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Sample records for cluster detection method

  1. An Examination of Three Spatial Event Cluster Detection Methods

    Directory of Open Access Journals (Sweden)

    Hensley H. Mariathas

    2015-03-01

    Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

  2. Method for detecting clusters of possible uranium deposits

    International Nuclear Information System (INIS)

    Conover, W.J.; Bement, T.R.; Iman, R.L.

    1978-01-01

    When a two-dimensional map contains points that appear to be scattered somewhat at random, a question that often arises is whether groups of points that appear to cluster are merely exhibiting ordinary behavior, which one can expect with any random distribution of points, or whether the clusters are too pronounced to be attributable to chance alone. A method for detecting clusters along a straight line is applied to the two-dimensional map of 214 Bi anomalies observed as part of the National Uranium Resource Evaluation Program in the Lubbock, Texas, region. Some exact probabilities associated with this method are computed and compared with two approximate methods. The two methods for approximating probabilities work well in the cases examined and can be used when it is not feasible to obtain the exact probabilities

  3. a Probabilistic Embedding Clustering Method for Urban Structure Detection

    Science.gov (United States)

    Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

    2017-09-01

    Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

  4. A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

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    X. Lin

    2017-09-01

    Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

  5. System and Method for Outlier Detection via Estimating Clusters

    Science.gov (United States)

    Iverson, David J. (Inventor)

    2016-01-01

    An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

  6. A method of detecting spatial clustering of disease

    International Nuclear Information System (INIS)

    Openshaw, S.; Wilkie, D.; Binks, K.; Wakeford, R.; Gerrard, M.H.; Croasdale, M.R.

    1989-01-01

    A statistical technique has been developed to identify extreme groupings of a disease and is being applied to childhood cancers, initially to acute lymphoblastic leukaemia incidence in the Northern and North-Western Regions of England. The method covers the area with a square grid, the size of which is varied over a wide range and whose origin is moved in small increments in two directions. The population at risk within any square is estimated using the 1971 and 1981 censuses. The significance of an excess of disease is determined by random simulation. In addition, tests to detect a general departure from a background Poisson process are carried out. Available results will be presented at the conference. (author)

  7. Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

    Science.gov (United States)

    Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

    2011-01-01

    Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

  8. A semi-supervised method to detect seismic random noise with fuzzy GK clustering

    International Nuclear Information System (INIS)

    Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

    2008-01-01

    We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

  9. A novel intrusion detection method based on OCSVM and K-means recursive clustering

    Directory of Open Access Journals (Sweden)

    Leandros A. Maglaras

    2015-01-01

    Full Text Available In this paper we present an intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition system, based on the combination of One-Class Support Vector Machine (OCSVM with RBF kernel and recursive k-means clustering. Important parameters of OCSVM, such as Gaussian width o and parameter v affect the performance of the classifier. Tuning of these parameters is of great importance in order to avoid false positives and over fitting. The combination of OCSVM with recursive k- means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters o and v, making it ideal for real-time intrusion detection mechanisms for SCADA systems. Extensive simulations have been conducted with datasets extracted from small and medium sized HTB SCADA testbeds, in order to compare the accuracy, false alarm rate and execution time against the base line OCSVM method.

  10. [A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

    Science.gov (United States)

    Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

    2011-04-01

    An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

  11. Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

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    Yonghao Gu

    2017-01-01

    Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

  12. An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

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    Deepa Devasenapathy

    2015-01-01

    Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

  13. An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

    Science.gov (United States)

    Devasenapathy, Deepa; Kannan, Kathiravan

    2015-01-01

    The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

  14. A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

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    Rosemary M McCloskey

    2017-11-01

    Full Text Available Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP, which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85% and specificity (91% than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46% as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where

  15. Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells

    International Nuclear Information System (INIS)

    Felfer, P.; Ceguerra, A.V.; Ringer, S.P.; Cairney, J.M.

    2015-01-01

    The analysis of the formation of clusters in solid solutions is one of the most common uses of atom probe tomography. Here, we present a method where we use the Voronoi tessellation of the solute atoms and its geometric dual, the Delaunay triangulation to test for spatial/chemical randomness of the solid solution as well as extracting the clusters themselves. We show how the parameters necessary for cluster extraction can be determined automatically, i.e. without user interaction, making it an ideal tool for the screening of datasets and the pre-filtering of structures for other spatial analysis techniques. Since the Voronoi volumes are closely related to atomic concentrations, the parameters resulting from this analysis can also be used for other concentration based methods such as iso-surfaces. - Highlights: • Cluster analysis of atom probe data can be significantly simplified by using the Voronoi cell volumes of the atomic distribution. • Concentration fields are defined on a single atomic basis using Voronoi cells. • All parameters for the analysis are determined by optimizing the separation probability of bulk atoms vs clustered atoms

  16. Cluster detection methods applied to the Upper Cape Cod cancer data

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    Ozonoff David

    2005-09-01

    Full Text Available Abstract Background A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. Methods We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. Results The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. Conclusion The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

  17. Comparison of Molecular Typing Methods Useful for Detecting Clusters of Campylobacter jejuni and C. coli Isolates through Routine Surveillance

    Science.gov (United States)

    Taboada, Eduardo; Grant, Christopher C. R.; Blakeston, Connie; Pollari, Frank; Marshall, Barbara; Rahn, Kris; MacKinnon, Joanne; Daignault, Danielle; Pillai, Dylan; Ng, Lai-King

    2012-01-01

    Campylobacter spp. may be responsible for unreported outbreaks of food-borne disease. The detection of these outbreaks is made more difficult by the fact that appropriate methods for detecting clusters of Campylobacter have not been well defined. We have compared the characteristics of five molecular typing methods on Campylobacter jejuni and C. coli isolates obtained from human and nonhuman sources during sentinel site surveillance during a 3-year period. Comparative genomic fingerprinting (CGF) appears to be one of the optimal methods for the detection of clusters of cases, and it could be supplemented by the sequencing of the flaA gene short variable region (flaA SVR sequence typing), with or without subsequent multilocus sequence typing (MLST). Different methods may be optimal for uncovering different aspects of source attribution. Finally, the use of several different molecular typing or analysis methods for comparing individuals within a population reveals much more about that population than a single method. Similarly, comparing several different typing methods reveals a great deal about differences in how the methods group individuals within the population. PMID:22162562

  18. K2: A NEW METHOD FOR THE DETECTION OF GALAXY CLUSTERS BASED ON CANADA-FRANCE-HAWAII TELESCOPE LEGACY SURVEY MULTICOLOR IMAGES

    International Nuclear Information System (INIS)

    Thanjavur, Karun; Willis, Jon; Crampton, David

    2009-01-01

    We have developed a new method, K2, optimized for the detection of galaxy clusters in multicolor images. Based on the Red Sequence approach, K2 detects clusters using simultaneous enhancements in both colors and position. The detection significance is robustly determined through extensive Monte Carlo simulations and through comparison with available cluster catalogs based on two different optical methods, and also on X-ray data. K2 also provides quantitative estimates of the candidate clusters' richness and photometric redshifts. Initially, K2 was applied to the two color (gri) 161 deg 2 images of the Canada-France-Hawaii Telescope Legacy Survey Wide (CFHTLS-W) data. Our simulations show that the false detection rate for these data, at our selected threshold, is only ∼1%, and that the cluster catalogs are ∼80% complete up to a redshift of z = 0.6 for Fornax-like and richer clusters and to z ∼ 0.3 for poorer clusters. Based on the g-, r-, and i-band photometric catalogs of the Terapix T05 release, 35 clusters/deg 2 are detected, with 1-2 Fornax-like or richer clusters every 2 deg 2 . Catalogs containing data for 6144 galaxy clusters have been prepared, of which 239 are rich clusters. These clusters, especially the latter, are being searched for gravitational lenses-one of our chief motivations for cluster detection in CFHTLS. The K2 method can be easily extended to use additional color information and thus improve overall cluster detection to higher redshifts. The complete set of K2 cluster catalogs, along with the supplementary catalogs for the member galaxies, are available on request from the authors.

  19. The detection of neutron clusters

    Energy Technology Data Exchange (ETDEWEB)

    Marques, F.M.; Labiche, M.; Orr, N.A.; Angelique, J.C. [Caen Univ., 14 (France). Lab. de Physique Corpusculaire] [and others

    2001-11-01

    A new approach to the production and detection of bound neutron clusters is presented. The technique is based on the breakup of beams of very neutron-rich nuclei and the subsequent detection of the recoiling proton in a liquid scintillator. The method has been tested in the breakup of {sup 11}Li, {sup 14}Be and {sup 15}B beams by a C target. Some 6 events were observed that exhibit the characteristics of a multi-neutron cluster liberated in the breakup of {sup 14}Be, most probably in the channel {sup 10}Be+{sup 4}n. The various backgrounds that may mimic such a signal are discussed in detail. (author)

  20. Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

    Science.gov (United States)

    Ma, Xiaoke; Wang, Bingbo; Yu, Liang

    2018-01-01

    Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

  1. Computerized detection method for asymptomatic white matter lesions in brain screening MR images using a clustering technique

    International Nuclear Information System (INIS)

    Kunieda, Takuya; Uchiyama, Yoshikazu; Hara, Takeshi

    2008-01-01

    Asymptomatic white matter lesions are frequently identified by the screening system known as Brain Dock, which is intended for the detection of asymptomatic brain diseases. The detection of asymptomatic white matter lesions is important because their presence is associated with an increased risk of stroke. Therefore, we have developed a computerized method for the detection of asymptomatic white matter lesions in order to assist radiologists in image interpretation as a ''second opinion''. Our database consisted of T 1 - and T 2 -weighted images obtained from 73 patients. The locations of the white matter lesions were determined by an experienced neuroradiologist. In order to restrict the area to be searched for white matter lesions, we first segmented the cerebral region in T 1 -weighted images by applying thresholding and region-growing techniques. To identify the initial candidate lesions, k-means clustering with pixel values in T 1 - and T 2 -weighted images was applied to the segmented cerebral region. To eliminate false positives (FPs), we determined the features, such as location, size, and circularity, of each of the initial candidate lesions. Finally, a rule-based scheme and a quadratic discriminant analysis with these features were employed to distinguish between white matter lesions and FPs. The results showed that the sensitivity for the detection of white matter lesions was 93.2%, with 4.3 FPs per image, suggesting that our computerized method may be useful for the detection of asymptomatic white matter lesions in T 1 - and T 2 -weighted images. (author)

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

    Science.gov (United States)

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

    2017-01-01

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

  3. Spatial cluster detection using dynamic programming

    Directory of Open Access Journals (Sweden)

    Sverchkov Yuriy

    2012-03-01

    Full Text Available Abstract Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic

  4. The use of cluster analysis method for the localization of acoustic emission sources detected during the hydrotest of PWR pressure vessels

    International Nuclear Information System (INIS)

    Liska, J.; Svetlik, M.; Slama, K.

    1982-01-01

    The acoustic emission method is a promising tool for checking reactor pressure vessel integrity. Localization of emission sources is the first and the most important step in processing emission signals. The paper describes the emission sources localization method which is based on cluster analysis of a set of points depicting the emission events in the plane of coordinates of their occurrence. The method is based on using this set of points for constructing the minimum spanning tree and its partition into fragments corresponding to point clusters. Furthermore, the laws are considered of probability distribution of the minimum spanning tree edge length for one and several clusters with the aim of finding the optimum length of the critical edge for the partition of the tree. Practical application of the method is demonstrated on localizing the emission sources detected during a hydrotest of a pressure vessel used for testing the reactor pressure vessel covers. (author)

  5. Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA

    2009-12-22

    Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

  6. Semi-supervised clustering methods.

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    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

  7. Fast clustering using adaptive density peak detection.

    Science.gov (United States)

    Wang, Xiao-Feng; Xu, Yifan

    2017-12-01

    Common limitations of clustering methods include the slow algorithm convergence, the instability of the pre-specification on a number of intrinsic parameters, and the lack of robustness to outliers. A recent clustering approach proposed a fast search algorithm of cluster centers based on their local densities. However, the selection of the key intrinsic parameters in the algorithm was not systematically investigated. It is relatively difficult to estimate the "optimal" parameters since the original definition of the local density in the algorithm is based on a truncated counting measure. In this paper, we propose a clustering procedure with adaptive density peak detection, where the local density is estimated through the nonparametric multivariate kernel estimation. The model parameter is then able to be calculated from the equations with statistical theoretical justification. We also develop an automatic cluster centroid selection method through maximizing an average silhouette index. The advantage and flexibility of the proposed method are demonstrated through simulation studies and the analysis of a few benchmark gene expression data sets. The method only needs to perform in one single step without any iteration and thus is fast and has a great potential to apply on big data analysis. A user-friendly R package ADPclust is developed for public use.

  8. Semi-supervised clustering methods

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

  9. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.

    Directory of Open Access Journals (Sweden)

    Yong He

    Full Text Available Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1 concave points clustering to determine the seed points of touching cells; and 2 random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.

  10. Could the clinical interpretability of subgroups detected using clustering methods be improved by using a novel two-stage approach?

    DEFF Research Database (Denmark)

    Kent, Peter; Stochkendahl, Mette Jensen; Wulff Christensen, Henrik

    2015-01-01

    participation, psychological factors, biomarkers and imaging. However, such ‘whole person’ research may result in data-driven subgroups that are complex, difficult to interpret and challenging to recognise clinically. This paper describes a novel approach to applying statistical clustering techniques that may...... potential benefits but requires broad testing, in multiple patient samples, to determine its clinical value. The usefulness of the approach is likely to be context-specific, depending on the characteristics of the available data and the research question being asked of it....

  11. Automated detection of microcalcification clusters in mammograms

    Science.gov (United States)

    Karale, Vikrant A.; Mukhopadhyay, Sudipta; Singh, Tulika; Khandelwal, Niranjan; Sadhu, Anup

    2017-03-01

    Mammography is the most efficient modality for detection of breast cancer at early stage. Microcalcifications are tiny bright spots in mammograms and can often get missed by the radiologist during diagnosis. The presence of microcalcification clusters in mammograms can act as an early sign of breast cancer. This paper presents a completely automated computer-aided detection (CAD) system for detection of microcalcification clusters in mammograms. Unsharp masking is used as a preprocessing step which enhances the contrast between microcalcifications and the background. The preprocessed image is thresholded and various shape and intensity based features are extracted. Support vector machine (SVM) classifier is used to reduce the false positives while preserving the true microcalcification clusters. The proposed technique is applied on two different databases i.e DDSM and private database. The proposed technique shows good sensitivity with moderate false positives (FPs) per image on both databases.

  12. The detection of clusters in rare diseases

    Energy Technology Data Exchange (ETDEWEB)

    Besag, J. (Washington Univ., Seattle, WA (USA) Newcastle upon Tyne Univ. (UK)); Newell, J. (Newcastle upon Tyne Univ. (UK))

    1991-01-01

    Tests for clustering of rare diseases investigate whether an observed pattern of cases in one or more geographical regions could reasonably have arisen by chance alone, bearing in mind the variation in background population density. In contrast, tests for the detection of clusters are concerned with screening a large region for evidence of individual 'hot spots' of disease but without any preconception about their likely locations; the results of such tests may form the basis for subsequent small area investigations, statistical or non-statistical, but will rarely be an end in themselves. The main intention of the paper is to describe and illustrate a new technique for the identification of small clusters of disease. A secondary purpose is to discuss some common pitfalls in the application of tests of clustering to epidemiological data. (author).

  13. Weighted community detection and data clustering using message passing

    Science.gov (United States)

    Shi, Cheng; Liu, Yanchen; Zhang, Pan

    2018-03-01

    Grouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.

  14. Clustering methods for the optimization of atomic cluster structure

    Science.gov (United States)

    Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

    2018-04-01

    In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

  15. Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

    OpenAIRE

    Cook, Andrea J.; Gold, Diane R.; Li, Yi

    2009-01-01

    Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information ...

  16. AMICO: optimized detection of galaxy clusters in photometric surveys

    Science.gov (United States)

    Bellagamba, Fabio; Roncarelli, Mauro; Maturi, Matteo; Moscardini, Lauro

    2018-02-01

    We present Adaptive Matched Identifier of Clustered Objects (AMICO), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximize the signal-to-noise ratio (S/N) of the clusters. In this work, we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than 50 per cent of the cases for objects at radial distance equal to 0.5 × R200 or redshift distance equal to 2 × σz, being σz the typical uncertainty of photometric redshifts. Running AMICO on mocks derived from N-body simulations and semi-analytical modelling of the galaxy evolution, we obtain a consistent mass-amplitude relation through the redshift range of 0.3 slope of ∼0.55 and a logarithmic scatter of ∼0.14. The fraction of false detections is steeply decreasing with S/N and negligible at S/N > 5.

  17. A Test for Cluster Bias: Detecting Violations of Measurement Invariance across Clusters in Multilevel Data

    Science.gov (United States)

    Jak, Suzanne; Oort, Frans J.; Dolan, Conor V.

    2013-01-01

    We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings…

  18. Detecting space-time cancer clusters using residential histories

    Science.gov (United States)

    Jacquez, Geoffrey M.; Meliker, Jaymie R.

    2007-04-01

    Methods for analyzing geographic clusters of disease typically ignore the space-time variability inherent in epidemiologic datasets, do not adequately account for known risk factors (e.g., smoking and education) or covariates (e.g., age, gender, and race), and do not permit investigation of the latency window between exposure and disease. Our research group recently developed Q-statistics for evaluating space-time clustering in cancer case-control studies with residential histories. This technique relies on time-dependent nearest neighbor relationships to examine clustering at any moment in the life-course of the residential histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness, each individual's probability of being a case is instead based on his/her risk factors and covariates. Case-control clusters will be presented using residential histories of 220 bladder cancer cases and 440 controls in Michigan. In preliminary analyses of this dataset, smoking, age, gender, race and education were sufficient to explain the majority of the clustering of residential histories of the cases. Clusters of unexplained risk, however, were identified surrounding the business address histories of 10 industries that emit known or suspected bladder cancer carcinogens. The clustering of 5 of these industries began in the 1970's and persisted through the 1990's. This systematic approach for evaluating space-time clustering has the potential to generate novel hypotheses about environmental risk factors. These methods may be extended to detect differences in space-time patterns of any two groups of people, making them valuable for security intelligence and surveillance operations.

  19. Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

    Directory of Open Access Journals (Sweden)

    Sami Ullah

    2017-11-01

    Full Text Available Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend.

  20. CCM: A Text Classification Method by Clustering

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results...... show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based...

  1. Comparing the performance of biomedical clustering methods

    DEFF Research Database (Denmark)

    Wiwie, Christian; Baumbach, Jan; Röttger, Richard

    2015-01-01

    expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future......Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene....... This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide...

  2. A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses.

    Science.gov (United States)

    Cebi, Nur; Yilmaz, Mustafa Tahsin; Sagdic, Osman

    2017-08-15

    Sibutramine may be illicitly included in herbal slimming foods and supplements marketed as "100% natural" to enhance weight loss. Considering public health and legal regulations, there is an urgent need for effective, rapid and reliable techniques to detect sibutramine in dietetic herbal foods, teas and dietary supplements. This research comprehensively explored, for the first time, detection of sibutramine in green tea, green coffee and mixed herbal tea using ATR-FTIR spectroscopic technique combined with chemometrics. Hierarchical cluster analysis and PCA principle component analysis techniques were employed in spectral range (2746-2656cm -1 ) for classification and discrimination through Euclidian distance and Ward's algorithm. Unadulterated and adulterated samples were classified and discriminated with respect to their sibutramine contents with perfect accuracy without any false prediction. The results suggest that existence of the active substance could be successfully determined at the levels in the range of 0.375-12mg in totally 1.75g of green tea, green coffee and mixed herbal tea by using FTIR-ATR technique combined with chemometrics. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. How to detect trap cluster systems?

    International Nuclear Information System (INIS)

    Mandowski, Arkadiusz

    2008-01-01

    Spatially correlated traps and recombination centres (trap-recombination centre pairs and larger clusters) are responsible for many anomalous phenomena that are difficult to explain in the framework of both classical models, i.e. model of localized transitions (LT) and the simple trap model (STM), even with a number of discrete energy levels. However, these 'anomalous' effects may provide a good platform for identifying trap cluster systems. This paper considers selected cluster-type effects, mainly relating to an anomalous dependence of TL on absorbed dose in the system of isolated clusters (ICs). Some consequences for interacting cluster (IAC) systems, involving both localized and delocalized transitions occurring simultaneously, are also discussed

  4. Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

    Directory of Open Access Journals (Sweden)

    Tushar H Jaware

    2013-10-01

    Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

  5. Nonlinear Multiantenna Detection Methods

    Directory of Open Access Journals (Sweden)

    Chen Sheng

    2004-01-01

    Full Text Available A nonlinear detection technique designed for multiple-antenna assisted receivers employed in space-division multiple-access systems is investigated. We derive the optimal solution of the nonlinear spatial-processing assisted receiver for binary phase shift keying signalling, which we refer to as the Bayesian detector. It is shown that this optimal Bayesian receiver significantly outperforms the standard linear beamforming assisted receiver in terms of a reduced bit error rate, at the expense of an increased complexity, while the achievable system capacity is substantially enhanced with the advent of employing nonlinear detection. Specifically, when the spatial separation expressed in terms of the angle of arrival between the desired and interfering signals is below a certain threshold, a linear beamformer would fail to separate them, while a nonlinear detection assisted receiver is still capable of performing adequately. The adaptive implementation of the optimal Bayesian detector can be realized using a radial basis function network. Two techniques are presented for constructing block-data-based adaptive nonlinear multiple-antenna assisted receivers. One of them is based on the relevance vector machine invoked for classification, while the other on the orthogonal forward selection procedure combined with the Fisher ratio class-separability measure. A recursive sample-by-sample adaptation procedure is also proposed for training nonlinear detectors based on an amalgam of enhanced -means clustering techniques and the recursive least squares algorithm.

  6. The polarizable embedding coupled cluster method

    DEFF Research Database (Denmark)

    Sneskov, Kristian; Schwabe, Tobias; Kongsted, Jacob

    2011-01-01

    We formulate a new combined quantum mechanics/molecular mechanics (QM/MM) method based on a self-consistent polarizable embedding (PE) scheme. For the description of the QM region, we apply the popular coupled cluster (CC) method detailing the inclusion of electrostatic and polarization effects...

  7. Locally adaptive decision in detection of clustered microcalcifications in mammograms

    Science.gov (United States)

    Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

    2018-02-01

    In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

  8. METHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERS

    Directory of Open Access Journals (Sweden)

    N. A. Novoselova

    2016-01-01

    Full Text Available The paper presents a method of construction of genetic data clusters (functional modules using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.

  9. Agglomerative concentric hypersphere clustering applied to structural damage detection

    Science.gov (United States)

    Silva, Moisés; Santos, Adam; Santos, Reginaldo; Figueiredo, Eloi; Sales, Claudomiro; Costa, João C. W. A.

    2017-08-01

    The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures. Continuous structural monitoring systems often require unsupervised approaches to automatically infer the health condition of a structure. However, when a structure is under linear and nonlinear effects caused by environmental and operational variability, data normalization procedures are also required to overcome these effects. The proposed approach aims, through a straightforward clustering procedure, to discover automatically the optimal number of clusters, representing the main state conditions of a structural system. Three initialization procedures are introduced to evaluate the impact of deterministic and stochastic initializations on the performance of this approach. The ACH is compared to state-of-the-art approaches, based on Gaussian mixture models and Mahalanobis squared distance, on standard data sets from a post-tensioned bridge located in Switzerland: the Z-24 Bridge. The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters. Furthermore, it reveals a better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, demonstrating a promising applicability in real-world structural health monitoring scenarios.

  10. Leak detection method

    International Nuclear Information System (INIS)

    1978-01-01

    This invention provides a method for removing nuclear fuel elements from a fabrication building while at the same time testing the fuel elements for leaks without releasing contaminants from the fabrication building or from the fuel elements. The vacuum source used, leak detecting mechanism and fuel element fabrication building are specified to withstand environmental hazards. (UK)

  11. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Jean Marie Vianney Kinani

    2017-01-01

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

  12. Radionuclide identification using subtractive clustering method

    International Nuclear Information System (INIS)

    Farias, Marcos Santana; Mourelle, Luiza de Macedo

    2011-01-01

    Radionuclide identification is crucial to planning protective measures in emergency situations. This paper presents the application of a method for a classification system of radioactive elements with a fast and efficient response. To achieve this goal is proposed the application of subtractive clustering algorithm. The proposed application can be implemented in reconfigurable hardware, a flexible medium to implement digital hardware circuits. (author)

  13. Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

    Science.gov (United States)

    Cook, Andrea J; Gold, Diane R; Li, Yi

    2009-10-01

    Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

  14. Community detection in complex networks using proximate support vector clustering

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-03-01

    Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

  15. Local Community Detection Algorithm Based on Minimal Cluster

    Directory of Open Access Journals (Sweden)

    Yong Zhou

    2016-01-01

    Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

  16. Detection of CO emission in Hydra 1 cluster galaxies

    International Nuclear Information System (INIS)

    Huchtmeier, W.K.

    1990-01-01

    A survey of bright Hydra cluster spiral galaxies for the CO(1-0) transition at 115 GHz was performed with the 15m Swedish-ESO submillimeter telescope (SEST). Five out of 15 galaxies observed have been detected in the CO(1-0) line. The largest spiral galaxy in the cluster, NGC 3312, got more CO than any spiral of the Virgo cluster. This Sa-type galaxy is optically largely distorted and disrupted on one side. It is a good candidate for ram pressure stripping while passing through the cluster's central region. A comparison with global CO properties of Virgo cluster spirals shows a relatively good agreement with the detected Hydra cluster galaxies

  17. Remote detection device and detection method therefor

    International Nuclear Information System (INIS)

    Kogure, Sumio; Yoshida, Yoji; Matsuo, Takashiro; Takehara, Hidetoshi; Kojima, Shinsaku.

    1997-01-01

    The present invention provides a non-destructive detection device for collectively, efficiently and effectively conducting maintenance and detection for confirming the integrity of a nuclear reactor by way of a shielding member for shielding radiation rays generated from an objective portion to be detected. Namely, devices for direct visual detection using an under water TV camera as a sensor, an eddy current detection using a coil as a sensor and each magnetic powder flow detection are integrated and applied collectively. Specifically, the visual detection by using the TV camera and the eddy current flaw detection are adopted together. The flaw detection with magnetic powder is applied as a means for confirming the results of the two kinds of detections by other method. With such procedures, detection techniques using respective specific theories are combined thereby enabling to enhance the accuracy for the evaluation of the detection. (I.S.)

  18. Human population structure detection via multilocus genotype clustering

    Directory of Open Access Journals (Sweden)

    Starmer Joshua

    2007-06-01

    Full Text Available Abstract Background We describe a hierarchical clustering algorithm for using Single Nucleotide Polymorphism (SNP genetic data to assign individuals to populations. The method does not assume Hardy-Weinberg equilibrium and linkage equilibrium among loci in sample population individuals. Results We show that the algorithm can assign sample individuals highly accurately to their corresponding ethnic groups in our tests using HapMap SNP data and it is also robust to admixed populations when tested with Perlegen SNP data. Moreover, it can detect fine-scale population structure as subtle as that between Chinese and Japanese by using genome-wide high-diversity SNP loci. Conclusion The algorithm provides an alternative approach to the popular STRUCTURE program, especially for fine-scale population structure detection in genome-wide association studies. This is the first successful separation of Chinese and Japanese samples using random SNP loci with high statistical support.

  19. A Novel Method to Automatically Detect and Measure the Ages of Star Clusters in Nearby Galaxies: Application to the Large Magellanic Cloud

    Czech Academy of Sciences Publication Activity Database

    Bitsakis, J.; Bonfini, P.; Gonzalez-Lopezlira, R.A.; Ramirez-Siordia, V.H.; Bruzual, G.; Charlot, S.; Maravelias, Grigorios; Zaritsky, D.

    2017-01-01

    Roč. 845, č. 1 (2017), 56/1-56/12 ISSN 0004-637X R&D Projects: GA ČR(CZ) GA14-21373S Institutional support: RVO:67985815 Keywords : catalogs * star clusters * Magellanic Cloud s Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics OBOR OECD: Astronomy (including astrophysics,space science) Impact factor: 5.533, year: 2016

  20. Recent advances in coupled-cluster methods

    CERN Document Server

    Bartlett, Rodney J

    1997-01-01

    Today, coupled-cluster (CC) theory has emerged as the most accurate, widely applicable approach for the correlation problem in molecules. Furthermore, the correct scaling of the energy and wavefunction with size (i.e. extensivity) recommends it for studies of polymers and crystals as well as molecules. CC methods have also paid dividends for nuclei, and for certain strongly correlated systems of interest in field theory.In order for CC methods to have achieved this distinction, it has been necessary to formulate new, theoretical approaches for the treatment of a variety of essential quantities

  1. A method of clustering observers with different visual characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Niimi, Takanaga [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Imai, Kuniharu [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Ikeda, Mitsuru [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Maeda, Hisatoshi [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan)

    2006-01-15

    Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnel with the similar perceptual predisposition when a C-D phantom was used in image quality control.

  2. A method of clustering observers with different visual characteristics

    International Nuclear Information System (INIS)

    Niimi, Takanaga; Imai, Kuniharu; Ikeda, Mitsuru; Maeda, Hisatoshi

    2006-01-01

    Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnel with the similar perceptual predisposition when a C-D phantom was used in image quality control

  3. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Directory of Open Access Journals (Sweden)

    Jingli Yang

    2016-12-01

    Full Text Available The k-nearest neighbour (kNN rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

  4. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Science.gov (United States)

    Yang, Jingli; Sun, Zhen; Chen, Yinsheng

    2016-01-01

    The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

  5. A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

    Directory of Open Access Journals (Sweden)

    A. Tajari Siahmarzkooh

    2016-12-01

    Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.

  6. Uncertainty of a detected spatial cluster in 1D: quantification and visualization

    KAUST Repository

    Lee, Junho; Gangnon, Ronald E.; Zhu, Jun; Liang, Jingjing

    2017-01-01

    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

  7. Uncertainty of a detected spatial cluster in 1D: quantification and visualization

    KAUST Repository

    Lee, Junho

    2017-10-19

    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

  8. Failed fuel detection method

    International Nuclear Information System (INIS)

    Utamura, Motoaki; Urata, Megumu.

    1976-01-01

    Object: To detect failed fuel element in a reactor with high precision by measuring the radioactivity concentrations for more than one nuclides of fission products ( 131 I and 132 I, for example) contained in each sample of coolant in fuel channel. Method: The radioactivity concentrations in the sampled coolant are obtained from gamma spectra measured by a pulse height analyser after suitable cooling periods according to the half-lives of the fission products to be measured. The first measurement for 132 I is made in two hours after sampling, and the second for 131 I is started one day after the sampling. Fuel element corresponding to the high radioactivity concentrations for both 131 I and 132 I is expected with certainty to have failed

  9. Blood detection in wireless capsule endoscopy using expectation maximization clustering

    Science.gov (United States)

    Hwang, Sae; Oh, JungHwan; Cox, Jay; Tang, Shou Jiang; Tibbals, Harry F.

    2006-03-01

    Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. Other endoscopies such as colonoscopy, upper gastrointestinal endoscopy, push enteroscopy, and intraoperative enteroscopy could be used to visualize up to the stomach, duodenum, colon, and terminal ileum, but there existed no method to view most of the small intestine without surgery. With the miniaturization of wireless and camera technologies came the ability to view the entire gestational track with little effort. A tiny disposable video capsule is swallowed, transmitting two images per second to a small data receiver worn by the patient on a belt. During an approximately 8-hour course, over 55,000 images are recorded to a worn device and then downloaded to a computer for later examination. Typically, a medical clinician spends more than two hours to analyze a WCE video. Research has been attempted to automatically find abnormal regions (especially bleeding) to reduce the time needed to analyze the videos. The manufacturers also provide the software tool to detect the bleeding called Suspected Blood Indicator (SBI), but its accuracy is not high enough to replace human examination. It was reported that the sensitivity and the specificity of SBI were about 72% and 85%, respectively. To address this problem, we propose a technique to detect the bleeding regions automatically utilizing the Expectation Maximization (EM) clustering algorithm. Our experimental results indicate that the proposed bleeding detection method achieves 92% and 98% of sensitivity and specificity, respectively.

  10. Membership determination of open clusters based on a spectral clustering method

    Science.gov (United States)

    Gao, Xin-Hua

    2018-06-01

    We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

  11. Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.

    Science.gov (United States)

    1982-02-26

    UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an

  12. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  13. Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering.

    Science.gov (United States)

    Shi, Lin; Wang, Defeng; Liu, Wen; Fang, Kui; Wang, Yi-Xiang J; Huang, Wenhua; King, Ann D; Heng, Pheng Ann; Ahuja, Anil T

    2014-05-01

    To automatically and robustly detect the arterial input function (AIF) with high detection accuracy and low computational cost in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we developed an automatic AIF detection method using an accelerated version (Fast-AP) of affinity propagation (AP) clustering. The validity of this Fast-AP-based method was proved on two DCE-MRI datasets, i.e., rat kidney and human head and neck. The detailed AIF detection performance of this proposed method was assessed in comparison with other clustering-based methods, namely original AP and K-means, as well as the manual AIF detection method. Both the automatic AP- and Fast-AP-based methods achieved satisfactory AIF detection accuracy, but the computational cost of Fast-AP could be reduced by 64.37-92.10% on rat dataset and 73.18-90.18% on human dataset compared with the cost of AP. The K-means yielded the lowest computational cost, but resulted in the lowest AIF detection accuracy. The experimental results demonstrated that both the AP- and Fast-AP-based methods were insensitive to the initialization of cluster centers, and had superior robustness compared with K-means method. The Fast-AP-based method enables automatic AIF detection with high accuracy and efficiency. Copyright © 2013 Wiley Periodicals, Inc.

  14. MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS

    Directory of Open Access Journals (Sweden)

    Jana Halčinová

    2014-06-01

    Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.

  15. Cluster temperature. Methods for its measurement and stabilization

    International Nuclear Information System (INIS)

    Makarov, G N

    2008-01-01

    Cluster temperature is an important material parameter essential to many physical and chemical processes involving clusters and cluster beams. Because of the diverse methods by which clusters can be produced, excited, and stabilized, and also because of the widely ranging values of atomic and molecular binding energies (approximately from 10 -5 to 10 eV) and numerous energy relaxation channels in clusters, cluster temperature (internal energy) ranges from 10 -3 to about 10 8 K. This paper reviews research on cluster temperature and describes methods for its measurement and stabilization. The role of cluster temperature in and its influence on physical and chemical processes is discussed. Results on the temperature dependence of cluster properties are presented. The way in which cluster temperature relates to cluster structure and to atomic and molecular interaction potentials in clusters is addressed. Methods for strong excitation of clusters and channels for their energy relaxation are discussed. Some applications of clusters and cluster beams are considered. (reviews of topical problems)

  16. Detection and quantification of solute clusters in a nanostructured ferritic alloy

    Energy Technology Data Exchange (ETDEWEB)

    Miller, M.K., E-mail: millermk@ornl.gov [Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6139 (United States); Reinhard, D., E-mail: David.Reinhard@ametek.com [CAMECA Instruments, Inc., 5500 Nobel Drive, Madison, WI 53711 (United States); Larson, D.J., E-mail: David.Larson@ametek.com [CAMECA Instruments, Inc., 5500 Nobel Drive, Madison, WI 53711 (United States)

    2015-07-15

    Highlights: • Simulated APT data indicate that solute clusters can be resolved at 80% detection efficiency. • Solute clusters containing 2–9 atoms were detected in a prototype ∼80% detection efficiency LEAP. • High densities, 1.8 × 10{sup 24} m{sup −3}, of solute clusters were detected in as-milled flakes of 14YWT. • Lower densities, 1.2 × 10{sup 24} m{sup −3}, were detected in the stir zone of a FSW. • Vacancies stabilize the clusters, which retard diffusion and confers excellent stability. - Abstract: A series of simulated atom probe datasets were examined with a friends-of-friends method to establish the detection efficiency required to resolve solute clusters in the ferrite phase of a 14YWT nanostructured ferritic alloy. The size and number densities of solute clusters in the ferrite of the as-milled mechanically-alloyed condition and the stir zone of a friction stir weld were estimated with a prototype high-detection-efficiency (∼80%) local electrode atom probe. High number densities, 1.8 × 10{sup 24} m{sup −3} and 1.2 × 10{sup 24} m{sup −3}, respectively of solute clusters containing between 2 and 9 solute atoms of Ti, Y and O and were detected for these two conditions. These results support first principle calculations that predicted that vacancies stabilize these Ti–Y–O– clusters, which retard diffusion and contribute to the excellent high temperature stability of the microstructure and radiation tolerance of nanostructured ferritic alloys.

  17. Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

    Directory of Open Access Journals (Sweden)

    Ji-wook Jeong

    2016-01-01

    Full Text Available We propose computer-aided detection (CADe algorithm for microcalcification (MC clusters in reconstructed digital breast tomosynthesis (DBT images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

  18. A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

    Directory of Open Access Journals (Sweden)

    Peixin Zhao

    2013-01-01

    Full Text Available Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.

  19. Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign

    OpenAIRE

    Maciej Kutera; Mirosława Lasek

    2010-01-01

    Clustering methods are recently so advanced elaborated algorithms for large collection data analysis that they have been already included today to data mining methods. Clustering methods are nowadays larger and larger group of methods, very quickly evolving and having more and more various applications. In the article, our research concerning usefulness of clustering methods in customer segmentation to manage advertisement campaign is presented. We introduce results obtained by using four sel...

  20. Integrated management of thesis using clustering method

    Science.gov (United States)

    Astuti, Indah Fitri; Cahyadi, Dedy

    2017-02-01

    Thesis is one of major requirements for student in pursuing their bachelor degree. In fact, finishing the thesis involves a long process including consultation, writing manuscript, conducting the chosen method, seminar scheduling, searching for references, and appraisal process by the board of mentors and examiners. Unfortunately, most of students find it hard to match all the lecturers' free time to sit together in a seminar room in order to examine the thesis. Therefore, seminar scheduling process should be on the top of priority to be solved. Manual mechanism for this task no longer fulfills the need. People in campus including students, staffs, and lecturers demand a system in which all the stakeholders can interact each other and manage the thesis process without conflicting their timetable. A branch of computer science named Management Information System (MIS) could be a breakthrough in dealing with thesis management. This research conduct a method called clustering to distinguish certain categories using mathematics formulas. A system then be developed along with the method to create a well-managed tool in providing some main facilities such as seminar scheduling, consultation and review process, thesis approval, assessment process, and also a reliable database of thesis. The database plays an important role in present and future purposes.

  1. An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

    Directory of Open Access Journals (Sweden)

    Tingquan Deng

    2016-01-01

    Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

  2. A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Bohui Zhu

    2013-01-01

    Full Text Available This paper presents a novel maximum margin clustering method with immune evolution (IEMMC for automatic diagnosis of electrocardiogram (ECG arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

  3. Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

    Science.gov (United States)

    Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.

    2018-04-01

    Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

  4. Crack detecting method

    International Nuclear Information System (INIS)

    Narita, Michiko; Aida, Shigekazu

    1998-01-01

    A penetration liquid or a slow drying penetration liquid prepared by mixing a penetration liquid and a slow drying liquid is filled to the inside of an artificial crack formed to a member to be detected such as of boiler power generation facilities and nuclear power facilities. A developing liquid is applied to the periphery of the artificial crack on the surface of a member to be detected. As the slow-drying liquid, an oil having a viscosity of 56 is preferably used. Loads are applied repeatedly to the member to be detected, and when a crack is caused to the artificial crack, the permeation liquid penetrates into the crack. The penetration liquid penetrated into the crack is developed by the developing liquid previously coated to the periphery of the artificial crack of the surface of the member to be detected. When a crack is caused, since the crack is developed clearly even if it is a small opening, the crack can be recognized visually reliably. (I.N.)

  5. Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.

    Directory of Open Access Journals (Sweden)

    Yuliang Wang

    Full Text Available Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

  6. Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.

    Science.gov (United States)

    Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

    2015-01-01

    Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

  7. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

    Directory of Open Access Journals (Sweden)

    Fonseca Carlos M

    2010-10-01

    Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

  8. A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.

    Science.gov (United States)

    Tango, Toshiro; Takahashi, Kunihiko

    2012-12-30

    Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Detecting edges in the X-ray surface brightness of galaxy clusters

    Science.gov (United States)

    Sanders, J. S.; Fabian, A. C.; Russell, H. R.; Walker, S. A.; Blundell, K. M.

    2016-08-01

    The effects of many physical processes in the intracluster medium of galaxy clusters imprint themselves in X-ray surface brightness images. It is therefore important to choose optimal methods for extracting information from and enhancing the interpretability of such images. We describe in detail a gradient filtering edge detection method that we previously applied to images of the Centaurus cluster of galaxies. The Gaussian gradient filter measures the gradient in the surface brightness distribution on particular spatial scales. We apply this filter on different scales to Chandra X-ray observatory images of two clusters with active galactic nucleus feedback, the Perseus cluster and M 87, and a merging system, A 3667. By combining filtered images on different scales using radial filters spectacular images of the edges in a cluster are produced. We describe how to assess the significance of features in filtered images. We find the gradient filtering technique to have significant advantages for detecting many kinds of features compared to other analysis techniques, such as unsharp masking. Filtering cluster images in this way in a hard energy band allows shocks to be detected.

  10. THE DETECTION AND STATISTICS OF GIANT ARCS BEHIND CLASH CLUSTERS

    International Nuclear Information System (INIS)

    Xu, Bingxiao; Zheng, Wei; Postman, Marc; Bradley, Larry; Meneghetti, Massimo; Koekemoer, Anton; Seitz, Stella; Zitrin, Adi; Merten, Julian; Maoz, Dani; Frye, Brenda; Umetsu, Keiichi; Vega, Jesus

    2016-01-01

    We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble (CLASH). Simulated CLASH images are created using the MOKA package and also clusters selected from the high-resolution, hydrodynamical simulations, MUSIC, over the same mass and redshift range as the CLASH sample. The algorithm's arc elongation accuracy, completeness, and false positive rate are determined and used to compute an estimate of the true arc abundance. We derive a lensing efficiency of 4 ± 1 arcs (with length ≥6″ and length-to-width ratio ≥7) per cluster for the X-ray-selected CLASH sample, 4 ± 1 arcs per cluster for the MOKA-simulated sample, and 3 ± 1 arcs per cluster for the MUSIC-simulated sample. The observed and simulated arc statistics are in full agreement. We measure the photometric redshifts of all detected arcs and find a median redshift z s = 1.9 with 33% of the detected arcs having z s  > 3. We find that the arc abundance does not depend strongly on the source redshift distribution but is sensitive to the mass distribution of the dark matter halos (e.g., the c–M relation). Our results show that consistency between the observed and simulated distributions of lensed arc sizes and axial ratios can be achieved by using cluster-lensing simulations that are carefully matched to the selection criteria used in the observations

  11. THE DETECTION AND STATISTICS OF GIANT ARCS BEHIND CLASH CLUSTERS

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Bingxiao; Zheng, Wei [Department of Physics and Astronomy, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Postman, Marc; Bradley, Larry [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21208 (United States); Meneghetti, Massimo; Koekemoer, Anton [INAF, Osservatorio Astronomico di Bologna, and INFN, Sezione di Bologna, Via Ranzani 1, I-40127 Bologna (Italy); Seitz, Stella [Universitaets-Sternwarte, Fakultaet fuer Physik, Ludwig-Maximilians Universitaet Muenchen, Scheinerstr. 1, D-81679 Muenchen (Germany); Zitrin, Adi [California Institute of Technology, MC 249-17, Pasadena, CA 91125 (United States); Merten, Julian [University of Oxford, Department of Physics, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH (United Kingdom); Maoz, Dani [School of Physics and Astronomy, Tel Aviv University, Tel-Aviv 69978 (Israel); Frye, Brenda [Steward Observatory/Department of Astronomy, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721 (United States); Umetsu, Keiichi [Institute of Astronomy and Astrophysics, Academia Sinica, P.O. Box 23-141, Taipei 10617, Taiwan (China); Vega, Jesus, E-mail: bxu6@jhu.edu [Universidad Autonoma de Madrid, Ciudad Universitaria de Cantoblanco, E-28049 Madrid (Spain)

    2016-02-01

    We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble (CLASH). Simulated CLASH images are created using the MOKA package and also clusters selected from the high-resolution, hydrodynamical simulations, MUSIC, over the same mass and redshift range as the CLASH sample. The algorithm's arc elongation accuracy, completeness, and false positive rate are determined and used to compute an estimate of the true arc abundance. We derive a lensing efficiency of 4 ± 1 arcs (with length ≥6″ and length-to-width ratio ≥7) per cluster for the X-ray-selected CLASH sample, 4 ± 1 arcs per cluster for the MOKA-simulated sample, and 3 ± 1 arcs per cluster for the MUSIC-simulated sample. The observed and simulated arc statistics are in full agreement. We measure the photometric redshifts of all detected arcs and find a median redshift z{sub s} = 1.9 with 33% of the detected arcs having z{sub s} > 3. We find that the arc abundance does not depend strongly on the source redshift distribution but is sensitive to the mass distribution of the dark matter halos (e.g., the c–M relation). Our results show that consistency between the observed and simulated distributions of lensed arc sizes and axial ratios can be achieved by using cluster-lensing simulations that are carefully matched to the selection criteria used in the observations.

  12. The Detection and Statistics of Giant Arcs behind CLASH Clusters

    Science.gov (United States)

    Xu, Bingxiao; Postman, Marc; Meneghetti, Massimo; Seitz, Stella; Zitrin, Adi; Merten, Julian; Maoz, Dani; Frye, Brenda; Umetsu, Keiichi; Zheng, Wei; Bradley, Larry; Vega, Jesus; Koekemoer, Anton

    2016-02-01

    We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble (CLASH). Simulated CLASH images are created using the MOKA package and also clusters selected from the high-resolution, hydrodynamical simulations, MUSIC, over the same mass and redshift range as the CLASH sample. The algorithm's arc elongation accuracy, completeness, and false positive rate are determined and used to compute an estimate of the true arc abundance. We derive a lensing efficiency of 4 ± 1 arcs (with length ≥6″ and length-to-width ratio ≥7) per cluster for the X-ray-selected CLASH sample, 4 ± 1 arcs per cluster for the MOKA-simulated sample, and 3 ± 1 arcs per cluster for the MUSIC-simulated sample. The observed and simulated arc statistics are in full agreement. We measure the photometric redshifts of all detected arcs and find a median redshift zs = 1.9 with 33% of the detected arcs having zs > 3. We find that the arc abundance does not depend strongly on the source redshift distribution but is sensitive to the mass distribution of the dark matter halos (e.g., the c-M relation). Our results show that consistency between the observed and simulated distributions of lensed arc sizes and axial ratios can be achieved by using cluster-lensing simulations that are carefully matched to the selection criteria used in the observations.

  13. A Voltage Quality Detection Method

    DEFF Research Database (Denmark)

    Chen, Zhe; Wei, Mu

    2008-01-01

    This paper presents a voltage quality detection method based on a phase-locked loop (PLL) technique. The technique can detect the voltage magnitude and phase angle of each individual phase under both normal and fault power system conditions. The proposed method has the potential to evaluate various...

  14. A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Xite Wang

    2017-01-01

    Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

  15. Efficient image duplicated region detection model using sequential block clustering

    Czech Academy of Sciences Publication Activity Database

    Sekeh, M. A.; Maarof, M. A.; Rohani, M. F.; Mahdian, Babak

    2013-01-01

    Roč. 10, č. 1 (2013), s. 73-84 ISSN 1742-2876 Institutional support: RVO:67985556 Keywords : Image forensic * Copy–paste forgery * Local block matching Subject RIV: IN - Informatics, Computer Science Impact factor: 0.986, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/mahdian-efficient image duplicated region detection model using sequential block clustering.pdf

  16. Orthology detection combining clustering and synteny for very large datasets

    OpenAIRE

    Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K.; Prohaska, Sonja J.; Stadler, Peter F.

    2014-01-01

    The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the ...

  17. Homological methods, representation theory, and cluster algebras

    CERN Document Server

    Trepode, Sonia

    2018-01-01

    This text presents six mini-courses, all devoted to interactions between representation theory of algebras, homological algebra, and the new ever-expanding theory of cluster algebras. The interplay between the topics discussed in this text will continue to grow and this collection of courses stands as a partial testimony to this new development. The courses are useful for any mathematician who would like to learn more about this rapidly developing field; the primary aim is to engage graduate students and young researchers. Prerequisites include knowledge of some noncommutative algebra or homological algebra. Homological algebra has always been considered as one of the main tools in the study of finite-dimensional algebras. The strong relationship with cluster algebras is more recent and has quickly established itself as one of the important highlights of today’s mathematical landscape. This connection has been fruitful to both areas—representation theory provides a categorification of cluster algebras, wh...

  18. Detection of secondary structure elements in proteins by hydrophobic cluster analysis.

    Science.gov (United States)

    Woodcock, S; Mornon, J P; Henrissat, B

    1992-10-01

    Hydrophobic cluster analysis (HCA) is a protein sequence comparison method based on alpha-helical representations of the sequences where the size, shape and orientation of the clusters of hydrophobic residues are primarily compared. The effectiveness of HCA has been suggested to originate from its potential ability to focus on the residues forming the hydrophobic core of globular proteins. We have addressed the robustness of the bidimensional representation used for HCA in its ability to detect the regular secondary structure elements of proteins. Various parameters have been studied such as those governing cluster size and limits, the hydrophobic residues constituting the clusters as well as the potential shift of the cluster positions with respect to the position of the regular secondary structure elements. The following results have been found to support the alpha-helical bidimensional representation used in HCA: (i) there is a positive correlation (clearly above background noise) between the hydrophobic clusters and the regular secondary structure elements in proteins; (ii) the hydrophobic clusters are centred on the regular secondary structure elements; (iii) the pitch of the helical representation which gives the best correspondence is that of an alpha-helix. The correspondence between hydrophobic clusters and regular secondary structure elements suggests a way to implement variable gap penalties during the automatic alignment of protein sequences.

  19. Single pass kernel k-means clustering method

    Indian Academy of Sciences (India)

    In unsupervised classification, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...

  20. Fault detection of flywheel system based on clustering and principal component analysis

    Directory of Open Access Journals (Sweden)

    Wang Rixin

    2015-12-01

    Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  1. A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

    2005-04-01

    Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

  2. Improving local clustering based top-L link prediction methods via asymmetric link clustering information

    Science.gov (United States)

    Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

    2018-02-01

    Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

  3. A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    ZiQi Hao

    2015-01-01

    Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

  4. Determining wood chip size: image analysis and clustering methods

    Directory of Open Access Journals (Sweden)

    Paolo Febbi

    2013-09-01

    Full Text Available One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010. Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm; the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors and size descriptors (area, perimeter, Feret diameters, eccentricity was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process.

  5. Detection methods for irradiated food

    International Nuclear Information System (INIS)

    Stevenson, M.H.

    1993-01-01

    The plenary lecture gives a brief historical review of the development of methods for the detection of food irradiation and defines the demands on such methods. The methods described in detail are as follows: 1) Physical methods: As examples of luminescence methods, thermoluminescence and chermoluminescence are mentioned; ESR spectroscopy is discussed in detail by means of individual examples (crustaceans, frutis and vegetables, spieces and herbs, nuts). 2) Chemical methods: Examples given for these are methods that make use of alterations in lipids through radiation (formation of long-chain hydrocarbons, formation of 2-alkyl butanones), respectively radiation-induced alterations in the DNA. 3) Microbiological methods. An extensive bibliography is appended. (VHE) [de

  6. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    Science.gov (United States)

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  7. A Web service substitution method based on service cluster nets

    Science.gov (United States)

    Du, YuYue; Gai, JunJing; Zhou, MengChu

    2017-11-01

    Service substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.

  8. A {sup 13}CO Detection in a Brightest Cluster Galaxy

    Energy Technology Data Exchange (ETDEWEB)

    Vantyghem, A. N.; McNamara, B. R.; Hogan, M. T. [Department of Physics and Astronomy, University of Waterloo, Waterloo, ON N2L 3G1 (Canada); Edge, A. C. [Department of Physics, Durham University, Durham DH1 3LE (United Kingdom); Combes, F.; Salomé, P. [LERMA, Observatoire de Paris, CNRS, UPMC, PSL Univ., 61 avenue de l’Observatoire, F-75014 Paris (France); Russell, H. R.; Fabian, A. C. [Institute of Astronomy, Madingley Road, Cambridge CB3 0HA (United Kingdom); McDonald, M. [Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States); Nulsen, P. E. J., E-mail: a2vantyg@uwaterloo.ca [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)

    2017-10-20

    We present ALMA Cycle 4 observations of CO(1-0), CO(3-2), and {sup 13}CO(3-2) line emission in the brightest cluster galaxy (BCG) of RXJ0821+0752. This is one of the first detections of {sup 13}CO line emission in a galaxy cluster. Half of the CO(3-2) line emission originates from two clumps of molecular gas that are spatially offset from the galactic center. These clumps are surrounded by diffuse emission that extends 8 kpc in length. The detected {sup 13}CO emission is confined entirely to the two bright clumps, with any emission outside of this region lying below our detection threshold. Two distinct velocity components with similar integrated fluxes are detected in the {sup 12}CO spectra. The narrower component (60 km s{sup −1} FWHM) is consistent in both velocity centroid and linewidth with {sup 13}CO(3-2) emission, while the broader (130–160 km s{sup −1}), slightly blueshifted wing has no associated {sup 13}CO(3-2) emission. A simple local thermodynamic model indicates that the {sup 13}CO emission traces 2.1 × 10{sup 9} M {sub ⊙} of molecular gas. Isolating the {sup 12}CO velocity component that accompanies the {sup 13}CO emission yields a CO-to-H{sub 2} conversion factor of α {sub CO} = 2.3 M {sub ⊙} (K km s{sup −1}){sup −1}, which is a factor of two lower than the Galactic value. Adopting the Galactic CO-to-H{sub 2} conversion factor in BCGs may therefore overestimate their molecular gas masses by a factor of two. This is within the object-to-object scatter from extragalactic sources, so calibrations in a larger sample of clusters are necessary in order to confirm a sub-Galactic conversion factor.

  9. Cluster analysis for DNA methylation profiles having a detection threshold

    Directory of Open Access Journals (Sweden)

    Siegmund Kimberly D

    2006-07-01

    Full Text Available Abstract Background DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data. Results We compare performance of existing methodology (such as k-means with two novel methods that explicitly allow for the preponderance of values at 0. We also consider how the ability to successfully cluster such data depends upon the number of informative genes for which methylation is measured and the correlation structure of the methylation values for those genes. We show that when data is collected for a sufficient number of genes, our models do improve clustering performance compared to methods, such as k-means, that do not explicitly respect the supposed biological realities of the situation. Conclusion The performance of analysis methods depends upon how well the assumptions of those methods reflect the properties of the data being analyzed. Differing technologies will lead to data with differing properties, and should therefore be analyzed differently. Consequently, it is prudent to give thought to what the properties of the data are likely to be, and which analysis method might therefore be likely to best capture those properties.

  10. Fuzzy C-means method for clustering microarray data.

    Science.gov (United States)

    Dembélé, Doulaye; Kastner, Philippe

    2003-05-22

    Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

  11. Progeny Clustering: A Method to Identify Biological Phenotypes

    Science.gov (United States)

    Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

    2015-01-01

    Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

  12. Image Registration Using Single Cluster PHD Methods

    Science.gov (United States)

    Campbell, M.; Schlangen, I.; Delande, E.; Clark, D.

    Cadets in the Department of Physics at the United States Air Force Academy are using the technique of slitless spectroscopy to analyze the spectra from geostationary satellites during glint season. The equinox periods of the year are particularly favorable for earth-based observers to detect specular reflections off satellites (glints), which have been observed in the past using broadband photometry techniques. Three seasons of glints were observed and analyzed for multiple satellites, as measured across the visible spectrum using a diffraction grating on the Academy’s 16-inch, f/8.2 telescope. It is clear from the results that the glint maximum wavelength decreases relative to the time periods before and after the glint, and that the spectral reflectance during the glint is less like a blackbody. These results are consistent with the presumption that solar panels are the predominant source of specular reflection. The glint spectra are also quantitatively compared to different blackbody curves and the solar spectrum by means of absolute differences and standard deviations. Our initial analysis appears to indicate a potential method of determining relative power capacity.

  13. THE RELATION BETWEEN COOL CLUSTER CORES AND HERSCHEL-DETECTED STAR FORMATION IN BRIGHTEST CLUSTER GALAXIES

    Energy Technology Data Exchange (ETDEWEB)

    Rawle, T. D.; Egami, E.; Rex, M.; Fiedler, A.; Haines, C. P.; Pereira, M. J.; Portouw, J.; Walth, G. [Steward Observatory, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721 (United States); Edge, A. C. [Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE (United Kingdom); Smith, G. P. [School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT (United Kingdom); Altieri, B.; Valtchanov, I. [Herschel Science Centre, ESAC, ESA, P.O. Box 78, Villanueva de la Canada, 28691 Madrid (Spain); Perez-Gonzalez, P. G. [Departamento de Astrofisica, Facultad de CC. Fisicas, Universidad Complutense de Madrid, E-28040 Madrid (Spain); Van der Werf, P. P. [Sterrewacht Leiden, Leiden University, P.O. Box 9513, 2300 RA, Leiden (Netherlands); Zemcov, M., E-mail: trawle@as.arizona.edu [Department of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125 (United States)

    2012-03-01

    We present far-infrared (FIR) analysis of 68 brightest cluster galaxies (BCGs) at 0.08 < z < 1.0. Deriving total infrared luminosities directly from Spitzer and Herschel photometry spanning the peak of the dust component (24-500 {mu}m), we calculate the obscured star formation rate (SFR). 22{sup +6.2}{sub -5.3}% of the BCGs are detected in the far-infrared, with SFR = 1-150 M{sub Sun} yr{sup -1}. The infrared luminosity is highly correlated with cluster X-ray gas cooling times for cool-core clusters (gas cooling time <1 Gyr), strongly suggesting that the star formation in these BCGs is influenced by the cluster-scale cooling process. The occurrence of the molecular gas tracing H{alpha} emission is also correlated with obscured star formation. For all but the most luminous BCGs (L{sub TIR} > 2 Multiplication-Sign 10{sup 11} L{sub Sun }), only a small ({approx}<0.4 mag) reddening correction is required for SFR(H{alpha}) to agree with SFR{sub FIR}. The relatively low H{alpha} extinction (dust obscuration), compared to values reported for the general star-forming population, lends further weight to an alternate (external) origin for the cold gas. Finally, we use a stacking analysis of non-cool-core clusters to show that the majority of the fuel for star formation in the FIR-bright BCGs is unlikely to originate from normal stellar mass loss.

  14. The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

    Science.gov (United States)

    Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

    2017-07-01

    Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

  15. A Latent Variable Clustering Method for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir

    2016-01-01

    In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...

  16. Single pass kernel k-means clustering method

    Indian Academy of Sciences (India)

    paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

  17. Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

    Science.gov (United States)

    Sa, Qila; Wang, Zhihui

    2018-03-01

    At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

  18. Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

    Science.gov (United States)

    Ing, Alex; Schwarzbauer, Christian

    2014-01-01

    Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

  19. Population clustering based on copy number variations detected from next generation sequencing data.

    Science.gov (United States)

    Duan, Junbo; Zhang, Ji-Gang; Wan, Mingxi; Deng, Hong-Wen; Wang, Yu-Ping

    2014-08-01

    Copy number variations (CNVs) can be used as significant bio-markers and next generation sequencing (NGS) provides a high resolution detection of these CNVs. But how to extract features from CNVs and further apply them to genomic studies such as population clustering have become a big challenge. In this paper, we propose a novel method for population clustering based on CNVs from NGS. First, CNVs are extracted from each sample to form a feature matrix. Then, this feature matrix is decomposed into the source matrix and weight matrix with non-negative matrix factorization (NMF). The source matrix consists of common CNVs that are shared by all the samples from the same group, and the weight matrix indicates the corresponding level of CNVs from each sample. Therefore, using NMF of CNVs one can differentiate samples from different ethnic groups, i.e. population clustering. To validate the approach, we applied it to the analysis of both simulation data and two real data set from the 1000 Genomes Project. The results on simulation data demonstrate that the proposed method can recover the true common CNVs with high quality. The results on the first real data analysis show that the proposed method can cluster two family trio with different ancestries into two ethnic groups and the results on the second real data analysis show that the proposed method can be applied to the whole-genome with large sample size consisting of multiple groups. Both results demonstrate the potential of the proposed method for population clustering.

  20. Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign

    Directory of Open Access Journals (Sweden)

    Maciej Kutera

    2010-10-01

    Full Text Available Clustering methods are recently so advanced elaborated algorithms for large collection data analysis that they have been already included today to data mining methods. Clustering methods are nowadays larger and larger group of methods, very quickly evolving and having more and more various applications. In the article, our research concerning usefulness of clustering methods in customer segmentation to manage advertisement campaign is presented. We introduce results obtained by using four selected methods which have been chosen because their peculiarities suggested their applicability to our purposes. One of the analyzed method k-means clustering with random selected initial cluster seeds gave very good results in customer segmentation to manage advertisement campaign and these results were presented in details in the article. In contrast one of the methods (hierarchical average linkage was found useless in customer segmentation. Further investigations concerning benefits of clustering methods in customer segmentation to manage advertisement campaign is worth continuing, particularly that finding solutions in this field can give measurable profits for marketing activity.

  1. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang

    2017-02-16

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  2. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke

    2017-01-01

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  3. Performance improvement of haptic collision detection using subdivision surface and sphere clustering.

    Directory of Open Access Journals (Sweden)

    A Ram Choi

    Full Text Available Haptics applications such as surgery simulations require collision detections that are more precise than others. An efficient collision detection method based on the clustering of bounding spheres was proposed in our prior study. This paper analyzes and compares the applied effects of the five most common subdivision surface methods on some 3D models for haptic collision detection. The five methods are Butterfly, Catmull-Clark, Mid-point, Loop, and LS3 (Least Squares Subdivision Surface. After performing a number of experiments, we have concluded that LS3 method is the most appropriate for haptic simulations. The more we applied surface subdivision, the more the collision detection results became precise. However, it is observed that the performance becomes better until a certain threshold and degrades afterward. In order to reduce the performance degradation, we adopted our prior work, which was the fast and precise collision detection method based on adaptive clustering. As a result, we obtained a notable improvement of the speed of collision detection.

  4. Regions of micro-calcifications clusters detection based on new features from imbalance data in mammograms

    Science.gov (United States)

    Wang, Keju; Dong, Min; Yang, Zhen; Guo, Yanan; Ma, Yide

    2017-02-01

    Breast cancer is the most common cancer among women. Micro-calcification cluster on X-ray mammogram is one of the most important abnormalities, and it is effective for early cancer detection. Surrounding Region Dependence Method (SRDM), a statistical texture analysis method is applied for detecting Regions of Interest (ROIs) containing microcalcifications. Inspired by the SRDM, we present a method that extract gray and other features which are effective to predict the positive and negative regions of micro-calcifications clusters in mammogram. By constructing a set of artificial images only containing micro-calcifications, we locate the suspicious pixels of calcifications of a SRDM matrix in original image map. Features are extracted based on these pixels for imbalance date and then the repeated random subsampling method and Random Forest (RF) classifier are used for classification. True Positive (TP) rate and False Positive (FP) can reflect how the result will be. The TP rate is 90% and FP rate is 88.8% when the threshold q is 10. We draw the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC Curve (AUC) value reaches 0.9224. The experiment indicates that our method is effective. A novel regions of micro-calcifications clusters detection method is developed, which is based on new features for imbalance data in mammography, and it can be considered to help improving the accuracy of computer aided diagnosis breast cancer.

  5. Alerts Visualization and Clustering in Network-based Intrusion Detection

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Dr. Li [University of Tennessee; Gasior, Wade C [ORNL; Dasireddy, Swetha [University of Tennessee

    2010-04-01

    Today's Intrusion detection systems when deployed on a busy network overload the network with huge number of alerts. This behavior of producing too much raw information makes it less effective. We propose a system which takes both raw data and Snort alerts to visualize and analyze possible intrusions in a network. Then we present with two models for the visualization of clustered alerts. Our first model gives the network administrator with the logical topology of the network and detailed information of each node that involves its associated alerts and connections. In the second model, flocking model, presents the network administrator with the visual representation of IDS data in which each alert is represented in different color and the alerts with maximum similarity move together. This gives network administrator with the idea of detecting various of intrusions through visualizing the alert patterns.

  6. Orthology detection combining clustering and synteny for very large datasets.

    Science.gov (United States)

    Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K; Prohaska, Sonja J; Stadler, Peter F

    2014-01-01

    The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

  7. Orthology detection combining clustering and synteny for very large datasets.

    Directory of Open Access Journals (Sweden)

    Marcus Lechner

    Full Text Available The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

  8. Spices, irradiation and detection methods

    International Nuclear Information System (INIS)

    Sjoeberg, A.M.; Manninen, M.

    1991-01-01

    This paper is about microbiological aspects of spices and microbiological methods to detect irradiated food. The proposed method is a combination of the Direct Epifluorescence Filter Technique (DEFT) and the Aerobic Plate Count (APC). The evidence for irradiation of spices is based on the demonstration of a higher DEFT count than the APC. The principle was first tested in our earlier investigation in the detection of irradiation of whole spices. The combined DEFT+APC procedure was found to give a fairly reliable indication of whether or not a whole spice sample had been irradiated. The results are given (8 figs, 22 refs)

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

    Science.gov (United States)

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

    2014-04-01

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

  10. Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

    Science.gov (United States)

    Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

    2018-06-01

    Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

  11. Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment.

    Directory of Open Access Journals (Sweden)

    Aline Guttmann

    Full Text Available In cluster detection of disease, the use of local cluster detection tests (CDTs is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps.

  12. Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

    Science.gov (United States)

    Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

    2015-01-01

    In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

  13. Detection methods for irradiated foods

    International Nuclear Information System (INIS)

    Dyakova, A.; Tsvetkova, E.; Nikolova, R.

    2005-01-01

    In connection with the ongoing world application of irradiation as a technology in Food industry for increasing food safety, it became a need for methods of identification of irradiation. It was required to control international trade of irradiated foods, because of the certain that legally imposed food laws are not violated; supervise correct labeling; avoid multiple irradiation. Physical, chemical and biological methods for detection of irradiated foods as well principle that are based, are introducing in this summary

  14. Adjunct methods for caries detection

    DEFF Research Database (Denmark)

    Twetman, Svante; Axelsson, Susanna Bihari; Dahlén, Gunnar

    2012-01-01

    Abstract Objective. To assess the diagnostic accuracy of adjunct methods used to detect and quantify dental caries. Study design. A systematic literature search for relevant papers was conducted with pre-determined inclusion and exclusion criteria. Abstracts and full text articles were assessed...

  15. The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

    Directory of Open Access Journals (Sweden)

    Ichiro IWASAKI

    2010-06-01

    Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

  16. Space-time clusters for early detection of grizzly bear predation.

    Science.gov (United States)

    Kermish-Wells, Joseph; Massolo, Alessandro; Stenhouse, Gordon B; Larsen, Terrence A; Musiani, Marco

    2018-01-01

    Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small-prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space-time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears ( Ursus arctos ) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013-2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006-2007, 187 of which were within space-time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013-2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based

  17. Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

    Science.gov (United States)

    Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

    2017-12-01

    To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

  18. Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

    Science.gov (United States)

    Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

    2016-01-01

    Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

  19. Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

    Science.gov (United States)

    Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

    2016-01-01

    Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

  20. 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...... of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster...... formation with maximum resolution on the edge of randomness. RESULTS: Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized datasets. In this case, the optimal value of the fuzzifier follows common rules that depend only...

  1. INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

    Directory of Open Access Journals (Sweden)

    S. Zourlidou

    2016-06-01

    Full Text Available The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

  2. Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  3. A clustering based method to evaluate soil corrosivity for pipeline external integrity management

    International Nuclear Information System (INIS)

    Yajima, Ayako; Wang, Hui; Liang, Robert Y.; Castaneda, Homero

    2015-01-01

    One important category of transportation infrastructure is underground pipelines. Corrosion of these buried pipeline systems may cause pipeline failures with the attendant hazards of property loss and fatalities. Therefore, developing the capability to estimate the soil corrosivity is important for designing and preserving materials and for risk assessment. The deterioration rate of metal is highly influenced by the physicochemical characteristics of a material and the environment of its surroundings. In this study, the field data obtained from the southeast region of Mexico was examined using various data mining techniques to determine the usefulness of these techniques for clustering soil corrosivity level. Specifically, the soil was classified into different corrosivity level clusters by k-means and Gaussian mixture model (GMM). In terms of physical space, GMM shows better separability; therefore, the distributions of the material loss of the buried petroleum pipeline walls were estimated via the empirical density within GMM clusters. The soil corrosivity levels of the clusters were determined based on the medians of metal loss. The proposed clustering method was demonstrated to be capable of classifying the soil into different levels of corrosivity severity. - Highlights: • The clustering approach is applied to the data extracted from a real-life pipeline system. • Soil properties in the right-of-way are analyzed via clustering techniques to assess corrosivity. • GMM is selected as the preferred method for detecting the hidden pattern of in-situ data. • K–W test is performed for significant difference of corrosivity level between clusters

  4. Method of detecting failed fuels

    International Nuclear Information System (INIS)

    Ishizaki, Hideaki; Suzumura, Takeshi.

    1982-01-01

    Purpose: To enable the settlement of the temperature of an adequate filling high temperature pure water by detecting the outlet temperature of a high temperature pure water filling tube to a fuel assembly to control the heating of the pure water and detecting the failed fuel due to the sampling of the pure water. Method: A temperature sensor is provided at a water tube connected to a sipping cap for filling high temperature pure water to detect the temperature of the high temperature pure water at the outlet of the tube, and the temperature is confirmed by a temperature indicator. A heater is controlled on the basis of this confirmation, an adequate high temperature pure water is filled in the fuel assembly, and the pure water is replaced with coolant. Then, it is sampled to settle the adequate temperature of the high temperature coolant used for detecting the failure of the fuel assembly. As a result, the sipping effect does not decrease, and the failed fuel can be precisely detected. (Yoshihara, H.)

  5. Advanced defect detection algorithm using clustering in ultrasonic NDE

    Science.gov (United States)

    Gongzhang, Rui; Gachagan, Anthony

    2016-02-01

    A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed A-scans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as `legitimate reflector' or `artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.

  6. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Yuchou Chang

    2008-02-01

    Full Text Available Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  7. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Hong Yi

    2008-01-01

    Full Text Available Abstract Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  8. Momentum-space cluster dual-fermion method

    Science.gov (United States)

    Iskakov, Sergei; Terletska, Hanna; Gull, Emanuel

    2018-03-01

    Recent years have seen the development of two types of nonlocal extensions to the single-site dynamical mean field theory. On one hand, cluster approximations, such as the dynamical cluster approximation, recover short-range momentum-dependent correlations nonperturbatively. On the other hand, diagrammatic extensions, such as the dual-fermion theory, recover long-ranged corrections perturbatively. The correct treatment of both strong short-ranged and weak long-ranged correlations within the same framework is therefore expected to lead to a quick convergence of results, and offers the potential of obtaining smooth self-energies in nonperturbative regimes of phase space. In this paper, we present an exact cluster dual-fermion method based on an expansion around the dynamical cluster approximation. Unlike previous formulations, our method does not employ a coarse-graining approximation to the interaction, which we show to be the leading source of error at high temperature, and converges to the exact result independently of the size of the underlying cluster. We illustrate the power of the method with results for the second-order cluster dual-fermion approximation to the single-particle self-energies and double occupancies.

  9. Polarizable Density Embedding Coupled Cluster Method

    DEFF Research Database (Denmark)

    Hršak, Dalibor; Olsen, Jógvan Magnus Haugaard; Kongsted, Jacob

    2018-01-01

    by an embedding potential consisting of a set of fragment densities obtained from calculations on isolated fragments with a quantum-chemistry method such as Hartree-Fock (HF) or Kohn-Sham density functional theory (KS-DFT) and dressed with a set of atom-centered anisotropic dipole-dipole polarizabilities...

  10. Detection methods of irradiated foodstuffs

    Energy Technology Data Exchange (ETDEWEB)

    Ponta, C C; Cutrubinis, M; Georgescu, R [IRASM Center, Horia Hulubei National Institute for Physics and Nuclear Engineering, PO Box MG-6, RO-077125 Magurele-Bucharest (Romania); Mihai, R [Life and Environmental Physics Department, Horia Hulubei National Institute for Physics and Nuclear Engineering, PO Box MG-6, RO-077125 Magurele-Bucharest (Romania); Secu, M [National Institute of Materials Physics, Bucharest (Romania)

    2005-07-01

    food is marketed as irradiated or if irradiated goods are sold without the appropriate labeling, then detection tests should be able to prove the authenticity of the product. For the moment in Romania there is not any food control laboratory able to detect irradiated foodstuffs. The Technological Irradiation Department coordinates and co finances a research project aimed to establish the first Laboratory of Irradiated Foodstuffs Detection. The detection methods studied in this project are the ESR methods (for cellulose EN 1787/2000, bone EN 1786/1996 and crystalline sugar EN 13708/2003), the TL method (EN 1788/2001), the PSL method (EN 13751/2002) and the DNA Comet Assay method (EN 13784/2001). The above detection methods will be applied on various foodstuffs such: garlic, onion, potatoes, rice, beans, wheat, maize, pistachio, sunflower seeds, raisins, figs, strawberries, chicken, beef, fish, pepper, paprika, thyme, laurel and mushrooms. As an example of the application of a detection method there are presented the ESR spectra of irradiated and nonirradiated paprika acquired according to ESR detection method for irradiated foodstuffs containing cellulose. First of all it can be noticed that the intensity of the signal of cellulose is much higher for the irradiated sample than that for the nonirradiated one and second that appear two radiation specific signals symmetrical to the cellulose signal. These two radiation specific signals prove the irradiation treatment of paprika. (author)

  11. A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

    Science.gov (United States)

    Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

    2007-01-01

    Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

  12. Trend analysis using non-stationary time series clustering based on the finite element method

    Science.gov (United States)

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-05-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

  13. A novel clustering and supervising users' profiles method

    Institute of Scientific and Technical Information of China (English)

    Zhu Mingfu; Zhang Hongbin; Song Fangyun

    2005-01-01

    To better understand different users' accessing intentions, a novel clustering and supervising method based on accessing path is presented. This method divides users' interest space to express the distribution of users' interests, and directly to instruct the constructing process of web pages indexing for advanced performance.

  14. Method of detecting irradiated pepper

    International Nuclear Information System (INIS)

    Doumaru, Takaaki; Furuta, Masakazu; Katayama, Tadashi; Toratani, Hirokazu; Takeda, Atsuhiko

    1989-01-01

    Spices represented by pepper are generally contaminated by microorganisms, and for using them as foodstuffs, some sterilizing treatment is indispensable. However, heating is not suitable to spices, accordingly ethylene oxide gas sterilization has been inevitably carried out, but its carcinogenic property is a problem. Food irradiation is the technology for killing microorganisms and noxious insects which cause the rotting and spoiling of foods and preventing the germination, which is an energy-conserving method without the fear of residual chemicals, therefore, it is most suitable to the sterilization of spices. In the irradiation of lower than 10 kGy, the toxicity test is not required for any food, and the irradiation of spices is permitted in 20 countries. However, in order to establish the international distribution organization for irradiated foods, the PR to consumers and the development of the means of detecting irradiation are the important subjects. The authors used pepper, and examined whether the hydrogen generated by irradiation remains in seeds and it can be detected or not. The experimental method and the results are reported. From the samples without irradiation, hydrogen was scarcely detected. The quantity of hydrogen generated was proportional to dose. The measuring instrument is only a gas chromatograph. (K.I.)

  15. GALAXY CLUSTERS IN THE SWIFT/BAT ERA. II. 10 MORE CLUSTERS DETECTED ABOVE 15 keV

    International Nuclear Information System (INIS)

    Ajello, M.; Reimer, O.; Rebusco, P.; Cappelluti, N.; Boehringer, H.; La Parola, V.; Cusumano, G.

    2010-01-01

    We report on the discovery of 10 additional galaxy clusters detected in the ongoing Swift/Burst Alert Telescope (BAT) all-sky survey. Among the newly BAT-discovered clusters there are Bullet, A85, Norma, and PKS 0745-19. Norma is the only cluster, among those presented here, which is resolved by BAT. For all the clusters, we perform a detailed spectral analysis using XMM-Newton and Swift/BAT data to investigate the presence of a hard (non-thermal) X-ray excess. We find that in most cases the clusters' emission in the 0.3-200 keV band can be explained by a multi-temperature thermal model confirming our previous results. For two clusters (Bullet and A3667), we find evidence for the presence of a hard X-ray excess. In the case of the Bullet cluster, our analysis confirms the presence of a non-thermal, power-law-like, component with a 20-100 keV flux of 3.4 x 10 -12 erg cm -2 s -1 as detected in previous studies. For A3667, the excess emission can be successfully modeled as a hot component (kT ∼ 13 keV). We thus conclude that the hard X-ray emission from galaxy clusters (except the Bullet) has most likely a thermal origin.

  16. Galaxy Clusters in the Swift/BAT era II: 10 more Clusters detected above 15 keV

    Energy Technology Data Exchange (ETDEWEB)

    Ajello, M.; /SLAC /KIPAC, Menlo Park; Rebusco, P.; /KIPAC, Menlo Park; Cappelluti, N.; /Garching, Max Planck Inst., MPE /Maryland U., Baltimore County; Reimer, O.; /SLAC /Palermo Observ.; Boehringer, H.; /Garching, Max Planck Inst., MPE; La Parola, V.; Cusumano, G.; /Palermo Observ.

    2010-10-27

    We report on the discovery of 10 additional galaxy clusters detected in the ongoing Swift/BAT all-sky survey. Among the newly BAT-discovered clusters there are: Bullet, Abell 85, Norma, and PKS 0745-19. Norma is the only cluster, among those presented here, which is resolved by BAT. For all the clusters we perform a detailed spectral analysis using XMM-Newton and Swift/BAT data to investigate the presence of a hard (non-thermal) X-ray excess. We find that in most cases the clusters emission in the 0.3-200 keV band can be explained by a multi-temperature thermal model confirming our previous results. For two clusters (Bullet and Abell 3667) we find evidence for the presence of a hard X-ray excess. In the case of the Bullet cluster, our analysis confirms the presence of a non-thermal, power-law like, component with a 20-100 keV flux of 3.4 x 10{sup -12} erg cm{sup -2} s{sup -1} as detected in previous studies. For Abell 3667 the excess emission can be successfully modeled as a hot component (kT = {approx}13 keV). We thus conclude that the hard X-ray emission from galaxy clusters (except the Bullet) has most likely thermal origin.

  17. Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

    OpenAIRE

    Satish Gajawada; Durga Toshniwal

    2012-01-01

    Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...

  18. Kernel method for clustering based on optimal target vector

    International Nuclear Information System (INIS)

    Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-01-01

    We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

  19. Agent-based method for distributed clustering of textual information

    Science.gov (United States)

    Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN

    2010-09-28

    A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.

  20. Lagrangian based methods for coherent structure detection

    Energy Technology Data Exchange (ETDEWEB)

    Allshouse, Michael R., E-mail: mallshouse@chaos.utexas.edu [Center for Nonlinear Dynamics and Department of Physics, University of Texas at Austin, Austin, Texas 78712 (United States); Peacock, Thomas, E-mail: tomp@mit.edu [Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States)

    2015-09-15

    There has been a proliferation in the development of Lagrangian analytical methods for detecting coherent structures in fluid flow transport, yielding a variety of qualitatively different approaches. We present a review of four approaches and demonstrate the utility of these methods via their application to the same sample analytic model, the canonical double-gyre flow, highlighting the pros and cons of each approach. Two of the methods, the geometric and probabilistic approaches, are well established and require velocity field data over the time interval of interest to identify particularly important material lines and surfaces, and influential regions, respectively. The other two approaches, implementing tools from cluster and braid theory, seek coherent structures based on limited trajectory data, attempting to partition the flow transport into distinct regions. All four of these approaches share the common trait that they are objective methods, meaning that their results do not depend on the frame of reference used. For each method, we also present a number of example applications ranging from blood flow and chemical reactions to ocean and atmospheric flows.

  1. A cluster approximation for the transfer-matrix method

    International Nuclear Information System (INIS)

    Surda, A.

    1990-08-01

    A cluster approximation for the transfer-method is formulated. The calculation of the partition function of lattice models is transformed to a nonlinear mapping problem. The method yields the free energy, correlation functions and the phase diagrams for a large class of lattice models. The high accuracy of the method is exemplified by the calculation of the critical temperature of the Ising model. (author). 14 refs, 2 figs, 1 tab

  2. Fuzzy Clustering Methods and their Application to Fuzzy Modeling

    DEFF Research Database (Denmark)

    Kroszynski, Uri; Zhou, Jianjun

    1999-01-01

    Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...

  3. Dynamic analysis of clustered building structures using substructures methods

    International Nuclear Information System (INIS)

    Leimbach, K.R.; Krutzik, N.J.

    1989-01-01

    The dynamic substructure approach to the building cluster on a common base mat starts with the generation of Ritz-vectors for each building on a rigid foundation. The base mat plus the foundation soil is subjected to kinematic constraint modes, for example constant, linear, quadratic or cubic constraints. These constraint modes are also imposed on the buildings. By enforcing kinematic compatibility of the complete structural system on the basis of the constraint modes a reduced Ritz model of the complete cluster is obtained. This reduced model can now be analyzed by modal time history or response spectrum methods

  4. Method to detect biological particles

    International Nuclear Information System (INIS)

    Giaever, I.

    1976-01-01

    A medical-diagnostic method to detect immunological as well as other specific reactions is described. According to the invention, first reactive particles (e.g. antibodies) are adsorbed on the surface of a solid, non-reactive substrate. The coated substrate is subjected to a solution which one assumes to contain the second biological particles (e.g. antigens) which are specific to the first and form complexes with these. A preferential radioactive labelling (e.g. with iodine 125) of the second biological particle is then directly or indirectly carried out. Clearage follows labelling in order to separate the second biological particles from the first ones. A specific splitting agent can selectively break the bond of both types of particle. The splitting agent solution is finally separated off and its content is investigated for the presence of labelling. (VJ) [de

  5. Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples.

    Science.gov (United States)

    Henry, David; Dymnicki, Allison B; Mohatt, Nathaniel; Allen, James; Kelly, James G

    2015-10-01

    Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a "real-world" example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities.

  6. Clustering Methods with Qualitative Data: A Mixed Methods Approach for Prevention Research with Small Samples

    Science.gov (United States)

    Henry, David; Dymnicki, Allison B.; Mohatt, Nathaniel; Allen, James; Kelly, James G.

    2016-01-01

    Qualitative methods potentially add depth to prevention research, but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data, but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-Means clustering, and latent class analysis produced similar levels of accuracy with binary data, and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a “real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities. PMID:25946969

  7. Molecular detection using Rydberg, autoionizing, and cluster states. Progress report

    Energy Technology Data Exchange (ETDEWEB)

    Wessel, J.

    1989-08-17

    Continuing investigations of multiphoton ionization processes in naphthalene have established the geometry and spectroscopy of trimer and tetramer cluster states. A new, highly efficient ionization mechanism has been identified in the trimer. It is closely related to autoionization of 2-electron atoms by resonant 2-photon excitation and to exciton fusion in larger clusters.

  8. Application of a Light-Front Coupled Cluster Method

    International Nuclear Information System (INIS)

    Chabysheva, S.S.; Hiller, J.R.

    2012-01-01

    As a test of the new light-front coupled-cluster method in a gauge theory, we apply it to the nonperturbative construction of the dressed-electron state in QED, for an arbitrary covariant gauge, and compute the electron's anomalous magnetic moment. The construction illustrates the spectator and Fock-sector independence of vertex and self-energy contributions and indicates resolution of the difficulties with uncanceled divergences that plague methods based on Fock-space truncation. (author)

  9. A Clustering Method for Data in Cylindrical Coordinates

    Directory of Open Access Journals (Sweden)

    Kazuhisa Fujita

    2017-01-01

    Full Text Available We propose a new clustering method for data in cylindrical coordinates based on the k-means. The goal of the k-means family is to maximize an optimization function, which requires a similarity. Thus, we need a new similarity to obtain the new clustering method for data in cylindrical coordinates. In this study, we first derive a new similarity for the new clustering method by assuming a particular probabilistic model. A data point in cylindrical coordinates has radius, azimuth, and height. We assume that the azimuth is sampled from a von Mises distribution and the radius and the height are independently generated from isotropic Gaussian distributions. We derive the new similarity from the log likelihood of the assumed probability distribution. Our experiments demonstrate that the proposed method using the new similarity can appropriately partition synthetic data defined in cylindrical coordinates. Furthermore, we apply the proposed method to color image quantization and show that the methods successfully quantize a color image with respect to the hue element.

  10. Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

    Science.gov (United States)

    Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

    2017-06-01

    The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

  11. Comparative analysis on the selection of number of clusters in community detection

    Science.gov (United States)

    Kawamoto, Tatsuro; Kabashima, Yoshiyuki

    2018-02-01

    We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

  12. Comparative Investigation of Guided Fuzzy Clustering and Mean Shift Clustering for Edge Detection in Electrical Resistivity Tomography Images of Mineral Deposits

    Science.gov (United States)

    Ward, Wil; Wilkinson, Paul; Chambers, Jon; Bai, Li

    2014-05-01

    Geophysical surveying using electrical resistivity tomography (ERT) can be used as a rapid non-intrusive method to investigate mineral deposits [1]. One of the key challenges with this approach is to find a robust automated method to assess and characterise deposits on the basis of an ERT image. Recent research applying edge detection techniques has yielded a framework that can successfully locate geological interfaces in ERT images using a minimal assumption data clustering technique, the guided fuzzy clustering method (gfcm) [2]. Non-parametric clustering techniques are statistically grounded methods of image segmentation that do not require any assumptions about the distribution of data under investigation. This study is a comparison of two such methods to assess geological structure based on the resistivity images. In addition to gfcm, a method called mean-shift clustering [3] is investigated with comparisons directed at accuracy, computational expense, and degree of user interaction. Neither approach requires the number of clusters as input (a common parameter and often impractical), rather they are based on a similar theory that data can be clustered based on peaks in the probability density function (pdf) of the data. Each local maximum in these functions represents the modal value of a particular population corresponding to a cluster and as such the data are assigned based on their relationships to these model values. The two methods differ in that gfcm approximates the pdf using kernel density estimation and identifies population means, assigning cluster membership probabilities to each resistivity value in the model based on its distance from the distribution averages. Whereas, in mean-shift clustering, the density function is not calculated, but a gradient ascent method creates a vector that leads each datum towards high density distributions iteratively using weighted kernels to calculate locally dense regions. The only parameter needed in both methods

  13. Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

    Science.gov (United States)

    Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

    2009-02-01

    Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

  14. Form gene clustering method about pan-ethnic-group products based on emotional semantic

    Science.gov (United States)

    Chen, Dengkai; Ding, Jingjing; Gao, Minzhuo; Ma, Danping; Liu, Donghui

    2016-09-01

    The use of pan-ethnic-group products form knowledge primarily depends on a designer's subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers' perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.

  15. Statistical method on nonrandom clustering with application to somatic mutations in cancer

    Directory of Open Access Journals (Sweden)

    Rejto Paul A

    2010-01-01

    Full Text Available Abstract Background Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. Results We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. Conclusions Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.

  16. Unbiased methods for removing systematics from galaxy clustering measurements

    Science.gov (United States)

    Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.

    2016-02-01

    Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.

  17. Advanced cluster methods for correlated-electron systems

    Energy Technology Data Exchange (ETDEWEB)

    Fischer, Andre

    2015-04-27

    In this thesis, quantum cluster methods are used to calculate electronic properties of correlated-electron systems. A special focus lies in the determination of the ground state properties of a 3/4 filled triangular lattice within the one-band Hubbard model. At this filling, the electronic density of states exhibits a so-called van Hove singularity and the Fermi surface becomes perfectly nested, causing an instability towards a variety of spin-density-wave (SDW) and superconducting states. While chiral d+id-wave superconductivity has been proposed as the ground state in the weak coupling limit, the situation towards strong interactions is unclear. Additionally, quantum cluster methods are used here to investigate the interplay of Coulomb interactions and symmetry-breaking mechanisms within the nematic phase of iron-pnictide superconductors. The transition from a tetragonal to an orthorhombic phase is accompanied by a significant change in electronic properties, while long-range magnetic order is not established yet. The driving force of this transition may not only be phonons but also magnetic or orbital fluctuations. The signatures of these scenarios are studied with quantum cluster methods to identify the most important effects. Here, cluster perturbation theory (CPT) and its variational extention, the variational cluster approach (VCA) are used to treat the respective systems on a level beyond mean-field theory. Short-range correlations are incorporated numerically exactly by exact diagonalization (ED). In the VCA, long-range interactions are included by variational optimization of a fictitious symmetry-breaking field based on a self-energy functional approach. Due to limitations of ED, cluster sizes are limited to a small number of degrees of freedom. For the 3/4 filled triangular lattice, the VCA is performed for different cluster symmetries. A strong symmetry dependence and finite-size effects make a comparison of the results from different clusters difficult

  18. An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

    Science.gov (United States)

    Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

    2015-01-01

    Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

  19. A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

    Directory of Open Access Journals (Sweden)

    Xiangbing Zhou

    2018-04-01

    Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

  20. A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data

    DEFF Research Database (Denmark)

    Kent, Peter; Jensen, Rikke K; Kongsted, Alice

    2014-01-01

    ). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold...... LCA and SNOB LCA). METHODS: The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program...... classify individuals into those subgroups. CONCLUSIONS: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data...

  1. AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA

    Directory of Open Access Journals (Sweden)

    A. Alizade Naeini

    2014-10-01

    Full Text Available K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF and Random methods are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.

  2. Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases.

    Science.gov (United States)

    Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M

    2018-06-01

    Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

    Science.gov (United States)

    Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

    2017-04-01

    In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

  4. Particle detection systems and methods

    Science.gov (United States)

    Morris, Christopher L.; Makela, Mark F.

    2010-05-11

    Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  5. Detection of enhancement in number densities of background galaxies due to magnification by massive galaxy clusters

    Energy Technology Data Exchange (ETDEWEB)

    Chiu, I.; Dietrich, J. P.; Mohr, J.; Applegate, D. E.; Benson, B. A.; Bleem, L. E.; Bayliss, M. B.; Bocquet, S.; Carlstrom, J. E.; Capasso, R.; Desai, S.; Gangkofner, C.; Gonzalez, A. H.; Gupta, N.; Hennig, C.; Hoekstra, H.; von der Linden, A.; Liu, J.; McDonald, M.; Reichardt, C. L.; Saro, A.; Schrabback, T.; Strazzullo, V.; Stubbs, C. W.; Zenteno, A.

    2016-02-18

    We present a detection of the enhancement in the number densities of background galaxies induced from lensing magnification and use it to test the Sunyaev-Zel'dovich effect (SZE-) inferred masses in a sample of 19 galaxy clusters with median redshift z similar or equal to 0.42 selected from the South Pole Telescope SPT-SZ survey. These clusters are observed by the Megacam on the Magellan Clay Telescope though gri filters. Two background galaxy populations are selected for this study through their photometric colours; they have median redshifts zmedian similar or equal to 0.9 (low-z background) and z(median) similar or equal to 1.8 (high-z background). Stacking these populations, we detect the magnification bias effect at 3.3 sigma and 1.3 sigma for the low-and high-z backgrounds, respectively. We fit Navarro, Frenk and White models simultaneously to all observed magnification bias profiles to estimate the multiplicative factor. that describes the ratio of the weak lensing mass to the mass inferred from the SZE observable-mass relation. We further quantify systematic uncertainties in. resulting from the photometric noise and bias, the cluster galaxy contamination and the estimations of the background properties. The resulting. for the combined background populations with 1 sigma uncertainties is 0.83 +/- 0.24(stat) +/- 0.074(sys), indicating good consistency between the lensing and the SZE-inferred masses. We use our best-fitting eta to predict the weak lensing shear profiles and compare these predictions with observations, showing agreement between the magnification and shear mass constraints. This work demonstrates the promise of using the magnification as a complementary method to estimate cluster masses in large surveys.

  6. Detection of wood failure by image processing method: influence of algorithm, adhesive and wood species

    Science.gov (United States)

    Lanying Lin; Sheng He; Feng Fu; Xiping Wang

    2015-01-01

    Wood failure percentage (WFP) is an important index for evaluating the bond strength of plywood. Currently, the method used for detecting WFP is visual inspection, which lacks efficiency. In order to improve it, image processing methods are applied to wood failure detection. The present study used thresholding and K-means clustering algorithms in wood failure detection...

  7. Comparison of Methods for Oscillation Detection

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Trangbæk, Klaus

    2006-01-01

    This paper compares a selection of methods for detecting oscillations in control loops. The methods are tested on measurement data from a coal-fired power plant, where some oscillations are occurring. Emphasis is put on being able to detect oscillations without having a system model and without...... using process knowledge. The tested methods show potential for detecting the oscillations, however, transient components in the signals cause false detections as well, motivating usage of models in order to remove the expected signals behavior....

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

    Science.gov (United States)

    Wang, Huiya; Feng, Jun; Wang, Hongyu

    2017-07-20

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

  9. An unexpected detection of bifurcated blue straggler sequences in the young globular cluster NGC 2173

    OpenAIRE

    Li, Chengyuan; Deng, Licai; de Grijs, Richard; Jiang, Dengkai; Xin, Yu

    2018-01-01

    Bifurcated patterns of blue straggler stars in their color--magnitude diagrams have atracted significant attention. This type of special (but rare) pattern of two distinct blue straggler sequences is commonly interpreted as evidence of cluster core-collapse-driven stellar collisions as an efficient formation mechanism. Here, we report the detection of a bifurcated blue straggler distribution in a young Large MagellanicCloud cluster, NGC 2173. Because of the cluster's low central stellar numbe...

  10. Mainshock-Aftershocks Clustering Detection in Volcanic Regions

    Science.gov (United States)

    Garza Giron, R.; Brodsky, E. E.; Prejean, S. G.

    2017-12-01

    Crustal earthquakes tend to break their general Poissonean process behavior by gathering into two main kinds of seismic bursts: swarms and mainshock-aftershocks sequences. The former is commonly related to volcanic or geothermal processes whereas the latter is a characteristic feature of tectonically driven seismicity. We explore the mainshock-aftershock clustering behavior of different active volcanic regions in Japan and its comparison to non-volcanic regions. We find that aftershock production in volcanoes shows mainshock-aftershocks clustering similar to what is observed in non-volcanic areas. The ratio of volanic areas that cluster in mainshock-aftershocks sequences vs the areas that do not is comparable to the ratio of non-volcanic regions that show clustering vs the ones that do not. Furthermore, the level of production of aftershocks for most volcanic areas where clustering is present seems to be of the same order of magnitude, or slightly higher, as the median of the non-volcanic regions. An interesting example of highly aftershock-productive volcanoes emerges from the 2000 Miyakejima dike intrusion. A big seismic cluster started to build up rapidly in the south-west flank of Miyakejima to later propagate to the north-west towards the Kozushima and Niijima volcanoes. In Miyakejima the seismicity showed a swarm-like signature with a constant earthquake rate, whereas Kozushima and Niijima both had expressions of highly productive mainshock-aftershocks sequences. These findings are surprising given the alternative mechanisms available in volcanic systems for releasing deviatoric strain. We speculate that aftershock behavior might hold a relationship with the rheological properties of the rocks of each system and with the capacity of a system to accumulate or release the internal pressures caused by magmatic or hydrothermal systems.

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

    Science.gov (United States)

    Karaca, Yeliz; Cattani, Carlo

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

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

    DEFF Research Database (Denmark)

    Drachen, Anders; Thurau, C.; Sifa, R.

    2013-01-01

    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......, interpretation of the resulting categories in terms of actual play behavior can be difficult if not impossible. In this paper, a range of unsupervised techniques are applied together with Archetypal Analysis to develop behavioral clusters from playtime data of 70,014 World of Warcraft players, covering a five......The analysis of user behavior in digital games has been aided by the introduction of user telemetry in game development, which provides unprecedented access to quantitative data on user behavior from the installed game clients of the entire population of players. Player behavior telemetry datasets...

  13. ICARES: a real-time automated detection tool for clusters of infectious diseases in the Netherlands.

    NARCIS (Netherlands)

    Groeneveld, Geert H; Dalhuijsen, Anton; Kara-Zaïtri, Chakib; Hamilton, Bob; de Waal, Margot W; van Dissel, Jaap T; van Steenbergen, Jim E

    2017-01-01

    Clusters of infectious diseases are frequently detected late. Real-time, detailed information about an evolving cluster and possible associated conditions is essential for local policy makers, travelers planning to visit the area, and the local population. This is currently illustrated in the Zika

  14. Cluster monte carlo method for nuclear criticality safety calculation

    International Nuclear Information System (INIS)

    Pei Lucheng

    1984-01-01

    One of the most important applications of the Monte Carlo method is the calculation of the nuclear criticality safety. The fair source game problem was presented at almost the same time as the Monte Carlo method was applied to calculating the nuclear criticality safety. The source iteration cost may be reduced as much as possible or no need for any source iteration. This kind of problems all belongs to the fair source game prolems, among which, the optimal source game is without any source iteration. Although the single neutron Monte Carlo method solved the problem without the source iteration, there is still quite an apparent shortcoming in it, that is, it solves the problem without the source iteration only in the asymptotic sense. In this work, a new Monte Carlo method called the cluster Monte Carlo method is given to solve the problem further

  15. Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

    Directory of Open Access Journals (Sweden)

    Hendrik Treutler

    2016-10-01

    Full Text Available Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92 % of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.

  16. Cancer Detection and Diagnosis Methods - Annual Plan

    Science.gov (United States)

    Early cancer detection is a proven life-saving strategy. Learn about the research opportunities NCI supports, including liquid biopsies and other less-invasive methods, for detecting early cancers and precancerous growths.

  17. Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis

    International Nuclear Information System (INIS)

    Wei, Jun; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Lu, Yao; Zhou, Chuan; Samala, Ravi

    2014-01-01

    Purpose: To investigate the feasibility of a new two-dimensional (2D) multichannel response (MCR) analysis approach for the detection of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT). Methods: With IRB approval and informed consent, a data set of two-view DBTs from 42 breasts containing biopsy-proven MC clusters was collected in this study. The authors developed a 2D approach for MC detection using projection view (PV) images rather than the reconstructed three-dimensional (3D) DBT volume. Signal-to-noise ratio (SNR) enhancement processing was first applied to each PV to enhance the potential MCs. The locations of MC candidates were then identified with iterative thresholding. The individual MCs were decomposed with Hermite–Gaussian (HG) and Laguerre–Gaussian (LG) basis functions and the channelized Hotelling model was trained to produce the MCRs for each MC on the 2D images. The MCRs from the PVs were fused in 3D by a coincidence counting method that backprojects the MC candidates on the PVs and traces the coincidence of their ray paths in 3D. The 3D MCR was used to differentiate the true MCs from false positives (FPs). Finally a dynamic clustering method was used to identify the potential MC clusters in the DBT volume based on the fact that true MCs of clinical significance appear in clusters. Using two-fold cross validation, the performance of the 3D MCR for classification of true and false MCs was estimated by the area under the receiver operating characteristic (ROC) curve and the overall performance of the MCR approach for detection of clustered MCs was assessed by free response receiver operating characteristic (FROC) analysis. Results: When the HG basis function was used for MCR analysis, the detection of MC cluster achieved case-based test sensitivities of 80% and 90% at the average FP rates of 0.65 and 1.55 FPs per DBT volume, respectively. With LG basis function, the average FP rates were 0.62 and 1.57 per DBT volume at

  18. Onto-clust--a methodology for combining clustering analysis and ontological methods for identifying groups of comorbidities for developmental disorders.

    Science.gov (United States)

    Peleg, Mor; Asbeh, Nuaman; Kuflik, Tsvi; Schertz, Mitchell

    2009-02-01

    Children with developmental disorders usually exhibit multiple developmental problems (comorbidities). Hence, such diagnosis needs to revolve on developmental disorder groups. Our objective is to systematically identify developmental disorder groups and represent them in an ontology. We developed a methodology that combines two methods (1) a literature-based ontology that we created, which represents developmental disorders and potential developmental disorder groups, and (2) clustering for detecting comorbid developmental disorders in patient data. The ontology is used to interpret and improve clustering results and the clustering results are used to validate the ontology and suggest directions for its development. We evaluated our methodology by applying it to data of 1175 patients from a child development clinic. We demonstrated that the ontology improves clustering results, bringing them closer to an expert generated gold-standard. We have shown that our methodology successfully combines an ontology with a clustering method to support systematic identification and representation of developmental disorder groups.

  19. Rapid methods for detection of bacteria

    DEFF Research Database (Denmark)

    Corfitzen, Charlotte B.; Andersen, B.Ø.; Miller, M.

    2006-01-01

    Traditional methods for detection of bacteria in drinking water e.g. Heterotrophic Plate Counts (HPC) or Most Probable Number (MNP) take 48-72 hours to give the result. New rapid methods for detection of bacteria are needed to protect the consumers against contaminations. Two rapid methods...

  20. Method of removing crud deposited on fuel element clusters

    International Nuclear Information System (INIS)

    Yokota, Tokunobu; Yashima, Akira; Tajima, Jun-ichiro.

    1982-01-01

    Purpose: To enable easy elimination of claddings deposited on the surface of fuel element. Method: An operator manipulates a pole from above a platform, engages the longitudinal flange of the cover to the opening at the upper end of a channel box and starts up a suction pump. The suction amount of the pump is set such that water flow becomes within the channel box at greater flow rate than the operational flow rate in the channel box of the fuel element clusters during reactor operation. This enables to remove crud deposited on the surface of individual fuel elements with ease and rapidly without detaching the channel box. (Moriyama, K.)

  1. Automatic detection of erythemato-squamous diseases using k-means clustering.

    Science.gov (United States)

    Ubeyli, Elif Derya; Doğdu, Erdoğan

    2010-04-01

    A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

  2. Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering.

    Science.gov (United States)

    Xia, Yong; Han, Junze; Wang, Kuanquan

    2015-01-01

    Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.

  3. Leak detection by vibrational diagnostic methods

    International Nuclear Information System (INIS)

    Siklossy, P.

    1983-01-01

    The possibilities and methods of leak detection due to mechanical failures in nuclear power plants are reviewed on the basis of the literature. Great importance is attributed to vibrational diagnostic methods for their adventageous characteristics which enable them to become final leak detecting methods. The problems of noise analysis, e.g. leak detection by impact sound measurements, probe characteristics, gain problems, probe selection, off-line analysis and correlation functions, types of leak noises etc. are summarized. Leak detection based on noise analysis can be installed additionally to power plants. Its maintenance and testing is simple. On the other hand, it requires special training and measuring methods. (Sz.J.)

  4. Detection of a Double Relic in the Torpedo Cluster: SPT-CL J0245-5302

    Science.gov (United States)

    Zheng, Q.; Johnston-Hollitt, M.; Duchesne, S. W.; Li, W. T.

    2018-06-01

    The Torpedo cluster, SPT-CL J0245-5302 (S0295) is a massive, merging cluster at a redshift of z = 0.300, which exhibits a strikingly similar morphology to the Bullet cluster 1E 0657-55.8 (z = 0.296), including a classic bow shock in the cluster's intra-cluster medium revealed by Chandra X-ray observations. We present Australia Telescope Compact Array data centred at 2.1 GHz and Murchison Widefield Array data at frequencies between 72 MHz and 231 MHz which we use to study the properties of the cluster. We characterise a number of discrete and diffuse radio sources in the cluster, including the detection of two previously unknown radio relics on the cluster periphery. The average spectral index of the diffuse emission between 70 MHz and 3.1 GHz is α =-1.63_{-0.10}^{+0.10} and a radio-derived Mach number for the shock in the west of the cluster is calculated as M = 2.04. The Torpedo cluster is thus a double relic system at moderate redshift.

  5. Detecting Gravitational Lensing of the Cosmic Microwave Background by Galaxy Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Baxter, Eric Jones [Univ. of Chicago, IL (United States)

    2014-08-01

    Clusters of galaxies gravitationally lens the Cosmic Microwave Background (CMB) leading to a distinct signal in the CMB on arcminute scales. Measurement of the cluster lensing effect offers the exciting possibility of constraining the masses of galaxy clusters using CMB data alone. Improved constraints on cluster masses are in turn essential to the use of clusters as cosmological probes: uncertainties in cluster masses are currently the dominant systematic affecting cluster abundance constraints on cosmology. To date, however, the CMB cluster lensing signal remains undetected because of its small magnitude and angular size. In this thesis, we develop a maximum likelihood approach to extracting the signal from CMB temperature data. We validate the technique by applying it to mock data designed to replicate as closely as possible real data from the South Pole Telescope’s (SPT) Sunyaev-Zel’dovich (SZ) survey: the effects of the SPT beam, transfer function, instrumental noise and cluster selection are incorporated. We consider the effects of foreground emission on the analysis and show that uncertainty in amount of foreground lensing results in a small systematic error on the lensing constraints. Additionally, we show that if unaccounted for, the SZ effect leads to unacceptably large biases on the lensing constraints and develop an approach for removing SZ contamination. The results of the mock analysis presented here suggest that a 4σ first detection of the cluster lensing effect can be achieved with current SPT-SZ data.

  6. Synthesis of colloidal silver nanoparticle clusters and their application in ascorbic acid detection by SERS.

    Science.gov (United States)

    Cholula-Díaz, Jorge L; Lomelí-Marroquín, Diana; Pramanick, Bidhan; Nieto-Argüello, Alfonso; Cantú-Castillo, Luis A; Hwang, Hyundoo

    2018-03-01

    Ascorbic acid (vitamin C) has an essential role in the human body mainly due to its antioxidant function. In this work, metallic silver nanoparticle (AgNP) colloids were used in SERS experiments to detect ascorbic acid in aqueous solution. The AgNPs were synthesized by a green method using potato starch as reducing and stabilizing agent, and water as the solvent. The optical properties of the yellowish as-synthesized silver colloids were characterized by UV-vis spectroscopy, in which besides a typical band at 410 nm related to the localized surface plasmon resonance of the silver nanoparticles, a shoulder band around 500 nm, due to silver nanoparticle cluster formation, is presented when relatively higher concentrations of starch are used in the synthesis. These starch-capped silver nanoparticles show an intrinsic Raman peak at 1386 cm -1 assigned to deformation modes of the starch structure. The increase of the intensity of the SERS peak at 1386 cm -1 with an increase in the concentration of the ascorbic acid is related to a decrease of the gap between dimers and trimers of the silver nanoparticle clusters produced by the presence of ascorbic acid in the colloid. The limit of detection of this technique for ascorbic acid is 0.02 mM with a measurement concentration range of 0.02-10 mM, which is relevant for the application of this method for detecting ascorbic acid in biological specimen. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. An improved data clustering algorithm for outlier detection

    Directory of Open Access Journals (Sweden)

    Anant Agarwal

    2016-12-01

    Full Text Available Data mining is the extraction of hidden predictive information from large databases. This is a technology with potential to study and analyze useful information present in data. Data objects which do not usually fit into the general behavior of the data are termed as outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. By definition, outliers are rare occurrences and hence represent a small portion of the data. However, the use of Outlier Detection for various purposes is not an easy task. This research proposes a modified PAM for detecting outliers. The proposed technique has been implemented in JAVA. The results produced by the proposed technique are found better than existing technique in terms of outliers detected and time complexity.

  8. Detecting Android Malwares with High-Efficient Hybrid Analyzing Methods

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2018-01-01

    Full Text Available In order to tackle the security issues caused by malwares of Android OS, we proposed a high-efficient hybrid-detecting scheme for Android malwares. Our scheme employed different analyzing methods (static and dynamic methods to construct a flexible detecting scheme. In this paper, we proposed some detecting techniques such as Com+ feature based on traditional Permission and API call features to improve the performance of static detection. The collapsing issue of traditional function call graph-based malware detection was also avoided, as we adopted feature selection and clustering method to unify function call graph features of various dimensions into same dimension. In order to verify the performance of our scheme, we built an open-access malware dataset in our experiments. The experimental results showed that the suggested scheme achieved high malware-detecting accuracy, and the scheme could be used to establish Android malware-detecting cloud services, which can automatically adopt high-efficiency analyzing methods according to the properties of the Android applications.

  9. Simple method to calculate percolation, Ising and Potts clusters

    International Nuclear Information System (INIS)

    Tsallis, C.

    1981-01-01

    A procedure ('break-collapse method') is introduced which considerably simplifies the calculation of two - or multirooted clusters like those commonly appearing in real space renormalization group (RG) treatments of bond-percolation, and pure and random Ising and Potts problems. The method is illustrated through two applications for the q-state Potts ferromagnet. The first of them concerns a RG calculation of the critical exponent ν for the isotropic square lattice: numerical consistence is obtained (particularly for q→0) with den Nijs conjecture. The second application is a compact reformulation of the standard star-triangle and duality transformations which provide the exact critical temperature for the anisotropic triangular and honeycomb lattices. (Author) [pt

  10. Expanding Comparative Literature into Comparative Sciences Clusters with Neutrosophy and Quad-stage Method

    Directory of Open Access Journals (Sweden)

    Fu Yuhua

    2016-08-01

    Full Text Available By using Neutrosophy and Quad-stage Method, the expansions of comparative literature include: comparative social sciences clusters, comparative natural sciences clusters, comparative interdisciplinary sciences clusters, and so on. Among them, comparative social sciences clusters include: comparative literature, comparative history, comparative philosophy, and so on; comparative natural sciences clusters include: comparative mathematics, comparative physics, comparative chemistry, comparative medicine, comparative biology, and so on.

  11. Electromagnetic Methods of Lightning Detection

    Science.gov (United States)

    Rakov, V. A.

    2013-11-01

    Both cloud-to-ground and cloud lightning discharges involve a number of processes that produce electromagnetic field signatures in different regions of the spectrum. Salient characteristics of measured wideband electric and magnetic fields generated by various lightning processes at distances ranging from tens to a few hundreds of kilometers (when at least the initial part of the signal is essentially radiation while being not influenced by ionospheric reflections) are reviewed. An overview of the various lightning locating techniques, including magnetic direction finding, time-of-arrival technique, and interferometry, is given. Lightning location on global scale, when radio-frequency electromagnetic signals are dominated by ionospheric reflections, is also considered. Lightning locating system performance characteristics, including flash and stroke detection efficiencies, percentage of misclassified events, location accuracy, and peak current estimation errors, are discussed. Both cloud and cloud-to-ground flashes are considered. Representative examples of modern lightning locating systems are reviewed. Besides general characterization of each system, the available information on its performance characteristics is given with emphasis on those based on formal ground-truth studies published in the peer-reviewed literature.

  12. Threshold selection for classification of MR brain images by clustering method

    Energy Technology Data Exchange (ETDEWEB)

    Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  13. bcl::Cluster : A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System.

    Science.gov (United States)

    Alexander, Nathan; Woetzel, Nils; Meiler, Jens

    2011-02-01

    Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

  14. TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    C. Jehan

    2016-06-01

    Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

  15. Indirect photometric detection of boron cluster anions electrophoretically separated in methanol.

    Science.gov (United States)

    Vítová, Lada; Fojt, Lukáš; Vespalec, Radim

    2014-04-18

    3,5-Dinitrobenzoate and picrate are light absorbing anions pertinent to indirect photometric detection of boron cluster anions in buffered methanolic background electrolytes (BGEs). Tris(hydroxymethyl)aminomethane and morpholine have been used as buffering bases, which eliminated baseline steps, and minimized the baseline noise. In methanolic BGEs, mobilities of boron cluster anions depend on both ionic constituents of the BGE buffer. This dependence can be explained by ion pair interaction of detected anions with BGE cations, which are not bonded into ion pairs with the BGE anions. The former ion pair interaction decreases sensitivity of the indirect photometric detection. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Application of clustering methods: Regularized Markov clustering (R-MCL) for analyzing dengue virus similarity

    Science.gov (United States)

    Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.

    2017-07-01

    Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.

  17. Substructure in clusters of galaxies

    International Nuclear Information System (INIS)

    Fitchett, M.J.

    1988-01-01

    Optical observations suggesting the existence of substructure in clusters of galaxies are examined. Models of cluster formation and methods used to detect substructure in clusters are reviewed. Consideration is given to classification schemes based on a departure of bright cluster galaxies from a spherically symmetric distribution, evidence for statistically significant substructure, and various types of substructure, including velocity, spatial, and spatial-velocity substructure. The substructure observed in the galaxy distribution in clusters is discussed, focusing on observations from general cluster samples, the Virgo cluster, the Hydra cluster, Centaurus, the Coma cluster, and the Cancer cluster. 88 refs

  18. In vivo fluorescent detection of Fe-S clusters coordinated by human GRX2.

    Science.gov (United States)

    Hoff, Kevin G; Culler, Stephanie J; Nguyen, Peter Q; McGuire, Ryan M; Silberg, Jonathan J; Smolke, Christina D

    2009-12-24

    A major challenge to studying Fe-S cluster biosynthesis in higher eukaryotes is the lack of simple tools for imaging metallocluster binding to proteins. We describe the first fluorescent approach for in vivo detection of 2Fe2S clusters that is based upon the complementation of Venus fluorescent protein fragments via human glutaredoxin 2 (GRX2) coordination of a 2Fe2S cluster. We show that Escherichia coli and mammalian cells expressing Venus fragments fused to GRX2 exhibit greater fluorescence than cells expressing fragments fused to a C37A mutant that cannot coordinate a metallocluster. In addition, we find that maximal fluorescence in the cytosol of mammalian cells requires the iron-sulfur cluster assembly proteins ISCU and NFS1. These findings provide evidence that glutaredoxins can dimerize within mammalian cells through coordination of a 2Fe2S cluster as observed with purified recombinant proteins. Copyright 2009 Elsevier Ltd. All rights reserved.

  19. A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB.

    Science.gov (United States)

    Kent, Peter; Jensen, Rikke K; Kongsted, Alice

    2014-10-02

    There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets

  20. The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis

    Directory of Open Access Journals (Sweden)

    Chen Yidong

    2004-01-01

    Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.

  1. A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

    Science.gov (United States)

    Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

    2016-02-01

    Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

  2. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  3. The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected Via the Sunyaev-Zel'dovich Effect

    Science.gov (United States)

    Sehgal, Neelima; Trac, Hy; Acquaviva, Viviana; Ade, Peter A. R.; Aguirre, Paula; Amiri, Mandana; Appel, John W.; Barrientos, L. Felipe; Battistelli, Elia S.; Bond, J. Richard; hide

    2010-01-01

    We present constraints on cosmological parameters based on a sample of Sunyaev-Zel'dovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148 GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives (sigma)8 = 0.851 +/- 0.115 and w = -1.14 +/- 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find (sigma)8 + 0.821 +/- 0.044 and w = -1.05 +/- 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernova which give (sigma)8 = 0.802 +/- 0.038 and w = -0.98 +/- 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.

  4. The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected via the Sunyaev-Zeldovich Effect

    International Nuclear Information System (INIS)

    Sehgal, N.

    2011-01-01

    We present constraints on cosmological parameters based on a sample of Sunyaev-Zeldovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives σ 8 = 0.851 ± 0.115 and w = -1.14 ± 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find σ 8 = 0.821 ± 0.044 and w = -1.05 ± 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernoava which give σ 8 = 0.802 ± 0.038 and w = -0.98 ± 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.

  5. The Views of Turkish Pre-Service Teachers about Effectiveness of Cluster Method as a Teaching Writing Method

    Science.gov (United States)

    Kitis, Emine; Türkel, Ali

    2017-01-01

    The aim of this study is to find out Turkish pre-service teachers' views on effectiveness of cluster method as a writing teaching method. The Cluster Method can be defined as a connotative creative writing method. The way the method works is that the person who brainstorms on connotations of a word or a concept in abscence of any kind of…

  6. ICGE: an R package for detecting relevant clusters and atypical units in gene expression

    Directory of Open Access Journals (Sweden)

    Irigoien Itziar

    2012-02-01

    Full Text Available Abstract Background Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample... belongs to one of these previously identified clusters or to a new group. Results ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. Conclusions We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

  7. Improved GLR method to instrument failure detection

    International Nuclear Information System (INIS)

    Jeong, Hak Yeoung; Chang, Soon Heung

    1985-01-01

    The generalized likehood radio(GLR) method performs statistical tests on the innovations sequence of a Kalman-Buchy filter state estimator for system failure detection and its identification. However, the major drawback of the convensional GLR is to hypothesize particular failure type in each case. In this paper, a method to solve this drawback is proposed. The improved GLR method is applied to a PWR pressurizer and gives successful results in detection and identification of any failure. Furthmore, some benefit on the processing time per each cycle of failure detection and its identification can be accompanied. (Author)

  8. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

    Science.gov (United States)

    Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

    2018-04-15

    In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  9. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

    Directory of Open Access Journals (Sweden)

    Vasileios Karyotis

    2018-04-01

    Full Text Available In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  10. GMDD: a database of GMO detection methods.

    Science.gov (United States)

    Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans J P; Guo, Rong; Liang, Wanqi; Zhang, Dabing

    2008-06-04

    Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.

  11. Detecting groups of coevolving positions in a molecule: a clustering approach

    Directory of Open Access Journals (Sweden)

    Galtier Nicolas

    2007-11-01

    Full Text Available Abstract Background Although the patterns of co-substitutions in RNA is now well characterized, detection of coevolving positions in proteins remains a difficult task. It has been recognized that the signal is typically weak, due to the fact that (i amino-acid are characterized by various biochemical properties, so that distinct amino acids changes are not functionally equivalent, and (ii a given mutation can be compensated by more than one mutation, at more than one position. Results We present a new method based on phylogenetic substitution mapping. The two above-mentioned problems are addressed by (i the introduction of a weighted mapping, which accounts for the biochemical effects (volume, polarity, charge of amino-acid changes, (ii the use of a clustering approach to detect groups of coevolving sites of virtually any size, and (iii the distinction between biochemical compensation and other coevolutionary mechanisms. We apply this methodology to a previously studied data set of bacterial ribosomal RNA, and to three protein data sets (myoglobin of vertebrates, S-locus Receptor Kinase and Methionine Amino-Peptidase. Conclusion We succeed in detecting groups of sites which significantly depart the null hypothesis of independence. Group sizes range from pairs to groups of size ≃ 10, depending on the substitution weights used. The structural and functional relevance of these groups of sites are assessed, and the various evolutionary processes potentially generating correlated substitution patterns are discussed.

  12. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

    Directory of Open Access Journals (Sweden)

    Yimei Wang

    2018-04-01

    Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

  13. Influence of the input database in detecting fire space-time clusters

    Science.gov (United States)

    Pereira, Mário; Costa, Ricardo; Tonini, Marj; Vega Orozco, Carmen; Parente, Joana

    2015-04-01

    Fire incidence variability is influenced by local environmental variables such as topography, land use, vegetation and weather conditions. These induce a cluster pattern of the fire events distribution. The space-time permutation scan statistics (STPSS) method developed by Kulldorff et al. (2005) and implemented in the SaTScanTM software (http://www.satscan.org/) proves to be able to detect space-time clusters in many different fields, even when using incomplete and/or inaccurate input data. Nevertheless, the dependence of the STPSS method on the different characteristics of different datasets describing the same environmental phenomenon has not been studied yet. In this sense, the objective of this study is to assess the robustness of the STPSS for detecting real clusters using different input datasets and to justify the obtained results. This study takes advantage of the existence of two very different official fire datasets currently available for Portugal, both provided by the Institute for the Conservation of Nature and Forests. The first one is the aggregated Portuguese Rural Fire Database PRFD (Pereira et al., 2011), which is based on ground measurements and provides detailed information about the ignition and extinction date/time and the area burnt by each fire in forest, scrubs and agricultural areas. However, in the PRFD, the fire location of each fire is indicated by the name of smallest administrative unit (the parish) where the ignition occurred. Consequently, since the application of the STPSS requires the geographic coordinates of the events, the centroid of the parishes was considered. The second fire dataset is the national mapping burnt areas (NMBA), which is based on satellite measurements and delivered in shape file format. The NMBA provides a detailed spatial information (shape and size of each fire) but the temporal information is restricted to the year of occurrence. Besides these differences, the two datasets cover different periods, they

  14. GC ‘Multi-Analyte’ Detection Method

    Energy Technology Data Exchange (ETDEWEB)

    Dudar, E. [Plant Protection & Soil Conservation Service of Budapest, Budapest (Hungary)

    2009-07-15

    Elaborated methodologies for GC multi-analyte detection are presented, comprising the steps of method development, chromatographic conditions and procedures including the determination of relative retention times and summary results tables. (author)

  15. Motion estimation using point cluster method and Kalman filter.

    Science.gov (United States)

    Senesh, M; Wolf, A

    2009-05-01

    The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal

  16. AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

    Directory of Open Access Journals (Sweden)

    Cooper James B

    2010-03-01

    Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.

  17. A density-based clustering model for community detection in complex networks

    Science.gov (United States)

    Zhao, Xiang; Li, Yantao; Qu, Zehui

    2018-04-01

    Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

  18. Clustering and Recurring Anomaly Identification: Recurring Anomaly Detection System (ReADS)

    Science.gov (United States)

    McIntosh, Dawn

    2006-01-01

    This viewgraph presentation reviews the Recurring Anomaly Detection System (ReADS). The Recurring Anomaly Detection System is a tool to analyze text reports, such as aviation reports and maintenance records: (1) Text clustering algorithms group large quantities of reports and documents; Reduces human error and fatigue (2) Identifies interconnected reports; Automates the discovery of possible recurring anomalies; (3) Provides a visualization of the clusters and recurring anomalies We have illustrated our techniques on data from Shuttle and ISS discrepancy reports, as well as ASRS data. ReADS has been integrated with a secure online search

  19. Detection of high mass cluster ions sputtered from Bi surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Shepard, A; Hewitt, R W; Slusser, G J; Baitinger, W E; Cooks, R G; Winograd, N [Purdue Univ., Lafayette, Ind. (USA). Dept. of Chemistry; Delgass, W N [Purdue Univ., Lafayette, Ind. (USA); Varon, A; Devant, G [Societe RIBER, 92 - Rueil-Malmaison (France)

    1976-12-01

    The technique of secondary ion mass spectrometry (SIMS) has been employed to detect Bi/sup 3 +/ ions and associated oxides Bi/sub 3/Osub(x)sup(+)(x=1 to 4) from a Bi foil. Using a 3 keV Ar/sup +/ ion primary beam of 5x10/sup -7/ A/cm/sup 2/, mass resolution to nearly 700 with the requisite sensitivity has been achieved. The Bi surface was also monitored by X-ray photoelectron spectroscopy (XPS or ESCA). The presence of a weak O 1s peak at 532.7 eV and a strong SIMS Bi/sup 3 +/ peak is interpreted to mean that the oxygen is weakly incorporated into the Bi lattice without disrupting metal-metal bonds.

  20. What if LIGO's gravitational wave detections are strongly lensed by massive galaxy clusters?

    Science.gov (United States)

    Smith, Graham P.; Jauzac, Mathilde; Veitch, John; Farr, Will M.; Massey, Richard; Richard, Johan

    2018-04-01

    Motivated by the preponderance of so-called `heavy black holes' in the binary black hole (BBH) gravitational wave (GW) detections to date, and the role that gravitational lensing continues to play in discovering new galaxy populations, we explore the possibility that the GWs are strongly lensed by massive galaxy clusters. For example, if one of the GW sources were actually located at z = 1, then the rest-frame mass of the associated BHs would be reduced by a factor of ˜2. Based on the known populations of BBH GW sources and strong-lensing clusters, we estimate a conservative lower limit on the number of BBH mergers detected per detector year at LIGO/Virgo's current sensitivity that are multiply-imaged, of Rdetect ≃ 10-5 yr-1. This is equivalent to rejecting the hypothesis that one of the BBH GWs detected to date was multiply-imaged at ≲4σ. It is therefore unlikely, but not impossible, that one of the GWs is multiply-imaged. We identify three spectroscopically confirmed strong-lensing clusters with well-constrained mass models within the 90 per cent credible sky localizations of the BBH GWs from LIGO's first observing run. In the event that one of these clusters multiply-imaged one of the BBH GWs, we predict that 20-60 per cent of the putative next appearances of the GWs would be detectable by LIGO, and that they would arrive at Earth within 3yr of first detection.

  1. Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering

    Directory of Open Access Journals (Sweden)

    Susan Worner

    2013-09-01

    Full Text Available For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the co-occurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM, a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k

  2. clusters

    Indian Academy of Sciences (India)

    2017-09-27

    Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

  3. clusters

    Indian Academy of Sciences (India)

    environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.

  4. Differences Between Ward's and UPGMA Methods of Cluster Analysis: Implications for School Psychology.

    Science.gov (United States)

    Hale, Robert L.; Dougherty, Donna

    1988-01-01

    Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…

  5. 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

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

    International Nuclear Information System (INIS)

    Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitrios; Skouroliakou, Aikaterini; Hazle, John D.; Kagadis, George C.

    2014-01-01

    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

  7. The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware

    Energy Technology Data Exchange (ETDEWEB)

    Tierney, Brian L; Vallentin, Matthias; Sommer, Robin; Lee, Jason; Leres, Craig; Paxson, Vern; Tierney, Brian

    2007-09-19

    In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.

  8. Motif-Independent De Novo Detection of Secondary Metabolite Gene Clusters – Towards Identification of Novel Secondary Metabolisms from Filamentous Fungi -

    Directory of Open Access Journals (Sweden)

    Myco eUmemura

    2015-05-01

    Full Text Available Secondary metabolites are produced mostly by clustered genes that are essential to their biosynthesis. The transcriptional expression of these genes is often cooperatively regulated by a transcription factor located inside or close to a cluster. Most of the secondary metabolism biosynthesis (SMB gene clusters identified to date contain so-called core genes with distinctive sequence features, such as polyketide synthase (PKS and non-ribosomal peptide synthetase (NRPS. Recent efforts in sequencing fungal genomes have revealed far more SMB gene clusters than expected based on the number of core genes in the genomes. Several bioinformatics tools have been developed to survey SMB gene clusters using the sequence motif information of the core genes, including SMURF and antiSMASH.More recently, accompanied by the development of sequencing techniques allowing to obtain large-scale genomic and transcriptomic data, motif-independent prediction methods of SMB gene clusters, including MIDDAS-M, have been developed. Most these methods detect the clusters in which the genes are cooperatively regulated at transcriptional levels, thus allowing the identification of novel SMB gene clusters regardless of the presence of the core genes. Another type of the method, MIPS-CG, uses the characteristics of SMB genes, which are highly enriched in non-syntenic blocks (NSBs, enabling the prediction even without transcriptome data although the results have not been evaluated in detail. Considering that large portion of SMB gene clusters might be sufficiently expressed only in limited uncommon conditions, it seems that prediction of SMB gene clusters by bioinformatics and successive experimental validation is an only way to efficiently uncover hidden SMB gene clusters. Here, we describe and discuss possible novel approaches for the determination of SMB gene clusters that have not been identified using conventional methods.

  9. An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

    Directory of Open Access Journals (Sweden)

    M. Anitha

    2012-04-01

    Full Text Available 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 clustering, Geostatistical Possibilistic clustering and Geostatistical Fuzzy clustering and 3.Optimization using Particle Swarm Optimization (PSO. The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 89%, specificity of 94% and overall accuracy of 93% over FCM and GPC. The clustered brain images are then subjected to Particle Swarm Optimization (PSO. The optimized result obtained from GFCM-PSO provides sensitivity of 90%, specificity of 94% and accuracy of 95%. The detection results reveals that GFCM and GFCMPSO better localizes the large regions of lesions and gives less false positive rate when compared to GPC and GPC-PSO which captures the largest loads of WMLs only in the upper ventral horns of the brain.

  10. Developing cluster strategy of apples dodol SMEs by integration K-means clustering and analytical hierarchy process method

    Science.gov (United States)

    Mustaniroh, S. A.; Effendi, U.; Silalahi, R. L. R.; Sari, T.; Ala, M.

    2018-03-01

    The purposes of this research were to determine the grouping of apples dodol small and medium enterprises (SMEs) in Batu City and to determine an appropriate development strategy for each cluster. The methods used for clustering SMEs was k-means. The Analytical Hierarchy Process (AHP) approach was then applied to determine the development strategy priority for each cluster. The variables used in grouping include production capacity per month, length of operation, investment value, average sales revenue per month, amount of SMEs assets, and the number of workers. Several factors were considered in AHP include industry cluster, government, as well as related and supporting industries. Data was collected using the methods of questionaire and interviews. SMEs respondents were selected among SMEs appels dodol in Batu City using purposive sampling. The result showed that two clusters were formed from five apples dodol SMEs. The 1stcluster of apples dodol SMEs, classified as small enterprises, included SME A, SME C, and SME D. The 2ndcluster of SMEs apples dodol, classified as medium enterprises, consisted of SME B and SME E. The AHP results indicated that the priority development strategy for the 1stcluster of apples dodol SMEs was improving quality and the product standardisation, while for the 2nd cluster was increasing the marketing access.

  11. Swarm: robust and fast clustering method for amplicon-based studies

    Science.gov (United States)

    Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

    2014-01-01

    Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PMID:25276506

  12. Swarm: robust and fast clustering method for amplicon-based studies

    Directory of Open Access Journals (Sweden)

    Frédéric Mahé

    2014-09-01

    Full Text Available Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.

  13. Lane Detection in Video-Based Intelligent Transportation Monitoring via Fast Extracting and Clustering of Vehicle Motion Trajectories

    Directory of Open Access Journals (Sweden)

    Jianqiang Ren

    2014-01-01

    Full Text Available Lane detection is a crucial process in video-based transportation monitoring system. This paper proposes a novel method to detect the lane center via rapid extraction and high accuracy clustering of vehicle motion trajectories. First, we use the activity map to realize automatically the extraction of road region, the calibration of dynamic camera, and the setting of three virtual detecting lines. Secondly, the three virtual detecting lines and a local background model with traffic flow feedback are used to extract and group vehicle feature points in unit of vehicle. Then, the feature point groups are described accurately by edge weighted dynamic graph and modified by a motion-similarity Kalman filter during the sparse feature point tracking. After obtaining the vehicle trajectories, a rough k-means incremental clustering with Hausdorff distance is designed to realize the rapid online extraction of lane center with high accuracy. The use of rough set reduces effectively the accuracy decrease, which results from the trajectories that run irregularly. Experimental results prove that the proposed method can detect lane center position efficiently, the affected time of subsequent tasks can be reduced obviously, and the safety of traffic surveillance systems can be enhanced significantly.

  14. Heartbeat detection from a hydraulic bed sensor using a clustering approach.

    Science.gov (United States)

    Rosales, Licet; Skubic, Marjorie; Heise, David; Devaney, Michael J; Schaumburg, Mark

    2012-01-01

    Encouraged by previous performance of a hydraulic bed sensor, this work presents a new hydraulic transducer configuration which improves the system's ability to capture a heartbeat signal from four subjects with different body weight and height, gender, age and cardiac history. It also proposes a new approach for detecting the occurrence of heartbeats from ballistocardiogram (BCG) signals through the use of the k-means clustering algorithm, based on finding the location of the J-peaks. Preliminary testing showed that the new transducer arrangement was able to capture the occurrence of heartbeats for all the participants, and the clustering approach achieved correct heartbeat detection ranging from 98.6 to 100% for three of them. Some considerations are discussed regarding adjustments that can be done in order to increase the correct detection of heartbeats for the participant whose percentage of correct detection ranged from 71.0 to 92.5%.

  15. Near-Duplicate Web Page Detection: An Efficient Approach Using Clustering, Sentence Feature and Fingerprinting

    Directory of Open Access Journals (Sweden)

    J. Prasanna Kumar

    2013-02-01

    Full Text Available Duplicate and near-duplicate web pages are the chief concerns for web search engines. In reality, they incur enormous space to store the indexes, ultimately slowing down and increasing the cost of serving results. A variety of techniques have been developed to identify pairs of web pages that are aldquo;similarardquo; to each other. The problem of finding near-duplicate web pages has been a subject of research in the database and web-search communities for some years. In order to identify the near duplicate web pages, we make use of sentence level features along with fingerprinting method. When a large number of web documents are in consideration for the detection of web pages, then at first, we use K-mode clustering and subsequently sentence feature and fingerprint comparison is used. Using these steps, we exactly identify the near duplicate web pages in an efficient manner. The experimentation is carried out on the web page collections and the results ensured the efficiency of the proposed approach in detecting the near duplicate web pages.

  16. Cluster-cell calculation using the method of generalized homogenization

    International Nuclear Information System (INIS)

    Laletin, N.I.; Boyarinov, V.F.

    1988-01-01

    The generalized-homogenization method (GHM), used for solving the neutron transfer equation, was applied to calculating the neutron distribution in the cluster cell with a series of cylindrical cells with cylindrically coaxial zones. Single-group calculations of the technological channel of the cell of an RBMK reactor were performed using GHM. The technological channel was understood to be the reactor channel, comprised of the zirconium rod, the water or steam-water mixture, the uranium dioxide fuel element, and the zirconium tube, together with the adjacent graphite layer. Calculations were performed for channels with no internal sources and with unit incoming current at the external boundary as well as for channels with internal sources and zero current at the external boundary. The PRAKTINETs program was used to calculate the symmetric neutron distributions in the microcell and in channels with homogenized annular zones. The ORAR-TsM program was used to calculate the antisymmetric distribution in the microcell. The accuracy of the calculations were compared for the two channel versions

  17. The Cluster Variation Method: A Primer for Neuroscientists.

    Science.gov (United States)

    Maren, Alianna J

    2016-09-30

    Effective Brain-Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables , is defined in terms of a single interaction enthalpy parameter ( h ) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.

  18. The Cluster Variation Method: A Primer for Neuroscientists

    Directory of Open Access Journals (Sweden)

    Alianna J. Maren

    2016-09-01

    Full Text Available Effective Brain–Computer Interfaces (BCIs require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h for the case of an equiprobable distribution of bistate (neural/neural ensemble units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.

  19. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    Directory of Open Access Journals (Sweden)

    Reilly John J

    2005-06-01

    Full Text Available Abstract Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical

  20. Feature selection for anomaly–based network intrusion detection using cluster validity indices

    CSIR Research Space (South Africa)

    Naidoo, T

    2015-09-01

    Full Text Available for Anomaly–Based Network Intrusion Detection Using Cluster Validity Indices Tyrone Naidoo_, Jules–Raymond Tapamoy, Andre McDonald_ Modelling and Digital Science, Council for Scientific and Industrial Research, South Africa 1tnaidoo2@csir.co.za 3...

  1. Using Clustering Techniques To Detect Usage Patterns in a Web-based Information System.

    Science.gov (United States)

    Chen, Hui-Min; Cooper, Michael D.

    2001-01-01

    This study developed an analytical approach to detecting groups with homogenous usage patterns in a Web-based information system. Principal component analysis was used for data reduction, cluster analysis for categorizing usage into groups. The methodology was demonstrated and tested using two independent samples of user sessions from the…

  2. Computer aided detection of clusters of microcalcifications on full field digital mammograms

    International Nuclear Information System (INIS)

    Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, H.-P.; Wei Jun; Helvie, Mark A.; Zhou Chuan

    2006-01-01

    We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from the artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case

  3. Comparative analysis of clustering methods for gene expression time course data

    Directory of Open Access Journals (Sweden)

    Ivan G. Costa

    2004-01-01

    Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

  4. Improvement of economic potential estimation methods for enterprise with potential branch clusters use

    Directory of Open Access Journals (Sweden)

    V.Ya. Nusinov

    2017-08-01

    Full Text Available The research determines that the current existing methods of enterprise’s economic potential estimation are based on the use of additive, multiplicative and rating models. It is determined that the existing methods have a row of defects. For example, not all the methods take into account the branch features of the analysis, and also the level of development of the enterprise comparatively with other enterprises. It is suggested to level such defects by an account at the estimation of potential integral level not only by branch features of enterprises activity but also by the intra-account economic clusterization of such enterprises. Scientific works which are connected with the using of clusters for the estimation of economic potential are generalized. According to the results of generalization it is determined that it is possible to distinguish 9 scientific approaches in this direction: the use of natural clusterization of enterprises with the purpose of estimation and increase of region potential; the use of natural clusterization of enterprises with the purpose of estimation and increase of industry potential; use of artificial clusterization of enterprises with the purpose of estimation and increase of region potential; use of artificial clusterization of enterprises with the purpose of estimation and increase of industry potential; the use of artificial clusterization of enterprises with the purpose of clustering potential estimation; the use of artificial clusterization of enterprises with the purpose of estimation of clustering competitiveness potential; the use of natural (artificial clusterization for the estimation of clustering efficiency; the use of natural (artificial clusterization for the increase of level at region (industries development; the use of methods of economic potential of region (industries estimation or its constituents for the construction of the clusters. It is determined that the use of clusterization method in

  5. Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications

    International Nuclear Information System (INIS)

    Mungan, Muhittin; Ramasco, José J

    2010-01-01

    Components of complex systems are often classified according to the way they interact with each other. In graph theory such groups are known as clusters or communities. Many different techniques have been recently proposed to detect them, some of which involve inference methods using either Bayesian or maximum likelihood approaches. In this paper, we study a statistical model designed for detecting clusters based on connection similarity. The basic assumption of the model is that the graph was generated by a certain grouping of the nodes and an expectation maximization algorithm is employed to infer that grouping. We show that the method admits further development to yield a stability analysis of the groupings that quantifies the extent to which each node influences its neighbors' group membership. Our approach naturally allows for the identification of the key elements responsible for the grouping and their resilience to changes in the network. Given the generality of the assumptions underlying the statistical model, such nodes are likely to play special roles in the original system. We illustrate this point by analyzing several empirical networks for which further information about the properties of the nodes is available. The search and identification of stabilizing nodes constitutes thus a novel technique to characterize the relevance of nodes in complex networks

  6. A statistical method (cross-validation) for bone loss region detection after spaceflight

    Science.gov (United States)

    Zhao, Qian; Li, Wenjun; Li, Caixia; Chu, Philip W.; Kornak, John; Lang, Thomas F.

    2010-01-01

    Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes. PMID:20632144

  7. DETECTION OF SOLAR-LIKE OSCILLATIONS FROM KEPLER PHOTOMETRY OF THE OPEN CLUSTER NGC 6819

    International Nuclear Information System (INIS)

    Stello, Dennis; Bedding, Timothy R.; Huber, Daniel; Basu, Sarbani; Bruntt, Hans; Mosser, BenoIt; Barban, Caroline; Goupil, Marie-Jo; Stevens, Ian R.; Chaplin, William J.; Elsworth, Yvonne P.; Hekker, Saskia; Brown, Timothy M.; Christensen-Dalsgaard, Joergen; Kjeldsen, Hans; Arentoft, Torben; Gilliland, Ronald L.; Ballot, Jerome; GarcIa, Rafael A.; Mathur, Savita

    2010-01-01

    Asteroseismology of stars in clusters has been a long-sought goal because the assumption of a common age, distance, and initial chemical composition allows strong tests of the theory of stellar evolution. We report results from the first 34 days of science data from the Kepler Mission for the open cluster NGC 6819-one of the four clusters in the field of view. We obtain the first clear detections of solar-like oscillations in the cluster red giants and are able to measure the large frequency separation, Δν, and the frequency of maximum oscillation power, ν max . We find that the asteroseismic parameters allow us to test cluster membership of the stars, and even with the limited seismic data in hand, we can already identify four possible non-members despite their having a better than 80% membership probability from radial velocity measurements. We are also able to determine the oscillation amplitudes for stars that span about 2 orders of magnitude in luminosity and find good agreement with the prediction that oscillation amplitudes scale as the luminosity to the power of 0.7. These early results demonstrate the unique potential of asteroseismology of the stellar clusters observed by Kepler.

  8. Atomic and electronic structure of clusters from car-Parrinello method

    International Nuclear Information System (INIS)

    Kumar, V.

    1994-06-01

    With the development of ab-initio molecular dynamics method, it has now become possible to study the static and dynamical properties of clusters containing up to a few tens of atoms. Here I present a review of the method within the framework of the density functional theory and pseudopotential approach to represent the electron-ion interaction and discuss some of its applications to clusters. Particular attention is focussed on the structure and bonding properties of clusters as a function of their size. Applications to clusters of alkali metals and Al, non-metal - metal transition in divalent metal clusters, molecular clusters of carbon and Sb are discussed in detail. Some results are also presented on mixed clusters. (author). 121 refs, 24 ifigs

  9. A Bayesian method for detecting stellar flares

    Science.gov (United States)

    Pitkin, M.; Williams, D.; Fletcher, L.; Grant, S. D. T.

    2014-12-01

    We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of `quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

  10. Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

    Science.gov (United States)

    Gaur, Pallavi; Chaturvedi, Anoop

    2017-07-22

    The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

  11. HOTSPOTS DETECTION FROM TRAJECTORY DATA BASED ON SPATIOTEMPORAL DATA FIELD CLUSTERING

    Directory of Open Access Journals (Sweden)

    K. Qin

    2017-09-01

    Full Text Available City hotspots refer to the areas where residents visit frequently, and large traffic flow exist, which reflect the people travel patterns and distribution of urban function area. Taxi trajectory data contain abundant information about urban functions and citizen activities, and extracting interesting city hotspots from them can be of importance in urban planning, traffic command, public travel services etc. To detect city hotspots and discover a variety of changing patterns among them, we introduce a data field-based cluster analysis technique to the pick-up and drop-off points of taxi trajectory data and improve the method by introducing the time weight, which has been normalized to estimate the potential value in data field. Thus, in the light of the new potential function in data field, short distance and short time difference play a powerful role. So the region full of trajectory points, which is regarded as hotspots area, has a higher potential value, while the region with thin trajectory points has a lower potential value. The taxi trajectory data of Wuhan city in China on May 1, 6 and 9, 2015, are taken as the experimental data. From the result, we find the sustaining hotspots area and inconstant hotspots area in Wuhan city based on the spatiotemporal data field method. Further study will focus on optimizing parameter and the interaction among hotspots area.

  12. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  13. Automated detection of microcalcification clusters in digital mammograms based on wavelet domain hidden Markov tree modeling

    International Nuclear Information System (INIS)

    Regentova, E.; Zhang, L.; Veni, G.; Zheng, J.

    2007-01-01

    A system is designed for detecting microcalcification clusters (MCC) in digital mammograms. The system is intended for computer-aided diagnostic prompting. Further discrimination of MCC as benign or malignant is assumed to be performed by radiologists. Processing of mammograms is based on the statistical modeling by means of wavelet domain hidden markov trees (WHMT). Segmentation is performed by the weighted likelihood evaluation followed by the classification based on spatial filters for a single microcalcification (MC) and a cluster of MC detection. The analysis is carried out on FROC curves for 40 mammograms from the mini-MIAS database and for 100 mammograms with 50 cancerous and 50 benign cases from DDSM database. The designed system is capable to detect 100% of true positive cases in these sets. The rate of false positives is 2.9 per case for mini-MIAS dataset; and 0.01 for the DDSM images. (orig.)

  14. Novel methods for detecting buried explosive devices

    Energy Technology Data Exchange (ETDEWEB)

    Kercel, S.W.; Burlage, R.S.; Patek, D.R.; Smith, C.M. [Oak Ridge National Lab., TN (United States); Hibbs, A.D.; Rayner, T.J. [Quantum Magnetics, Inc., San Diego, CA (United States)

    1997-04-01

    Oak Ridge National Laboratory (ORNL) and Quantum Magnetics, Inc. (QM) are exploring novel landmine detection technologies. Technologies considered here include bioreporter bacteria, swept acoustic resonance, nuclear quadrupole resonance (NQR), and semiotic data fusion. Bioreporter bacteria look promising for third-world humanitarian applications; they are inexpensive, and deployment does not require high-tech methods. Swept acoustic resonance may be a useful adjunct to magnetometers in humanitarian demining. For military demining, NQR is a promising method for detecting explosive substances; of 50,000 substances that have been tested, none has an NQR signature that can be mistaken for RDX or TNT. For both military and commercial demining, sensor fusion entails two daunting tasks, identifying fusible features in both present-day and emerging technologies, and devising a fusion algorithm that runs in real-time on cheap hardware. Preliminary research in these areas is encouraging. A bioreporter bacterium for TNT detection is under development. Investigation has just started in swept acoustic resonance as an approach to a cheap mine detector for humanitarian use. Real-time wavelet processing appears to be a key to extending NQR bomb detection into mine detection, including TNT-based mines. Recent discoveries in semiotics may be the breakthrough that will lead to a robust fused detection scheme.

  15. Evaluation of null-point detection methods on simulation data

    Science.gov (United States)

    Olshevsky, Vyacheslav; Fu, Huishan; Vaivads, Andris; Khotyaintsev, Yuri; Lapenta, Giovanni; Markidis, Stefano

    2014-05-01

    We model the measurements of artificial spacecraft that resemble the configuration of CLUSTER propagating in the particle-in-cell simulation of turbulent magnetic reconnection. The simulation domain contains multiple isolated X-type null-points, but the majority are O-type null-points. Simulations show that current pinches surrounded by twisted fields, analogous to laboratory pinches, are formed along the sequences of O-type nulls. In the simulation, the magnetic reconnection is mainly driven by the kinking of the pinches, at spatial scales of several ion inertial lentghs. We compute the locations of magnetic null-points and detect their type. When the satellites are separated by the fractions of ion inertial length, as it is for CLUSTER, they are able to locate both the isolated null-points, and the pinches. We apply the method to the real CLUSTER data and speculate how common are pinches in the magnetosphere, and whether they play a dominant role in the dissipation of magnetic energy.

  16. Trend analysis using non-stationary time series clustering based on the finite element method

    OpenAIRE

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-01-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods ...

  17. Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

    Science.gov (United States)

    Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

    2017-01-01

    Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

  18. Fuel rod failure detection method and system

    International Nuclear Information System (INIS)

    Assmann, H.; Janson, W.; Stehle, H.; Wahode, P.

    1975-01-01

    The inventor claims a method for the detection of a defective fuel rod cladding tube or of inleaked water in the cladding tube of a fuel rod in the fuel assembly of a pressurized-water reactor. The fuel assembly is not disassembled but examined as a whole. In the examination, the cladding tube is heated near one of its two end plugs, e.g. with an attached high-frequency inductor. The water contained in the cladding tube evaporates, and steam bubbles or a condensate are detected by the ultrasonic impulse-echo method. It is also possible to measure the delay of the temperature rise at the end plug or to determine the cooling energy required to keep the end plug temperature stable and thus to detect water ingression. (DG/AK) [de

  19. Characteristics of Clusters of Salmonella and Escherichia coli O157 Detected by Pulsed-Field Gel Electrophoresis that Predict Identification of Outbreaks.

    Science.gov (United States)

    Jones, Timothy F; Sashti, Nupur; Ingram, Amanda; Phan, Quyen; Booth, Hillary; Rounds, Joshua; Nicholson, Cyndy S; Cosgrove, Shaun; Crocker, Kia; Gould, L Hannah

    2016-12-01

    Molecular subtyping of pathogens is critical for foodborne disease outbreak detection and investigation. Many clusters initially identified by pulsed-field gel electrophoresis (PFGE) are not confirmed as point-source outbreaks. We evaluated characteristics of clusters that can help prioritize investigations to maximize effective use of limited resources. A multiagency collaboration (FoodNet) collected data on Salmonella and Escherichia coli O157 clusters for 3 years. Cluster size, timing, extent, and nature of epidemiologic investigations were analyzed to determine associations with whether the cluster was identified as a confirmed outbreak. During the 3-year study period, 948 PFGE clusters were identified; 849 (90%) were Salmonella and 99 (10%) were E. coli O157. Of those, 192 (20%) were ultimately identified as outbreaks (154 [18%] of Salmonella and 38 [38%] of E. coli O157 clusters). Successful investigation was significantly associated with larger cluster size, more rapid submission of isolates (e.g., for Salmonella, 6 days for outbreaks vs. 8 days for nonoutbreaks) and PFGE result reporting to investigators (16 days vs. 29 days, respectively), and performance of analytic studies (completed in 33% of Salmonella outbreaks vs. 1% of nonoutbreaks) and environmental investigations (40% and 1%, respectively). Intervals between first and second cases in a cluster did not differ significantly between outbreaks and nonoutbreaks. Molecular subtyping of pathogens is a rapidly advancing technology, and successfully identifying outbreaks will vary by pathogen and methods used. Understanding criteria for successfully investigating outbreaks is critical for efficiently using limited resources.

  20. Anharmonic effects in the quantum cluster equilibrium method

    Science.gov (United States)

    von Domaros, Michael; Perlt, Eva

    2017-03-01

    The well-established quantum cluster equilibrium (QCE) model provides a statistical thermodynamic framework to apply high-level ab initio calculations of finite cluster structures to macroscopic liquid phases using the partition function. So far, the harmonic approximation has been applied throughout the calculations. In this article, we apply an important correction in the evaluation of the one-particle partition function and account for anharmonicity. Therefore, we implemented an analytical approximation to the Morse partition function and the derivatives of its logarithm with respect to temperature, which are required for the evaluation of thermodynamic quantities. This anharmonic QCE approach has been applied to liquid hydrogen chloride and cluster distributions, and the molar volume, the volumetric thermal expansion coefficient, and the isobaric heat capacity have been calculated. An improved description for all properties is observed if anharmonic effects are considered.

  1. A crystalline cluster method for deep impurities in insulators

    International Nuclear Information System (INIS)

    Guimaraes, P.S.

    1983-01-01

    An 'ab initio' self-consistent-field crystalline-cluster approach to the study of deep impurity states in insulators is proposed. It is shown that, in spite of being a cluster calculation, the interaction of the impurity with the crystal environment is fully taken into account. It is also shown that the present representation of the impurity states is, at least, as precise as the crystalline cluster representation of the pure crystal electronic structure. The procedure has been tested by performing the calculation of the electronic structure of the U center in a sodium chloride crystal, and it has been observed that the calculated GAMMA 1 - GAMMA 15 absorption energy is in good agreement with experiment. (Author) [pt

  2. A crystalline cluster method for deep impurities in insulators

    International Nuclear Information System (INIS)

    Guimaraes, P.S.

    1983-01-01

    An ''ab initio'' self-consistent-field crysttalline-cluster approach to the study of deep impurity states in insulators is proposed. It is shown that, in spite of being a cluster calculation, the interaction of the impurity with the crystal environment is fully taken into account. It is also shown that the present representation of the impurity states is, at least, as precise as the crystalline cluster representation of the pure crystal electronic structure. The procedure has been tested by performing the calculation of the electronic structure of the U center in a sodium chloride crystal, and it has been observed that the calculated γ 1 - γ 15 absorption energy is in good agreement with experiment. (author) [pt

  3. A method for detecting hydrophobic patches protein

    NARCIS (Netherlands)

    Lijnzaad, P.; Berendsen, H.J.C.; Argos, P.

    1996-01-01

    A method for the detection of hydrophobic patches on the surfaces of protein tertiary structures is presented, it delineates explicit contiguous pieces of surface of arbitrary size and shape that consist solely of carbon and sulphur atoms using a dot representation of the solvent-accessible surface,

  4. Radioimmunoassay method for detection of gonorrhea antibodies

    International Nuclear Information System (INIS)

    1975-01-01

    A novel radioimmunoassay for the detection of gonorrhea antibodies in serum is described. A radionuclide is bound to gonorrhea antigens produced by a growth culture. In the presence of gonorrhea antibodies in the serum, an antigen-antibody conjugate is formed, the concentration of which can be measured with conventional radiometric methods. The radioimmunoassay is highly specific

  5. GMDD: a database of GMO detection methods

    NARCIS (Netherlands)

    Dong, W.; Yang, L.; Shen, K.; Kim, B.; Kleter, G.A.; Marvin, H.J.P.; Guo, R.; Liang, W.; Zhang, D.

    2008-01-01

    Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been

  6. Method for discovering relationships in data by dynamic quantum clustering

    Science.gov (United States)

    Weinstein, Marvin; Horn, David

    2014-10-28

    Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

  7. A dynamic lattice searching method with rotation operation for optimization of large clusters

    International Nuclear Information System (INIS)

    Wu Xia; Cai Wensheng; Shao Xueguang

    2009-01-01

    Global optimization of large clusters has been a difficult task, though much effort has been paid and many efficient methods have been proposed. During our works, a rotation operation (RO) is designed to realize the structural transformation from decahedra to icosahedra for the optimization of large clusters, by rotating the atoms below the center atom with a definite degree around the fivefold axis. Based on the RO, a development of the previous dynamic lattice searching with constructed core (DLSc), named as DLSc-RO, is presented. With an investigation of the method for the optimization of Lennard-Jones (LJ) clusters, i.e., LJ 500 , LJ 561 , LJ 600 , LJ 665-667 , LJ 670 , LJ 685 , and LJ 923 , Morse clusters, silver clusters by Gupta potential, and aluminum clusters by NP-B potential, it was found that both the global minima with icosahedral and decahedral motifs can be obtained, and the method is proved to be efficient and universal.

  8. Quantifying clutter: A comparison of four methods and their relationship to bat detection

    Science.gov (United States)

    Joy M. O’Keefe; Susan C. Loeb; Hoke S. Hill Jr.; J. Drew Lanham

    2014-01-01

    The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these...

  9. Transmitted ion energy loss distributions to detect cluster formation in silicon

    International Nuclear Information System (INIS)

    Selen, L.J.M.; Loon, A. van; IJzendoorn, L.J. van; Voigt, M.J.A. de

    2002-01-01

    The energy loss distribution of ions transmitted through a 5.7±0.2 μm thick Si crystal was measured and simulated with the Monte Carlo channeling simulation code FLUX. A general resemblance between the measured and simulated energy loss distributions was obtained after incorporation of an energy dependent energy loss in the simulation program. The energy loss calculations are used to investigate the feasibility to detect the presence of light element dopant clusters in a host crystal from the shape of the energy loss distribution, with transmission ion channeling. A curved crystal structure is used as a model for a region in the host crystal with clusters. The presence of the curvature does have a large influence on the transmitted energy distribution, which offers the possibility to determine the presence of dopant clusters in a host crystal with transmission ion channeling

  10. Toward the detection of pure carbon clusters in the Interstellar Medium (ISM)

    Science.gov (United States)

    Heath, J. R.; Van Orden, A.; Hwang, H. J.; Kuo, E. W.; Tanaka, K.; Saykally, R. J.

    1995-01-01

    Determination of the form and distribution of carbon in the universe is critical to understanding the origin of life on Earth and elsewhere. Two potentially large reservoirs of carbon in the interstellar medium (ISM) remain unexplored. These are polycyclic aromatic hydrocarbons (PAH) and pure carbon clusters. Little information exists on the structures, properties, and transition frequencies of pure carbon clusters. The work described is designed to provide a specific inventory of laboratory frequencies and physical properties of this carbon clusters so that efforts can be made to detect them in cold interstellar sources by far-infrared astronomy. Data is given from infrared laser spectroscopy determination of the structure of C3, C4, C5, C6, C7, and C9.

  11. CHANDRA DETECTION OF A NEW DIFFUSE X-RAY COMPONENT FROM THE GLOBULAR CLUSTER 47 TUCANAE

    Energy Technology Data Exchange (ETDEWEB)

    Wu, E. M. H.; Cheng, K. S. [Department of Physics, University of Hong Kong, Pokfulam Road (Hong Kong); Hui, C. Y. [Department of Astronomy and Space Science, Chungnam National University, Daejeon (Korea, Republic of); Kong, A. K. H.; Tam, P. H. T. [Institute of Astronomy and Department of Physics, National Tsing Hua University, Hsinchu, Taiwan (China); Dogiel, V. A., E-mail: cyhui@cnu.ac.kr [I. E. Tamm Theoretical Physics Division of P. N. Lebedev Institute of Physics, Leninskii pr. 53, 119991 Moscow (Russian Federation)

    2014-06-20

    In re-analyzing the archival Chandra data of the globular cluster 47 Tucanae, we have detected a new diffuse X-ray emission feature within the half-mass radius of the cluster. The spectrum of the diffuse emission can be described by a power-law model plus a plasma component with photon index Γ ∼ 1.0 and plasma temperature kT ∼ 0.2 keV. While the thermal component is apparently uniform, the non-thermal contribution falls off exponentially from the core. The observed properties could possibly be explained in the context of multiple shocks resulting from the collisions among the stellar wind in the cluster and the inverse Compton scattering between the pulsar wind and the relic photons.

  12. A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Wen Chen

    2011-08-01

    Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.

  13. Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.

    Science.gov (United States)

    Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H

    2017-10-25

    Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.

  14. Automated detection of very Low Surface Brightness galaxies in the Virgo Cluster

    Science.gov (United States)

    Prole, D. J.; Davies, J. I.; Keenan, O. C.; Davies, L. J. M.

    2018-04-01

    We report the automatic detection of a new sample of very low surface brightness (LSB) galaxies, likely members of the Virgo cluster. We introduce our new software, DeepScan, that has been designed specifically to detect extended LSB features automatically using the DBSCAN algorithm. We demonstrate the technique by applying it over a 5 degree2 portion of the Next-Generation Virgo Survey (NGVS) data to reveal 53 low surface brightness galaxies that are candidate cluster members based on their sizes and colours. 30 of these sources are new detections despite the region being searched specifically for LSB galaxies previously. Our final sample contains galaxies with 26.0 ≤ ⟨μe⟩ ≤ 28.5 and 19 ≤ mg ≤ 21, making them some of the faintest known in Virgo. The majority of them have colours consistent with the red sequence, and have a mean stellar mass of 106.3 ± 0.5M⊙ assuming cluster membership. After using ProFit to fit Sérsic profiles to our detections, none of the new sources have effective radii larger than 1.5 Kpc and do not meet the criteria for ultra-diffuse galaxy (UDG) classification, so we classify them as ultra-faint dwarfs.

  15. Bayesian Methods for Radiation Detection and Dosimetry

    CERN Document Server

    Groer, Peter G

    2002-01-01

    We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...

  16. A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

    Science.gov (United States)

    Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

    2013-01-01

    As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

  17. A novel community detection method in bipartite networks

    Science.gov (United States)

    Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

    2018-02-01

    Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

  18. Metagenomic Detection Methods in Biopreparedness Outbreak Scenarios

    DEFF Research Database (Denmark)

    Karlsson, Oskar Erik; Hansen, Trine; Knutsson, Rickard

    2013-01-01

    In the field of diagnostic microbiology, rapid molecular methods are critically important for detecting pathogens. With rapid and accurate detection, preventive measures can be put in place early, thereby preventing loss of life and further spread of a disease. From a preparedness perspective...... of a clinical sample, creating a metagenome, in a single week of laboratory work. As new technologies emerge, their dissemination and capacity building must be facilitated, and criteria for use, as well as guidelines on how to report results, must be established. This article focuses on the use of metagenomics...

  19. Minimal disease detection of B-cell lymphoproliferative disorders by flow cytometry: multidimensional cluster analysis.

    Science.gov (United States)

    Duque, Ricardo E

    2012-04-01

    Flow cytometric analysis of cell suspensions involves the sequential 'registration' of intrinsic and extrinsic parameters of thousands of cells in list mode files. Thus, it is almost irresistible to describe phenomena in numerical terms or by 'ratios' that have the appearance of 'accuracy' due to the presence of numbers obtained from thousands of cells. The concepts involved in the detection and characterization of B cell lymphoproliferative processes are revisited in this paper by identifying parameters that, when analyzed appropriately, are both necessary and sufficient. The neoplastic process (cluster) can be visualized easily because the parameters that distinguish it form a cluster in multidimensional space that is unique and distinguishable from neighboring clusters that are not of diagnostic interest but serve to provide a background. For B cell neoplasia it is operationally necessary to identify the multidimensional space occupied by a cluster whose kappa:lambda ratio is 100:0 or 0:100. Thus, the concept of kappa:lambda ratio is without meaning and would not detect B cell neoplasia in an unacceptably high number of cases.

  20. Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

    Directory of Open Access Journals (Sweden)

    Harris Ann

    2004-10-01

    Full Text Available Abstract Background Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Centers for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. Results Several significant (p Conclusion The findings of the present study provide evidence for the existence of accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation.

  1. A Centralized Detection of Sinkhole Attacks Based on Energy Level of the Nodes on Cluster-Based Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Merve Nilay Aydın

    2017-10-01

    Full Text Available Wireless Sensor Networks is consist of thousands of small and low-cost devices, which communicate over wireless medium. Due to locating in harsh environment and having limited resources, WSN is prone to various attacks. One of the most dangerous attacks threatening WSN is the sinkhole attack. In this paper, sinkhole attack is modelled on a cluster-based WSN, and a centralized detection algorithm based on the remaining energies of the nodes is proposed. The simulations were run for different values of energy thresholds and various numbers of nodes. The performance of the system was investigated over total energy consumption in the system, the number of packets arrived at base station and true detection rate of the sinkhole node(s. The results showed that the proposed method is energy-efficient and detects the malicious nodes with a 100% accuracy for all number of nodes.

  2. Detection method of internal leakage from valve using acoustic method

    International Nuclear Information System (INIS)

    Kumagai, Hiromichi; Kitajima, Akira; Suzuki, Akio.

    1990-01-01

    The objective of this study is to estimate the feasibility of the acoustic method for the internal leakage from the valves in power plants. From the experimental results, it was suggested that the acoustic method for the monitoring of leakage was feasible. When the background levels are higher than the acoustic signals from leakage, we can detect the leakage analyzing the spectrum of the remainders which take the background noise from the acoustic signals. (author)

  3. Molecular methods for the detection of mutations.

    Science.gov (United States)

    Monteiro, C; Marcelino, L A; Conde, A R; Saraiva, C; Giphart-Gassler, M; De Nooij-van Dalen, A G; Van Buuren-van Seggelen, V; Van der Keur, M; May, C A; Cole, J; Lehmann, A R; Steinsgrimsdottir, H; Beare, D; Capulas, E; Armour, J A

    2000-01-01

    We report the results of a collaborative study aimed at developing reliable, direct assays for mutation in human cells. The project used common lymphoblastoid cell lines, both with and without mutagen treatment, as a shared resource to validate the development of new molecular methods for the detection of low-level mutations in the presence of a large excess of normal alleles. As the "gold standard, " hprt mutation frequencies were also measured on the same samples. The methods under development included i) the restriction site mutation (RSM) assay, in which mutations lead to the destruction of a restriction site; ii) minisatellite length-change mutation, in which mutations lead to alleles containing new numbers of tandem repeat units; iii) loss of heterozygosity for HLA epitopes, in which antibodies can be used to direct selection for mutant cells; iv) multiple fluorescence-based long linker arm nucleotides assay (mf-LLA) technology, for the detection of substitutional mutations; v) detection of alterations in the TP53 locus using a (CA) array as the target for the screening; and vi) PCR analysis of lymphocytes for the presence of the BCL2 t(14:18) translocation. The relative merits of these molecular methods are discussed, and a comparison made with more "traditional" methods.

  4. A novel method for detection of apoptosis

    International Nuclear Information System (INIS)

    Zagariya, Alexander M.

    2012-01-01

    There are two different Angiotensin II (ANG II) peptides in nature: Human type (ANG II) and Bovine type (ANG II*). These eight amino acid peptides differ only at position 5 where Valine is replaced by Isoleucine in the Bovine type. They are present in all species studied so far. These amino acids are different by only one atom of carbon. This difference is so small, that it will allow any of ANG II, Bovine or Human antibodies to interact with all species and create a universal method for apoptosis detection. ANG II concentrations are found at substantially higher levels in apoptotic, compared to non-apoptotic, tissues. ANG II accumulation can lead to DNA damage, mutations, carcinogenesis and cell death. We demonstrate that Bovine antiserum can be used for universal detection of apoptosis. In 2010, the worldwide market for apoptosis detection reached the $20 billion mark and significantly increases each year. Most commercially available methods are related to Annexin V and TUNNEL. Our new method based on ANG II is more widely known to physicians and scientists compared to previously used methods. Our approach offers a novel alternative for assessing apoptosis activity with enhanced sensitivity, at a lower cost and ease of use.

  5. Hough transform methods used for object detection

    International Nuclear Information System (INIS)

    Qussay A Salih; Abdul Rahman Ramli; Md Mahmud Hassan Prakash

    2001-01-01

    The Hough transform (HT) is a robust parameter estimator of multi-dimensional features in images. The HT is an established technique which evidences a shape by mapping image edge points into a parameter space. The HT is technique which is used to isolate curves of a give shape in an image. The classical HT requires that the curve be specified in some parametric from and, hence is most commonly used in the detection of regular curves. The HT has been generalized so that it is capable of detecting arbitrary curved shapes. The main advantage of this transform technique is that it is very tolerant of gaps in the actual object boundaries the classical HT for the detection of line , we will indicate how it can be applied to the detection of arbitrary shapes. Sometimes the straight line HT is efficient enough to detect features such as artificial curves. The HT is an established technique for extracting geometric shapes based on the duality definition of the points on a curve and their parameters. This technique has been developed for extracting simple geometric shapes such as lines, circles and ellipses as well as arbitrary shapes. The HT provides robustness against discontinuous or missing features, points or edges are mapped into a partitioned parameter of Hough space as individual votes where peaks denote the feature of interest represented in a non-analytically tabular form. The main drawback of the HT technique is the computational requirement which has an exponential growth of memory space and processing time as the number of parameters used to represent a primitive increases. For this reason most of the research on the HT has focused on reducing the computational burden for extracting of arbitrary shapes under more general transformations include a overview of describing the methods for the detection image processing programs are frequently required to detect and particle classification in an industrial setting, a standard algorithms for this detection lines

  6. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

    Directory of Open Access Journals (Sweden)

    Huanhuan Li

    2017-08-01

    Full Text Available The Shipboard Automatic Identification System (AIS is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW, a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our

  7. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.

    Science.gov (United States)

    Li, Huanhuan; Liu, Jingxian; Liu, Ryan Wen; Xiong, Naixue; Wu, Kefeng; Kim, Tai-Hoon

    2017-08-04

    The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with

  8. Detection of food irradiation - two analytical methods

    International Nuclear Information System (INIS)

    1994-01-01

    This publication summarizes the activities of Nordic countries in the field of detection of irradiated food. The National Food Agency of Denmark has coordinated the project. The two analytical methods investigated were: the gas-chromatographic determination of the hydrocarbon/lipid ratio in irradiated chicken meat, and a bioassay based on microelectrophoresis of DNA from single cells. Also a method for determination of o-tyrosine in the irradiated and non-irradiated chicken meat has been tested. The first method based on radiolytical changes in fatty acids, contained in chicken meat, has been tested and compared in the four Nordic countries. Four major hydrocarbons (C16:2, C16:3, C17:1 and C17:2) have been determined and reasonable agreement was observed between the dose level and hydrocarbons concentration. Results of a bioassay, where strand breaks of DNA are demonstrated by microelectrophoresis of single cells, prove a correlation between the dose levels and the pattern of DNA fragments migration. The hydrocarbon method can be applied to detect other irradiated, fat-containing foods, while the DNA method can be used for some animal and some vegetable foods as well.Both methods allow to determine the fact of food irradiation beyond any doubt, thus making them suitable for food control analysis. The detailed determination protocols are given. (EG)

  9. A cluster merging method for time series microarray with production values.

    Science.gov (United States)

    Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

    2014-09-01

    A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

  10. Research and Design of Rootkit Detection Method

    Science.gov (United States)

    Liu, Leian; Yin, Zuanxing; Shen, Yuli; Lin, Haitao; Wang, Hongjiang

    Rootkit is one of the most important issues of network communication systems, which is related to the security and privacy of Internet users. Because of the existence of the back door of the operating system, a hacker can use rootkit to attack and invade other people's computers and thus he can capture passwords and message traffic to and from these computers easily. With the development of the rootkit technology, its applications are more and more extensive and it becomes increasingly difficult to detect it. In addition, for various reasons such as trade secrets, being difficult to be developed, and so on, the rootkit detection technology information and effective tools are still relatively scarce. In this paper, based on the in-depth analysis of the rootkit detection technology, a new kind of the rootkit detection structure is designed and a new method (software), X-Anti, is proposed. Test results show that software designed based on structure proposed is much more efficient than any other rootkit detection software.

  11. Hough transform for clustered microcalcifications detection in full-field digital mammograms

    Science.gov (United States)

    Fanizzi, A.; Basile, T. M. A.; Losurdo, L.; Amoroso, N.; Bellotti, R.; Bottigli, U.; Dentamaro, R.; Didonna, V.; Fausto, A.; Massafra, R.; Moschetta, M.; Tamborra, P.; Tangaro, S.; La Forgia, D.

    2017-09-01

    Many screening programs use mammography as principal diagnostic tool for detecting breast cancer at a very early stage. Despite the efficacy of the mammograms in highlighting breast diseases, the detection of some lesions is still doubtless for radiologists. In particular, the extremely minute and elongated salt-like particles of microcalcifications are sometimes no larger than 0.1 mm and represent approximately half of all cancer detected by means of mammograms. Hence the need for automatic tools able to support radiologists in their work. Here, we propose a computer assisted diagnostic tool to support radiologists in identifying microcalcifications in full (native) digital mammographic images. The proposed CAD system consists of a pre-processing step, that improves contrast and reduces noise by applying Sobel edge detection algorithm and Gaussian filter, followed by a microcalcification detection step performed by exploiting the circular Hough transform. The procedure performance was tested on 200 images coming from the Breast Cancer Digital Repository (BCDR), a publicly available database. The automatically detected clusters of microcalcifications were evaluated by skilled radiologists which asses the validity of the correctly identified regions of interest as well as the system error in case of missed clustered microcalcifications. The system performance was evaluated in terms of Sensitivity and False Positives per images (FPi) rate resulting comparable to the state-of-art approaches. The proposed model was able to accurately predict the microcalcification clusters obtaining performances (sensibility = 91.78% and FPi rate = 3.99) which favorably compare to other state-of-the-art approaches.

  12. Detection method of a failed fuel

    International Nuclear Information System (INIS)

    Urata, Megumu; Uchida, Shunsuke; Utamura, Motoaki.

    1976-01-01

    Object: To divide a tank arrangement into a heating tank for the exclusive use of heating and a mixing tank for the exclusive use of mixing to thereby minimize the purifying amount of reactor water pumped from the interior of reactor and to considerably minimize the capacity of a purifier. Structure: In a detection method of a failed fuel comprising stopping a flow of coolant within fuel assemblies arranged in the coolant in a reactor container, sampling said coolant within the fuel assemblies, and detecting a radioactivity level of sampling liquid, the improvement of the method comprising the steps of heating a part of said coolant removed from the interior of said reactor container, mixing said heated coolant into the remainder of said removed coolant, pouring said mixed liquid into said fuel assemblies, and after a lapse of given time, sampling the liquid poured into said fuel assemblies. (Kawakami, Y.)

  13. Method for detecting a failed fuel

    International Nuclear Information System (INIS)

    Utamura, Motoaki; Urata, Megumu; Uchida, Shunsuke.

    1976-01-01

    Purpose: To provide a method for the detection of failed fuel by pouring hot water, in which pouring speed of liquid to be poured and temperature of the liquid are controlled to prevent the leakage of the liquid. Constitution: The method comprises blocking the top of a fuel assembly arranged in coolant to stop a flow of coolant, pouring a liquid higher in temperature than that of coolant into the fuel assembly, sampling the liquid poured, and measuring the concentration of radioactivity of coolant already subjected to sampling to detect a failed fuel. At this time, controlling is made so that the pouring speed of the poured liquid is set to about 25 l/min, and an increased portion of temperature from the temperature of liquid to the temperature of coolant is set to a level less than about 15 0 C. (Furukawa, Y.)

  14. System and method for anomaly detection

    Science.gov (United States)

    Scherrer, Chad

    2010-06-15

    A system and method for detecting one or more anomalies in a plurality of observations is provided. In one illustrative embodiment, the observations are real-time network observations collected from a stream of network traffic. The method includes performing a discrete decomposition of the observations, and introducing derived variables to increase storage and query efficiencies. A mathematical model, such as a conditional independence model, is then generated from the formatted data. The formatted data is also used to construct frequency tables which maintain an accurate count of specific variable occurrence as indicated by the model generation process. The formatted data is then applied to the mathematical model to generate scored data. The scored data is then analyzed to detect anomalies.

  15. Fermi Detection of a Luminous gamma-ray Pulsar in a Globular Cluster

    Science.gov (United States)

    Freire, P. C. C.; Abdo, A. A.; Ajello, M.; Allafort, A.; Ballet, J.; Barbiellini, G.; Bastieri, D.; Bechtol, K.; Bellazzini, R.; Blandford, R. D.; hide

    2011-01-01

    We report the Fermi Large Area Telescope detection of gamma -ray (>100 mega-electron volts) pulsations from pulsar J1823--3021A in the globular cluster NGC 6624 with high significance (approx 7 sigma). Its gamma-ray luminosity L (sub 3) = (8:4 +/- 1:6) X 10(exp 34) ergs per second, is the highest observed for any millisecond pulsar (MSP) to date, and it accounts for most of the cluster emission. The non-detection of the cluster in the off-pulse phase implies that its contains < 32 gamma-ray MSPs, not approx 100 as previously estimated. The gamma -ray luminosity indicates that the unusually large rate of change of its period is caused by its intrinsic spin-down. This implies that J1823--3021A has the largest magnetic field and is the youngest MSP ever detected, and that such anomalous objects might be forming at rates comparable to those of the more normal MSPs.

  16. Consensus of satellite cluster flight using an energy-matching optimal control method

    Science.gov (United States)

    Luo, Jianjun; Zhou, Liang; Zhang, Bo

    2017-11-01

    This paper presents an optimal control method for consensus of satellite cluster flight under a kind of energy matching condition. Firstly, the relation between energy matching and satellite periodically bounded relative motion is analyzed, and the satellite energy matching principle is applied to configure the initial conditions. Then, period-delayed errors are adopted as state variables to establish the period-delayed errors dynamics models of a single satellite and the cluster. Next a novel satellite cluster feedback control protocol with coupling gain is designed, so that the satellite cluster periodically bounded relative motion consensus problem (period-delayed errors state consensus problem) is transformed to the stability of a set of matrices with the same low dimension. Based on the consensus region theory in the research of multi-agent system consensus issues, the coupling gain can be obtained to satisfy the requirement of consensus region and decouple the satellite cluster information topology and the feedback control gain matrix, which can be determined by Linear quadratic regulator (LQR) optimal method. This method can realize the consensus of satellite cluster period-delayed errors, leading to the consistency of semi-major axes (SMA) and the energy-matching of satellite cluster. Then satellites can emerge the global coordinative cluster behavior. Finally the feasibility and effectiveness of the present energy-matching optimal consensus for satellite cluster flight is verified through numerical simulations.

  17. An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

    Directory of Open Access Journals (Sweden)

    XiuLi Zhao

    2015-01-01

    Full Text Available Affinity propagation (AP algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

  18. Method and device for detecting radiatons

    International Nuclear Information System (INIS)

    Borel, J.; Goascoz, V.

    1979-01-01

    The method consists in fabricating an MOS transistor comprising a drain region and a source region separated from each other by a bulk region of opposite doping type relative to the first two regions, in delivering the radiation to be detected into the carrier-collection region of the MOS transistor, in leaving the bulk region at a floating potential and in collecting the drain-source current of the transistor

  19. Fluorescence detection of a protein-bound 2Fe2S cluster.

    Science.gov (United States)

    Hoff, Kevin G; Goodlitt, Rochelle; Li, Rui; Smolke, Christina D; Silberg, Jonathan J

    2009-03-02

    A fluorescent biosensor is described for 2Fe2S clusters that is composed of green fluorescent protein (GFP) fused to glutaredoxin 2 (Grx2), as illustrated here. 2Fe2S detection is based on the reduction of GFP fluorescence upon the 2Fe2S-induced dimerization of GFP-Grx2. This assay is sufficiently sensitive to detect submicromolar changes in 2Fe2S levels, thus making it suitable for high-throughput measurements of metallocluster degradation and synthesis reactions.

  20. DIAGNOSTIC METHODS IN BREAST CANCER DETECTION

    Directory of Open Access Journals (Sweden)

    Kristijana Hertl

    2018-02-01

    Full Text Available Background. In the world as well as in Slovenia, breast cancer is the most frequent female cancer. Due to its high incidence, it appears to be a serious health and economic problem. Content. Among other, tumour size at diagnosis, is an important prognostic factors of the course of the disease. The probability of axillary lymph node involvement as well as distant metastases is greater in larger tumours. This is the reason that encouraged the development of various diagnostic methods for early detection of small, clinically non-palpable breast tumours. Mammography, however, remains the »golden standard« of early breast cancer detection. It is the basic diagnostic method applied in all symptomatic women over 35 years of age and in asymptomatic women over 40 years of age. Ultrasonography (US, additional projections, magnetic resonance imaging (MRI and ductography are regarded as complementary diagnostic breast imaging techniques in addition to mammography. The detected changes in the breast can be further confirmed by US-, MR-guided or stereotactic biopsy. If necessary, surgical biopsy and the excision of a tissue sample, after wire or isotope localisation of the nonpalpable lesion, can be performed. Conclusions. Any of the above mentioned diagnostic methods has advantages as well as drawbacks and only detailed knowledge and understanding of each of them may assure the best option.

  1. Detection method of internal leakage from valve using acoustic method

    International Nuclear Information System (INIS)

    Kumagai, Horomichi

    1990-01-01

    The purpose of this study is to estimate the availability of acoustic method for detecting the internal leakage of valves at power plants. Experiments have been carried out on the characteristics of acoustic noise caused by the leak simulated flow. From the experimental results, the mechanism of the acoustic noisegenerated from flow, the relation between acoustic intensity and leak flow velocity, and the characteristics of the acoustic frequency spectrum were clarified. The acoustic method was applied to valves at site, and the background noises were measured in abnormal plant conditions. When the background level is higher than the acoustic signal, the difference between the background noise frequency spectrum and the acoustic signal spectrum provide a very useful leak detection method. (author)

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

    Science.gov (United States)

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

    2017-12-01

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

  3. A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES

    Energy Technology Data Exchange (ETDEWEB)

    Andrade-Santos, Felipe; Lima Neto, Gastao B.; Lagana, Tatiana F. [Departamento de Astronomia, Instituto de Astronomia, Geofisica e Ciencias Atmosfericas, Universidade de Sao Paulo, Geofisica e Ciencias Atmosfericas, Rua do Matao 1226, Cidade Universitaria, 05508-090 Sao Paulo, SP (Brazil)

    2012-02-20

    We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model ({beta}-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z in [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high- and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high- and low-substructure level clusters) are different (they present an offset, i.e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.

  4. Gold atomic cluster mediated electrochemical aptasensor for the detection of lipopolysaccharide.

    Science.gov (United States)

    Posha, Biyas; Nambiar, Sindhu R; Sandhyarani, N

    2018-03-15

    We have constructed an aptamer immobilized gold atomic cluster mediated, ultrasensitive electrochemical biosensor (Apt/AuAC/Au) for LPS detection without any additional signal amplification strategy. The aptamer self-assemble onto the gold atomic clusters makes Apt/AuAC/Au an excellent platform for the LPS detection. Differential pulse voltammetry and EIS were used for the quantitative LPS detection. The Apt/AuAC/Au sensor offers an ultrasensitive and selective detection of LPS down to 7.94 × 10 -21 M level with a wide dynamic range from 0.01 attomolar to 1pM. The sensor exhibited excellent selectivity and stability. The real sample analysis was performed by spiking the diluted insulin sample with various concentration of LPS and obtained recovery within 2% error value. The sensor is found to be more sensitive than most of the literature reports. The simple and easy way of construction of this sensor provides an efficient and promising detection of an even trace amount of LPS. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. 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.

  6. Analytical detection methods for irradiated foods

    International Nuclear Information System (INIS)

    1991-03-01

    The present publication is a review of scientific literature on the analytical identification of foods treated with ionizing radiation and the quantitative determination of absorbed dose of radiation. Because of the extremely low level of chemical changes resulting from irradiation or because of the lack of specificity to irradiation of any chemical changes, a few methods of quantitative determination of absorbed dose have shown promise until now. On the other hand, the present review has identified several possible methods, which could be used, following further research and testing, for the identification of irradiated foods. An IAEA Co-ordinated Research Programme on Analytical Detection Methods for Irradiation Treatment of Food ('ADMIT'), established in 1990, is currently investigating many of the methods cited in the present document. Refs and tab

  7. Delamination detection using methods of computational intelligence

    Science.gov (United States)

    Ihesiulor, Obinna K.; Shankar, Krishna; Zhang, Zhifang; Ray, Tapabrata

    2012-11-01

    Abstract Reliable delamination prediction scheme is indispensable in order to prevent potential risks of catastrophic failures in composite structures. The existence of delaminations changes the vibration characteristics of composite laminates and hence such indicators can be used to quantify the health characteristics of laminates. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using numerical models of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used via inverse modeling to determine the position, size and location of delaminations using changes in measured natural frequencies. The results clearly highlight the efficiency and the robustness of the approach.

  8. Investigation of the cluster formation in lithium niobate crystals by computer modeling method

    Energy Technology Data Exchange (ETDEWEB)

    Voskresenskii, V. M.; Starodub, O. R., E-mail: ol-star@mail.ru; Sidorov, N. V.; Palatnikov, M. N. [Russian Academy of Sciences, Tananaev Institute of Chemistry and Technology of Rare Earth Elements and Mineral Raw Materials, Kola Science Centre (Russian Federation)

    2017-03-15

    The processes occurring upon the formation of energetically equilibrium oxygen-octahedral clusters in the ferroelectric phase of a stoichiometric lithium niobate (LiNbO{sub 3}) crystal have been investigated by the computer modeling method within the semiclassical atomistic model. An energetically favorable cluster size (at which a structure similar to that of a congruent crystal is organized) is shown to exist. A stoichiometric cluster cannot exist because of the electroneutrality loss. The most energetically favorable cluster is that with a Li/Nb ratio of about 0.945, a value close to the lithium-to-niobium ratio for a congruent crystal.

  9. Near-IR search for lensed supernovae behind galaxy clusters. II. First detection and future prospects

    OpenAIRE

    Goobar, A.; Paech, K.; Stanishev, V.; Amanullah, R.; Dahlén, T.; Jönsson, J.; Kneib, J. P.; Lidman, C.; Limousin, M.; Mörtsell, E.; Nobili, S.; Richard, J.; Riehm, T.; von Strauss, M.

    2009-01-01

    Aims. Powerful gravitational telescopes in the form of massive galaxy clusters can be used to enhance the light collecting power over a limited field of view by about an order of magnitude in flux. This effect is exploited here to increase the depth of a survey for lensed supernovae at near-IR wavelengths. Methods. We present a pilot supernova search programme conducted with the ISAAC camera at VLT. Lensed galaxies behind the massive clusters A1689, A1835, and AC114 were observed for a tot...

  10. Clustering Methods; Part IV of Scientific Report No. ISR-18, Information Storage and Retrieval...

    Science.gov (United States)

    Cornell Univ., Ithaca, NY. Dept. of Computer Science.

    Two papers are included as Part Four of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Controlled Single Pass Classification Algorithm with Application to Multilevel Clustering" by D. B. Johnson and J. M. Laferente presents a single pass clustering method which compares favorably…

  11. An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

    Science.gov (United States)

    Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

    2013-05-01

    Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

  12. A Novel Fusion-Based Ship Detection Method from Pol-SAR Images

    Directory of Open Access Journals (Sweden)

    Wenguang Wang

    2015-09-01

    Full Text Available A novel fusion-based ship detection method from polarimetric Synthetic Aperture Radar (Pol-SAR images is proposed in this paper. After feature extraction and constant false alarm rate (CFAR detection, the detection results of HH channel, diplane scattering by Pauli decomposition and helical factor by Barnes decomposition are fused together. The confirmed targets and potential target pixels can be obtained after the fusion process. Using the difference degree of the target, potential target pixels can be classified. The fusion-based ship detection method works accurately by utilizing three different features comprehensively. The result of applying the technique to measured Airborne Synthetic Radar (AIRSAR data shows that the novel detection method can achieve better performance in both ship’s detection and ship’s shape preservation compared to the result of K-means clustering method and the Notch Filter method.

  13. Waterborne Pathogens: Detection Methods and Challenges

    Directory of Open Access Journals (Sweden)

    Flor Yazmín Ramírez-Castillo

    2015-05-01

    Full Text Available Waterborne pathogens and related diseases are a major public health concern worldwide, not only by the morbidity and mortality that they cause, but by the high cost that represents their prevention and treatment. These diseases are directly related to environmental deterioration and pollution. Despite the continued efforts to maintain water safety, waterborne outbreaks are still reported globally. Proper assessment of pathogens on water and water quality monitoring are key factors for decision-making regarding water distribution systems’ infrastructure, the choice of best water treatment and prevention waterborne outbreaks. Powerful, sensitive and reproducible diagnostic tools are developed to monitor pathogen contamination in water and be able to detect not only cultivable pathogens but also to detect the occurrence of viable but non-culturable microorganisms as well as the presence of pathogens on biofilms. Quantitative microbial risk assessment (QMRA is a helpful tool to evaluate the scenarios for pathogen contamination that involve surveillance, detection methods, analysis and decision-making. This review aims to present a research outlook on waterborne outbreaks that have occurred in recent years. This review also focuses in the main molecular techniques for detection of waterborne pathogens and the use of QMRA approach to protect public health.

  14. Performance Analysis of Entropy Methods on K Means in Clustering Process

    Science.gov (United States)

    Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

    2017-12-01

    K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

  15. Doppler method leak detection for LMFBR steam generators. Pt. 3. Investigation of detection sensitivity and method

    International Nuclear Information System (INIS)

    Kumagai, Hiromichi; Kinoshita, Izumi

    2001-01-01

    To prevent the expansion of tube damage and to maintain structural integrity in the steam generators (SGs) of a fast breeder reactor (FBR), it is necessary to detect precisely and immediately any leakage of water from heat transfer tubes. Therefore, the Doppler method was developed. Previous studies have revealed that, in the SG full-sector model that simulates actual SGs, the Doppler method can detect bubbles of 0.4 l/s within a few seconds. However in consideration of the dissolution rate of hydrogen generated by a sodium-water reaction even from a small water leak, it is necessary to detect smaller leakages of water from the heat transfer tubes. The detection sensitivity of the Doppler method and the influence of background noise were experimentally investigated. In-water experiments were performed using the SG model. The results show that the Doppler method can detect bubbles of 0.01 l/s (equivalent to a water leak rate of about 0.01 g/s) within a few seconds and that the background noise has little effect on water leak detection performance. The Doppler method thus has great potential for the detection of water leakage in SGs. (author)

  16. Novel Methods of Hydrogen Leak Detection

    International Nuclear Information System (INIS)

    Pushpinder S Puri

    2006-01-01

    With the advent of the fuel cell technology and a drive for clean fuel, hydrogen gas is emerging as a leading candidate for the fuel of choice. For hydrogen to become a consumer fuel for automotive and domestic power generation, safety is paramount. It is, therefore, desired to have a method and system for hydrogen leak detection using odorant which can incorporate a uniform concentration of odorant in the hydrogen gas, when odorants are mixed in the hydrogen storage or delivery means. It is also desired to develop methods where the odorant is not added to the bulk hydrogen, keeping it free of the odorization additives. When odorants are not added to the hydrogen gas in the storage or delivery means, methods must be developed to incorporate odorant in the leaking gas so that leaks can be detected by small. Further, when odorants are not added to the stored hydrogen, it may also be desirable to observe leaks by sight by discoloration of the surface of the storage or transportation vessels. A series of novel solutions are proposed which address the issues raised above. These solutions are divided into three categories as follows: 1. Methods incorporating an odorant in the path of hydrogen leak as opposed to adding it to the hydrogen gas. 2. Methods where odorants are generated in-situ by chemical reaction with the leaking hydrogen 3. Methods of dispensing and storing odorants in high pressure hydrogen gas which release odorants to the gas at a uniform and predetermined rates. Use of one or more of the methods described here in conjunction with appropriate engineering solutions will assure the ultimate safety of hydrogen use as a commercial fuel. (authors)

  17. Sensing Methods for Detecting Analog Television Signals

    Science.gov (United States)

    Rahman, Mohammad Azizur; Song, Chunyi; Harada, Hiroshi

    This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

  18. PARTIAL TRAINING METHOD FOR HEURISTIC ALGORITHM OF POSSIBLE CLUSTERIZATION UNDER UNKNOWN NUMBER OF CLASSES

    Directory of Open Access Journals (Sweden)

    D. A. Viattchenin

    2009-01-01

    Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible  clusterization with partial  training is proposed in the  paper.  The  method  is  based  on  data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.

  19. Detection of food irradiation with luminescence methods

    International Nuclear Information System (INIS)

    Anderle, H.

    1997-06-01

    Food irradiation is applied as method for the preservation of foods, the prevention of food spoilage and the inhibition of food-borne pathogens. Doses exceeding 10 kGy (10 kJ/kg) are not recommended by the WHO. The different legislation requires methods for the detection and the closimetry of irradiated foods. Among the physical methods based on the radiation-induced changes in inorganic, nonhygroscopic crystalline solids are thermoluminescence (TL), photostimulated luminescence (PSL) and lyoluminescence (LL) measurement. The luminescence methods were tested on natural minerals. Pure quartz, feldspars, calcite, aragonite and dolomite of known origin were irradiated, read out and analyzed to determine the influence of luminescence-activators and deactivators. Carbonate minerals show an orange-red TL easily detectable by blue-sensitive photomultiplier tubes. TIL-inactive carbonate samples may be identified by a lyoluminescence method using the reaction of trapped irradiation-generated charge carriers with the solvent during crystal-lattice breakup. The fine-ground mineral is dissolved in an alkaline complexing agent/chemiluminescence sensitizer/chemiluminescence catalyst (EDTA/luminol/hemin) reagent mixture. The TL and PSL of quartz is too weak to contribute a significant part for the corresponding signals in polymineral dust. Alkali and soda feldspar show intense TL and PSL. The temperature maxima in the TL glow curves allow a clear distinction. PSL does not give this additional information, it suffers from bleaching by ambient light and requires light-protection. Grain disinfestated with low irradiation doses (500 Gy) may not identified by both TL and PSL measurement. The natural TL of feldspar particles may be overlap with the irradiation-induced TL of other minerals. As a routine method, irradiated spices are identified with TL measurement. The dust particles have to be enriched by heavy-liquid flotation and centrifugation. The PSL method allows a clear

  20. A two-stage method for microcalcification cluster segmentation in mammography by deformable models

    International Nuclear Information System (INIS)

    Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.; Karahaliou, A.; Costaridou, L.; Vassiou, K.

    2015-01-01

    Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST cluster , average of minimum distance—AMINDIST cluster ) and the area overlap measure (AOM cluster ). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing tenfold cross

  1. Characterization and detection of a widely distributed gene cluster that predicts anaerobic choline utilization by human gut bacteria.

    Science.gov (United States)

    Martínez-del Campo, Ana; Bodea, Smaranda; Hamer, Hilary A; Marks, Jonathan A; Haiser, Henry J; Turnbaugh, Peter J; Balskus, Emily P

    2015-04-14

    Elucidation of the molecular mechanisms underlying the human gut microbiota's effects on health and disease has been complicated by difficulties in linking metabolic functions associated with the gut community as a whole to individual microorganisms and activities. Anaerobic microbial choline metabolism, a disease-associated metabolic pathway, exemplifies this challenge, as the specific human gut microorganisms responsible for this transformation have not yet been clearly identified. In this study, we established the link between a bacterial gene cluster, the choline utilization (cut) cluster, and anaerobic choline metabolism in human gut isolates by combining transcriptional, biochemical, bioinformatic, and cultivation-based approaches. Quantitative reverse transcription-PCR analysis and in vitro biochemical characterization of two cut gene products linked the entire cluster to growth on choline and supported a model for this pathway. Analyses of sequenced bacterial genomes revealed that the cut cluster is present in many human gut bacteria, is predictive of choline utilization in sequenced isolates, and is widely but discontinuously distributed across multiple bacterial phyla. Given that bacterial phylogeny is a poor marker for choline utilization, we were prompted to develop a degenerate PCR-based method for detecting the key functional gene choline TMA-lyase (cutC) in genomic and metagenomic DNA. Using this tool, we found that new choline-metabolizing gut isolates universally possessed cutC. We also demonstrated that this gene is widespread in stool metagenomic data sets. Overall, this work represents a crucial step toward understanding anaerobic choline metabolism in the human gut microbiota and underscores the importance of examining this microbial community from a function-oriented perspective. Anaerobic choline utilization is a bacterial metabolic activity that occurs in the human gut and is linked to multiple diseases. While bacterial genes responsible for

  2. Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

    Science.gov (United States)

    Bhola, Rishav; Krishna, Nandigam Hari; Ramesh, K N; Senthilnath, J; Anand, Gautham

    2018-01-15

    In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. The swift UVOT stars survey. I. Methods and test clusters

    Energy Technology Data Exchange (ETDEWEB)

    Siegel, Michael H.; Porterfield, Blair L.; Linevsky, Jacquelyn S.; Bond, Howard E.; Hoversten, Erik A.; Berrier, Joshua L.; Gronwall, Caryl A. [Department of Astronomy and Astrophysics, The Pennsylvania State University, 525 Davey Laboratory, University Park, PA 16802 (United States); Holland, Stephen T. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Breeveld, Alice A. [Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey RH5 6NT (United Kingdom); Brown, Peter J., E-mail: siegel@astro.psu.edu, E-mail: blp14@psu.edu, E-mail: heb11@psu.edu, E-mail: caryl@astro.psu.edu, E-mail: sholland@stsci.edu, E-mail: aab@mssl.ucl.ac.uk, E-mail: grbpeter@yahoo.com [George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Texas A. and M. University, Department of Physics and Astronomy, 4242 TAMU, College Station, TX 77843 (United States)

    2014-12-01

    We describe the motivations and background of a large survey of nearby stellar populations using the Ultraviolet Optical Telescope (UVOT) on board the Swift Gamma-Ray Burst Mission. UVOT, with its wide field, near-UV sensitivity, and 2.″3 spatial resolution, is uniquely suited to studying nearby stellar populations and providing insight into the near-UV properties of hot stars and the contribution of those stars to the integrated light of more distant stellar populations. We review the state of UV stellar photometry, outline the survey, and address problems specific to wide- and crowded-field UVOT photometry. We present color–magnitude diagrams of the nearby open clusters M67, NGC 188, and NGC 2539, and the globular cluster M79. We demonstrate that UVOT can easily discern the young- and intermediate-age main sequences, blue stragglers, and hot white dwarfs, producing results consistent with previous studies. We also find that it characterizes the blue horizontal branch of M79 and easily identifies a known post-asymptotic giant branch star.

  4. The swift UVOT stars survey. I. Methods and test clusters

    International Nuclear Information System (INIS)

    Siegel, Michael H.; Porterfield, Blair L.; Linevsky, Jacquelyn S.; Bond, Howard E.; Hoversten, Erik A.; Berrier, Joshua L.; Gronwall, Caryl A.; Holland, Stephen T.; Breeveld, Alice A.; Brown, Peter J.

    2014-01-01

    We describe the motivations and background of a large survey of nearby stellar populations using the Ultraviolet Optical Telescope (UVOT) on board the Swift Gamma-Ray Burst Mission. UVOT, with its wide field, near-UV sensitivity, and 2.″3 spatial resolution, is uniquely suited to studying nearby stellar populations and providing insight into the near-UV properties of hot stars and the contribution of those stars to the integrated light of more distant stellar populations. We review the state of UV stellar photometry, outline the survey, and address problems specific to wide- and crowded-field UVOT photometry. We present color–magnitude diagrams of the nearby open clusters M67, NGC 188, and NGC 2539, and the globular cluster M79. We demonstrate that UVOT can easily discern the young- and intermediate-age main sequences, blue stragglers, and hot white dwarfs, producing results consistent with previous studies. We also find that it characterizes the blue horizontal branch of M79 and easily identifies a known post-asymptotic giant branch star.

  5. Fast optimization of binary clusters using a novel dynamic lattice searching method

    International Nuclear Information System (INIS)

    Wu, Xia; Cheng, Wen

    2014-01-01

    Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd) 79 clusters with DFT-fit parameters of Gupta potential

  6. Fermi detection of a luminous γ-ray pulsar in a globular cluster.

    Science.gov (United States)

    2011-11-25

    We report on the Fermi Large Area Telescope's detection of γ-ray (>100 mega-electron volts) pulsations from pulsar J1823-3021A in the globular cluster NGC 6624 with high significance (~7 σ). Its γ-ray luminosity, L(γ) = (8.4 ± 1.6) × 10(34) ergs per second, is the highest observed for any millisecond pulsar (MSP) to date, and it accounts for most of the cluster emission. The nondetection of the cluster in the off-pulse phase implies that it contains <32 γ-ray MSPs, not ~100 as previously estimated. The γ-ray luminosity indicates that the unusually large rate of change of its period is caused by its intrinsic spin-down. This implies that J1823-3021A has the largest magnetic field and is the youngest MSP ever detected and that such anomalous objects might be forming at rates comparable to those of the more normal MSPs.

  7. Heuristic methods using grasp, path relinking and variable neighborhood search for the clustered traveling salesman problem

    Directory of Open Access Journals (Sweden)

    Mário Mestria

    2013-08-01

    Full Text Available The Clustered Traveling Salesman Problem (CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. The CTSP is NP-hard and, in this context, we are proposed heuristic methods for the CTSP using GRASP, Path Relinking and Variable Neighborhood Descent (VND. The heuristic methods were tested using Euclidean instances with up to 2000 vertices and clusters varying between 4 to 150 vertices. The computational tests were performed to compare the performance of the heuristic methods with an exact algorithm using the Parallel CPLEX software. The computational results showed that the hybrid heuristic method using VND outperforms other heuristic methods.

  8. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    International Nuclear Information System (INIS)

    Anderson, Ryan B.; Bell, James F.; Wiens, Roger C.; Morris, Richard V.; Clegg, Samuel M.

    2012-01-01

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO 2 at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by ∼ 3 wt.%. The statistical significance of these improvements was ∼ 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and specifically

  9. Bayesian Methods for Radiation Detection and Dosimetry

    International Nuclear Information System (INIS)

    Peter G. Groer

    2002-01-01

    We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed compartmental activities. From the estimated probability densities of the model parameters we were able to derive the densities for compartmental activities for a two compartment catenary model at different times. We also calculated the average activities and their standard deviation for a simple two compartment model

  10. Detection of an unidentified emission line in the stacked X-ray spectrum of galaxy clusters

    Energy Technology Data Exchange (ETDEWEB)

    Bulbul, Esra; Foster, Adam; Smith, Randall K.; Randall, Scott W. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Markevitch, Maxim [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Loewenstein, Michael, E-mail: ebulbul@cfa.harvard.edu [CRESST and X-ray Astrophysics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States)

    2014-07-01

    We detect a weak unidentified emission line at E = (3.55-3.57) ± 0.03 keV in a stacked XMM-Newton spectrum of 73 galaxy clusters spanning a redshift range 0.01-0.35. When the full sample is divided into three subsamples (Perseus, Centaurus+Ophiuchus+Coma, and all others), the line is seen at >3σ statistical significance in all three independent MOS spectra and the PN 'all others' spectrum. It is also detected in the Chandra spectra of the Perseus Cluster. However, it is very weak and located within 50-110 eV of several known lines. The detection is at the limit of the current instrument capabilities. We argue that there should be no atomic transitions in thermal plasma at this energy. An intriguing possibility is the decay of sterile neutrino, a long-sought dark matter particle candidate. Assuming that all dark matter is in sterile neutrinos with m{sub s} = 2E = 7.1 keV, our detection corresponds to a neutrino decay rate consistent with previous upper limits. However, based on the cluster masses and distances, the line in Perseus is much brighter than expected in this model, significantly deviating from other subsamples. This appears to be because of an anomalously bright line at E = 3.62 keV in Perseus, which could be an Ar XVII dielectronic recombination line, although its emissivity would have to be 30 times the expected value and physically difficult to understand. Another alternative is the above anomaly in the Ar line combined with the nearby 3.51 keV K line also exceeding expectation by a factor of 10-20. Confirmation with Astro-H will be critical to determine the nature of this new line.

  11. A 3D clustering approach for point clouds to detect and quantify changes at a rock glacier front

    Science.gov (United States)

    Micheletti, Natan; Tonini, Marj; Lane, Stuart N.

    2016-04-01

    Terrestrial Laser Scanners (TLS) are extensively used in geomorphology to remotely-sense landforms and surfaces of any type and to derive digital elevation models (DEMs). Modern devices are able to collect many millions of points, so that working on the resulting dataset is often troublesome in terms of computational efforts. Indeed, it is not unusual that raw point clouds are filtered prior to DEM creation, so that only a subset of points is retained and the interpolation process becomes less of a burden. Whilst this procedure is in many cases necessary, it implicates a considerable loss of valuable information. First, and even without eliminating points, the common interpolation of points to a regular grid causes a loss of potentially useful detail. Second, it inevitably causes the transition from 3D information to only 2.5D data where each (x,y) pair must have a unique z-value. Vector-based DEMs (e.g. triangulated irregular networks) partially mitigate these issues, but still require a set of parameters to be set and a considerable burden in terms of calculation and storage. Because of the reasons above, being able to perform geomorphological research directly on point clouds would be profitable. Here, we propose an approach to identify erosion and deposition patterns on a very active rock glacier front in the Swiss Alps to monitor sediment dynamics. The general aim is to set up a semiautomatic method to isolate mass movements using 3D-feature identification directly from LiDAR data. An ultra-long range LiDAR RIEGL VZ-6000 scanner was employed to acquire point clouds during three consecutive summers. In order to isolate single clusters of erosion and deposition we applied the Density-Based Scan Algorithm with Noise (DBSCAN), previously successfully employed by Tonini and Abellan (2014) in a similar case for rockfall detection. DBSCAN requires two input parameters, strongly influencing the number, shape and size of the detected clusters: the minimum number of

  12. Apparatus and method for detecting explosives

    International Nuclear Information System (INIS)

    Griffith, B.

    1976-01-01

    An apparatus is described for use in situations such as airports to detect explosives hidden in containers (for eg. suitcases). The method involves the evaluation of the quantities of oxygen and nitrogen within the container by neutron activation analysis and the determination of whether these quantities exceed predetermined limits. The equipment includes a small sub-critical lower powered reactor for thermal (0.01 to 0.10 eV) neutron production, a radium beryllium primary source, a deuterium-tritium reactor as a high energy (> 1.06 MeV) neutron source and Geiger counter detector arrays. (UK)

  13. An Unexpected Detection of Bifurcated Blue Straggler Sequences in the Young Globular Cluster NGC 2173

    Science.gov (United States)

    Li, Chengyuan; Deng, Licai; de Grijs, Richard; Jiang, Dengkai; Xin, Yu

    2018-03-01

    The bifurcated patterns in the color–magnitude diagrams of blue straggler stars (BSSs) have attracted significant attention. This type of special (but rare) pattern of two distinct blue straggler sequences is commonly interpreted as evidence that cluster core-collapse-driven stellar collisions are an efficient formation mechanism. Here, we report the detection of a bifurcated blue straggler distribution in a young Large Magellanic Cloud cluster, NGC 2173. Because of the cluster’s low central stellar number density and its young age, dynamical analysis shows that stellar collisions alone cannot explain the observed BSSs. Therefore, binary evolution is instead the most viable explanation of the origin of these BSSs. However, the reason why binary evolution would render the color–magnitude distribution of BSSs bifurcated remains unclear. C. Li, L. Deng, and R. de Grijs jointly designed this project.

  14. Cluster cosmological analysis with X ray instrumental observables: introduction and testing of AsPIX method

    International Nuclear Information System (INIS)

    Valotti, Andrea

    2016-01-01

    Cosmology is one of the fundamental pillars of astrophysics, as such it contains many unsolved puzzles. To investigate some of those puzzles, we analyze X-ray surveys of galaxy clusters. These surveys are possible thanks to the bremsstrahlung emission of the intra-cluster medium. The simultaneous fit of cluster counts as a function of mass and distance provides an independent measure of cosmological parameters such as Ω m , σ s , and the dark energy equation of state w0. A novel approach to cosmological analysis using galaxy cluster data, called top-down, was developed in N. Clerc et al. (2012). This top-down approach is based purely on instrumental observables that are considered in a two-dimensional X-ray color-magnitude diagram. The method self-consistently includes selection effects and scaling relationships. It also provides a means of bypassing the computation of individual cluster masses. My work presents an extension of the top-down method by introducing the apparent size of the cluster, creating a three-dimensional X-ray cluster diagram. The size of a cluster is sensitive to both the cluster mass and its angular diameter, so it must also be included in the assessment of selection effects. The performance of this new method is investigated using a Fisher analysis. In parallel, I have studied the effects of the intrinsic scatter in the cluster size scaling relation on the sample selection as well as on the obtained cosmological parameters. To validate the method, I estimate uncertainties of cosmological parameters with MCMC method Amoeba minimization routine and using two simulated XMM surveys that have an increasing level of complexity. The first simulated survey is a set of toy catalogues of 100 and 10000 deg 2 , whereas the second is a 1000 deg 2 catalogue that was generated using an Aardvark semi-analytical N-body simulation. This comparison corroborates the conclusions of the Fisher analysis. In conclusion, I find that a cluster diagram that accounts

  15. Novel Methods of Hydrogen Leak Detection

    International Nuclear Information System (INIS)

    Pushpinder S Puri

    2006-01-01

    For hydrogen to become a consumer fuel for automotive and domestic power generation, safety is paramount. Today's hydrogen systems are built with inherent safety measures and multiple levels of protection. However, human senses, in particular, the sense of smell, is considered the ultimate safeguards against leaks. Since hydrogen is an odorless gas, use of odorants to detect leaks, as is done in case of natural gas, is obvious solution. The odorants required for hydrogen used in fuel cells have a unique requirement which must be met. This is because almost all of the commercial odorants used in gas leak detection contain sulfur which acts as poison for the catalysts used in hydrogen based fuel cells, most specifically for the PEM (polymer electrolyte membrane or proton exchange membrane) fuel cells. A possible solution to this problem is to use non-sulfur containing odorants. Chemical compounds based on mixtures of acrylic acid and nitrogen compounds have been adopted to achieve a sulfur-free odorization of a gas. It is, therefore, desired to have a method and system for hydrogen leak detection using odorant which can incorporate a uniform concentration of odorant in the hydrogen gas, when odorants are mixed in the hydrogen storage or delivery means. It is also desired to develop methods where the odorant is not added to the bulk hydrogen, keeping it free of the odorization additives. A series of novel solutions are proposed which address the issues raised above. These solutions are divided into three categories as follows: 1. Methods incorporating an odorant in the path of hydrogen leak as opposed to adding it to the hydrogen gas. 2. Methods where odorants are generated in-situ by chemical reaction with the leaking hydrogen 3. Methods of dispensing and storing odorants in high pressure hydrogen gas which release odorants to the gas at a uniform and predetermined rates. Use of one or more of the methods described here in conjunction with appropriate engineering

  16. Detection of sensor degradation using K-means clustering and support vector regression in nuclear power plant

    International Nuclear Information System (INIS)

    Seo, Inyong; Ha, Bokam; Lee, Sungwoo; Shin, Changhoon; Lee, Jaeyong; Kim, Seongjun

    2011-01-01

    In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be rectified. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this study, an on-line calibration monitoring called KPCSVR using k-means clustering and principal component based Auto-Associative support vector regression (PCSVR) is proposed for nuclear power plant. To reduce the training time of the model, k-means clustering method was used. Response surface methodology is employed to efficiently determine the optimal values of support vector regression hyperparameters. The proposed KPCSVR model was confirmed with actual plant data of Kori Nuclear Power Plant Unit 3 which were measured from the primary and secondary systems of the plant, and compared with the PCSVR model. By using data clustering, the average accuracy of PCSVR improved from 1.228×10 -4 to 0.472×10 -4 and the average sensitivity of PCSVR from 0.0930 to 0.0909, which results in good detection of sensor drift. Moreover, the training time is greatly reduced from 123.5 to 31.5 sec. (author)

  17. Radiation sensitive area detection device and method

    Science.gov (United States)

    Carter, Daniel C. (Inventor); Hecht, Diana L. (Inventor); Witherow, William K. (Inventor)

    1991-01-01

    A radiation sensitive area detection device for use in conjunction with an X ray, ultraviolet or other radiation source is provided which comprises a phosphor containing film which releases a stored diffraction pattern image in response to incoming light or other electromagnetic wave. A light source such as a helium-neon laser, an optical fiber capable of directing light from the laser source onto the phosphor film and also capable of channelling the fluoresced light from the phosphor film to an integrating sphere which directs the light to a signal processing means including a light receiving means such as a photomultiplier tube. The signal processing means allows translation of the fluoresced light in order to detect the original pattern caused by the diffraction of the radiation by the original sample. The optical fiber is retained directly in front of the phosphor screen by a thin metal holder which moves up and down across the phosphor screen and which features a replaceable pinhole which allows easy adjustment of the resolution of the light projected onto the phosphor film. The device produces near real time images with high spatial resolution and without the distortion that accompanies prior art devices employing photomultiplier tubes. A method is also provided for carrying out radiation area detection using the device of the invention.

  18. Some methods for the detection of fissionable matter; Quelques methodes de detection des corps fissiles

    Energy Technology Data Exchange (ETDEWEB)

    Guery, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

    1967-03-01

    A number of equipments or processes allowing to detect uranium or plutonium in industrial plants, and in particular to measure solution concentrations, are studied here. Each method has its own field of applications and has its own performances, which we have tried to define by calculations and by experiments. The following topics have been treated: {gamma} absorptiometer with an Am source, detection test by neutron multiplication, apparatus for the measurement of the {alpha} activity of a solution, fissionable matter detection by {gamma} emission, fissionable matter detection by neutron emission. (author) [French] On examine ici plusieurs appareils ou procedes qui permettent de detecter l'uranium ou le plutonium dans les installations industrielles, et en particulier de mesurer les concentrations de solutions. Chacune des methodes a son domaine d'application et ses performances, qu'on a tente de definir par le calcul et par des experiences. Les sujets traites sont les suivants: absorptiometre {gamma} a source d'americium, essais de detection par multiplication neutronique, appareil de mesure de l'activite {alpha} d'une solution, detection des matieres fissiles par leur emission {gamma}, detection des matieres fissiles par leur emission neutronique. (auteur)

  19. Relation between financial market structure and the real economy: comparison between clustering methods.

    Science.gov (United States)

    Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

    2015-01-01

    We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

  20. Relation between financial market structure and the real economy: comparison between clustering methods.

    Directory of Open Access Journals (Sweden)

    Nicoló Musmeci

    Full Text Available We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

  1. Nucleic acid detection system and method for detecting influenza

    Science.gov (United States)

    Cai, Hong; Song, Jian

    2015-03-17

    The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

  2. Detection of structural defects in lecithin membranes by the small-angle neutron scattering method

    International Nuclear Information System (INIS)

    Bezzabotnov, V.Yu.; Gordelij, V.I.; Ostanevich, Yu.M.; Yaguzhinskij, L.S.

    1989-01-01

    Irregularities interpreted as interdomain defects have been detected in model lipid membranes of dipalmitoil lecithin in liquid L α -phase by the method of small-angle scattering (lateral diffraction). The dimensions and concentrations of the defects were about those supposed within the dynamic cluster model of bilayer (Ivkov, 1984). No irregularities were detected in the solid Lβ ' -phase (the diffusion scattering intensity was at least ten times less)

  3. Phenotypic clustering: a novel method for microglial morphology analysis.

    Science.gov (United States)

    Verdonk, Franck; Roux, Pascal; Flamant, Patricia; Fiette, Laurence; Bozza, Fernando A; Simard, Sébastien; Lemaire, Marc; Plaud, Benoit; Shorte, Spencer L; Sharshar, Tarek; Chrétien, Fabrice; Danckaert, Anne

    2016-06-17

    Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex "dendritic-like" aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. Using the knock-in mouse model CX3CR1(GFP/+), we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. Our results counteract the classical view of a homogenous regional resting

  4. Supersonic wave detection method and supersonic detection device

    International Nuclear Information System (INIS)

    Machida, Koichi; Seto, Takehiro; Ishizaki, Hideaki; Asano, Rin-ichi.

    1996-01-01

    The present invention provides a method of and device for a detection suitable to a channel box which is used while covering a fuel assembly of a BWR type reactor. Namely, a probe for transmitting/receiving supersonic waves scans on the surface of the channel box. A data processing device determines an index showing a selective orientation degree of crystal direction of the channel box based on the signals received by the probe. A judging device compares the determined index with a previously determined allowable range to judge whether the channel box is satisfactory or not based on the result of the comparison. The judgement are on the basis that (1) the bending of the channel box is caused by the difference of elongation of opposed surfaces, (2) the elongation due to irradiation is caused by the selective orientation of crystal direction, and (3) the bending of the channel box can be suppressed within a predetermined range by suppressing the index determined by the measurement of supersonic waves having a correlation with the selective orientation of the crystal direction. As a result, the performance of the channel box capable of enduring high burnup region can be confirmed in a nondestructive manner. (I.S.)

  5. Correction for dispersion and Coulombic interactions in molecular clusters with density functional derived methods: Application to polycyclic aromatic hydrocarbon clusters

    Science.gov (United States)

    Rapacioli, Mathias; Spiegelman, Fernand; Talbi, Dahbia; Mineva, Tzonka; Goursot, Annick; Heine, Thomas; Seifert, Gotthard

    2009-06-01

    The density functional based tight binding (DFTB) is a semiempirical method derived from the density functional theory (DFT). It inherits therefore its problems in treating van der Waals clusters. A major error comes from dispersion forces, which are poorly described by commonly used DFT functionals, but which can be accounted for by an a posteriori treatment DFT-D. This correction is used for DFTB. The self-consistent charge (SCC) DFTB is built on Mulliken charges which are known to give a poor representation of Coulombic intermolecular potential. We propose to calculate this potential using the class IV/charge model 3 definition of atomic charges. The self-consistent calculation of these charges is introduced in the SCC procedure and corresponding nuclear forces are derived. Benzene dimer is then studied as a benchmark system with this corrected DFTB (c-DFTB-D) method, but also, for comparison, with the DFT-D. Both methods give similar results and are in agreement with references calculations (CCSD(T) and symmetry adapted perturbation theory) calculations. As a first application, pyrene dimer is studied with the c-DFTB-D and DFT-D methods. For coronene clusters, only the c-DFTB-D approach is used, which finds the sandwich configurations to be more stable than the T-shaped ones.

  6. Novel Clustering Method Based on K-Medoids and Mobility Metric

    Directory of Open Access Journals (Sweden)

    Y. Hamzaoui

    2018-06-01

    Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.

  7. Thermoluminescence method for detection of irradiated food

    International Nuclear Information System (INIS)

    Pinnioja, S.

    1998-01-01

    A method of thermoluminescence (TL) analysis was developed for the detection of irradiated foods. The TL method is based on the determination of thermoluminescence of adhering or contaminating minerals separated from foods by wet sieving and treatment with high density liquid. Carbon tetrachloride provided a suitable alternative for foods that form gels with water. Thermoluminescence response of minerals in a first TL measurement is normalised with a second TL measurement of the same mineral sample after calibration irradiation to a dose of 5 kGy. The decision about irradiation is made on the basis of a comparison of the two TL spectra: if the two TL glow curves match in shape and intensity the sample has been irradiated, and if they are clearly different it has not been irradiated. An attractive feature of TL analysis is that the mineral material itself is used for calibration; no reference material is required. Foods of interest in the investigation were herbs, spices, berries and seafood. The presence of minerals in samples is a criterion for application of the method, and appropriate minerals were found in all herbs, spices and berries. The most common minerals in terrestrial food were tecto-silicates - quartz and feldspars - which with their intense and stable thermoluminescence were well suited for the analysis. Mica proved to be useless for detection purposes, whereas carbonate in the form of calcite separated from intestines of seafood was acceptable. Fading of the TL signal is considerable in the low temperature part of the glow curve during a storage of several months after irradiation. However, spices and herbs could easily be identified as irradiated even after two years storage. Conditions for seafood, which is stored in a freezer, are different, and only slight fading was observed after one year. The effect of mineral composition and structure on TL was studied for feldspars. Feldspars originating from subtropical and tropical regions exhibit lower TL

  8. Thermoluminescence method for detection of irradiated food

    Energy Technology Data Exchange (ETDEWEB)

    Pinnioja, S

    1998-12-31

    A method of thermoluminescence (TL) analysis was developed for the detection of irradiated foods. The TL method is based on the determination of thermoluminescence of adhering or contaminating minerals separated from foods by wet sieving and treatment with high density liquid. Carbon tetrachloride provided a suitable alternative for foods that form gels with water. Thermoluminescence response of minerals in a first TL measurement is normalised with a second TL measurement of the same mineral sample after calibration irradiation to a dose of 5 kGy. The decision about irradiation is made on the basis of a comparison of the two TL spectra: if the two TL glow curves match in shape and intensity the sample has been irradiated, and if they are clearly different it has not been irradiated. An attractive feature of TL analysis is that the mineral material itself is used for calibration; no reference material is required. Foods of interest in the investigation were herbs, spices, berries and seafood. The presence of minerals in samples is a criterion for application of the method, and appropriate minerals were found in all herbs, spices and berries. The most common minerals in terrestrial food were tecto-silicates - quartz and feldspars - which with their intense and stable thermoluminescence were well suited for the analysis. Mica proved to be useless for detection purposes, whereas carbonate in the form of calcite separated from intestines of seafood was acceptable. Fading of the TL signal is considerable in the low temperature part of the glow curve during a storage of several months after irradiation. However, spices and herbs could easily be identified as irradiated even after two years storage. Conditions for seafood, which is stored in a freezer, are different, and only slight fading was observed after one year. The effect of mineral composition and structure on TL was studied for feldspars. Feldspars originating from subtropical and tropical regions exhibit lower TL

  9. De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units

    Directory of Open Access Journals (Sweden)

    Sarah L. Westcott

    2015-12-01

    VSEARCH have a high level of sensitivity to detect reference sequences, the specificity of those matches was poor relative to the true best match.Discussion. Our analysis calls into question the quality and stability of OTU assignments generated by the open and closed-reference methods as implemented in current version of QIIME. This study demonstrates that de novo methods are the optimal method of assigning sequences into OTUs and that the quality of these assignments needs to be assessed for multiple methods to identify the optimal clustering method for a particular dataset.

  10. Shocks and cold fronts in merging and massive galaxy clusters: new detections with Chandra

    Science.gov (United States)

    Botteon, A.; Gastaldello, F.; Brunetti, G.

    2018-06-01

    A number of merging galaxy clusters show the presence of shocks and cold fronts, i.e. sharp discontinuities in surface brightness and temperature. The observation of these features requires an X-ray telescope with high spatial resolution like Chandra, and allows to study important aspects concerning the physics of the intracluster medium (ICM), such as its thermal conduction and viscosity, as well as to provide information on the physical conditions leading to the acceleration of cosmic rays and magnetic field amplification in the cluster environment. In this work we search for new discontinuities in 15 merging and massive clusters observed with Chandra by using different imaging and spectral techniques of X-ray observations. Our analysis led to the discovery of 22 edges: six shocks, eight cold fronts, and eight with uncertain origin. All the six shocks detected have Mdiverse approaches aimed to identify edges in the ICM. A radio follow-up of the shocks discovered in this paper will be useful to study the connection between weak shocks and radio relics.

  11. Kinetic methods for measuring the temperature of clusters and nanoparticles in molecular beams

    International Nuclear Information System (INIS)

    Makarov, Grigorii N

    2011-01-01

    The temperature (internal energy) of clusters and nanoparticles is an important physical parameter which affects many of their properties and the character of processes they are involved in. At the same time, determining the temperature of free clusters and nanoparticles in molecular beams is a rather complicated problem because the temperature of small particles depends on their size. In this paper, recently developed kinetic methods for measuring the temperature of clusters and nanoparticles in molecular beams are reviewed. The definition of temperature in the present context is given, and how the temperature affects the properties of and the processes involving the particles is discussed. The temperature behavior of clusters and nanoparticles near a phase transition point is analyzed. Early methods for measuring the temperature of large clusters are briefly described. It is shown that, compared to other methods, new kinetic methods are more universal and applicable for determining the temperature of clusters and nanoparticles of practically any size and composition. The future development and applications of these methods are outlined. (reviews of topical problems)

  12. Odour detection methods: olfactometry and chemical sensors.

    Science.gov (United States)

    Brattoli, Magda; de Gennaro, Gianluigi; de Pinto, Valentina; Loiotile, Annamaria Demarinis; Lovascio, Sara; Penza, Michele

    2011-01-01

    The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective "analytical instrument" for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through

  13. Odour Detection Methods: Olfactometry and Chemical Sensors

    Directory of Open Access Journals (Sweden)

    Sara Lovascio

    2011-05-01

    Full Text Available The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc. and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality; this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective “analytical instrument” for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses are then described, focusing on their better performances for environmental analysis. Odour emission monitoring

  14. SCREENING METHODS FOR THE DETECTION OF CARTELS

    Directory of Open Access Journals (Sweden)

    Mihail BUŞU

    2014-06-01

    Full Text Available During their everyday activities, the economic operators conclude a multitude of agreements in tacit or written form, such as: contracts or conventions. Some of these arrangements are absolutely necessary for the development of their current activities. These are agreements which, by respecting the rules of competition, are able to bring benefits to consumers and to the entire economy, as a whole. On the other hand, the economic operators often conclude agreements which are harmful to the economy as well as to the consumers, violating the competition rules. Some examples in this respect are: operators’ agreements on price fixing, on market or customers sharing. Before investigating the violation of competition rules, the relevant authorities should identify the possibility of the existence of such illegalities. The theoretical models for detecting the cartels do represent a proactive tool concerning the antitrust activity of competition authorities. The present paper furnishes a review of the methods for detecting cartels as well as a part of their practical application.

  15. A Method to Detect AAC Audio Forgery

    Directory of Open Access Journals (Sweden)

    Qingzhong Liu

    2015-08-01

    Full Text Available Advanced Audio Coding (AAC, a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate.

  16. Detection methods for irradiated mites and insects

    International Nuclear Information System (INIS)

    Ignatowicz, S.

    1999-01-01

    Results of the study on the following tests for separation of irradiated pests from untreated ones are reported: (a) test for identification of irradiated mites (Acaridae) based on lack of fecundity of treated females; (b) test for identification of irradiated beetles based on their locomotor activity; (c) test for identification of irradiated pests based on electron spin resonance (ESR) signal derived from treated insects; (d) test for identification of irradiated pests based on changes in the midgut induced by gamma radiation; and (e) test for identification of irradiated pests based on the alterations in total proteins of treated adults. Of these detection methods, only the test based on the pathological changes induced by irradiation in the insect midgut may identify consistently either irradiated larvae or adults. This test is simple and convenient when a rapid processing technique for dehydrating and embedding the midgut is used. (author)

  17. Method of detecting a fuel element failure

    International Nuclear Information System (INIS)

    Cohen, P.

    1975-01-01

    A method is described for detecting a fuel element failure in a liquid-sodium-cooled fast breeder reactor consisting of equilibrating a sample of the coolant with a molten salt consisting of a mixture of barium iodide and strontium iodide (or other iodides) whereby a large fraction of any radioactive iodine present in the liquid sodium coolant exchanges with the iodine present in the salt; separating the molten salt and sodium; if necessary, equilibrating the molten salt with nonradioactive sodium and separating the molten salt and sodium; and monitoring the molten salt for the presence of iodine, the presence of iodine indicating that the cladding of a fuel element has failed. (U.S.)

  18. Liquid chromatography detection unit, system, and method

    Science.gov (United States)

    Derenzo, Stephen E.; Moses, William W.

    2015-10-27

    An embodiment of a liquid chromatography detection unit includes a fluid channel and a radiation detector. The radiation detector is operable to image a distribution of a radiolabeled compound as the distribution travels along the fluid channel. An embodiment of a liquid chromatography system includes an injector, a separation column, and a radiation detector. The injector is operable to inject a sample that includes a radiolabeled compound into a solvent stream. The position sensitive radiation detector is operable to image a distribution of the radiolabeled compound as the distribution travels along a fluid channel. An embodiment of a method of liquid chromatography includes injecting a sample that comprises radiolabeled compounds into a solvent. The radiolabeled compounds are then separated. A position sensitive radiation detector is employed to image distributions of the radiolabeled compounds as the radiolabeled compounds travel along a fluid channel.

  19. Detection of the YORP Effect for Small Asteroids in the Karin Cluster

    Science.gov (United States)

    Carruba, V.; Nesvorný, D.; Vokrouhlický, D.

    2016-06-01

    The Karin cluster is a young asteroid family thought to have formed only ≃ 5.75 Myr ago. The young age can be demonstrated by numerically integrating the orbits of Karin cluster members backward in time and showing the convergence of the perihelion and nodal longitudes (as well as other orbital elements). Previous work has pointed out that the convergence is not ideal if the backward integration only accounts for the gravitational perturbations from the solar system planets. It improves when the thermal radiation force known as the Yarkovsky effect is accounted for. This argument can be used to estimate the spin obliquities of the Karin cluster members. Here we take advantage of the fast growing membership of the Karin cluster and show that the obliquity distribution of diameter D≃ 1{--}2 km Karin asteroids is bimodal, as expected if the YORP effect acted to move obliquities toward extreme values (0° or 180°). The measured magnitude of the effect is consistent with the standard YORP model. The surface thermal conductivity is inferred to be 0.07-0.2 W m-1 K-1 (thermal inertia ≃ 300{--}500 J m-2 K-1 s{}-1/2). We find that the strength of the YORP effect is roughly ≃ 0.7 of the nominal strength obtained for a collection of random Gaussian spheroids. These results are consistent with a surface composed of rough, rocky regolith. The obliquity values predicted here for 480 members of the Karin cluster can be validated by the light-curve inversion method.

  20. Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006-2011.

    Science.gov (United States)

    Faires, Meredith C; Pearl, David L; Ciccotelli, William A; Berke, Olaf; Reid-Smith, Richard J; Weese, J Scott

    2014-05-12

    In hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital. Bacteriology reports of patients identified with a CDI from August 2006 to February 2011 were collected. For patients detected with CDI from March 2010 to February 2011, stool specimens were obtained. Clostridium difficile isolates were characterized by ribotyping and investigated for the presence of toxin genes by PCR. CDI clusters were investigated using a retrospective temporal scan test statistic. Statistically significant clusters were compared to known CDI outbreaks within the hospital. A negative binomial regression model was used to identify associations between year, season, month and the rate of CDI cases. Overall, 86 CDI cases were identified. Eighteen specimens were analyzed and nine ribotypes were classified with ribotype 027 (n = 6) the most prevalent. The temporal scan statistic identified significant CDI clusters at the hospital (n = 5), service (n = 6), and ward (n = 4) levels (P ≤ 0.05). Three clusters were concordant with the one C. difficile outbreak identified by hospital personnel. Two clusters were identified as potential outbreaks. The negative binomial model indicated years 2007-2010 (P ≤ 0.05) had decreased CDI rates compared to 2006 and spring had an increased CDI rate compared to the fall (P = 0.023). Application of the temporal scan statistic identified several clusters, including potential outbreaks not detected by hospital

  1. Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

    International Nuclear Information System (INIS)

    Samala, Ravi K; Chan, Heang-Ping; Lu, Yao; Hadjiiski, Lubomir M; Wei, Jun; Helvie, Mark A

    2014-01-01

    This paper describes a new approach to detect microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 60° in 3° increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). A simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network trained to recognize the image pattern of microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis. (paper)

  2. Health-related hot topic detection in online communities using text clustering.

    Directory of Open Access Journals (Sweden)

    Yingjie Lu

    Full Text Available Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients' needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards.

  3. Two different hematocrit detection methods: Different methods, different results?

    Directory of Open Access Journals (Sweden)

    Schuepbach Reto A

    2010-03-01

    Full Text Available Abstract Background Less is known about the influence of hematocrit detection methodology on transfusion triggers. Therefore, the aim of the present study was to compare two different hematocrit-assessing methods. In a total of 50 critically ill patients hematocrit was analyzed using (1 blood gas analyzer (ABLflex 800 and (2 the central laboratory method (ADVIA® 2120 and compared. Findings Bland-Altman analysis for repeated measurements showed a good correlation with a bias of +1.39% and 2 SD of ± 3.12%. The 24%-hematocrit-group showed a correlation of r2 = 0.87. With a kappa of 0.56, 22.7% of the cases would have been transfused differently. In the-28%-hematocrit group with a similar correlation (r2 = 0.8 and a kappa of 0.58, 21% of the cases would have been transfused differently. Conclusions Despite a good agreement between the two methods used to determine hematocrit in clinical routine, the calculated difference of 1.4% might substantially influence transfusion triggers depending on the employed method.

  4. An improved K-means clustering method for cDNA microarray image segmentation.

    Science.gov (United States)

    Wang, T N; Li, T J; Shao, G F; Wu, S X

    2015-07-14

    Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

  5. Application Of WIMS Code To Calculation Kartini Reactor Parameters By Pin-Cell And Cluster Method

    International Nuclear Information System (INIS)

    Sumarsono, Bambang; Tjiptono, T.W.

    1996-01-01

    Analysis UZrH fuel element parameters calculation in Kartini Reactor by WIMS Code has been done. The analysis is done by pin cell and cluster method. The pin cell method is done as a function percent burn-up and by 8 group 3 region analysis and cluster method by 8 group 12 region analysis. From analysis and calculation resulted K ∼ = 1.3687 by pin cell method and K ∼ = 1.3162 by cluster method and so deviation is 3.83%. By pin cell analysis as a function percent burn-up at the percent burn-up greater than 59.50%, the multiplication factor is less than one (k ∼ < 1) it is mean that the fuel element reactivity is negative

  6. Recent developments in optical detection methods for microchip separations

    NARCIS (Netherlands)

    Götz, S.; Karst, U.

    2007-01-01

    This paper summarizes the features and performances of optical detection systems currently applied in order to monitor separations on microchip devices. Fluorescence detection, which delivers very high sensitivity and selectivity, is still the most widely applied method of detection. Instruments

  7. CutL: an alternative to Kulldorff's scan statistics for cluster detection with a specified cut-off level.

    Science.gov (United States)

    Więckowska, Barbara; Marcinkowska, Justyna

    2017-11-06

    When searching for epidemiological clusters, an important tool can be to carry out one's own research with the incidence rate from the literature as the reference level. Values exceeding this level may indicate the presence of a cluster in that location. This paper presents a method of searching for clusters that have significantly higher incidence rates than those specified by the investigator. The proposed method uses the classic binomial exact test for one proportion and an algorithm that joins areas with potential clusters while reducing the number of multiple comparisons needed. The sensitivity and specificity are preserved by this new method, while avoiding the Monte Carlo approach and still delivering results comparable to the commonly used Kulldorff's scan statistics and other similar methods of localising clusters. A strong contributing factor afforded by the statistical software that makes this possible is that it allows analysis and presentation of the results cartographically.

  8. The use of different clustering methods in the evaluation of genetic diversity in upland cotton

    Directory of Open Access Journals (Sweden)

    Laíse Ferreira de Araújo

    Full Text Available The continuous development and evaluation of new genotypes through crop breeding is essential in order to obtain new cultivars. The objective of this work was to evaluate the genetic divergences between cultivars of upland cotton (Gossypium hirsutum L. using the agronomic and technological characteristics of the fibre, in order to select superior parent plants. The experiment was set up during 2010 at the Federal University of Ceará in Fortaleza, Ceará, Brazil. Eleven cultivars of upland cotton were used in an experimental design of randomised blocks with three replications. In order to evaluate the genetic diversity among cultivars, the generalised Mahalanobis distance matrix was calculated, with cluster analysis then being applied, employing various methods: single linkage, Ward, complete linkage, median, average linkage within a cluster and average linkage between clusters. Genetic variability exists among the evaluated genotypes. The most consistant clustering method was that employing average linkage between clusters. Among the characteristics assessed, mean boll weight presented the highest contribution to genetic diversity, followed by elongation at rupture. Employing the method of mean linkage between clusters, the cultivars with greater genetic divergence were BRS Acacia and LD Frego; those of greater similarity were BRS Itaúba and BRS Araripe.

  9. A semantics-based method for clustering of Chinese web search results

    Science.gov (United States)

    Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong

    2014-01-01

    Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.

  10. A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation

    Directory of Open Access Journals (Sweden)

    Jian-Yong Lou

    2015-01-01

    error of 7.18% for well-defined masses (or 8.06% for ill-defined masses was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55% and 6.14% (or 5.27% improvements as compared to the standard DA and fuzzy c-means methods.

  11. Adaptive cluster sampling: An efficient method for assessing inconspicuous species

    Science.gov (United States)

    Andrea M. Silletti; Joan Walker

    2003-01-01

    Restorationistis typically evaluate the success of a project by estimating the population sizes of species that have been planted or seeded. Because total census is raely feasible, they must rely on sampling methods for population estimates. However, traditional random sampling designs may be inefficient for species that, for one reason or another, are challenging to...

  12. A PVC/polypyrrole sensor designed for beef taste detection using electrochemical methods and sensory evaluation.

    Science.gov (United States)

    Zhu, Lingtao; Wang, Xiaodan; Han, Yunxiu; Cai, Yingming; Jin, Jiahui; Wang, Hongmei; Xu, Liping; Wu, Ruijia

    2018-03-01

    An electrochemical sensor for detection of beef taste was designed in this study. This sensor was based on the structure of polyvinyl chloride/polypyrrole (PVC/PPy), which was polymerized onto the surface of a platinum (Pt) electrode to form a Pt-PPy-PVC film. Detecting by electrochemical methods, the sensor was well characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The sensor was applied to detect 10 rib-eye beef samples and the accuracy of the new sensor was validated by sensory evaluation and ion sensor detection. Several cluster analysis methods were used in the study to distinguish the beef samples. According to the obtained results, the designed sensor showed a high degree of association of electrochemical detection and sensory evaluation, which proved a fast and precise sensor for beef taste detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Robustness of serial clustering of extratropical cyclones to the choice of tracking method

    Directory of Open Access Journals (Sweden)

    Joaquim G. Pinto

    2016-07-01

    Full Text Available Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have shown that serial clustering of cyclones generally occurs on both flanks and downstream regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the western side of the North Atlantic basin near Newfoundland. This study explores the sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track data from 15 methods derived from ERA-Interim data (1979–2010. Clustering is estimated by the dispersion (ratio of variance to mean of winter [December – February (DJF] cyclone passages near each grid point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are compared between methods, revealing considerable differences, particularly for the latter. Results show that all different tracking methods qualitatively capture similar large-scale spatial patterns of underdispersion and overdispersion over the study region. The quantitative differences can primarily be attributed to the differences in the variance of cyclone counts between the methods. Nevertheless, overdispersion is statistically significant for almost all methods over parts of the eastern North Atlantic and Western Europe, and is therefore considered as a robust feature. The influence of the North Atlantic Oscillation (NAO on cyclone clustering displays a similar pattern for all tracking methods, with one maximum near Iceland and another between the Azores and Iberia. The differences in variance between methods are not related with different sensitivities to the NAO, which can account to over 50% of the clustering in some regions. We conclude that the general features of underdispersion and overdispersion of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO on cyclone dispersion.

  14. A speeded-up saliency region-based contrast detection method for small targets

    Science.gov (United States)

    Li, Zhengjie; Zhang, Haiying; Bai, Jiaojiao; Zhou, Zhongjun; Zheng, Huihuang

    2018-04-01

    To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain "integrity" property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of "clustering segmentation", the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.

  15. An effective trust-based recommendation method using a novel graph clustering algorithm

    Science.gov (United States)

    Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

    2015-10-01

    Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

  16. Image Processing Methods Usable for Object Detection on the Chessboard

    Directory of Open Access Journals (Sweden)

    Beran Ladislav

    2016-01-01

    Full Text Available Image segmentation and object detection is challenging problem in many research. Although many algorithms for image segmentation have been invented, there is no simple algorithm for image segmentation and object detection. Our research is based on combination of several methods for object detection. The first method suitable for image segmentation and object detection is colour detection. This method is very simply, but there is problem with different colours. For this method it is necessary to have precisely determined colour of segmented object before all calculations. In many cases it is necessary to determine this colour manually. Alternative simply method is method based on background removal. This method is based on difference between reference image and detected image. In this paper several methods suitable for object detection are described. Thisresearch is focused on coloured object detection on chessboard. The results from this research with fusion of neural networks for user-computer game checkers will be applied.

  17. MHCcluster, a method for functional clustering of MHC molecules

    DEFF Research Database (Denmark)

    Thomsen, Martin Christen Frølund; Lundegaard, Claus; Buus, Søren

    2013-01-01

    The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially...... binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where...

  18. Pseudo-potential method for taking into account the Pauli principle in cluster systems

    International Nuclear Information System (INIS)

    Krasnopol'skii, V.M.; Kukulin, V.I.

    1975-01-01

    In order to take account of the Pauli principle in cluster systems (such as 3α, α + α + n) a convenient method of renormalization of the cluster-cluster deep attractive potentials with forbidden states is suggested. The renormalization consists of adding projectors upon the occupied states with an infinite coupling constant to the initial deep potential which means that we pass to pseudo-potentials. The pseudo-potential approach in projecting upon the noneigenstates is shown to be equivalent to the orthogonality condition model of Saito et al. The orthogonality of the many-particle wave function to the forbidden states of each two-cluster sub-system is clearly demonstrated

  19. Test computations on the dynamical evolution of star clusters. [Fluid dynamic method

    Energy Technology Data Exchange (ETDEWEB)

    Angeletti, L; Giannone, P. (Rome Univ. (Italy))

    1977-01-01

    Test calculations have been carried out on the evolution of star clusters using the fluid-dynamical method devised by Larson (1970). Large systems of stars have been considered with specific concern with globular clusters. With reference to the analogous 'standard' model by Larson, the influence of varying in turn the various free parameters (cluster mass, star mass, tidal radius, mass concentration of the initial model) has been studied for the results. Furthermore, the partial release of some simplifying assumptions with regard to the relaxation time and distribution of the 'target' stars has been considered. The change of the structural properties is discussed, and the variation of the evolutionary time scale is outlined. An indicative agreement of the results obtained here with structural properties of globular clusters as deduced from previous theoretical models is pointed out.

  20. The resonating group method three cluster approach to the ground state 9 Li nucleus structure

    International Nuclear Information System (INIS)

    Filippov, G.F.; Pozdnyakov, Yu.A.; Terenetsky, K.O.; Verbitsky, V.P.

    1994-01-01

    The three-cluster approach for light atomic nuclei is formulated in frame of the algebraic version of resonating group method. Overlap integral and Hamiltonian matrix elements on generating functions are obtained for 9 Li nucleus. All permissible by Pauli principle 9 Li different cluster nucleon permutations were taken into account in the calculations. The results obtained can be easily generalised on any three-cluster system up to 12 C. Matrix elements obtained in the work were used in the variational calculations of the ground state energetic and geometric 9 Li characteristics. It is shown that 9 Li ground state is not adequate to the shell model limit and has pronounced three-cluster structure. (author). 16 refs., 4 tab., 2 figs

  1. A New Soft Computing Method for K-Harmonic Means Clustering.

    Science.gov (United States)

    Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

    2016-01-01

    The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

  2. Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Sen Zhang

    2015-01-01

    Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

  3. Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

    Directory of Open Access Journals (Sweden)

    Yajie Zou

    2017-01-01

    Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

  4. Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: A Comparison of Methods

    NARCIS (Netherlands)

    Moisl, Hermann; Jones, Valerie M.

    2005-01-01

    This article examines the feasibility of an empirical approach to sociolinguistic analysis of the Newcastle Electronic Corpus of Tyneside English using exploratory multivariate methods. It addresses a known problem with one class of such methods, hierarchical cluster analysis—that different

  5. Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: In A Comparison of Methods

    NARCIS (Netherlands)

    Moisl, Hermann; Jones, Valerie M.

    2005-01-01

    This article examines the feasibility of an empirical approach to sociolinguistic analysis of the Newcastle Electronic Corpus of Tyneside English using exploratory multivariate methods. It addresses a known problem with one class of such methods, hierarchical cluster analysis—that different

  6. Development of detection methods for irradiated foods

    International Nuclear Information System (INIS)

    Yang, Jae Seung; Nam, Hye Seon; Oh, Kyong Nam; Woo, Si Ho; Kim, Kyeung Eun; Yi, Sang Duk; Park, Jun Young; Kim, Kyong Su; Hwang, Keum Taek

    2000-04-01

    In 1999, we have been studied (1) on the detection of irradiated foods by ESR spectroscopy, by thermoluminescence, and by viscometry for physical measurements, (2) on the detection of hydrocarbons and 2-alkylcyclobutanones derived from fatty foods by GC/MS for chemical measurements, (3) on the screening and detection of irradiated foods by Comet assay and immunochemical (ELISA) technique for biological or biochemical measurements

  7. Development of detection methods for irradiated foods

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Jae Seung; Nam, Hye Seon; Oh, Kyong Nam; Woo, Si Ho; Kim, Kyeung Eun; Yi, Sang Duk; Park, Jun Young; Kim, Kyong Su; Hwang, Keum Taek

    2000-04-01

    In 1999, we have been studied (1) on the detection of irradiated foods by ESR spectroscopy, by thermoluminescence, and by viscometry for physical measurements, (2) on the detection of hydrocarbons and 2-alkylcyclobutanones derived from fatty foods by GC/MS for chemical measurements, (3) on the screening and detection of irradiated foods by Comet assay and immunochemical (ELISA) technique for biological or biochemical measurements.

  8. DETECTABILITY OF FREE FLOATING PLANETS IN OPEN CLUSTERS WITH THE JAMES WEBB SPACE TELESCOPE

    International Nuclear Information System (INIS)

    Pacucci, Fabio; Ferrara, Andrea; D'Onghia, Elena

    2013-01-01

    Recent observations have shown the presence of extra-solar planets in Galactic open stellar clusters, such as in Praesepe (M44). These systems provide a favorable environment for planetary formation due to the high heavy-element content exhibited by the majority of their population. The large stellar density, and corresponding high close-encounter event rate, may induce strong perturbations of planetary orbits with large semimajor axes. Here we present a set of N-body simulations implementing a novel scheme to treat the tidal effects of external stellar perturbers on planetary orbit eccentricity and inclination. By simulating five nearby open clusters, we determine the rate of occurrence of bodies extracted from their parent stellar system by quasi-impulsive tidal interactions. We find that the specific free-floating planet production rate N-dot o (total number of free-floating planets per unit of time, normalized by the total number of stars), is proportional to the stellar density ρ * of the cluster: N-dot o =αρ ⋆ , with α = (23 ± 5) × 10 –6 pc 3 Myr –1 . For the Pleiades (M45), we predict that ∼26% of stars should have lost their planets. This raises the exciting possibility of directly observing these wandering planets with the James Webb Space Telescope in the near-infrared band. Assuming a surface temperature for the planet of ∼500 K, a free-floating planet of Jupiter size inside the Pleiades would have a specific flux of F ν (4.4 μm) ≈4 × 10 2  nJy, which would lead to a very clear detection (S/N ∼ 100) in only one hour of integration

  9. Detectability of Free Floating Planets in Open Clusters with the James Webb Space Telescope

    Science.gov (United States)

    Pacucci, Fabio; Ferrara, Andrea; D'Onghia, Elena

    2013-12-01

    Recent observations have shown the presence of extra-solar planets in Galactic open stellar clusters, such as in Praesepe (M44). These systems provide a favorable environment for planetary formation due to the high heavy-element content exhibited by the majority of their population. The large stellar density, and corresponding high close-encounter event rate, may induce strong perturbations of planetary orbits with large semimajor axes. Here we present a set of N-body simulations implementing a novel scheme to treat the tidal effects of external stellar perturbers on planetary orbit eccentricity and inclination. By simulating five nearby open clusters, we determine the rate of occurrence of bodies extracted from their parent stellar system by quasi-impulsive tidal interactions. We find that the specific free-floating planet production rate \\dot{N}_o (total number of free-floating planets per unit of time, normalized by the total number of stars), is proportional to the stellar density ρsstarf of the cluster: \\dot{N}_o = \\alpha \\rho _\\star, with α = (23 ± 5) × 10-6 pc3 Myr-1. For the Pleiades (M45), we predict that ~26% of stars should have lost their planets. This raises the exciting possibility of directly observing these wandering planets with the James Webb Space Telescope in the near-infrared band. Assuming a surface temperature for the planet of ~500 K, a free-floating planet of Jupiter size inside the Pleiades would have a specific flux of F ν (4.4 μm) ≈4 × 102 nJy, which would lead to a very clear detection (S/N ~ 100) in only one hour of integration.

  10. DETECTABILITY OF FREE FLOATING PLANETS IN OPEN CLUSTERS WITH THE JAMES WEBB SPACE TELESCOPE

    Energy Technology Data Exchange (ETDEWEB)

    Pacucci, Fabio; Ferrara, Andrea [Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa (Italy); D' Onghia, Elena [University of Wisconsin, 475 Charter St., Madison, WI 53706 (United States)

    2013-12-01

    Recent observations have shown the presence of extra-solar planets in Galactic open stellar clusters, such as in Praesepe (M44). These systems provide a favorable environment for planetary formation due to the high heavy-element content exhibited by the majority of their population. The large stellar density, and corresponding high close-encounter event rate, may induce strong perturbations of planetary orbits with large semimajor axes. Here we present a set of N-body simulations implementing a novel scheme to treat the tidal effects of external stellar perturbers on planetary orbit eccentricity and inclination. By simulating five nearby open clusters, we determine the rate of occurrence of bodies extracted from their parent stellar system by quasi-impulsive tidal interactions. We find that the specific free-floating planet production rate N-dot {sub o} (total number of free-floating planets per unit of time, normalized by the total number of stars), is proportional to the stellar density ρ{sub *} of the cluster: N-dot {sub o}=αρ{sub ⋆}, with α = (23 ± 5) × 10{sup –6} pc{sup 3} Myr{sup –1}. For the Pleiades (M45), we predict that ∼26% of stars should have lost their planets. This raises the exciting possibility of directly observing these wandering planets with the James Webb Space Telescope in the near-infrared band. Assuming a surface temperature for the planet of ∼500 K, a free-floating planet of Jupiter size inside the Pleiades would have a specific flux of F {sub ν} (4.4 μm) ≈4 × 10{sup 2} nJy, which would lead to a very clear detection (S/N ∼ 100) in only one hour of integration.

  11. A method for determining the radius of an open cluster from stellar proper motions

    Science.gov (United States)

    Sánchez, Néstor; Alfaro, Emilio J.; López-Martínez, Fátima

    2018-04-01

    We propose a method for calculating the radius of an open cluster in an objective way from an astrometric catalogue containing, at least, positions and proper motions. It uses the minimum spanning tree in the proper motion space to discriminate cluster stars from field stars and it quantifies the strength of the cluster-field separation by means of a statistical parameter defined for the first time in this paper. This is done for a range of different sampling radii from where the cluster radius is obtained as the size at which the best cluster-field separation is achieved. The novelty of this strategy is that the cluster radius is obtained independently of how its stars are spatially distributed. We test the reliability and robustness of the method with both simulated and real data from a well-studied open cluster (NGC 188), and apply it to UCAC4 data for five other open clusters with different catalogued radius values. NGC 188, NGC 1647, NGC 6603, and Ruprecht 155 yielded unambiguous radius values of 15.2 ± 1.8, 29.4 ± 3.4, 4.2 ± 1.7, and 7.0 ± 0.3 arcmin, respectively. ASCC 19 and Collinder 471 showed more than one possible solution, but it is not possible to know whether this is due to the involved uncertainties or due to the presence of complex patterns in their proper motion distributions, something that could be inherent to the physical object or due to the way in which the catalogue was sampled.

  12. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  13. Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods

    DEFF Research Database (Denmark)

    Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne

    studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log......-transformation of the cost data and apply the normal theory on these data. Again we try to account for the cluster effect. The performance of these two methods is investigated in a simulation study. The advantages and disadvantages of the different approaches are discussed.......  We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...

  14. Detection of Arctic and European cluster of canine distemper virus in north and center of Iran.

    Science.gov (United States)

    Namroodi, Somayeh; Rostami, Amir; Majidzadeh-Ardebili, Keyvan; Ghalyanchi Langroudi, Arash; Morovvati, Abbas

    2015-01-01

    Canine distemper virus (CDV) creates a very contagious viral multi-systemic canine distemper (CD) disease that affects most species of Carnivora order. The virus is genetically heterogeneous, particularly in section of the hemagglutinin (H) gene. Sequence analysis of the H gene can be useful to investigate distinction of various lineages related to geographical distribution and CDV molecular epidemiology. Since vaccination program is conducted only in large cities of Iran, CD still remains as one of the major causes of death in dogs in this country. In order to monitor H gene, CDV has been detected in 14 out of 19 sampled dogs through the amplification of nucleoprotein (NP) gene in nested-PCR assay. In the next step 665 bp of H gene was amplified in 9 out of 14 NP-gene positive dogs. Phylogenetic analysis distinguished two distinct CDV genotypes in Iran. JN941238 has been embedded in European cluster and JN941239 has been embedded in Arctic cluster. Nucleic analysis has been shown high difference among both Iranian CDV lineages with CDV vaccine strains.

  15. Identification of rural landscape classes through a GIS clustering method

    Directory of Open Access Journals (Sweden)

    Irene Diti

    2013-09-01

    Full Text Available The paper presents a methodology aimed at supporting the rural planning process. The analysis of the state of the art of local and regional policies focused on rural and suburban areas, and the study of the scientific literature in the field of spatial analysis methodologies, have allowed the definition of the basic concept of the research. The proposed method, developed in a GIS, is based on spatial metrics selected and defined to cover various agricultural, environmental, and socio-economic components. The specific goal of the proposed methodology is to identify homogeneous extra-urban areas through their objective characterization at different scales. Once areas with intermediate urban-rural characters have been identified, the analysis is then focused on the more detailed definition of periurban agricultural areas. The synthesis of the results of the analysis of the various landscape components is achieved through an original interpretative key which aims to quantify the potential impacts of rural areas on the urban system. This paper presents the general framework of the methodology and some of the main results of its first implementation through an Italian case study.

  16. Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life.

    Science.gov (United States)

    Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M

    2016-01-01

    Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  17. The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected via the Sunyaev-Zel'dovich Effect

    Energy Technology Data Exchange (ETDEWEB)

    Sehgal, Neelima; Trac, Hy; Acquaviva, Viviana; Ade, Peter A.R.; Aguirre, Paula; Amiri, Mandana; Appel, John W.; Barrientos, L.Felipe; Battistelli, Elia S.; Bond, J.Richard; Brown, Ben; Burger, Bryce; Chervenak, Jay; Das, Sudeep; Devlin, Mark J.; Dicker, Simon R.; Doriese, W.Bertrand; Dunkley, Joanna; Dunner, Rolando; Essinger-Hileman, Thomas; Fisher, Ryan P.

    2011-08-18

    We present constraints on cosmological parameters based on a sample of Sunyaev-Zeldovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives {sigma}{sub 8} = 0.851 {+-} 0.115 and w = -1.14 {+-} 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find {sigma}{sub 8} = 0.821 {+-} 0.044 and w = -1.05 {+-} 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernoava which give {sigma}{sub 8} = 0.802 {+-} 0.038 and w = -0.98 {+-} 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.

  18. 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.

  19. Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications.

    Science.gov (United States)

    Bria, A; Karssemeijer, N; Tortorella, F

    2014-02-01

    Finding abnormalities in diagnostic images is a difficult task even for expert radiologists because the normal tissue locations largely outnumber those with suspicious signs which may thus be missed or incorrectly interpreted. For the same reason the design of a Computer-Aided Detection (CADe) system is very complex because the large predominance of normal samples in the training data may hamper the ability of the classifier to recognize the abnormalities on the images. In this paper we present a novel approach for computer-aided detection which faces the class imbalance with a cascade of boosting classifiers where each node is trained by a learning algorithm based on ranking instead of classification error. Such approach is used to design a system (CasCADe) for the automated detection of clustered microcalcifications (μCs), which is a severely unbalanced classification problem because of the vast majority of image locations where no μC is present. The proposed approach was evaluated with a dataset of 1599 full-field digital mammograms from 560 cases and compared favorably with the Hologic R2CAD ImageChecker, one of the most widespread commercial CADe systems. In particular, at the same lesion sensitivity of R2CAD (90%) on biopsy proven malignant cases, CasCADe and R2CAD detected 0.13 and 0.21 false positives per image (FPpi), respectively (p-value=0.09), whereas at the same FPpi of R2CAD (0.21), CasCADe and R2CAD detected 93% and 90% of true lesions respectively (p-value=0.11) thus showing that CasCADe can compete with high-end CADe commercial systems. Copyright © 2013 Elsevier B.V. All rights reserved.

  20. NGC 1866: First Spectroscopic Detection of Fast-rotating Stars in a Young LMC Cluster

    Energy Technology Data Exchange (ETDEWEB)

    Dupree, A. K.; Dotter, A.; Johnson, C. I. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Marino, A. F.; Milone, A. P. [Australian National University, The Research School of Astronomy and Astrophysics, Mount Stromlo Observatory, Weston Creek, ACT 2611 (Australia); Bailey, J. I. III [Leiden Observatory, Niels Bohrweg 2, NL-2333 CA Leiden (Netherlands); Crane, J. D. [The Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101 (United States); Mateo, M. [Department of Astronomy, University of Michigan, Ann Arbor, MI 48109 (United States); Olszewski, E. W. [The University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85721 (United States)

    2017-09-01

    High-resolution spectroscopic observations were taken of 29 extended main-sequence turnoff (eMSTO) stars in the young (∼200 Myr) Large Magellanic Cloud (LMC) cluster, NGC 1866, using the Michigan/ Magellan Fiber System and MSpec spectrograph on the Magellan -Clay 6.5 m telescope. These spectra reveal the first direct detection of rapidly rotating stars whose presence has only been inferred from photometric studies. The eMSTO stars exhibit H α emission (indicative of Be-star decretion disks), others have shallow broad H α absorption (consistent with rotation ≳150 km s{sup −1}), or deep H α core absorption signaling lower rotation velocities (≲150 km s{sup −1}). The spectra appear consistent with two populations of stars—one rapidly rotating, and the other, younger and slowly rotating.

  1. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)

    2012-04-15

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and

  2. CHANDRA DETECTION OF X-RAY EMISSION FROM ULTRACOMPACT DWARF GALAXIES AND EXTENDED STAR CLUSTERS

    Energy Technology Data Exchange (ETDEWEB)

    Hou, Meicun; Li, Zhiyuan, E-mail: lizy@nju.edu.cn [School of Astronomy and Space Science, Nanjing University, Nanjing 210046 (China)

    2016-03-10

    We have conducted a systematic study of X-ray emission from ultracompact dwarf (UCD) galaxies and extended star clusters (ESCs), based on archival Chandra observations. Among a sample of 511 UCDs and ESCs complied from the literature, 17 X-ray counterparts with 0.5–8 keV luminosities above ∼5 × 10{sup 36} erg s{sup −1} are identified, which are distributed in eight early-type host galaxies. To facilitate comparison, we also identify X-ray counterparts of 360 globular clusters (GCs) distributed in four of the eight galaxies. The X-ray properties of the UCDs and ESCs are found to be broadly similar to those of the GCs. The incidence rate of X-ray-detected UCDs and ESCs, 3.3% ± 0.8%, while lower than that of the X-ray-detected GCs (7.0% ± 0.4%), is substantially higher than expected from the field populations of external galaxies. A stacking analysis of the individually undetected UCDs/ESCs further reveals significant X-ray signals, which corresponds to an equivalent 0.5–8 keV luminosity of ∼4 × 10{sup 35} erg s{sup −1} per source. Taken together, these provide strong evidence that the X-ray emission from UCDs and ESCs is dominated by low-mass X-ray binaries having formed from stellar dynamical interactions, consistent with the stellar populations in these dense systems being predominantly old. For the most massive UCDs, there remains the possibility that a putative central massive black hole gives rise to the observed X-ray emission.

  3. CHANDRA DETECTION OF X-RAY EMISSION FROM ULTRACOMPACT DWARF GALAXIES AND EXTENDED STAR CLUSTERS

    International Nuclear Information System (INIS)

    Hou, Meicun; Li, Zhiyuan

    2016-01-01

    We have conducted a systematic study of X-ray emission from ultracompact dwarf (UCD) galaxies and extended star clusters (ESCs), based on archival Chandra observations. Among a sample of 511 UCDs and ESCs complied from the literature, 17 X-ray counterparts with 0.5–8 keV luminosities above ∼5 × 10 36 erg s −1 are identified, which are distributed in eight early-type host galaxies. To facilitate comparison, we also identify X-ray counterparts of 360 globular clusters (GCs) distributed in four of the eight galaxies. The X-ray properties of the UCDs and ESCs are found to be broadly similar to those of the GCs. The incidence rate of X-ray-detected UCDs and ESCs, 3.3% ± 0.8%, while lower than that of the X-ray-detected GCs (7.0% ± 0.4%), is substantially higher than expected from the field populations of external galaxies. A stacking analysis of the individually undetected UCDs/ESCs further reveals significant X-ray signals, which corresponds to an equivalent 0.5–8 keV luminosity of ∼4 × 10 35 erg s −1 per source. Taken together, these provide strong evidence that the X-ray emission from UCDs and ESCs is dominated by low-mass X-ray binaries having formed from stellar dynamical interactions, consistent with the stellar populations in these dense systems being predominantly old. For the most massive UCDs, there remains the possibility that a putative central massive black hole gives rise to the observed X-ray emission

  4. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    Directory of Open Access Journals (Sweden)

    Qiao Wei

    2017-01-01

    Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

  5. A new fault detection method for computer networks

    International Nuclear Information System (INIS)

    Lu, Lu; Xu, Zhengguo; Wang, Wenhai; Sun, Youxian

    2013-01-01

    Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method

  6. Multishell method: Exact treatment of a cluster in an effective medium

    International Nuclear Information System (INIS)

    Gonis, A.; Garland, J.W.

    1977-01-01

    A method is presented for the exact determination of the Green's function of a cluster embedded in a given effective medium. This method, the multishell method, is applicable even to systems with off-diagonal disorder, extended-range hopping, multiple bands, and/or hybridization, and is computationally practicable for any system described by a tight-binding or interpolation-scheme Hamiltonian. It allows one to examine the effects of local environment on the densities of states and site spectral weight functions of disordered systems. For any given analytic effective medium characterized by a non-negative density of states the method yields analytic cluster Green's functions and non-negative site spectral weight functions. Previous methods used for the calculation of the Green's function of a cluster embedded in a given effective medium have not been exact. The results of numerical calculations for model systems show that even the best of these previous methods can lead to substantial errors, at least for small clusters in two- and three-dimensional lattices. These results also show that fluctuations in local environment have large effects on site spectral weight functions, even in cases in which the single-site coherent-potential approximation yields an accurate overall density of states

  7. Open-Source Sequence Clustering Methods Improve the State Of the Art.

    Science.gov (United States)

    Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob

    2016-01-01

    Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http

  8. Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.

    Science.gov (United States)

    Leyrat, Clémence; Caille, Agnès; Foucher, Yohann; Giraudeau, Bruno

    2016-01-22

    Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.

  9. Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics

    Science.gov (United States)

    Sato, Takeshi; Pathak, Himadri; Orimo, Yuki; Ishikawa, Kenichi L.

    2018-02-01

    Time-dependent coupled-cluster method with time-varying orbital functions, called time-dependent optimized coupled-cluster (TD-OCC) method, is formulated for multielectron dynamics in an intense laser field. We have successfully derived the equations of motion for CC amplitudes and orthonormal orbital functions based on the real action functional, and implemented the method including double excitations (TD-OCCD) and double and triple excitations (TD-OCCDT) within the optimized active orbitals. The present method is size extensive and gauge invariant, a polynomial cost-scaling alternative to the time-dependent multiconfiguration self-consistent-field method. The first application of the TD-OCC method of intense-laser driven correlated electron dynamics in Ar atom is reported.

  10. Clustering of attitudes towards obesity: a mixed methods study of Australian parents and children.

    Science.gov (United States)

    Olds, Tim; Thomas, Samantha; Lewis, Sophie; Petkov, John

    2013-10-12

    Current population-based anti-obesity campaigns often target individuals based on either weight or socio-demographic characteristics, and give a 'mass' message about personal responsibility. There is a recognition that attempts to influence attitudes and opinions may be more effective if they resonate with the beliefs that different groups have about the causes of, and solutions for, obesity. Limited research has explored how attitudinal factors may inform the development of both upstream and downstream social marketing initiatives. Computer-assisted face-to-face interviews were conducted with 159 parents and 184 of their children (aged 9-18 years old) in two Australian states. A mixed methods approach was used to assess attitudes towards obesity, and elucidate why different groups held various attitudes towards obesity. Participants were quantitatively assessed on eight dimensions relating to the severity and extent, causes and responsibility, possible remedies, and messaging strategies. Cluster analysis was used to determine attitudinal clusters. Participants were also able to qualify each answer. Qualitative responses were analysed both within and across attitudinal clusters using a constant comparative method. Three clusters were identified. Concerned Internalisers (27% of the sample) judged that obesity was a serious health problem, that Australia had among the highest levels of obesity in the world and that prevalence was rapidly increasing. They situated the causes and remedies for the obesity crisis in individual choices. Concerned Externalisers (38% of the sample) held similar views about the severity and extent of the obesity crisis. However, they saw responsibility and remedies as a societal rather than an individual issue. The final cluster, the Moderates, which contained significantly more children and males, believed that obesity was not such an important public health issue, and judged the extent of obesity to be less extreme than the other clusters

  11. Radiation detection device and a radiation detection method

    International Nuclear Information System (INIS)

    Blum, A.

    1975-01-01

    A radiation detection device is described including at least one scintillator in the path of radiation emissions from a distributed radiation source; a plurality of photodetectors for viewing each scintillator; a signal processing means, a storage means, and a data processing means that are interconnected with one another and connected to said photodetectors; and display means connected to the data processing means to locate a plurality of radiation sources in said distributed radiation source and to provide an image of the distributed radiation sources. The storage means includes radiation emission response data and location data from a plurality of known locations for use by the data processing means to derive a more accurate image by comparison of radiation responses from known locations with radiation responses from unknown locations. (auth)

  12. Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method

    DEFF Research Database (Denmark)

    Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels

    2014-01-01

    The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models the probabi......The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models...... the probability distribution of the Y-STR haplotypes. Creating a consistent statistical model of the haplotypes enables us to perform a wide range of analyses. Previously, haplotype frequency estimation using the discrete Laplace method has been validated. In this paper we investigate how the discrete Laplace...... method can be used for cluster analysis to further validate the discrete Laplace method. A very important practical fact is that the calculations can be performed on a normal computer. We identified two sub-clusters of the Eastern and Western European Y-STR haplotypes similar to results of previous...

  13. ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

    Science.gov (United States)

    Oluwadare, Oluwatosin; Cheng, Jianlin

    2017-11-14

    With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts. The identification of the TADs for a genome is useful for studying gene regulation, genomic interaction, and genome function. Here, we formulate the TAD identification problem as an unsupervised machine learning (clustering) problem, and develop a new TAD identification method called ClusterTAD. We introduce a novel method to represent chromosomal contacts as features to be used by the clustering algorithm. Our results show that ClusterTAD can accurately predict the TADs on a simulated Hi-C data. Our method is also largely complementary and consistent with existing methods on the real Hi-C datasets of two mouse cells. The validation with the chromatin immunoprecipitation (ChIP) sequencing (ChIP-Seq) data shows that the domain boundaries identified by ClusterTAD have a high enrichment of CTCF binding sites, promoter-related marks, and enhancer-related histone modifications. As ClusterTAD is based on a proven clustering approach, it opens a new avenue to apply a large array of clustering methods developed in the machine learning field to the TAD identification problem. The source code, the results, and the TADs generated for the simulated and real Hi-C datasets are available here: https://github.com/BDM-Lab/ClusterTAD .

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

    Science.gov (United States)

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

    2017-07-01

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

  15. LeARN: a platform for detecting, clustering and annotating non-coding RNAs

    Directory of Open Access Journals (Sweden)

    Schiex Thomas

    2008-01-01

    Full Text Available Abstract Background In the last decade, sequencing projects have led to the development of a number of annotation systems dedicated to the structural and functional annotation of protein-coding genes. These annotation systems manage the annotation of the non-protein coding genes (ncRNAs in a very crude way, allowing neither the edition of the secondary structures nor the clustering of ncRNA genes into families which are crucial for appropriate annotation of these molecules. Results LeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation. Conclusion This software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website http://bioinfo.genopole-toulouse.prd.fr/LeARN

  16. Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

    Science.gov (United States)

    Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

    2017-08-01

    Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

  17. Method of detecting a failed fuel

    International Nuclear Information System (INIS)

    Utamura, Motoaki; Urata, Megumi; Uchida, Shunsuke.

    1976-01-01

    Object: To improve detection accuracy of a failed fuel by eliminating a coolant temperature distribution in a fuel assembly. Structure: A failed fuel is detected from contents of nuclear fission products in a coolant by shutting off an upper portion of a fuel assembly provided in the coolant and by sampling the coolant in the fuel assembly. Temperature distribution in the fuel assembly is eliminated, by injecting the higher temperature coolant than that of the coolant inside and outside the fuel assembly when sampling, and thereby replacing the existing coolant in the fuel assembly for the higher temperature coolant. The failed fuel is detected from contents of the fission products existing in the coolant, by sampling the higher temperature coolant of the fuel assembly after a temperature passed. (Moriyama, K.)

  18. A method to determine the number of nanoparticles in a cluster using conventional optical microscopes

    International Nuclear Information System (INIS)

    Kang, Hyeonggon; Attota, Ravikiran; Tondare, Vipin; Vladár, András E.; Kavuri, Premsagar

    2015-01-01

    We present a method that uses conventional optical microscopes to determine the number of nanoparticles in a cluster, which is typically not possible using traditional image-based optical methods due to the diffraction limit. The method, called through-focus scanning optical microscopy (TSOM), uses a series of optical images taken at varying focus levels to achieve this. The optical images cannot directly resolve the individual nanoparticles, but contain information related to the number of particles. The TSOM method makes use of this information to determine the number of nanoparticles in a cluster. Initial good agreement between the simulations and the measurements is also presented. The TSOM method can be applied to fluorescent and non-fluorescent as well as metallic and non-metallic nano-scale materials, including soft materials, making it attractive for tag-less, high-speed, optical analysis of nanoparticles down to 45 nm diameter

  19. Method to detect steam generator tube leakage

    International Nuclear Information System (INIS)

    Watabe, Kiyomi

    1994-01-01

    It is important for plant operation to detect minor leakages from the steam generator tube at an early stage, thus, leakage detection has been performed using a condenser air ejector gas monitor and a steam generator blow down monitor, etc. In this study highly-sensitive main steam line monitors have been developed in order to identify leakages in the steam generator more quickly and accurately. The performance of the monitors was verified and the demonstration test at the actual plant was conducted for their intended application to the plants. (author)

  20. Clustering method to process signals from a CdZnTe detector

    International Nuclear Information System (INIS)

    Zhang, Lan; Takahashi, Hiroyuki; Fukuda, Daiji; Nakazawa, Masaharu

    2001-01-01

    The poor mobility of holes in a compound semiconductor detector results in the imperfect collection of the primary charge deposited in the detector. Furthermore the fluctuation of the charge loss efficiency due to the change in the hole collection path length seriously degrades the energy resolution of the detector. Since the charge collection efficiency varies with the signal waveform, we can expect the improvement of the energy resolution through a proper waveform signal processing method. We developed a new digital signal processing technique, a clustering method which derives typical patterns containing the information on the real situation inside a detector from measured signals. The obtained typical patterns for the detector are then used for the pattern matching method. Measured signals are classified through analyzing the practical waveform variation due to the charge trapping, the electric field and the crystal defect etc. Signals with similar shape are placed into the same cluster. For each cluster we calculate an average waveform as a reference pattern. Using these reference patterns obtained from all the clusters, we can classify other measured signal waveforms from the same detector. Then signals are independently processed according to the classified category and form corresponding spectra. Finally these spectra are merged into one spectrum by multiplying normalization coefficients. The effectiveness of this method was verified with a CdZnTe detector of 2 mm thick and a 137 Cs gamma-ray source. The obtained energy resolution as improved to about 8 keV (FWHM). Because the clustering method is only related to the measured waveforms, it can be applied to any type and size of detectors and compatible with any type of filtering methods. (author)

  1. Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

    Directory of Open Access Journals (Sweden)

    Lee Yun-Shien

    2008-03-01

    Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

  2. A comparison of moving object detection methods for real-time moving object detection

    Science.gov (United States)

    Roshan, Aditya; Zhang, Yun

    2014-06-01

    Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

  3. Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

    Science.gov (United States)

    Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

    2017-06-01

    Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

  4. Optical detection of CO and CO2 temperature dependent desorption from carbon nanotube clusters

    International Nuclear Information System (INIS)

    Chistiakova, M V; Armani, A M

    2014-01-01

    The development of new materials relies on high precision methods to quantify adsorption/desorption of gases from surfaces. One commonly used approach is temperature programmed desorption spectroscopy. While this approach is very accurate, it requires complex instrumentation, and it is limited to performing experiments under high vacuum, thus restricting experimental scope. An alternative approach is to integrate the surface of interest directly onto a detector face, creating an active substrate. One surface that has applications in numerous areas is the carbon nanotube (CNT). As such, an active substrate that integrates a CNT surface on a sensor and is able to perform measurements in ambient environments will have significant impact. In the present work, we have developed an active substrate that combines an optical sensor with a CNT cluster substrate. The optical sensor is able to accurately probe the temperature dependent desorption of carbon monoxide and carbon dioxide gases from the CNT cluster surface. This active substrate will enable a wide range of temperature dependent desorption measurements to be performed from a scientifically interesting material system. (paper)

  5. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  6. Detection methods for centrifugal microfluidic platforms

    DEFF Research Database (Denmark)

    Burger, Robert; Amato, Letizia; Boisen, Anja

    2016-01-01

    Centrifugal microfluidics has attracted much interest from academia as well as industry, since it potentially offers solutions for affordable, user-friendly and portable biosensing. A wide range of so-called fluidic unit operations, e.g. mixing, metering, liquid routing, and particle separation...... for the centrifugal microfluidics platform and cover optical as well as mechanical and electrical detection principles....

  7. Molecular Methods for Detection of Antimicrobial Resistance

    DEFF Research Database (Denmark)

    Anjum, Muna F.; Zankari, Ea; Hasman, Henrik

    2017-01-01

    The increase in bacteria harboring antimicrobial resistance (AMR) is a global problem because there is a paucity of antibiotics available to treat multidrug-resistant bacterial infections in humans and animals. Detection of AMR present in bacteria that may pose a threat to veterinary and public...

  8. Steam generator leak detection using acoustic method

    International Nuclear Information System (INIS)

    Goluchko, V.V.; Sokolov, B.M.; Bulanov, A.N.

    1982-05-01

    The main requirements to meet by a device for leak detection in sodium - water steam generators are determined. The potentialities of instrumentation designed based on the developed requirements have been tested using a model of a 550 kw steam generator [fr

  9. Ultrasound Imaging Methods for Breast Cancer Detection

    NARCIS (Netherlands)

    Ozmen, N.

    2014-01-01

    The main focus of this thesis is on modeling acoustic wavefield propagation and implementing imaging algorithms for breast cancer detection using ultrasound. As a starting point, we use an integral equation formulation, which can be used to solve both the forward and inverse problems. This thesis

  10. A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

    Science.gov (United States)

    Chen, Jianrui; Wang, Hua; Wang, Lina; Liu, Weiwei

    2016-04-01

    Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

  11. Annotated Computer Output for Illustrative Examples of Clustering Using the Mixture Method and Two Comparable Methods from SAS.

    Science.gov (United States)

    1987-06-26

    BUREAU OF STANDAR-S1963-A Nw BOM -ILE COPY -. 4eo .?3sa.9"-,,A WIN* MAT HEMATICAL SCIENCES _*INSTITUTE AD-A184 687 DTICS!ELECTE ANNOTATED COMPUTER OUTPUT...intoduction to the use of mixture models in clustering. Cornell University Biometrics Unit Technical Report BU-920-M and Mathematical Sciences Institute...mixture method and two comparable methods from SAS. Cornell University Biometrics Unit Technical Report BU-921-M and Mathematical Sciences Institute

  12. Cluster-cluster clustering

    International Nuclear Information System (INIS)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

    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. 30 references

  13. Research of the Space Clustering Method for the Airport Noise Data Minings

    Directory of Open Access Journals (Sweden)

    Jiwen Xie

    2014-03-01

    Full Text Available Mining the distribution pattern and evolution of the airport noise from the airport noise data and the geographic information of the monitoring points is of great significance for the scientific and rational governance of airport noise pollution problem. However, most of the traditional clustering methods are based on the closeness of space location or the similarity of non-spatial features, which split the duality of space elements, resulting in that the clustering result has difficult in satisfying both the closeness of space location and the similarity of non-spatial features. This paper, therefore, proposes a spatial clustering algorithm based on dual-distance. This algorithm uses a distance function as the similarity measure function in which spatial features and non-spatial features are combined. The experimental results show that the proposed algorithm can discover the noise distribution pattern around the airport effectively.

  14. Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models

    KAUST Repository

    Elsheikh, Ahmed H.

    2013-05-01

    A novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.

  15. Implementation of K-Means Clustering Method for Electronic Learning Model

    Science.gov (United States)

    Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni

    2017-12-01

    Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.

  16. Study of methods to increase cluster/dislocation loop densities in electrodes

    Science.gov (United States)

    Yang, Xiaoling; Miley, George H.

    2009-03-01

    Recent research has developed a technique for imbedding ultra-high density deuterium ``clusters'' (50 to 100 atoms per cluster) in various metals such as Palladium (Pd), Beryllium (Be) and Lithium (Li). It was found the thermally dehydrogenated PdHx retained the clusters and exhibited up to 12 percent lower resistance compared to the virginal Pd samplesootnotetextA. G. Lipson, et al. Phys. Solid State. 39 (1997) 1891. SQUID measurements showed that in Pd these condensed matter clusters approach metallic conditions, exhibiting superconducting propertiesootnotetextA. Lipson, et al. Phys. Rev. B 72, 212507 (2005ootnotetextA. G. Lipson, et al. Phys. Lett. A 339, (2005) 414-423. If the fabrication methods under study are successful, a large packing fraction of nuclear reactive clusters can be developed in the electrodes by electrolyte or high pressure gas loading. This will provide a much higher low-energy-nuclear- reaction (LENR) rate than achieved with earlier electrodeootnotetextCastano, C.H., et al. Proc. ICCF-9, Beijing, China 19-24 May, 2002..

  17. Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

    Directory of Open Access Journals (Sweden)

    Wen Liu

    2016-12-01

    Full Text Available Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS. Due to the absence of satellite signal in Global Navigation Satellite System (GNSS, various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP, which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC, is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1 and the XiDan Joy City (Floors 1 and 2, as Test-bed 2, and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

  18. Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments.

    Science.gov (United States)

    Liu, Wen; Fu, Xiao; Deng, Zhongliang

    2016-12-02

    Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

  19. IP2P K-means: an efficient method for data clustering on sensor networks

    Directory of Open Access Journals (Sweden)

    Peyman Mirhadi

    2013-03-01

    Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.

  20. Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

    Directory of Open Access Journals (Sweden)

    Youngmin Lee

    2016-08-01

    Full Text Available Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.

  1. Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, John R.; Marshall, P.J.; /KIPAC, Menlo Park; Andersson, K.; /Stockholm U. /SLAC

    2005-08-05

    We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature, location, size, and elemental abundances. Hundreds to thousands of these particles are used to construct a model cluster of galaxies, with the appropriate complexity estimated from the data quality. This model is then compared iteratively with X-ray data in the form of adaptively binned photon lists via a two-sample likelihood statistic and iterated via Markov Chain Monte Carlo. The complex cluster model is propagated through the X-ray instrument response using direct sampling Monte Carlo methods. Using this approach the method can reproduce many of the features observed in the X-ray emission in a less assumption-dependent way that traditional analyses, and it allows for a more detailed characterization of the density, temperature, and metal abundance structure of clusters. Multi-instrument X-ray analyses and simultaneous X-ray, Sunyaev-Zeldovich (SZ), and lensing analyses are a straight-forward extension of this methodology. Significant challenges still exist in understanding the degeneracy in these models and the statistical noise induced by the complexity of the models.

  2. Standardized Methods for Detection of Poliovirus Antibodies.

    Science.gov (United States)

    Weldon, William C; Oberste, M Steven; Pallansch, Mark A

    2016-01-01

    Testing for neutralizing antibodies against polioviruses has been an established gold standard for assessing individual protection from disease, population immunity, vaccine efficacy studies, and other vaccine clinical trials. Detecting poliovirus specific IgM and IgA in sera and mucosal specimens has been proposed for evaluating the status of population mucosal immunity. More recently, there has been a renewed interest in using dried blood spot cards as a medium for sample collection to enhance surveillance of poliovirus immunity. Here, we describe the modified poliovirus microneutralization assay, poliovirus capture IgM and IgA ELISA assays, and dried blood spot polio serology procedures for the detection of antibodies against poliovirus serotypes 1, 2, and 3.

  3. Methods and systems for detection of radionuclides

    Science.gov (United States)

    Coates, Jr., John T.; DeVol, Timothy A.

    2010-05-25

    Disclosed are materials and systems useful in determining the existence of radionuclides in an aqueous sample. The materials provide the dual function of both extraction and scintillation to the systems. The systems can be both portable and simple to use, and as such can beneficially be utilized to determine presence and optionally concentration of radionuclide contamination in an aqueous sample at any desired location and according to a relatively simple process without the necessity of complicated sample handling techniques. The disclosed systems include a one-step process, providing simultaneous extraction and detection capability, and a two-step process, providing a first extraction step that can be carried out in a remote field location, followed by a second detection step that can be carried out in a different location.

  4. Developing methods for detecting radioactive scrap

    International Nuclear Information System (INIS)

    Bellian, J.G.; Johnston, J.G.

    1995-01-01

    During the last 10 years, there have been major developments in radiation detection systems used for catching shielded radioactive sources in scrap metal. The original testing required to determine the extent of the problem and the preliminary designs of the first instruments will be discussed. Present systems available today will be described listing their advantages and disadvantages. In conclusion, the newest developments and state of the art equipment will also be included describing the limits and most appropriate locations for the systems

  5. Development of detection methods for irradiated foods

    International Nuclear Information System (INIS)

    Yang, Jae Seung; Kim, Chong Ki; Lee, Hae Jung; Kim, Kyong Su

    1999-04-01

    To identify irradiated foods, studies have been carried out with electron spin resonance (ESR) spectroscopy on bone containing foods, such as chicken, pork, and beef. The intensity of the signal induced in bones increased linearly with irradiation doses in the range of 1.0 kGy to 5.0 kGy, and it was possible to distinguish between samples given low and high doses of irradiation. The signal stability for 6 weeks made them ideal for the quick and easy identification of irradiated meats. The analysis of DNA damage made on single cells by agarose gel electrophoresis (DNA 'comet assay') can be used to detect irradiated food. All the samples irradiated with over 0.3 kGy were identified to detect post-irradiation by the tail length of their comets. Irradiated samples showed comets with long tails, and the tail length of the comets increased with the dose, while unirradiated samples showed no or very short tails. As a result of the above experiment, the DNA 'comet assay' might be applied to the detection of irradiated grains as a simple, low-cost and rapid screening test. When fats are irradiated, hydrocarbons contained one or two fewer carbon atoms are formed from the parent fatty acids. The major hydrocarbons in irradiated beef, pork and chicken were 1,7-hexadecadiene and 8-heptadecene originating from leic acid. 1,7 hexadecadiene was the highest amount in irradiated beef, pork and chicken. Eight kinds of hydrocarbons were identified from irradiated chicken, among which 1,7-hexadecadiene and 8-heptadecen were detected as major compounds. The concentration of radiation-induced hydrocarbons was relatively constant during 16 weeks

  6. Development of detection methods for irradiated foods

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Jae Seung; Kim, Chong Ki; Lee, Hae Jung [Korea Atomic Energy Research Insitiute, Taejon (Korea, Republic of); Kim, Kyong Su [Chosun University, Kwangju (Korea, Republic of)

    1999-04-01

    To identify irradiated foods, studies have been carried out with electron spin resonance (ESR) spectroscopy on bone containing foods, such as chicken, pork, and beef. The intensity of the signal induced in bones increased linearly with irradiation doses in the range of 1.0 kGy to 5.0 kGy, and it was possible to distinguish between samples given low and high doses of irradiation. The signal stability for 6 weeks made them ideal for the quick and easy identification of irradiated meats. The analysis of DNA damage made on single cells by agarose gel electrophoresis (DNA 'comet assay') can be used to detect irradiated food. All the samples irradiated with over 0.3 kGy were identified to detect post-irradiation by the tail length of their comets. Irradiated samples showed comets with long tails, and the tail length of the comets increased with the dose, while unirradiated samples showed no or very short tails. As a result of the above experiment, the DNA 'comet assay' might be applied to the detection of irradiated grains as a simple, low-cost and rapid screening test. When fats are irradiated, hydrocarbons contained one or two fewer carbon atoms are formed from the parent fatty acids. The major hydrocarbons in irradiated beef, pork and chicken were 1,7-hexadecadiene and 8-heptadecene originating from leic acid. 1,7 hexadecadiene was the highest amount in irradiated beef, pork and chicken. Eight kinds of hydrocarbons were identified from irradiated chicken, among which 1,7-hexadecadiene and 8-heptadecen were detected as major compounds. The concentration of radiation-induced hydrocarbons was relatively constant during 16 weeks.

  7. Blind Methods for Detecting Image Fakery

    Czech Academy of Sciences Publication Activity Database

    Mahdian, Babak; Saic, Stanislav

    2010-01-01

    Roč. 25, č. 4 (2010), s. 18-24 ISSN 0885-8985 R&D Projects: GA ČR GA102/08/0470 Institutional research plan: CEZ:AV0Z10750506 Keywords : Image forensics * Image Fakery * Forgery detection * Authentication Subject RIV: BD - Theory of Information Impact factor: 0.179, year: 2010 http://library.utia.cas.cz/separaty/2010/ZOI/saic-0343316.pdf

  8. Relativistic rise measurement by cluster counting method in time expansion chamber

    International Nuclear Information System (INIS)

    Rehak, P.; Walenta, A.H.

    1979-10-01

    A new approach to the measurement of the ionization energy loss for the charged particle identification in the region of the relativistic rise was tested experimentally. The method consists of determining in a special drift chamber (TEC) the number of clusters of the primary ionization. The method gives almost the full relativistic rise and narrower landau distribution. The consequences for a practical detector are discussed

  9. Stepwise threshold clustering: a new method for genotyping MHC loci using next-generation sequencing technology.

    Directory of Open Access Journals (Sweden)

    William E Stutz

    Full Text Available Genes of the vertebrate major histocompatibility complex (MHC are of great interest to biologists because of their important role in immunity and disease, and their extremely high levels of genetic diversity. Next generation sequencing (NGS technologies are quickly becoming the method of choice for high-throughput genotyping of multi-locus templates like MHC in non-model organisms. Previous approaches to genotyping MHC genes using NGS technologies suffer from two problems:1 a "gray zone" where low frequency alleles and high frequency artifacts can be difficult to disentangle and 2 a similar sequence problem, where very similar alleles can be difficult to distinguish as two distinct alleles. Here were present a new method for genotyping MHC loci--Stepwise Threshold Clustering (STC--that addresses these problems by taking full advantage of the increase in sequence data provided by NGS technologies. Unlike previous approaches for genotyping MHC with NGS data that attempt to classify individual sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based criteria to clusters rather than individual sequences, STC is able to successfully identify clusters of sequences that correspond to individual or similar alleles present in the genomes of individual samples. Furthermore, STC does not require duplicate runs of all samples, increasing the number of samples that can be genotyped in a given project. We show how the STC method works using a single sample library. We then apply STC to 295 threespine stickleback (Gasterosteus aculeatus samples from four populations and show that neighboring populations differ significantly in MHC allele pools. We show that STC is a reliable, accurate, efficient, and flexible method for genotyping MHC that will be of use to biologists interested in a variety of downstream applications.

  10. HPLC ‘Multi-Analyte’ Detection Method

    Energy Technology Data Exchange (ETDEWEB)

    Dudar, E. [Plant Protection & Soil Conservation Service of Budapest, Budapest (Hungary)

    2009-07-15

    The application of multi-analyte methods for pesticides carrying chromophoric structures by HPLC is described. Details are given on the materials and methods used. Recorded UV spectra of active substances are presented for allowing the verification of purity and the confirmation of substances eluting from the HPLC column. (author)

  11. Distance Measurement Methods for Improved Insider Threat Detection

    Directory of Open Access Journals (Sweden)

    Owen Lo

    2018-01-01

    Full Text Available Insider threats are a considerable problem within cyber security and it is often difficult to detect these threats using signature detection. Increasing machine learning can provide a solution, but these methods often fail to take into account changes of behaviour of users. This work builds on a published method of detecting insider threats and applies Hidden Markov method on a CERT data set (CERT r4.2 and analyses a number of distance vector methods (Damerau–Levenshtein Distance, Cosine Distance, and Jaccard Distance in order to detect changes of behaviour, which are shown to have success in determining different insider threats.

  12. Detection of irradiated meats by hydrocarbon method

    International Nuclear Information System (INIS)

    Goto, Michiko; Miyakawa, Hiroyuki; Fujinuma, Kenji; Ozawa, Hideki

    2005-01-01

    Meats, for example, lamb, razorback, wild duck and turkey were irradiated by gamma ray, and the amounts of hydrocarbons formed from fatty acids were measured. Since C 20:0 was found from wild duck and turkey. C 1-18:1 was recommended for internal standard. Good correlation was found between the amount of hydrocarbons and the doses of gamma irradiation. This study shows that such hydrocarbons induced after radiation procedure as C 1,7-16:2 , C 8-17:1 , C 1-14:1 , and C 15:0 may make it possible to detect irradiated lamb, razorback, wild duck and turkey. (author)

  13. a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

    Science.gov (United States)

    Huang, W.; Li, S.; Xu, S.

    2016-06-01

    How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the

  14. A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Huang

    2016-06-01

    Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The

  15. Rootkits. Methods of detecting and removing

    International Nuclear Information System (INIS)

    Lagutina, A.M.; Bogdanovich, A.A.; Ivanov, M.A.

    2012-01-01

    The problems connected with the threat of the infection of computer systems by rootkits have been examined, and the methods for providing a guard from this type of malicious software have been analyzed [ru

  16. Pre-crash scenarios at road junctions: A clustering method for car crash data.

    Science.gov (United States)

    Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth

    2017-10-01

    Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

    Science.gov (United States)

    Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong

    2017-02-28

    The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.

  18. Research on the method of information system risk state estimation based on clustering particle filter

    Science.gov (United States)

    Cui, Jia; Hong, Bei; Jiang, Xuepeng; Chen, Qinghua

    2017-05-01

    With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.

  19. Research on the method of information system risk state estimation based on clustering particle filter

    Directory of Open Access Journals (Sweden)

    Cui Jia

    2017-05-01

    Full Text Available With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.

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

    Science.gov (United States)

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

    2018-03-01

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

  1. Knowledge representation methods for early failure detection

    International Nuclear Information System (INIS)

    Scherer, K.P.; Stiller, P.

    1990-01-01

    To supervise technical processes like nuclear power plants, it is very important to detect failure modes in an early stage. In the nuclear research center at Karlsruhe an expert system is developed, embedded in a computer network of autonomous computers, which are used for intelligent prepocessing. Events, process data and actual parameter values are stored in slots of special frames in the knowledge base of the expert system. Both rule based and fact based knowledge representations are employed to generate cause consequence chains of failure states. By on-line surveillance of the reactor process, the slots of the frames are dynamically actualized. Immediately after the evaluation, the inference engine starts in the special domain experts (triggered by metarules from a manager) and detects the correspondend failures or anomaly state. Matching the members of the chain and regarding a catalogue of instructions and messages, what is to do by the operator, future failure states can be estimated and propagation can be prohibited. That means qualitative failure prediction based on cause consequence in the static part of the knowledge base. Also, a time series of physical data can be used to predict on analytical way future process state and to continue such a theoretical propagation with matching the cause consuquence chain

  2. Marine Biotoxins: Occurrence, Toxicity, and Detection Methods

    Science.gov (United States)

    Asakawa, M.

    2017-04-01

    This review summarizes the role of marine organisms as vectors of marine biotoxins, and discusses the need for surveillance to protect public health and ensure the quality of seafood. I Paralytic shellfish poison (PSP) and PSP-bearing organisms-PSP is produced by toxic dinoflagellates species belonging to the genera Alexandrium, Gymnodinium, and Pyrodinium. Traditionally, PSP monitoring programs have only considered filter-feeding molluscs that concentrate these toxic algae, however, increasing attention is now being paid to higher-order predators that carry PSP, such as carnivorous gastropods and crustaceans. II. Tetrodotoxin (TTX) and TTX-bearing organisms - TTX is the most common natural marine toxin that causes food poisonings in Japan, and poses a serious public health risk. TTX was long believed to be present only in pufferfish. However, TTX was detected in the eggs of California newt Taricha torosa in 1964, and since then it has been detected in a wide variety of species belonging to several different phyla. In this study, the main toxic components in the highly toxic ribbon worm Cephalothrix simula and the greater blue-ringed octopus Hapalochlaena lunulata from Japan were purified and analysed.

  3. Polarization sensitive optical coherence tomography detection method

    International Nuclear Information System (INIS)

    Colston, B W; DaSilva, L B; Everett, M J; Featherstone, J D B; Fried, D; Ragadio, J N; Sathyam, U S.

    1999-01-01

    This study demonstrates the potential of polarization sensitive optical coherence tomography (PS-OCT) for non-invasive in vivo detection and characterization of early, incipient caries lesions. PS-OCT generates cross-sectional images of biological tissue while measuring the effect of the tissue on the polarization state of incident light. Clear discrimination between regions of normal and demineralized enamel is first shown in PS-OCT images of bovine enamel blocks containing well-characterized artificial lesions. High-resolution, cross-sectional images of extracted human teeth are then generated that clearly discriminate between the normal and carious regions on both the smooth and occlusal surfaces. Regions of the teeth that appeared to be demineralized in the PS-OCT images were verified using histological thin sections examined under polarized light microscopy. The PS-OCT system discriminates between normal and carious regions by measuring the polarization state of the back-scattered 1310 nm light, which is affected by the state of demineralization of the enamel. Demineralization of enamel increases the scattering coefficient, thus depolarizing the incident light. This study shows that PS-OCT has great potential for the detection, characterization, and monitoring of incipient caries lesions

  4. Detecting Massive, High-Redshift Galaxy Clusters Using the Thermal Sunyaev-Zel'dovich Effect

    Science.gov (United States)

    Adams, Carson; Steinhardt, Charles L.; Loeb, Abraham; Karim, Alexander; Staguhn, Johannes; Erler, Jens; Capak, Peter L.

    2017-01-01

    We develop the thermal Sunyaev-Zel'dovich (SZ) effect as a direct astrophysical measure of the mass distribution of dark matter halos. The SZ effect increases with cosmological distance, a unique astronomical property, and is highly sensitive to halo mass. We find that this presents a powerful methodology for distinguishing between competing models of the halo mass function distribution, particularly in the high-redshift domain just a few hundred million years after the Big Bang. Recent surveys designed to probe this epoch of initial galaxy formation such as CANDELS and SPLASH report an over-abundance of highly massive halos as inferred from stellar ultraviolet (UV) luminosities and the stellar mass to halo mass ratio estimated from nearby galaxies. If these UV luminosity to halo mass relations hold to high-redshift, observations estimate several orders of magnitude more highly massive halos than predicted by hierarchical merging and the standard cosmological paradigm. Strong constraints on the masses of these galaxy clusters are essential to resolving the current tension between observation and theory. We conclude that detections of thermal SZ sources are plausible at high-redshift only for the halo masses inferred from observation. Therefore, future SZ surveys will provide a robust determination between theoretical and observational predictions.

  5. Detection of gold cluster ions by ion-to-ion conversion using a CsI-converter

    International Nuclear Information System (INIS)

    Nguyen, V.-T.; Novilkov, A.C.; Obnorskii, V.V.

    1997-01-01

    Gold cluster ions in the m/z range of 10 4 -2 x 10 6 u were produced by bombarding a thin film of gold with 252 Cf-fission fragments. The gold covering a C-Al substrate formed islets having a mean diameter of 44 A. Their size- and mass-distribution was determined by means of electron microscopy. The main task was to measure the m/z distribution of the cluster ions ejected from the sample surface. For this purpose we built a time-of-flight (TOF) mass spectrometer, which could be used as a linear TOF instrument or, alternatively, as a tandem-TOF instrument being equipped with an ion-to-ion converter. Combining the results obtained in both modes, it turned out that the linear TOF instrument equipped with micro-channel plates had a mean detection efficiency for 20 keV cluster ions of about 40%. In the tandem mode, the cluster ions hit a CsI converter with energies of 40z keV (z = charge state), from where secondary ions - mainly Cs + and (CsI) n Cs + cluster ions - were ejected. These ions were used to measure the TOF spectrum of the gold cluster ions. The detection efficiency of the cluster ions was found to vary in the available mass range from 99.7% to 96.5%. The complete mass distribution between 4 x 10 4 and 4 x 10 6 u was determined and compared with the corresponding mass distribution of the gold islets covering the substrate. (orig.)

  6. Thermal History Devices, Systems For Thermal History Detection, And Methods For Thermal History Detection

    KAUST Repository

    Caraveo Frescas, Jesus Alfonso; Alshareef, Husam N.

    2015-01-01

    Embodiments of the present disclosure include nanowire field-effect transistors, systems for temperature history detection, methods for thermal history detection, a matrix of field effect transistors, and the like.

  7. Thermal History Devices, Systems For Thermal History Detection, And Methods For Thermal History Detection

    KAUST Repository

    Caraveo Frescas, Jesus Alfonso

    2015-05-28

    Embodiments of the present disclosure include nanowire field-effect transistors, systems for temperature history detection, methods for thermal history detection, a matrix of field effect transistors, and the like.

  8. X-RAY DETECTION OF THE CLUSTER CONTAINING THE CEPHEID S MUS

    Energy Technology Data Exchange (ETDEWEB)

    Evans, Nancy Remage; Pillitteri, Ignazio; Wolk, Scott; Karovska, Margarita; DePasquale, Joseph; Tingle, Evan [Smithsonian Astrophysical Observatory, MS 4, 60 Garden Street, Cambridge, MA 02138 (United States); Guinan, Edward; Engle, Scott [Department of Astronomy and Astrophysics, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085 (United States); Bond, Howard E. [Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802 (United States); Schaefer, Gail H., E-mail: nevans@cfa.harvard.edu [The CHARA Array of Georgia State University, Mount Wilson, CA 91023 (United States)

    2014-04-20

    The galactic Cepheid S Muscae has recently been added to the important list of Cepheids linked to open clusters, in this case the sparse young cluster ASCC 69. Low-mass members of a young cluster are expected to have rapid rotation and X-ray activity, making X-ray emission an excellent way to discriminate them from old field stars. We have made an XMM-Newton observation centered on S Mus and identified a population of X-ray sources whose near-IR Two Micron All Sky Survey counterparts lie at locations in the J, (J – K) color-magnitude diagram consistent with cluster membership at the distance of S Mus. Their median energy and X-ray luminosity are consistent with young cluster members as distinct from field stars. These strengthen the association of S Mus with the young cluster, making it a potential Leavitt law (period-luminosity relation) calibrator.

  9. X-Ray Detection of the Cluster Containing the Cepheid S Mus

    Science.gov (United States)

    Evans, Nancy Remage; Pillitteri, Ignazio; Wolk, Scott; Guinan, Edward; Engle, Scott; Bond, Howard E.; Schaefer, Gail H.; Karovska, Margarita; DePasquale, Joseph; Tingle, Evan

    2014-04-01

    The galactic Cepheid S Muscae has recently been added to the important list of Cepheids linked to open clusters, in this case the sparse young cluster ASCC 69. Low-mass members of a young cluster are expected to have rapid rotation and X-ray activity, making X-ray emission an excellent way to discriminate them from old field stars. We have made an XMM-Newton observation centered on S Mus and identified a population of X-ray sources whose near-IR Two Micron All Sky Survey counterparts lie at locations in the J, (J - K) color-magnitude diagram consistent with cluster membership at the distance of S Mus. Their median energy and X-ray luminosity are consistent with young cluster members as distinct from field stars. These strengthen the association of S Mus with the young cluster, making it a potential Leavitt law (period-luminosity relation) calibrator.

  10. X-RAY DETECTION OF THE CLUSTER CONTAINING THE CEPHEID S MUS

    International Nuclear Information System (INIS)

    Evans, Nancy Remage; Pillitteri, Ignazio; Wolk, Scott; Karovska, Margarita; DePasquale, Joseph; Tingle, Evan; Guinan, Edward; Engle, Scott; Bond, Howard E.; Schaefer, Gail H.

    2014-01-01

    The galactic Cepheid S Muscae has recently been added to the important list of Cepheids linked to open clusters, in this case the sparse young cluster ASCC 69. Low-mass members of a young cluster are expected to have rapid rotation and X-ray activity, making X-ray emission an excellent way to discriminate them from old field stars. We have made an XMM-Newton observation centered on S Mus and identified a population of X-ray sources whose near-IR Two Micron All Sky Survey counterparts lie at locations in the J, (J – K) color-magnitude diagram consistent with cluster membership at the distance of S Mus. Their median energy and X-ray luminosity are consistent with young cluster members as distinct from field stars. These strengthen the association of S Mus with the young cluster, making it a potential Leavitt law (period-luminosity relation) calibrator

  11. Development and Establishment of Detection Method of Irradiated Foods

    International Nuclear Information System (INIS)

    Byun, Myung Woo; Lee, Ju Woon; Kim, Dong Ho; Jo, Cheo Run; Kim, Jang Ho; Kim, Kyong Su

    2004-12-01

    The present project was related to the development and establishment of the detection techniques for the safety management of gamma-irradiated food and particularly conducted for the establishment of standard detection method for gamma-irradiated dried spices and raw materials, dried meat and fish powder for processed foods, bean paste powder, red pepper paste powder, soy sauce powder, and starch for flavoring ingredients described in 3, 6, 7 section of Korean Food Standard. Since the approvement of gamma-irradiated food items will be enlarged due to the international tendency for gamma-irradiated food, it was concluded that the establishment of detailed detection methods for each food group is not efficient for the enactment and enforcement of related regulations. For this reason, in order to establish the standard detection method, a detection system for gamma-irradiated food suitable for domestic operation was studied using comparative analysis of domestic and foreign research data classified by items and methods and European Standard as a reference. According to the comparative analyses of domestic and foreign research data and regulations of detection for gamma-irradiated food, it was concluded to be desirable that the optimal detection method should be decided after principal detection tests such as physical, chemical, and biological detection methods are established as standard methods and that the specific descriptions such as pre-treatment of raw materials, test methods, and the evaluation of results should be separately prescribed