange (ETDEWEB)

    Neto, J.C. [Companhia Energetica de Goias (CELG-D), Goiania, GO (Brazil)], E-mail: joao.cn@celg.com.br; Lima, W.S. [Votorantim Siderurgia, Resende, Rio de Janeiro, RJ (Brazil). Gerencia Geral de Tecnologia], E-mail: wagner.lima@vmetais.com.br

    2009-07-01

    The power distribution companies live with an antagonistic reality: an increasing energy demand, due to steady economic and population growth, and a limitation on their financial resources to expand its network. Therefore, it is essential an improvement in activity of planning of the power distribution system trying to improve the application of available resources. In this context fits the application of Geographic Information System combined with clustering techniques and classification in order to enhance the planning process, giving the planner a more complete picture of the consumer market by the distributor. This paper presents a system that makes use of Geographic Information System combined with the technique of clustering and classification Fuzzy C-Means, with the aim of analyzing the distribution of network load and the performance of the technique. Each group performed leads to a spatial representation (scenario). This, together with an index measuring the performance of the group (intra-group and inter-group) implemented in this work, provides a favorable environment for spatial analysis of the electric power market.

  13. New Embedded Denotes Fuzzy C-Mean Application for Breast Cancer Density Segmentation in Digital Mammograms

    Science.gov (United States)

    Othman, Khairulnizam; Ahmad, Afandi

    2016-11-01

    In this research we explore the application of normalize denoted new techniques in advance fast c-mean in to the problem of finding the segment of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if new denotes fuzzy c- mean algorithm could separate different densities for the different breast patterns. The new density segmentation is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. New denotes fuzzy c- mean have been explored on images of various imaging modalities but not on huge format digital mammograms just yet. Therefore, this project is mainly focused on using normalize denoted new techniques employed in fuzzy c-mean to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database and Trueta Database.

  14. Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill

    Institute of Scientific and Technical Information of China (English)

    JosAngel BARRIOS; Csar VILLANUEVA; Alberto CAVAZOS; Rafael COLS

    2016-01-01

    Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes.The fuzzy C-means algorithm was evaluated for rule-base generation for fuzzy and fuzzy grey-box temperature estimation.Experimental data were collected from a real-life mill and three different sets were randomly drawn.The first set was used for rule-generation,the second set was used for training those systems with learning capabilities,while the third one was used for validation.The perform-ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant.The results show that the fuzzy C-means generated rule-bases improve temperature estimation;however,the best results are obtained when fuzzy C-means algorithm,grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.

  15. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET.

    Science.gov (United States)

    Belhassen, Saoussen; Zaidi, Habib

    2010-03-01

    Accurate and robust image segmentation was identified as one of the most challenging issues facing PET quantification in oncological imaging. This difficulty is compounded by the low spatial resolution and high noise characteristics of PET images. The fuzzy C-means (FCM) clustering algorithm was largely used in various medical image segmentation approaches. However, the algorithm is sensitive to both noise and intensity heterogeneity since it does not take into account spatial contextual information. To overcome this limitation, a new fuzzy segmentation technique adapted to typical noisy and low resolution oncological PET data is proposed. PET images smoothed using a nonlinear anisotropic diffusion filter are added as a second input to the proposed FCM algorithm to incorporate spatial information (FCM-S). In addition, a methodology was developed to integrate the a trous wavelet transform in the standard FCM algorithm (FCM-SW) to allow handling of heterogeneous lesions' uptake. The algorithm was applied to the simulated data of the NCAT phantom, incorporating heterogeneous lesions in the lung and clinical PET/CT images of 21 patients presenting with histologically proven nonsmall-cell lung cancer (NSCLC) and 7 patients presenting with laryngeal squamous cell carcinoma (LSCC) to assess its performance for segmenting tumors with arbitrary size, shape, and tracer uptake. For NSCLC patients, the maximal tumor diameters measured from the macroscopic examination of the surgical specimen served as the ground truth for comparison with the maximum diameter estimated by the segmentation technique, whereas for LSCC patients, the 3D macroscopic tumor volume was considered as the ground truth for comparison with the corresponding PET-based volume. The proposed algorithm was also compared to the classical FCM segmentation technique. There is a good correlation (R2 = 0.942) between the actual maximal diameter of primary NSCLC tumors estimated using the proposed PET segmentation

  16. A Hybrid Constrained Semi-Supervised Clustering Algorithm%一种混合约束的半监督聚类算法

    Institute of Scientific and Technical Information of China (English)

    李雪梅; 王立宏; 宋宜斌

    2011-01-01

    提出一种混合约束的半监督聚类算法(HCC),综合考虑标号点和成对点约束信息的作用,使两种先验信息在聚类的过程中能以不同的方式发挥作用.给出理论推导、具体算法步骤、实验及分析.实验表明在HCC算法中,标号点对提高聚类结果的作用要比成对点约束信息的作用更明显,算法得到的CRI、聚类数、运行时间等多项指标都比对比算法好.%A hybrid constrained semi-supervised clustering algorithm (HCC) is proposed based on consistency algorithm. To get a better clustering result, both labeled data and pairwise constraints are considered in clustering to make use of two types of prior knowledge supplementary to each other. The theoretical derivation and the algorithm are presented in detail. Experimental results show that labeled data outperform pairwise constraints in promoting the quality of clustering. Additionally, for many indices, such as CRI, number of clusters and running time, HCC is better than comparative algorithms.

  17. Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain

    OpenAIRE

    S.Madhukumar; N. Santhiyakumari

    2015-01-01

    This paper does the qualitative comparison of Fuzzy C-means (FCM) and k-Means segmentation, with histogram guided initialization, on tumor edema complex MR images. The accuracy of any segmentation scheme depends on its ability to distinguish different tissue classes, separately. Hence, there is a serious pre-requisite to evaluate this ability before employing the segmentation scheme on medical images. This paper evaluates the ability of FCM and k-Means to segment Gray Matter (GM), White Matte...

  18. An incremental clustering algorithm based on Mahalanobis distance

    Science.gov (United States)

    Aik, Lim Eng; Choon, Tan Wee

    2014-12-01

    Classical fuzzy c-means clustering algorithm is insufficient to cluster non-spherical or elliptical distributed datasets. The paper replaces classical fuzzy c-means clustering euclidean distance with Mahalanobis distance. It applies Mahalanobis distance to incremental learning for its merits. A Mahalanobis distance based fuzzy incremental clustering learning algorithm is proposed. Experimental results show the algorithm is an effective remedy for the defect in fuzzy c-means algorithm but also increase training accuracy.

  19. Fuzzy clustering, genetic algorithms and neuro-fuzzy methods compared for hybrid fuzzy-first principles modeling

    NARCIS (Netherlands)

    van Lith, Pascal; van Lith, P.F.; Betlem, Bernardus H.L.; Roffel, B.

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and

  20. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and

  1. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe

  2. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe

  3. Document Clustering Based on Semi-Supervised Term Clustering

    Directory of Open Access Journals (Sweden)

    Hamid Mahmoodi

    2012-05-01

    Full Text Available The study is conducted to propose a multi-step feature (term selection process and in semi-supervised fashion, provide initial centers for term clusters. Then utilize the fuzzy c-means (FCM clustering algorithm for clustering terms. Finally assign each of documents to closest associated term clusters. While most text clustering algorithms directly use documents for clustering, we propose to first group the terms using FCM algorithm and then cluster documents based on terms clusters. We evaluate effectiveness of our technique on several standard text collections and compare our results with the some classical text clustering algorithms.

  4. An Analysis of Gene Expression Data using Penalized Fuzzy C-Means Approach

    OpenAIRE

    Banu, P. K. Nizar; Inbarani, H. Hannah

    2013-01-01

    With the rapid advances of microarray technologies, large amounts of high-dimensional gene expression data are being generated, which poses significant computational challenges. A first step towards addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. A robust gene expression clustering approach to minimize undesirable clustering is proposed. In this p...

  5. Detecting lesions in MRI brain images combining pseudo-color segmentation with fuzzy C-Means clustering

    OpenAIRE

    Azar, Fariba Beiramzadeh Azar

    2013-01-01

    ABSTRACT: As biomedical image analysis has been improved over the last decades, the widespread advancement of detection/estimation approaches has aided the rapid development of new technologies for monitoring and diagnosis, as well as, treatment of patients. Image segmentation plays a substantial role as part of the preprocessing in various biomedical applications. The segmentation technique is widely used by the radiologists to interpret the input medical image into meaningful data to be use...

  6. Photodissociation of Cl 2 in helium clusters: an application of hybrid method of quantum wavepacket dynamics and path integral centroid molecular dynamics

    Science.gov (United States)

    Takayanagi, Toshiyuki; Shiga, Motoyuki

    2003-04-01

    The photodissociation dynamics of Cl 2 embedded in helium clusters is studied by numerical simulation with an emphasis on the effect of quantum character of helium motions. The simulation is based on the hybrid model in which Cl-Cl internuclear dynamics is treated in a wavepacket technique, while the helium motions are described by a path integral centroid molecular dynamics approach. It is found that the cage effect largely decreases when the helium motion is treated quantum mechanically. The mechanism is affected not only by the zero-point vibration in the helium solvation structure, but also by the quantum dynamics of helium.

  7. Development of a Hybrid Piezo Natural Rubber Piezoelectricity and Piezoresistivity Sensor with Magnetic Clusters Made by Electric and Magnetic Field Assistance and Filling with Magnetic Compound Fluid

    Directory of Open Access Journals (Sweden)

    Kunio Shimada

    2017-02-01

    Full Text Available Piezoelements used in robotics require large elasticity and extensibility to be installed in an artificial robot skin. However, the piezoelements used until recently are vulnerable to large forces because of the thin solid materials employed. To resolve this issue, we utilized a natural rubber and applied our proposed new method of aiding with magnetic and electric fields as well as filling with magnetic compound fluid (MCF and doping. We have verified the piezoproperties of the resulting MCF rubber. The effect of the created magnetic clusters is featured in a new two types of multilayered structures of the piezoelement. By measuring the piezoelectricity response to pressure, the synergetic effects of the magnetic clusters, the doping and the electric polymerization on the piezoelectric effect were clarified. In addition, by examining the relation between the piezoelectricity and the piezoresistivity created in the MCF piezo element, we propose a hybrid piezoelement.

  8. Development of a Hybrid Piezo Natural Rubber Piezoelectricity and Piezoresistivity Sensor with Magnetic Clusters Made by Electric and Magnetic Field Assistance and Filling with Magnetic Compound Fluid.

    Science.gov (United States)

    Shimada, Kunio; Saga, Norihiko

    2017-02-10

    Piezoelements used in robotics require large elasticity and extensibility to be installed in an artificial robot skin. However, the piezoelements used until recently are vulnerable to large forces because of the thin solid materials employed. To resolve this issue, we utilized a natural rubber and applied our proposed new method of aiding with magnetic and electric fields as well as filling with magnetic compound fluid (MCF) and doping. We have verified the piezoproperties of the resulting MCF rubber. The effect of the created magnetic clusters is featured in a new two types of multilayered structures of the piezoelement. By measuring the piezoelectricity response to pressure, the synergetic effects of the magnetic clusters, the doping and the electric polymerization on the piezoelectric effect were clarified. In addition, by examining the relation between the piezoelectricity and the piezoresistivity created in the MCF piezo element, we propose a hybrid piezoelement.

  9. Development of a Hybrid Piezo Natural Rubber Piezoelectricity and Piezoresistivity Sensor with Magnetic Clusters Made by Electric and Magnetic Field Assistance and Filling with Magnetic Compound Fluid

    Science.gov (United States)

    Shimada, Kunio; Saga, Norihiko

    2017-01-01

    Piezoelements used in robotics require large elasticity and extensibility to be installed in an artificial robot skin. However, the piezoelements used until recently are vulnerable to large forces because of the thin solid materials employed. To resolve this issue, we utilized a natural rubber and applied our proposed new method of aiding with magnetic and electric fields as well as filling with magnetic compound fluid (MCF) and doping. We have verified the piezoproperties of the resulting MCF rubber. The effect of the created magnetic clusters is featured in a new two types of multilayered structures of the piezoelement. By measuring the piezoelectricity response to pressure, the synergetic effects of the magnetic clusters, the doping and the electric polymerization on the piezoelectric effect were clarified. In addition, by examining the relation between the piezoelectricity and the piezoresistivity created in the MCF piezo element, we propose a hybrid piezoelement. PMID:28208625

  10. Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation.

    Science.gov (United States)

    Mekhmoukh, Abdenour; Mokrani, Karim

    2015-11-01

    In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm.

  11. Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

    Energy Technology Data Exchange (ETDEWEB)

    Baraldi, Piero; Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, Via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico, E-mail: enrico.zio@polimi.it [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, Via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

    2011-05-15

    Research highlights: > A bagged ensemble of classifiers is applied for nuclear transient identification. > Fuzzy C-Means classifiers are used as base classifiers of the ensemble. > Transients are simulated in the feedwater system of a boiling water reactor. > Ensemble is compared with a supervised, evolutionary-optimized FCM classifier. > Ensemble improves classification accuracy in cases of large or very small sizes data. - Abstract: This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR).

  12. A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction

    Institute of Scientific and Technical Information of China (English)

    Wen-Qian Deng; Xue-Mei Li; Xifeng Gao; Cai-Ming Zhang

    2016-01-01

    In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.

  13. IMPROVED HYBRID SEGMENTATION OF BRAIN MRI TISSUE AND TUMOR USING STATISTICAL FEATURES

    Directory of Open Access Journals (Sweden)

    S. Allin Christe

    2010-08-01

    Full Text Available Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM segmentation and Kohonen means(K means segmentation were implemented. In addition to this, new hybrid segmentation technique, namely, Fuzzy Kohonen means of image segmentation based on statistical feature clustering is proposed and implemented along with standard pixel value clustering method. The clustered segmented tissue images are compared with the Ground truth and its performance metric is also found. It is found that the feature based hybrid segmentation gives improved performance metric and improved classification accuracy rather than pixel based segmentation.

  14. 两阶段混合粒子群优化聚类%Two-Step Hybrid PSO-Based Clustering Algorithm

    Institute of Scientific and Technical Information of China (English)

    王纵虎; 刘志镜; 陈东辉

    2012-01-01

    In order to solve the problems of the existing PSO (particle swarm optimization) K-means algorithms, i. e. , their calculation speeds are slow and the clustering results are unstable when samples have a high dimension, some high-quality sub-clusters were generated by hierarchical agglomerative clustering. These sub-clusters were used as the search space of candidate centroids of the PSO K-means. In order to reduce the computational complexity when the dimension of a sample is high, a simplified particle encoding method was proposed. In addition, chaotic idea was introduced to keep the diversity of particle swarm to avoid premature. By two-step hybrid clustering the advantages of the hierarchical clustering, the partitioning clustering and the PSO were combined. The experimental results on several UCI data sets show that compared with the best results of several contrastive algorithms, the purity of its clustering result increases by 1% to 8% and the consuming time reduces by 50% at least.%为解决数据集样本维数较高时已有粒子群优化K均值算法计算速度较慢且聚类结果不稳定的问题,利用第1阶段聚类层次凝聚聚类获得准确率较高的子簇集合,作为粒子群优化K均值聚类算法初始聚类中心的搜索空间,进行第2阶段聚类.提出了一种简化的粒子编码方法,以减小样本维数对计算复杂度的影响;引入混沌的思想,以保持粒子种群的多样性,从而避免粒子群优化算法可能出现的早熟现象.通过两阶段聚类,有效地融合了粒子群优化、层次聚类与划分聚类算法的优点.在多个UCI数据集上的聚类结果表明,与几种对比算法聚类结果的最优值相比,其纯度分别提高了1%~8%,且耗时减少50%以上.

  15. Comparision of Clustering Algorithms usingNeural Network Classifier for Satellite Image Classification

    Directory of Open Access Journals (Sweden)

    S.Praveena

    2015-06-01

    Full Text Available This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM, Moving K-means, Artificial Bee Colony(ABC algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.

  16. Research for color image segmentation based on hybrid clustering%混合聚类彩色图像分割方法研究

    Institute of Scientific and Technical Information of China (English)

    施海滨; 周勇

    2011-01-01

    提出了一种基于K-均值算法和EM算法混合聚类的彩色图像分割方法.首先将待分割的RGB彩色图像转化成YUV空间模型,然后将该图像分割成n小块,对每个块的颜色分量用改进的K-均值聚类算法进行聚类分析,最后用EM聚类算法对每个块进行聚类,分割源图像.对K-均值算法和EM算法的初始聚类中心引进了改进算法,加快了算法的收敛速度.并与相似的分割方法进行了比较实验,给出了详细的实验结果与分析.实验表明该方法分割速度快,效果好,具有较高的实用价值.%A new color image segmentation algorithm is introduced based on hybrid clustering including K-means algorithm and EM algorithm, which firstly converts RGB color image into YUV-Space model, and then divides the whole image to n-blocks,clusters the color components of each block with K-means improved in this paper,and finally,segments the source image by clustering each block with EM clustering algorithm.In this paper,a new method is proposed to set initial cluster centers in K-means algorithm and EM algorithm, accelerates the convergence speed.The experiment results and the comparison results with similar approach are provided.Experiment results show the proposed algorithm is effective and has high practical value.

  17. COSMOS--improving the quality of life in nursing home patients: protocol for an effectiveness-implementation cluster randomized clinical hybrid trial.

    Science.gov (United States)

    Husebo, Bettina S; Flo, Elisabeth; Aarsland, Dag; Selbaek, Geir; Testad, Ingelin; Gulla, Christine; Aasmul, Irene; Ballard, Clive

    2015-09-15

    Nursing home patients have complex mental and physical health problems, disabilities and social needs, combined with widespread prescription of psychotropic drugs. Preservation of their quality of life is an important goal. This can only be achieved within nursing homes that offer competent clinical conditions of treatment and care. COmmunication, Systematic assessment and treatment of pain, Medication review, Occupational therapy, Safety (COSMOS) is an effectiveness-implementation hybrid trial that combines and implements organization of activities evidence-based interventions to improve staff competence and thereby the patients' quality of life, mental health and safety. The aim of this paper is to describe the development, content and implementation process of the COSMOS trial. COSMOS includes a 2-month pilot study with 128 participants distributed among nine Norwegian nursing homes, and a 4-month multicenter, cluster randomized effectiveness-implementation clinical hybrid trial with follow-up at month 9, including 571 patients from 67 nursing home units (one unit defined as one cluster). Clusters are randomized to COSMOS intervention or current best practice (control group). The intervention group will receive a 2-day education program including written guidelines, repeated theoretical and practical training (credited education of caregivers, physicians and nursing home managers), case discussions and role play. The 1-day midway evaluation, information and interviews of nursing staff and a telephone hotline all support the implementation process. Outcome measures include quality of life in late-stage dementia, neuropsychiatric symptoms, activities of daily living, pain, depression, sleep, medication, cost-utility analysis, hospital admission and mortality. Despite complex medical and psychosocial challenges, nursing home patients are often treated by staff possessing low level skills, lacking education and in facilities with a high staff turnover

  18. Electrooculogram Signals Analysis for Process Control Operator Based on Fuzzy c-Means

    Directory of Open Access Journals (Sweden)

    Jiangwen Song

    2015-09-01

    Full Text Available Biomedical signals of human can reflect the body's task load, fatigue and other psychological information. Compared with other biomedical signals, electrooculogram (EOG has higher amplitude, less interference, and is easy to detect. In this paper, the EOG signals of operator’s were analyzed. Wavelet transform was used to remove the high-frequency artifacts. Then fuzzy c-means was adopted to detect the eye blink peak points of EOG. After that, eye blink interval (EBI of operator was calculated. Four EOG features (the average of EBI, variance of EBI, standard deviation of EBI and variation coefficient of EBI were extracted. Finally, the relationship between EOG features and operator’s fatigue, effort, anxiety and task load were analyzed. The experimental results illustrate that EOG features had some relation to the operator’s fatigue, effort, anxiety and task load respectively.

  19. New Magnetic Thin Film Hybrid Materials Built by the Incorporation of Octanickel(II)-oxamato Clusters Between Clay Mineral Platelets

    NARCIS (Netherlands)

    Toma, Luminita M.; Gengler, Regis Y. N.; Cangussu, Danielle; Pardo, Emilio; Lloret, Francesc; Rudolf, Petra

    2011-01-01

    We report on a new method based on the combination of Langmuir-Schaefer deposition with self-assembly to insert highly anisotropic Ni(8) molecules in a hybrid organic-inorganic nanostructure. Spectroscopic, crystallographic, and magnetic data prove the successful insertion of the guest cationic mole

  20. Possibilistic Exponential Fuzzy Clustering

    Institute of Scientific and Technical Information of China (English)

    Kiatichai Treerattanapitak; Chuleerat Jaruskulchai

    2013-01-01

    Generally,abnormal points (noise and outliers) cause cluster analysis to produce low accuracy especially in fuzzy clustering.These data not only stay in clusters but also deviate the centroids from their true positions.Traditional fuzzy clustering like Fuzzy C-Means (FCM) always assigns data to all clusters which is not reasonable in some circumstances.By reformulating objective function in exponential equation,the algorithm aggressively selects data into the clusters.However noisy data and outliers cannot be properly handled by clustering process therefore they are forced to be included in a cluster because of a general probabilistic constraint that the sum of the membership degrees across all clusters is one.In order to improve this weakness,possibilistic approach relaxes this condition to improve membership assignment.Nevertheless,possibilistic clustering algorithms generally suffer from coincident clusters because their membership equations ignore the distance to other clusters.Although there are some possibilistic clustering approaches that do not generate coincident clusters,most of them require the right combination of multiple parameters for the algorithms to work.In this paper,we theoretically study Possibilistic Exponential Fuzzy Clustering (PXFCM) that integrates possibilistic approach with exponential fuzzy clustering.PXFCM has only one parameter and not only partitions the data but also filters noisy data or detects them as outliers.The comprehensive experiments show that PXFCM produces high accuracy in both clustering results and outlier detection without generating coincident problems.

  1. 基于混合概率潜在语义分析模型的Web聚类%Web clustering based on hybrid probabilistic latent semantic analysis model

    Institute of Scientific and Technical Information of China (English)

    王治和; 王凌云; 党辉; 潘丽娜

    2012-01-01

    In E-commerce, in order to know more about the inherent characteristics of user access and make better marketing strategies, a Web clustering algorithm based on Hybrid Probabilistic Latent Semantic Analysis (H-PLSA) model was proposed in this paper. The Probabilistic Latent Semantic Analysis ( PLSA) models were established respectively on user browsing data, page information and enhanced user transaction data by using PLSA technology. Using log-likelihood function, three PLSA models were merged to get the user clustering H-PLSA model and the page clustering H-PLSA model. Similarity calculation was based on the conditional probability among latent themes and user, page as well as site in the clustering analysis. The k-medoids algorithm based on distance was adopted in this clustering algorithm. The H-PLSA model was designed and constructed in this article, and the Web clustering algorithm was verified on this H-PLSA model. Then it is proved that the algorithm is effective.%在电子商务应用中,为了更好地了解用户的内在特征,制定有效的营销策略,提出一种基于混合概率潜在语义分析(H-PLSA)模型的Web聚类算法.利用概率潜在语义分析-(PLSA)技术分别对用户浏览数据、页面内容信息及内容增强型用户事务数据建立PLSA模型,通过对数一似然函数对三个PLSA模型进行合并得到用户聚类的H-PLSA模型和页面聚类的H-PLSA模型.聚类分析中以潜在主题与用户、页面以及站点之间的条件概率作为相似度计算依据,聚类算法采用基于距离的k-medoids算法.设计并构建了H-PLSA模型,在该模型上对Web聚类算法进行验证,表明该算法是可行的.

  2. A pixel-based color image segmentation using support vector machine and fuzzy C-means.

    Science.gov (United States)

    Wang, Xiang-Yang; Zhang, Xian-Jin; Yang, Hong-Ying; Bu, Juan

    2012-09-01

    Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

  3. Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain

    Directory of Open Access Journals (Sweden)

    S. Madhukumar

    2015-06-01

    Full Text Available This paper does the qualitative comparison of Fuzzy C-means (FCM and k-Means segmentation, with histogram guided initialization, on tumor edema complex MR images. The accuracy of any segmentation scheme depends on its ability to distinguish different tissue classes, separately. Hence, there is a serious pre-requisite to evaluate this ability before employing the segmentation scheme on medical images. This paper evaluates the ability of FCM and k-Means to segment Gray Matter (GM, White Matter (WM, Cerebro-Spinal Fluid (CSF, Necrotic Focus of Glioblastoma Multiforme (GBM and the perifocal vasogenic edema from pre-processed T1 contrast axial plane MR images of tumor edema complex. The experiment reveals that FCM identifies the vasogenic edema and the white matter as a single tissue class and similarly gray matter and necrotic focus, also. k-Means is able to characterize these regions comparatively better than FCM. FCM identifies only three tissue classes whereas; k-Means identifies all the six classes. The experimental evaluation of k-Means and FCM, with histogram guided initialization is performed in Matlab®.

  4. Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

    Science.gov (United States)

    Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

    2011-10-01

    Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

  5. Novel inorganic-organic hybrids constructed from multinuclear copper cluster and Keggin polyanions: from 1D wave-like chain to 2D network.

    Science.gov (United States)

    Wang, Xiuli; Wang, Yufei; Liu, Guocheng; Tian, Aixiang; Zhang, Juwen; Lin, Hongyan

    2011-09-28

    Two novel inorganic-organic hybrids constructed from Keggin-type polyanions and multinuclear copper clusters based on 1-H-1,2,3-benzotriazole (HBTA), [Cu(I)(8)(BTA)(4)(HBTA)(8)(SiMo(12)O(40))]·2H(2)O (1) and [Cu(II)(6)(OH)(4)(BTA)(4)(SiW(12)O(40))(H(2)O)(6)]·6H(2)O (2), have been hydrothermally synthesized and structurally characterized by single crystal X-ray diffraction, elemental analyses, IR spectra and thermogravimetric (TG) analyses. In compound 1, eight Cu(I) ions were linked by twelve HBTA/BTA ligands to form an octanuclear Cu(I) cluster, which is connected by SiMo(12)O(40)(4-) anion with two bridging O atoms and two terminal O atoms to construct a one-dimensional (1D) wave-like chain. The octanuclear copper unit represents the maximum subunit linked just by amine ligands in the POMs system. In 2, four BTA ligands linked five Cu(II) ions constructing a pentanuclear "porphyrin-like" subunit, which is connected by another Cu(II) ion to form a 1D metal-organic band. The SiW(12)O(40)(4-) polyanions as tetradentate inorganic linkages extend the 1D band into a two-dimensional (2D) network with (8(3))(2)(8(5)·10) topology. To the best of our knowledge, compounds 1 and 2 represent the first examples of inorganic-organic hybrids based on metal-HBTA multinuclear subunits and polyoxometalates. The photocatalysis and electrochemical properties have been investigated in this paper.

  6. CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING

    Institute of Scientific and Technical Information of China (English)

    Huang Ning; Zhu Minhui; Zhang Shourong

    2002-01-01

    Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.

  7. 基于模糊C均值算法的入侵检测方法%An Approach for Intrusion Detection Based on Fuzzy C-means Algorithm

    Institute of Scientific and Technical Information of China (English)

    林荣亮; 张文波

    2012-01-01

    Clustering analysis is an effective method of anomaly intrusion detection,which can find normal flow and abnormal flow in the network data set. Fuzzy C-means clustering algorithm is applied to classify the network traffic data into normal flow and abnormal flow. A new clustering center method which is designed for intrusion detecting problem specially is provided in this paper. Finally. KDD 99 data set is used for the illustrative example , and the result proves that this algorithm could discover the abnormal flows effectively.%聚类分析是一种有效的异常入侵检测方法,可用以在网络数据集中区分正常流量和异常流量.采用模糊C均值聚类算法对网络流量样本集进行划分,从中区分正常流量和异常流量,并针对入侵检测问题的特性提出了聚类中心确定方法.最后,利用KDD 99数据集进行实验,证明该算法能够有效地发现异常流量.

  8. The calculation of the highest leak level of water pipe lines region at PDAM Tirta Kahuripan using fuzzy C-means and ArcGIS method analysis

    Science.gov (United States)

    Parwatiningtyas, D.; Ambarsari, E. W.; Mariko, S.

    2017-07-01

    Water is a basic necessity for human's life. Water, which is distributed to the public, should in decent condition, healthy, and protected from metal pollutants. In Indonesia, it is handled by a government institution, commonly is PDAM (Indonesian regional water utility company). A PDAM Tirta Kahuripan handles water distribution in Bogor area and part of Depok cities. Based on data, PDAM Tirta Kahuripan had approximately more than 46 % water loss, due to geological factor, human activity, etc. Therefore in this paper, we try to make a decision system of water loss at PDAM pipelines, using cluster Fuzzy C - Means method analysis. Then, mapped into ArcGIS software. Based on this method, we can be determine the region which shows the most water loss and also identify the highest leaks level from water pipelines at PDAM Tirta Kahuripan.

  9. 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

    Science.gov (United States)

    Kim, Chan Moon; Parnichkun, Manukid

    2017-02-01

    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.

  10. A mixed methods protocol for developing and testing implementation strategies for evidence-based obesity prevention in childcare: a cluster randomized hybrid type III trial.

    Science.gov (United States)

    Swindle, Taren; Johnson, Susan L; Whiteside-Mansell, Leanne; Curran, Geoffrey M

    2017-07-18

    Despite the potential to reach at-risk children in childcare, there is a significant gap between current practices and evidence-based obesity prevention in this setting. There are few investigations of the impact of implementation strategies on the uptake of evidence-based practices (EBPs) for obesity prevention and nutrition promotion. This study protocol describes a three-phase approach to developing and testing implementation strategies to support uptake of EBPs for obesity prevention practices in childcare (i.e., key components of the WISE intervention). Informed by the i-PARIHS framework, we will use a stakeholder-driven evidence-based quality improvement (EBQI) process to apply information gathered in qualitative interviews on barriers and facilitators to practice to inform the design of implementation strategies. Then, a Hybrid Type III cluster randomized trial will compare a basic implementation strategy (i.e., intervention as usual) with an enhanced implementation strategy informed by stakeholders. All Head Start centers (N = 12) within one agency in an urban area in a southern state in the USA will be randomized to receive the basic or enhanced implementation with approximately 20 classrooms per group (40 educators, 400 children per group). The educators involved in the study, the data collectors, and the biostastician will be blinded to the study condition. The basic and enhanced implementation strategies will be compared on outcomes specified by the RE-AIM model (e.g., Reach to families, Effectiveness of impact on child diet and health indicators, Adoption commitment of agency, Implementation fidelity and acceptability, and Maintenance after 6 months). Principles of formative evaluation will be used throughout the hybrid trial. This study will test a stakeholder-driven approach to improve implementation, fidelity, and maintenance of EBPs for obesity prevention in childcare. Further, this study provides an example of a systematic process to develop

  11. Adaptive fuzzy leader clustering of complex data sets in pattern recognition

    Science.gov (United States)

    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.

  12. Self-organization and clustering algorithms

    Science.gov (United States)

    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.

  13. Organic-inorganic hybrid materials starting from the novel nanoscaled bismuth oxido methacrylate cluster [Bi38O45(OMc)24(DMSO)9]·2DMSO·7H2O.

    Science.gov (United States)

    Miersch, Linda; Rüffer, Tobias; Mehring, Michael

    2011-06-14

    The reaction of the basic bismuth nitrate [Bi(6)O(4)(OH)(4)](NO(3))(6)·H(2)O with sodium methacrylate in DMSO gave [Bi(38)O(45)(OMc)(24)(DMSO)(9)]·2DMSO·7H(2)O (OMc = O(2)CC(3)H(5)), which is highly soluble in organic solvents. By copolymerization of the bismuth oxido cluster with methyl methacrylate transparent, radiopaque organic-inorganic hybrid materials were obtained.

  14. Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm

    Institute of Scientific and Technical Information of China (English)

    李侃; 刘玉树

    2004-01-01

    A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.

  15. Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    A. M. Chandrashekhar

    2013-02-01

    Full Text Available Intrusion Detection Systems (IDS form a key part of system defence, where it identifies abnormalactivities happening in a computer system. In recent years different soft computing based techniques havebeen proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfecttechnology. This has provided an opportunity for data mining to make quite a lot of importantcontributions in the field of intrusion detection. In this paper we have proposed a new hybrid techniqueby utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzyand radial basis function(RBF SVM for fortification of the intrusion detection system. Theproposed technique has five major steps in which, first step is to perform the relevance analysis, and theninput data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that eachof the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.Subsequently, a vector for SVM classification is formed and in the last step, classification using RBFSVMis performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 datasetand we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Ourtechnique could achieve better accuracy for all types of intrusions. The results of proposed technique arecompared with the other existing techniques. These comparisons proved the effectiveness of ourtechnique.

  16. Retinal Vessel Segmentation: A Comparative Study of Fuzzy C-means and Sum Entropy Information on Phase Congruency

    Directory of Open Access Journals (Sweden)

    Temitope Mapayi

    2015-09-01

    Full Text Available As the use of robotic-assisted surgery systems continue to increase, highly accurate and timely efficient automatic vasculature detection techniques for large and thin vessels in the retinal images are needed. Vascular segmentation has however been challenging due to uneven illumination in retinal images. The use of efficient pre-processing techniques as well as good segmentation techniques are highly needed to produce good vessel segmentation results. This paper presents an investigatory study on the combination of phase congruence with fuzzy c-means and the combination of phase congruence with gray level co-occurrence (GLCM matrix sum entropy for the segmentation of retinal vessels. Fuzzy C-Means combined with phase congruence yields a higher accuracy rate but a longer running time while compared to GLCM sum entropy combined with phase congruence. While compared with the widely previously used techniques on DRIVE and STARE databases, the techniques investigated yield high average accuracy rates.

  17. Kernel method-based fuzzy clustering algorithm

    Institute of Scientific and Technical Information of China (English)

    Wu Zhongdong; Gao Xinbo; Xie Weixin; Yu Jianping

    2005-01-01

    The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

  18. Remote Sensing Image Classification Based on Improved Fuzzy c-Means%基于改进的模糊c-均值聚类方法遥感影像分类研究

    Institute of Scientific and Technical Information of China (English)

    余洁; 郭培煌; 陈品祥; 张中山; 软文斌

    2008-01-01

    Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.

  19. An improved approach based on fuzzy clustering and Back-Propagation Neural Networks with adaptive learning rate for sales forecasting: Case study of PCB industry

    Directory of Open Access Journals (Sweden)

    Attariuas Hicham

    2012-05-01

    Full Text Available This paper describes new hybrid sales forecasting system based on fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate (FCBPN.The proposed approach is composed of three stages: (1 Winters Exponential Smoothing method will be utilized to take the trend effect into consideration; (2 utilizing Fuzzy C-Means clustering method (Used in an clusters memberships fuzzy system (CMFS, the clusters membership levels of each normalized data records will be extracted; (3 Each cluster will be fed into parallel BP networks with a learning rate adapted as the level of cluster membership of training data records. Compared to many researches which use Hard clustering, we employ fuzzy clustering which permits each data record to belong to each cluster to a certain degree, which allows the clusters to be larger which consequently increases the accuracy of the proposed forecasting system . Printed Circuit Board (PCB will be used as a case study to evaluate the precision of our proposed architecture. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising solution for industrial forecasting.

  20. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  1. Fuzzy clustering with Minkowski distance

    NARCIS (Netherlands)

    P.J.F. Groenen (Patrick); U. Kaymak (Uzay); J.M. van Rosmalen (Joost)

    2006-01-01

    textabstractDistances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L_1-distance and Bobrowski and Bezdek (1991) also used the L_inf

  2. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    B. Ojeda-Magaña

    2013-01-01

    Full Text Available We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means and an absolute typicality (typicality value, Possibilistic c-means. Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find.

  3. A physical analogy to fuzzy clustering

    DEFF Research Database (Denmark)

    Jantzen, Jan

    2004-01-01

    This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain...

  4. A physical analogy to fuzzy clustering

    DEFF Research Database (Denmark)

    Jantzen, Jan

    2004-01-01

    This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain ...

  5. Sistem Perencanaan Penambahan Stok Barang menggunakan Metode Fuzzy C-Means dan Fuzzy Tsukamoto (Studi Kasus di Distributor Alfamart Semarang

    Directory of Open Access Journals (Sweden)

    Tono Puryanto

    2016-08-01

    Full Text Available Gudang barang suatu perusahaan merupakan tempat penyimpanan barang yang akan dijual kepada pelanggan. Permasalahan utama pada gudang barang suatu perusahaan adalah terjadinya penumpukan barang atau barang keluar lebih banyak daripada barang masuk yang dapat mengakibatkan kerugian bagi perusahaan. Penambahan stok barang pada gudang dilakukan berdasarkan permintaan pelanggan dan stok barang saat itu. Banyak permintaan pelanggan setiap waktu selalu berubah yang dapat menyebabkan terjadinya penumpukan barang atau kekurangan barang. Hal ini menyebabkan sulit dalam pengambilan keputusan jumlah barang yang akan dikirim. Salah satu cara untuk membantu pengambilan keputusan tersebut yaitu dengan pembangunan aplikasi perencanaan penambahan stok barang yang menggunakan konsep logika fuzzy. Fuzzy merupakan suatu cara untuk menyelesaikan masalah ketidakpastian. Pada aplikasi perencanaan penambahan stok barang, proses penentuan penambahan stok barang dilakukan dengan menggunakan metode fuzzy C-Means dan mekanisme inferensi fuzzy Tsukamoto. Hasil akhir dari aplikasi ini berupa jumlah barang yang akan dikirim. Hasil tersebut menjadi saran yang dapat dipertimbangkan oleh admin bagian pengiriman barang. Pengujian dilakukan menggunakan data Coca-Cola pada bulan September 2014 sampai Oktober 2014. Pada pengujian sistem dilakukan 11 kali pengujian dengan memasukkan stok dan permintaan data asli menghasilkan tingkat keakuratan sistem sebesar 80,22 %. Tingkat keakuratan sistem dapat berubah tergantung pada data pelatihan yang digunakan pada proses pelatihan fuzzy C-Means.

  6. Clustering of resting state networks.

    Directory of Open Access Journals (Sweden)

    Megan H Lee

    Full Text Available BACKGROUND: The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm. METHODOLOGY/PRINCIPAL FINDINGS: The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization. CONCLUSIONS/SIGNIFICANCE: The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

  7. Hybrid secure beamforming and vehicle selection using hierarchical agglomerative clustering for C-RAN-based vehicle-to-infrastructure communications in vehicular cyber-physical systems

    National Research Council Canada - National Science Library

    Xu, Dongyang; Ren, Pinyi; Du, Qinghe; Sun, Li

    2016-01-01

    ...–enhancing mechanisms in the physical layer. In this article, we propose a hybrid beamforming and vehicle-selection framework for vehicle-to-infrastructure communications to broadcast high-speed confidential messages...

  8. Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images.

    Science.gov (United States)

    Meena Prakash, R; Shantha Selva Kumari, R

    2017-01-01

    The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.

  9. Clustering Techniques in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Muhammad Ali Masood

    2015-01-01

    Full Text Available Dealing with data means to group information into a set of categories either in order to learn new artifacts or understand new domains. For this purpose researchers have always looked for the hidden patterns in data that can be defined and compared with other known notions based on the similarity or dissimilarity of their attributes according to well-defined rules. Data mining, having the tools of data classification and data clustering, is one of the most powerful techniques to deal with data in such a manner that it can help researchers identify the required information. As a step forward to address this challenge, experts have utilized clustering techniques as a mean of exploring hidden structure and patterns in underlying data. Improved stability, robustness and accuracy of unsupervised data classification in many fields including pattern recognition, machine learning, information retrieval, image analysis and bioinformatics, clustering has proven itself as a reliable tool. To identify the clusters in datasets algorithm are utilized to partition data set into several groups based on the similarity within a group. There is no specific clustering algorithm, but various algorithms are utilized based on domain of data that constitutes a cluster and the level of efficiency required. Clustering techniques are categorized based upon different approaches. This paper is a survey of few clustering techniques out of many in data mining. For the purpose five of the most common clustering techniques out of many have been discussed. The clustering techniques which have been surveyed are: K-medoids, K-means, Fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN and Self-Organizing Map (SOM clustering.

  10. Maximum-entropy clustering algorithm and its global convergence analysis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.

  11. Two generalizations of Kohonen clustering

    Science.gov (United States)

    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.

  12. Fuzzy C Means Clustering Remote Sensing Image Classification%模糊C均值聚类遥感影像分类

    Institute of Scientific and Technical Information of China (English)

    田晓娜; 董静

    2011-01-01

    模糊C均值聚类算法可有效的解决遥感信息的不确定性和混合像元的划分.文中基于matlab平台、采用模糊C均值聚类对遥感影像进行分类,并运用混淆矩阵对分类结果进行了精度评定.实验结果表明,基于模糊C均值聚类使得分类后的图像很好地区分了地物类别,取得了较好效果.

  13. Research on fuzzy C-means clustering algorithm parallel%模糊C均值聚类算法的并行化研究

    Institute of Scientific and Technical Information of China (English)

    张建强; 郑晓薇; 吴华平

    2010-01-01

    使用Intel Parallel Amplifier高性能工具,针对模糊C均值聚类算法在多核平台的性能问题,找出串行程序的热点和并发性,提出并行化设计方案.基于Intel并行库TBB(线程构建模块)和OpenMP运行时库函数,对多核平台下的串行程序进行循环并行化和任务分配的并行化设计.

  14. Experiences Using Hybrid MPI/OpenMP in the Real World: Parallelization of a 3D CFD Solver for Multi-Core Node Clusters

    Directory of Open Access Journals (Sweden)

    Gabriele Jost

    2010-01-01

    Full Text Available Today most systems in high-performance computing (HPC feature a hierarchical hardware design: shared-memory nodes with several multi-core CPUs are connected via a network infrastructure. When parallelizing an application for these architectures it seems natural to employ a hierarchical programming model such as combining MPI and OpenMP. Nevertheless, there is the general lore that pure MPI outperforms the hybrid MPI/OpenMP approach. In this paper, we describe the hybrid MPI/OpenMP parallelization of IR3D (Incompressible Realistic 3-D code, a full-scale real-world application, which simulates the environmental effects on the evolution of vortices trailing behind control surfaces of underwater vehicles. We discuss performance, scalability and limitations of the pure MPI version of the code on a variety of hardware platforms and show how the hybrid approach can help to overcome certain limitations.

  15. The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

    Science.gov (United States)

    Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

    2014-05-01

    Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the

  16. Fuzzy Clustering with Novel Separable Criterion

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis. An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means (FCM) to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method's properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method.

  17. Empirical evaluation of DArT, SNP, and SSR marker-systems for genotyping, clustering, and assigning sugar beet hybrid varieties into populations

    Science.gov (United States)

    Dominant and co-dominant molecular markers are routinely used in plant genetic diversity research. In the present study we assessed the success-rate of three marker-systems for estimating genotypic diversity, clustering varieties into populations, and assigning a single variety into the expected pop...

  18. Two new inorganic-organic hybrid materials based on inorganic cluster, [X2Mo18O62]6− (X=P, As)

    Indian Academy of Sciences (India)

    Fatma Hmida; Meriem Ayed; Brahim Ayed; Amor Haddad

    2015-09-01

    Two new inorganic-organic hybrid materials based on heteropolyoxometalates, (C4H10N)6 (P2 Mo18O62).4H2O I, and (C4H10N)6 (As2Mo18O62).4H2O II, where C4H10N is protonated pyrrolidine have been synthesized and structurally characterized by physic-chemical methods. Single-crystal X-ray diffraction method, infrared, ultraviolet spectroscopy, Thermogravimetricanalysis andcyclic voltammetry measurements of the title hybrid materials indicate that there are hydrogen bond interaction between O atoms of the hetero-polyoxometalates and water molecules as well as the N and O atoms of the organic compound. The molecular structures of synthesized hybrid materials contain discrete entities of pyrrolidinumion and water molecules surround every [X2Mo18O62]6− anion over the extended crystalline network that the [X2Mo18O62]6− anion retains its ``Dawson structure". Crystal data: I monoclinic, space group P21/a, a = 13,453(1) Å, b = 24,046 (1) Å, c = 24,119(1) = 97, 99(1)°, V = 7726,30(5) Å3 and Z = 4; II monoclinic, space group P21/a, a = 13.4900(1) Å, 24.0900(1) Å, 24.2740(1) Å, = 98.320(1)°, V = 7805.40(7) Å3 and Z = 4.

  19. 多群粒子输运问题在多核集群系统上的混合并行计算%Hybrid Parallel Computation of Multi-Group Particle transport Equations on Multi-Core Cluster Systems

    Institute of Scientific and Technical Information of China (English)

    迟利华; 刘杰; 龚春叶; 徐涵; 蒋杰; 胡庆丰

    2009-01-01

    The parallel performance of solving the multi-group particle transport equations on the unstructure meshes is analyzed Adapting to the characteristics of multi-core cluster systems, this paper desgins a MPI/OpenMP hybrid parallel code. For the meshes, the space domain decomposition is adopted, and MPI between the computations of multi-core CPU nodes is used. When each MPI process begin to compute the variables of the energy groups, several OpenMP threads will be forked, and the threads start to compute simultaneously in the same mutli-core CPU node. Using the MPI/OpenMP hybrid parallel code, we solve a 2D mutli-group particle transport equation on a cluster with mutli-core CPU nodes, and the results show that the code has good scalability and can be scaled to 1024 CPU cores.%本文分析了非结构网格多群粒子输运Sn方程求解的并行性,拟合多核机群系统的特点,设计了MPI/OpenMP混合程序,针对空间网格点采用区域分解划分,计算结点间基于消息传递MPI编程,每个MPI计算进程在计算过程中碰到关于能群的计算,就生成多个OpenMP线程,计算结点内针对能群进行多线程并行计算.数值测试结果表明,非结构网格上的粒子输运问题的混合并行计算能较好地匹配多核机群系统的硬件结构,具有良好的可扩展性,可以扩展到1 024个CPU核.

  20. Ionothermal Synthesis and Phase Transformation of Organic-inorganic Hybrid Neutral Zincophosphate Cluster[Zn(HPO4)(H2PO4)][C6H10N3O2

    Institute of Scientific and Technical Information of China (English)

    DONG Zhao-jun; YAN Yan; ZHENG Rong-feng; LIU Dan; LI Ji-yang; HAN Zhen-guo; YU Ji-hong

    2011-01-01

    Ionothermal synthesis was used to prepare a novel amino acid containing hybrid zincophosphate monomer,[Zn(HPO4)(H2PO4)][C6H10N3O2](denoted as ZnPO-CJ58).The inorganic framework of[Zn(HPO4)(H2PO4)]·[C6H10N3O2]consists of 4-membered rings formed by ZnO3OH is and PO2(OH)2 tetrahedra.The HPO4 and amino acid moieties hang on the Zn center.Such a framework is stabilized by extensive multipoint hydrogen bonds involving the phosphate units and histidine molecules to form a pseudo-3D supramolecular structure.It is noteworthy that ZnPO-CJ58 is the first zinc phosphate cluster with amino acid acting as the ligand.It exhibits photoluminescence excited at a wavelength of 220 nm.Interestingly,ZnPO-CJ58 can transform into a layered structure (C6H10N3O2)Zn2·(HPO4)(PO4)·H2O(ZnPO-CJ36) through further reacting with water or zinc acetate dihydrate in water at 85 ℃ for 1 h.This work will be helpful for the synthesis of crystalline inorganic-organic hybrid materials with biofunctional molecules.

  1. Molecular evidence for hybridization in Colias (Lepidoptera: Pieridae): are Colias hybrids really hybrids?

    Science.gov (United States)

    Dwyer, Heather E; Jasieniuk, Marie; Okada, Miki; Shapiro, Arthur M

    2015-01-01

    Gene flow and hybridization among species dramatically affect our understanding of the species as a biological unit, species relationships, and species adaptations. In North American Colias eurytheme and Colias eriphyle, there has been historical debate over the extent of hybridization occurring and the identity of phenotypically intermediate individuals as genetic hybrids. This study assesses the population structure of these two species to measure the extent of hybridization and the genetic identity of phenotypic intermediates as hybrids. Amplified fragment length polymorphism (AFLP) marker analysis was performed on 378 specimens collected from northern California and Nevada. Population structure was inferred using a Bayesian/Markov chain Monte Carlo method, which probabilistically assigns individuals to genetic clusters. Three genetic clusters provided the best fit for the data. C. eurytheme individuals were primarily assigned to two closely related clusters, and C. eriphyle individuals were mostly assigned to a third, more distantly related cluster. There appeared to be significant hybridization between the two species. Individuals of intermediate phenotype (putative hybrids) were found to be genetically indistinguishable from C. eriphyle, indicating that previous work based on the assumption that these intermediate forms are hybrids may warrant reconsideration. PMID:26306172

  2. A hybrid clustering algorithm combining K-harmonic means and simulated annealing particle swarm optimization%融合K-调和均值和模拟退火粒子群的混合聚类算法

    Institute of Scientific and Technical Information of China (English)

    毛力; 刘兴阳; 沈明明

    2011-01-01

    In view of the advantages and disadvantages of K-harmonic means (KHM) and simulated annealing particle swarm optimization (SAPSO), a hybrid clustering algorithm combining KHM and SAPSO (KHM-SAPSO) was presented in this paper. With KHM, the particle swarm was divided into several sub-groups. Each particle iteratively updated its location based on its individual extreme value and the global extreme value of the sub-group it belonged to. With simulated annealing technique, the algorithm prevented premature convergence and improved the calculation accuracy. Using the databases of Iris, Zoo, Wine and Image Segmentation, and taking F-measure as a measure to evaluate the clustering effect, this paper qualified the new hybrid algorithm. Our experimental results indicated that the new algorithm significantly improved the clustering effectiveness by avoiding being trapped in local optimum, enhanced the global search capability while achieved faster convergence rate. This algorithm is adopted by an aquaculture water quality analysis system of a freshwater breeding base in Wuxi, which is running effectively.%针对K-调和均值和模拟退火粒子群聚类算法的优缺点,提出了1种融合K-调和均值和模拟退火粒子群的混合聚类算法.首先通过K-调和均值方法将粒子群分成若干个子群,每个粒子根据其个体极值和所在子种群的全局极值来更新位置.同时引入模拟退火思想,抑制了早期收敛,提高了计算精度.本文使用Iris、Zoo、Wine和Image Segmentation,4个数据库,以F-measure为评价聚类效果的标准,对混合聚类算法进行了验证.研究发现,该混合聚类算法可以有效地避免陷入局部最优,在保证收敛速度的同时增强了算法的全局搜索能力,明显改善了聚类效果.该算法目前已用于无锡一淡水养殖基地的水产健康养殖水质分析系统,运行效果良好.

  3. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina, E-mail: despina.kontos@uphs.upenn.edu [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)

    2013-12-15

    Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a

  4. Potential emission flux to aerosol pollutants over Bengal Gangetic plain through combined trajectory clustering and aerosol source fields analysis

    Science.gov (United States)

    Kumar, D. Bharath; Verma, S.

    2016-09-01

    A hybrid source-receptor analysis was carried out to evaluate the potential emission flux to winter monsoon (WinMon) aerosols over Bengal Gangetic plain urban (Kolkata, Kol) and semi-urban atmospheres (Kharagpur, Kgp). This was done through application of fuzzy c-mean clustering to back-trajectory data combined with emission flux and residence time weighted aerosols analysis. WinMon mean aerosol optical depth (AOD) and angstrom exponent (AE) at Kol (AOD: 0.77; AE: 1.17) were respectively slightly higher than and nearly equal to that at Kgp (AOD: 0.71; AE: 1.18). Out of six source region clusters over Indian subcontinent and two over Indian oceanic region, the cluster mean AOD was the highest when associated with the mean path of air mass originating from the Bay of Bengal and the Arabian sea clusters at Kol and that from the Indo-Gangetic plain (IGP) cluster at Kgp. Spatial distribution of weighted AOD fields showed the highest potential source of aerosols over the IGP, primarily over upper IGP (e.g. Punjab, Haryana), lower IGP (e.g. Uttarpradesh) and eastern region (e.g. west Bengal, Bihar, northeast India) clusters. The emission flux contribution potential (EFCP) of fossil fuel (FF) emissions at surface (SL) of Kol/Kgp, elevated layer (EL) of Kol, and of biomass burning (BB) emissions at SL of Kol were primarily from upper, lower, upper/lower IGP clusters respectively. The EFCP of FF/BB emissions at Kgp-EL/SL, and that of BB at EL of Kol/Kgp were mainly from eastern region and Africa (AFR) clusters respectively. Though the AFR cluster was constituted of significantly high emission flux source potential of dust emissions, the EFCP of dust from northwest India (NWI) was comparable to that from AFR at Kol SL/EL.

  5. Clustering Student Data to Characterize Performance Patterns

    Directory of Open Access Journals (Sweden)

    Bindiya M Varghese

    2011-09-01

    Full Text Available Over the years the academic records of thousands of students have accumulated in educational institutions and most of these data are available in digital format. Mining these huge volumes of data may gain a deeper insight and can throw some light on planning pedagogical approaches and strategies in the future. We propose to formulate this problem as a data mining task and use k-means clustering and fuzzy c-means clustering algorithms to evolve hidden patterns.

  6. Syntheses, structures and photocatalytic properties of five new praseodymium-antimony oxochlorides: from discrete clusters to 3D inorganic-organic hybrid racemic compounds.

    Science.gov (United States)

    Zou, Guo-Dong; Wang, Ze-Ping; Song, Ying; Hu, Bing; Huang, Xiao-Ying

    2014-07-14

    Five novel praseodymium-antimony oxochloride (Pr-Sb-O-Cl) cluster-based compounds, namely (2-MepyH)2[Fe(1,10-phen)3]2[Pr4Sb12O18Cl14.6(OH)2.4(Hsal)]·H2O (1), (2-MepyH)2[Fe(1,10-phen)3]4{[Pr4Sb12O18Cl13.5(OH)0.5](bcpb)2[Pr4Sb12O18Cl13.5(OH)0.5]}·42H2O (2), (3-MepyH)2[Fe(1,10-phen)3]{[Pr4Sb12O18Cl13(H2O)2](bcpb)}·2(3-Mepy)·3H2O (3), [Fe(1,10-phen)3]2{[Pr4Sb12O18Cl10(H2O)2](bcpb)2}·3(3-Mepy)·13H2O (4), and (2-MepyH)6[Fe(1,10-phen)3]10{[Pr4Sb12O18Cl13(OH)2]2[Pr4Sb12O18Cl9][Pr4Sb12O18Cl9(OH)2]2(Hpdc)10(pdc)2}·110H2O (5) (2-Mepy = 2-methylpyridine, 3-Mepy = 3-methylpyridine, 1,10-phen = 1,10-phenanthroline, H2sal = salicylic acid, H3bcpb = 3,5-bis(4-carboxyphenoxy)benzoic acid, H3pdc = 3,5-pyrazoledicarboxylic acid) have been solvothermally synthesized and structurally characterized. Compound 1 is the first zero-dimensional (0D) Pr-Sb-O-Cl cluster decorated by an organic ligand. Compounds 2-4 are constructed from the same H3bcpb ligands but adopt different structures: 2 represents a rare example of a one-dimensional (1D) nanotubular structure based on high-nuclearity clusters; 3 exhibits a two-dimensional (2D) mono-layered structure, in which left-handed and right-handed helical chains are alternately arranged, while 4 features a double-layered structure with an unprecedented (3,3,6)-connected 3-nodal topological net. Compound 5 is a unique three-dimensional (3D) 2-fold interpenetrating racemic compound, simultaneously containing three kinds of Pr-Sb-O-Cl-pdc clusters. UV-light photocatalytic H2 evolution activity was observed for compound 3 with Pt as a co-catalyst and MeOH as a sacrificial electron donor. In addition, the magnetic properties of compounds 1 and 5 are also studied.

  7. Possibilistic clustering for shape recognition

    Science.gov (United States)

    Keller, James M.; Krishnapuram, Raghu

    1993-01-01

    Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

  8. Kernel Generalized Noise Clustering Algorithm

    Institute of Scientific and Technical Information of China (English)

    WU Xiao-hong; ZHOU Jian-jiang

    2007-01-01

    To deal with the nonlinear separable problem, the generalized noise clustering (GNC) algorithm is extended to a kernel generalized noise clustering (KGNC) model. Different from the fuzzy c-means (FCM) model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do itjust in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.

  9. Cluster headache

    Science.gov (United States)

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... A cluster headache begins as a severe, sudden headache. The headache commonly strikes 2 to 3 hours after you fall ...

  10. Cluster Forests

    CERN Document Server

    Yan, Donghui; Jordan, Michael I

    2011-01-01

    Inspired by Random Forests (RF) in the context of classification, we propose a new clustering ensemble method---Cluster Forests (CF). Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure $\\kappa$. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis shows that the $\\kappa$ criterion is shown to grow each local clustering in a desirable way---it is "noise-resistant." A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.

  11. Fe-Cluster Pushing Electrons to N-Doped Graphitic Layers with Fe3C(Fe) Hybrid Nanostructure to Enhance O2 Reduction Catalysis of Zn-Air Batteries.

    Science.gov (United States)

    Yang, Jie; Hu, Jiangtao; Weng, Mouyi; Tan, Rui; Tian, Leilei; Yang, Jinlong; Amine, Joseph; Zheng, Jiaxin; Chen, Haibiao; Pan, Feng

    2017-02-08

    Non-noble metal catalysts with catalytic activity toward oxygen reduction reaction (ORR) comparable or even superior to that of Pt/C are extremely important for the wide application of metal-air batteries and fuel cells. Here, we develop a simple and controllable strategy to synthesize Fe-cluster embedded in Fe3C nanoparticles (designated as Fe3C(Fe)) encased in nitrogen-doped graphitic layers (NDGLs) with graphitic shells as a novel hybrid nanostructure as an effective ORR catalyst by directly pyrolyzing a mixture of Prussian blue (PB) and glucose. The pyrolysis temperature was found to be the key parameter for obtaining a stable Fe3C(Fe)@NDGL core-shell nanostructure with an optimized content of nitrogen. The optimized Fe3C(Fe)@NDGL catalyst showed high catalytic performance of ORR comparable to that of the Pt/C (20 wt %) catalyst and better stability than that of the Pt/C catalyst in alkaline electrolyte. According to the experimental results and first principle calculation, the high activity of the Fe3C(Fe)@NDGL catalyst can be ascribed to the synergistic effect of an adequate content of nitrogen doping in graphitic carbon shells and Fe-cluster pushing electrons to NDGL. A zinc-air battery utilizing the Fe3C(Fe)@NDGL catalyst demonstrated a maximum power density of 186 mW cm(-2), which is slightly higher than that of a zinc-air battery utilizing the commercial Pt/C catalyst (167 mW cm(-2)), mostly because of the large surface area of the N-doped graphitic carbon shells. Theoretical calculation verified that O2 molecules can spontaneously adsorb on both pristine and nitrogen doped graphene surfaces and then quickly diffuse to the catalytically active nitrogen sites. Our catalyst can potentially become a promising replacement for Pt catalysts in metal-air batteries and fuel cells.

  12. Star Clusters

    OpenAIRE

    Gieles, M.

    1993-01-01

    Star clusters are observed in almost every galaxy. In this thesis we address several fundamental problems concerning the formation, evolution and disruption of star clusters. From observations of (young) star clusters in the interacting galaxy M51, we found that clusters are formed in complexes of stars and star clusters. These complexes share similar properties with giant molecular clouds, from which they are formed. Many (70%) of the young clusters will not survive the fist 10 Myr, due to t...

  13. Hybrid Baryons

    CERN Document Server

    Page, P R

    2003-01-01

    We review the status of hybrid baryons. The only known way to study hybrids rigorously is via excited adiabatic potentials. Hybrids can be modelled by both the bag and flux-tube models. The low-lying hybrid baryon is N 1/2^+ with a mass of 1.5-1.8 GeV. Hybrid baryons can be produced in the glue-rich processes of diffractive gamma N and pi N production, Psi decays and p pbar annihilation.

  14. A Three-dimensional Organic-inorganic Hybrid Material Supported by Decavanadate Clusters and Na-O Chains:Synthesis and Crystal Structure of [Na6(H2O)16(dod)4V10O28

    Institute of Scientific and Technical Information of China (English)

    张献明; 武海顺; 陈小明

    2004-01-01

    A new organic-inorganic hybrid material [Na6(H2O)16(dod)2V10O28](dod=1,4-diazoniabicyclo[2,2,2]octane-1,4-diacetate)has been synthesized and X-ray single-crystal structural analysis reveals it crystallizes in triclinic,space group Pī with a=11.533(7),b=12.031(7),c=12.187(4)A,α=72.47(1),β=73.16(1),γ=68.21(1)°,C20H64N4Na6O52V10,V=1467(1)A3,Z=1,Mr=1840.1,Dc=2.083 g/cm3,MoKα,λ=0.71073A,μ=1.686,F(000)=924,S=1.027,the final R=0.0538 and wR=0.1272 for 4398 observed reflections.The compound has a three-dimensional frame- work constructed from decavanadate clusters,Na-O chains and dod ligands.A variety of O-H…O and C-H…O hydrogen bonds play an important role in stabilizing the framework.

  15. A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Subodh; Srivastava, Rajeev

    2017-07-01

    For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other

  16. A possibilistic approach to clustering

    Science.gov (United States)

    Krishnapuram, Raghu; Keller, James M.

    1993-01-01

    Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

  17. Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

  18. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ranganathan Mohanasundaram

    2015-01-01

    Full Text Available The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

  19. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  20. 基于优化模糊C均值聚类算法的路面不平度识别%Road roughness recognition based on improved fuzzy C-mean algorithm combined with genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    刘庆华; 周帏; 何仁; 张利敏

    2014-01-01

    模糊C均值(fuzzy C-mean,FCM)聚类算法具有良好的抗噪声性能,但FCM是一种局部搜索算法,易陷入局部最优,而遗传算法则具有全局优化搜索的优点。基于此该文提出了一种改进的 FCM 算法与遗传算法结合的聚类方法,先运用遗传算法得到聚类中心,然后用改进的 FCM 聚类算法得到最优解。并基于真实采集的道路谱数据,利用该算法对路面不平度进行识别。试验结果表明,改进的 FCM 算法与遗传算法结合的聚类算法路面识别率为94.54%,比FCM聚类算法高出4.98个百分点,比改进FCM算法高出4.67个百分点,具有更好的处理噪声数据的能力,提高了聚类的准确率和路面的识别率。%With the development of Chinese economy and the increasing instruction of highway, road detection and identification has become the focus for infrastructure, thus the requirements of the road roughness detection accuracy are also urgent. In road recognition cases, due to different road load spectrum has obvious clustering features, it is feasible for road recognition by using clustering analysis method. However, clustering result is sensitive to the initial center, and often cannot achieve the result of the global optimal. FCM (fuzzy C-mean) clustering algorithm and improved FCM algorithm were used to measure the effect of clustering by using deterministic objective function, the existence of the local minimum points of the objective function made clustering result sensitive to the initial center. So FCM algorithm and improved FCM algorithm both have shortcomings and cannot solve the problem that the effect of clustering is bad when sample contains noise. In this paper a new method of improved FCM clustering algorithm combined with genetic algorithm was proposed. The method mainly included the following four steps: Firstly, genetic algorithm was adopted to get the clustering center. Secondly, improved FCM clustering

  1. Fuzzy Document Clustering Approach using WordNet Lexical Categories

    Science.gov (United States)

    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.

  2. Hybrid vehicles

    Energy Technology Data Exchange (ETDEWEB)

    West, J.G.W. [Electrical Machines (United Kingdom)

    1997-07-01

    The reasons for adopting hybrid vehicles result mainly from the lack of adequate range from electric vehicles at an acceptable cost. Hybrids can offer significant improvements in emissions and fuel economy. Series and parallel hybrids are compared. A combination of series and parallel operation would be the ideal. This can be obtained using a planetary gearbox as a power split device allowing a small generator to transfer power to the propulsion motor giving the effect of a CVT. It allows the engine to run at semi-constant speed giving better fuel economy and reduced emissions. Hybrid car developments are described that show the wide range of possible hybrid systems. (author)

  3. Meaningful Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.

    2004-05-26

    We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.

  4. Cluster Lenses

    CERN Document Server

    Kneib, Jean-Paul; 10.1007/s00159-011-0047-3

    2012-01-01

    Clusters of galaxies are the most recently assembled, massive, bound structures in the Universe. As predicted by General Relativity, given their masses, clusters strongly deform space-time in their vicinity. Clusters act as some of the most powerful gravitational lenses in the Universe. Light rays traversing through clusters from distant sources are hence deflected, and the resulting images of these distant objects therefore appear distorted and magnified. Lensing by clusters occurs in two regimes, each with unique observational signatures. The strong lensing regime is characterized by effects readily seen by eye, namely, the production of giant arcs, multiple-images, and arclets. The weak lensing regime is characterized by small deformations in the shapes of background galaxies only detectable statistically. Cluster lenses have been exploited successfully to address several important current questions in cosmology: (i) the study of the lens(es) - understanding cluster mass distributions and issues pertaining...

  5. Particle identification using clustering algorithms

    CERN Document Server

    Wirth, R; Löher, B; Savran, D; Silva, J; Pol, H Álvarez; Gil, D Cortina; Pietras, B; Bloch, T; Kröll, T; Nácher, E; Perea, Á; Tengblad, O; Bendel, M; Dierigl, M; Gernhäuser, R; Bleis, T Le; Winkel, M

    2013-01-01

    A method that uses fuzzy clustering algorithms to achieve particle identification based on pulse shape analysis is presented. The fuzzy c-means clustering algorithm is used to compute mean (principal) pulse shapes induced by different particle species in an automatic and unsupervised fashion from a mixed set of data. A discrimination amplitude is proposed using these principal pulse shapes to identify the originating particle species of a detector pulse. Since this method does not make any assumptions about the specific features of the pulse shapes, it is very generic and suitable for multiple types of detectors. The method is applied to discriminate between photon- and proton-induced signals in CsI(Tl) scintillator detectors and the results are compared to the well-known integration method.

  6. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

  7. Cluster Chemistry

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    @@ Cansisting of eight scientists from the State Key Laboratory of Physical Chemistry of Solid Surfaces and Xiamen University, this creative research group is devoted to the research of cluster chemistry and creation of nanomaterials.After three-year hard work, the group scored a series of encouraging progresses in synthesis of clusters with special structures, including novel fullerenes, fullerene-like metal cluster compounds as well as other related nanomaterials, and their properties study.

  8. Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

    DEFF Research Database (Denmark)

    Rutkowski Olesen, Jakob; Cordero, Jorge; Zeng, Yifeng

    2009-01-01

    This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data...

  9. Clustered regression with unknown clusters

    CERN Document Server

    Barman, Kishor

    2011-01-01

    We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...

  10. Subspace clustering through attribute clustering

    Institute of Scientific and Technical Information of China (English)

    Kun NIU; Shubo ZHANG; Junliang CHEN

    2008-01-01

    Many recently proposed subspace clustering methods suffer from two severe problems. First, the algorithms typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters. Second, the clustering results are often sensitive to input parameters. In this paper, a fast algorithm of subspace clustering using attribute clustering is proposed to over-come these limitations. This algorithm first filters out redundant attributes by computing the Gini coefficient. To evaluate the correlation of every two non-redundant attributes, the relation matrix of non-redundant attributes is constructed based on the relation function of two dimensional united Gini coefficients. After applying an overlapping clustering algorithm on the relation matrix, the candidate of all interesting subspaces is achieved. Finally, all subspace clusters can be derived by clustering on interesting subspaces. Experiments on both synthesis and real datasets show that the new algorithm not only achieves a significant gain of runtime and quality to find subspace clusters, but also is insensitive to input parameters.

  11. 基于半监督模糊聚类的黄瓜霜霉病受害程度识别研究%Research on Disease Level of Cucumber Downy Mildew Based on Partial Fuzzy C-means Algorithm

    Institute of Scientific and Technical Information of China (English)

    施伟民; 杨昔阳; 李志伟

    2012-01-01

    通过定义一种新的半监督模糊聚类算法,提高聚类算法的运行效率和可解释性,结合Fisher线性判别分析,对黄瓜霜霉病的受害程度进行识别研究.针对7个关于叶片色调信息的统计特征,利用判别分析提取出2个主分量.结合一部分叶片的类别属性,对所有叶片的这2个主分量,进行半监督聚类分析.结果表明,对于类别属性已知的叶片,聚类结果与已知类别的一致率达100%,而对于类别未知的数据,一致率也达到95%以上.%A novel fuzzy C-means algorithm is proposed to improve the intelligibility and computing efficiency of semi-supervised cluster algorithm. Based on Fisher linear discriminant and the proposed algorithm, the disease level of cucumber leaves infected by cucumber downy mildew are studied. 7 hue statistics of cucumber leaves are measured,and 2 feature parameters are founded based on linear discriminant analysis. The result of the partial Fuzzy Gmeuns alyorithm shows that the correct recognition rata is 100% on sample data,and above 95% on test data.

  12. Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

    Directory of Open Access Journals (Sweden)

    Nour-Eddine El Harchaoui

    2013-01-01

    Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

  13. The Cluster as Market Organization

    DEFF Research Database (Denmark)

    Maskell, Peter; Lorenzen, Mark

    2003-01-01

    The many competing schools of thought concerning themselves with industrial clusters have atleast one thing in common: they all agree that clusters are real life phenomena characterized bythe co-localization of separate economic entities, which are in some sense related, but not joinedtogether...... by any common ownership or management. So hierarchies they are certainly not.Yet, it is usually taken for granted that clusters, almost regardless of how they are defined, allexpatriate the 'swollen middle' of various hybrid 'forms of long-term contracting, reciprocaltrading, regulation, franchising...... organization or market form. The cluster is one suchspecific market organization that is structured along territorial lines because this enables thebuilding of a set of institutions that are helpful in conducting certain kinds of economicactivities....

  14. Gene Expression Data Knowledge Discovery using Global and Local Clustering

    CERN Document Server

    H, Swathi

    2010-01-01

    To understand complex biological systems, the research community has produced huge corpus of gene expression data. A large number of clustering approaches have been proposed for the analysis of gene expression data. However, extracting important biological knowledge is still harder. To address this task, clustering techniques are used. In this paper, hybrid Hierarchical k-Means algorithm is used for clustering and biclustering gene expression data is used. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. A validation technique, Figure of Merit is used to determine the quality of clustering results. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. To determine the quality of clustering results, a validation technique, Figure of Merit is used. Appropriate ...

  15. Hybrid Metaheuristics

    CERN Document Server

    2013-01-01

    The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.

  16. Visible Watermarking within the Region of Non-Interest of Medical Images Based on Fuzzy C-Means and Harris Corner Detection

    Directory of Open Access Journals (Sweden)

    Debalina Biswas

    2013-05-01

    Full Text Available Transfer of medical information amongst various hos pitals and diagnostic centers for mutual availability of diagnostic and therapeutic case stu dies is a very common process. Watermarking is adding “ownership” information in multimedia con tents to verify signal integrity, prove authenticity and achieve control over the copy proc ess. Distortion in Region of Interest (ROI of a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment. Therefore, proper selection of Region of Non-Intere st (RONI in a medical image is very crucial for adding watermark. First part of the present wor k proposes proper selection of Region of Non-Interest based on Fuzzy C-Means segmentation an d Harris corner detection, to improve retention of diagnostic value lost in embedding own ership information. The second part of the work presents watermark embedding in the selected a rea of RONI based on alpha blending technique. In this approach, the generated watermar ked image having an acceptable level of imperceptibility and distortion is compared to the original image. The Peak Signal to Noise Ratio (PSNR of the original image vs. watermarked image is calculated to prove the efficacy of the proposed method.

  17. Optimization of automated segmentation of monkeypox virus-induced lung lesions from normal lung CT images using hard C-means algorithm

    Science.gov (United States)

    Castro, Marcelo A.; Thomasson, David; Avila, Nilo A.; Hufton, Jennifer; Senseney, Justin; Johnson, Reed F.; Dyall, Julie

    2013-03-01

    Monkeypox virus is an emerging zoonotic pathogen that results in up to 10% mortality in humans. Knowledge of clinical manifestations and temporal progression of monkeypox disease is limited to data collected from rare outbreaks in remote regions of Central and West Africa. Clinical observations show that monkeypox infection resembles variola infection. Given the limited capability to study monkeypox disease in humans, characterization of the disease in animal models is required. A previous work focused on the identification of inflammatory patterns using PET/CT image modality in two non-human primates previously inoculated with the virus. In this work we extended techniques used in computer-aided detection of lung tumors to identify inflammatory lesions from monkeypox virus infection and their progression using CT images. Accurate estimation of partial volumes of lung lesions via segmentation is difficult because of poor discrimination between blood vessels, diseased regions, and outer structures. We used hard C-means algorithm in conjunction with landmark based registration to estimate the extent of monkeypox virus induced disease before inoculation and after disease progression. Automated estimation is in close agreement with manual segmentation.

  18. New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method

    Directory of Open Access Journals (Sweden)

    Katayoon Ahmadi

    2016-01-01

    Full Text Available Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of ​​class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA is applied for segmentation of liver’s blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.

  19. Cluster editing

    DEFF Research Database (Denmark)

    Böcker, S.; Baumbach, Jan

    2013-01-01

    . The problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications......The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side...

  20. Weighted Clustering

    CERN Document Server

    Ackerman, Margareta; Branzei, Simina; Loker, David

    2011-01-01

    In this paper we investigate clustering in the weighted setting, in which every data point is assigned a real valued weight. We conduct a theoretical analysis on the influence of weighted data on standard clustering algorithms in each of the partitional and hierarchical settings, characterising the precise conditions under which such algorithms react to weights, and classifying clustering methods into three broad categories: weight-responsive, weight-considering, and weight-robust. Our analysis raises several interesting questions and can be directly mapped to the classical unweighted setting.

  1. Cluster analysis

    CERN Document Server

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  2. Hybrid intermediaries

    OpenAIRE

    Cetorelli, Nicola

    2014-01-01

    I introduce the concept of hybrid intermediaries: financial conglomerates that control a multiplicity of entity types active in the "assembly line" process of modern financial intermediation, a system that has become known as shadow banking. The complex bank holding companies of today are the best example of hybrid intermediaries, but I argue that financial firms from the "nonbank" space can just as easily evolve into conglomerates with similar organizational structure, thus acquiring the cap...

  3. Hybrid composites

    CSIR Research Space (South Africa)

    Jacob John, Maya

    2009-04-01

    Full Text Available effect was observed for the elongation at break of the hybrid composites. The impact strength of the hybrid composites increased with the addition of glass fibres. The tensile and impact properties of thermoplastic natural rubber reinforced short... panels made from conventional structural materials. Figure 3 illustrates the performance of cellular biocomposite panels against conventional systems used for building and residential construction, namely a pre- cast pre-stressed hollow core concrete...

  4. The interrelation between plus-hybrid effect on grain yield and genetic distance of studied hybrids

    Directory of Open Access Journals (Sweden)

    Božinović Sofija

    2010-01-01

    Full Text Available The combined effect of cytoplasmic male sterility and xenia is referred to as the Plus-Hybrid effect. A mixture of hybrids, in which one is a sterile female component and the other is a fertile pollinator, was sown. The objective of the present study was to determine whether the increase of a hybrid genetic distance would result in the increased gain from Plus-hybrid effects on grain yield. Two ZP hybrids (ZP 1 and ZP 2, i.e. their sterile and fertile counterparts, as well as, five hybrid pollinators (ZP 1, ZP 2, ZP 3, ZP 4 and ZP 5 were selected for the studies. The three-replicate trail was set up according to the randomized split-plot design at Zemun Polje in 2009. SSR markers were used to determine the genetic distance between hybrids. Ten out of total 12 applied primers gave results. Coefficients of similarity were estimated according to Dice and Jaccard. The greatest (0.37, i.e. smallest genetic distance (0.08, according to Dice, was obtained between hybrids ZP 1 and ZP 5, i.e. ZP 1 and ZP 4, respectively. Values of genetic distance according to Jaccard were between 0.14 (ZP 1 and ZP 4 and 0.54 (ZP 1 i ZP 5 . By using the cluster analysis, four hybrids (ZP 1, ZP 4, ZP 3 and ZP 2 were grouped into one sub-cluster that was loosely linked to ZP 5. The Plus-hybrid effect on grain yield of the hybrid ZP 1 was negative. The greatest gain was detected in the ZP 2st ' ZP 1 combination, between two hybrids that were genetically very similar and belonged to the same sub-cluster, and then in ZP 2st x ZP 3 and ZP 2st x ZP 4 combinations, between hybrids that also belonged to the same sub-cluster. It can be concluded that the Plus-hybrid effect, after all, depends not on the hybrid genetic distance but on the hybrid genotype.

  5. Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD and fuzzy c-means (FCM clustering method based on Local Mean Decomposition (LMD and time segmentation energy entropy (TSEE is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs. Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.

  6. Cluster forcing

    DEFF Research Database (Denmark)

    Christensen, Thomas Budde

    .g. sustainability or quality of life. The purpose of this paper is to explore how and to what extent public sector interventions that aim at forcing cluster development in industries can support sustainable development as defined in the Brundtland tradition and more recently elaborated in such concepts as eco......, Portugal and New Zealand have adopted the concept. Public sector interventions that aim to support cluster development in industries most often focus upon economic policy goals such as enhanced employment and improved productivity, but rarely emphasise broader societal policy goals relating to e...... to the automotive sector in Wales. Specifically, the paper evaluates the "Accelerates" programme initiated by the Welsh Development Agency and elaborates on how and to what extent the Accelerate programme supports the development of a sustainable automotive industry cluster. The Accelerate programme was set up...

  7. Effects of Cluster Size on Platinum-Oxygen Bonds Formation in Small Platinum Clusters

    Science.gov (United States)

    Oemry, Ferensa; Padama, Allan Abraham B.; Kishi, Hirofumi; Kunikata, Shinichi; Nakanishi, Hiroshi; Kasai, Hideaki; Maekawa, Hiroyoshi; Osumi, Kazuo; Sato, Kaoru

    2012-03-01

    We present the results of density functional theory calculation in oxygen dissociative adsorption process on two types of isolated platinum (Pt) clusters: Pt4 and Pt10, by taking into account the effect of cluster reconstruction. The strength of Pt-Pt bonds in the clusters is mainly defined by d-d hybridization and interstitial bonding orbitals (IBO). Oxygen that adsorbed on the clusters is weakening the IBO and thus inducing geometry reconstruction as occurred in Pt10 cluster. However, cluster that could undergo structural deformation is found to promote oxygen dissociation with no energy barrier. The details show that maintaining well-balanced of attractive and repulsive (Hellmann-Feynman) forces between atoms is considered to be the main key to avoid any considerable rise of energy barrier. Furthermore, a modest energy barrier that gained in Pt4 cluster is presumed to be originate from inequality of intramolecular forces between atoms.

  8. A novel segmentation approach for implementation of MRAC in head PET/MRI employing Short-TE MRI and 2-point Dixon method in a fuzzy C-means framework

    Energy Technology Data Exchange (ETDEWEB)

    Khateri, Parisa; Rad, Hamidreza Saligheh [Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Jafari, Amir Homayoun [Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Ay, Mohammad Reza, E-mail: mohammadreza_ay@tums.ac.ir [Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of)

    2014-01-11

    Quantitative PET image reconstruction requires an accurate map of attenuation coefficients of the tissue under investigation at 511 keV (μ-map), and in order to correct the emission data for attenuation. The use of MRI-based attenuation correction (MRAC) has recently received lots of attention in the scientific literature. One of the major difficulties facing MRAC has been observed in the areas where bone and air collide, e.g. ethmoidal sinuses in the head area. Bone is intrinsically not detectable by conventional MRI, making it difficult to distinguish air from bone. Therefore, development of more versatile MR sequences to label the bone structure, e.g. ultra-short echo-time (UTE) sequences, certainly plays a significant role in novel methodological developments. However, long acquisition time and complexity of UTE sequences limit its clinical applications. To overcome this problem, we developed a novel combination of Short-TE (ShTE) pulse sequence to detect bone signal with a 2-point Dixon technique for water–fat discrimination, along with a robust image segmentation method based on fuzzy clustering C-means (FCM) to segment the head area into four classes of air, bone, soft tissue and adipose tissue. The imaging protocol was set on a clinical 3 T Tim Trio and also 1.5 T Avanto (Siemens Medical Solution, Erlangen, Germany) employing a triple echo time pulse sequence in the head area. The acquisition parameters were as follows: TE1/TE2/TE3=0.98/4.925/6.155 ms, TR=8 ms, FA=25 on the 3 T system, and TE1/TE2/TE3=1.1/2.38/4.76 ms, TR=16 ms, FA=18 for the 1.5 T system. The second and third echo-times belonged to the Dixon decomposition to distinguish soft and adipose tissues. To quantify accuracy, sensitivity and specificity of the bone segmentation algorithm, resulting classes of MR-based segmented bone were compared with the manual segmented one by our expert neuro-radiologist. Results for both 3 T and 1.5 T systems show that bone segmentation applied in several

  9. A novel segmentation approach for implementation of MRAC in head PET/MRI employing Short-TE MRI and 2-point Dixon method in a fuzzy C-means framework

    Science.gov (United States)

    Khateri, Parisa; Rad, Hamidreza Saligheh; Jafari, Amir Homayoun; Ay, Mohammad Reza

    2014-01-01

    Quantitative PET image reconstruction requires an accurate map of attenuation coefficients of the tissue under investigation at 511 keV (μ-map), and in order to correct the emission data for attenuation. The use of MRI-based attenuation correction (MRAC) has recently received lots of attention in the scientific literature. One of the major difficulties facing MRAC has been observed in the areas where bone and air collide, e.g. ethmoidal sinuses in the head area. Bone is intrinsically not detectable by conventional MRI, making it difficult to distinguish air from bone. Therefore, development of more versatile MR sequences to label the bone structure, e.g. ultra-short echo-time (UTE) sequences, certainly plays a significant role in novel methodological developments. However, long acquisition time and complexity of UTE sequences limit its clinical applications. To overcome this problem, we developed a novel combination of Short-TE (ShTE) pulse sequence to detect bone signal with a 2-point Dixon technique for water-fat discrimination, along with a robust image segmentation method based on fuzzy clustering C-means (FCM) to segment the head area into four classes of air, bone, soft tissue and adipose tissue. The imaging protocol was set on a clinical 3 T Tim Trio and also 1.5 T Avanto (Siemens Medical Solution, Erlangen, Germany) employing a triple echo time pulse sequence in the head area. The acquisition parameters were as follows: TE1/TE2/TE3=0.98/4.925/6.155 ms, TR=8 ms, FA=25 on the 3 T system, and TE1/TE2/TE3=1.1/2.38/4.76 ms, TR=16 ms, FA=18 for the 1.5 T system. The second and third echo-times belonged to the Dixon decomposition to distinguish soft and adipose tissues. To quantify accuracy, sensitivity and specificity of the bone segmentation algorithm, resulting classes of MR-based segmented bone were compared with the manual segmented one by our expert neuro-radiologist. Results for both 3 T and 1.5 T systems show that bone segmentation applied in several

  10. Genomic networks of hybrid sterility.

    Directory of Open Access Journals (Sweden)

    Leslie M Turner

    2014-02-01

    Full Text Available Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities". The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL. Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is

  11. Genomic networks of hybrid sterility.

    Science.gov (United States)

    Turner, Leslie M; White, Michael A; Tautz, Diethard; Payseur, Bret A

    2014-02-01

    Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities"). The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus) provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL). Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is applicable in a broad

  12. Image Segmentation with Fuzzy C-means Clustering Based on Image Patch%基于图像片的模糊C均值聚类图像分割

    Institute of Scientific and Technical Information of China (English)

    顾建伟

    2011-01-01

    本文提出了一种全新的基于图像片的模糊C均值聚类的图像分割方法.将图像片的思想引入聚类分割中,提出IPFCM方法,用局部的图像片来代替聚类分割中的像素点,从而增大不同类别之间的差异,并对隶属度更新函数进行改造使隶属度函数分布具有单峰值性.实验结果表明,本文方法具有较强的抗噪性和较高的分割精度,图像的隶属度函数与理想隶属度函数十分接近.同时无需过多控制参数,具有较强的可靠性和适应性.另一方面,本文将聚类中心的每一个成员扩展为一个向量,并给出了向量聚类中心的更新公式,为日后将多种图像特征加入FCM对图像进行分割提供了充分的理论基础.

  13. Cluster forcing

    DEFF Research Database (Denmark)

    Christensen, Thomas Budde

    The cluster theory attributed to Michael Porter has significantly influenced industrial policies in countries across Europe and North America since the beginning of the 1990s. Institutions such as the EU, OECD and the World Bank and governments in countries such as the UK, France, The Netherlands...

  14. Fuzzy Rules for Ant Based Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Amira Hamdi

    2016-01-01

    Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.

  15. Electronic Structure of Au25 Clusters: Between Discrete and Continuous

    KAUST Repository

    Katsiev, Khabiboulakh

    2016-07-15

    Here, an approach based on synchrotron resonant photoemission is emplyed to explore the transition between quantization and hybridization of the electronic structure in atomically precise ligand-stabilized nanoparticles. While the presence of ligands maintains quantization in Au25 clusters, their removal renders increased hybridization of the electronic states at the vicinity of the Fermi level. These observations are supported by DFT studies.

  16. Quotients of cluster categories

    OpenAIRE

    Jorgensen, Peter

    2007-01-01

    Higher cluster categories were recently introduced as a generalization of cluster categories. This paper shows that in Dynkin types A and D, half of all higher cluster categories are actually just quotients of cluster categories. The other half can be obtained as quotients of 2-cluster categories, the "lowest" type of higher cluster categories. Hence, in Dynkin types A and D, all higher cluster phenomena are implicit in cluster categories and 2-cluster categories. In contrast, the same is not...

  17. Regional Innovation Clusters

    Data.gov (United States)

    Small Business Administration — The Regional Innovation Clusters serve a diverse group of sectors and geographies. Three of the initial pilot clusters, termed Advanced Defense Technology clusters,...

  18. A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation

    Directory of Open Access Journals (Sweden)

    Liming Tang

    2014-01-01

    Full Text Available The fuzzy C means clustering algorithm with spatial constraint (FCMS is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

  19. Cluster Radioactivity

    Science.gov (United States)

    Poenaru, Dorin N.; Greiner, Walter

    One of the rare examples of phenomena predicted before experimental discovery, offers the opportunity to introduce fission theory based on the asymmetric two center shell model. The valleys within the potential energy surfaces are due to the shell effects and are clearly showing why cluster radioactivity was mostly detected in parent nuclei leading to a doubly magic lead daughter. Saddle point shapes can be determined by solving an integro-differential equation. Nuclear dynamics allows us to calculate the half-lives. The following cluster decay modes (or heavy particle radioactivities) have been experimentally confirmed: 14C, 20O, 23F, 22,24-26Ne, 28,30Mg, 32,34Si with half-lives in good agreement with predicted values within our analytical superasymmetric fission model. The preformation probability is calculated as the internal barrier penetrability. An universal curve is described and used as an alternative for the estimation of the half-lives. The macroscopic-microscopic method was extended to investigate two-alpha accompanied fission and true ternary fission. The methods developed in nuclear physics are also adapted to study the stability of deposited atomic clusters on the planar surfaces.

  20. Hybrid microelectronic technology

    Science.gov (United States)

    Moran, P.

    Various areas of hybrid microelectronic technology are discussed. The topics addressed include: basic thick film processing, thick film pastes and substrates, add-on components and attachment methods, thin film processing, and design of thick film hybrid circuits. Also considered are: packaging hybrid circuits, automating the production of hybrid circuits, application of hybrid techniques, customer's view of hybrid technology, and quality control and assurance in hybrid circuit production.

  1. Biomolecular hybrid material and process for preparing same and uses for same

    Science.gov (United States)

    Kim, Jungbae [Richland, WA

    2010-11-23

    Disclosed is a composition and method for fabricating novel hybrid materials comprised of, e.g., carbon nanotubes (CNTs) and crosslinked enzyme clusters (CECs). In one method, enzyme-CNT hybrids are prepared by precipitation of enzymes which are subsequently crosslinked, yielding crosslinked enzyme clusters (CECs) on the surface of the CNTs. The CEC-enzyme-CNT hybrids exhibit high activity per unit area or mass as well as improved enzyme stability and longevity over hybrid materials known in the art. The CECs in the disclosed materials permit multilayer biocatalytic coatings to be applied to surfaces providing hybrid materials suitable for use in, e.g., biocatalytic applications and devices as described herein.

  2. Gene ordering in partitive clustering using microarray expressions.

    Science.gov (United States)

    Ray, Shubhra Sankar; Bandyopadhyay, Sanghamitra; Pal, Sankar K

    2007-08-01

    A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarray gene expressions.Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fibroblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution.

  3. Gene ordering in partitive clustering using microarray expressions

    Indian Academy of Sciences (India)

    Shubhra Sankar Ray; Sanghamitra Bandyopadhyay; Sankar K Pal

    2007-08-01

    A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarray gene expressions. Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fibroblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution.

  4. DNA-Protected Silver Clusters for Nanophotonics

    Directory of Open Access Journals (Sweden)

    Elisabeth Gwinn

    2015-02-01

    Full Text Available DNA-protected silver clusters (AgN-DNA possess unique fluorescence properties that depend on the specific DNA template that stabilizes the cluster. They exhibit peak emission wavelengths that range across the visible and near-IR spectrum. This wide color palette, combined with low toxicity, high fluorescence quantum yields of some clusters, low synthesis costs, small cluster sizes and compatibility with DNA are enabling many applications that employ AgN-DNA. Here we review what is known about the underlying composition and structure of AgN-DNA, and how these relate to the optical properties of these fascinating, hybrid biomolecule-metal cluster nanomaterials. We place AgN-DNA in the general context of ligand-stabilized metal clusters and compare their properties to those of other noble metal clusters stabilized by small molecule ligands. The methods used to isolate pure AgN-DNA for analysis of composition and for studies of solution and single-emitter optical properties are discussed. We give a brief overview of structurally sensitive chiroptical studies, both theoretical and experimental, and review experiments on bringing silver clusters of distinct size and color into nanoscale DNA assemblies. Progress towards using DNA scaffolds to assemble multi-cluster arrays is also reviewed.

  5. Hybrid manifold embedding.

    Science.gov (United States)

    Liu, Yang; Liu, Yan; Chan, Keith C C; Hua, Kien A

    2014-12-01

    In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy called geodesic clustering is proposed to divide the original data set into several subsets with minimum nonlinearity. In the second layer, a supervised dimensionality reduction scheme called locally conjugate discriminant projection is performed on each subset for maximizing the discriminant information and minimizing the dimension redundancy simultaneously in the reduced low-dimensional space. By integrating these two layers in a unified mapping function, a supervised manifold embedding framework is established to describe both global and local manifold structure as well as to preserve the discriminative ability in the learned subspace. Experiments on various data sets validate the effectiveness of the proposed method.

  6. An Automatic Clustering Technique for Optimal Clusters

    CERN Document Server

    Pavan, K Karteeka; Rao, A V Dattatreya; 10.5121/ijcsea.2011.1412

    2011-01-01

    This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.

  7. CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS

    Directory of Open Access Journals (Sweden)

    M. Tan

    2017-09-01

    Full Text Available In change detection (CD of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method.

  8. Hybrid Gear

    Science.gov (United States)

    Handschuh, Robert F. (Inventor); Roberts, Gary D. (Inventor)

    2016-01-01

    A hybrid gear consisting of metallic outer rim with gear teeth and metallic hub in combination with a composite lay up between the shaft interface (hub) and gear tooth rim is described. The composite lay-up lightens the gear member while having similar torque carrying capability and it attenuates the impact loading driven noise/vibration that is typical in gear systems. The gear has the same operational capability with respect to shaft speed, torque, and temperature as an all-metallic gear as used in aerospace gear design.

  9. Hybrid Qualifications

    DEFF Research Database (Denmark)

    has turned out as a major focus of European education and training policies and certainly is a crucial principle underlying the European Qualifications Framework (EQF). In this context, «hybrid qualifications» (HQ) may be seen as an interesting approach to tackle these challenges as they serve «two...... masters», i.e. by producing skills for the labour market and enabling individuals to progress more or less directly to higher education. The specific focus of this book is placed on conditions, structures and processes which help to combine VET with qualifications leading into higher education...

  10. Small Al clusters on the Cu(111) surface: Atomic relaxation and vibrational properties

    Science.gov (United States)

    Rusina, G. G.; Borisova, S. D.; Chulkov, E. V.

    2010-11-01

    The relaxation and vibrational properties of both Al clusters and the (111) surface of a copper sub-strate were studied using the interatomic interaction potentials obtained in a tight-binding approximation. The presence of small aluminum clusters led to modification of the vibrational states of the substrate, a shift of the Rayleigh mode, and excitation of new Z-polarized modes. Hybridized modes localized on the cluster adatoms and the neighboring atoms of the substrate were found in the phonon spectrum. The localized dipole-active modes of the cluster and their strong hybridization with vibrations of the substrate points to desorption stability of the tri- and heptaatomic clusters.

  11. Identification of Counterfeit Alcoholic Beverages Using Cluster Analysis in Principal-Component Space

    Science.gov (United States)

    Khodasevich, M. A.; Sinitsyn, G. V.; Gres'ko, M. A.; Dolya, V. M.; Rogovaya, M. V.; Kazberuk, A. V.

    2017-07-01

    A study of 153 brands of commercial vodka products showed that counterfeit samples could be identified by introducing a unified additive at the minimum concentration acceptable for instrumental detection and multivariate analysis of UV-Vis transmission spectra. Counterfeit products were detected with 100% probability by using hierarchical cluster analysis or the C-means method in two-dimensional principal-component space.

  12. Effective FCM noise clustering algorithms in medical images.

    Science.gov (United States)

    Kannan, S R; Devi, R; Ramathilagam, S; Takezawa, K

    2013-02-01

    The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.

  13. Cluster Analysis of Customer Reviews Extracted from Web Pages

    Directory of Open Access Journals (Sweden)

    S. Shivashankar

    2010-01-01

    Full Text Available As e-commerce is gaining popularity day by day, the web has become an excellent source for gathering customer reviews / opinions by the market researchers. The number of customer reviews that a product receives is growing at very fast rate (It could be in hundreds or thousands. Customer reviews posted on the websites vary greatly in quality. The potential customer has to read necessarily all the reviews irrespective of their quality to make a decision on whether to purchase the product or not. In this paper, we make an attempt to assess are view based on its quality, to help the customer make a proper buying decision. The quality of customer review is assessed as most significant, more significant, significant and insignificant.A novel and effective web mining technique is proposed for assessing a customer review of a particular product based on the feature clustering techniques, namely, k-means method and fuzzy c-means method. This is performed in three steps : (1Identify review regions and extract reviews from it, (2 Extract and cluster the features of reviews by a clustering technique and then assign weights to the features belonging to each of the clusters (groups and (3 Assess the review by considering the feature weights and group belongingness. The k-means and fuzzy c-means clustering techniques are implemented and tested on customer reviews extracted from web pages. Performance of these techniques are analyzed.

  14. Personal credit scoring based on hybrid support vector machines with cluster analysis%基于聚类和支持向量机的个人信誉评估方法

    Institute of Scientific and Technical Information of China (English)

    刘夫成; 高尚

    2013-01-01

    There are some problems exist in traditional individual credit assessment system. To solve those problems, a credit assessment model basesed on k-means method and support vector method is proposed. First the training samples are clustered using the K-means method. Then, the new samples defined according the feature of samples in cluster train the support vector machines, and to classify the test set by SVM. The result shows the approach improves training precision and test precision of the whole model compared with the traditional support vector classification method and improved the training speed.%针对传统的个人信誉评估方法存在的缺陷,提出了一种基于K均值聚类和支持向量机结合的个人信誉评估方法.该方法先将测试数据集进行聚类,根据数据离聚类的数据分布来选取合适数据训练支持向量机,然后利用支持向量机进行分类.结果表明,同单一利用支持向量机分类进行比较,该方法减少了训练时间,同时具有较高的测试精度,比传统的个人信誉评估模型有更好的效果.

  15. Intuitionistic hybrid logic

    DEFF Research Database (Denmark)

    Braüner, Torben

    2011-01-01

    Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area.......Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area....

  16. Continuity Controlled Hybrid Automata

    OpenAIRE

    Bergstra, J. A.; Middelburg, C.A.

    2004-01-01

    We investigate the connections between the process algebra for hybrid systems of Bergstra and Middelburg and the formalism of hybrid automata of Henzinger et al. We give interpretations of hybrid automata in the process algebra for hybrid systems and compare them with the standard interpretation of hybrid automata as timed transition systems. We also relate the synchronized product operator on hybrid automata to the parallel composition operator of the process algebra. It turns out that the f...

  17. Heavy hitters via cluster-preserving clustering

    DEFF Research Database (Denmark)

    Larsen, Kasper Green; Nelson, Jelani; Nguyen, Huy L.

    2016-01-01

    , providing correctness whp. In fact, a simpler version of our algorithm for p = 1 in the strict turnstile model answers queries even faster than the "dyadic trick" by roughly a log n factor, dominating it in all regards. Our main innovation is an efficient reduction from the heavy hitters to a clustering...... problem in which each heavy hitter is encoded as some form of noisy spectral cluster in a much bigger graph, and the goal is to identify every cluster. Since every heavy hitter must be found, correctness requires that every cluster be found. We thus need a "cluster-preserving clustering" algorithm......, that partitions the graph into clusters with the promise of not destroying any original cluster. To do this we first apply standard spectral graph partitioning, and then we use some novel combinatorial techniques to modify the cuts obtained so as to make sure that the original clusters are sufficiently preserved...

  18. An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining

    Directory of Open Access Journals (Sweden)

    Amreen Khan,

    2010-07-01

    Full Text Available Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI has recently emerged that meets these requirements and has successfully been applied to a number ofreal world clustering problems. This paper looks into the use ofParticle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.

  19. Hybridized tetraquarks

    Directory of Open Access Journals (Sweden)

    A. Esposito

    2016-07-01

    Full Text Available We propose a new interpretation of the neutral and charged X,Z exotic hadron resonances. Hybridized-tetraquarks are neither purely compact tetraquark states nor bound or loosely bound molecules but rather a manifestation of the interplay between the two. While meson molecules need a negative or zero binding energy, its counterpart for h-tetraquarks is required to be positive. The formation mechanism of this new class of hadrons is inspired by that of Feshbach metastable states in atomic physics. The recent claim of an exotic resonance in the Bs0π± channel by the D0 Collaboration and the negative result presented subsequently by the LHCb Collaboration are understood in this scheme, together with a considerable portion of available data on X,Z particles. Considerations on a state with the same quantum numbers as the X(5568 are also made.

  20. Hybridized Tetraquarks

    CERN Document Server

    Esposito, A.; Polosa, A.D.

    2016-01-01

    We propose a new interpretation of the neutral and charged X, Z exotic hadron resonances. Hybridized-tetraquarks are neither purely compact tetraquark states nor bound or loosely bound molecules. The latter would require a negative or zero binding energy whose counterpart in h-tetraquarks is a positive quantity. The formation mechanism of this new class of hadrons is inspired by that of Feshbach metastable states in atomic physics. The recent claim of an exotic resonance in the Bs pi+- channel by the D0 collaboration and the negative result presented subsequently by the LHCb collaboration are understood in this scheme, together with a considerable portion of available data on X, Z particles. Considerations on a state with the same quantum numbers as the X(5568) are also made.

  1. Artificial spacecraft in hybrid simulations of the quasi-parallel Earth's bow shock: analysis of time series versus spatial profiles and a separation strategy for Cluster

    Directory of Open Access Journals (Sweden)

    J. Giacalone

    Full Text Available We construct artificial "software" spacecraft consisting of magnetometers and 3D thermal and energetic ion detectors. Four such spacecraft are "flown" through a 1D simulation of a quasi-parallel shock. We analyze the resulting time series from the spacecraft, and then use the more complete simulational information to evaluate our interpretations based on the limited times series information. The separation strategy used, with two closely spaced spacecraft pairs separated by a large distance, was helpful in the interpretation, since a variety of important processes operate over several different scale lengths. This work highlights the ability to draw inferences about spatially and temporally varying phenomena based on multiple-spacecraft time series data, and suggests that many spacecraft configurations which bear little resemblance to the classic Cluster tetrahedron may be necessary when multiple scale lengths are present.

  2. Electricity customer classification based on optimized FCM clustering by hybrid CSO%纵横交叉算法优化FCM在电力客户分类中的应用

    Institute of Scientific and Technical Information of China (English)

    孟安波; 卢海明; 李海亮; 谭火超; 郭壮志

    2015-01-01

    电力客户分类是供电企业客户关系管理的基石,为了提高聚类算法的稳定性和精确性,提出了一种纵横交叉算法(CSO)与模糊 C 均值算法(FCM)有机结合的新聚类算法(CSO-FCM),并用新算法进行客户分类.新方法有效弥补了单一算法的不足,拥有模糊理论处理不确定信息的能力以及纵横交叉算法全局收敛性强的特点.利用新算法对电力客户数据进行客观、科学的挖掘分析,实现了对电力大客户较全面和准确的精细化分类,为供电企业制定有针对性的营销策略提供了依据.%Electrical consumers segmentation is the cornerstone of consumers relation management of electrical supply enterprises, in order to improve stability and exactness of clustering algorithm, this paper proposes a novel clustering algorithm to conduct consumers segmentation, which is organic combination by crisscross optimization algorithm and FCM. This method effectively compensates the demerits of single intelligent algorithm, which not only has the ability to dispose unstable information of fuzzy theory, but also has an advantage of global convergence of CSO. The new algorithm is used to objectively and scientifically analyze the electrical consumers data, achieving comprehensive and accurate segmentation, which can offer a pointed marketing strategies for enterprises.

  3. Cluster headache

    Directory of Open Access Journals (Sweden)

    Ducros Anne

    2008-07-01

    Full Text Available Abstract Cluster headache (CH is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye. It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name in bouts that can occur during specific months of the year. Alcohol is the only dietary trigger of CH, strong odors (mainly solvents and cigarette smoke and napping may also trigger CH attacks. During bouts, attacks may happen at precise hours, especially during the night. During the attacks, patients tend to be restless. CH may be episodic or chronic, depending on the presence of remission periods. CH is associated with trigeminovascular activation and neuroendocrine and vegetative disturbances, however, the precise cautive mechanisms remain unknown. Involvement of the hypothalamus (a structure regulating endocrine function and sleep-wake rhythms has been confirmed, explaining, at least in part, the cyclic aspects of CH. The disease is familial in about 10% of cases. Genetic factors play a role in CH susceptibility, and a causative role has been suggested for the hypocretin receptor gene. Diagnosis is clinical. Differential diagnoses include other primary headache diseases such as migraine, paroxysmal hemicrania and SUNCT syndrome. At present, there is no curative treatment. There are efficient treatments to shorten the painful attacks (acute treatments and to reduce the number of daily attacks (prophylactic treatments. Acute treatment is based on subcutaneous administration of sumatriptan and high-flow oxygen. Verapamil, lithium, methysergide, prednisone, greater occipital nerve blocks and topiramate may be used for prophylaxis. In refractory cases, deep-brain stimulation of the

  4. Partitional clustering algorithms

    CERN Document Server

    2015-01-01

    This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

  5. An improved algorithm for clustering gene expression data.

    Science.gov (United States)

    Bandyopadhyay, Sanghamitra; Mukhopadhyay, Anirban; Maulik, Ujjwal

    2007-11-01

    Recent advancements in microarray technology allows simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important tool for analyzing such microarray data, typical properties of which are its inherent uncertainty, noise and imprecision. In this article, a two-stage clustering algorithm, which employs a recently proposed variable string length genetic scheme and a multiobjective genetic clustering algorithm, is proposed. It is based on the novel concept of points having significant membership to multiple classes. An iterated version of the well-known Fuzzy C-Means is also utilized for clustering. The significant superiority of the proposed two-stage clustering algorithm as compared to the average linkage method, Self Organizing Map (SOM) and a recently developed weighted Chinese restaurant-based clustering method (CRC), widely used methods for clustering gene expression data, is established on a variety of artificial and publicly available real life data sets. The biological relevance of the clustering solutions are also analyzed.

  6. Clustering and Community Detection with Imbalanced Clusters

    OpenAIRE

    Aksoylar, Cem; Qian, Jing; Saligrama, Venkatesh

    2016-01-01

    Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes since they tend to emphasize cut sizes over cut values. We propose a graph partitioning problem that seeks minimum cut partitions under minimum size constraints on partitions to de...

  7. Continuity Controlled Hybrid Automata

    NARCIS (Netherlands)

    Bergstra, J.A.; Middelburg, C.A.

    2004-01-01

    We investigate the connections between the process algebra for hybrid systems of Bergstra and Middelburg and the formalism of hybrid automata of Henzinger et al. We give interpretations of hybrid automata in the process algebra for hybrid systems and compare them with the standard interpretation of

  8. Continuity controlled Hybrid Automata

    NARCIS (Netherlands)

    Bergstra, J.A.; Middelburg, C.A.

    2008-01-01

    We investigate the connections between the process algebra for hybrid systems of Bergstra and Middelburg and the formalism of hybrid automata of Henzinger et al. We give interpretations of hybrid automata in the process algebra for hybrid systems and compare them with the standard interpretation of

  9. Cluster headaches.

    Science.gov (United States)

    Ryan, R E; Ryan, R E

    1989-12-01

    The patient with cluster headaches will be afflicted with the most severe type of pain that one will encounter. If the physician can do something to help this patient either by symptomatic or, more importantly, prophylactic treatment, he or she will have a most thankful patient. This type of headache is seen most frequently in men, and occurs in a cyclic manner. During an acute cycle, the patient will experience a daily type of pain that may occur many times per day. The pain is usually unilateral and may be accompanied by unilateral lacrimation, conjunctivitis, and clear rhinorrhea. Prednisone is the first treatment we employ. Patients are seen for follow-up approximately twice a week, and their medication is lowered in an appropriate manner, depending on their response to the treatment. Regulation of dosage has to be individualized, and when one reaches the lower dose such as 5 to 10 mg per day, the drug may have to be tapered more slowly, or even maintained at that level for a period of time to prevent further recurrence of symptoms. We frequently will use an intravenous histamine desensitization technique to prevent further attacks. We will give the patient an ergotamine preparation to use for symptomatic relief. As these patients often have headaches during the middle of the night, we will place the patient on a 2-mg ergotamine preparation to take prior to going to bed in the evening. This often works in a prophylactic nature, and prevents the nighttime occurrence of a headache. We believe that following these principles to make the accurate diagnosis and institute the proper therapy will help the practicing otolaryngologist recognize and treat patients suffering from this severe pain.

  10. Classifying OECD Countries According to Health Indicators Using Fuzzy Clustering Ana lysis

    Directory of Open Access Journals (Sweden)

    Nesrin Alptekin

    2015-12-01

    Full Text Available This study was conducted in order to classify OECD countries according to health indicators using fuzzy clustering analysis, to identify the cluster in which Turkey is in and the other countries located in the same cluster with Turkey and to determine whether Turkey shows similar characteristics with other countries located in the same cluster or not. In the study, 34 OECD member countries were discussed. With ten variables that directly and indirectly affect the health, c- means clustering analysis was performed. The NCSS 10 software package was used to analyze the data.In the analysis, it was determined that the most appropriate cluster number is five; three countries involved in the first cluster, nine countries involved in the second cluster, nine countries involved in the third cluster, six countries involved in the fourth cluster and seven countries involved in the fifth cluster. Turkey is located in the fourth cluster. Other countries in the same cluster along with Turkey are Estonia, Hungary, Mexico, Poland and Chile

  11. Implementation of Clustering Algorithms for real datasets in Medical Diagnostics using MATLAB

    Directory of Open Access Journals (Sweden)

    B. Venkataramana

    2017-03-01

    Full Text Available As in the medical field, for one disease there require samples given by diagnosis. The samples will be analyzed by a doctor or a pharmacist. As the no. of patients increases their samples also increases, there require more time to analyze samples for deciding the stage of the disease. To analyze the sample every time requires a skilled person. The samples can be classified by applying them to clustering algorithms. Data clustering has been considered as the most important raw data analysis method used in data mining technology. Most of the clustering techniques proved their efficiency in many applications such as decision making systems, medical sciences, earth sciences etc. Partition based clustering is one of the main approach in clustering. There are various algorithms of data clustering, every algorithm has its own advantages and disadvantages. This work reports the results of classification performance of three such widely used algorithms namely K-means (KM, Fuzzy c-means and Fuzzy Possibilistic c-Means (FPCM clustering algorithms. To analyze these algorithms three known data sets from UCI machine learning repository are taken such as thyroid data, liver and wine. The efficiency of clustering output is compared with the classification performance, percentage of correctness. The experimental results show that K-means and FCM give same performance for liver data. And FCM and FPCM are giving same performance for thyroid and wine data. FPCM has more efficient classification performance in all the given data sets.

  12. Factorial PD-Clustering

    CERN Document Server

    Tortora, Cristina; Summa, Mireille Gettler

    2011-01-01

    Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed data optimizing a common criterion. Factorial PD-clustering is based on Probabilistic Distance clustering (PD-clustering). PD-clustering is an iterative, distribution free, probabilistic, clustering method. Factorial PD-clustering make a linear transformation of original variables into a reduced number of orthogonal ones using a common criterion with PD-Clustering. It is demonstrated that Tucker 3 decomposition allows to obtain this transformation. Factorial PD-clustering makes alternatively a Tucker 3 decomposition and a PD-clustering on transformed data until convergence. This method could significantly improve the algorithm performance and allows to work with large dataset, to improve the stability and the robustness of the method.

  13. Comparative Analysis of Cluster Validity Indices in Identifying Some Possible Genes Mediating Certain Cancers.

    Science.gov (United States)

    Ghosh, Anupam; Dhara, Bibhas Chandra; De, Rajat K

    2013-04-01

    In this article, we compare the performance of 19 cluster validity indices, in identifying some possible genes mediating certain cancers, based on gene expression data. For the purpose of this comparison, we have developed a method. The proposed method involves cluster generation, selection of the best k-value or c-values, cluster identification, identifying the altered gene cluster, scoring an altered gene cluster and determining the best k-value or c-value exploring through biological repositories. The effectiveness of the method has been demonstrated on three gene expression data sets dealing with human lung cancer, colon cancer, and leukemia. Here, we have used three clustering algorithms, i.e., k-means, PAM and fuzzy c-means. We have used biochemical pathways related to these cancers and p-value statistics for validating the study. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Application of Bibliographic Coupling versus Cited Titles Words in Patent Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Anahita Kermani

    2013-03-01

    Full Text Available Attribute selection is one of the steps before patent clustering. Various attributes can be used for clustering. In this study, the effect of using citation and citation title words, respectively, in form of bibliographic coupling and citation title words sharing, were measured and compared with each other, as patent attributes. This study was done in an experimental method, on a collection of 717 US Patent cited in the patents belong to 977/774 subclass of US Patent Classification. Fuzzy C-means was used for patent clustering and extended BCubed precision and extended BCubed recall were used as evaluation measure. The results showed that the clustering produced by bibliographic coupling had better performance than clustering used citation title words and existence of cluster structure were in a wider range of exhaustivity than citation title words.

  15. Structures of Mn clusters

    Indian Academy of Sciences (India)

    Tina M Briere; Marcel H F Sluiter; Vijay Kumar; Yoshiyuki Kawazoe

    2003-01-01

    The geometries of several Mn clusters in the size range Mn13–Mn23 are studied via the generalized gradient approximation to density functional theory. For the 13- and 19-atom clusters, the icosahedral structures are found to be most stable, while for the 15-atom cluster, the bcc structure is more favoured. The clusters show ferrimagnetic spin configurations.

  16. Dissolution of Globular Clusters

    OpenAIRE

    Baumgardt, Holger

    2006-01-01

    Globular clusters are among the oldest objects in galaxies, and understanding the details of their formation and evolution can bring valuable insight into the early history of galaxies. This review summarises the current knowledge about the dissolution of star clusters and discusses the implications of star cluster dissolution for the evolution of the mass function of star cluster systems in galaxies.

  17. Clustering of correlated networks

    OpenAIRE

    Dorogovtsev, S. N.

    2003-01-01

    We obtain the clustering coefficient, the degree-dependent local clustering, and the mean clustering of networks with arbitrary correlations between the degrees of the nearest-neighbor vertices. The resulting formulas allow one to determine the nature of the clustering of a network.

  18. Atmospheric Ion Clusters: Properties and Size Distributions

    Science.gov (United States)

    D'Auria, R.; Turco, R. P.

    2002-12-01

    Ions are continuously generated in the atmosphere by the action of galactic cosmic radiation. Measured charge concentrations are of the order of 103 ~ {cm-3} throughout the troposphere, increasing to about 5 x 103 ~ {cm-3} in the lower stratosphere [Cole and Pierce, 1965; Paltridge, 1965, 1966]. The lifetimes of these ions are sufficient to allow substantial clustering with common trace constituents in air, including water, nitric and sulfuric acids, ammonia, and a variety of organic compounds [e.g., D'Auria and Turco, 2001 and references cited therein]. The populations of the resulting charged molecular clusters represent a pre-nucleation phase of particle formation, and in this regard comprise a key segment of the over-all nucleation size spectrum [e.g., Castleman and Tang, 1972]. It has been suggested that these clusters may catalyze certain heterogeneous reactions, and given their characteristic crystal-like structures may act as freezing nuclei for supercooled droplets. To investigate these possibilities, basic information on cluster thermodynamic properties and chemical kinetics is needed. Here, we present new results for several relevant atmospheric ion cluster families. In particular, predictions based on quantum mechanical simulations of cluster structure, and related thermodynamic parameters, are compared against laboratory data. We also describe a hybrid approach for modeling cluster sequences that combines laboratory measurements and quantum predictions with the classical liquid droplet (Thomson) model to treat a wider range of cluster sizes. Calculations of cluster mass distributions based on this hybrid model are illustrated, and the advantages and limitations of such an analysis are summarized. References: Castelman, A. W., Jr., and I. N. Tang, Role of small clusters in nucleation about ions, J. Chem. Phys., 57, 3629-3638, 1972. Cole, R. K., and E. T. Pierce, Electrification in the Earth's atmosphere for altitudes between 0 and 100 kilometers, J

  19. Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods

    Science.gov (United States)

    Lange, Oliver; Meyer-Baese, Anke; Wismuller, Axel; Hurdal, Monica

    2005-03-01

    We employ unsupervised clustering techniques for the analysis of dynamic contrast-enhanced perfusion MRI time-series in patients with and without stroke. "Neural gas" network, fuzzy clustering based on deterministic annealing, self-organizing maps, and fuzzy c-means clustering enable self-organized data-driven segmentation w.r.t.fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.

  20. Contextualizing the Cluster

    DEFF Research Database (Denmark)

    Giacomin, Valeria

    This dissertation examines the case of the palm oil cluster in Malaysia and Indonesia, today one of the largest agricultural clusters in the world. My analysis focuses on the evolution of the cluster from the 1880s to the 1970s in order to understand how it helped these two countries to integrate......-researched topic in the cluster literature – the emergence of clusters, their governance and institutional change, and competition between rival cluster locations – through the case of the Southeast Asian palm oil cluster....

  1. Contextualizing the Cluster

    DEFF Research Database (Denmark)

    Giacomin, Valeria

    This dissertation examines the case of the palm oil cluster in Malaysia and Indonesia, today one of the largest agricultural clusters in the world. My analysis focuses on the evolution of the cluster from the 1880s to the 1970s in order to understand how it helped these two countries to integrate......-researched topic in the cluster literature – the emergence of clusters, their governance and institutional change, and competition between rival cluster locations – through the case of the Southeast Asian palm oil cluster....

  2. Facile assembly of tetragonal Pt clusters on graphene oxide for enhanced nonlinear optical properties

    Science.gov (United States)

    Zheng, Chan; Li, Yubing; Huang, Li; Li, Wei; Chen, Wenzhe

    2015-11-01

    A facile method to assemble tetragonal Pt clusters on the surface of graphene oxide (Pt-cluster/GO) using anatase TiO2 as a template is proposed. The morphology and structure of Pt-cluster/GO were investigated, revealing that tetragonal Pt clusters with a diameter of 20-50 nm composed of 2-3 nm Pt nanoparticles (NPs) were homogenously decorated on the surface of GO. The nonlinear optical properties were characterized by the open-aperture Z-scan technique in the nanosecond regime using a laser with wavelength of 532 nm. The as-prepared Pt-cluster/GO hybrid was found to show strong optical limiting (OL) effects for nanosecond laser pulses at 532 nm, and the OL performance is superior to that of carbon nanotubes, a benchmark optical limiter. Furthermore, the Z-scan results showed that the OL performance of the Pt-cluster/GO hybrid is superior to that of GO and the Pt-NP/GO hybrid. The OL behavior of the metal/GO composite nanostructure can be effectively tailored by altering the aggregation means of metal NPs. Scattering measurements suggested that nonlinear scattering (NLS) played an important role in the observed OL behavior in the Pt-cluster/GO hybrid. The OL properties of the Pt-cluster/GO hybrid are attributed to the reverse saturable absorption in the GO sheet and NLS in the metal NPs.

  3. Classification of excessive domestic water consumption using Fuzzy Clustering Method

    Science.gov (United States)

    Zairi Zaidi, A.; Rasmani, Khairul A.

    2016-08-01

    Demand for clean and treated water is increasing all over the world. Therefore it is crucial to conserve water for better use and to avoid unnecessary, excessive consumption or wastage of this natural resource. Classification of excessive domestic water consumption is a difficult task due to the complexity in determining the amount of water usage per activity, especially as the data is known to vary between individuals. In this study, classification of excessive domestic water consumption is carried out using a well-known Fuzzy C-Means (FCM) clustering algorithm. Consumer data containing information on daily, weekly and monthly domestic water usage was employed for the purpose of classification. Using the same dataset, the result produced by the FCM clustering algorithm is compared with the result obtained from a statistical control chart. The finding of this study demonstrates the potential use of the FCM clustering algorithm for the classification of domestic consumer water consumption data.

  4. Analysis of protein profiles using fuzzy clustering methods

    DEFF Research Database (Denmark)

    Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya

    clustering methods for their classification followed by various validation  measures.    The  clustering  algorithms  used  for  the  study  were  K-  means,  K- medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva.  The results presented in this study  conclude  that  the  protein  profiles  of  tissue......  samples  recorded  by  using  the  HPLC- LIF  system  and  the  data  analyzed  by  clustering  algorithms  quite  successfully  classifies them as belonging from normal and malignant conditions....

  5. Clustering in analytical chemistry.

    Science.gov (United States)

    Drab, Klaudia; Daszykowski, Michal

    2014-01-01

    Data clustering plays an important role in the exploratory analysis of analytical data, and the use of clustering methods has been acknowledged in different fields of science. In this paper, principles of data clustering are presented with a direct focus on clustering of analytical data. The role of the clustering process in the analytical workflow is underlined, and its potential impact on the analytical workflow is emphasized.

  6. Extension of K-Means Algorithm for clustering mixed data | Onuodu ...

    African Journals Online (AJOL)

    Extension of K-Means Algorithm for clustering mixed data. ... PROMOTING ACCESS TO AFRICAN RESEARCH ... In this work, a new hybrid method has been proposed which extends K-means algorithm to categorical domain and mixed-type ...

  7. Implementation of Hybrid Neuro-fuzzy Classifier%混合神经模糊分类器的实现

    Institute of Scientific and Technical Information of China (English)

    刘淑英

    2013-01-01

    Artificial neural network and fuzzy system were considered the main components of computation intelligence,the hybrid system about them was one of study topics in recent years. Classification is a research focus in data analysis,as data is complicated and diversi-fied,the requirements for classification will be increasingly high,sometimes only by experience and professional knowledge not to accu-rately classify. In view of their powerful data analysis functions,using neuro-fuzzy algorithm for data analysis will be meaningful and useful. In this paper,fuzzy C-means clustering algorithm model and Gath-Geva clustering algorithm model are proposed for the parame-ter classification,which is simulated,and obtain good results.%人工神经网络与模糊系统是计算智能的核心内容,二者的混合系统是近年来的一个研究热点。分类是数据分析中的研究重点,随着数据的复杂化和多样化,对分类的要求越来越高,有时仅凭经验和专业知识难以确切地进行分类,因此研究如何运用神经模糊分类算法进行数据分析具有重要意义与实用价值。鉴于其强大的数据分析功能,研究中采用模糊C均值聚类算法和Gath-Geva聚类算法对数据进行分类,并对测试数据进行仿真试验,其测试结果良好。

  8. Freeway incident detection based on improved fuzzy clustering arithmetic and ANFIS%基于改进模糊聚类与ANFIS的高速公路事件检测

    Institute of Scientific and Technical Information of China (English)

    姚磊; 刘渊

    2013-01-01

    为了准确并及时地发现高速公路上的交通事故隐患,减少事故引发的交通延迟,提高高速公路运行安全性,结合减法聚类与模糊C均值(FCM)聚类算法对输入样本数据进行聚类,建成初始模糊推理系统,然后通过神经网络的自学习机制,训练模糊系统参数,确定模糊推理规则,建立最终模糊模型。通过仿真实验结果对比,验证了基于改进模糊聚类与自适应神经模糊推理系统(ANFIS)建模方法的有效性。%In order to accurately and timely detect highway traffic accident, reduce traffic delay and improve highway safety, this paper combines subtractive clustering and Fuzzy C-Means(FCM) clustering method to cluster the input sample data to build the initial fuzzy inference system, then the hybrid algorithm is used to train the parameters of the fuzzy system, determine the fuzzy reasoning rules, and establish a final training fuzzy model. Compared with the simulation experimental results, the method obtains excellent performance on ROC(Receiver Operation Characteristic)curve, shows the validity of the modeling method based on the improved fuzzy clustering and Adaptive Neural Fuzzy Inference System(ANFIS).

  9. Fuzzy Clustering - Principles, Methods and Examples

    DEFF Research Database (Denmark)

    Kroszynski, Uri; Zhou, Jianjun

    1998-01-01

    One of the most remarkable advances in the field of identification and control of systems -in particular mechanical systems- whose behaviour can not be described by means of the usual mathematical models, has been achieved by the application of methods of fuzzy theory.In the framework of a study...... about identification of "black-box" properties by analysis of system input/output data sets, we have prepared an introductory note on the principles and the most popular data classification methods used in fuzzy modeling. This introductory note also includes some examples that illustrate the use...... of the methods. The examples were solved by hand and served as a test bench for exploration of the MATLAB capabilities included in the Fuzzy Control Toolbox. The fuzzy clustering methods described include Fuzzy c-means (FCM), Fuzzy c-lines (FCL) and Fuzzy c-elliptotypes (FCE)....

  10. From hybrid swarms to swarms of hybrids

    Science.gov (United States)

    The introgression of modern humans (Homo sapiens) with Neanderthals 40,000 YBP after a half-million years of separation, may have led to the best example of a hybrid swarm on earth. Modern trade and transportation in support of the human hybrids has continued to introduce additional species, genotyp...

  11. The Hybrid Museum: Hybrid Economies of Meaning

    DEFF Research Database (Denmark)

    Vestergaard, Vitus

    2013-01-01

    this article shows that there are two different museum mindsets where the second mindset leans towards participatory practices. It is shown how a museum can support a hybrid economy of meaning that builds on both a user generated economy of meaning and an institutional economy of meaning and adds value to both....... Such a museum is referred to as a hybrid museum....

  12. Hydraulic Hybrid Vehicles

    Science.gov (United States)

    EPA and the United Parcel Service (UPS) have developed a hydraulic hybrid delivery vehicle to explore and demonstrate the environmental benefits of the hydraulic hybrid for urban pick-up and delivery fleets.

  13. Hybrid Management in Hospitals

    DEFF Research Database (Denmark)

    Byrkjeflot, Haldor; Jespersen, Peter Kragh

    2010-01-01

    Artiklen indeholder et litteraturbaseret studium af ledelsesformer i sygehuse, hvor sundhedsfaglig ledelse og generel ledelse mikses til hybride ledelsesformer......Artiklen indeholder et litteraturbaseret studium af ledelsesformer i sygehuse, hvor sundhedsfaglig ledelse og generel ledelse mikses til hybride ledelsesformer...

  14. Resin Catalyst Hybrids

    Institute of Scientific and Technical Information of China (English)

    S. Asaoka

    2005-01-01

    @@ 1Introduction: What are resin catalyst hybrids? There are typically two types of resin catalyst. One is acidic resin which representative is polystyrene sulfonic acid. The other is basic resin which is availed as metal complex support. The objective items of this study on resin catalyst are consisting of pellet hybrid, equilibrium hybrid and function hybrid of acid and base,as shown in Fig. 1[1-5].

  15. Mesoscale hybrid calibration artifact

    Science.gov (United States)

    Tran, Hy D.; Claudet, Andre A.; Oliver, Andrew D.

    2010-09-07

    A mesoscale calibration artifact, also called a hybrid artifact, suitable for hybrid dimensional measurement and the method for make the artifact. The hybrid artifact has structural characteristics that make it suitable for dimensional measurement in both vision-based systems and touch-probe-based systems. The hybrid artifact employs the intersection of bulk-micromachined planes to fabricate edges that are sharp to the nanometer level and intersecting planes with crystal-lattice-defined angles.

  16. Hybrid evolving clique-networks and their communicability

    Science.gov (United States)

    Ding, Yimin; Zhou, Bin; Chen, Xiaosong

    2014-08-01

    Aiming to understand real-world hierarchical networks whose degree distributions are neither power law nor exponential, we construct a hybrid clique network that includes both homogeneous and inhomogeneous parts, and introduce an inhomogeneity parameter to tune the ratio between the homogeneous part and the inhomogeneous one. We perform Monte-Carlo simulations to study various properties of such a network, including the degree distribution, the average shortest-path-length, the clustering coefficient, the clustering spectrum, and the communicability.

  17. Realizing the Hybrid Library.

    Science.gov (United States)

    Pinfield, Stephen; Eaton, Jonathan; Edwards, Catherine; Russell, Rosemary; Wissenburg, Astrid; Wynne, Peter

    1998-01-01

    Outlines five projects currently funded by the United Kingdom's Electronic Libraries Program (eLib): HyLiFe (Hybrid Library of the Future), MALIBU (MAnaging the hybrid Library for the Benefit of Users), HeadLine (Hybrid Electronic Access and Delivery in the Library Networked Environment), ATHENS (authentication scheme), and BUILDER (Birmingham…

  18. Homoploid hybrid expectations

    Science.gov (United States)

    Homoploid hybrid speciation occurs when a stable, fertile, and reproductively isolated lineage results from hybridization between two distinct species without a change in ploidy level. Reproductive isolation between a homoploid hybrid species and its parents is generally attained via chromosomal re...

  19. Hybrid armature projectile

    Science.gov (United States)

    Hawke, Ronald S.; Asay, James R.; Hall, Clint A.; Konrad, Carl H.; Sauve, Gerald L.; Shahinpoor, Mohsen; Susoeff, Allan R.

    1993-01-01

    A projectile for a railgun that uses a hybrid armature and provides a seed block around part of the outer surface of the projectile to seed the hybrid plasma brush. In addition, the hybrid armature is continuously vaporized to replenish plasma in a plasma armature to provide a tandem armature and provides a unique ridge and groove to reduce plasama blowby.

  20. Intraply Hybrid Composite Design

    Science.gov (United States)

    Chamis, C. C.; Sinclair, J. H.

    1986-01-01

    Several theoretical approaches combined in program. Intraply hybrid composites investigated theoretically and experimentally at Lewis Research Center. Theories developed during investigations and corroborated by attendant experiments used to develop computer program identified as INHYD (Intraply Hybrid Composite Design). INHYD includes several composites micromechanics theories, intraply hybrid composite theories, and integrated hygrothermomechanical theory. Equations from theories used by program as appropriate for user's specific applications.

  1. Hybrid quantum information processing

    Energy Technology Data Exchange (ETDEWEB)

    Furusawa, Akira [Department of Applied Physics, School of Engineering, The University of Tokyo (Japan)

    2014-12-04

    I will briefly explain the definition and advantage of hybrid quantum information processing, which is hybridization of qubit and continuous-variable technologies. The final goal would be realization of universal gate sets both for qubit and continuous-variable quantum information processing with the hybrid technologies. For that purpose, qubit teleportation with a continuousvariable teleporter is one of the most important ingredients.

  2. What Makes Clusters Decline?

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    2015-01-01

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark....... The longitudinal study on the high-tech cluster reveals that technological lock-in and exit of key firms have contributed to decline. Entrepreneurship has a positive effect on the cluster’s adaptive capabilities, while multinational companies have contradicting effects by bringing in new resources to the cluster...

  3. Comprehensive cluster analysis with Transitivity Clustering.

    Science.gov (United States)

    Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

    2011-03-01

    Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

  4. The Cluster Substructure - Alignment Connection

    OpenAIRE

    Plionis, Manolis

    2001-01-01

    Using the APM cluster data we investigate whether the dynamical status of clusters is related to the large-scale structure of the Universe. We find that cluster substructure is strongly correlated with the tendency of clusters to be aligned with their nearest neighbour and in general with the nearby clusters that belong to the same supercluster. Furthermore, dynamically young clusters are more clustered than the overall cluster population. These are strong indications that cluster develop in ...

  5. The hydrogen hybrid option

    Energy Technology Data Exchange (ETDEWEB)

    Smith, J.R.

    1993-10-15

    The energy efficiency of various piston engine options for series hybrid automobiles are compared with conventional, battery powered electric, and proton exchange membrane (PEM) fuel cell hybrid automobiles. Gasoline, compressed natural gas (CNG), and hydrogen are considered for these hybrids. The engine and fuel comparisons are done on a basis of equal vehicle weight, drag, and rolling resistance. The relative emissions of these various fueled vehicle options are also presented. It is concluded that a highly optimized, hydrogen fueled, piston engine, series electric hybrid automobile will have efficiency comparable to a similar fuel cell hybrid automobile and will have fewer total emissions than the battery powered vehicle, even without a catalyst.

  6. Clustering-based limb identification for pressure ulcer risk assessment.

    Science.gov (United States)

    Baran Pouyan, M; Nourani, M; Pompeo, M

    2015-01-01

    Bedridden patients have a high risk of developing pressure ulcers. Risk assessment for pressure ulceration is critical for preventive care. For a reliable assessment, we need to identify and track the limbs continuously and accurately. In this paper, we propose a method to identify body limbs using a pressure mat. Three prevalent sleep postures (supine, left and right postures) are considered. Then, predefined number of limbs (body parts) are identified by applying Fuzzy C-Means (FCM) clustering on key attributes. We collected data from 10 adult subjects and achieved average accuracy of 93.2% for 10 limbs in supine and 7 limbs in left/right postures.

  7. A Comparison of Methods for Player Clustering via Behavioral Telemetry

    DEFF Research Database (Denmark)

    Drachen, Anders; Thurau, Christian; Sifa, Rafet

    2013-01-01

    can be exceptionally complex, with features recorded for a varying population of users over a temporal segment that can reach years in duration. Categorization of behaviors, whether through descriptive methods (e.g. segmentation) or unsupervised/supervised learning techniques, is valuable for finding...... patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Nonnegative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations...

  8. Nuclear Clusters in Astrophysics

    Energy Technology Data Exchange (ETDEWEB)

    Kubono, S.; Binh, Dam N.; Hayakawa, S.; Hashimoto, H.; Kahl, D.; Wakabayashi, Y.; Yamaguchi, H. [Center for Nuclear Study (CNS), University of Tokyo, Wako Branch at RIKEN 2-1 Hirosawa, Wako, Saitama, 351-0198 (Japan); Teranishi, T. [Department of Physics, Kyushu University, Fukuoka, 812-8581 (Japan); Iwasa, N. [Department of Physics, Tohoku University, Sendai, 980-8578 (Japan); Komatsubara, T. [Department of Physics, Tsukuba University, Ibaraki, 305-8571 (Japan); Kato, S. [Department of Physics, Yamagata University, Yamagata, 990-8560 (Japan); Khiem, Le H. [Institute of Physics, Vietnam Academy for Science and Technology, Hanoi (Viet Nam)

    2010-03-01

    The role of nuclear clustering is discussed for nucleosynthesis in stellar evolution with Cluster Nucleosynthesis Diagram (CND) proposed before. Special emphasis is placed on alpha-induced stellar reactions together with molecular states for O and C burning.

  9. Hybridization and extinction.

    Science.gov (United States)

    Todesco, Marco; Pascual, Mariana A; Owens, Gregory L; Ostevik, Katherine L; Moyers, Brook T; Hübner, Sariel; Heredia, Sylvia M; Hahn, Min A; Caseys, Celine; Bock, Dan G; Rieseberg, Loren H

    2016-08-01

    Hybridization may drive rare taxa to extinction through genetic swamping, where the rare form is replaced by hybrids, or by demographic swamping, where population growth rates are reduced due to the wasteful production of maladaptive hybrids. Conversely, hybridization may rescue the viability of small, inbred populations. Understanding the factors that contribute to destructive versus constructive outcomes of hybridization is key to managing conservation concerns. Here, we survey the literature for studies of hybridization and extinction to identify the ecological, evolutionary, and genetic factors that critically affect extinction risk through hybridization. We find that while extinction risk is highly situation dependent, genetic swamping is much more frequent than demographic swamping. In addition, human involvement is associated with increased risk and high reproductive isolation with reduced risk. Although climate change is predicted to increase the risk of hybridization-induced extinction, we find little empirical support for this prediction. Similarly, theoretical and experimental studies imply that genetic rescue through hybridization may be equally or more probable than demographic swamping, but our literature survey failed to support this claim. We conclude that halting the introduction of hybridization-prone exotics and restoring mature and diverse habitats that are resistant to hybrid establishment should be management priorities.

  10. Analysis of genetic diversity of maize hybrids in the regional tests in Sichuan and Southwest China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In this study,analyses of phenotypic characters,SSR molecular markers and pedigrees were done to study the genetic diversity in 186 maize hybrids that were tested in regional trials in Sichuan and Southwest China.The results showed that there were differences in the variation coefficients of different characteristics,but all of the variation coefficients changed within a narrow range.Sixty pairs of simple sequence repeat (SSR) primer distributed on the ten chromosomes of maize produced stable amplified bands and 608 alleles were detected among the hybrids.The average number of alleles per locus was 10.1 ranging from 3 to 23.The values of polymorphism information content (PIC) for each SSR locus varied from 0.5179 to 0.9256 with an average of 0.7826.The genetic similarities of SSR marker pattern among the 186 hybrids ranged from 0.6067 to 0.9162,with an average of 0.7722.There were 16499 pairs of genetic similarity,in which 96.9% were 0.70000 to 0.9256.The cluster analysis showed that the hybrids could be classified into ten clusters,with 88.2% of the hybrids included in Cluster 4,Cluster 8 and Cluster 10.The analysis of pedigree sources of 51 hybrids showed that 36 hybrids had close genetic relationships with the hybrids developed by the Pioneer Company in the late 1980s and early 1990s in the United States,such as Y78599,Y7865 and Y78698,accounting for 70.58%.Meanwhile,13 hybrids had close genetic relationships with Y78599,accounting for 8.66%.The genetic similarities of SSR marker pattern among the 51 hybrids ranged from 0.66192 to 0.8799,with an average of 0.7686.There were 1196 pairs of genetic similarity ranged between 0.7000 to 0.8796,accounting for 93.80% of all the genetic similarity pairs.The cluster analysis showed that 88.2% of the 51 hybrids were in Cluster 4,Cluster 8 and Cluster 10,which indicated that similarity was high and genetic diversity narrow among the 186 hybrids.This showed that it is necessary to broaden the genetic basis of breeding

  11. [Pathophysiology of cluster headache].

    Science.gov (United States)

    Donnet, Anne

    2015-11-01

    The aetiology of cluster headache is partially unknown. Three areas are involved in the pathogenesis of cluster headache: the trigeminal nociceptive pathways, the autonomic system and the hypothalamus. The cluster headache attack involves activation of the trigeminal autonomic reflex. A dysfunction located in posterior hypothalamic gray matter is probably pivotal in the process. There is a probable association between smoke exposure, a possible genetic predisposition and the development of cluster headache.

  12. A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

    Directory of Open Access Journals (Sweden)

    Zhi-Yong Li

    2015-10-01

    Full Text Available As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.

  13. Spoof Plasmon Hybridization

    CERN Document Server

    Zhang, Jingjing; Luo, Yu; Shen, Xiaopeng; Maier, Stefan A; Cui, Tie Jun

    2016-01-01

    Plasmon hybridization between closely spaced nanoparticles yields new hybrid modes not found in individual constituents, allowing for the engineering of resonance properties and field enhancement capabilities of metallic nanostructure. Experimental verifications of plasmon hybridization have been thus far mostly limited to optical frequencies, as metals cannot support surface plasmons at longer wavelengths. Here, we introduce the concept of 'spoof plasmon hybridization' in highly conductive metal structures and investigate experimentally the interaction of localized surface plasmon resonances (LSPR) in adjacent metal disks corrugated with subwavelength spiral patterns. We show that the hybridization results in the splitting of spoof plasmon modes into bonding and antibonding resonances analogous to molecular orbital rule and plasmonic hybridization in optical spectrum. These hybrid modes can be manipulated to produce enormous field enhancements (larger than 5000) by tuning the separation between disks or alte...

  14. 一种基于PSO优化HWFCM的快速水下图像分割算法%A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

    Institute of Scientific and Technical Information of China (English)

    王士龙; 徐玉如; 庞永杰

    2011-01-01

    The S/N of an underwater image is low and has a fuzzy edge. Ifusing traditional methods to process it directly, the result is not satisfying. Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background, its time-consuming computation is often an obstacle. The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task. So,by using the statistical characteristics of the gray image histogram, a fast and effective fuzzy C-means underwater image segmentation algorithm was presented. With the weighted histogram modifying the fuzzy membership, the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm, so as to speed up the efficiency of the segmentation, but also improve the quality of underwater image segmentation. Finally, particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above. It made up for the shortcomings that the FCM algorithm can not get the global optimal solution. Thus, on the one hand,it considers the global impact and achieves the local optimal solution, and on the other hand, further greatly increases the computing speed. Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced. They enhance efficiency and satisfy the requirements of a highly effective, real-time AUV.

  15. Cluster Ions and Atmospheric Processes

    Science.gov (United States)

    D'Auria, R.; Turco, R. P.

    We investigate the properties and possible roles of naturally occurring ions under at- mospheric conditions. Among other things, the formation of stable charged molecular clusters represents the initial stages of aerosol nucleation [e.g., Keesee and Castle- man, 1982], while the conversion of vapor to aggregates is the first step in certain atmospheric phase transitions [e.g. Hamill and Turco, 2000]. We analyze the stability and size distributions of common ionic clusters by solving the differential equations describing their growth and loss. The necessary reaction rate coefficients are deter- mined using kinetic and thermodynamic data. The latter are derived from direct labo- ratory measurements of equilibrium constants, from the classical charged liquid drop model applied to large aggregates (i.e., the Thomson model [Thomson, 1906]), and from quantum mechanical calculations of the thermodynamic potentials associated with the cluster structures. This approach allows us to characterize molecular clusters across the entire size range from true molecular species to larger aggregates exhibiting macroscopic behavior [D'Auria, 2001]. Cluster systems discussed in this talk include the proton hydrates (PHs) and nitrate-water and nitrate-nitric acid series [D'Auria and Turco, 2001]. These ions have frequently been detected in the stratosphere and tropo- sphere [e.g., Arnold et al., 1977; Viggiano and Arnold, 1981]. We show how the pro- posed hybrid cluster model can be extended to a wide range of ion systems, including non-proton hydrates (NPHs), mixed-ligand clusters such as nitrate-water-nitric acid and sulfate-sulfuric acid-water, as well as more exotic species containing ammonia, pyridine and other organic compounds found on ions [e.g., Eisele, 1988; Tanner and Eisele, 1991]. References: Arnold, F., D. Krankowsky and K. H. Marien, First mass spectrometric measurements of posi- tive ions in the stratosphere, Nature, 267, 30-32, 1977. D'Auria, R., A study of ionic

  16. Cluster Physics with Merging Galaxy Clusters

    Directory of Open Access Journals (Sweden)

    Sandor M. Molnar

    2016-02-01

    Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

  17. The Durban Auto Cluster

    DEFF Research Database (Denmark)

    Lorentzen, Jochen; Robbins, Glen; Barnes, Justin

    2004-01-01

    The paper describes the formation of the Durban Auto Cluster in the context of trade liberalization. It argues that the improvement of operational competitiveness of firms in the cluster is prominently due to joint action. It tests this proposition by comparing the gains from cluster activities i...

  18. The Durban Auto Cluster

    DEFF Research Database (Denmark)

    Lorentzen, Jochen; Robbins, Glen; Barnes, Justin

    2004-01-01

    The paper describes the formation of the Durban Auto Cluster in the context of trade liberalization. It argues that the improvement of operational competitiveness of firms in the cluster is prominently due to joint action. It tests this proposition by comparing the gains from cluster activities i...

  19. Marketing research cluster analysis

    Directory of Open Access Journals (Sweden)

    Marić Nebojša

    2002-01-01

    Full Text Available One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

  20. Cluster Correspondence Analysis

    NARCIS (Netherlands)

    M. van de Velden (Michel); A. Iodice D' Enza; F. Palumbo

    2014-01-01

    markdownabstract__Abstract__ A new method is proposed that combines dimension reduction and cluster analysis for categorical data. A least-squares objective function is formulated that approximates the cluster by variables cross-tabulation. Individual observations are assigned to clusters

  1. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    Science.gov (United States)

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

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

  2. Marine Fish Hybridization

    KAUST Repository

    He, Song

    2017-04-01

    Natural hybridization is reproduction (without artificial influence) between two or more species/populations which are distinguishable from each other by heritable characters. Natural hybridizations among marine fishes were highly underappreciated due to limited research effort; it seems that this phenomenon occurs more often than is commonly recognized. As hybridization plays an important role in biodiversity processes in the marine environment, detecting hybridization events and investigating hybridization is important to understand and protect biodiversity. The first chapter sets the framework for this disseration study. The Cohesion Species Concept was selected as the working definition of a species for this study as it can handle marine fish hybridization events. The concept does not require restrictive species boundaries. A general history and background of natural hybridization in marine fishes is reviewed during in chapter as well. Four marine fish hybridization cases were examed and documented in Chapters 2 to 5. In each case study, at least one diagnostic nuclear marker, screened from among ~14 candidate markers, was found to discriminate the putative hybridizing parent species. To further investigate genetic evidence to support the hybrid status for each hybrid offspring in each case, haploweb analysis on diagnostic markers (nuclear and/or mitochondrial) and the DAPC/PCA analysis on microsatellite data were used. By combining the genetic evidences, morphological traits, and ecological observations together, the potential reasons that triggered each hybridization events and the potential genetic/ecology effects could be discussed. In the last chapter, sequences from 82 pairs of hybridizing parents species (for which COI barcoding sequences were available either on GenBank or in our lab) were collected. By comparing the COI fragment p-distance between each hybridizing parent species, some general questions about marine fish hybridization were discussed: Is

  3. Clusters as a Form of Spatial Organisation of Economic Activity: Theory and Practical Observations

    Directory of Open Access Journals (Sweden)

    Shastitko Andrey

    2009-06-01

    Full Text Available This article aims at explaining the clustering of economic activity using instruments of new institutional economics, taking into account well-known descriptive characteristics of the cluster, as well as recent developments in research on hybrid institutional agreements, primarily, the research conducted by Michael Porter, Claude Ménard and others.

  4. Clusters as a Form of Spatial Organisation of Economic Activity: Theory and Practical Observations

    Directory of Open Access Journals (Sweden)

    Shastitko A.

    2009-01-01

    Full Text Available This article aims at explaining the clustering of economic activity using instruments of new institutional economics, taking into account well-known descriptive characteristics of the cluster, as well as recent developments in research on hybrid institutional agreements, primarily, the research conducted by Michael Porter, Claude Ménard and others.

  5. MST-BASED CLUSTERING TOPOLOGY CONTROL ALGORITHM FOR WIRELESS SENSOR NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Cai Wenyu; Zhang Meiyan

    2010-01-01

    In this paper,we propose a novel clustering topology control algorithm named Minimum Spanning Tree (MST)-based Clustering Topology Control (MCTC) for Wireless Sensor Networks (WSNs),which uses a hybrid approach to adjust sensor nodes' transmission power in two-tiered hierarchical WSNs. MCTC algorithm employs a one-hop Maximum Energy & Minimum Distance (MEMD) clustering algorithm to decide clustering status. Each cluster exchanges information between its own Cluster Members (CMs) locally and then deliveries information to the Cluster Head (CH). Moreover,CHs exchange information between CH and CH and afterwards transmits aggregated information to the base station finally. The intra-cluster topology control scheme uses MST to decide CMs' transmission radius,similarly,the inter-cluster topology control scheme applies MST to decide CHs' transmission radius. Since the intra-cluster topology control is a full distributed approach and the inter-cluster topology control is a pure centralized approach performed by the base station,therefore,MCTC algorithm belongs to one kind of hybrid clustering topology control algorithms and can obtain scalability topology and strong connectivity guarantees simultaneously. As a result,the network topology will be reduced by MCTC algorithm so that network energy efficiency will be improved. The simulation results verify that MCTC outperforms traditional topology control schemes such as LMST,DRNG and MEMD at the aspects of average node's degree,average node's power radius and network lifetime,respectively.

  6. Cluster analysis for applications

    CERN Document Server

    Anderberg, Michael R

    1973-01-01

    Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

  7. Range-Clustering Queries

    OpenAIRE

    Abrahamsen, Mikkel; de Berg, Mark; Buchin, Kevin; Mehr, Mehran; Mehrabi, Ali D.

    2017-01-01

    In a geometric $k$-clustering problem the goal is to partition a set of points in $\\mathbb{R}^d$ into $k$ subsets such that a certain cost function of the clustering is minimized. We present data structures for orthogonal range-clustering queries on a point set $S$: given a query box $Q$ and an integer $k>2$, compute an optimal $k$-clustering for $S\\setminus Q$. We obtain the following results. We present a general method to compute a $(1+\\epsilon)$-approximation to a range-clustering query, ...

  8. Cluster Decline and Resilience

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    -2011. Our longitudinal study reveals that technological lock-in and exit of key firms have contributed to impairment of the cluster’s resilience in adapting to disruptions. Entrepreneurship has a positive effect on cluster resilience, while multinational companies have contradicting effects by bringing......Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark, 1963...

  9. Management of cluster headache

    DEFF Research Database (Denmark)

    Tfelt-Hansen, Peer C; Jensen, Rigmor H

    2012-01-01

    and agitation. Patients may have up to eight attacks per day. Episodic cluster headache (ECH) occurs in clusters of weeks to months duration, whereas chronic cluster headache (CCH) attacks occur for more than 1 year without remissions. Management of cluster headache is divided into acute attack treatment....... In drug-resistant CCH, neuromodulation with either occipital nerve stimulation or deep brain stimulation of the hypothalamus is an alternative treatment strategy. For most cluster headache patients there are fairly good treatment options both for acute attacks and for prophylaxis. The big problem...

  10. Effective Transparency: A Test of Atomistic Laser-Cluster Models

    CERN Document Server

    Pandit, Rishi; Teague, Thomas; Hartwick, Zachary; Bigaouette, Nicolas; Ramunno, Lora; Ackad, Edward

    2016-01-01

    The effective transparency of rare-gas clusters, post-interaction with an extreme ultraviolet (XUV) pump pulse, is studied by using an atomistic hybrid quantum-classical molecular dynamics model. We find there is an intensity range in which an XUV probe pulse has no lasting effect on the average charge state of a cluster after being saturated by an XUV pump pulse: the cluster is effectively transparent to the probe pulse. The range of this phenomena increases with the size of the cluster and thus provides an excellent candidate for an experimental test of the effective transparency effect. We present predictions for the clusters at the peak of the laser pulse as well as the experimental time-of-flight signal expected along with trends which can be compared with. Significant deviations from these predictions would provide evidence for enhanced photoionization mechanism(s).

  11. Cluster State Quantum Computation and the Repeat-Until Scheme

    Science.gov (United States)

    Kwek, L. C.

    Cluster state computation or the one way quantum computation (1WQC) relies on an initially highly entangled state (called a cluster state) and an appropriate sequence of single qubit measurements along different directions, together with feed-forward based on the measurement results, to realize a quantum computation process. The final result of the computation is obtained by measuring the last remaining qubits in the computational basis. In this short tutorial on cluster state quantum computation, we will also describe the basic ideas of a cluster state and proceed to describe how a single qubit operation can be done on a cluster state. Recently, we proposed a repeat-until-success (RUS) scheme that could effectively be used to realize one-way quantum computer on a hybrid system of photons and atoms. We will briefly describe this RUS scheme and show how it can be used to entangled two distant stationary qubits.

  12. Bulk synthesis of polymer-inorganic colloidal clusters.

    Science.gov (United States)

    Perro, Adeline; Manoharan, Vinothan N

    2010-12-21

    We describe a procedure to synthesize colloidal clusters with polyhedral morphologies in high yield (liter quantities at up to 70% purity) using a combination of emulsion polymerization and inorganic surface chemistry. We show that the synthesis initially used for silica-polystyrene hybrid clusters can be generalized to create clusters from other inorganic and polymer particles. We also show that high yields of particular morphologies can be obtained by precise control of the inorganic seed particle size, a finding that can be explained using a hard-sphere packing model. These clusters can be further chemically modified for a variety of applications. Introducing a cross-linker leads to colloidal clusters that can be index matched in an appropriate solvent, allowing them to be used for particle tracking or optical studies of colloidal self-assembly. Also, depositing a thin silica layer on these colloids allows the surface properties to be controlled using silane chemistry.

  13. Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data

    Institute of Scientific and Technical Information of China (English)

    Haiyan Pan; Jun Zhu; Danfu Han

    2003-01-01

    A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance of HGACLUS and other methods was compared by using simulated data and open microarray gene-expression datasets. HGACLUS was generally found to be more accurate and robust than other methods discussed in this paper by the exact validation strategy and the explicit cluster number.

  14. Clusters in nuclei

    CERN Document Server

    Following the pioneering discovery of alpha clustering and of molecular resonances, the field of nuclear clustering is today one of those domains of heavy-ion nuclear physics that faces the greatest challenges, yet also contains the greatest opportunities. After many summer schools and workshops, in particular over the last decade, the community of nuclear molecular physicists has decided to collaborate in producing a comprehensive collection of lectures and tutorial reviews covering the field. This third volume follows the successful Lect. Notes Phys. 818 (Vol. 1) and 848 (Vol. 2), and comprises six extensive lectures covering the following topics:  - Gamma Rays and Molecular Structure - Faddeev Equation Approach for Three Cluster Nuclear Reactions - Tomography of the Cluster Structure of Light Nuclei Via Relativistic Dissociation - Clustering Effects Within the Dinuclear Model : From Light to Hyper-heavy Molecules in Dynamical Mean-field Approach - Clusterization in Ternary Fission - Clusters in Light N...

  15. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  16. Unconventional methods for clustering

    Science.gov (United States)

    Kotyrba, Martin

    2016-06-01

    Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. The topic of this paper is one of the modern methods of clustering namely SOM (Self Organising Map). The paper describes the theory needed to understand the principle of clustering and descriptions of algorithm used with clustering in our experiments.

  17. An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

    OpenAIRE

    Anitha, M.; P. Tamije Selvy

    2012-01-01

    Settings White Matter lesions (WMLs) are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM) and Grey Matter (GM). The aim of this paper is to
    automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means cluste...

  18. CLEAN: CLustering Enrichment ANalysis

    Directory of Open Access Journals (Sweden)

    Medvedovic Mario

    2009-07-01

    Full Text Available Abstract Background Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation. Results We developed a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score. The score is derived by correlating the clustering structure as a whole with functional categories of interest. We directly demonstrate that integrating biological knowledge in this way improves the reproducibility of conclusions derived from cluster analysis. The CLEAN score differentiates between the levels of functional coherence for genes within the same cluster based on their membership in enriched functional categories. We show that this aspect results in higher reproducibility across independent datasets and produces more informative genes for distinguishing different sample types than the scores based on the traditional cluster-wide analysis. We also demonstrate the utility of the CLEAN framework in comparing clusterings produced by different algorithms. CLEAN was implemented as an add-on R package and can be downloaded at http://Clusteranalysis.org. The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView. Conclusion Our results indicate that using the gene-specific functional coherence score improves the reproducibility of the

  19. Survey on Text Document Clustering

    OpenAIRE

    M.Thangamani; Dr.P.Thangaraj

    2010-01-01

    Document clustering is also referred as text clustering, and its concept is merely equal to data clustering. It is hardly difficult to find the selective information from an ‘N’number of series information, so that document clustering came into picture. Basically cluster means a group of similar data, document clustering means segregating the data into different groups of similar data. Clustering can be of mathematical, statistical or numerical domain. Clustering is a fundamental data analysi...

  20. Brain tumor segmentation based on a hybrid clustering technique

    OpenAIRE

    Eman Abdel-Maksoud; Mohammed Elmogy; Rashid Al-Awadi

    2015-01-01

    Image segmentation refers to the process of partitioning an image into mutually exclusive regions. It can be considered as the most essential and crucial process for facilitating the delineation, characterization, and visualization of regions of interest in any medical image. Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms...

  1. Introgressive hybridization as a promoter of genome reshuffling in natural homoploid fish hybrids (Cyprinidae, Leuciscinae).

    Science.gov (United States)

    Pereira, C S A; Aboim, M A; Ráb, P; Collares-Pereira, M J

    2014-03-01

    Understanding the mechanisms underlying diversification and speciation by introgressive hybridization is currently one of the major challenges in evolutionary biology. Here, the analysis of hybridization between two pairs of Iberian Leuciscinae provided new data on independent hybrid zones involving Achondrostoma oligolepis (AOL) and Pseudochondrostoma duriense (PDU), and confirmed the occurrence of hybrids between AOL and Pseudochondrostoma polylepis (PPO). A multilevel survey combining morphological, genetic and cytogenomic markers on a vast population screening successfully sorted the selected fishes as admixed. Results were similar in both AOL × PDU and AOL × PPO systems. Overall, hybrid morphotypes, cytogenomic data and genetic profiling indicated preferential backcrossing and suggested AOL as a major genomic contributor. Moreover, results implied AOL as more permissive to introgression than PDU or PPO. Although PDU- and PPO-like individuals appeared more resilient to genome modifications, AOL appeared to be more involved and affected by the ongoing hybridization events, as chromosomal translocations were only found in AOL-like individuals. All hybrids analysed evidenced extensive ribosomal DNA (rDNA) polymorphism that was not found in parental species, but usually seen falling within the range of possible parental combinations. Yet, transgressive phenotypes that cannot be explained by normal recombination, including more rDNA clusters than expected or the occurrence of syntenic rDNAs, were also detected. Present results proved rapid genomic evolution providing the genetic novelty for species to persist. In addition, although the ultimate consequences of such apparently extensive and recurrent events remain unknown, modern genome-wide methodologies are of great promise towards answering questions concerning the causes, dynamics and impacts of hybridization.

  2. Henkin and Hybrid Logic

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Huertas, Antonia; Manzano, Maria;

    2014-01-01

    Leon Henkin was not a modal logician, but there is a branch of modal logic that has been deeply influenced by his work. That branch is hybrid logic, a family of logics that extend orthodox modal logic with special proposition symbols (called nominals) that name worlds. This paper explains why...... Henkin’s techniques are so important in hybrid logic. We do so by proving a completeness result for a hybrid type theory called HTT, probably the strongest hybrid logic that has yet been explored. Our completeness result builds on earlier work with a system called BHTT, or basic hybrid type theory...... is due to the first-order perspective, which lies at the heart of Henin’s best known work and hybrid logic....

  3. Agricultural Clusters in the Netherlands

    NARCIS (Netherlands)

    Schouten, M.A.; Heijman, W.J.M.

    2012-01-01

    Michael Porter was the first to use the term cluster in an economic context. He introduced the term in The Competitive Advantage of Nations (1990). The term cluster is also known as business cluster, industry cluster, competitive cluster or Porterian cluster. This article aims at determining and

  4. Agricultural Clusters in the Netherlands

    NARCIS (Netherlands)

    Schouten, M.A.; Heijman, W.J.M.

    2012-01-01

    Michael Porter was the first to use the term cluster in an economic context. He introduced the term in The Competitive Advantage of Nations (1990). The term cluster is also known as business cluster, industry cluster, competitive cluster or Porterian cluster. This article aims at determining and mea

  5. Agricultural Clusters in the Netherlands

    NARCIS (Netherlands)

    Schouten, M.A.; Heijman, W.J.M.

    2012-01-01

    Michael Porter was the first to use the term cluster in an economic context. He introduced the term in The Competitive Advantage of Nations (1990). The term cluster is also known as business cluster, industry cluster, competitive cluster or Porterian cluster. This article aims at determining and mea

  6. Energetics of H$_2$ clusters from density functional and coupled cluster theories

    CERN Document Server

    Trail, J R; Needs, R J

    2016-01-01

    We use coupled-cluster quantum chemical methods to calculate the energetics of molecular clusters cut out of periodic molecular hydrogen structures that model observed phases of solid hydrogen. The hydrogen structures are obtained from Kohn-Sham density functional theory (DFT) calculations at pressures of 150, 250 and 350 GPa, which are within the pressure range in which phases II, III and IV are found to be stable. The calculated deviations in the DFT energies from the coupled-cluster data are reported for different functionals, and optimized functionals are generated which provide reduced errors. We give recommendations for semi-local and hybrid density functionals that are expected to accurately describe hydrogen at high pressures.

  7. Energetics of H2 clusters from density functional and coupled cluster theories

    Science.gov (United States)

    Trail, J. R.; López Ríos, P.; Needs, R. J.

    2017-03-01

    We use coupled-cluster quantum chemical methods to calculate the energetics of molecular clusters cut out of periodic molecular hydrogen structures that model observed phases of solid hydrogen. The hydrogen structures are obtained from Kohn-Sham density functional theory (DFT) calculations at pressures of 150, 250, and 350 GPa, which are within the pressure range in which phases II, III, and IV are found to be stable. The calculated deviations in the DFT energies from the coupled-cluster data are reported for different functionals, and optimized functionals are generated which provide reduced errors. We give recommendations for semilocal and hybrid density functionals that are expected to provide an accurate description of hydrogen at high pressures.

  8. BSA Hybrid Synthesized Polymer

    Institute of Scientific and Technical Information of China (English)

    Zong Bin LIU; Xiao Pei DENG; Chang Sheng ZHAO

    2006-01-01

    Bovine serum albumin (BSA), a naturally occurring biopolymer, was regarded as a polymeric material to graft to an acrylic acid (AA)-N-vinyl pyrrolidone (NVP) copolymer to form a biomacromolecular hybrid polymer. The hybrid polymer can be blended with polyethersulfone (PES) to increase the hydrophilicity of the PES membrane, which suggested that the hybrid polymer might have a wide application in the modification of biomaterials.

  9. Hybrid Action Systems

    DEFF Research Database (Denmark)

    Ronkko, Mauno; Ravn, Anders P.

    1997-01-01

    a differential action, which allows differential equations as primitive actions. The extension allows us to model hybrid systems with both continuous and discrete behaviour. The main result of this paper is an extension of such a hybrid action system with parallel composition. The extension does not change...... the original meaning of the parallel composition, and therefore also the ordinary action systems can be composed in parallel with the hybrid action systems....

  10. HYBRID VEHICLE CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    V. Dvadnenko

    2016-06-01

    Full Text Available The hybrid vehicle control system includes a start–stop system for an internal combustion engine. The system works in a hybrid mode and normal vehicle operation. To simplify the start–stop system, there were user new possibilities of a hybrid car, which appeared after the conversion. Results of the circuit design of the proposed system of basic blocks are analyzed.

  11. Nanoscale Organic Hybrid Electrolytes

    KAUST Repository

    Nugent, Jennifer L.

    2010-08-20

    Nanoscale organic hybrid electrolytes are composed of organic-inorganic hybrid nanostructures, each with a metal oxide or metallic nanoparticle core densely grafted with an ion-conducting polyethylene glycol corona - doped with lithium salt. These materials form novel solvent-free hybrid electrolytes that are particle-rich, soft glasses at room temperature; yet manifest high ionic conductivity and good electrochemical stability above 5V. © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Hybrid radiator cooling system

    Science.gov (United States)

    France, David M.; Smith, David S.; Yu, Wenhua; Routbort, Jules L.

    2016-03-15

    A method and hybrid radiator-cooling apparatus for implementing enhanced radiator-cooling are provided. The hybrid radiator-cooling apparatus includes an air-side finned surface for air cooling; an elongated vertically extending surface extending outwardly from the air-side finned surface on a downstream air-side of the hybrid radiator; and a water supply for selectively providing evaporative cooling with water flow by gravity on the elongated vertically extending surface.

  13. Hybrid Unifying Variable Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    In order to compare new phenomenon of topology change,evolution,hybrid ratio and network characteristics of unified hybrid network theoretical model with unified hybrid supernetwork model,this paper constructed unified hybrid variable supernetwork model(HUVSM).The first layer introduces a hybrid ratio dr,the

  14. Large Unifying Hybrid Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    For depicting multi-hybrid process,large unifying hybrid network model(so called LUHNM)has two sub-hybrid ratios except dr.They are deterministic hybrid ratio(so called fd)and random hybrid ratio(so called gr),respectively.

  15. Hybrid Rocket Technology

    National Research Council Canada - National Science Library

    Sankaran Venugopal; K K Rajesh; V Ramanujachari

    2011-01-01

    With their unique operational characteristics, hybrid rockets can potentially provide safer, lower-cost avenues for spacecraft and missiles than the current solid propellant and liquid propellant systems...

  16. Hybrid FOSS Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Armstrong researchers are continuing their efforts to further develop FOSS technologies. A hybrid FOSS technique (HyFOSS) employs conventional continuous grating...

  17. Stable isotope phenotyping via cluster analysis of NanoSIMS data as a method for characterizing distinct microbial ecophysiologies and sulfur-cycling in the environment

    Directory of Open Access Journals (Sweden)

    Katherine S Dawson

    2016-05-01

    Full Text Available Stable isotope probing (SIP is a valuable tool for gaining insights into ecophysiology and biogeochemical cycling of environmental microbial communities by tracking isotopically labeled compounds into cellular macromolecules as well as into byproducts of respiration. SIP, in conjunction with nanoscale secondary ion mass spectrometry (NanoSIMS, allows for the visualization of isotope incorporation at the single cell level. In this manner, both active cells within a diverse population as well as heterogeneity in metabolism within a homogeneous population can be observed. The ecophysiological implications of these single cell stable isotope measurements are often limited to the taxonomic resolution of paired fluorescence in situ hybridization (FISH microscopy. Here we introduce a taxonomy-independent method using multi-isotope SIP and NanoSIMS for identifying and grouping phenotypically similar microbial cells by their chemical and isotopic fingerprint. This method was applied to SIP experiments in a sulfur-cycling biofilm collected from sulfidic intertidal vents amended with 13C-acetate, 15N-ammonium, and 33S-sulfate. Using a cluster analysis technique based on fuzzy c-means to group cells according to their isotope (13C/12C, 15N/14N, and 33S/32S and elemental ratio (C/CN and S/CN profiles, our analysis partitioned ~2200 cellular regions of interest (ROIs into 5 distinct groups. These isotope phenotype groupings are reflective of the variation in labeled substrate uptake by cells in a multispecies metabolic network dominated by Gamma- and Deltaproteobacteria. Populations independently grouped by isotope phenotype were subsequently compared with paired FISH data, demonstrating a single coherent deltaproteobacterial cluster and multiple gammaproteobacterial groups, highlighting the distinct ecophysiologies of spatially-associated microbes within the sulfur-cycling biofilm from White Point Beach, CA.

  18. Stable isotope phenotyping via cluster analysis of NanoSIMS data as a method for characterizing distinct microbial ecophysiologies and sulfur-cycling in the environment

    Science.gov (United States)

    Dawson, K.; Scheller, S.; Dillon, J. G.; Orphan, V. J.

    2016-12-01

    Stable isotope probing (SIP) is a valuable tool for gaining insights into ecophysiology and biogeochemical cycling of environmental microbial communities by tracking isotopically labeled compounds into cellular macromolecules as well as into byproducts of respiration. SIP, in conjunction with nanoscale secondary ion mass spectrometry (NanoSIMS), allows for the visualization of isotope incorporation at the single cell level. In this manner, both active cells within a diverse population as well as heterogeneity in metabolism within a homogeneous population can be observed. The ecophysiological implications of these single cell stable isotope measurements are often limited to the taxonomic resolution of paired fluorescence in situ hybridization (FISH) microscopy. Here we introduce a taxonomy-independent method using multi-isotope SIP and NanoSIMS for identifying and grouping phenotypically similar microbial cells by their chemical and isotopic fingerprint. This method was applied to SIP experiments in a sulfur-cycling biofilm collected from sulfidic intertidal vents amended with 13C-acetate, 15N-ammonium, and 33S-sulfate. Using a cluster analysis technique based on fuzzy c-means to group cells according to their isotope (13C/12C, 15N/14N, and 33S/32S) and elemental ratio (C/CN and S/CN) profiles, our analysis partitioned 2200 cellular regions of interest (ROIs) into 5 distinct groups. These isotope phenotype groupings are reflective of the variation in labeled substrate uptake by cells in a multispecies metabolic network dominated by Gamma- and Deltaproteobacteria. Populations independently grouped by isotope phenotype were subsequently compared with paired FISH data, demonstrating a single coherent deltaproteobacterial cluster and multiple gammaproteobacterial groups, highlighting the distinct ecophysiologies of spatially-associated microbes within the sulfur-cycling biofilm from White Point Beach, CA.

  19. FCM Clustering Algorithms for Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    Yogita K. Dubey

    2016-01-01

    Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

  20. An axiomatic approach to soft learning vector quantization and clustering.

    Science.gov (United States)

    Karayiannis, N B

    1999-01-01

    This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (ECFC) algorithms. This analysis indicates that minimization of admissible reformulation functions using gradient descent leads to a broad variety of soft learning vector quantization and clustering algorithms. According to the proposed approach, the development of specific algorithms reduces to the selection of a generator function. Linear generator functions lead to the FCM and fuzzy learning vector quantization (FLVQ) algorithms while exponential generator functions lead to ECFC and entropy-constrained learning vector quantization (ECLVQ) algorithms. The reformulation of LVQ and clustering algorithms also provides the basis for developing uncertainty measures that can identify feature vectors equidistant from all prototypes. These measures are employed by a procedure developed to make soft LVQ and clustering algorithms capable of identifying outliers in the data set. This procedure is evaluated by testing the algorithms generated by linear and exponential generator functions on speech data.

  1. Improved fuzzy identification method based on Hough transformation and fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    刘福才; 路平立; 潘江华; 裴润

    2004-01-01

    This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

  2. A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection

    Directory of Open Access Journals (Sweden)

    Burhan Ergen

    2014-01-01

    Full Text Available This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT and Magnetic Resonance Imaging (MRI devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

  3. A fusion method of Gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection.

    Science.gov (United States)

    Ergen, Burhan

    2014-01-01

    This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

  4. From hybrid swarms to swarms of hybrids

    Science.gov (United States)

    Stohlgren, Thomas J.; Szalanski, Allen L; Gaskin, John F.; Young, Nicholas E.; West, Amanda; Jarnevich, Catherine S.; Tripodi, Amber

    2015-01-01

    Science has shown that the introgression or hybridization of modern humans (Homo sapiens) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis, was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica, showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.

  5. CSR in Industrial Clusters

    DEFF Research Database (Denmark)

    Lund-Thomsen, Peter; Pillay, Renginee G.

    2012-01-01

    Purpose – The paper seeks to review the literature on CSR in industrial clusters in developing countries, identifying the main strengths, weaknesses, and gaps in this literature, pointing to future research directions and policy implications in the area of CSR and industrial cluster development...... in this field and their comments incorporated in the final version submitted to Corporate Governance. Findings – The article traces the origins of the debate on industrial clusters and CSR in developing countries back to the early 1990s when clusters began to be seen as an important vehicle for local economic...... development in the South. At the turn of the millennium the industrial cluster debate expanded as clusters were perceived as a potential source of poverty reduction, while their role in promoting CSR among small and medium-sized enterprises began to take shape from 2006 onwards. At present, there is still...

  6. Cosmology with cluster surveys

    Indian Academy of Sciences (India)

    Subhabrata Majumdar

    2004-10-01

    Surveys of clusters of galaxies provide us with a powerful probe of the density and nature of the dark energy. The red-shift distribution of detected clusters is highly sensitive to the dark energy equation of state parameter . Upcoming Sunyaev–Zel'dovich (SZ) surveys would provide us large yields of clusters to very high red-shifts. Self-calibration of cluster scaling relations, possible for such a huge sample, would be able to constrain systematic biases on mass estimators. Combining cluster red-shift abundance with limited mass follow-up and cluster mass power spectrum can then give constraints on , as well as on 8 and to a few per cents.

  7. Disentangling Porterian Clusters

    DEFF Research Database (Denmark)

    Jagtfelt, Tue

    This dissertation investigates the contemporary phenomenon of industrial clusters based on the work of Michael E. Porter, the central progenitor and promoter of the cluster notion. The dissertation pursues two central questions: 1) What is a cluster? and 2) How could Porter’s seemingly fuzzy......, contested theory become so widely disseminated and applied as a normative and prescriptive strategy for economic development? The dissertation traces the introduction of the cluster notion into the EU’s Lisbon Strategy and demonstrates how its inclusion originates from Porter’s colleagues: Professor Örjan...... Sölvell, Dr. Christian Ketels and Dr. Göran Lindqvist. Taking departure in Porter’s works and the cluster literature, the dissertations shows a considerable paradigmatic shift has occurred from the first edition of Nations to the present state of cluster cooperation. To elaborate on this change...

  8. Melting of sodium clusters

    CERN Document Server

    Reyes-Nava, J A; Beltran, M R; Michaelian, K

    2002-01-01

    Thermal stability properties and the melting-like transition of Na_n, n=13-147, clusters are studied through microcanonical molecular dynamics simulations. The metallic bonding in the sodium clusters is mimicked by a many-body Gupta potential based on the second moment approximation of a tight-binding Hamiltonian. The characteristics of the solid-to-liquid transition in the sodium clusters are analyzed by calculating physical quantities like caloric curves, heat capacities, and root-mean-square bond length fluctuations using simulation times of several nanoseconds. Distinct melting mechanisms are obtained for the sodium clusters in the size range investigated. The calculated melting temperatures show an irregular variation with the cluster size, in qualitative agreement with recent experimental results. However, the calculated melting point for the Na_55 cluster is about 40 % lower than the experimental value.

  9. Online Correlation Clustering

    CERN Document Server

    Mathieu, Claire; Schudy, Warren

    2010-01-01

    We study the online clustering problem where data items arrive in an online fashion. The algorithm maintains a clustering of data items into similarity classes. Upon arrival of v, the relation between v and previously arrived items is revealed, so that for each u we are told whether v is similar to u. The algorithm can create a new cluster for v and merge existing clusters. When the objective is to minimize disagreements between the clustering and the input, we prove that a natural greedy algorithm is O(n)-competitive, and this is optimal. When the objective is to maximize agreements between the clustering and the input, we prove that the greedy algorithm is .5-competitive; that no online algorithm can be better than .834-competitive; we prove that it is possible to get better than 1/2, by exhibiting a randomized algorithm with competitive ratio .5+c for a small positive fixed constant c.

  10. Cluster Management Institutionalization

    DEFF Research Database (Denmark)

    Normann, Leo; Agger Nielsen, Jeppe

    2015-01-01

    This article explores a new management form – cluster management – in Danish public sector day care. Although cluster management has been widely adopted in Danish day care at the municipality level, it has attracted only sparse research attention. We use theoretical insights from Scandinavian...... institutionalism together with a longitudinal case-based inquiry into how cluster management has entered and penetrated the management practices of day care in Denmark. We demonstrate how cluster management became widely adopted in the day care field not only because of its intrinsic properties but also because...... of how it was legitimized as a “ready-to-use” management model. Further, our account reveals how cluster management translated into considerably different local variants as it travelled into specific organizations. However, these processes have not occurred sequentially with cluster management first...

  11. Cluster Correspondence Analysis.

    Science.gov (United States)

    van de Velden, M; D'Enza, A Iodice; Palumbo, F

    2017-03-01

    A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent to GROUPALS applied to categorical data. Performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with the so-called tandem approach, a sequential analysis of dimension reduction followed by cluster analysis. The tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure. Our simulation study confirms this conjecture. Moreover, the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods.

  12. Cluster Management Institutionalization

    DEFF Research Database (Denmark)

    Normann, Leo; Agger Nielsen, Jeppe

    2015-01-01

    of how it was legitimized as a “ready-to-use” management model. Further, our account reveals how cluster management translated into considerably different local variants as it travelled into specific organizations. However, these processes have not occurred sequentially with cluster management first......This article explores a new management form – cluster management – in Danish public sector day care. Although cluster management has been widely adopted in Danish day care at the municipality level, it has attracted only sparse research attention. We use theoretical insights from Scandinavian...... institutionalism together with a longitudinal case-based inquiry into how cluster management has entered and penetrated the management practices of day care in Denmark. We demonstrate how cluster management became widely adopted in the day care field not only because of its intrinsic properties but also because...

  13. Clustering Categorical Data:A Cluster Ensemble Approach

    Institute of Scientific and Technical Information of China (English)

    He Zengyou(何增友); Xu Xiaofei; Deng Shengchun

    2003-01-01

    Clustering categorical data, an integral part of data mining,has attracted much attention recently. In this paper, the authors formally define the categorical data clustering problem as an optimization problem from the viewpoint of cluster ensemble, and apply cluster ensemble approach for clustering categorical data. Experimental results on real datasets show that better clustering accuracy can be obtained by comparing with existing categorical data clustering algorithms.

  14. Spatial Scan Statistic: Selecting clusters and generating elliptic clusters

    DEFF Research Database (Denmark)

    Christiansen, Lasse Engbo; Andersen, Jens Strodl

    2004-01-01

    The spatial scan statistic is widely used to search for clusters. This paper shows that the usually applied elimination of overlapping clusters to find secondary clusters is sensitive to smooth changes in the shape of the clusters. We present an algorithm for generation of set of confocal elliptic...... clusters. In addition, we propose a new way to present the information in a given set of clusters based on the significance of the clusters....

  15. Ciassification and Semi-Quantitation of Oiive Oii Aduiteration by Adaptive Fuzzy C Means%改进的模糊C均值算法用于掺杂橄榄油的分类和半定量研究

    Institute of Scientific and Technical Information of China (English)

    林伟琦; 孙晓丹; 王志莹; 申琦

    2016-01-01

    Determination of the authenticity of extra virgin olive oils has become more and more important in recent years. In this paper,the adaptive fuzzy C means optimized by particle swarm optimization algorithm( AFC-MPSO)is proposed to obtain classification and semi-quantitative information of olive oils. To semi-quantify the a-dulteration in olive oil,a new objective function is proposed for AFCMPSO. The results show that this chemomet-ric method can classify and semi-quantify the adulterated oil simultaneously and is a stable and reliable method for identifying olive oils.%针对橄榄油的掺杂检测问题,提出了基于粒子群优化算法的模糊C均值分类算法,可以同时进行分类和半定量分析,并建立了新的目标函数评价算法。结果表明改进的模糊C均值算法简单、快速、有效。

  16. AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

    Directory of Open Access Journals (Sweden)

    V.G. Biju

    2015-11-01

    Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

  17. Clustering of Absorbers

    CERN Document Server

    Cristiani, S; D'Odorico, V; Fontana, A; Giallongo, E; Moscardini, L; Savaglio, S

    1997-01-01

    The observed clustering of Lyman-$\\alpha$ lines is reviewed and compared with the clustering of CIV systems. We argue that a continuity of properties exists between Lyman-$\\alpha$ and metal systems and show that the small-scale clustering of the absorbers is consistent with a scenario of gravitationally induced correlations. At large scales statistically significant over and under-densities (including voids) are found on scales of tens of Mpc.

  18. Structures in Galaxy Clusters

    CERN Document Server

    Escalera, E; Girardi, M; Giuricin, G; Mardirossian, F; Mazure, A; Mezzetti, M

    1993-01-01

    The analysis of the presence of substructures in 16 well-sampled clusters of galaxies suggests a stimulating hypothesis: Clusters could be classified as unimodal or bimodal, on the basis of to the sub-clump distribution in the {\\em 3-D} space of positions and velocities. The dynamic study of these clusters shows that their fundamental characteristics, in particular the virial masses, are not severely biased by the presence of subclustering if the system considered is bound.

  19. 基于双核铜簇和[β-Mo8O26]4-阴离子的杂化化合物的合成及结构%Synthesis and Structure of One Hybrid Constructed by Dimeric Copper Clusters and [β-Mo8O26]4-Polyanions

    Institute of Scientific and Technical Information of China (English)

    杜晓迪; 李春阳; 王振领; 常加忠; 金刚

    2012-01-01

    An organic-inorganic hybrid [Cu2(1,4-bth)3(H2O)(β-Mo8O26)] (1) (1,4-bth=4-(6-(1H-1,2,4-triazol-1-yl)hexyl)-4H-1,2,4-triazole)) based on dimeric copper clusters [Cu2(1,4-bth)3]4+ and [β-Mo8O26]4-polyanions has been synthesized and characterized by element analysis,IR spectra,single-crystal X-ray diffraction,thermal analysis.The crystallographic data shows that complex 1 crystallizes in triclinic space group P1 with a=1.195 1 (2) nm,b =1.252 0(2) nm,c=2.146 0(4) nm,α=85.893(3)°,β=76.229(3)°,γ=62.714(3)°,V=2.769 0(8) nm3,C30H50Cu2Mo8 N18O27,Mr=1989.48,Dc=2.386 g·cm-3,μ(Mo Kα)=2.598 mm-1,F(000)=1 932,GOF=1.001,Z=2,the final R1=0.062 0 andwR2=0.153 4 for I>2σ(Ⅰ).In complex 1,each 1,4-bth ligand acted as a tridentate-linker to bridge three Cu2+ ions and dimeric copper clusters [Cu2(1,4-bth)3]4+ were formed.Each dimeric copper cluster is connected to four same clusters forming parallel two dimensional (2D) sheets [Cu2(1,4-bth)3]n4n+.Then the [β-Mo8O26]4-anions through bonded to the Cu2+ ions as the pillars to construct a three dimensional (3D) framework,with the pcu alpha-Po primitive cubic topology; the thermal analysis illustrate that compound 1 retains a comparatively good thermal stability.CCDC:865416.%通过水热合成方法得到了一个基于双核铜簇[Cu2(1,4-bth)3]4+和[β-Mo8O26]4-阴离子的有机-无机杂化化合物,[Cu2(1,4-bth)3(H2O)(β-Mo8O26)] (1)(1,4-bth=4-(6-(1H-1,2,4-三氮唑-1-基)正己烷)-4H-1,2,4-三氮唑),并通过元素分析、红外光谱、X-射线单晶衍射、热分析等测试对其进行了表征.晶体数据表明该化合物属于三斜晶系,P(1)空间群.在化合物1中,1,4-bth配体都以三齿配体的形式与3个铜离子相连,形成双核铜簇[Cu2(1,4-bth)3]4+,每个簇单元进一步和其相邻的4个同类型单元相连形成了相互平行的层状结构[Cu2(1,4-bth)3]n4n+,层与层之间又通过[β-Mo8O26]4-阴离子相连构筑成1个三维框架结构,其拓扑类型为pcu alpha-Po 简单

  20. The Youngest Globular Clusters

    CERN Document Server

    Beck, Sara

    2014-01-01

    It is likely that all stars are born in clusters, but most clusters are not bound and disperse. None of the many protoclusters in our Galaxy are likely to develop into long-lived bound clusters. The Super Star Clusters (SSCs) seen in starburst galaxies are more massive and compact and have better chances of survival. The birth and early development of SSCs takes place deep in molecular clouds, and during this crucial stage the embedded clusters are invisible to optical or UV observations but are studied via the radio-infared supernebulae (RISN) they excite. We review observations of embedded clusters and identify RISN within 10 Mpc whose exciting clusters have a million solar masses or more in volumes of a few cubic parsecs and which are likely to not only survive as bound clusters, but to evolve into objects as massive and compact as Galactic globulars. These clusters are distinguished by very high star formation efficiency eta, at least a factor of 10 higher than the few percent seen in the Galaxy, probably...

  1. Star Clusters within FIRE

    Science.gov (United States)

    Perez, Adrianna; Moreno, Jorge; Naiman, Jill; Ramirez-Ruiz, Enrico; Hopkins, Philip F.

    2017-01-01

    In this work, we analyze the environments surrounding star clusters of simulated merging galaxies. Our framework employs Feedback In Realistic Environments (FIRE) model (Hopkins et al., 2014). The FIRE project is a high resolution cosmological simulation that resolves star forming regions and incorporates stellar feedback in a physically realistic way. The project focuses on analyzing the properties of the star clusters formed in merging galaxies. The locations of these star clusters are identified with astrodendro.py, a publicly available dendrogram algorithm. Once star cluster properties are extracted, they will be used to create a sub-grid (smaller than the resolution scale of FIRE) of gas confinement in these clusters. Then, we can examine how the star clusters interact with these available gas reservoirs (either by accreting this mass or blowing it out via feedback), which will determine many properties of the cluster (star formation history, compact object accretion, etc). These simulations will further our understanding of star formation within stellar clusters during galaxy evolution. In the future, we aim to enhance sub-grid prescriptions for feedback specific to processes within star clusters; such as, interaction with stellar winds and gas accretion onto black holes and neutron stars.

  2. Galaxy Clusters with Chandra

    CERN Document Server

    Forman, W; Markevitch, M L; Vikhlinin, A A; Churazov, E

    2002-01-01

    We discuss Chandra results related to 1) cluster mergers and cold fronts and 2) interactions between relativistic plasma and hot cluster atmospheres. We describe the properties of cold fronts using NGC1404 in the Fornax cluster and A3667 as examples. We discuss multiple surface brightness discontinuities in the cooling flow cluster ZW3146. We review the supersonic merger underway in CL0657. Finally, we summarize the interaction between plasma bubbles produced by AGN and hot gas using M87 and NGC507 as examples.

  3. 15th Cluster workshop

    CERN Document Server

    Laakso, Harri; Escoubet, C. Philippe; The Cluster Active Archive : Studying the Earth’s Space Plasma Environment

    2010-01-01

    Since the year 2000 the ESA Cluster mission has been investigating the small-scale structures and processes of the Earth's plasma environment, such as those involved in the interaction between the solar wind and the magnetospheric plasma, in global magnetotail dynamics, in cross-tail currents, and in the formation and dynamics of the neutral line and of plasmoids. This book contains presentations made at the 15th Cluster workshop held in March 2008. It also presents several articles about the Cluster Active Archive and its datasets, a few overview papers on the Cluster mission, and articles reporting on scientific findings on the solar wind, the magnetosheath, the magnetopause and the magnetotail.

  4. Coscheduling in Clusters: Is It a Viable Alternative?

    Energy Technology Data Exchange (ETDEWEB)

    Choi, G S; Kim, J H; Ersoz, D; Yoo, A B; Das, C R

    2003-11-10

    As clusters are widely accepted as cost-effective infrastructures for many scientific and commercial applications, improving the deliverable performance and reducing the energy consumption of such systems has become a pressing issue. In this paper, we exploit the feasibility of achieving these objectives through efficiently scheduling the communicating processes of parallel applications. In this context, we conduct an in-depth evaluation of a broad spectrum of scheduling alternatives for clusters. These include the widely used batch scheduling, local scheduling, gang scheduling, all prior communication-driven coscheduling algorithms, and a newly proposed HYBRID coscheduling algorithm. In order to provide ease of implementation and portability across many cluster platforms, we propose a generic framework for deploying any coscheduling algorithm. We have implemented four prior coscheduling algorithms (Dynamic Coscheduling (DCS), Spin Block (SB), Periodic Boost (PB), and Co-ordinated Coscheduling (CC)) and the HYBRID coscheduling using this framework on a 16-node, Myrinet connected Linux cluster that uses GM as the communication layer. In addition, we use PBS as the batch scheduler and a previously proposed gang scheduler (SCore) to analyze all classes of scheduling techniques. Performance and energy measurements using several NAS and LLNL benchmarks on the Linux cluster provide several interesting conclusions. First, although batch scheduling is currently used in most clusters, all blocking-based coscheduling techniques such as SB, CC and HYBRID and the gang scheduling can provide much better performance even in a dedicated cluster platform. Under high system load, these coscheduling schemes can provide orders of magnitude reduction in average response time and much better performance-energy behavior compared to the PBS scheme. Second, in contrast to some of the prior studies, we observe that blocking-based schemes like SB and HYBRID can provide better performance

  5. FUZZY BASED CLUSTERING AND ENERGY EFFICIENT ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Sihem Souiki

    2015-03-01

    Full Text Available Underwater Wireless Sensor Network (UWSN is a particular kind of sensor networks which is characterized by using acoustic channels for communication. UWSN is challenged by great issues specially the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the closeness to the center of the cluster, then the node having the higher residual energy elects itself as a cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data aggregation and transmits the data directly to the base station. The second algorithm uses the same principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data from cluster heads to the underwater sink (uw-sink. Furthermore the two proposed algorithms are tested for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed algorithms resulting in an extension of the network lifetime.

  6. Cardiac hybrid imaging

    Energy Technology Data Exchange (ETDEWEB)

    Gaemperli, Oliver [University Hospital Zurich, Cardiac Imaging, Zurich (Switzerland); University Hospital Zurich, Nuclear Cardiology, Cardiovascular Center, Zurich (Switzerland); Kaufmann, Philipp A. [University Hospital Zurich, Cardiac Imaging, Zurich (Switzerland); Alkadhi, Hatem [University Hospital Zurich, Institute of Diagnostic and Interventional Radiology, Zurich (Switzerland)

    2014-05-15

    Hybrid cardiac single photon emission computed tomography (SPECT)/CT imaging allows combined assessment of anatomical and functional aspects of cardiac disease. In coronary artery disease (CAD), hybrid SPECT/CT imaging allows detection of coronary artery stenosis and myocardial perfusion abnormalities. The clinical value of hybrid imaging has been documented in several subsets of patients. In selected groups of patients, hybrid imaging improves the diagnostic accuracy to detect CAD compared to the single imaging techniques. Additionally, this approach facilitates functional interrogation of coronary stenoses and guidance with regard to revascularization procedures. Moreover, the anatomical information obtained from CT coronary angiography or coronary artery calcium scores (CACS) adds prognostic information over perfusion data from SPECT. The use of cardiac hybrid imaging has been favoured by the dissemination of dedicated hybrid systems and the release of dedicated image fusion software, which allow simple patient throughput for hybrid SPECT/CT studies. Further technological improvements such as more efficient detector technology to allow for low-radiation protocols, ultra-fast image acquisition and improved low-noise image reconstruction algorithms will be instrumental to further promote hybrid SPECT/CT in research and clinical practice. (orig.)

  7. Hybrid intelligent engineering systems

    CERN Document Server

    Jain, L C; Adelaide, Australia University of

    1997-01-01

    This book on hybrid intelligent engineering systems is unique, in the sense that it presents the integration of expert systems, neural networks, fuzzy systems, genetic algorithms, and chaos engineering. It shows that these new techniques enhance the capabilities of one another. A number of hybrid systems for solving engineering problems are presented.

  8. A Hybrid Imagination

    DEFF Research Database (Denmark)

    Jamison, Andrew; Christensen, Steen Hyldgaard; Botin, Lars

    contexts, or sites, for mixing scientific knowledge and technical skills from different fields and social domains into new combinations, thus fostering what the authors term a “hybrid imagination”. Such a hybrid imagination is especially important today, as a way to counter the competitive and commercial...

  9. Hybrid trajectory spaces

    NARCIS (Netherlands)

    Collins, P.J.

    2005-01-01

    In this paper, we present a general framework for describing and studying hybrid systems. We represent the trajectories of the system as functions on a hybrid time domain, and the system itself by its trajectory space, which is the set of all possible trajectories. The trajectory space is given a na

  10. Editorial: Hybrid Systems

    DEFF Research Database (Denmark)

    Olderog, Ernst-Rüdiger; Ravn, Anders Peter

    2007-01-01

    An introduction to three papers in a special issue on Hybrid Systems. These paper were first presented at an IFIP WG 2.2 meeting in Skagen 2005.......An introduction to three papers in a special issue on Hybrid Systems. These paper were first presented at an IFIP WG 2.2 meeting in Skagen 2005....

  11. Statistical properties of convex clustering

    OpenAIRE

    Tan, Kean Ming; Witten, Daniela

    2015-01-01

    In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and $k$-means clustering. In addition, we derive the range of the tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimator of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to so...

  12. Multiuser hybrid switched-selection diversity systems

    KAUST Repository

    Shaqfeh, Mohammad

    2011-09-01

    A new multiuser scheduling scheme is proposed and analyzed in this paper. The proposed system combines features of conventional full-feedback selection-based diversity systems and reduced-feedback switch-based diversity systems. The new hybrid system provides flexibility in trading-off the channel information feedback overhead with the prospected multiuser diversity gains. The users are clustered into groups, and the users\\' groups are ordered into a sequence. Per-group feedback thresholds are used and optimized to maximize the system overall achievable rate. The proposed hybrid system applies switched diversity criterion to choose one of the groups, and a selection criterion to decide the user to be scheduled from the chosen group. Numerical results demonstrate that the system capacity increases as the number of users per group increases, but at the cost of more required feedback messages. © 2011 IEEE.

  13. Hybrid reactors. [Fuel cycle

    Energy Technology Data Exchange (ETDEWEB)

    Moir, R.W.

    1980-09-09

    The rationale for hybrid fusion-fission reactors is the production of fissile fuel for fission reactors. A new class of reactor, the fission-suppressed hybrid promises unusually good safety features as well as the ability to support 25 light-water reactors of the same nuclear power rating, or even more high-conversion-ratio reactors such as the heavy-water type. One 4000-MW nuclear hybrid can produce 7200 kg of /sup 233/U per year. To obtain good economics, injector efficiency times plasma gain (eta/sub i/Q) should be greater than 2, the wall load should be greater than 1 MW.m/sup -2/, and the hybrid should cost less than 6 times the cost of a light-water reactor. Introduction rates for the fission-suppressed hybrid are usually rapid.

  14. Hybrid propulsion technology program

    Science.gov (United States)

    1990-01-01

    Technology was identified which will enable application of hybrid propulsion to manned and unmanned space launch vehicles. Two design concepts are proposed. The first is a hybrid propulsion system using the classical method of regression (classical hybrid) resulting from the flow of oxidizer across a fuel grain surface. The second system uses a self-sustaining gas generator (gas generator hybrid) to produce a fuel rich exhaust that was mixed with oxidizer in a separate combustor. Both systems offer cost and reliability improvement over the existing solid rocket booster and proposed liquid boosters. The designs were evaluated using life cycle cost and reliability. The program consisted of: (1) identification and evaluation of candidate oxidizers and fuels; (2) preliminary evaluation of booster design concepts; (3) preparation of a detailed point design including life cycle costs and reliability analyses; (4) identification of those hybrid specific technologies needing improvement; and (5) preperation of a technology acquisition plan and large scale demonstration plan.

  15. Recognition of Spontaneous Combustion in Coal Mines Based on Genetic Clustering

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is therefore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effectiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.

  16. Cluster Analysis of the Rat Olfactory Bulb Activity in Response to Different Odorants

    Science.gov (United States)

    Falasconi, M.; Gutierrez, A.; Auffarth, B.; Sberveglieri, G.; Marco, S.

    2009-05-01

    With the goal of deepen in the understanding of coding of chemical information in the olfactory system, a large data set consisting of rat's olfactory bulb activity values in response to several different volatile compounds has been analyzed by fuzzy c-means clustering methods. Clustering should help to discover groups of glomeruli that are similary activated according to their response profiles across the odorants. To investigate the significance of the achieved fuzzy partitions we developed and applied a novel validity approach based on cluster stability. Our results show certain level of glomerular clustering in the olfactory bulb and indicate that exist a main chemo-topic subdivision of the glomerular layer in few macro-area which are rather specific to particular functional groups of the volatile molecules.

  17. Fuzzy clustering-based segmented attenuation correction in whole-body PET

    CERN Document Server

    Zaidi, H; Boudraa, A; Slosman, DO

    2001-01-01

    Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

  18. An On-Line Oxygen Forecasting System for Waterless Live Transportation of Flatfish Based on Feature Clustering

    Directory of Open Access Journals (Sweden)

    Yongjun Zhang

    2017-09-01

    Full Text Available Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not fully consider current ambient influence. This is likely lead to a greater deviation in on-line oxygen level forecasting in real situations. Therefore, it is not advisable for the system to perform early warning and on-line air adjustment in delivery. In this paper, we propose a hybrid method and its implementation system that combines a gray model (GM (1, 1 with least squares support vector machines (LSSVM that can be used effectively as a forecasting model to perform early warning effectively according to the dynamic changes of oxygen in a closed system. For accurately forecasting of the oxygen level, the fuzzy C-means clustering (FCM algorithm was utilized for classification according to the flatfish’s physical features—i.e., length and weight—for more pertinent training. The performance of the gray model-particle swarm optimization-least squares support vector machines (GM-PSO-LSSVM model was compared with the traditional modeling approaches of GM (1, 1 and LSSVM by applying it to predict on-line oxygen level, and the results showed that its predictions were more accurate than those of the LSSVM and grey model. Therefore, it is a suitable and effective method for abnormal condition forecasting and timely control in the waterless live transportation of flatfish.

  19. Lateral transfer of the lux gene cluster.

    Science.gov (United States)

    Kasai, Sabu; Okada, Kazuhisa; Hoshino, Akinori; Iida, Tetsuya; Honda, Takeshi

    2007-02-01

    The lux operon is an uncommon gene cluster. To find the pathway through which the operon has been transferred, we sequenced the operon and both flanking regions in four typical luminous species. In Vibrio cholerae NCIMB 41, a five-gene cluster, most genes of which were highly similar to orthologues present in Gram-positive bacteria, along with the lux operon, is inserted between VC1560 and VC1563, on chromosome 1. Because this entire five-gene cluster is present in Photorhabdus luminescens TT01, about 1.5 Mbp upstream of the operon, we deduced that the operon and the gene cluster were transferred from V. cholerae to an ancestor of Pr. luminescens. Because in both V. fischeri and Shewanella hanedai, luxR and luxI were found just upstream of the operon, we concluded that the operon was transferred from either species to the other. Because most of the genes flanking the operon were highly similar to orthologues present on chromosome 2 of vibrios, we speculated that the operon of most species is located on this chromosome. The undigested genomic DNAs of five luminous species were analysed by pulsed-field gel electrophoresis and Southern hybridization. In all the species except V. cholerae, the operons are located on chromosome 2.

  20. Chicken rRNA Gene Cluster Structure.

    Directory of Open Access Journals (Sweden)

    Alexander G Dyomin

    Full Text Available Ribosomal RNA (rRNA genes, whose activity results in nucleolus formation, constitute an extremely important part of genome. Despite the extensive exploration into avian genomes, no complete description of avian rRNA gene primary structure has been offered so far. We publish a complete chicken rRNA gene cluster sequence here, including 5'ETS (1836 bp, 18S rRNA gene (1823 bp, ITS1 (2530 bp, 5.8S rRNA gene (157 bp, ITS2 (733 bp, 28S rRNA gene (4441 bp and 3'ETS (343 bp. The rRNA gene cluster sequence of 11863 bp was assembled from raw reads and deposited to GenBank under KT445934 accession number. The assembly was validated through in situ fluorescent hybridization analysis on chicken metaphase chromosomes using computed and synthesized specific probes, as well as through the reference assembly against de novo assembled rRNA gene cluster sequence using sequenced fragments of BAC-clone containing chicken NOR (nucleolus organizer region. The results have confirmed the chicken rRNA gene cluster validity.