A Fast General-Purpose Clustering Algorithm Based on FPGAs for High-Throughput Data Processing
Annovi, A; The ATLAS collaboration; Castegnaro, A; Gatta, M
2012-01-01
We present a fast general-purpose algorithm for high-throughput clustering of data ”with a two dimensional organization”. The algorithm is designed to be implemented with FPGAs or custom electronics. The key feature is a processing time that scales linearly with the amount of data to be processed. This means that clustering can be performed in pipeline with the readout, without suffering from combinatorial delays due to looping multiple times through all the data. This feature makes this algorithm especially well suited for problems where the data has high density, e.g. in the case of tracking devices working under high-luminosity condition such as those of LHC or Super-LHC. The algorithm is organized in two steps: the first step (core) clusters the data; the second step analyzes each cluster of data to extract the desired information. The current algorithm is developed as a clustering device for modern high-energy physics pixel detectors. However, the algorithm has much broader field of applications. In ...
The ATLAS Trigger Algorithms for General Purpose Graphics Processor Units
Tavares Delgado, Ademar; The ATLAS collaboration
2016-01-01
The ATLAS Trigger Algorithms for General Purpose Graphics Processor Units Type: Talk Abstract: We present the ATLAS Trigger algorithms developed to exploit General Purpose Graphics Processor Units. ATLAS is a particle physics experiment located on the LHC collider at CERN. The ATLAS Trigger system has two levels, hardware-based Level 1 and the High Level Trigger implemented in software running on a farm of commodity CPU. Performing the trigger event selection within the available farm resources presents a significant challenge that will increase future LHC upgrades. are being evaluated as a potential solution for trigger algorithms acceleration. Key factors determining the potential benefit of this new technology are the relative execution speedup, the number of GPUs required and the relative financial cost of the selected GPU. We have developed a trigger demonstrator which includes algorithms for reconstructing tracks in the Inner Detector and Muon Spectrometer and clusters of energy deposited in the Cal...
Foam: A general purpose Monte Carlo cellular algorithm
International Nuclear Information System (INIS)
Jadach, S.
2003-01-01
A general-purpose, self-adapting Monte Carlo (MC) algorithm implemented in the program Foam is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain into small cells. The cells can be n-dimensional simplices, hyperrectangles cells. The next cell to be divided and the position/direction of the division hyperplane is chosen by the algorithm which optimizes the ratio of the maximum weight to the average weight or (optionally) the total variance. The algorithm is able to deal, in principle, with an arbitrary pattern of the singularities in the distribution
Using general-purpose compression algorithms for music analysis
DEFF Research Database (Denmark)
Louboutin, Corentin; Meredith, David
2016-01-01
General-purpose compression algorithms encode files as dictionaries of substrings with the positions of these strings’ occurrences. We hypothesized that such algorithms could be used for pattern discovery in music. We compared LZ77, LZ78, Burrows–Wheeler and COSIATEC on classifying folk song...... in the input data, COSIATEC outperformed LZ77 with a mean F1 score of 0.123, compared with 0.053 for LZ77. However, when the music was processed a voice at a time, the F1 score for LZ77 more than doubled to 0.124. We also discovered a significant correlation between compression factor and F1 score for all...
Selecting a general-purpose data compression algorithm
Mathews, Gary Jason
1995-01-01
The National Space Science Data Center's Common Data Formate (CDF) is capable of storing many types of data such as scalar data items, vectors, and multidimensional arrays of bytes, integers, or floating point values. However, regardless of the dimensionality and data type, the data break down into a sequence of bytes that can be fed into a data compression function to reduce the amount of data without losing data integrity and thus remaining fully reconstructible. Because of the diversity of data types and high performance speed requirements, a general-purpose, fast, simple data compression algorithm is required to incorporate data compression into CDF. The questions to ask are how to evaluate and compare compression algorithms, and what compression algorithm meets all requirements. The object of this paper is to address these questions and determine the most appropriate compression algorithm to use within the CDF data management package that would be applicable to other software packages with similar data compression needs.
Foam: A general purpose Monte Carlo cellular algorithm
International Nuclear Information System (INIS)
Jadach, S.
2002-01-01
A general-purpose, self-adapting Monte Carlo (MC) algorithm implemented in the program Foam is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain into small cells. The cells can be n-dimensional simplices, hyperrectangles or a Cartesian product of them. The grid of cells, called 'foam', is produced in the process of the binary split of the cells. The choice of the next cell to be divided and the position/direction of the division hyperplane is driven by the algorithm which optimizes the ratio of the maximum weight to the average weight or (optionally) the total variance. The algorithm is able to deal, in principle, with an arbitrary pattern of the singularities in the distribution. (author)
Foam A General purpose Monte Carlo Cellular Algorithm
Jadach, Stanislaw
2002-01-01
A general-purpose, self-adapting Monte Carlo (MC) algorithm implemented in the program {\\tt Foam} is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain into small cells. The cells can be $n$-dimensional simplices, hyperrectangles or a Cartesian product of them. The grid of cells, ``foam'', is produced in the process of the binary split of the cells. The next cell to be divided and the position/direction of the division hyperplane is chosen by the algorithm which optimizes the ratio of the maximum weight to the average weight or (optionally) the total variance. The algorithm is able to deal, in principle, with an arbitrary pattern of the singularities in the distribution.
High-Speed General Purpose Genetic Algorithm Processor.
Hoseini Alinodehi, Seyed Pourya; Moshfe, Sajjad; Saber Zaeimian, Masoumeh; Khoei, Abdollah; Hadidi, Khairollah
2016-07-01
In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in [Formula: see text] process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.
General purpose graphic processing unit implementation of adaptive pulse compression algorithms
Cai, Jingxiao; Zhang, Yan
2017-07-01
This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.
Schallhorn, Paul; Majumdar, Alok
2012-01-01
This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.
Conde Mui\\~no, Patricia; The ATLAS collaboration
2016-01-01
General purpose Graphics Processor Units (GPGPU) are being evaluated for possible future inclusion in an upgraded ATLAS High Level Trigger farm. We have developed a demonstrator including GPGPU implementations of Inner Detector and Muon tracking and Calorimeter clustering within the ATLAS software framework. ATLAS is a general purpose particle physics experiment located on the LHC collider at CERN. The ATLAS Trigger system consists of two levels, with level 1 implemented in hardware and the High Level Trigger implemented in software running on a farm of commodity CPU. The High Level Trigger reduces the trigger rate from the 100 kHz level 1 acceptance rate to 1 kHz for recording, requiring an average per-event processing time of ~250 ms for this task. The selection in the high level trigger is based on reconstructing tracks in the Inner Detector and Muon Spectrometer and clusters of energy deposited in the Calorimeter. Performing this reconstruction within the available farm resources presents a significant ...
Partitional clustering algorithms
2015-01-01
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...
Non-convex polygons clustering algorithm
Directory of Open Access Journals (Sweden)
Kruglikov Alexey
2016-01-01
Full Text Available A clustering algorithm is proposed, to be used as a preliminary step in motion planning. It is tightly coupled to the applied problem statement, i.e. uses parameters meaningful only with respect to it. Use of geometrical properties for polygons clustering allows for a better calculation time as opposed to general-purpose algorithms. A special form of map optimized for quick motion planning is constructed as a result.
International Nuclear Information System (INIS)
Chandrasekharan, Shailesh
2000-01-01
Cluster algorithms have been recently used to eliminate sign problems that plague Monte-Carlo methods in a variety of systems. In particular such algorithms can also be used to solve sign problems associated with the permutation of fermion world lines. This solution leads to the possibility of designing fermion cluster algorithms in certain cases. Using the example of free non-relativistic fermions we discuss the ideas underlying the algorithm
International Nuclear Information System (INIS)
Yang Lei; Gong Xueyu; Wang Ling
2013-01-01
Combined with standard mathematical model for evaluating quality of deploying results, a new high-performance parallel algorithm for source pencils' deployment was obtained by using parallel plant growth simulation algorithm which was completely parallelized with CUDA execute model, and the corresponding code can run on GPU. Based on such work, several instances in various scales were used to test the new version of algorithm. The results show that, based on the advantage of old versions. the performance of new one is improved more than 500 times comparing with the CPU version, and also 30 times with the CPU plus GPU hybrid version. The computation time of new version is less than ten minutes for the irradiator of which the activity is less than 111 PBq. For a single GTX275 GPU, the maximum computing power of new version is no more than 167 PBq as well as the computation time is no more than 25 minutes, and for multiple GPUs, the power can be improved more. Overall, the new version of algorithm running on GPU can satisfy the requirement of source pencils' deployment of any domestic irradiator, and it is of high competitiveness. (authors)
A cluster algorithm for graphs
S. van Dongen
2000-01-01
textabstractA cluster algorithm for graphs called the emph{Markov Cluster algorithm (MCL~algorithm) is introduced. The algorithm provides basically an interface to an algebraic process defined on stochastic matrices, called the MCL~process. The graphs may be both weighted (with nonnegative weight)
Data clustering algorithms and applications
Aggarwal, Charu C
2013-01-01
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea
K-means Clustering: Lloyd's algorithm
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. K-means Clustering: Lloyd's algorithm. Refines clusters iteratively. Cluster points using Voronoi partitioning of the centers; Centroids of the clusters determine the new centers. Bad example k = 3, n =4.
Normalization based K means Clustering Algorithm
Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika
2015-01-01
K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...
Parallel algorithms and cluster computing
Hoffmann, Karl Heinz
2007-01-01
This book presents major advances in high performance computing as well as major advances due to high performance computing. It contains a collection of papers in which results achieved in the collaboration of scientists from computer science, mathematics, physics, and mechanical engineering are presented. From the science problems to the mathematical algorithms and on to the effective implementation of these algorithms on massively parallel and cluster computers we present state-of-the-art methods and technology as well as exemplary results in these fields. This book shows that problems which seem superficially distinct become intimately connected on a computational level.
Determination of atomic cluster structure with cluster fusion algorithm
DEFF Research Database (Denmark)
Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.
2005-01-01
We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....
A new cluster algorithm for graphs
S. van Dongen
1998-01-01
textabstractA new cluster algorithm for graphs called the emph{Markov Cluster algorithm ($MCL$ algorithm) is introduced. The graphs may be both weighted (with nonnegative weight) and directed. Let~$G$~be such a graph. The $MCL$ algorithm simulates flow in $G$ by first identifying $G$ in a
Text Clustering Algorithm Based on Random Cluster Core
Directory of Open Access Journals (Sweden)
Huang Long-Jun
2016-01-01
Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.
Frequent Pattern Mining Algorithms for Data Clustering
DEFF Research Database (Denmark)
Zimek, Arthur; Assent, Ira; Vreeken, Jilles
2014-01-01
that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed into an independent research field. In this chapter, we discuss how frequent pattern mining algorithms have been extended and generalized towards the discovery of local clusters in high-dimensional data......Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say....... In particular, we discuss several example algorithms for subspace clustering or projected clustering as well as point out recent research questions and open topics in this area relevant to researchers in either clustering or pattern mining...
Moorthy, Arun S; Wallace, William E; Kearsley, Anthony J; Tchekhovskoi, Dmitrii V; Stein, Stephen E
2017-12-19
A mass spectral library search algorithm that identifies compounds that differ from library compounds by a single "inert" structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compounds, to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum. When the spectra being compared differ by a single structural feature, these matching neutral loss peaks should contain that structural feature. This method extends the scope of the library to include spectra of "nearest-neighbor" compounds that differ from library compounds by a single chemical moiety. Additionally, determination of the structural origin of the shifted peaks can aid in the determination of the chemical structure and fragmentation mechanism of the query compound. A variety of examples are presented, including the identification of designer drugs and chemical derivatives not present in the library.
General Purpose (office) Network reorganisation
IT Department
2016-01-01
On Saturday 27 August, the IT Department’s Communication Systems group will perform a major reorganisation of CERN’s General Purpose Network. This reorganisation will cause network interruptions on Saturday 27 August (and possibly Sunday 28 August) and will be followed by a change to the IP addresses of connected systems that will come into effect on Monday 3 October. For further details and information about the actions you may need to take, please see: https://information-technology.web.cern.ch/news/general-purpose-office-network-reorganisation.
General purpose programmable accelerator board
Robertson, Perry J.; Witzke, Edward L.
2001-01-01
A general purpose accelerator board and acceleration method comprising use of: one or more programmable logic devices; a plurality of memory blocks; bus interface for communicating data between the memory blocks and devices external to the board; and dynamic programming capabilities for providing logic to the programmable logic device to be executed on data in the memory blocks.
Algorithm for Spatial Clustering with Obstacles
El-Sharkawi, Mohamed E.; El-Zawawy, Mohamed A.
2009-01-01
In this paper, we propose an efficient clustering technique to solve the problem of clustering in the presence of obstacles. The proposed algorithm divides the spatial area into rectangular cells. Each cell is associated with statistical information that enables us to label the cell as dense or non-dense. We also label each cell as obstructed (i.e. intersects any obstacle) or non-obstructed. Then the algorithm finds the regions (clusters) of connected, dense, non-obstructed cells. Finally, th...
Data clustering theory, algorithms, and applications
Gan, Guojun; Wu, Jianhong
2007-01-01
Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB® programming languages.
Semantic based cluster content discovery in description first clustering algorithm
International Nuclear Information System (INIS)
Khan, M.W.; Asif, H.M.S.
2017-01-01
In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase. (author)
General Purpose Heat Source Simulator
Emrich, Bill
2008-01-01
The General Purpose Heat Source (GPHS) simulator project is designed to replicate through the use of electrical heaters, the form, fit, and function of actual GPHS modules which generate heat through the radioactive decay of Pu238. The use of electrically heated modules rather than modules containing Pu238 facilitates the testing of spacecraft subsystems and systems without sacrificing the quantity and quality of the test data gathered. Previous GPHS activities are centered around developing robust heater designs with sizes and weights that closely matched those of actual Pu238 fueled GPHS blocks. These efforts were successful, although their maximum temperature capabilities were limited to around 850 C. New designs are being pursued which also replicate the sizes and weights of actual Pu238 fueled GPHS blocks but will allow operation up to 1100 C.
General-purpose RFQ design program
International Nuclear Information System (INIS)
Wadlinger, E.A.
1984-01-01
We have written a general-purpose, radio-frequency quadrupole (RFQ) design program that allows maximum flexibility in picking design algorithms. This program optimizes the RFQ on any combination of design parameters while simultaneously satisfying mutually compatible, physically required constraint equations. It can be very useful for deriving various scaling laws for RFQs. This program has a friendly user interface in addition to checking the consistency of the user-defined requirements and is written to minimize the effort needed to incorporate additional constraint equations. We describe the program and present some examples
Robust MST-Based Clustering Algorithm.
Liu, Qidong; Zhang, Ruisheng; Zhao, Zhili; Wang, Zhenghai; Jiao, Mengyao; Wang, Guangjing
2018-06-01
Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.
Maximum-entropy clustering algorithm and its global convergence analysis
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.
Fuzzy Rules for Ant Based Clustering Algorithm
Directory of Open Access Journals (Sweden)
Amira Hamdi
2016-01-01
Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.
Genetic algorithm optimization of atomic clusters
International Nuclear Information System (INIS)
Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E.; Iowa State Univ., Ames, IA
1996-01-01
The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process
Efficient probabilistic model checking on general purpose graphic processors
Bosnacki, D.; Edelkamp, S.; Sulewski, D.; Pasareanu, C.S.
2009-01-01
We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). For this purpose we exploit the fact that some of the basic algorithms for probabilistic model checking rely on matrix vector multiplication. Since this kind of linear algebraic
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
Directory of Open Access Journals (Sweden)
Dieter Hendricks
2016-02-01
Full Text Available We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.
Application for Suggesting Restaurants Using Clustering Algorithms
Directory of Open Access Journals (Sweden)
Iulia Alexandra IANCU
2014-10-01
Full Text Available The aim of this article is to present an application whose purpose is to make suggestions of restaurants to users. The application uses as input the descriptions of restaurants, reviews, user reviews available on the specialized Internet sites and blogs. In the application there are used processing techniques of natural language implemented using parsers, clustering algorithms and techniques for data collection from the Internet through web crawlers.
7 CFR 254.1 - General purpose.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose. 254.1 Section 254.1 Agriculture... GENERAL REGULATIONS AND POLICIES-FOOD DISTRIBUTION ADMINISTRATION OF THE FOOD DISTRIBUTION PROGRAM FOR INDIAN HOUSEHOLDS IN OKLAHOMA § 254.1 General purpose. This part sets the requirement under which...
22 CFR 309.1 - General purpose.
2010-04-01
... 22 Foreign Relations 2 2010-04-01 2010-04-01 true General purpose. 309.1 Section 309.1 Foreign Relations PEACE CORPS DEBT COLLECTION General Provisions § 309.1 General purpose. This part prescribes the procedures to be used by the United States Peace Corps (Peace Corps) in the collection and/or disposal of non...
10 CFR 205.350 - General purpose.
2010-01-01
... 10 Energy 3 2010-01-01 2010-01-01 false General purpose. 205.350 Section 205.350 Energy DEPARTMENT OF ENERGY OIL ADMINISTRATIVE PROCEDURES AND SANCTIONS Electric Power System Permits and Reports....350 General purpose. The purpose of this rule is to establish a procedure for the Office of...
12 CFR 1703.31 - General purposes.
2010-01-01
... 12 Banks and Banking 7 2010-01-01 2010-01-01 false General purposes. 1703.31 Section 1703.31 Banks and Banking OFFICE OF FEDERAL HOUSING ENTERPRISE OVERSIGHT, DEPARTMENT OF HOUSING AND URBAN... Legal Proceedings in Which OFHEO Is Not a Named Party § 1703.31 General purposes. The purposes of this...
A cluster algorithm for jet studies
International Nuclear Information System (INIS)
Daum, H.J.; Meyer, H.; Buerger, J.
1980-10-01
A procedure is described which determines the number of jets in hadronic final states by means of a cluster algorithm. In addition it yields a measurement of the energy and the direction of each jet. The properties of this method are studied using Monte Carlo simulations of different types of e + e - -annihilation final states. It is shown that in case of 3-jet events direct comparison with the underlying parton structure can be made. Possible further applications of this method are discussed. (orig.)
Improved Ant Colony Clustering Algorithm and Its Performance Study
Gao, Wei
2016-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533
Development of a General Purpose Gamification Framework
Vea, Eivind
2016-01-01
This report describes the design and implementation of a general purpose gamification framework developed in JavaScript on the Metor platform. Gamification is described as the use of game elements in none-game contexts. The purpose is to encourage and change user behaviour. Examples of existing gamification use cases and frameworks are described. A demo game shows how a general purpose framework can be used.
General purpose code for Monte Carlo simulations
International Nuclear Information System (INIS)
Wilcke, W.W.
1983-01-01
A general-purpose computer called MONTHY has been written to perform Monte Carlo simulations of physical systems. To achieve a high degree of flexibility the code is organized like a general purpose computer, operating on a vector describing the time dependent state of the system under simulation. The instruction set of the computer is defined by the user and is therefore adaptable to the particular problem studied. The organization of MONTHY allows iterative and conditional execution of operations
Energy Aware Clustering Algorithms for Wireless Sensor Networks
Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian
2011-09-01
The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.
A Novel Clustering Algorithm Inspired by Membrane Computing
Directory of Open Access Journals (Sweden)
Hong Peng
2015-01-01
Full Text Available P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.
Evaluation of Hierarchical Clustering Algorithms for Document Datasets
National Research Council Canada - National Science Library
Zhao, Ying; Karypis, George
2002-01-01
Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters...
Algorithms of maximum likelihood data clustering with applications
Giada, Lorenzo; Marsili, Matteo
2002-12-01
We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.
URL Mining Using Agglomerative Clustering Algorithm
Directory of Open Access Journals (Sweden)
Chinmay R. Deshmukh
2015-02-01
Full Text Available Abstract The tremendous growth of the web world incorporates application of data mining techniques to the web logs. Data Mining and World Wide Web encompasses an important and active area of research. Web log mining is analysis of web log files with web pages sequences. Web mining is broadly classified as web content mining web usage mining and web structure mining. Web usage mining is a technique to discover usage patterns from Web data in order to understand and better serve the needs of Web-based applications. URL mining refers to a subclass of Web mining that helps us to investigate the details of a Uniform Resource Locator. URL mining can be advantageous in the fields of security and protection. The paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is a clickthrough data each record consist of a users query to a search engine along with the URL which the user selected from among the candidates offered by search engine. By viewing this dataset as a bipartite graph with the vertices on one side corresponding to queries and on the other side to URLs one can apply an agglomerative clustering algorithm to the graphs vertices to identify related queries and URLs.
A Dynamic Fuzzy Cluster Algorithm for Time Series
Directory of Open Access Journals (Sweden)
Min Ji
2013-01-01
clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.
General purpose computers in real time
International Nuclear Information System (INIS)
Biel, J.R.
1989-01-01
I see three main trends in the use of general purpose computers in real time. The first is more processing power. The second is the use of higher speed interconnects between computers (allowing more data to be delivered to the processors). The third is the use of larger programs running in the computers. Although there is still work that needs to be done, I believe that all indications are that the online need for general purpose computers should be available for the SCC and LHC machines. 2 figs
Robustness of Multiple Clustering Algorithms on Hyperspectral Images
National Research Council Canada - National Science Library
Williams, Jason P
2007-01-01
.... Various clustering algorithms were employed, including a hierarchical method, ISODATA, K-means, and X-means, and were used on a simple two dimensional dataset in order to discover potential problems with the algorithms...
Performance Evaluation of Spectral Clustering Algorithm using Various Clustering Validity Indices
M. T. Somashekara; D. Manjunatha
2014-01-01
In spite of the popularity of spectral clustering algorithm, the evaluation procedures are still in developmental stage. In this article, we have taken benchmarking IRIS dataset for performing comparative study of twelve indices for evaluating spectral clustering algorithm. The results of the spectral clustering technique were also compared with k-mean algorithm. The validity of the indices was also verified with accuracy and (Normalized Mutual Information) NMI score. Spectral clustering algo...
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.
Cluster fusion algorithm: application to Lennard-Jones clusters
DEFF Research Database (Denmark)
Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter
2006-01-01
paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...
Cluster fusion algorithm: application to Lennard-Jones clusters
DEFF Research Database (Denmark)
Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter
2008-01-01
paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...
A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm
Directory of Open Access Journals (Sweden)
Wenping Zou
2010-01-01
Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
Lipseys Quest for the Micro-foundations of GPT-the General Purpose Engine
Van der Kooij, B.J.G.
2016-01-01
The construct of the General Purpose Technology misses its micro-foundation (as observed by Richard Lipsey). We present a possible solution in the General Purpose Engines. These are the basic innovations and the clusters of contributing and derived innovation, that appear in a Schumpeterian 'cluster
A Flocking Based algorithm for Document Clustering Analysis
Energy Technology Data Exchange (ETDEWEB)
Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL
2006-01-01
Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.
Mining the National Career Assessment Examination Result Using Clustering Algorithm
Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.
2018-03-01
Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.
Summary of JENDL-2 general purpose file
Energy Technology Data Exchange (ETDEWEB)
Nakagawa, Tsuneo [ed.
1984-06-15
The general purpose file of the second version of Japanese Evaluated Nuclear Data Library (JENDL-2) was released in December 1982. Recently, descriptive data were added to JENDL-2 and at the same time the first revision of numerical data was performed. JENDL-2 (Rev.1) consists of the data for 89 nuclides and about 211,000 records in the ENDF/B-IV format. In this report, full listings of presently added descriptive data are given to summarize the JENDL-2 general purpose file. The 2200-m/sec and 14-MeV cross sections, resonance integrals, Maxwellian and fission spectrum averaged cross sections are given in a table. Average cross sections were also calculated in suitable energy intervals.
Summary of JENDL-2 general purpose file
International Nuclear Information System (INIS)
Nakagawa, Tsuneo
1984-06-01
The general purpose file of the second version of Japanese Evaluated Nuclear Data Library (JENDL-2) was released in December 1982. Recently, descriptive data were added to JENDL-2 and at the same time the first revision of numerical data was performed. JENDL-2 (Rev1) consists of the data for 89 nuclides and about 211,000 records in the ENDF/B-IV format. In this report, full listings of presently added descriptive data are given to summarize the JENDL-2 general purpose file. The 2200-m/sec and 14-MeV cross sections, resonance integrals, Maxwellian and fission spectrum averaged cross sections are given in a table. Average cross sections were also calculated in suitable energy intervals. (author)
Android Malware Classification Using K-Means Clustering Algorithm
Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah
2017-08-01
Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.
An event driven algorithm for fractal cluster formation
González, S.; Thornton, Anthony Richard; Luding, Stefan
2010-01-01
A new cluster based event-driven algorithm is developed to simulate the formation of clusters in a two dimensional gas: particles move freely until they collide and "stick" together irreversibly. These clusters aggregate into bigger structures in an isotompic way, forming fractal structures whose
An event driven algorithm for fractal cluster formation
González, S.; Gonzalez Briones, Sebastián; Thornton, Anthony Richard; Luding, Stefan
2011-01-01
A new cluster based event-driven algorithm is developed to simulate the formation of clusters in a two dimensional gas: particles move freely until they collide and "stick" together irreversibly. These clusters aggregate into bigger structures in an isotompic way, forming fractal structures whose
Clustering algorithms for Stokes space modulation format recognition
DEFF Research Database (Denmark)
Boada, Ricard; Borkowski, Robert; Tafur Monroy, Idelfonso
2015-01-01
influences the performance of the detection process, particularly at low signal-to-noise ratios. This paper reports on an extensive study of six different clustering algorithms: k-means, expectation maximization, density-based DBSCAN and OPTICS, spectral clustering and maximum likelihood clustering, used...
Performance Evaluation of Incremental K-means Clustering Algorithm
Chakraborty, Sanjay; Nagwani, N. K.
2014-01-01
The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algorithm using air pollution database. This paper also describes the comparison on the performance evaluations between existing K-means clustering and i...
Co-clustering models, algorithms and applications
Govaert, Gérard
2013-01-01
Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixture
General-purpose radiographic and fluoroscopic table
International Nuclear Information System (INIS)
Ishizaki, Noritaka
1982-01-01
A new series of diagnostic tables, Model DT-KEL, was developed for general-purpose radiographic and fluoroscopic systems. Through several investigations, the table was so constructed that the basic techniques be general radiography and GI examination, and other techniques be optionally added. The diagnostic tables involve the full series of the type for various purposes and are systematized with the surrounding equipment. A retractable mechanism of grids was adopted first for general use. The fine grids with a density of 57 lines per cm, which was adopted in KEL-2, reduced the X-ray doses by 16 percent. (author)
Report of the general purpose detector group
International Nuclear Information System (INIS)
Barbaro-Galtieri, A.; Bartel, W.; Bulos, F.; Cool, R.; Hanson, G.; Koetz, U.; Kottahaus, R.; Loken, S.; Luke, D.; Rothenberg, A.
1975-01-01
A general purpose detector for PEP is described. The main components of this detector are a l meter radius, 15 kilogauss superconducting solenoidal magnet with drift chambers to detect and measure the momentum of charged particles, a liquid argon neutral detector and hadron calorimeter, and a system of Cherenkov and time-of-flight counters for identification of charged hadrons. A major consideration in the design of this detector was that it be flexible: the magnet coil and drift chambers form a core around which various apparatus for specialized detection can be placed
A novel clustering algorithm based on quantum games
International Nuclear Information System (INIS)
Li Qiang; He Yan; Jiang Jingping
2009-01-01
Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.
General-purpose parallel simulator for quantum computing
International Nuclear Information System (INIS)
Niwa, Jumpei; Matsumoto, Keiji; Imai, Hiroshi
2002-01-01
With current technologies, it seems to be very difficult to implement quantum computers with many qubits. It is therefore of importance to simulate quantum algorithms and circuits on the existing computers. However, for a large-size problem, the simulation often requires more computational power than is available from sequential processing. Therefore, simulation methods for parallel processors are required. We have developed a general-purpose simulator for quantum algorithms/circuits on the parallel computer (Sun Enterprise4500). It can simulate algorithms/circuits with up to 30 qubits. In order to test efficiency of our proposed methods, we have simulated Shor's factorization algorithm and Grover's database search, and we have analyzed robustness of the corresponding quantum circuits in the presence of both decoherence and operational errors. The corresponding results, statistics, and analyses are presented in this paper
Modification of MSDR algorithm and ITS implementation on graph clustering
Prastiwi, D.; Sugeng, K. A.; Siswantining, T.
2017-07-01
Maximum Standard Deviation Reduction (MSDR) is a graph clustering algorithm to minimize the distance variation within a cluster. In this paper we propose a modified MSDR by replacing one technical step in MSDR which uses polynomial regression, with a new and simpler step. This leads to our new algorithm called Modified MSDR (MMSDR). We implement the new algorithm to separate a domestic flight network of an Indonesian airline into two large clusters. Further analysis allows us to discover a weak link in the network, which should be improved by adding more flights.
General Purpose Crate (GPC) for control applications
International Nuclear Information System (INIS)
Singh, Kundan; Munda, Deepak K.; Jain, Mamta; Archunan, M.; Barua, P.; Ajith Kumar, B.P.
2011-01-01
A General Purpose Crate (GPC) capable of handling digital and analog Inputs/Outputs signals has been developed at Inter University Accelerator Centre (IUAC), New Delhi, for accelerator control system applications. The system includes back-plane bus with on board plugged-in single board computer with PC104 and Ethernet interface, running Linux operating system. The bus control logic is designed on the back-plane pcb itself, making the system more rugged. The various types of digital and analog input/output modules can be plugged into the back plane bus randomly with standard euro connectors, which provides highly reliable and dust free contacts. Maximum eight modules can be inserted into the crate. The total power consumption for various types of modules and back-plane controller is approximately 50 watts. The multi-output DC power supply from COSEL has been used in the crate. The general purpose crate is software compatible with the CAMAC crates used in the accelerator control system. (author)
Speed Control of General Purpose Engine with Electronic Governor
Sawut, Umerujan; Tohti, Gheyret; Takigawa, Buso; Tsuji, Teruo
This paper presents a general purpose engine speed control system with an electronic governor in order to improve the current system with a mechanical governor which shows unstable characteristics by change of mecanical friction or A/F ratio (Air/Fuel ratio). For the control system above, there are problems that the feedback signal is only a crank angle because of cost and the controlled object is a general purpose engine which is strongly nonlinear. In order to overcome these problems, the system model is shown for the dynamic estimation of the amount of air flow and the robust controller is designed. That is, the proposed system includes the robust sliding-mode controller by the feedback signal of only a crank angle where Genetic Algorithm is applied for the controller design. The simulation and the experiments by MATLAB/Simulink are performed to show the effectiveness of our proposal.
APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags
DEFF Research Database (Denmark)
Zong, Yu; Xu, Guandong; Jin, Pin
2011-01-01
algorithm for Tags (APPECT). The main steps of APPECT are: (1) we execute the K-means algorithm on a tag similarity matrix for M times and collect a set of tag clustering results Z={C1,C2,…,Cm}; (2) we form the approximate backbone of Z by executing a greedy search; (3) we fix the approximate backbone...... as the initial tag clustering result and then assign the rest tags into the corresponding clusters based on the similarity. Experimental results on three real world datasets namely MedWorm, MovieLens and Dmoz demonstrate the effectiveness and the superiority of the proposed method against the traditional...... Agglomerative Clustering on tagging data, which possess the inherent drawbacks, such as the sensitivity of initialization. In this paper, we instead make use of the approximate backbone of tag clustering results to find out better tag clusters. In particular, we propose an APProximate backbonE-based Clustering...
Improved multi-objective clustering algorithm using particle swarm optimization.
Directory of Open Access Journals (Sweden)
Congcong Gong
Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.
Improved multi-objective clustering algorithm using particle swarm optimization.
Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang
2017-01-01
Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.
A High-Order CFS Algorithm for Clustering Big Data
Directory of Open Access Journals (Sweden)
Fanyu Bu
2016-01-01
Full Text Available With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i an adaptive dropout deep learning model to learn features from each type of data, (ii a feature tensor model to capture the correlations of heterogeneous data, and (iii a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.
Parallel clustering algorithm for large-scale biological data sets.
Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang
2014-01-01
Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.
General-Purpose Software For Computer Graphics
Rogers, Joseph E.
1992-01-01
NASA Device Independent Graphics Library (NASADIG) is general-purpose computer-graphics package for computer-based engineering and management applications which gives opportunity to translate data into effective graphical displays for presentation. Features include two- and three-dimensional plotting, spline and polynomial interpolation, control of blanking of areas, multiple log and/or linear axes, control of legends and text, control of thicknesses of curves, and multiple text fonts. Included are subroutines for definition of areas and axes of plots; setup and display of text; blanking of areas; setup of style, interpolation, and plotting of lines; control of patterns and of shading of colors; control of legends, blocks of text, and characters; initialization of devices; and setting of mixed alphabets. Written in FORTRAN 77.
A new hybrid imperialist competitive algorithm on data clustering
Indian Academy of Sciences (India)
Modified imperialist competitive algorithm; simulated annealing; ... Clustering is one of the unsupervised learning branches where a set of patterns, usually vectors ..... machine classification is based on design, operation, and/or purpose.
An AK-LDMeans algorithm based on image clustering
Chen, Huimin; Li, Xingwei; Zhang, Yongbin; Chen, Nan
2018-03-01
Clustering is an effective analytical technique for handling unmarked data for value mining. Its ultimate goal is to mark unclassified data quickly and correctly. We use the roadmap for the current image processing as the experimental background. In this paper, we propose an AK-LDMeans algorithm to automatically lock the K value by designing the Kcost fold line, and then use the long-distance high-density method to select the clustering centers to further replace the traditional initial clustering center selection method, which further improves the efficiency and accuracy of the traditional K-Means Algorithm. And the experimental results are compared with the current clustering algorithm and the results are obtained. The algorithm can provide effective reference value in the fields of image processing, machine vision and data mining.
Flowbca : A flow-based cluster algorithm in Stata
Meekes, J.; Hassink, W.H.J.
In this article, we introduce the Stata implementation of a flow-based cluster algorithm written in Mata. The main purpose of the flowbca command is to identify clusters based on relational data of flows. We illustrate the command by providing multiple applications, from the research fields of
Efficient Record Linkage Algorithms Using Complete Linkage Clustering.
Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar
2016-01-01
Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times.
Collaborative filtering recommendation model based on fuzzy clustering algorithm
Yang, Ye; Zhang, Yunhua
2018-05-01
As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.
Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis
Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song
2018-01-01
To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.
SRAC95; general purpose neutronics code system
International Nuclear Information System (INIS)
Okumura, Keisuke; Tsuchihashi, Keichiro; Kaneko, Kunio.
1996-03-01
SRAC is a general purpose neutronics code system applicable to core analyses of various types of reactors. Since the publication of JAERI-1302 for the revised SRAC in 1986, a number of additions and modifications have been made for nuclear data libraries and programs. Thus, the new version SRAC95 has been completed. The system consists of six kinds of nuclear data libraries(ENDF/B-IV, -V, -VI, JENDL-2, -3.1, -3.2), five modular codes integrated into SRAC95; collision probability calculation module (PIJ) for 16 types of lattice geometries, Sn transport calculation modules(ANISN, TWOTRAN), diffusion calculation modules(TUD, CITATION) and two optional codes for fuel assembly and core burn-up calculations(newly developed ASMBURN, revised COREBN). In this version, many new functions and data are implemented to support nuclear design studies of advanced reactors, especially for burn-up calculations. SRAC95 is available not only on conventional IBM-compatible computers but also on scalar or vector computers with the UNIX operating system. This report is the SRAC95 users manual which contains general description, contents of revisions, input data requirements, detail information on usage, sample input data and list of available libraries. (author)
SRAC95; general purpose neutronics code system
Energy Technology Data Exchange (ETDEWEB)
Okumura, Keisuke; Tsuchihashi, Keichiro [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment; Kaneko, Kunio
1996-03-01
SRAC is a general purpose neutronics code system applicable to core analyses of various types of reactors. Since the publication of JAERI-1302 for the revised SRAC in 1986, a number of additions and modifications have been made for nuclear data libraries and programs. Thus, the new version SRAC95 has been completed. The system consists of six kinds of nuclear data libraries(ENDF/B-IV, -V, -VI, JENDL-2, -3.1, -3.2), five modular codes integrated into SRAC95; collision probability calculation module (PIJ) for 16 types of lattice geometries, Sn transport calculation modules(ANISN, TWOTRAN), diffusion calculation modules(TUD, CITATION) and two optional codes for fuel assembly and core burn-up calculations(newly developed ASMBURN, revised COREBN). In this version, many new functions and data are implemented to support nuclear design studies of advanced reactors, especially for burn-up calculations. SRAC95 is available not only on conventional IBM-compatible computers but also on scalar or vector computers with the UNIX operating system. This report is the SRAC95 users manual which contains general description, contents of revisions, input data requirements, detail information on usage, sample input data and list of available libraries. (author).
Research on retailer data clustering algorithm based on Spark
Huang, Qiuman; Zhou, Feng
2017-03-01
Big data analysis is a hot topic in the IT field now. Spark is a high-reliability and high-performance distributed parallel computing framework for big data sets. K-means algorithm is one of the classical partition methods in clustering algorithm. In this paper, we study the k-means clustering algorithm on Spark. Firstly, the principle of the algorithm is analyzed, and then the clustering analysis is carried out on the supermarket customers through the experiment to find out the different shopping patterns. At the same time, this paper proposes the parallelization of k-means algorithm and the distributed computing framework of Spark, and gives the concrete design scheme and implementation scheme. This paper uses the two-year sales data of a supermarket to validate the proposed clustering algorithm and achieve the goal of subdividing customers, and then analyze the clustering results to help enterprises to take different marketing strategies for different customer groups to improve sales performance.
Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering.
He, Zhaoshui; Xie, Shengli; Zdunek, Rafal; Zhou, Guoxu; Cichocki, Andrzej
2011-12-01
Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.
Robustness of the ATLAS pixel clustering neural network algorithm
AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration
2016-01-01
Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. In the ATLAS track reconstruction algorithm, an artificial neural network is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The robustness of the neural network algorithm is presented, probing its sensitivity to uncertainties in the detector conditions. The robustness is studied by evaluating the stability of the algorithm's performance under a range of variations in the inputs to the neural networks. Within reasonable variation magnitudes, the neural networks prove to be robust to most variation types.
Cluster algorithms with empahsis on quantum spin systems
International Nuclear Information System (INIS)
Gubernatis, J.E.; Kawashima, Naoki
1995-01-01
The purpose of this lecture is to discuss in detail the generalized approach of Kawashima and Gubernatis for the construction of cluster algorithms. We first present a brief refresher on the Monte Carlo method, describe the Swendsen-Wang algorithm, show how this algorithm follows from the Fortuin-Kastelyn transformation, and re=interpret this transformation in a form which is the basis of the generalized approach. We then derive the essential equations of the generalized approach. This derivation is remarkably simple if done from the viewpoint of probability theory, and the essential assumptions will be clearly stated. These assumptions are implicit in all useful cluster algorithms of which we are aware. They lead to a quite different perspective on cluster algorithms than found in the seminal works and in Ising model applications. Next, we illustrate how the generalized approach leads to a cluster algorithm for world-line quantum Monte Carlo simulations of Heisenberg models with S = 1/2. More succinctly, we also discuss the generalization of the Fortuin- Kasetelyn transformation to higher spin models and illustrate the essential steps for a S = 1 Heisenberg model. Finally, we summarize how to go beyond S = 1 to a general spin, XYZ model
A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering
Chahine, Firas Safwan
2012-01-01
Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…
Personalized PageRank Clustering: A graph clustering algorithm based on random walks
A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali
2013-11-01
Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.
AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION
Directory of Open Access Journals (Sweden)
V.G. Biju
2015-11-01
Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.
Spin chain simulations with a meron cluster algorithm
International Nuclear Information System (INIS)
Boyer, T.; Bietenholz, W.; Deutsches Elektronen-Synchrotron; Wuilloud, J.; Geneve Univ.
2007-01-01
We apply a meron cluster algorithm to the XY spin chain, which describes a quantum rotor. This is a multi-cluster simulation supplemented by an improved estimator, which deals with objects of half-integer topological charge. This method is powerful enough to provide precise results for the model with a θ-term - it is therefore one of the rare examples, where a system with a complex action can be solved numerically. In particular we measure the correlation length, as well as the topological and magnetic susceptibility. We discuss the algorithmic efficiency in view of the critical slowing down. Due to the excellent performance that we observe, it is strongly motivated to work on new applications of meron cluster algorithms in higher dimensions. (orig.)
A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis
Directory of Open Access Journals (Sweden)
Shaoning Li
2017-01-01
Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.
Core Business Selection Based on Ant Colony Clustering Algorithm
Directory of Open Access Journals (Sweden)
Yu Lan
2014-01-01
Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.
Identifying multiple influential spreaders by a heuristic clustering algorithm
Energy Technology Data Exchange (ETDEWEB)
Bao, Zhong-Kui [School of Mathematical Science, Anhui University, Hefei 230601 (China); Liu, Jian-Guo [Data Science and Cloud Service Research Center, Shanghai University of Finance and Economics, Shanghai, 200133 (China); Zhang, Hai-Feng, E-mail: haifengzhang1978@gmail.com [School of Mathematical Science, Anhui University, Hefei 230601 (China); Department of Communication Engineering, North University of China, Taiyuan, Shan' xi 030051 (China)
2017-03-18
The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.
Identifying multiple influential spreaders by a heuristic clustering algorithm
International Nuclear Information System (INIS)
Bao, Zhong-Kui; Liu, Jian-Guo; Zhang, Hai-Feng
2017-01-01
The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.
General-purpose event generators for LHC physics
Buckley, Andy; Gieseke, Stefan; Grellscheid, David; Hoche, Stefan; Hoeth, Hendrik; Krauss, Frank; Lonnblad, Leif; Nurse, Emily; Richardson, Peter; Schumann, Steffen; Seymour, Michael H.; Sjostrand, Torbjorn; Skands, Peter; Webber, Bryan
2011-01-01
We review the physics basis, main features and use of general-purpose Monte Carlo event generators for the simulation of proton-proton collisions at the Large Hadron Collider. Topics included are: the generation of hard-scattering matrix elements for processes of interest, at both leading and next-to-leading QCD perturbative order; their matching to approximate treatments of higher orders based on the showering approximation; the parton and dipole shower formulations; parton distribution functions for event generators; non-perturbative aspects such as soft QCD collisions, the underlying event and diffractive processes; the string and cluster models for hadron formation; the treatment of hadron and tau decays; the inclusion of QED radiation and beyond-Standard-Model processes. We describe the principal features of the ARIADNE, Herwig++, PYTHIA 8 and SHERPA generators, together with the Rivet and Professor validation and tuning tools, and discuss the physics philosophy behind the proper use of these generators ...
A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
2017-01-01
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
The Efficiency of Linda for General Purpose Scientific Programming
Directory of Open Access Journals (Sweden)
Timothy G. Mattson
1994-01-01
Full Text Available Linda (Linda is a registered trademark of Scientific Computing Associates, Inc. is a programming language for coordinating the execution and interaction of processes. When combined with a language for computation (such as C or Fortran, the resulting hybrid language can be used to write portable programs for parallel and distributed multiple instruction multiple data (MIMD computers. The Linda programming model is based on operations that read, write, and erase a virtual shared memory. It is easy to use, and lets the programmer code in a very expressive, uncoupled programming style. These benefits, however, are of little value unless Linda programs execute efficiently. The goal of this article is to demonstrate that Linda programs are efficient making Linda an effective general purpose tool for programming MIMD parallel computers. Two arguments for Linda's efficiency are given; the first is based on Linda's implementation and the second on a range of case studies spanning a complete set of parallel algorithm classes.
A similarity based agglomerative clustering algorithm in networks
Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong
2018-04-01
The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.
Fuzzy cluster means algorithm for the diagnosis of confusable disease
African Journals Online (AJOL)
... end platform while Microsoft Access was used as the database application. The system gives a measure of each disease within a set of confusable disease. The proposed system had a classification accuracy of 60%. Keywords: Artificial Intelligence, expert system Fuzzy cluster – means Algorithm, physician, Diagnosis ...
Modified genetic algorithms to model cluster structures in medium-size silicon clusters
International Nuclear Information System (INIS)
Bazterra, Victor E.; Ona, Ofelia; Caputo, Maria C.; Ferraro, Marta B.; Fuentealba, Patricio; Facelli, Julio C.
2004-01-01
This paper presents the results obtained using a genetic algorithm (GA) to search for stable structures of medium size silicon clusters. In this work the GA uses a semiempirical energy function to find the best cluster structures, which are further optimized using density-functional theory. For small clusters our results agree well with previously reported structures, but for larger ones different structures appear. This is the case of Si 36 where we report a different structure, with significant lower energy than those previously found using limited search approaches on common structural motifs. This demonstrates the need for global optimization schemes when searching for stable structures of medium-size silicon clusters
The C4 clustering algorithm: Clusters of galaxies in the Sloan Digital Sky Survey
Energy Technology Data Exchange (ETDEWEB)
Miller, Christopher J.; Nichol, Robert; Reichart, Dan; Wechsler, Risa H.; Evrard, August; Annis, James; McKay, Timothy; Bahcall, Neta; Bernardi, Mariangela; Boehringer,; Connolly, Andrew; Goto, Tomo; Kniazev, Alexie; Lamb, Donald; Postman, Marc; Schneider, Donald; Sheth, Ravi; Voges, Wolfgang; /Cerro-Tololo InterAmerican Obs. /Portsmouth U.,
2005-03-01
We present the ''C4 Cluster Catalog'', a new sample of 748 clusters of galaxies identified in the spectroscopic sample of the Second Data Release (DR2) of the Sloan Digital Sky Survey (SDSS). The C4 cluster-finding algorithm identifies clusters as overdensities in a seven-dimensional position and color space, thus minimizing projection effects that have plagued previous optical cluster selection. The present C4 catalog covers {approx}2600 square degrees of sky and ranges in redshift from z = 0.02 to z = 0.17. The mean cluster membership is 36 galaxies (with redshifts) brighter than r = 17.7, but the catalog includes a range of systems, from groups containing 10 members to massive clusters with over 200 cluster members with redshifts. The catalog provides a large number of measured cluster properties including sky location, mean redshift, galaxy membership, summed r-band optical luminosity (L{sub r}), velocity dispersion, as well as quantitative measures of substructure and the surrounding large-scale environment. We use new, multi-color mock SDSS galaxy catalogs, empirically constructed from the {Lambda}CDM Hubble Volume (HV) Sky Survey output, to investigate the sensitivity of the C4 catalog to the various algorithm parameters (detection threshold, choice of passbands and search aperture), as well as to quantify the purity and completeness of the C4 cluster catalog. These mock catalogs indicate that the C4 catalog is {approx_equal}90% complete and 95% pure above M{sub 200} = 1 x 10{sup 14} h{sup -1}M{sub {circle_dot}} and within 0.03 {le} z {le} 0.12. Using the SDSS DR2 data, we show that the C4 algorithm finds 98% of X-ray identified clusters and 90% of Abell clusters within 0.03 {le} z {le} 0.12. Using the mock galaxy catalogs and the full HV dark matter simulations, we show that the L{sub r} of a cluster is a more robust estimator of the halo mass (M{sub 200}) than the galaxy line-of-sight velocity dispersion or the richness of the cluster
Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.
Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy
2016-01-01
Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.
High-dimensional cluster analysis with the Masked EM Algorithm
Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.
2014-01-01
Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694
A HYBRID HEURISTIC ALGORITHM FOR THE CLUSTERED TRAVELING SALESMAN PROBLEM
Directory of Open Access Journals (Sweden)
Mário Mestria
2016-04-01
Full Text Available ABSTRACT This paper proposes a hybrid heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedure, Iterated Local Search and Variable Neighborhood Descent, to solve the Clustered Traveling Salesman Problem (CTSP. Hybrid Heuristic algorithm uses several variable neighborhood structures combining the intensification (using local search operators and diversification (constructive heuristic and perturbation routine. In the CTSP, the vertices are partitioned into clusters and all vertices of each cluster have to be visited contiguously. The CTSP is -hard since it includes the well-known Traveling Salesman Problem (TSP as a special case. Our hybrid heuristic is compared with three heuristics from the literature and an exact method. Computational experiments are reported for different classes of instances. Experimental results show that the proposed hybrid heuristic obtains competitive results within reasonable computational time.
7 CFR 2902.48 - General purpose household cleaners.
2010-01-01
... 7 Agriculture 15 2010-01-01 2010-01-01 false General purpose household cleaners. 2902.48 Section... PROCUREMENT Designated Items § 2902.48 General purpose household cleaners. (a) Definition. Products designed... procurement preference for qualifying biobased general purpose household cleaners. By that date, Federal...
7 CFR 2902.37 - General purpose de-icers.
2010-01-01
... 7 Agriculture 15 2010-01-01 2010-01-01 false General purpose de-icers. 2902.37 Section 2902.37... Items § 2902.37 General purpose de-icers. (a) Definition. Chemical products (e.g., salt, fluids) that... preference for qualifying biobased general purpose de-icers. By that date, Federal agencies that have the...
47 CFR 32.6124 - General purpose computers expense.
2010-10-01
... 47 Telecommunication 2 2010-10-01 2010-10-01 false General purpose computers expense. 32.6124... General purpose computers expense. This account shall include the costs of personnel whose principal job is the physical operation of general purpose computers and the maintenance of operating systems. This...
Clustering Using Boosted Constrained k-Means Algorithm
Directory of Open Access Journals (Sweden)
Masayuki Okabe
2018-03-01
Full Text Available This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learning-based methods. Since it simply adds a function into the data assignment process of the k-means algorithm to check for constraint violations, it often exploits only a small number of constraints. Metric learning-based methods, which exploit constraints to create a new metric for data similarity, have shown promising results although the methods proposed so far are often slow depending on the amount of data or number of feature dimensions. We present a method that exploits the advantages of the constrained k-means and metric learning approaches. It incorporates a mechanism for accepting constraint priorities and a metric learning framework based on the boosting principle into a constrained k-means algorithm. In the framework, a metric is learned in the form of a kernel matrix that integrates weak cluster hypotheses produced by the constrained k-means algorithm, which works as a weak learner under the boosting principle. Experimental results for 12 data sets from 3 data sources demonstrated that our method has performance competitive to those of state-of-the-art constrained clustering methods for most data sets and that it takes much less computation time. Experimental evaluation demonstrated the effectiveness of controlling the constraint priorities by using the boosting principle and that our constrained k-means algorithm functions correctly as a weak learner of boosting.
Which clustering algorithm is better for predicting protein complexes?
Directory of Open Access Journals (Sweden)
Moschopoulos Charalampos N
2011-12-01
Full Text Available Abstract Background Protein-Protein interactions (PPI play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks. Results In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H and Tandem Affinity Purification (TAP methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases. Conclusions While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm
Evaluation of clustering algorithms for protein-protein interaction networks
Directory of Open Access Journals (Sweden)
van Helden Jacques
2006-11-01
Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This
A heuristic approach to possibilistic clustering algorithms and applications
Viattchenin, Dmitri A
2013-01-01
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects. The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientist...
Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering
Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang
2012-01-01
Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043
ABCluster: the artificial bee colony algorithm for cluster global optimization.
Zhang, Jun; Dolg, Michael
2015-10-07
Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.
Robustness of the ATLAS pixel clustering neural network algorithm
AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration
2016-01-01
Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. The algorithms depend heavily on accurate estimation of the position of particles as they traverse the inner detector elements. An artificial neural network algorithm is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The method recovers otherwise lost tracks in dense environments where particles are separated by distances comparable to the size of the detector read-out elements. Such environments are highly relevant for LHC run 2, e.g. in searches for heavy resonances. Within the scope of run 2 track reconstruction performance and upgrades, the robustness of the neural network algorithm will be presented. The robustness has been studied by evaluating the stability of the algorithm’s performance under a range of variations in the pixel detector conditions.
Stochastic cluster algorithms for discrete Gaussian (SOS) models
International Nuclear Information System (INIS)
Evertz, H.G.; Hamburg Univ.; Hasenbusch, M.; Marcu, M.; Tel Aviv Univ.; Pinn, K.; Muenster Univ.; Solomon, S.
1990-10-01
We present new Monte Carlo cluster algorithms which eliminate critical slowing down in the simulation of solid-on-solid models. In this letter we focus on the two-dimensional discrete Gaussian model. The algorithms are based on reflecting the integer valued spin variables with respect to appropriately chosen reflection planes. The proper choice of the reflection plane turns out to be crucial in order to obtain a small dynamical exponent z. Actually, the successful versions of our algorithm are a mixture of two different procedures for choosing the reflection plane, one of them ergodic but slow, the other one non-ergodic and also slow when combined with a Metropolis algorithm. (orig.)
clusterMaker: a multi-algorithm clustering plugin for Cytoscape
Directory of Open Access Journals (Sweden)
Morris John H
2011-11-01
Full Text Available Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view, k-means, k-medoid, SCPS, AutoSOME, and native (Java MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin cluster
Exploring New Clustering Algorithms for the CMS Tracker FED
Gamboa Alvarado, Jose Leandro
2013-01-01
In the current Front End (FE) firmware clusters of hits within the APV frames are found using a simple threshold comparison (which is made between the data and a 3 or 5 sigma strip noise cut) on reordered pedestal and Common Mode (CM) noise subtracted data. In addition the CM noise subtraction requires the baseline of each APV frame to be approximately uniform. Therefore, the current algorithm will fail if the APV baseline exhibits large-scale non-uniform behavior. Under very high luminosity conditions the assumption of a uniform APV baseline breaks down and the FED is unable to maintain a high efficiency of cluster finding. \
Foam A General Purpose Cellular Monte Carlo Event Generator
Jadach, Stanislaw
2003-01-01
A general purpose, self-adapting, Monte Carlo (MC) event generator (simulator) is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain into small cells. The cells can be $n$-dimensional simplices, hyperrectangles or Cartesian product of them. The grid of cells, called ``foam'', is produced in the process of the binary split of the cells. The choice of the next cell to be divided and the position/direction of the division hyper-plane is driven by the algorithm which optimizes the ratio of the maximum weight to the average weight or (optionally) the total variance. The algorithm is able to deal, in principle, with an arbitrary pattern of the singularities in the distribution. As any MC generator, it can also be used for the MC integration. With the typical personal computer CPU, the program is able to perform adaptive integration/simulation at relatively small number of dimensions ($\\leq 16$). With the continu...
FCM Clustering Algorithms for Segmentation of Brain MR Images
Directory of Open Access Journals (Sweden)
Yogita K. Dubey
2016-01-01
Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.
Synchronous Firefly Algorithm for Cluster Head Selection in WSN
Directory of Open Access Journals (Sweden)
Madhusudhanan Baskaran
2015-01-01
Full Text Available Wireless Sensor Network (WSN consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.
Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy
Directory of Open Access Journals (Sweden)
Ning Li
2016-01-01
Full Text Available A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.
Advanced defect detection algorithm using clustering in ultrasonic NDE
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.
Аdaptive clustering algorithm for recommender systems
Stekh, Yu.; Artsibasov, V.
2012-01-01
In this article adaptive clustering algorithm for recommender systems is developed. Розроблено адаптивний алгоритм кластеризації для рекомендаційних систем.
A cluster analysis on road traffic accidents using genetic algorithms
Saharan, Sabariah; Baragona, Roberto
2017-04-01
The analysis of traffic road accidents is increasingly important because of the accidents cost and public road safety. The availability or large data sets makes the study of factors that affect the frequency and severity accidents are viable. However, the data are often highly unbalanced and overlapped. We deal with the data set of the road traffic accidents recorded in Christchurch, New Zealand, from 2000-2009 with a total of 26440 accidents. The data is in a binary set and there are 50 factors road traffic accidents with four level of severity. We used genetic algorithm for the analysis because we are in the presence of a large unbalanced data set and standard clustering like k-means algorithm may not be suitable for the task. The genetic algorithm based on clustering for unknown K, (GCUK) has been used to identify the factors associated with accidents of different levels of severity. The results provided us with an interesting insight into the relationship between factors and accidents severity level and suggest that the two main factors that contributes to fatal accidents are "Speed greater than 60 km h" and "Did not see other people until it was too late". A comparison with the k-means algorithm and the independent component analysis is performed to validate the results.
Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion
Directory of Open Access Journals (Sweden)
Jin Qi
2015-01-01
Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.
An improved clustering algorithm based on reverse learning in intelligent transportation
Qiu, Guoqing; Kou, Qianqian; Niu, Ting
2017-05-01
With the development of artificial intelligence and data mining technology, big data has gradually entered people's field of vision. In the process of dealing with large data, clustering is an important processing method. By introducing the reverse learning method in the clustering process of PAM clustering algorithm, to further improve the limitations of one-time clustering in unsupervised clustering learning, and increase the diversity of clustering clusters, so as to improve the quality of clustering. The algorithm analysis and experimental results show that the algorithm is feasible.
General-purpose event generators for LHC physics
Energy Technology Data Exchange (ETDEWEB)
Buckley, Andy [PPE Group, School of Physics and Astronomy, University of Edinburgh, EH25 9PN (United Kingdom); Butterworth, Jonathan [Department of Physics and Astronomy, University College London, WC1E 6BT (United Kingdom); Gieseke, Stefan [Institute for Theoretical Physics, Karlsruhe Institute of Technology, D-76128 Karlsruhe (Germany); Grellscheid, David [Institute for Particle Physics Phenomenology, Durham University, DH1 3LE (United Kingdom); Hoeche, Stefan [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Hoeth, Hendrik; Krauss, Frank [Institute for Particle Physics Phenomenology, Durham University, DH1 3LE (United Kingdom); Loennblad, Leif [Department of Astronomy and Theoretical Physics, Lund University (Sweden); PH Department, TH Unit, CERN, CH-1211 Geneva 23 (Switzerland); Nurse, Emily [Department of Physics and Astronomy, University College London, WC1E 6BT (United Kingdom); Richardson, Peter [Institute for Particle Physics Phenomenology, Durham University, DH1 3LE (United Kingdom); Schumann, Steffen [Institute for Theoretical Physics, University of Heidelberg, 69120 Heidelberg (Germany); Seymour, Michael H. [School of Physics and Astronomy, University of Manchester, M13 9PL (United Kingdom); Sjoestrand, Torbjoern [Department of Astronomy and Theoretical Physics, Lund University (Sweden); Skands, Peter [PH Department, TH Unit, CERN, CH-1211 Geneva 23 (Switzerland); Webber, Bryan, E-mail: webber@hep.phy.cam.ac.uk [Cavendish Laboratory, J.J. Thomson Avenue, Cambridge CB3 0HE (United Kingdom)
2011-07-15
We review the physics basis, main features and use of general-purpose Monte Carlo event generators for the simulation of proton-proton collisions at the Large Hadron Collider. Topics included are: the generation of hard scattering matrix elements for processes of interest, at both leading and next-to-leading QCD perturbative order; their matching to approximate treatments of higher orders based on the showering approximation; the parton and dipole shower formulations; parton distribution functions for event generators; non-perturbative aspects such as soft QCD collisions, the underlying event and diffractive processes; the string and cluster models for hadron formation; the treatment of hadron and tau decays; the inclusion of QED radiation and beyond Standard Model processes. We describe the principal features of the ARIADNE, Herwig++, PYTHIA 8 and SHERPA generators, together with the Rivet and Professor validation and tuning tools, and discuss the physics philosophy behind the proper use of these generators and tools. This review is aimed at phenomenologists wishing to understand better how parton-level predictions are translated into hadron-level events as well as experimentalists seeking a deeper insight into the tools available for signal and background simulation at the LHC.
General-purpose event generators for LHC physics
International Nuclear Information System (INIS)
Buckley, Andy; Butterworth, Jonathan; Gieseke, Stefan; Grellscheid, David; Hoeche, Stefan; Hoeth, Hendrik; Krauss, Frank; Loennblad, Leif; Nurse, Emily; Richardson, Peter; Schumann, Steffen; Seymour, Michael H.; Sjoestrand, Torbjoern; Skands, Peter; Webber, Bryan
2011-01-01
We review the physics basis, main features and use of general-purpose Monte Carlo event generators for the simulation of proton-proton collisions at the Large Hadron Collider. Topics included are: the generation of hard scattering matrix elements for processes of interest, at both leading and next-to-leading QCD perturbative order; their matching to approximate treatments of higher orders based on the showering approximation; the parton and dipole shower formulations; parton distribution functions for event generators; non-perturbative aspects such as soft QCD collisions, the underlying event and diffractive processes; the string and cluster models for hadron formation; the treatment of hadron and tau decays; the inclusion of QED radiation and beyond Standard Model processes. We describe the principal features of the ARIADNE, Herwig++, PYTHIA 8 and SHERPA generators, together with the Rivet and Professor validation and tuning tools, and discuss the physics philosophy behind the proper use of these generators and tools. This review is aimed at phenomenologists wishing to understand better how parton-level predictions are translated into hadron-level events as well as experimentalists seeking a deeper insight into the tools available for signal and background simulation at the LHC.
General-purpose event generators for LHC physics
Energy Technology Data Exchange (ETDEWEB)
Buckley, Andy; /Edinburgh U.; Butterworth, Jonathan; /University Coll. London; Gieseke, Stefan; /Karlsruhe U., ITP; Grellscheid, David; /Durham U., IPPP; Hoche, Stefan; /SLAC; Hoeth, Hendrik; Krauss, Frank; /Durham U., IPPP; Lonnblad, Leif; /Lund U., Dept. Theor. Phys. /CERN; Nurse, Emily; /University Coll. London; Richardson, Peter; /Durham U., IPPP; Schumann, Steffen; /Heidelberg U.; Seymour, Michael H.; /Manchester U.; Sjostrand, Torbjorn; /Lund U., Dept. Theor. Phys.; Skands, Peter; /CERN; Webber, Bryan; /Cambridge U.
2011-03-03
We review the physics basis, main features and use of general-purpose Monte Carlo event generators for the simulation of proton-proton collisions at the Large Hadron Collider. Topics included are: the generation of hard-scattering matrix elements for processes of interest, at both leading and next-to-leading QCD perturbative order; their matching to approximate treatments of higher orders based on the showering approximation; the parton and dipole shower formulations; parton distribution functions for event generators; non-perturbative aspects such as soft QCD collisions, the underlying event and diffractive processes; the string and cluster models for hadron formation; the treatment of hadron and tau decays; the inclusion of QED radiation and beyond-Standard-Model processes. We describe the principal features of the Ariadne, Herwig++, Pythia 8 and Sherpa generators, together with the Rivet and Professor validation and tuning tools, and discuss the physics philosophy behind the proper use of these generators and tools. This review is aimed at phenomenologists wishing to understand better how parton-level predictions are translated into hadron-level events as well as experimentalists wanting a deeper insight into the tools available for signal and background simulation at the LHC.
Finding reproducible cluster partitions for the k-means algorithm.
Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Chambers, Simon J
2013-01-01
K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.
7 CFR 225.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 225.1 Section 225.1... AGRICULTURE CHILD NUTRITION PROGRAMS SUMMER FOOD SERVICE PROGRAM General § 225.1 General purpose and scope... primary purpose of the Program is to provide food service to children from needy areas during periods when...
7 CFR 253.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 253.1 Section 253.1... AGRICULTURE GENERAL REGULATIONS AND POLICIES-FOOD DISTRIBUTION ADMINISTRATION OF THE FOOD DISTRIBUTION PROGRAM FOR HOUSEHOLDS ON INDIAN RESERVATIONS § 253.1 General purpose and scope. This part describes the terms...
7 CFR 277.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 277.1 Section 277.1 Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF... AGENCIES § 277.1 General purpose and scope. (a) Purpose. This part establishes uniform requirements for the...
7 CFR 245.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 245.1 Section 245.1 Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF... SCHOOLS § 245.1 General purpose and scope. (a) This part established the responsibilities of State...
7 CFR 285.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 285.1 Section 285.1... COMMONWEALTH OF PUERTO RICO § 285.1 General purpose and scope. This part describes the general terms and... government of the Commonwealth of Puerto Rico for the purpose of designing and conducting a nutrition...
7 CFR 220.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 220.1 Section 220.1 Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF AGRICULTURE CHILD NUTRITION PROGRAMS SCHOOL BREAKFAST PROGRAM § 220.1 General purpose and scope. This part...
7 CFR 1485.10 - General purpose and scope.
2010-01-01
... 7 Agriculture 10 2010-01-01 2010-01-01 false General purpose and scope. 1485.10 Section 1485.10... FOREIGN MARKETS FOR AGRICULTURAL COMMODITIES Market Access Program § 1485.10 General purpose and scope. (a.../Market Access Program (EIP/MAP). It also establishes the general terms and conditions applicable to MAP...
7 CFR 281.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 281.1 Section 281.1 Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF... RESERVATIONS § 281.1 General purpose and scope. (a) These regulations govern the operation of the Food Stamp...
7 CFR 215.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 215.1 Section 215.1... AGRICULTURE CHILD NUTRITION PROGRAMS SPECIAL MILK PROGRAM FOR CHILDREN § 215.1 General purpose and scope. This part announces the policies and prescribes the general regulations with respect to the Special Milk...
7 CFR 248.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 248.1 Section 248.1... AGRICULTURE CHILD NUTRITION PROGRAMS WIC FARMERS' MARKET NUTRITION PROGRAM (FMNP) General § 248.1 General purpose and scope. This part announces regulations under which the Secretary of Agriculture shall carry...
46 CFR 7.1 - General purpose of boundary lines.
2010-10-01
... 46 Shipping 1 2010-10-01 2010-10-01 false General purpose of boundary lines. 7.1 Section 7.1 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES General § 7.1 General purpose of boundary lines. The lines in this part delineate the application of the...
7 CFR 246.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 246.1 Section 246.1... General § 246.1 General purpose and scope. This part announces regulations under which the Secretary of... health by reason of inadequate nutrition or health care, or both. The purpose of the Program is to...
7 CFR 250.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 250.1 Section 250.1... AGRICULTURE GENERAL REGULATIONS AND POLICIES-FOOD DISTRIBUTION DONATION OF FOODS FOR USE IN THE UNITED STATES, ITS TERRITORIES AND POSSESSIONS AND AREAS UNDER ITS JURISDICTION General § 250.1 General purpose and...
7 CFR 1728.10 - General purpose and scope.
2010-01-01
... 7 Agriculture 11 2010-01-01 2010-01-01 false General purpose and scope. 1728.10 Section 1728.10 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE ELECTRIC STANDARDS AND SPECIFICATIONS FOR MATERIALS AND CONSTRUCTION § 1728.10 General purpose and...
7 CFR 251.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 251.1 Section 251.1... AGRICULTURE GENERAL REGULATIONS AND POLICIES-FOOD DISTRIBUTION THE EMERGENCY FOOD ASSISTANCE PROGRAM § 251.1 General purpose and scope. This part announces the policies and prescribes the regulations necessary to...
7 CFR 235.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 235.1 Section 235.1 Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF AGRICULTURE CHILD NUTRITION PROGRAMS STATE ADMINISTRATIVE EXPENSE FUNDS § 235.1 General purpose and scope...
7 CFR 226.1 - General purpose and scope.
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false General purpose and scope. 226.1 Section 226.1 Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF AGRICULTURE CHILD NUTRITION PROGRAMS CHILD AND ADULT CARE FOOD PROGRAM General § 226.1 General purpose and...
21 CFR 864.4010 - General purpose reagent.
2010-04-01
... 21 Food and Drugs 8 2010-04-01 2010-04-01 false General purpose reagent. 864.4010 Section 864.4010 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Specimen Preparation Reagents § 864.4010 General purpose...
47 CFR 32.2124 - General purpose computers.
2010-10-01
... 47 Telecommunication 2 2010-10-01 2010-10-01 false General purpose computers. 32.2124 Section 32... General purpose computers. (a) This account shall include the original cost of computers and peripheral... financial, statistical, or other business analytical reports; preparation of payroll, customer bills, and...
Standalone General Purpose Data Logger Design and Implementation
African Journals Online (AJOL)
This paper describes the design of a general purpose data logger that is compatible with a variety of transducers, potentially permitting the measurement and recording of a wide range of phenomena. The recorded data can be retrieved to a PC via an RS-232 serial port. The standalone general purpose data logger ...
Depth data research of GIS based on clustering analysis algorithm
Xiong, Yan; Xu, Wenli
2018-03-01
The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data. In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.
SNAP: A General Purpose Network Analysis and Graph Mining Library.
Leskovec, Jure; Sosič, Rok
2016-10-01
Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and meta-data on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic, they can be modified during the computation at low cost. SNAP is provided as an open source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.
Gravitation field algorithm and its application in gene cluster
Directory of Open Access Journals (Sweden)
Zheng Ming
2010-09-01
Full Text Available Abstract Background Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena. Results This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus. Conclusions The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab are publicly available at http://ccst.jlu.edu.cn/CSBG/GFA.
Development of Automatic Cluster Algorithm for Microcalcification in Digital Mammography
International Nuclear Information System (INIS)
Choi, Seok Yoon; Kim, Chang Soo
2009-01-01
Digital Mammography is an efficient imaging technique for the detection and diagnosis of breast pathological disorders. Six mammographic criteria such as number of cluster, number, size, extent and morphologic shape of microcalcification, and presence of mass, were reviewed and correlation with pathologic diagnosis were evaluated. It is very important to find breast cancer early when treatment can reduce deaths from breast cancer and breast incision. In screening breast cancer, mammography is typically used to view the internal organization. Clusterig microcalcifications on mammography represent an important feature of breast mass, especially that of intraductal carcinoma. Because microcalcification has high correlation with breast cancer, a cluster of a microcalcification can be very helpful for the clinical doctor to predict breast cancer. For this study, three steps of quantitative evaluation are proposed : DoG filter, adaptive thresholding, Expectation maximization. Through the proposed algorithm, each cluster in the distribution of microcalcification was able to measure the number calcification and length of cluster also can be used to automatically diagnose breast cancer as indicators of the primary diagnosis.
Study on distributed re-clustering algorithm for moblie wireless sensor networks
Directory of Open Access Journals (Sweden)
XU Chaojie
2016-04-01
Full Text Available In mobile wireless sensor networks,node mobility influences the topology of the hierarchically clustered network,thus affects packet delivery ratio and energy consumption of communications in clusters.To reduce the influence of node mobility,a distributed re-clustering algorithm is proposed in this paper.In this algorithm,basing on the clustered network,nodes estimate their current locations with particle algorithm and predict the most possible locations of next time basing on the mobility model.Each boundary node of a cluster periodically estimates the need for re-clustering and re-cluster itself to the optimal cluster through communicating with the cluster headers when needed.The simulation results indicate that,with small re-clustering periods,the proposed algorithm can be effective to keep appropriate communication distance and outperforms existing schemes on packet delivery ratio and energy consumption.
A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters
Directory of Open Access Journals (Sweden)
Weiwei Lin
2016-01-01
Full Text Available Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS. As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.
Ternary alloy material prediction using genetic algorithm and cluster expansion
Energy Technology Data Exchange (ETDEWEB)
Chen, Chong [Iowa State Univ., Ames, IA (United States)
2015-12-01
This thesis summarizes our study on the crystal structures prediction of Fe-V-Si system using genetic algorithm and cluster expansion. Our goal is to explore and look for new stable compounds. We started from the current ten known experimental phases, and calculated formation energies of those compounds using density functional theory (DFT) package, namely, VASP. The convex hull was generated based on the DFT calculations of the experimental known phases. Then we did random search on some metal rich (Fe and V) compositions and found that the lowest energy structures were body centered cube (bcc) underlying lattice, under which we did our computational systematic searches using genetic algorithm and cluster expansion. Among hundreds of the searched compositions, thirteen were selected and DFT formation energies were obtained by VASP. The stability checking of those thirteen compounds was done in reference to the experimental convex hull. We found that the composition, 24-8-16, i.e., Fe_{3}VSi_{2} is a new stable phase and it can be very inspiring to the future experiments.
A general purpose code for Monte Carlo simulations
International Nuclear Information System (INIS)
Wilcke, W.W.; Rochester Univ., NY
1984-01-01
A general-purpose computer code MONTHY has been written to perform Monte Carlo simulations of physical systems. To achieve a high degree of flexibility the code is organized like a general purpose computer, operating on a vector describing the time dependent state of the system under simulation. The instruction set of the 'computer' is defined by the user and is therefore adaptable to the particular problem studied. The organization of MONTHY allows iterative and conditional execution of operations. (orig.)
jClustering, an open framework for the development of 4D clustering algorithms.
Directory of Open Access Journals (Sweden)
José María Mateos-Pérez
Full Text Available We present jClustering, an open framework for the design of clustering algorithms in dynamic medical imaging. We developed this tool because of the difficulty involved in manually segmenting dynamic PET images and the lack of availability of source code for published segmentation algorithms. Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code. The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing. This tool has been coded in Java and is presented as an ImageJ plugin in order to take advantage of all the functionalities offered by this imaging analysis platform. Both binary packages and source code have been published, the latter under a free software license (GNU General Public License to allow modification if necessary.
Directory of Open Access Journals (Sweden)
Mingwei Leng
2013-01-01
Full Text Available The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.
A roadmap of clustering algorithms: finding a match for a biomedical application.
Andreopoulos, Bill; An, Aijun; Wang, Xiaogang; Schroeder, Michael
2009-05-01
Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms' benefits and drawbacks as a basis for matching them to biomedical applications.
KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance
Directory of Open Access Journals (Sweden)
Zhiwen ZU
2018-04-01
Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.
Using internal evaluation measures to validate the quality of diverse stream clustering algorithms
Hassani, M.; Seidl, T.
2017-01-01
Measuring the quality of a clustering algorithm has shown to be as important as the algorithm itself. It is a crucial part of choosing the clustering algorithm that performs best for an input data. Streaming input data have many features that make them much more challenging than static ones. They
Gong, Lina; Xu, Tao; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen
2017-03-01
The traditional microblog recommendation algorithm has the problems of low efficiency and modest effect in the era of big data. In the aim of solving these issues, this paper proposed a mixed recommendation algorithm with user clustering. This paper first introduced the situation of microblog marketing industry. Then, this paper elaborates the user interest modeling process and detailed advertisement recommendation methods. Finally, this paper compared the mixed recommendation algorithm with the traditional classification algorithm and mixed recommendation algorithm without user clustering. The results show that the mixed recommendation algorithm with user clustering has good accuracy and recall rate in the microblog advertisements promotion.
Directory of Open Access Journals (Sweden)
Guohua Zou
2016-12-01
Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.
An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets
Directory of Open Access Journals (Sweden)
Kang Zhang
2014-01-01
Full Text Available Clustering has been widely used in different fields of science, technology, social science, and so forth. In real world, numeric as well as categorical features are usually used to describe the data objects. Accordingly, many clustering methods can process datasets that are either numeric or categorical. Recently, algorithms that can handle the mixed data clustering problems have been developed. Affinity propagation (AP algorithm is an exemplar-based clustering method which has demonstrated good performance on a wide variety of datasets. However, it has limitations on processing mixed datasets. In this paper, we propose a novel similarity measure for mixed type datasets and an adaptive AP clustering algorithm is proposed to cluster the mixed datasets. Several real world datasets are studied to evaluate the performance of the proposed algorithm. Comparisons with other clustering algorithms demonstrate that the proposed method works well not only on mixed datasets but also on pure numeric and categorical datasets.
Clustering for Binary Data Sets by Using Genetic Algorithm-Incremental K-means
Saharan, S.; Baragona, R.; Nor, M. E.; Salleh, R. M.; Asrah, N. M.
2018-04-01
This research was initially driven by the lack of clustering algorithms that specifically focus in binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithms (GA). For the purpose of this research, GA was combined with the Incremental K-means (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. The implementation of IKM will give an advantage in terms of fast convergence. The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. In conclusion, the GAIKM outperformed other clustering algorithms such as GCUK, IKM, Scalable K-means (SKM) and K-means clustering and paves the way for future research involving missing data and outliers.
Robust K-Median and K-Means Clustering Algorithms for Incomplete Data
Directory of Open Access Journals (Sweden)
Jinhua Li
2016-01-01
Full Text Available Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms.
7 CFR 227.1 - General purpose and scope.
2010-01-01
... management training of school foodservice personnel, and (c) the conduct of nutrition education activities in... Agriculture Regulations of the Department of Agriculture (Continued) FOOD AND NUTRITION SERVICE, DEPARTMENT OF AGRICULTURE CHILD NUTRITION PROGRAMS NUTRITION EDUCATION AND TRAINING PROGRAM General § 227.1 General purpose...
On the System and Engineering Design of the General Purpose ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 13; Issue 5. On the System and Engineering Design of the General Purpose Electronic Digital Computer at TIFR. Rangaswamy Narasimhan. Classics Volume 13 Issue 5 May 2008 pp 490-501 ...
Geographical parthenogenesis: General purpose genotypes and frozen niche variation
DEFF Research Database (Denmark)
Vrijenhoek, Robert C.; Parker, Dave
2009-01-01
hypotheses concerning the evolution of niche breadth in asexual species - the "general-purpose genotype" (GPG) and "frozen niche-variation" (FNV) models. The two models are often portrayed as mutually exclusive, respectively viewing clonal lineages as generalists versus specialists. Nonetheless...
Weldability of general purpose heat source new-process iridium
International Nuclear Information System (INIS)
Kanne, W.R.
1987-01-01
Weldability tests on General Purpose Heat Source (GPHS) iridium capsules showed that a new iridium fabrication process reduced susceptibility to underbead cracking. Seventeen capsules were welded (a total of 255 welds) in four categories and the number of cracks in each weld was measured
General-purpose heat source development. Phase I: design requirements
International Nuclear Information System (INIS)
Snow, E.C.; Zocher, R.W.
1978-09-01
Studies have been performed to determine the necessary design requirements for a 238 PuO 2 General-Purpose Heat Source (GPHS). Systems and missions applications, as well as accident conditions, were considered. The results of these studies, along with the recommended GPHS design requirements, are given in this report
General-Purpose Data Containers for Science and Engineering
International Nuclear Information System (INIS)
2015-01-01
In 2012 the SG38 international committee was formed to develop a modern structure to replace the ENDF-6 format for storing evaluated nuclear reaction data on a computer system. This committee divided the project into seven tasks. One of these tasks, the design of General-Purpose Data Containers (GPDCs), is described in this article. What type of data does SG38 need to store and why is the task called General-Purpose Data Containers? The most common types of data in an evaluated nuclear reaction database are representations of physical functions in tabulated forms. There is also a need to store 1-dimensional functions using truncated Legendre or polynomial (or others) expansions. The phrase General-Purpose implies that the containers are to be designed to store generic forms of tabulated data rather than one for each physical function. Also, where possible, it would be beneficial to design containers that can store data forms not currently used in evaluated nuclear database or at least be easily extended. In addition to containers for storing physical functions as tabulated data, other types of containers are needed. There exists a desire within SG38 to support the storage of documentation at various levels within an evaluated file. Containers for storing non-functional data (e.g., a list of numbers) as well as units and labels for axes are also needed. Herein, containers for storing physical functions are called functional containers. One of the goals for the general-purpose data containers task is to design containers that will be useful to other scientific and engineering applications. To meet this goal, task members should think outside of the immediate needs of evaluated nuclear data to ensure that the containers are general- purpose rather than simply repackaged versions of existing containers. While the examples in this article may be specific to nuclear reaction data, it is hoped that the end product will be useful for other applications. To this end, some
Higher-spin cluster algorithms: the Heisenberg spin and U(1) quantum link models
Energy Technology Data Exchange (ETDEWEB)
Chudnovsky, V
2000-03-01
I discuss here how the highly-efficient spin-1/2 cluster algorithm for the Heisenberg antiferromagnet may be extended to higher-dimensional representations; some numerical results are provided. The same extensions can be used for the U(1) flux cluster algorithm, but have not yielded signals of the desired Coulomb phase of the system.
Higher-spin cluster algorithms: the Heisenberg spin and U(1) quantum link models
International Nuclear Information System (INIS)
Chudnovsky, V.
2000-01-01
I discuss here how the highly-efficient spin-1/2 cluster algorithm for the Heisenberg antiferromagnet may be extended to higher-dimensional representations; some numerical results are provided. The same extensions can be used for the U(1) flux cluster algorithm, but have not yielded signals of the desired Coulomb phase of the system
Study of parameters of the nearest neighbour shared algorithm on clustering documents
Mustika Rukmi, Alvida; Budi Utomo, Daryono; Imro’atus Sholikhah, Neni
2018-03-01
Document clustering is one way of automatically managing documents, extracting of document topics and fastly filtering information. Preprocess of clustering documents processed by textmining consists of: keyword extraction using Rapid Automatic Keyphrase Extraction (RAKE) and making the document as concept vector using Latent Semantic Analysis (LSA). Furthermore, the clustering process is done so that the documents with the similarity of the topic are in the same cluster, based on the preprocesing by textmining performed. Shared Nearest Neighbour (SNN) algorithm is a clustering method based on the number of "nearest neighbors" shared. The parameters in the SNN Algorithm consist of: k nearest neighbor documents, ɛ shared nearest neighbor documents and MinT minimum number of similar documents, which can form a cluster. Characteristics The SNN algorithm is based on shared ‘neighbor’ properties. Each cluster is formed by keywords that are shared by the documents. SNN algorithm allows a cluster can be built more than one keyword, if the value of the frequency of appearing keywords in document is also high. Determination of parameter values on SNN algorithm affects document clustering results. The higher parameter value k, will increase the number of neighbor documents from each document, cause similarity of neighboring documents are lower. The accuracy of each cluster is also low. The higher parameter value ε, caused each document catch only neighbor documents that have a high similarity to build a cluster. It also causes more unclassified documents (noise). The higher the MinT parameter value cause the number of clusters will decrease, since the number of similar documents can not form clusters if less than MinT. Parameter in the SNN Algorithm determine performance of clustering result and the amount of noise (unclustered documents ). The Silhouette coeffisient shows almost the same result in many experiments, above 0.9, which means that SNN algorithm works well
A Small Acoustic Goniometer for General Purpose Research.
Pook, Michael L; Loo, Sin Ming
2016-04-29
Understanding acoustic events and monitoring their occurrence is a useful aspect of many research projects. In particular, acoustic goniometry allows researchers to determine the source of an event based solely on the sound it produces. The vast majority of acoustic goniometry research projects used custom hardware targeted to the specific application under test. Unfortunately, due to the wide range of sensing applications, a flexible general purpose hardware/firmware system does not exist for this purpose. This article focuses on the development of such a system which encourages the continued exploration of general purpose hardware/firmware and lowers barriers to research in projects requiring the use of acoustic goniometry. Simulations have been employed to verify system feasibility, and a complete hardware implementation of the acoustic goniometer has been designed and field tested. The results are reported, and suggested areas for improvement and further exploration are discussed.
A heart disease recognition embedded system with fuzzy cluster algorithm.
de Carvalho, Helton Hugo; Moreno, Robson Luiz; Pimenta, Tales Cleber; Crepaldi, Paulo C; Cintra, Evaldo
2013-06-01
This article presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram - ECG signal processing by reducing the amount of data samples, without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson-Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicated common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database (EDB) as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan(®)-3A FPGA. The field programmable gate array (FPGA) implemented a Xilinx Microblaze(®) Soft-Core Processor running at a 50MHz clock rate. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Space shuttle general purpose computers (GPCs) (current and future versions)
1988-01-01
Current and future versions of general purpose computers (GPCs) for space shuttle orbiters are represented in this frame. The two boxes on the left (AP101B) represent the current GPC configuration, with the input-output processor at far left and the central processing unit (CPU) at its side. The upgraded version combines both elements in a single unit (far right, AP101S).
New Generation General Purpose Computer (GPC) compact IBM unit
1991-01-01
New Generation General Purpose Computer (GPC) compact IBM unit replaces a two-unit earlier generation computer. The new IBM unit is documented in table top views alone (S91-26867, S91-26868), with the onboard equipment it supports including the flight deck CRT screen and keypad (S91-26866), and next to the two earlier versions it replaces (S91-26869).
Survey of advanced general-purpose software for robot manipulators
International Nuclear Information System (INIS)
Latombe, J.C.
1983-01-01
Computer-controlled sensor-based robots will more and more common in industry. This paper attempts to survey the main trends of the development of advanced general-purpose software for robot manipulators. It is intended to make clear that robots are not only mechanical devices. They are truly programmable machines, and their programming, which occurs in an imperfectly modelled world,is somewhat different from conventional computer programming. (orig.)
Development of General Purpose Data Acquisition Shell (GPDAS)
International Nuclear Information System (INIS)
Chung, Y.; Kim, K.
1995-01-01
This note is intended as an abbreviated introduction to the concept and the structure of General Purpose Data Acquisitions Shell (GPDAS) and assumes the reader has a certain level of familiarity with programming in general. The structure of the following sections consists of brief explanations of the concepts and commands of GPDAS, followed by several examples. Some of these are tabulated in the appendices at the end of this note
General Purpose Multimedia Dataset - GarageBand 2008
DEFF Research Database (Denmark)
Meng, Anders
This document describes a general purpose multimedia data-set to be used in cross-media machine learning problems. In more detail we describe the genre taxonomy applied at http://www.garageband.com, from where the data-set was collected, and how the taxonomy have been fused into a more human...... understandable taxonomy. Finally, a description of various features extracted from both the audio and text are presented....
ENDF/B-4 General Purpose File 1974
International Nuclear Information System (INIS)
Schwerer, O.
1980-04-01
This document summarizes contents and documentation of the 1974 version of the General Purpose File of the ENDF/B Library maintained by the National Nuclear Data Center (NNDC) at the Brookhaven National Laboratory, USA. The Library contains numerical neutron reaction data for 90 isotopes or elements. The entire Library or selective retrievals from it can be obtained on magnetic tape from the IAEA Nuclear Data Section. (author)
Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model
Directory of Open Access Journals (Sweden)
Mi-Yuan Shan
2013-01-01
Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.
A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters
Wang, Zhihao; Yi, Jing
2016-01-01
For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291
The global kernel k-means algorithm for clustering in feature space.
Tzortzis, Grigorios F; Likas, Aristidis C
2009-07-01
Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.
A fast readout algorithm for Cluster Counting/Timing drift chambers on a FPGA board
Energy Technology Data Exchange (ETDEWEB)
Cappelli, L. [Università di Cassino e del Lazio Meridionale (Italy); Creti, P.; Grancagnolo, F. [Istituto Nazionale di Fisica Nucleare, Lecce (Italy); Pepino, A., E-mail: Aurora.Pepino@le.infn.it [Istituto Nazionale di Fisica Nucleare, Lecce (Italy); Tassielli, G. [Istituto Nazionale di Fisica Nucleare, Lecce (Italy); Fermilab, Batavia, IL (United States); Università Marconi, Roma (Italy)
2013-08-01
A fast readout algorithm for Cluster Counting and Timing purposes has been implemented and tested on a Virtex 6 core FPGA board. The algorithm analyses and stores data coming from a Helium based drift tube instrumented by 1 GSPS fADC and represents the outcome of balancing between cluster identification efficiency and high speed performance. The algorithm can be implemented in electronics boards serving multiple fADC channels as an online preprocessing stage for drift chamber signals.
Design of tallying function for general purpose Monte Carlo particle transport code JMCT
International Nuclear Information System (INIS)
Shangguan Danhua; Li Gang; Deng Li; Zhang Baoyin
2013-01-01
A new postponed accumulation algorithm was proposed. Based on JCOGIN (J combinatorial geometry Monte Carlo transport infrastructure) framework and the postponed accumulation algorithm, the tallying function of the general purpose Monte Carlo neutron-photon transport code JMCT was improved markedly. JMCT gets a higher tallying efficiency than MCNP 4C by 28% for simple geometry model, and JMCT is faster than MCNP 4C by two orders of magnitude for complicated repeated structure model. The available ability of tallying function for JMCT makes firm foundation for reactor analysis and multi-step burnup calculation. (authors)
Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.
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
Genetic algorithm based two-mode clustering of metabolomics data
Hageman, J.A.; van den Berg, R.A.; Westerhuis, J.A.; van der Werf, M.J.; Smilde, A.K.
2008-01-01
Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering
A Tabu Search Algorithm for application placement in computer clustering
van der Gaast, Jelmer; Rietveld, Cornelieus A.; Gabor, Adriana; Zhang, Yingqian
2014-01-01
This paper presents and analyzes a model for the problem of placing applications on computer clusters (APP). In this problem, organizations requesting a set of software applications have to be assigned to computer clusters such that the costs of opening clusters and installing the necessary
A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Shawkat K. Guirguis
2017-01-01
Full Text Available Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN. Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the noders residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.
Clustering performance comparison using K-means and expectation maximization algorithms.
Jung, Yong Gyu; Kang, Min Soo; Heo, Jun
2014-11-14
Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.
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.
Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji
2014-01-01
An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.
Directory of Open Access Journals (Sweden)
Liping Sun
2014-01-01
Full Text Available An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.
The architecture of Newton, a general-purpose dynamics simulator
Cremer, James F.; Stewart, A. James
1989-01-01
The architecture for Newton, a general-purpose system for simulating the dynamics of complex physical objects, is described. The system automatically formulates and analyzes equations of motion, and performs automatic modification of this system equations when necessitated by changes in kinematic relationships between objects. Impact and temporary contact are handled, although only using simple models. User-directed influence of simulations is achieved using Newton's module, which can be used to experiment with the control of many-degree-of-freedom articulated objects.
General-purpose heat source development. Phase II: conceptual designs
International Nuclear Information System (INIS)
Snow, E.C.; Zocher, R.W.; Grinberg, I.M.; Hulbert, L.E.
1978-11-01
Basic geometric module shapes and fuel arrays were studied to determine how well they could be expected to meet the General Purpose Heat Source (GPHS) design requirements. Seven conceptual designs were selected, detailed drawings produced, and these seven concepts analyzed. Three of these design concepts were selected as GPHS Trial Designs to be reanalyzed in more detail and tested. The geometric studies leading to the selection of the seven conceptual designs, the analyses of these designs, and the selection of the three trial designs are discussed
Installation of new Generation General Purpose Computer (GPC) compact unit
1991-01-01
In the Kennedy Space Center's (KSC's) Orbiter Processing Facility (OPF) high bay 2, Spacecraft Electronics technician Ed Carter (right), wearing clean suit, prepares for (26864) and installs (26865) the new Generation General Purpose Computer (GPC) compact IBM unit in Atlantis', Orbiter Vehicle (OV) 104's, middeck avionics bay as Orbiter Systems Quality Control technician Doug Snider looks on. Both men work for NASA contractor Lockheed Space Operations Company. All three orbiters are being outfitted with the compact IBM unit, which replaces a two-unit earlier generation computer.
How General-Purpose can a GPU be?
Directory of Open Access Journals (Sweden)
Philip Machanick
2015-12-01
Full Text Available The use of graphics processing units (GPUs in general-purpose computation (GPGPU is a growing field. GPU instruction sets, while implementing a graphics pipeline, draw from a range of single instruction multiple datastream (SIMD architectures characteristic of the heyday of supercomputers. Yet only one of these SIMD instruction sets has been of application on a wide enough range of problems to survive the era when the full range of supercomputer design variants was being explored: vector instructions. This paper proposes a reconceptualization of the GPU as a multicore design with minimal exotic modes of parallelism so as to make GPGPU truly general.
A Scheduling Algorithm for Minimizing the Packet Error Probability in Clusterized TDMA Networks
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Arash T. Toyserkani
2009-01-01
Full Text Available We consider clustered wireless networks, where transceivers in a cluster use a time-slotted mechanism (TDMA to access a wireless channel that is shared among several clusters. An approximate expression for the packet-loss probability is derived for networks with one or more mutually interfering clusters in Rayleigh fading environments, and the approximation is shown to be good for relevant scenarios. We then present a scheduling algorithm, based on Lagrangian duality, that exploits the derived packet-loss model in an attempt to minimize the average packet-loss probability in the network. Computer simulations of the proposed scheduling algorithm show that a significant increase in network throughput can be achieved compared to uncoordinated scheduling. Empirical trials also indicate that the proposed optimization algorithm almost always converges to an optimal schedule with a reasonable number of iterations. Thus, the proposed algorithm can also be used for bench-marking suboptimal scheduling algorithms.
An improved initialization center k-means clustering algorithm based on distance and density
Duan, Yanling; Liu, Qun; Xia, Shuyin
2018-04-01
Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.
A highly efficient multi-core algorithm for clustering extremely large datasets
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Kraus Johann M
2010-04-01
Full Text Available Abstract Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.
Nidheesh, N; Abdul Nazeer, K A; Ameer, P M
2017-12-01
Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Simon Fong
2014-01-01
Full Text Available Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
Deb, Suash; Yang, Xin-She
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730
K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data
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Cheng Lu
2015-01-01
Full Text Available The Affinity Propagation (AP algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.
A new clustering algorithm for scanning electron microscope images
Yousef, Amr; Duraisamy, Prakash; Karim, Mohammad
2016-04-01
A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons. The electrons interact with the sample atoms, producing various signals that are collected by detectors. The gathered signals contain information about the sample's surface topography and composition. The electron beam is generally scanned in a raster scan pattern, and the beam's position is combined with the detected signal to produce an image. The most common configuration for an SEM produces a single value per pixel, with the results usually rendered as grayscale images. The captured images may be produced with insufficient brightness, anomalous contrast, jagged edges, and poor quality due to low signal-to-noise ratio, grained topography and poor surface details. The segmentation of the SEM images is a tackling problems in the presence of the previously mentioned distortions. In this paper, we are stressing on the clustering of these type of images. In that sense, we evaluate the performance of the well-known unsupervised clustering and classification techniques such as connectivity based clustering (hierarchical clustering), centroid-based clustering, distribution-based clustering and density-based clustering. Furthermore, we propose a new spatial fuzzy clustering technique that works efficiently on this type of images and compare its results against these regular techniques in terms of clustering validation metrics.
General Purpose Data-Driven Monitoring for Space Operations
Iverson, David L.; Martin, Rodney A.; Schwabacher, Mark A.; Spirkovska, Liljana; Taylor, William McCaa; Castle, Joseph P.; Mackey, Ryan M.
2009-01-01
As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault
Novel density-based and hierarchical density-based clustering algorithms for uncertain data.
Zhang, Xianchao; Liu, Han; Zhang, Xiaotong
2017-09-01
Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing
A scalable and practical one-pass clustering algorithm for recommender system
Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali
2015-12-01
KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.
Constructing a graph of connections in clustering algorithm of complex objects
Directory of Open Access Journals (Sweden)
Татьяна Шатовская
2015-05-01
Full Text Available The article describes the results of modifying the algorithm Chameleon. Hierarchical multi-level algorithm consists of several phases: the construction of the count, coarsening, the separation and recovery. Each phase can be used various approaches and algorithms. The main aim of the work is to study the quality of the clustering of different sets of data using a set of algorithms combinations at different stages of the algorithm and improve the stage of construction by the optimization algorithm of k choice in the graph construction of k of nearest neighbors
High vacuum general purpose scattering chamber for nuclear reaction study
International Nuclear Information System (INIS)
Suresh Kumar; Ojha, S.C.
2003-01-01
To study the nuclear reactions induced by beam from medium energy accelerators, one of the most common facility required is a scattering chamber. In the scattering chamber, projectile collides with the target nucleus and the scattered reaction products are detected with various type of nuclear detector at different angles with respect to the beam. The experiments are performed under high vacuum to minimize the background reaction and the energy losses of the charged particles. To make the chamber general purpose various requirement of the experiments are incorporated into it. Changing of targets, changing angle of various detectors while in vacuum are the most desired features. The other features like ascertaining the beam spot size and position on the target, minimizing the background counts by proper beam dump, accurate positioning of the detector as per plan etc. are some of the important requirements
Incremental and developmental perspectives for general-purpose learning systems
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Fernando Martínez-Plumed
2017-02-01
Full Text Available The stupefying success of Articial Intelligence (AI for specic problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of (dierent problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities. It presents a declarative general-purpose learning system and a developmental and lifelong approach for knowledge acquisition, consolidation and forgetting. It also analyses the use of the use of more ability-oriented evaluation techniques for AI evaluation and provides further insight for the understanding of the concepts of development and incremental learning in AI systems.
General purpose heat source task group. Final report
International Nuclear Information System (INIS)
1979-01-01
The results of thermal analyses and impact tests on a modified design of a 238 Pu-fueled general purpose heat source (GPHS) for spacecraft power supplies are presented. This work was performed to establish the safety of a heat source with pyrolytic graphite insulator shells located either inside or outside the graphite impact shell. This safety is dependent on the degree of aerodynamic heating of the heat source during reentry and on the ability of the heat source capsule to withstand impact after reentry. Analysis of wind tunnel and impact test data result in a recommended GPHS design which should meet all temperature and safety requirements. Further wind tunnel tests, drop tests, and impact tests are recommended to verify the safety of this design
Using the general-purpose reactivity indicator: challenging examples.
Anderson, James S M; Melin, Junia; Ayers, Paul W
2016-03-01
We elucidate the regioselectivity of nucleophilic attack on substituted benzenesulfonates, quinolines, and pyridines using a general-purpose reactivity indicator (GPRI) for electrophiles. We observe that the GPRI is most accurate when the incoming nucleophile resembles a point charge. We further observe that the GPRI often chooses reactive "dead ends" as the most reactive sites as well as sterically hindered reactive sites. This means that care must be taken to remove sites that are inherently unreactive. Generally, among sites where reactions actually occur, the GPRI identifies the sites in the molecule that lead to the kinetically favored product(s). Furthermore, the GPRI can discern which sites react with hard reagents and which sites react with soft reagents. Because it is currently impossible to use the mathematical framework of conceptual DFT to identify sterically inaccessible sites and reactive dead ends, the GPRI is primarily useful as an interpretative, not a predictive, tool.
The RHIC general purpose multiplexed analog to digital converter system
International Nuclear Information System (INIS)
Michnoff, R.
1995-01-01
A general purpose multiplexed analog to digital converter system is currently under development to support acquisition of analog signals for the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. The system consists of a custom intelligent VME based controller module (V113) and a 14-bit 64 channel multiplexed A/D converter module (V114). The design features two independent scan groups, where one scan group is capable of acquiring 64 channels at 60 Hz, concurrently with the second scan group acquiring data at an aggregate rate of up to 80 k samples/second. An interface to the RHIC serially encoded event line is used to synchronize acquisition. Data is stored in a circular static RAM buffer on the controller module, then transferred to a commercial VMEbus CPU board and higher level workstations for plotting, report Generation, analysis and storage
Using a cognitive architecture for general purpose service robot control
Puigbo, Jordi-Ysard; Pumarola, Albert; Angulo, Cecilio; Tellez, Ricardo
2015-04-01
A humanoid service robot equipped with a set of simple action skills including navigating, grasping, recognising objects or people, among others, is considered in this paper. By using those skills the robot should complete a voice command expressed in natural language encoding a complex task (defined as the concatenation of a number of those basic skills). As a main feature, no traditional planner has been used to decide skills to be activated, as well as in which sequence. Instead, the SOAR cognitive architecture acts as the reasoner by selecting which action the robot should complete, addressing it towards the goal. Our proposal allows to include new goals for the robot just by adding new skills (without the need to encode new plans). The proposed architecture has been tested on a human-sized humanoid robot, REEM, acting as a general purpose service robot.
General-purpose software for science technology calculation
International Nuclear Information System (INIS)
Aikawa, Hiroshi
1999-01-01
We have developed many general-purpose softwares for parallel processing of science technology calculation. This paper reported six softwares such as STA (Seamless Thinking Aid) basic soft, parallel numerical computation library, grid formation software for parallel computer, real-time visualizing system, parallel benchmark test system and object-oriented parallel programing method. STA is a user interface software to perform a total environment for parallel programing, a network computing environment for various parallel computers and a desktop computing environment via Web. Some examples using the above softwares are explained. One of them is a simultaneous parallel calculation of both analysis of flow and structure of supersonic transport to design of them. The other is various kinds of computer parallel calculations for nuclear fusion reaction such as a molecular dynamic calculation and a calculation of reactor structure and fluid. These softs are opened to the public by the home page {http://guide.tokai.jaeri.go.jp/ccse/}. (S.Y.)
Medical Image Retrieval Based On the Parallelization of the Cluster Sampling Algorithm
Ali, Hesham Arafat; Attiya, Salah; El-henawy, Ibrahim
2017-01-01
In this paper we develop parallel cluster sampling algorithms and show that a multi-chain version is embarrassingly parallel and can be used efficiently for medical image retrieval among other applications.
A novel artificial bee colony based clustering algorithm for categorical data.
Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang
2015-01-01
Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.
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.
Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
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Yu Feng
2017-01-01
Full Text Available This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt
Directory of Open Access Journals (Sweden)
Qingming Zhan
2017-08-01
Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.
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.
Loewenstein, Yaniv; Portugaly, Elon; Fromer, Menachem; Linial, Michal
2008-01-01
Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. Application: We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without ex...
Ant colony algorithm for clustering in portfolio optimization
Subekti, R.; Sari, E. R.; Kusumawati, R.
2018-03-01
This research aims to describe portfolio optimization using clustering methods with ant colony approach. Two stock portfolios of LQ45 Indonesia is proposed based on the cluster results obtained from ant colony optimization (ACO). The first portfolio consists of assets with ant colony displacement opportunities beyond the defined probability limits of the researcher, where the weight of each asset is determined by mean-variance method. The second portfolio consists of two assets with the assumption that each asset is a cluster formed from ACO. The first portfolio has a better performance compared to the second portfolio seen from the Sharpe index.
Clustered K nearest neighbor algorithm for daily inflow forecasting
Akbari, M.; Van Overloop, P.J.A.T.M.; Afshar, A.
2010-01-01
Instance based learning (IBL) algorithms are a common choice among data driven algorithms for inflow forecasting. They are based on the similarity principle and prediction is made by the finite number of similar neighbors. In this sense, the similarity of a query instance is estimated according to
A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems
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Ke Niu
2015-01-01
Full Text Available In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.
Clustering Algorithm As A Planning Support Tool For Rural Electrification Optimization
Directory of Open Access Journals (Sweden)
Ronaldo Pornillosa Parreno Jr
2015-08-01
Full Text Available Abstract In this study clustering algorithm was developed to optimize electrification plans by screening and grouping potential customers to be supplied with electricity. The algorithm provided adifferent approach in clustering problem which combines conceptual and distance-based clustering algorithmsto analyze potential clusters using spanning tree with the shortest possible edge weight and creating final cluster trees based on the test of inconsistency for the edges. The clustering criteria consists of commonly used distance measure with the addition of household information as basis for the ability to pay ATP value. The combination of these two parameters resulted to a more significant and realistic clusters since distance measure alone could not take the effect of the household characteristics in screening the most sensible groupings of households. In addition the implications of varying geographical features were incorporated in the algorithm by using routing index across the locations of the households. This new approach of connecting the households in an area was applied in an actual case study of one village or barangay that was not yet energized. The results of clustering algorithm generated cluster trees which could becomethetheoretical basis for power utilities to plan the initial network arrangement of electrification. Scenario analysis conducted on the two strategies of clustering the households provideddifferent alternatives for the optimization of the cost of electrification. Futhermorethe benefits associated with the two strategies formulated from the two scenarios was evaluated using benefit cost ratio BC to determine which is more economically advantageous. The results of the study showed that clustering algorithm proved to be effective in solving electrification optimization problem and serves its purpose as a planning support tool which can facilitate electrification in rural areas and achieve cost-effectiveness.
Chang, Bingguo; Chen, Xiaofei
2018-05-01
Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.
Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.
Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong
2016-01-01
In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.
Design of the SLAC RCE Platform: A General Purpose ATCA Based Data Acquisition System
International Nuclear Information System (INIS)
Herbst, R.; Claus, R.; Freytag, M.; Haller, G.; Huffer, M.; Maldonado, S.; Nishimura, K.; O'Grady, C.; Panetta, J.; Perazzo, A.; Reese, B.; Ruckman, L.; Thayer, J.G.; Weaver, M.
2015-01-01
The SLAC RCE platform is a general purpose clustered data acquisition system implemented on a custom ATCA compliant blade, called the Cluster On Board (COB). The core of the system is the Reconfigurable Cluster Element (RCE), which is a system-on-chip design based upon the Xilinx Zynq family of FPGAs, mounted on custom COB daughter-boards. The Zynq architecture couples a dual core ARM Cortex A9 based processor with a high performance 28nm FPGA. The RCE has 12 external general purpose bi-directional high speed links, each supporting serial rates of up to 12Gbps. 8 RCE nodes are included on a COB, each with a 10Gbps connection to an on-board 24-port Ethernet switch integrated circuit. The COB is designed to be used with a standard full-mesh ATCA backplane allowing multiple RCE nodes to be tightly interconnected with minimal interconnect latency. Multiple shelves can be clustered using the front panel 10-gbps connections. The COB also supports local and inter-blade timing and trigger distribution. An experiment specific Rear Transition Module adapts the 96 high speed serial links to specific experiments and allows an experiment-specific timing and busy feedback connection. This coupling of processors with a high performance FPGA fabric in a low latency, multiple node cluster allows high speed data processing that can be easily adapted to any physics experiment. RTEMS and Linux are both ported to the module. The RCE has been used or is the baseline for several current and proposed experiments (LCLS, HPS, LSST, ATLAS-CSC, LBNE, DarkSide, ILC-SiD, etc).
Correia, J R C C C; Martins, C J A P
2017-10-01
Topological defects unavoidably form at symmetry breaking phase transitions in the early universe. To probe the parameter space of theoretical models and set tighter experimental constraints (exploiting the recent advances in astrophysical observations), one requires more and more demanding simulations, and therefore more hardware resources and computation time. Improving the speed and efficiency of existing codes is essential. Here we present a general purpose graphics-processing-unit implementation of the canonical Press-Ryden-Spergel algorithm for the evolution of cosmological domain wall networks. This is ported to the Open Computing Language standard, and as a consequence significant speedups are achieved both in two-dimensional (2D) and 3D simulations.
Correia, J. R. C. C. C.; Martins, C. J. A. P.
2017-10-01
Topological defects unavoidably form at symmetry breaking phase transitions in the early universe. To probe the parameter space of theoretical models and set tighter experimental constraints (exploiting the recent advances in astrophysical observations), one requires more and more demanding simulations, and therefore more hardware resources and computation time. Improving the speed and efficiency of existing codes is essential. Here we present a general purpose graphics-processing-unit implementation of the canonical Press-Ryden-Spergel algorithm for the evolution of cosmological domain wall networks. This is ported to the Open Computing Language standard, and as a consequence significant speedups are achieved both in two-dimensional (2D) and 3D simulations.
International Nuclear Information System (INIS)
Wu, Xia; Wu, Genhua
2014-01-01
Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron
Clinical assessment using an algorithm based on clustering Fuzzy c-means
Guijarro-Rodriguez, A.; Cevallos-Torres, L.; Yepez-Holguin, J.; Botto-Tobar, M.; Valencia-García, R.; Lagos-Ortiz, K.; Alcaraz-Mármol, G.; Del Cioppo, J.; Vera-Lucio, N.; Bucaram-Leverone, M.
2017-01-01
The Fuzzy c-means (FCM) algorithms dene a grouping criterion from a function, which seeks to minimize iteratively the function up to an optimal fuzzy partition is obtained. In the execution of this algorithm relates each element to the clusters that were determined in the same n-dimensional space,
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan
2011-01-01
comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we...
Jing Chen
2015-01-01
This study takes the concept of food logistics distribution as the breakthrough point, by means of the aim of optimization of food logistics distribution routes and analysis of the optimization model of food logistics route, as well as the interpretation of the genetic algorithm, it discusses the optimization of food logistics distribution route based on genetic and cluster scheme algorithm.
GenClust: A genetic algorithm for clustering gene expression data
Directory of Open Access Journals (Sweden)
Raimondi Alessandra
2005-12-01
Full Text Available Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a a novel coding of the search space that is simple, compact and easy to update; (b it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data sets, both with the use of validation measures and in comparison with other algorithms, i.e., Average Link, Cast, Click and K-means. Conclusion Experiments show that none of the algorithms we have used is markedly superior to the others across data sets and validation measures; i.e., in many cases the observed differences between the worst and best performing algorithm may be statistically insignificant and they could be considered equivalent. However, there are cases in which an algorithm may be better than others and therefore worthwhile. In particular, experiments for GenClust show that, although simple in its data representation, it converges very rapidly to a local optimum and that its ability to identify meaningful clusters is comparable, and sometimes superior, to that of more sophisticated algorithms. In addition, it is well suited for use in conjunction with data driven internal validation measures and, in particular, the FOM methodology.
Soil data clustering by using K-means and fuzzy K-means algorithm
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E. Hot
2016-06-01
Full Text Available A problem of soil clustering based on the chemical characteristics of soil, and proper visual representation of the obtained results, is analysed in the paper. To that aim, K-means and fuzzy K-means algorithms are adapted for soil data clustering. A database of soil characteristics sampled in Montenegro is used for a comparative analysis of implemented algorithms. The procedure of setting proper values for control parameters of fuzzy K-means is illustrated on the used database. In addition, validation of clustering is made through visualisation. Classified soil data are presented on the static Google map and dynamic Open Street Map.
A General Purpose High Performance Linux Installation Infrastructure
International Nuclear Information System (INIS)
Wachsmann, Alf
2002-01-01
With more and more and larger and larger Linux clusters, the question arises how to install them. This paper addresses this question by proposing a solution using only standard software components. This installation infrastructure scales well for a large number of nodes. It is also usable for installing desktop machines or diskless Linux clients, thus, is not designed for cluster installations in particular but is, nevertheless, highly performant. The infrastructure proposed uses PXE as the network boot component on the nodes. It uses DHCP and TFTP servers to get IP addresses and a bootloader to all nodes. It then uses kickstart to install Red Hat Linux over NFS. We have implemented this installation infrastructure at SLAC with our given server hardware and installed a 256 node cluster in 30 minutes. This paper presents the measurements from this installation and discusses the bottlenecks in our installation
General Purpose Technologies and their Implications for International Trade
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Petsas Iordanis
2015-09-01
Full Text Available This paper develops a simple model of trade and “quality-ladders” growth without scale effects to study the implications of general purpose technologies (GPTs for international trade. GPTs refer to a certain type of drastic innovations, such as electrification, the transistor, and the Internet, that are characterized by the pervasiveness in use, innovational complementarities, and technological dynamism. The model presents a two-country (Home and Foreign dynamic general equilibrium framework and incorporates GPT diffusion within Home that exhibits endogenous Schumpeterian growth. The model analyzes the long-run and transitional dynamic effects of a new GPT on the pattern of trade and relative wages. The main findings of the paper are: 1 when the GPT diffusion across industries is governed by S-curve dynamics, there are two steady-state equilibria: the initial steadystate arises before the adoption of the new GPT and the final one is reached after the GPT diffusion process has been completed, 2 when all industries at Home have adopted the new GPT, Home enjoys comparative advantage in a greater range of industries compared to Foreign, 3 during the transitional dynamics, Foreign gains back its competitiveness in some of the industries that lost its comparative advantage to Home.
A VMEbus general-purpose data acquisition system
International Nuclear Information System (INIS)
Ninane, A.; Nemry, M.; Martou, J.L.; Somers, F.
1992-01-01
We present a general-purpose, VMEbus based, multiprocessor data acquisition and monitoring system. Events, handled by a master CPU, are kept at the disposal of data storage and monitoring processes which can run on distinct processors. They access either the complete set of data or a fraction of them, minimizing the acquisition dead-time. The system is built with the VxWorks 5.0 real time kernel to which we have added device drivers for data acquisition and monitoring. The acquisition is controlled and the data are displayed on a workstation. The user interface is written in C ++ and re-uses the classes of the Interviews and the NIH libraries. The communication between the control workstation and the VMEbus processors is made through SUN RPCs on an Ethernet link. The system will be used for, CAMAC based, data acquisition for nuclear physics experiments as well as for the VXI data taking with the 4π configuration (100 neutron detectors) of the Brussels-Caen-Louvian-Strasbourg DEMON collaboration. (author)
Interfacial Properties of EXXPRO(TM) and General Purpose Elastomers
Zhang, Y.; Rafailovich, M.; Sokolov, Jon; Qu, S.; Ge, S.; Ngyuen, D.; Li, Z.; Peiffer, D.; Song, L.; Dias, J. A.; McElrath, K. O.
1998-03-01
EXXPRO(Trademark) elastomers are used for tires and many other applications. This elastomer (denoted as BIMS) is a random copolymer of p-methylstyrene (MS) and polyisobutylene (I) with varying degrees of PMS content and bromination (B) on the p-methyl group. BIMS is impermeable to gases, and has good heat, ozone and flex resistance. Very often general purpose elastomers are blended with BIMS. The interfacial width between polybutadiene and BIMS is a sensitive function of the Br level and PMS content. By neutron reflectivity (NR), we studied the dynamics of interface formation as a function of time and temperature for BIMS with varying degrees of PMS and Br. We found that in addition to the bulk parameters, the total film thickness and the proximity of an interactive surface can affect the interfacial interaction rates. The interfacial properties can also be modified by inclusion of particles, such as carbon black (a filler component in tire rubbers). Results will be presented on the relation between the interfacial width as measured by NR and compatibilization studies via AFM and LFM.
Directory of Open Access Journals (Sweden)
Arvind Sharma
2016-01-01
Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.
Reconstruction of a digital core containing clay minerals based on a clustering algorithm
He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling
2017-10-01
It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K -means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.
Reconstruction of a digital core containing clay minerals based on a clustering algorithm.
He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling
2017-10-01
It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.
Graph-based clustering and data visualization algorithms
Vathy-Fogarassy, Ágnes
2013-01-01
This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.
Vimalarani, C; Subramanian, R; Sivanandam, S N
2016-01-01
Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network
Directory of Open Access Journals (Sweden)
C. Vimalarani
2016-01-01
Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
Ju, Chunhua; Xu, Chonghuan
2013-01-01
Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.
Directory of Open Access Journals (Sweden)
Chunhua Ju
2013-01-01
Full Text Available Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.
Data Clustering on Breast Cancer Data Using Firefly Algorithm with Golden Ratio Method
Directory of Open Access Journals (Sweden)
DEMIR, M.
2015-05-01
Full Text Available Heuristic methods are problem solving methods. In general, they obtain near-optimal solutions, and they do not take the care of provability of this case. The heuristic methods do not guarantee to obtain the optimal results; however, they guarantee to obtain near-optimal solutions in considerable time. In this paper, an application was performed by using firefly algorithm - one of the heuristic methods. The golden ratio was applied to different steps of firefly algorithm and different parameters of firefly algorithm to develop a new algorithm - called Firefly Algorithm with Golden Ratio (FAGR. It was shown that the golden ratio made firefly algorithm be superior to the firefly algorithm without golden ratio. At this aim, the developed algorithm was applied to WBCD database (breast cancer database to cluster data obtained from breast cancer patients. The highest obtained success rate among all executions is 96% and the highest obtained average success rate in all executions is 94.5%.
Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Teng Gao
2015-01-01
Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
Performance quantification of clustering algorithms for false positive removal in fMRI by ROC curves
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André Salles Cunha Peres
Full Text Available Abstract Introduction Functional magnetic resonance imaging (fMRI is a non-invasive technique that allows the detection of specific cerebral functions in humans based on hemodynamic changes. The contrast changes are about 5%, making visual inspection impossible. Thus, statistic strategies are applied to infer which brain region is engaged in a task. However, the traditional methods like general linear model and cross-correlation utilize voxel-wise calculation, introducing a lot of false-positive data. So, in this work we tested post-processing cluster algorithms to diminish the false-positives. Methods In this study, three clustering algorithms (the hierarchical cluster, k-means and self-organizing maps were tested and compared for false-positive removal in the post-processing of cross-correlation analyses. Results Our results showed that the hierarchical cluster presented the best performance to remove the false positives in fMRI, being 2.3 times more accurate than k-means, and 1.9 times more accurate than self-organizing maps. Conclusion The hierarchical cluster presented the best performance in false-positive removal because it uses the inconsistency coefficient threshold, while k-means and self-organizing maps utilize a priori cluster number (centroids and neurons number; thus, the hierarchical cluster avoids clustering scattered voxels, as the inconsistency coefficient threshold allows only the voxels to be clustered that are at a minimum distance to some cluster.
Comparison of Clustering Algorithms for the Identification of Topics on Twitter
Directory of Open Access Journals (Sweden)
Marjori N. M. Klinczak
2016-05-01
Full Text Available Topic Identification in Social Networks has become an important task when dealing with event detection, particularly when global communities are affected. In order to attack this problem, text processing techniques and machine learning algorithms have been extensively used. In this paper we compare four clustering algorithms – k-means, k-medoids, DBSCAN and NMF (Non-negative Matrix Factorization – in order to detect topics related to textual messages obtained from Twitter. The algorithms were applied to a database initially composed by tweets having hashtags related to the recent Nepal earthquake as initial context. Obtained results suggest that the NMF clustering algorithm presents superior results, providing simpler clusters that are also easier to interpret.
Online cluster-finding algorithms for the PANDA electromagnetic calorimeter
Tiemens, Marcel
2017-01-01
Om zeldzame processen zoals de vorming van exotische deeltjes te kunnen bestuderen, is het PANDA experiment opgezet. Om de grote hoeveelheden data te kunnen verwerken, verwerken de subsystemen de data voor. Een voorbeeld is het algoritme om online naar clusters te zoeken in de data van de
A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.
Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong
2015-12-01
Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.
A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream
Ying Wah, Teh
2014-01-01
Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753
A fast density-based clustering algorithm for real-time Internet of Things stream.
Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut
2014-01-01
Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.
MINING ON CAR DATABASE EMPLOYING LEARNING AND CLUSTERING ALGORITHMS
Muhammad Rukunuddin Ghalib; Shivam Vohra; Sunish Vohra; Akash Juneja
2013-01-01
In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the known learning algorithms used are Naïve Bayesian (NB) and SMO (Self-Minimal-Optimisation) .Thus the following two learning algorithms are used on a Car review database and thus a model is hence created which predicts the characteristic of a review comment after getting trained. It was found that model successfully predicted correctly about the review comm...
Use of general purpose graphics processing units with MODFLOW
Hughes, Joseph D.; White, Jeremy T.
2013-01-01
To evaluate the use of general-purpose graphics processing units (GPGPUs) to improve the performance of MODFLOW, an unstructured preconditioned conjugate gradient (UPCG) solver has been developed. The UPCG solver uses a compressed sparse row storage scheme and includes Jacobi, zero fill-in incomplete, and modified-incomplete lower-upper (LU) factorization, and generalized least-squares polynomial preconditioners. The UPCG solver also includes options for sequential and parallel solution on the central processing unit (CPU) using OpenMP. For simulations utilizing the GPGPU, all basic linear algebra operations are performed on the GPGPU; memory copies between the central processing unit CPU and GPCPU occur prior to the first iteration of the UPCG solver and after satisfying head and flow criteria or exceeding a maximum number of iterations. The efficiency of the UPCG solver for GPGPU and CPU solutions is benchmarked using simulations of a synthetic, heterogeneous unconfined aquifer with tens of thousands to millions of active grid cells. Testing indicates GPGPU speedups on the order of 2 to 8, relative to the standard MODFLOW preconditioned conjugate gradient (PCG) solver, can be achieved when (1) memory copies between the CPU and GPGPU are optimized, (2) the percentage of time performing memory copies between the CPU and GPGPU is small relative to the calculation time, (3) high-performance GPGPU cards are utilized, and (4) CPU-GPGPU combinations are used to execute sequential operations that are difficult to parallelize. Furthermore, UPCG solver testing indicates GPGPU speedups exceed parallel CPU speedups achieved using OpenMP on multicore CPUs for preconditioners that can be easily parallelized.
General Purpose Probabilistic Programming Platform with Effective Stochastic Inference
2018-04-01
REFERENCES 74 LIST OF ACRONYMS 80 ii List of Figures Figure 1. The problem of inferring curves from data while simultaneously choosing the...bottom path) as the inverse problem to computer graphics (top path). ........ 18 Figure 18. An illustration of generative probabilistic graphics for 3D...Building these systems involves simultaneously developing mathematical models, inference algorithms and optimized software implementations. Small changes
GPUs: An Emerging Platform for General-Purpose Computation
2007-08-01
programming; real-time cinematic quality graphics Peak stream (26) License required (limited time no- cost evaluation program) Commercially...folding.stanford.edu (accessed 30 March 2007). 2. Fan, Z.; Qiu, F.; Kaufman, A.; Yoakum-Stover, S. GPU Cluster for High Performance Computing. ACM/IEEE...accessed 30 March 2007). 8. Goodnight, N.; Wang, R.; Humphreys, G. Computation on Programmable Graphics Hardware. IEEE Computer Graphics and
A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters
Weiwei Lin; Wentai Wu; James Z. Wang
2016-01-01
Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual ...
Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance
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Ahmad Sanmorino
2012-06-01
Full Text Available Batik is a fabric or clothes that are made with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.
CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms.
Kohlhoff, Kai J; Sosnick, Marc H; Hsu, William T; Pande, Vijay S; Altman, Russ B
2011-08-15
Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large datasets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created Clustering Algorithms for Massively Parallel Architectures, Including GPU Nodes (CAMPAIGN), a central resource for data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures. CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA' for Nvidia GPUs. The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. New modules from the community will be accepted into the library and the layout of it is such that it can easily be extended to promising future platforms such as OpenCL. Releases of the CAMPAIGN library are freely available for download under the LGPL from https://simtk.org/home/campaign. Source code can also be obtained through anonymous subversion access as described on https://simtk.org/scm/?group_id=453. kjk33@cantab.net.
SPIDR, a general-purpose readout system for pixel ASICs
International Nuclear Information System (INIS)
Heijden, B. van der; Visser, J.; Beuzekom, M. van; Boterenbrood, H.; Munneke, B.; Schreuder, F.; Kulis, S.
2017-01-01
The SPIDR (Speedy PIxel Detector Readout) system is a flexible general-purpose readout platform that can be easily adapted to test and characterize new and existing detector readout ASICs. It is originally designed for the readout of pixel ASICs from the Medipix/Timepix family, but other types of ASICs or front-end circuits can be read out as well. The SPIDR system consists of an FPGA board with memory and various communication interfaces, FPGA firmware, CPU subsystem and an API library on the PC . The FPGA firmware can be adapted to read out other ASICs by re-using IP blocks. The available IP blocks include a UDP packet builder, 1 and 10 Gigabit Ethernet MAC's and a 'soft core' CPU . Currently the firmware is targeted at the Xilinx VC707 development board and at a custom board called Compact-SPIDR . The firmware can easily be ported to other Xilinx 7 series and ultra scale FPGAs. The gap between an ASIC and the data acquisition back-end is bridged by the SPIDR system. Using the high pin count VITA 57 FPGA Mezzanine Card (FMC) connector only a simple chip carrier PCB is required. A 1 and a 10 Gigabit Ethernet interface handle the connection to the back-end. These can be used simultaneously for high-speed data and configuration over separate channels. In addition to the FMC connector, configurable inputs and outputs are available for synchronization with other detectors. A high resolution (≈ 27 ps bin size) Time to Digital converter is provided for time stamping events in the detector. The SPIDR system is frequently used as readout for the Medipix3 and Timepix3 ASICs. Using the 10 Gigabit Ethernet interface it is possible to read out a single chip at full bandwidth or up to 12 chips at a reduced rate. Another recent application is the test-bed for the VeloPix ASIC, which is developed for the Vertex Detector of the LHCb experiment. In this case the SPIDR system processes the 20 Gbps scrambled data stream from the VeloPix and distributes it over four
A damage mechanics based general purpose interface/contact element
Yan, Chengyong
laboratory test data presented in the literature. The results demonstrate that the proposed element and the damage law perform very well. The most important scientific contribution of this dissertation is the proposed damage criterion based on second law of thermodynamic and entropy of the system. The proposed general purpose interface/contact element is another contribution of this research. Compared to the previous adhoc interface elements proposed in the literature, the new one is, much more powerful and includes creep, plastic deformations, sliding, temperature, damage, cyclic behavior and fatigue life in a unified formulation.
CLOUDCLOUD : general-purpose instrument monitoring and data managing software
Dias, António; Amorim, António; Tomé, António
2016-04-01
An effective experiment is dependent on the ability to store and deliver data and information to all participant parties regardless of their degree of involvement in the specific parts that make the experiment a whole. Having fast, efficient and ubiquitous access to data will increase visibility and discussion, such that the outcome will have already been reviewed several times, strengthening the conclusions. The CLOUD project aims at providing users with a general purpose data acquisition, management and instrument monitoring platform that is fast, easy to use, lightweight and accessible to all participants of an experiment. This work is now implemented in the CLOUD experiment at CERN and will be fully integrated with the experiment as of 2016. Despite being used in an experiment of the scale of CLOUD, this software can also be used in any size of experiment or monitoring station, from single computers to large networks of computers to monitor any sort of instrument output without influencing the individual instrument's DAQ. Instrument data and meta data is stored and accessed via a specially designed database architecture and any type of instrument output is accepted using our continuously growing parsing application. Multiple databases can be used to separate different data taking periods or a single database can be used if for instance an experiment is continuous. A simple web-based application gives the user total control over the monitored instruments and their data, allowing data visualization and download, upload of processed data and the ability to edit existing instruments or add new instruments to the experiment. When in a network, new computers are immediately recognized and added to the system and are able to monitor instruments connected to them. Automatic computer integration is achieved by a locally running python-based parsing agent that communicates with a main server application guaranteeing that all instruments assigned to that computer are
Channel Parameter Estimation for Scatter Cluster Model Using Modified MUSIC Algorithm
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Jinsheng Yang
2012-01-01
Full Text Available Recently, the scatter cluster models which precisely evaluate the performance of the wireless communication system have been proposed in the literature. However, the conventional SAGE algorithm does not work for these scatter cluster-based models because it performs poorly when the transmit signals are highly correlated. In this paper, we estimate the time of arrival (TOA, the direction of arrival (DOA, and Doppler frequency for scatter cluster model by the modified multiple signal classification (MUSIC algorithm. Using the space-time characteristics of the multiray channel, the proposed algorithm combines the temporal filtering techniques and the spatial smoothing techniques to isolate and estimate the incoming rays. The simulation results indicated that the proposed algorithm has lower complexity and is less time-consuming in the dense multipath environment than SAGE algorithm. Furthermore, the estimations’ performance increases with elements of receive array and samples length. Thus, the problem of the channel parameter estimation of the scatter cluster model can be effectively addressed with the proposed modified MUSIC algorithm.
Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems
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Sotirios Kontogiannis
2017-11-01
Full Text Available Modern irrigation systems utilize sensors and actuators, interconnected together as a single entity. In such entities, A.I. algorithms are implemented, which are responsible for the irrigation process. In this paper, the authors present an irrigation Open Watering System (OWS architecture that spatially clusters the irrigation process into autonomous irrigation sections. Authors’ OWS implementation includes a Neuro-Fuzzy decision algorithm called FITRA, which originates from the Greek word for seed. In this paper, the FITRA algorithm is described in detail, as are experimentation results that indicate significant water conservations from the use of the FITRA algorithm. Furthermore, the authors propose a new communication protocol over LoRa radio as an alternative low-energy and long-range OWS clusters communication mechanism. The experimental scenarios confirm that the FITRA algorithm provides more efficient irrigation on clustered areas than existing non-clustered, time scheduled or threshold adaptive algorithms. This is due to the FITRA algorithm’s frequent monitoring of environmental conditions, fuzzy and neural network adaptation as well as adherence to past irrigation preferences.
A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks
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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.
Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks
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Rajeev Kumar
2016-01-01
Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.
Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm
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Xia Hanyu
2016-01-01
Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.
Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization
Liu, Zexi
2018-01-01
Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.
A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.
Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue
2016-02-19
Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.
A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network
Directory of Open Access Journals (Sweden)
Yuzhong Chen
2016-02-01
Full Text Available Vehicular ad hoc networks (VANETs have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.
A harmony search algorithm for clustering with feature selection
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Carlos Cobos
2010-01-01
Full Text Available En este artículo se presenta un nuevo algoritmo de clustering denominado IHSK, con la capacidad de seleccionar características en un orden de complejidad lineal. El algoritmo es inspirado en la combinación de los algoritmos de búsqueda armónica y K-means. Para la selección de las características se usó el concepto de variabilidad y un método heurístico que penaliza la presencia de dimensiones con baja probabilidad de aportar en la solución actual. El algoritmo fue probado con conjuntos de datos sintéticos y reales, obteniendo resultados prometedores.
Segmentation of Mushroom and Cap width Measurement using Modified K-Means Clustering Algorithm
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Eser Sert
2014-01-01
Full Text Available Mushroom is one of the commonly consumed foods. Image processing is one of the effective way for examination of visual features and detecting the size of a mushroom. We developed software for segmentation of a mushroom in a picture and also to measure the cap width of the mushroom. K-Means clustering method is used for the process. K-Means is one of the most successful clustering methods. In our study we customized the algorithm to get the best result and tested the algorithm. In the system, at first mushroom picture is filtered, histograms are balanced and after that segmentation is performed. Results provided that customized algorithm performed better segmentation than classical K-Means algorithm. Tests performed on the designed software showed that segmentation on complex background pictures is performed with high accuracy, and 20 mushrooms caps are measured with 2.281 % relative error.
Directory of Open Access Journals (Sweden)
Ioannis P. Panapakidis
2018-02-01
Full Text Available Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS. A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.
An adaptive clustering algorithm for image matching based on corner feature
Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song
2018-04-01
The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.
Energy Technology Data Exchange (ETDEWEB)
Reddy, N.M.; Reddy, K.R. [G. Narayanamma Inst. of Technology and Science, Hyderabad (India). Dept. of Electrical Engineering; Ramana, N.V. [JNTU College of Engineering, Jagityala (India). Dept. of Electrical Engineering
2008-07-01
Thermal power plants consist of several generating units with different generating capacities, fuel cost per MWH generated, minimum up/down times, and start-up or shut-down costs. The Unit Commitment (UC) problem in power systems involves determining the start-up and shut-down schedules of thermal generating units to meet forecasted load over a future short term for a period of one to seven days. This paper presented a new approach for the most complex UC problem using agglomerative and divisive hierarchical clustering. Euclidean costs, which are a measure of differences in fuel cost and start-up costs of any two units, were first calculated. Then, depending on the value of Euclidean costs, similar type of units were placed in a cluster. The proposed methodology has 2 individual algorithms. An agglomerative cluster algorithm is used while the load is increasing, and a divisive cluster algorithm is used when the load is decreasing. A search was conducted for an optimal solution for a minimal number of clusters and cluster data points. A standard ten-unit thermal unit power system was used to test and evaluate the performance of the method for a period of 24 hours. The new approach proved to be quite effective and satisfactory. 15 refs., 9 tabs., 5 figs.
Shen, Wenfeng; Wei, Daming; Xu, Weimin; Zhu, Xin; Yuan, Shizhong
2010-10-01
Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Online tracking applications of the general purpose EDRO Board
Annovi, A; The ATLAS collaboration; Cervini, F; Crescioli, F; Fabbri, L; Franchini, M; Giannetti, P; Giannuzzi, F; Giorgi, F; Magalotti, D; Piendibene, M; Sbarra, C; Valentinetti, S; Mauro, V; Zoccoli, A
2012-01-01
The capability to perform extremely fast track reconstruction online is becoming more and more important for the LHC upgrade as well as the next generation of HEP experiments, where the expected instantaneous luminosities (in excess of 10^34 /cm2/s) and the very low signal/background ratio ask for fast and clean identification of the main characteristics of interesting events. The Slim5 R&D project studied different aspects of fast and high-precision tracking in dedicated hardware: data-push silicon sensors, high bandwidth DAQ systems and Associative Memories (AM) for fast track identification. The central element of the development system is a high traffic board, called EDRO, capable of collecting and processing digital data with an input rate of 16 Gbps. The input hits, suitably formatted or clusterized, are sent to an AM board sending back candidate tracks, which are identified at a rate of 40 MHz. The EDRO board is then able to deliver triggers and formatted events for further processing. The EDRO-AM ...
An improved K-means clustering algorithm in agricultural image segmentation
Cheng, Huifeng; Peng, Hui; Liu, Shanmei
Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.
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.
Loewenstein, Yaniv; Portugaly, Elon; Fromer, Menachem; Linial, Michal
2008-07-01
UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation. We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families. A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request.
An Effective Tri-Clustering Algorithm Combining Expression Data with Gene Regulation Information
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Ao Li
2009-04-01
Full Text Available Motivation: Bi-clustering algorithms aim to identify sets of genes sharing similar expression patterns across a subset of conditions. However direct interpretation or prediction of gene regulatory mechanisms may be difficult as only gene expression data is used. Information about gene regulators may also be available, most commonly about which transcription factors may bind to the promoter region and thus control the expression level of a gene. Thus a method to integrate gene expression and gene regulation information is desirable for clustering and analyzing. Methods: By incorporating gene regulatory information with gene expression data, we define regulated expression values (REV as indicators of how a gene is regulated by a specific factor. Existing bi-clustering methods are extended to a three dimensional data space by developing a heuristic TRI-Clustering algorithm. An additional approach named Automatic Boundary Searching algorithm (ABS is introduced to automatically determine the boundary threshold. Results: Results based on incorporating ChIP-chip data representing transcription factor-gene interactions show that the algorithms are efficient and robust for detecting tri-clusters. Detailed analysis of the tri-cluster extracted from yeast sporulation REV data shows genes in this cluster exhibited significant differences during the middle and late stages. The implicated regulatory network was then reconstructed for further study of defined regulatory mechanisms. Topological and statistical analysis of this network demonstrated evidence of significant changes of TF activities during the different stages of yeast sporulation, and suggests this approach might be a general way to study regulatory networks undergoing transformations.
Chaos control of ferroresonance system based on RBF-maximum entropy clustering algorithm
International Nuclear Information System (INIS)
Liu Fan; Sun Caixin; Sima Wenxia; Liao Ruijin; Guo Fei
2006-01-01
With regards to the ferroresonance overvoltage of neutral grounded power system, a maximum-entropy learning algorithm based on radial basis function neural networks is used to control the chaotic system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers. It improves the regression and learning ability of neural networks. The numerical experiment of ferroresonance system testifies the effectiveness and feasibility of using the algorithm to control chaos in neutral grounded system
Edge corrections to electromagnetic Casimir energies from general-purpose Mathieu-function routines
Blose, Elizabeth Noelle; Ghimire, Biswash; Graham, Noah; Stratton-Smith, Jeremy
2015-01-01
Scattering theory methods make it possible to calculate the Casimir energy of a perfectly conducting elliptic cylinder opposite a perfectly conducting plane in terms of Mathieu functions. In the limit of zero radius, the elliptic cylinder becomes a finite-width strip, which allows for the study of edge effects. However, existing packages for computing Mathieu functions are insufficient for this calculation because none can compute Mathieu functions of both the first and second kind for complex arguments. To address this shortcoming, we have written a general-purpose Mathieu-function package, based on algorithms developed by Alhargan. We use these routines to find edge corrections to the proximity force approximation for the Casimir energy of a perfectly conducting strip opposite a perfectly conducting plane.
International Nuclear Information System (INIS)
Nakao, Yoshinobu; Koda, Eiichi; Takahashi, Toru
2009-01-01
We have developed the general-purpose software by which static thermal characteristic of the power generation system is analyzed easily. This software has the notable features as follows. It has the new algorithm to solve non-linear simultaneous equations to analyze the static thermal characteristics such as heat and mass balance, efficiencies, etc. of various power generation systems. It has the flexibility for setting calculation conditions. It is able to be executed on the personal computer easily and quickly. We ensured that it is able to construct heat and mass balance diagrams of main steam system of nuclear power plant and calculate the power output and efficiencies of the system. Furthermore, we evaluated various heat recovery measures of steam generator blowdown water and found that this software could be a useful operation aid for planning effective changes in support of power stretch. (author)
Energy Technology Data Exchange (ETDEWEB)
Loughry, Thomas A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-02-01
As the volume of data acquired by space-based sensors increases, mission data compression/decompression and forward error correction code processing performance must likewise scale. This competency development effort was explored using the General Purpose Graphics Processing Unit (GPGPU) to accomplish high-rate Rice Decompression and high-rate Reed-Solomon (RS) decoding at the satellite mission ground station. Each algorithm was implemented and benchmarked on a single GPGPU. Distributed processing across one to four GPGPUs was also investigated. The results show that the GPGPU has considerable potential for performing satellite communication Data Signal Processing, with three times or better performance improvements and up to ten times reduction in cost over custom hardware, at least in the case of Rice Decompression and Reed-Solomon Decoding.
A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
Hruz, Tomas; Lucas, Christoph; Laule, Oliver; Zimmermann, Philip
2013-01-01
Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. PMID:23864855
An effective trust-based recommendation method using a novel graph clustering algorithm
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.
AUTHOR|(INSPIRE)INSPIRE-00372074; The ATLAS collaboration; Sotiropoulou, Calliope Louisa; Annovi, Alberto; Kordas, Kostantinos
2016-01-01
In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole avail...
Gkaitatzis, Stamatios; The ATLAS collaboration
2016-01-01
In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100 µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole avai...
Khan, Fouad
2016-01-01
K-means is one of the most widely used clustering algorithms in various disciplines, especially for large datasets. However the method is known to be highly sensitive to initial seed selection of cluster centers. K-means++ has been proposed to overcome this problem and has been shown to have better accuracy and computational efficiency than k-means. In many clustering problems though -such as when classifying georeferenced data for mapping applications- standardization of clustering methodolo...
Directory of Open Access Journals (Sweden)
Taegwon Jeong
2011-05-01
Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.
Lee, Chongdeuk; Jeong, Taegwon
2011-01-01
Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.
MixSim : An R Package for Simulating Data to Study Performance of Clustering Algorithms
Directory of Open Access Journals (Sweden)
Volodymyr Melnykov
2012-11-01
Full Text Available The R package MixSim is a new tool that allows simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim, there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. All features of the package are illustrated in great detail. The utility of the package is highlighted through a small comparison study of several popular clustering algorithms.
An improved optimum-path forest clustering algorithm for remote sensing image segmentation
Chen, Siya; Sun, Tieli; Yang, Fengqin; Sun, Hongguang; Guan, Yu
2018-03-01
Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.
The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure
Euán, Carolina
2018-04-12
We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data.
Directory of Open Access Journals (Sweden)
Татьяна Борисовна Шатовская
2015-03-01
Full Text Available In this work results of modified Chameleon algorithm are discussed. Hierarchical multilevel algorithms consist of several stages: building the graph, coarsening, partitioning, recovering. Exploring of clustering quality for different data sets with different combinations of algorithms on different stages of the algorithm is the main aim of the article. And also aim is improving the construction phase through the optimization algorithm of choice k in the building the graph k-nearest neighbors
A priori data-driven multi-clustered reservoir generation algorithm for echo state network.
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Xiumin Li
Full Text Available Echo state networks (ESNs with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-07-03
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.
Directory of Open Access Journals (Sweden)
Liling Sun
2015-01-01
Full Text Available An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II, the multiobjective particle swarm optimizer (MOPSO, and the multiobjective ABC (MOABC. Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.
A clustering algorithm for sample data based on environmental pollution characteristics
Chen, Mei; Wang, Pengfei; Chen, Qiang; Wu, Jiadong; Chen, Xiaoyun
2015-04-01
Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To improve the accuracy of source apportionment and classify the sample data for these models, this study proposes an easy-to-use, high-dimensional EPC algorithm that not only organizes all of the sample data into different groups according to the similarities in pollution characteristics such as pollution sources and concentrations but also simultaneously detects outliers. The main clustering process consists of selecting the first unlabelled point as the cluster centre, then assigning each data point in the sample dataset to its most similar cluster centre according to both the user-defined threshold and the value of similarity function in each iteration, and finally modifying the clusters using a method similar to k-Means. The validity and accuracy of the algorithm are tested using both real and synthetic datasets, which makes the EPC algorithm practical and effective for appropriately classifying sample data for source apportionment models and helpful for better understanding and interpreting the sources of pollution.
Cluster-Based Multipolling Sequencing Algorithm for Collecting RFID Data in Wireless LANs
Choi, Woo-Yong; Chatterjee, Mainak
2015-03-01
With the growing use of RFID (Radio Frequency Identification), it is becoming important to devise ways to read RFID tags in real time. Access points (APs) of IEEE 802.11-based wireless Local Area Networks (LANs) are being integrated with RFID networks that can efficiently collect real-time RFID data. Several schemes, such as multipolling methods based on the dynamic search algorithm and random sequencing, have been proposed. However, as the number of RFID readers associated with an AP increases, it becomes difficult for the dynamic search algorithm to derive the multipolling sequence in real time. Though multipolling methods can eliminate the polling overhead, we still need to enhance the performance of the multipolling methods based on random sequencing. To that extent, we propose a real-time cluster-based multipolling sequencing algorithm that drastically eliminates more than 90% of the polling overhead, particularly so when the dynamic search algorithm fails to derive the multipolling sequence in real time.
Channel processor in 2D cluster finding algorithm for high energy physics application
International Nuclear Information System (INIS)
Paul, Rourab; Chakrabarti, Amlan; Mitra, Jubin; Khan, Shuaib A.; Nayak, Tapan; Mukherjee, Sanjoy
2016-01-01
In a Large Ion Collider Experiment (ALICE) at CERN 1 TB/s (approximately) data comes from front end electronics. Previously, we had 1 GBT link operated with a cluster clock frequencies of 133 MHz and 320 MHz in Run 1 and Run 2 respectively. The cluster algorithm proposed in Run 1 and 2 could not work in Run 3 as the data speed increased almost 20 times. Older version cluster algorithm receives data sequentially as a stream. It has 2 main sub processes - Channel Processor, Merging process. The initial step of channel processor finds a peak Q max and sums up pads (sensors) data from -2 time bin to +2 time bin in the time direction. The computed value stores in a register named cluster fragment data (cfd o ). The merging process merges cfd o in pad direction. The data streams in Run 2 comes sequentially, which processed by the channel processor and merging block in a sequential manner with very less resource over head. In Run 3 data comes parallely, 1600 data from 1600 pads of a single time instant comes at each 200 ns interval (5 MHz) which is very challenging to process in the budgeted resource platform of Arria 10 FPGA hardware with 250 to 320 MHz cluster clock
Energy Efficient and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks
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Amine Dahane
2015-01-01
Full Text Available The main concern of clustering approaches for mobile wireless sensor networks (WSNs is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach the need of a powerful mechanism to safely elect a cluster head remains a challenging task in many research works that take into account the mobility of the network. The approach based on the computing of the weight of each node in the network is one of the proposed techniques to deal with this problem. In this paper, we propose an energy efficient and safe weighted clustering algorithm (ES-WCA for mobile WSNs using a combination of five metrics. Among these metrics lies the behavioral level metric which promotes a safe choice of a cluster head in the sense where this last one will never be a malicious node. Moreover, the highlight of our work is summarized in a comprehensive strategy for monitoring the network, in order to detect and remove the malicious nodes. We use simulation study to demonstrate the performance of the proposed algorithm.
An algorithm of discovering signatures from DNA databases on a computer cluster.
Lee, Hsiao Ping; Sheu, Tzu-Fang
2014-10-05
Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved. In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discovery and have proposed a parallel signature discovery algorithm on a computer cluster. The algorithm applies the divide-and-conquer strategy to solve the problem posed to the existing algorithms where they are unable to process large databases and uses a parallel computing mechanism to effectively improve the efficiency of signature discovery. Even when run with just the memory of regular personal computers, the algorithm can still process large databases such as the human whole-genome EST database which were previously unable to be processed by the existing algorithms. The algorithm proposed in this research is not limited by the amount of usable memory and can rapidly find signatures in large databases, making it useful in applications such as Next Generation Sequencing and other large database analysis and processing. The implementation of the proposed algorithm is available at http://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm.
Clustering methods and visualization algorithms to aid nuclear reactor operative diagnostics
International Nuclear Information System (INIS)
Pepelyshev, Yu.N.; Dzwinel, W.
1990-01-01
The software system developed plays the role of the aid to an operator for nuclear reactor diagnostics. The noise analysis of the reactor parameters such as power, temperature and coolant flow rate constitutes the basis of the system. Combination of data acquisition, data preprocessing, clustering and cluster visualization algorithms with heuristic techniques of results analysis, determine the way of its implementation. Two regimes are available. The first one - extended - is recommended for a long term investigations and the second - suppressed for the aid to the reactor operation monitoring. The system has been tested and developed at the JINR IBR-2 pulsed reactor. 13 refs.; 4 figs.; 2 tabs
K-mean clustering algorithm for processing signals from compound semiconductor detectors
International Nuclear Information System (INIS)
Tada, Tsutomu; Hitomi, Keitaro; Wu, Yan; Kim, Seong-Yun; Yamazaki, Hiromichi; Ishii, Keizo
2011-01-01
The K-mean clustering algorithm was employed for processing signal waveforms from TlBr detectors. The signal waveforms were classified based on its shape reflecting the charge collection process in the detector. The classified signal waveforms were processed individually to suppress the pulse height variation of signals due to the charge collection loss. The obtained energy resolution of a 137 Cs spectrum measured with a 0.5 mm thick TlBr detector was 1.3% FWHM by employing 500 clusters.
What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm.
Raykov, Yordan P; Boukouvalas, Alexis; Baig, Fahd; Little, Max A
The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.
Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
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Ahmad Ali
2018-03-01
Full Text Available Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs. Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF. In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle.
Hopfield-K-Means clustering algorithm: A proposal for the segmentation of electricity customers
Energy Technology Data Exchange (ETDEWEB)
Lopez, Jose J.; Aguado, Jose A.; Martin, F.; Munoz, F.; Rodriguez, A.; Ruiz, Jose E. [Department of Electrical Engineering, University of Malaga, C/ Dr. Ortiz Ramos, sn., Escuela de Ingenierias, 29071 Malaga (Spain)
2011-02-15
Customer classification aims at providing electric utilities with a volume of information to enable them to establish different types of tariffs. Several methods have been used to segment electricity customers, including, among others, the hierarchical clustering, Modified Follow the Leader and K-Means methods. These, however, entail problems with the pre-allocation of the number of clusters (Follow the Leader), randomness of the solution (K-Means) and improvement of the solution obtained (hierarchical algorithm). Another segmentation method used is Hopfield's autonomous recurrent neural network, although the solution obtained only guarantees that it is a local minimum. In this paper, we present the Hopfield-K-Means algorithm in order to overcome these limitations. This approach eliminates the randomness of the initial solution provided by K-Means based algorithms and it moves closer to the global optimun. The proposed algorithm is also compared against other customer segmentation and characterization techniques, on the basis of relative validation indexes. Finally, the results obtained by this algorithm with a set of 230 electricity customers (residential, industrial and administrative) are presented. (author)
[Automatic Sleep Stage Classification Based on an Improved K-means Clustering Algorithm].
Xiao, Shuyuan; Wang, Bei; Zhang, Jian; Zhang, Qunfeng; Zou, Junzhong
2016-10-01
Sleep stage scoring is a hotspot in the field of medicine and neuroscience.Visual inspection of sleep is laborious and the results may be subjective to different clinicians.Automatic sleep stage classification algorithm can be used to reduce the manual workload.However,there are still limitations when it encounters complicated and changeable clinical cases.The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data.In the proposed improved K-means clustering algorithm,points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm.Meanwhile,the cluster centers were updated according to the‘Three-Sigma Rule’during the iteration to abate the influence of the outliers.The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure(CPAP)treatment.The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%.With the analysis of morphological diversity of sleep data,it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.
Hopfield-K-Means clustering algorithm: A proposal for the segmentation of electricity customers
International Nuclear Information System (INIS)
Lopez, Jose J.; Aguado, Jose A.; Martin, F.; Munoz, F.; Rodriguez, A.; Ruiz, Jose E.
2011-01-01
Customer classification aims at providing electric utilities with a volume of information to enable them to establish different types of tariffs. Several methods have been used to segment electricity customers, including, among others, the hierarchical clustering, Modified Follow the Leader and K-Means methods. These, however, entail problems with the pre-allocation of the number of clusters (Follow the Leader), randomness of the solution (K-Means) and improvement of the solution obtained (hierarchical algorithm). Another segmentation method used is Hopfield's autonomous recurrent neural network, although the solution obtained only guarantees that it is a local minimum. In this paper, we present the Hopfield-K-Means algorithm in order to overcome these limitations. This approach eliminates the randomness of the initial solution provided by K-Means based algorithms and it moves closer to the global optimun. The proposed algorithm is also compared against other customer segmentation and characterization techniques, on the basis of relative validation indexes. Finally, the results obtained by this algorithm with a set of 230 electricity customers (residential, industrial and administrative) are presented. (author)
Ningrum, R. W.; Surarso, B.; Farikhin; Safarudin, Y. M.
2018-03-01
This paper proposes the combination of Firefly Algorithm (FA) and Chen Fuzzy Time Series Forecasting. Most of the existing fuzzy forecasting methods based on fuzzy time series use the static length of intervals. Therefore, we apply an artificial intelligence, i.e., Firefly Algorithm (FA) to set non-stationary length of intervals for each cluster on Chen Method. The method is evaluated by applying on the Jakarta Composite Index (IHSG) and compare with classical Chen Fuzzy Time Series Forecasting. Its performance verified through simulation using Matlab.
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Xiangbing Zhou
2017-12-01
Full Text Available Rapidly growing Global Positioning System (GPS data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices. In order to employ K-means to mine the better origins and destinations (OD behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA with density and noise for K-means clustering (NoiseClust. In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city’s situations in four taxi GPS data sets.
Dynamic connectivity algorithms for Monte Carlo simulations of the random-cluster model
International Nuclear Information System (INIS)
Elçi, Eren Metin; Weigel, Martin
2014-01-01
We review Sweeny's algorithm for Monte Carlo simulations of the random cluster model. Straightforward implementations suffer from the problem of computational critical slowing down, where the computational effort per edge operation scales with a power of the system size. By using a tailored dynamic connectivity algorithm we are able to perform all operations with a poly-logarithmic computational effort. This approach is shown to be efficient in keeping online connectivity information and is of use for a number of applications also beyond cluster-update simulations, for instance in monitoring droplet shape transitions. As the handling of the relevant data structures is non-trivial, we provide a Python module with a full implementation for future reference.
Ishii, Satoshi; Kadota, Koji; Senoo, Keishi
2009-09-01
DNA fingerprinting analysis such as amplified ribosomal DNA restriction analysis (ARDRA), repetitive extragenic palindromic PCR (rep-PCR), ribosomal intergenic spacer analysis (RISA), and denaturing gradient gel electrophoresis (DGGE) are frequently used in various fields of microbiology. The major difficulty in DNA fingerprinting data analysis is the alignment of multiple peak sets. We report here an R program for a clustering-based peak alignment algorithm, and its application to analyze various DNA fingerprinting data, such as ARDRA, rep-PCR, RISA, and DGGE data. The results obtained by our clustering algorithm and by BioNumerics software showed high similarity. Since several R packages have been established to statistically analyze various biological data, the distance matrix obtained by our R program can be used for subsequent statistical analyses, some of which were not previously performed but are useful in DNA fingerprinting studies.
A New Waveform Signal Processing Method Based on Adaptive Clustering-Genetic Algorithms
International Nuclear Information System (INIS)
Noha Shaaban; Fukuzo Masuda; Hidetsugu Morota
2006-01-01
We present a fast digital signal processing method for numerical analysis of individual pulses from CdZnTe compound semiconductor detectors. Using Maxi-Mini Distance Algorithm and Genetic Algorithms based discrimination technique. A parametric approach has been used for classifying the discriminated waveforms into a set of clusters each has a similar signal shape with a corresponding pulse height spectrum. A corrected total pulse height spectrum was obtained by applying a normalization factor for the full energy peak for each cluster with a highly improvements in the energy spectrum characteristics. This method applied successfully for both simulated and real measured data, it can be applied to any detector suffers from signal shape variation. (authors)
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric
2017-01-01
This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was developed to implement a robotics application consisting in sorting and storing objects in an unsupervised way. The toy dataset used to validate such application consists of Lego bricks of different shapes and colors. The uncontrolled lighting conditions together with the use of RGB color features, respectively involve data...
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.
Bourobou, Serge Thomas Mickala; Yoo, Younghwan
2015-05-21
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm
Directory of Open Access Journals (Sweden)
Serge Thomas Mickala Bourobou
2015-05-01
Full Text Available This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.
Rajab, Maher I
2011-11-01
Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.
Big Data GPU-Driven Parallel Processing Spatial and Spatio-Temporal Clustering Algorithms
Konstantaras, Antonios; Skounakis, Emmanouil; Kilty, James-Alexander; Frantzeskakis, Theofanis; Maravelakis, Emmanuel
2016-04-01
Advances in graphics processing units' technology towards encompassing parallel architectures [1], comprised of thousands of cores and multiples of parallel threads, provide the foundation in terms of hardware for the rapid processing of various parallel applications regarding seismic big data analysis. Seismic data are normally stored as collections of vectors in massive matrices, growing rapidly in size as wider areas are covered, denser recording networks are being established and decades of data are being compiled together [2]. Yet, many processes regarding seismic data analysis are performed on each seismic event independently or as distinct tiles [3] of specific grouped seismic events within a much larger data set. Such processes, independent of one another can be performed in parallel narrowing down processing times drastically [1,3]. This research work presents the development and implementation of three parallel processing algorithms using Cuda C [4] for the investigation of potentially distinct seismic regions [5,6] present in the vicinity of the southern Hellenic seismic arc. The algorithms, programmed and executed in parallel comparatively, are the: fuzzy k-means clustering with expert knowledge [7] in assigning overall clusters' number; density-based clustering [8]; and a selves-developed spatio-temporal clustering algorithm encompassing expert [9] and empirical knowledge [10] for the specific area under investigation. Indexing terms: GPU parallel programming, Cuda C, heterogeneous processing, distinct seismic regions, parallel clustering algorithms, spatio-temporal clustering References [1] Kirk, D. and Hwu, W.: 'Programming massively parallel processors - A hands-on approach', 2nd Edition, Morgan Kaufman Publisher, 2013 [2] Konstantaras, A., Valianatos, F., Varley, M.R. and Makris, J.P.: 'Soft-Computing Modelling of Seismicity in the Southern Hellenic Arc', Geoscience and Remote Sensing Letters, vol. 5 (3), pp. 323-327, 2008 [3] Papadakis, S. and
Directory of Open Access Journals (Sweden)
Yongwei Zhang
2017-01-01
Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.
Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan
2018-04-01
Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.
Luo, Junhai; Fu, Liang
2017-06-09
With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.
Directory of Open Access Journals (Sweden)
Junhai Luo
2017-06-01
Full Text Available With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS, which is collected from Access Points (APs. The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.
An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm
Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin
2018-04-01
Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.
Mckee, James W.
1988-01-01
This final report describes the accomplishments of the General Purpose Intelligent Sensor Interface task of the Applications of Artificial Intelligence to Space Station grant for the period from October 1, 1987 through September 30, 1988. Portions of the First Biannual Report not revised will not be included but only referenced. The goal is to develop an intelligent sensor system that will simplify the design and development of expert systems using sensors of the physical phenomena as a source of data. This research will concentrate on the integration of image processing sensors and voice processing sensors with a computer designed for expert system development. The result of this research will be the design and documentation of a system in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, television camera selection, voice recognition hardware selection, or analog signal processing. The user will be able to access data from video or voice sensors through standard LISP statements without any need to know about the sensor hardware or software.
Computing OpenSURF on OpenCL and General Purpose GPU
Directory of Open Access Journals (Sweden)
Wanglong Yan
2013-10-01
Full Text Available Speeded-Up Robust Feature (SURF algorithm is widely used for image feature detecting and matching in computer vision area. Open Computing Language (OpenCL is a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. This paper introduces how to implement an open-sourced SURF program, namely OpenSURF, on general purpose GPU by OpenCL, and discusses the optimizations in terms of the thread architectures and memory models in detail. Our final OpenCL implementation of OpenSURF is on average 37% and 64% faster than the OpenCV SURF v2.4.5 CUDA implementation on NVidia's GTX660 and GTX460SE GPUs, repectively. Our OpenCL program achieved real-time performance (>25 Frames Per Second for almost all the input images with different sizes from 320*240 to 1024*768 on NVidia's GTX660 GPU, NVidia's GTX460SE GPU and AMD's Radeon HD 6850 GPU. Our OpenCL approach on NVidia's GTX660 GPU is more than 22.8 times faster than its original CPU version on Intel's Dual-Core E5400 2.7G on average.
2013-10-31
... (NOA) for General Purpose Warehouse and Information Technology Center Construction (GPW/IT)--Tracy Site... proposed action to construct a General Purpose Warehouse and Information Technology Center at Defense..., Suite 02G09, Alexandria, VA 22350- 3100. FOR FURTHER INFORMATION CONTACT: Ann Engelberger at (703) 767...
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division
Directory of Open Access Journals (Sweden)
Dawen Xia
2015-01-01
Full Text Available Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs. Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K-Means (Par3PKM algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K-Means and then employ a MapReduce paradigm to redesign the optimized K-Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K-Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.
A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data
Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart
2017-01-01
The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.
Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations
Directory of Open Access Journals (Sweden)
Yingfeng Zhang
2014-01-01
Full Text Available This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS and precision forging stage (PFS of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.
Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei
2013-01-01
This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…
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.
Development of a Genetic Algorithm to Automate Clustering of a Dependency Structure Matrix
Rogers, James L.; Korte, John J.; Bilardo, Vincent J.
2006-01-01
Much technology assessment and organization design data exists in Microsoft Excel spreadsheets. Tools are needed to put this data into a form that can be used by design managers to make design decisions. One need is to cluster data that is highly coupled. Tools such as the Dependency Structure Matrix (DSM) and a Genetic Algorithm (GA) can be of great benefit. However, no tool currently combines the DSM and a GA to solve the clustering problem. This paper describes a new software tool that interfaces a GA written as an Excel macro with a DSM in spreadsheet format. The results of several test cases are included to demonstrate how well this new tool works.
Implementation of the ALICE HLT hardware cluster finder algorithm in Vivado HLS
Energy Technology Data Exchange (ETDEWEB)
Gruell, Frederik; Engel, Heiko; Kebschull, Udo [Infrastructure and Computer Systems in Data Processing, Goethe University Frankfurt (Germany); Collaboration: ALICE-Collaboration
2016-07-01
The FastClusterFinder algorithm running in the ALICE High-Level Trigger (HLT) read-out boards extracts clusters from raw data from the Time Projection Chamber (TPC) detector and forwards them to the HLT data processing framework for tracking, event reconstruction and compression. It serves as an early stage of feature extraction in the FPGA of the board. Past and current implementations are written in VHDL on reconfigurable hardware for high throughput and low latency. We examine Vivado HLS, a high-level language that promises an increased developer productivity, as an alternative. The implementation of the application is compared to descriptions in VHDL and MaxJ in terms of productivity, resource usage and maximum clock frequency.
Rasim; Junaeti, E.; Wirantika, R.
2018-01-01
Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.
Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm
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.
Mobility-Aware and Load Balancing Based Clustering Algorithm for Energy Conservation in MANET
Institute of Scientific and Technical Information of China (English)
XU Li; ZHENG Bao-yu; GUO Gong-de
2005-01-01
Mobile ad hoc network (MANET) is one of wireless communication network architecture that has received a lot of attention. MANET is characterized by dynamic network topology and limited energy. With mobility-aware and load balancing based clustering algorithm (MLCA), this paper proposes a new topology management strategy to conserve energy. Performance simulation results show that the proposed MLCA strategy can balances the traffic load inside the whole network, so as to prolong the network lifetime, meanly, at the same time, achieve higher throughput ratio and network stability.
Parallel implementation of D-Phylo algorithm for maximum likelihood clusters.
Malik, Shamita; Sharma, Dolly; Khatri, Sunil Kumar
2017-03-01
This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of k -means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8 × 800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.
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.
Genetic algorithm with fuzzy clustering for optimization of nuclear reactor problems
International Nuclear Information System (INIS)
Machado, Marcelo Dornellas; Sacco, Wagner Figueiredo; Schirru, Roberto
2000-01-01
Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to introduce a new niching technique based on the fuzzy clustering method FCM, bearing in mind its eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. tests are performed using widely known test functions and their results show that the new method is quite promising, specially to a future application in real world problems like the nuclear reactor core reload. (author)
Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna
2017-12-01
The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.
Kamali, Tahereh; Stashuk, Daniel
2016-10-01
Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity. Copyright
Directory of Open Access Journals (Sweden)
Simon Fong
2012-01-01
Full Text Available Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers’ gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm.
Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.
Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B
2011-02-01
This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.
Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal
2015-08-13
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.
Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong
2015-12-10
In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network's running and the degree of candidate nodes' effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime.
Ebrahimi, A.; Pahlavani, P.; Masoumi, Z.
2017-09-01
Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.
Directory of Open Access Journals (Sweden)
A. Ebrahimi
2017-09-01
Full Text Available Traffic monitoring and managing in urban intelligent transportation systems (ITS can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs; moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH, and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.
A DISTRIBUTED ENERGY EFFICIENT CLUSTERING ALGORITHM FOR DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
Seyed Mohammad Bagher Musavi Shirazi
2018-06-01
Full Text Available Wireless sensor networks (WSNs are a new generation of networks typically consisting of a large number of inexpensive nodes with wireless communications. The main purpose of these networks is to collect information from the environment for further processing. Nodes in the network have been equipped with limited battery lifetime, so energy saving is one of the major issues in WSNs. If we balance the load among cluster heads and prevent having an extra load on just a few nodes in the network, we can reach longer network lifetime. One solution to control energy consumption and balance the load among nodes is to use clustering techniques. In this paper, we propose a new distributed energy-efficient clustering algorithm for data aggregation in wireless sensor networks, called Distributed Clustering for Data Aggregation (DCDA. In our new approach, an optimal transmission tree is constructed among sensor nodes with a new greedy method. Base station (BS is the root, cluster heads (CHs and relay nodes are intermediate nodes, and other nodes (cluster member nodes are the leaves of this transmission tree. DCDA balances load among CHs in intra-cluster and inter-cluster data communications using different cluster sizes. For efficient inter-cluster communications, some relay nodes will transfer data between CHs. Energy consumption, distance to the base station, and cluster heads’ centric metric are three main adjustment parameters for the cluster heads election. Simulation results show that the proposed protocol leads to the reduction of individual sensor nodes’ energy consumption and prolongs network lifetime, in comparison with other known methods. ABSTRAK: Rangkaian sensor wayarles (WSN adalah rangkaian generasi baru yang terdiri daripada nod-nod murah komunikasi wayarles. Tujuan rangkaian-rangkaian ini adalah bagi mengumpul maklumat sekeliling untuk proses seterusnya. Nod dalam rangkaian ini dilengkapi bateri kurang jangka hayat, jadi simpanan tenaga
KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.
Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C
2014-06-01
KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.
Directory of Open Access Journals (Sweden)
Daniel M de Brito
Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.
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.
Are judgments a form of data clustering? Reexamining contrast effects with the k-means algorithm.
Boillaud, Eric; Molina, Guylaine
2015-04-01
A number of theories have been proposed to explain in precise mathematical terms how statistical parameters and sequential properties of stimulus distributions affect category ratings. Various contextual factors such as the mean, the midrange, and the median of the stimuli; the stimulus range; the percentile rank of each stimulus; and the order of appearance have been assumed to influence judgmental contrast. A data clustering reinterpretation of judgmental relativity is offered wherein the influence of the initial choice of centroids on judgmental contrast involves 2 combined frequency and consistency tendencies. Accounts of the k-means algorithm are provided, showing good agreement with effects observed on multiple distribution shapes and with a variety of interaction effects relating to the number of stimuli, the number of response categories, and the method of skewing. Experiment 1 demonstrates that centroid initialization accounts for contrast effects obtained with stretched distributions. Experiment 2 demonstrates that the iterative convergence inherent to the k-means algorithm accounts for the contrast reduction observed across repeated blocks of trials. The concept of within-cluster variance minimization is discussed, as is the applicability of a backward k-means calculation method for inferring, from empirical data, the values of the centroids that would serve as a representation of the judgmental context. (c) 2015 APA, all rights reserved.
An Aerodynamic Database for the Mk 82 General Purpose Low Drag Bomb
National Research Council Canada - National Science Library
Krishnamoorthy, L
1997-01-01
The drag database of the Mk 82 General Purpose Low Drag bomb, the primary gravity weapon in the RAAF inventory, has some shortcomings in the quality and traceability of data, and in the variations due...
1997-01-01
Conversion of a general-purpose freeway into a high-occupancy-vehicle (HOV) lane is an alternative to infrastructure addition for HOV system implementation. Research indicates that lane conversion is feasible technically if sufficient HOV usage and m...
Low Overhead Real-Time Computing With General Purpose Operating Systems
National Research Council Canada - National Science Library
Raymond, Michael
2004-01-01
.... In larger systems and more recently, general-purpose operating systems such as SGI IRIX and Linux are used for new projects because they already have multiprocessor and device driver support as well a large user base...
Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.
2D evaluation of spectral LIBS data derived from heterogeneous materials using cluster algorithm
Gottlieb, C.; Millar, S.; Grothe, S.; Wilsch, G.
2017-08-01
Laser-induced Breakdown Spectroscopy (LIBS) is capable of providing spatially resolved element maps in regard to the chemical composition of the sample. The evaluation of heterogeneous materials is often a challenging task, especially in the case of phase boundaries. In order to determine information about a certain phase of a material, the need for a method that offers an objective evaluation is necessary. This paper will introduce a cluster algorithm in the case of heterogeneous building materials (concrete) to separate the spectral information of non-relevant aggregates and cement matrix. In civil engineering, the information about the quantitative ingress of harmful species like Cl-, Na+ and SO42- is of great interest in the evaluation of the remaining lifetime of structures (Millar et al., 2015; Wilsch et al., 2005). These species trigger different damage processes such as the alkali-silica reaction (ASR) or the chloride-induced corrosion of the reinforcement. Therefore, a discrimination between the different phases, mainly cement matrix and aggregates, is highly important (Weritz et al., 2006). For the 2D evaluation, the expectation-maximization-algorithm (EM algorithm; Ester and Sander, 2000) has been tested for the application presented in this work. The method has been introduced and different figures of merit have been presented according to recommendations given in Haddad et al. (2014). Advantages of this method will be highlighted. After phase separation, non-relevant information can be excluded and only the wanted phase displayed. Using a set of samples with known and unknown composition, the EM-clustering method has been validated regarding to Gustavo González and Ángeles Herrador (2007).
Chen, Xiao; Li, Yaan; Yu, Jing; Li, Yuxing
2018-01-01
For fast and more effective implementation of tracking multiple targets in a cluttered environment, we propose a multiple targets tracking (MTT) algorithm called maximum entropy fuzzy c-means clustering joint probabilistic data association that combines fuzzy c-means clustering and the joint probabilistic data association (PDA) algorithm. The algorithm uses the membership value to express the probability of the target originating from measurement. The membership value is obtained through fuzzy c-means clustering objective function optimized by the maximum entropy principle. When considering the effect of the public measurement, we use a correction factor to adjust the association probability matrix to estimate the state of the target. As this algorithm avoids confirmation matrix splitting, it can solve the high computational load problem of the joint PDA algorithm. The results of simulations and analysis conducted for tracking neighbor parallel targets and cross targets in a different density cluttered environment show that the proposed algorithm can realize MTT quickly and efficiently in a cluttered environment. Further, the performance of the proposed algorithm remains constant with increasing process noise variance. The proposed algorithm has the advantages of efficiency and low computational load, which can ensure optimum performance when tracking multiple targets in a dense cluttered environment.
Directory of Open Access Journals (Sweden)
Iman Aghayan
2012-11-01
Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.
Ferraro Petrillo, Umberto; Roscigno, Gianluca; Cattaneo, Giuseppe; Giancarlo, Raffaele
2018-06-01
Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e. how many times each k-mer in {A,C,G,T}k occurs in a DNA sequence. Although this problem is computationally very simple and efficiently solvable on a conventional computer, the sheer amount of data available now in applications demands to resort to parallel and distributed computing. Indeed, those type of algorithms have been developed to collect k-mer statistics in the realm of genome assembly. However, they are so specialized to this domain that they do not extend easily to the computation of informational and linguistic indices, concurrently on sets of genomes. Following the well-established approach in many disciplines, and with a growing success also in bioinformatics, to resort to MapReduce and Hadoop to deal with 'Big Data' problems, we present KCH, the first set of MapReduce algorithms able to perform concurrently informational and linguistic analysis of large collections of genomic sequences on a Hadoop cluster. The benchmarking of KCH that we provide indicates that it is quite effective and versatile. It is also competitive with respect to the parallel and distributed algorithms highly specialized to k-mer statistics collection for genome assembly problems. In conclusion, KCH is a much needed addition to the growing number of algorithms and tools that use MapReduce for bioinformatics core applications. The software, including instructions for running it over Amazon AWS, as well as the datasets are available at http://www.di-srv.unisa.it/KCH. umberto.ferraro@uniroma1.it. Supplementary data are available at Bioinformatics online.
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.
Directory of Open Access Journals (Sweden)
Wei Xiaozhao
2016-03-01
Full Text Available For the development of the construction industry, the construction of data era is approaching, BIM (building information model with the actual needs of the construction industry has been widely used as a building information clan system software, different software for the practical application of different maturity, through the expert scoring method for the application of BIM technology maturity index mark, establish the evaluation index system, using PCA - Q clustering algorithm for the evaluation index system of classification, comprehensive evaluation in combination with the Choquet integral on the classification of evaluation index system, to achieve a reasonable assessment of the application of BIM technology maturity index. To lay a foundation for the future development of BIM Technology in various fields of construction, at the same time provides direction for the comprehensive application of BIM technology.
Directory of Open Access Journals (Sweden)
Amir Ahmad
2016-01-01
Full Text Available The early diagnosis of breast cancer is an important step in a fight against the disease. Machine learning techniques have shown promise in improving our understanding of the disease. As medical datasets consist of data points which cannot be precisely assigned to a class, fuzzy methods have been useful for studying of these datasets. Sometimes breast cancer datasets are described by categorical features. Many fuzzy clustering algorithms have been developed for categorical datasets. However, in most of these methods Hamming distance is used to define the distance between the two categorical feature values. In this paper, we use a probabilistic distance measure for the distance computation among a pair of categorical feature values. Experiments demonstrate that the distance measure performs better than Hamming distance for Wisconsin breast cancer data.
International Nuclear Information System (INIS)
Altarelli, F; Monasson, R; Zamponi, F
2008-01-01
We study the performances of stochastic heuristic search algorithms on Uniquely Extendible Constraint Satisfaction Problems with random inputs. We show that, for any heuristic preserving the Poissonian nature of the underlying instance, the (heuristic-dependent) largest ratio α a of constraints per variables for which a search algorithm is likely to find solutions is smaller than the critical ratio α d above which solutions are clustered and highly correlated. In addition we show that the clustering ratio can be reached when the number k of variables per constraints goes to infinity by the so-called Generalized Unit Clause heuristic
Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.
Lan, Haidong; Chan, Yuandong; Xu, Kai; Schmidt, Bertil; Peng, Shaoliang; Liu, Weiguo
2016-07-19
Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency. Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .
a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering
Toori, S.; Esmaeily, A.
2017-09-01
Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.
A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization.
He, Sheng; Samara, Petros; Burgers, Jan; Schomaker, Lambert
2016-11-01
It is of essential importance for historians to know the date and place of origin of the documents they study. It would be a huge advancement for historical scholars if it would be possible to automatically estimate the geographical and temporal provenance of a handwritten document by inferring them from the handwriting style of such a document. We propose a multiple-label guided clustering algorithm to discover the correlations between the concrete low-level visual elements in historical documents and abstract labels, such as date and location. First, a novel descriptor, called histogram of orientations of handwritten strokes, is proposed to extract and describe the visual elements, which is built on a scale-invariant polar-feature space. In addition, the multi-label self-organizing map (MLSOM) is proposed to discover the correlations between the low-level visual elements and their labels in a single framework. Our proposed MLSOM can be used to predict the labels directly. Moreover, the MLSOM can also be considered as a pre-structured clustering method to build a codebook, which contains more discriminative information on date and geography. The experimental results on the medieval paleographic scale data set demonstrate that our method achieves state-of-the-art results.
Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok
2015-01-01
This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.
Srinivasan, A.; Galbán, C.J.; Johnson, T.D.; Chenevert, T.L.; Ross, B.D.; Mukherji, S.K.
2014-01-01
Purpose The objective of our study was to analyze the differences between apparent diffusion coefficient (ADC) partitions (created using the K-Means algorithm) between benign and malignant neck lesions and evaluate its benefit in distinguishing these entities. Material and methods MRI studies of 10 benign and 10 malignant proven neck pathologies were post-processed on a PC using in-house software developed in MATLAB (The MathWorks, Inc., Natick, MA). Lesions were manually contoured by two neuroradiologists with the ADC values within each lesion clustered into two (low ADC-ADCL, high ADC-ADCH) and three partitions (ADCL, intermediate ADC-ADCI, ADCH) using the K-Means clustering algorithm. An unpaired two-tailed Student’s t-test was performed for all metrics to determine statistical differences in the means between the benign and malignant pathologies. Results Statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3 cluster models of both readers (p=0.03, 0.022 respectively) and the 2 cluster model of reader 2 (p=0.04) with the other metrics (ADCH, ADCI, whole lesion mean ADC) not revealing any significant differences. Receiver operating characteristics curves demonstrated the quantitative difference in mean ADCH and ADCL in both the 2 and 3 cluster models to be predictive of malignancy (2 clusters: p=0.008, area under curve=0.850, 3 clusters: p=0.01, area under curve=0.825). Conclusion The K-Means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared to whole lesion mean ADC alone. PMID:20007723
Srinivasan, A; Galbán, C J; Johnson, T D; Chenevert, T L; Ross, B D; Mukherji, S K
2010-04-01
Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies. MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC(L); high, ADC-ADC(H)) and 3 partitions (ADC(L); intermediate, ADC-ADC(I); ADC(H)) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies. A statistically significant difference between the mean ADC(L) clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (P = .03 and .022, respectively) and the 2-cluster model of reader 2 (P = .04), with the other metrics (ADC(H), ADC(I); whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC(H) and ADC(L) in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825). The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.
Catalog of physical protection equipment. Book 3: Volume VII. General purpose display components
International Nuclear Information System (INIS)
1977-06-01
A catalog of commercially available physical protection equipment has been prepared under MITRE contract AT(49-24)-0376 for use by the U. S. Nuclear Regulatory Commission (NRC). Included is information on barrier structures and equipment, interior and exterior intrusion detection sensors, entry (access) control devices, surveillance and alarm assessment equipment, contraband detection sensors, automated response equipment, general purpose displays and general purpose communications, with one volume devoted to each of these eight areas. For each item of equipment the information included consists of performance, physical, cost and supply/logistics data. The entire catalog is contained in three notebooks for ease in its use by licensing and inspection staff at NRC
Scalable Algorithms for Clustering Large Geospatiotemporal Data Sets on Manycore Architectures
Mills, R. T.; Hoffman, F. M.; Kumar, J.; Sreepathi, S.; Sripathi, V.
2016-12-01
The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery using data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe a massively parallel implementation of accelerated k-means clustering and some optimizations to boost computational intensity and utilization of wide SIMD lanes on state-of-the art multi- and manycore processors, including the second-generation Intel Xeon Phi ("Knights Landing") processor based on the Intel Many Integrated Core (MIC) architecture, which includes several new features, including an on-package high-bandwidth memory. We also analyze the code in the context of a few practical applications to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.
Report on the operation and utilization of general purpose use computer system 2001
Energy Technology Data Exchange (ETDEWEB)
Watanabe, Kunihiko; Watanabe, Reiko; Tsugawa, Kazuko; Tsuda, Kenzo; Yamamoto, Takashi; Nakamura, Osamu; Kamimura, Tetsuo [National Inst. for Fusion Science, Toki, Gifu (Japan)
2001-09-01
The General Purpose Use Computer System of National Institute for Fusion Science was replaced in January, 2001. The System is almost fully used after the first three months operation. Reported here is the process of the introduction of the new system and the state of the operation and utilization of the System between January and March, 2001, especially the detailed utilization of March. (author)
Some thermo-electromagnetic applications to fusion technology of a general purpose CAD package
International Nuclear Information System (INIS)
Girdinio, P.; Molfino, P.; Molinari, G.; Raia, G.; Rosatelli, F.; Viviani, A.
1985-01-01
A general purpose CAD package is applied to the solution of problems related to fusion technology. The problems solved are the interacting electromagnetic and thermal fields in a resistive toroidal coil and the design of the poloidal field coils in Tokamak machines. In both cases, the procedure used is reported and the results obtained are displayed and discussed
Some thermo-electromagnetic applications to fusion technology of a general purpose CAD package
International Nuclear Information System (INIS)
Girdinio, P.; Molfino, P.; Molinari, G.; Viviani, A.; Raia, G.; Rosatelli, F.
1984-01-01
A general purpose CAD package is applied to the solution of problems related to fusion technology. The problems solved are the interacting electromagnetic and thermal fields in a resistive toroidal coil and the design of the poloidal field coils in Tokamak machines. In both cases, the procedure used is reported and the results obtained are displayed and discussed. (author)
International Nuclear Information System (INIS)
Zazula, J.M.
1983-01-01
The general purpose code BALTORO was written for coupling the three-dimensional Monte-Carlo /MC/ with the one-dimensional Discrete Ordinates /DO/ radiation transport calculations. The quantity of a radiation-induced /neutrons or gamma-rays/ nuclear effect or the score from a radiation-yielding nuclear effect can be analysed in this way. (author)
An Evaluation of Classroom Activities and Exercises in ELT Classroom for General Purposes Course
Zohrabi, Mohammad
2011-01-01
It is through effective implementation of activities and exercises which students can be motivated and consequently lead to language learning. However, as an insider, the experience of teaching English for General Purposes (EGP) course indicates that it has some problems which need to be modified. In order to evaluate the EGP course,…
General-purpose Monte Carlo codes for neutron and photon transport calculations. MVP version 3
International Nuclear Information System (INIS)
Nagaya, Yasunobu
2017-01-01
JAEA has developed a general-purpose neutron/photon transport Monte Carlo code MVP. This paper describes the recent development of the MVP code and reviews the basic features and capabilities. In addition, capabilities implemented in Version 3 are also described. (author)
International Nuclear Information System (INIS)
Grishchuk, M.Kh.
1979-01-01
An experience of application of the general-purpose pressure and pressure drop transformers at the Nuclear Power Engineering Institute of the BSSR Academy of Sciences for measurements on nitrogen tetroxide has been described. The concrete recommendations on the types of transformers and the volume of preparational work before putting them into operation have been given
A general-purpose trigger processor system and its application to fast vertex trigger
International Nuclear Information System (INIS)
Hazumi, M.; Banas, E.; Natkaniec, Z.; Ostrowicz, W.
1997-12-01
A general-purpose hardware trigger system has been developed. The system comprises programmable trigger processors and pattern generator/samplers. The hardware design of the system is described. An application as a prototype of the very fast vertex trigger in an asymmetric B-factory at KEK is also explained. (author)
A general purpose program system for high energy physics experiment data acquisition and analysis
International Nuclear Information System (INIS)
Li Shuren; Xing Yuguo; Jin Bingnian
1985-01-01
This paper introduced the functions, structure and system generation of a general purpose program system (Fermilab MULTI) for high energy physics experiment data acquisition and analysis. Works concerning the reconstruction of MULTI system level 0.5 which can be run on the computer PDP-11/23 are also introduced briefly
Perbandingan Kemampuan Embedded Computer dengan General Purpose Computer untuk Pengolahan Citra
Directory of Open Access Journals (Sweden)
Herryawan Pujiharsono
2017-08-01
Full Text Available Perkembangan teknologi komputer membuat pengolahan citra saat ini banyak dikembangkan untuk dapat membantu manusia di berbagai bidang pekerjaan. Namun, tidak semua bidang pekerjaan dapat dikembangkan dengan pengolahan citra karena tidak mendukung penggunaan komputer sehingga mendorong pengembangan pengolahan citra dengan mikrokontroler atau mikroprosesor khusus. Perkembangan mikrokontroler dan mikroprosesor memungkinkan pengolahan citra saat ini dapat dikembangkan dengan embedded computer atau single board computer (SBC. Penelitian ini bertujuan untuk menguji kemampuan embedded computer dalam mengolah citra dan membandingkan hasilnya dengan komputer pada umumnya (general purpose computer. Pengujian dilakukan dengan mengukur waktu eksekusi dari empat operasi pengolahan citra yang diberikan pada sepuluh ukuran citra. Hasil yang diperoleh pada penelitian ini menunjukkan bahwa optimasi waktu eksekusi embedded computer lebih baik jika dibandingkan dengan general purpose computer dengan waktu eksekusi rata-rata embedded computer adalah 4-5 kali waktu eksekusi general purpose computer dan ukuran citra maksimal yang tidak membebani CPU terlalu besar untuk embedded computer adalah 256x256 piksel dan untuk general purpose computer adalah 400x300 piksel.
Dasarathy, B. V.
1976-01-01
An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.
Wang, Xueyi
2012-02-08
The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.
Directory of Open Access Journals (Sweden)
Li Ma
2015-01-01
Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA. The proposed algorithm combines artificial fish swarm algorithm (AFSA with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM.
Directory of Open Access Journals (Sweden)
Guang Yang
2018-04-01
Full Text Available Ultra‐dense small cell networks (UD‐SCNs have been identified as a promising scheme for next‐generation wireless networks capable of meeting the ever‐increasing demand for higher transmission rates and better quality of service. However, UD‐SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software‐defined networking (SDN‐based hierarchical agglomerative clustering (SDN‐HAC framework, which leverages SDN to centrally control all sub‐channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non‐cooperative scenarios, respectively.
Energy Technology Data Exchange (ETDEWEB)
Marchal, Rémi; Carbonnière, Philippe; Pouchan, Claude [Université de Pau et des Pays de l' Adour, IPREM/ECP, UMR CNRS 5254 (France)
2015-01-22
The study of atomic clusters has become an increasingly active area of research in the recent years because of the fundamental interest in studying a completely new area that can bridge the gap between atomic and solid state physics. Due to their specific properties, such compounds are of great interest in the field of nanotechnology [1,2]. Here, we would present our GSAM algorithm based on a DFT exploration of the PES to find the low lying isomers of such compounds. This algorithm includes the generation of an intial set of structure from which the most relevant are selected. Moreover, an optimization process, called raking optimization, able to discard step by step all the non physically reasonnable configurations have been implemented to reduce the computational cost of this algorithm. Structural properties of Ga{sub n}Asm clusters will be presented as an illustration of the method.
Huang, Fang; Liu, Dingsheng; Tan, Xicheng; Wang, Jian; Chen, Yunping; He, Binbin
2011-04-01
To design and implement an open-source parallel GIS (OP-GIS) based on a Linux cluster, the parallel inverse distance weighting (IDW) interpolation algorithm has been chosen as an example to explore the working model and the principle of algorithm parallel pattern (APP), one of the parallelization patterns for OP-GIS. Based on an analysis of the serial IDW interpolation algorithm of GRASS GIS, this paper has proposed and designed a specific parallel IDW interpolation algorithm, incorporating both single process, multiple data (SPMD) and master/slave (M/S) programming modes. The main steps of the parallel IDW interpolation algorithm are: (1) the master node packages the related information, and then broadcasts it to the slave nodes; (2) each node calculates its assigned data extent along one row using the serial algorithm; (3) the master node gathers the data from all nodes; and (4) iterations continue until all rows have been processed, after which the results are outputted. According to the experiments performed in the course of this work, the parallel IDW interpolation algorithm can attain an efficiency greater than 0.93 compared with similar algorithms, which indicates that the parallel algorithm can greatly reduce processing time and maximize speed and performance.
2010-04-01
... 21 Food and Drugs 8 2010-04-01 2010-04-01 false General purpose laboratory equipment labeled or... TOXICOLOGY DEVICES Clinical Laboratory Instruments § 862.2050 General purpose laboratory equipment labeled or promoted for a specific medical use. (a) Identification. General purpose laboratory equipment labeled or...
DEFF Research Database (Denmark)
Ackerman, Margareta; Ben-David, Shai; Branzei, Simina
2012-01-01
We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
Directory of Open Access Journals (Sweden)
Yifei Tong
2016-02-01
Full Text Available Energy efficiency is a buzzword of the 21st century. With the ever growing need for energy efficient and low-carbon production, it is a big challenge for high energy-consumption enterprises to reduce their energy consumption. To this aim, a forging enterprise, DVR (the abbreviation of a forging enterprise, is researched. Firstly, an investigation into the production processes of DVR is given as well as an analysis of forging production. Then, the energy-saving forging scheduling is decomposed into two sub-problems. One is for cutting and machining scheduling, which is similar to traditional machining scheduling. The other one is for forging and heat treatment scheduling. Thirdly, former forging production scheduling is presented and solved based on an improved genetic algorithm. Fourthly, the latter is discussed in detail, followed by proposed dynamic clustering and stacking combination optimization. The proposed stacking optimization requires making the gross weight of forgings as close to the maximum batch capacity as possible. The above research can help reduce the heating times, and increase furnace utilization with high energy efficiency and low carbon emissions.
Discrete-Event Execution Alternatives on General Purpose Graphical Processing Units
International Nuclear Information System (INIS)
Perumalla, Kalyan S.
2006-01-01
Graphics cards, traditionally designed as accelerators for computer graphics, have evolved to support more general-purpose computation. General Purpose Graphical Processing Units (GPGPUs) are now being used as highly efficient, cost-effective platforms for executing certain simulation applications. While most of these applications belong to the category of time-stepped simulations, little is known about the applicability of GPGPUs to discrete event simulation (DES). Here, we identify some of the issues and challenges that the GPGPU stream-based interface raises for DES, and present some possible approaches to moving DES to GPGPUs. Initial performance results on simulation of a diffusion process show that DES-style execution on GPGPU runs faster than DES on CPU and also significantly faster than time-stepped simulations on either CPU or GPGPU.
Design method of general-purpose driving circuit for CCD based on CPLD
International Nuclear Information System (INIS)
Zhang Yong; Tang Benqi; Xiao Zhigang; Wang Zujun; Huang Shaoyan
2005-01-01
It is very important for studying the radiation damage effects and mechanism systematically about CCD to develop a general-purpose test platform. The paper discusses the design method of general-purpose driving circuit for CCD based on CPLD and the realization approach. A main controller has being designed to read the data file from the outer memory, setup the correlative parameter registers and produce the driving pulses according with parameter request strictly, which is based on MAX7000S by using MAX-PLUS II software. The basic driving circuit module has being finished based on this method. The output waveform of the module is the same figure as the simulation waveform. The result indicates that the design method is feasible. (authors)
Application of a general purpose finite element program system in pressure vessel technology
International Nuclear Information System (INIS)
Aamodt, B.; Sandsmark, N.; Medonos, S.
1977-01-01
Main advantages of using general purpose finite element program systems in structural analysis are summarized. Several illustrative applications of the program system SESAM-69 to pressure vessel problems are described. The first example is a dynamic analysis of the motor housing of the internal main circulation pump of a BWR nuclear reactor. The next example is a transient heat conduction and stress analysis of deflector of feeding nozzle of PWR nuclear reactor. Then, numerical calculations of stress intensity factors and fatigue crack growth of semi-elliptical surface cracks are discussed. And finally, an elasto-plastic analysis of a thick plate with edge-cracks is considered. It is concluded that due to the fact that general purpose finite element program systems are general and user-orientated, they will gain increasingly higher popularity in the years ahead
General-purpose heat source project and space nuclear safety and fuels program. Progress report
International Nuclear Information System (INIS)
Maraman, W.J.
1979-12-01
This formal monthly report covers the studies related to the use of 238 PuO 2 in radioisotopic power systems carried out for the Advanced Nuclear Systems and Projects Division of the Los Alamos Scientific Laboratory. The two programs involved are general-purpose heat source development and space nuclear safety and fuels. Most of the studies discussed hear are of a continuing nature. Results and conclusions described may change as the work continues
Development of general-purpose particle and heavy ion transport monte carlo code
International Nuclear Information System (INIS)
Iwase, Hiroshi; Nakamura, Takashi; Niita, Koji
2002-01-01
The high-energy particle transport code NMTC/JAM, which has been developed at JAERI, was improved for the high-energy heavy ion transport calculation by incorporating the JQMD code, the SPAR code and the Shen formula. The new NMTC/JAM named PHITS (Particle and Heavy-Ion Transport code System) is the first general-purpose heavy ion transport Monte Carlo code over the incident energies from several MeV/nucleon to several GeV/nucleon. (author)
Jaschinski, Wolfgang; König, Mirjam; Mekontso, Tiofil M; Ohlendorf, Arne; Welscher, Monique
2015-05-01
Two types of progressive addition lenses (PALs) were compared in an office field study: 1. General purpose PALs with continuous clear vision between infinity and near reading distances and 2. Computer vision PALs with a wider zone of clear vision at the monitor and in near vision but no clear distance vision. Twenty-three presbyopic participants wore each type of lens for two weeks in a double-masked four-week quasi-experimental procedure that included an adaptation phase (Weeks 1 and 2) and a test phase (Weeks 3 and 4). Questionnaires on visual and musculoskeletal conditions as well as preferences regarding the type of lenses were administered. After eight more weeks of free use of the spectacles, the preferences were assessed again. The ergonomic conditions were analysed from photographs. Head inclination when looking at the monitor was significantly lower by 2.3 degrees with the computer vision PALs than with the general purpose PALs. Vision at the monitor was judged significantly better with computer PALs, while distance vision was judged better with general purpose PALs; however, the reported advantage of computer vision PALs differed in extent between participants. Accordingly, 61 per cent of the participants preferred the computer vision PALs, when asked without information about lens design. After full information about lens characteristics and additional eight weeks of free spectacle use, 44 per cent preferred the computer vision PALs. On average, computer vision PALs were rated significantly better with respect to vision at the monitor during the experimental part of the study. In the final forced-choice ratings, approximately half of the participants preferred either the computer vision PAL or the general purpose PAL. Individual factors seem to play a role in this preference and in the rated advantage of computer vision PALs. © 2015 The Authors. Clinical and Experimental Optometry © 2015 Optometry Australia.
A new general purpose event horizon finder for 3D numerical spacetimes
International Nuclear Information System (INIS)
Diener, Peter
2003-01-01
I present a new general purpose event horizon finder for full 3D numerical spacetimes. It works by evolving a complete null surface backwards in time. The null surface is described as the zero-level set of a scalar function, which in principle is defined everywhere. This description of the surface allows the surface, trivially, to change topology, making this event horizon finder able to handle numerical spacetimes where two (or more) black holes merge into a single final black hole
A prepaid case study: Ready Credit’s general-purpose & transit-fare programs
Philip Keitel
2012-01-01
Today, prepaid cards are used in dozens of payment applications. To examine the most recent developments, the Payment Cards Center of the Federal Reserve Bank of Philadelphia hosted a workshop on August 22, 2011. Leading the workshop was Tim Walsh, president and chief executive officer of Ready Credit Corporation, a firm that developed network-branded prepaid cards for use in transit-fare systems and also markets general-purpose, reloadable prepaid cards to consumers. Walsh discussed the uniq...
Design of a general purpose (RS-232C) analog-to-digital data converter
International Nuclear Information System (INIS)
Ali, Q.
1995-01-01
The purpose of this project is to design a general purpose hardware that interfaces analog devices with any desirable computer supporting the RS-232 interface. The hardware incorporates bidirectional data transmission at 1,200 bps, 2,400 bps, 4800 bps, 9,600 bps, 19,200 pbs and 38400 bps. The communication / processing software has been written in C language that incorporates the idea of the potability of the software from one environment to the other. (author)
Design of low-cost general purpose microcontroller based neuromuscular stimulator.
Koçer, S; Rahmi Canal, M; Güler, I
2000-04-01
In this study, a general purpose, low-cost, programmable, portable and high performance stimulator is designed and implemented. For this purpose, a microcontroller is used in the design of the stimulator. The duty cycle and amplitude of the designed system can be controlled using a keyboard. The performance test of the system has shown that the results are reliable. The overall system can be used as the neuromuscular stimulator under safe conditions.
Design of General-purpose Industrial signal acquisition system in a large scientific device
Ren, Bin; Yang, Lei
2018-02-01
In order to measure the industrial signal of a large scientific device experiment, a set of industrial data general-purpose acquisition system has been designed. It can collect 4~20mA current signal and 0~10V voltage signal. Through the practical experiments, it shows that the system is flexible, reliable, convenient and economical, and the system has characters of high definition and strong anti-interference ability. Thus, the system fully meets the design requirements..
Child first language and adult second language are both tied to general-purpose learning systems.
Hamrick, Phillip; Lum, Jarrad A G; Ullman, Michael T
2018-02-13
Do the mechanisms underlying language in fact serve general-purpose functions that preexist this uniquely human capacity? To address this contentious and empirically challenging issue, we systematically tested the predictions of a well-studied neurocognitive theory of language motivated by evolutionary principles. Multiple metaanalyses were performed to examine predicted links between language and two general-purpose learning systems, declarative and procedural memory. The results tied lexical abilities to learning only in declarative memory, while grammar was linked to learning in both systems in both child first language and adult second language, in specific ways. In second language learners, grammar was associated with only declarative memory at lower language experience, but with only procedural memory at higher experience. The findings yielded large effect sizes and held consistently across languages, language families, linguistic structures, and tasks, underscoring their reliability and validity. The results, which met the predicted pattern, provide comprehensive evidence that language is tied to general-purpose systems both in children acquiring their native language and adults learning an additional language. Crucially, if language learning relies on these systems, then our extensive knowledge of the systems from animal and human studies may also apply to this domain, leading to predictions that might be unwarranted in the more circumscribed study of language. Thus, by demonstrating a role for these systems in language, the findings simultaneously lay a foundation for potentially important advances in the study of this critical domain.
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.
Yuan, Y.; Li, C.; Yang, Y.; Zhang, Xiangliang; Li, L.
2014-01-01
Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria. 2014 Yali Yuan et al.
Yuan, Y.
2014-04-28
Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria. 2014 Yali Yuan et al.
Application of k-means clustering algorithm in grouping the DNA sequences of hepatitis B virus (HBV)
Bustamam, A.; Tasman, H.; Yuniarti, N.; Frisca, Mursidah, I.
2017-07-01
Based on WHO data, an estimated of 15 millions people worldwide who are infected with hepatitis B (HBsAg+), which is caused by HBV virus, are also infected by hepatitis D, which is caused by HDV virus. Hepatitis D infection can occur simultaneously with hepatitis B (co infection) or after a person is exposed to chronic hepatitis B (super infection). Since HDV cannot live without HBV, HDV infection is closely related to HBV infection, hence it is very realistic that every effort of prevention against hepatitis B can indirectly prevent hepatitis D. This paper presents clustering of HBV DNA sequences by using k-means clustering algorithm and R programming. Clustering processes are started with collecting HBV DNA sequences from GenBank, then performing extraction HBV DNA sequences using n-mers frequency and furthermore the extraction results are collected as a matrix and normalized using the min-max normalization with interval [0, 1] which will later be used as an input data. The number of clusters is two and the initial centroid selected of the cluster is chosen randomly. In each iteration, the distance of every object to each centroid are calculated using the Euclidean distance and the minimum distance is selected to determine the membership in a cluster until two convergent clusters are created. As the result, the HBV viruses in the first cluster is more virulent than the HBV viruses in the second cluster, so the HBV viruses in the first cluster can potentially evolve with HDV viruses that cause hepatitis D.
Alfarizy, A. D.; Indahwati; Sartono, B.
2017-03-01
Indonesia is the largest Hollywood movie industry target market in Southeast Asia in 2015. Hollywood movies distributed in Indonesia targeted people in all range of ages including children. Low awareness of guiding children while watching movies make them could watch any rated films even the unsuitable ones for their ages. Even after being translated into Bahasa and passed the censorship phase, words that uncomfortable for children to watch still exist. The purpose of this research is to cluster box office Hollywood movies based on Indonesian subtitle, revenue, IMDb user rating and genres as one of the reference for adults to choose right movies for their children to watch. Text mining is used to extract words from the subtitles and count the frequency for three group of words (bad words, sexual words and terror words), while Partition Around Medoids (PAM) Algorithm with Gower similarity coefficient as proximity matrix is used as clustering method. We clustered 624 movies from 2006 until first half of 2016 from IMDb. Cluster with highest silhouette coefficient value (0.36) is the one with 5 clusters. Animation, Adventure and Comedy movies with high revenue like in cluster 5 is recommended for children to watch, while Comedy movies with high revenue like in cluster 4 should be avoided to watch.
Directory of Open Access Journals (Sweden)
Shanen Yu
2016-12-01
Full Text Available Most existing deployment algorithms for event coverage in underwater wireless sensor networks (UWSNs usually do not consider that network communication has non-uniform characteristics on three-dimensional underwater environments. Such deployment algorithms ignore that the nodes are distributed at different depths and have different probabilities for data acquisition, thereby leading to imbalances in the overall network energy consumption, decreasing the network performance, and resulting in poor and unreliable late network operation. Therefore, in this study, we proposed an uneven cluster deployment algorithm based network layered for event coverage. First, according to the energy consumption requirement of the communication load at different depths of the underwater network, we obtained the expected value of deployment nodes and the distribution density of each layer network after theoretical analysis and deduction. Afterward, the network is divided into multilayers based on uneven clusters, and the heterogeneous communication radius of nodes can improve the network connectivity rate. The recovery strategy is used to balance the energy consumption of nodes in the cluster and can efficiently reconstruct the network topology, which ensures that the network has a high network coverage and connectivity rate in a long period of data acquisition. Simulation results show that the proposed algorithm improves network reliability and prolongs network lifetime by significantly reducing the blind movement of overall network nodes while maintaining a high network coverage and connectivity rate.
Dolnicar, Sara; Leisch, Friedrich; Steiner, Gottfried; Weingessel, Andreas
1998-01-01
The search for clusters in empirical data is an important and often encountered research problem. Numerous algorithms exist that are able to render groups of objects or individuals. Of course each algorithm has its strengths and weaknesses. In order to identify these crucial points artificial data was generated - based primarily on experience with structures of empirical data - and used as benchmark for evaluating the results of numerous cluster algorithms. This work is an addition to SFB Wor...
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
A Local Search Algorithm for Clustering in Software as a Service Networks
J.P. van der Gaast (Jelmer); C.A. Rietveld (Niels); A.F. Gabor (Adriana); Y. Zhang (Yingqian)
2011-01-01
textabstractIn this paper we present and analyze a model for clustering in networks that offer Software as a Service (SaaS). In this problem, organizations requesting a set of applications have to be assigned to clusters such that the costs of opening clusters and installing the necessary
Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm
DEFF Research Database (Denmark)
Grotkjær, Thomas; Winther, Ole; Regenberg, Birgitte
2006-01-01
Motivation: Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods...... analysis by collecting re-occurring clustering patterns in a co-occurrence matrix. The results show that consensus clustering obtained from clustering multiple times with Variational Bayes Mixtures of Gaussians or K-means significantly reduces the classification error rate for a simulated dataset...
Yu, Miao; Li, Yan; Shu, Tong; Zhang, Yifan; Hong, Xiaobin; Qiu, Jifang; Zuo, Yong; Guo, Hongxiang; Li, Wei; Wu, Jian
2018-02-01
A method of recognizing 16QAM signal based on k-means clustering algorithm is proposed to mitigate the impact of transmitter finite extinction ratio. There are pilot symbols with 0.39% overhead assigned to be regarded as initial centroids of k-means clustering algorithm. Simulation result in 10 GBaud 16QAM system shows that the proposed method obtains higher precision of identification compared with traditional decision method for finite ER and IQ mismatch. Specially, the proposed method improves the required OSNR by 5.5 dB, 4.5 dB, 4 dB and 3 dB at FEC limit with ER= 12 dB, 16 dB, 20 dB and 24 dB, respectively, and the acceptable bias error and IQ mismatch range is widened by 767% and 360% with ER =16 dB, respectively.
TOWARDS FINDING A NEW KERNELIZED FUZZY C-MEANS CLUSTERING ALGORITHM
Directory of Open Access Journals (Sweden)
Samarjit Das
2014-04-01
Full Text Available Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performance due to the fact that here also the initial centroids are obtained based on the randomly initialized membership values of the objects. Our present work proposes a new method where we have applied the Subtractive clustering technique of Chiu as a preprocessor to Kernelized Fuzzy CMeans clustering technique. With this new method we have tried not only to remove the inconsistency of Kernelized Fuzzy C-Means clustering technique but also to deal with the situations where the number of clusters is not predetermined. We have also provided a comparison of our method with the Subtractive clustering technique of Chiu and Kernelized Fuzzy C-Means clustering technique using two validity measures namely Partition Coefficient and Clustering Entropy.
Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X
2015-12-26
Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.
Directory of Open Access Journals (Sweden)
Liu Yang
2015-12-01
Full Text Available Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs in the network act as routers to transmit data to base station (BS cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.
BiCluE - Exact and heuristic algorithms for weighted bi-cluster editing of biomedical data
DEFF Research Database (Denmark)
Sun, Peng; Guo, Jiong; Baumbach, Jan
2013-01-01
to solve the weighted bi-cluster editing problem. It implements (1) an exact algorithm based on fixed-parameter tractability and (2) a polynomial-time greedy heuristics based on solving the hardest part, edge deletions, first. We evaluated its performance on artificial graphs. Afterwards we exemplarily...... problem. BiCluE as well as the supplementary results are available online at http://biclue.mpi-inf.mpg.de webcite....
General-purpose chemical analyzer for on-line analyses of radioactive solutions
International Nuclear Information System (INIS)
Spencer, W.A.; Kronberg, J.W.
1983-01-01
An automated analyzer is being developed to perform analytical measurements on radioactive solutions on-line in a hostile environment. This General Purpose Chemical Analyzer (GPCA) samples a process stream, adds reagents, measures solution absorbances or electrode potentials, and automatically calculates the results. The use of modular components, under microprocessor control, permits a single analyzer design to carry out many types of analyses. This paper discusses the more important design criteria for the GPCA, and describes the equipment being tested in a prototype unit
Software design of a general purpose data acquisition and control executive
International Nuclear Information System (INIS)
Labiak, W.G.; Minor, E.G.
1981-01-01
The software design of an executive which performs general purpose data acquisition, monitoring, and control is presented. The executive runs on a memory-based mini or micro-computer and communicates with a disk-based computer where data analysis and display are done. The executive design stresses reliability and versatility, and has yielded software which can provide control and monitoring for widely different hardware systems. Applications of this software on two major fusion energy experiments at Lawrence Livermore National Laboratory will be described
Specialized Monte Carlo codes versus general-purpose Monte Carlo codes
International Nuclear Information System (INIS)
Moskvin, Vadim; DesRosiers, Colleen; Papiez, Lech; Lu, Xiaoyi
2002-01-01
The possibilities of Monte Carlo modeling for dose calculations and optimization treatment are quite limited in radiation oncology applications. The main reason is that the Monte Carlo technique for dose calculations is time consuming while treatment planning may require hundreds of possible cases of dose simulations to be evaluated for dose optimization. The second reason is that general-purpose codes widely used in practice, require an experienced user to customize them for calculations. This paper discusses the concept of Monte Carlo code design that can avoid the main problems that are preventing wide spread use of this simulation technique in medical physics. (authors)
Majumdar, Alok; Leclair, Andre; Moore, Ric; Schallhorn, Paul
2011-01-01
GFSSP stands for Generalized Fluid System Simulation Program. It is a general-purpose computer program to compute pressure, temperature and flow distribution in a flow network. GFSSP calculates pressure, temperature, and concentrations at nodes and calculates flow rates through branches. It was primarily developed to analyze Internal Flow Analysis of a Turbopump Transient Flow Analysis of a Propulsion System. GFSSP development started in 1994 with an objective to provide a generalized and easy to use flow analysis tool for thermo-fluid systems.
A Real-Time Programmer's Tour of General-Purpose L4 Microkernels
Ruocco Sergio
2008-01-01
Abstract L4-embedded is a microkernel successfully deployed in mobile devices with soft real-time requirements. It now faces the challenges of tightly integrated systems, in which user interface, multimedia, OS, wireless protocols, and even software-defined radios must run on a single CPU. In this paper we discuss the pros and cons of L4-embedded for real-time systems design, focusing on the issues caused by the extreme speed optimisations it inherited from its general-purpose ancestors. Sinc...
A Real-Time Programmer's Tour of General-Purpose L4 Microkernels
Sergio Ruocco
2008-01-01
L4-embedded is a microkernel successfully deployed in mobile devices with soft real-time requirements. It now faces the challenges of tightly integrated systems, in which user interface, multimedia, OS, wireless protocols, and even software-defined radios must run on a single CPU. In this paper we discuss the pros and cons of L4-embedded for real-time systems design, focusing on the issues caused by the extreme speed optimisations it inherited from its general-purpose ancestors. Since these i...
Development of a large-scale general purpose two-phase flow analysis code
International Nuclear Information System (INIS)
Terasaka, Haruo; Shimizu, Sensuke
2001-01-01
A general purpose three-dimensional two-phase flow analysis code has been developed for solving large-scale problems in industrial fields. The code uses a two-fluid model to describe the conservation equations for two-phase flow in order to be applicable to various phenomena. Complicated geometrical conditions are modeled by FAVOR method in structured grid systems, and the discretization equations are solved by a modified SIMPLEST scheme. To reduce computing time a matrix solver for the pressure correction equation is parallelized with OpenMP. Results of numerical examples show that the accurate solutions can be obtained efficiently and stably. (author)
International Nuclear Information System (INIS)
Maraman, W.J.
1980-05-01
This formal monthly report covers the studies related to the use of 238 PuO 2 in radioisotopic power systems carried out for the Advanced Nuclear Systems and Projects Division of the Los Alamos Scientific Laboratory. The two programs involved are: General-Purpose Heat Source Development and Space Nuclear Safety and Fuels. Most of the studies discussed here are of a continuing nature. Results and conclusions described may change as the work continues. Published reference to the results cited in this report should not be made without the explicit permission of the person in charge of the work
Real-time radar signal processing using GPGPU (general-purpose graphic processing unit)
Kong, Fanxing; Zhang, Yan Rockee; Cai, Jingxiao; Palmer, Robert D.
2016-05-01
This study introduces a practical approach to develop real-time signal processing chain for general phased array radar on NVIDIA GPUs(Graphical Processing Units) using CUDA (Compute Unified Device Architecture) libraries such as cuBlas and cuFFT, which are adopted from open source libraries and optimized for the NVIDIA GPUs. The processed results are rigorously verified against those from the CPUs. Performance benchmarked in computation time with various input data cube sizes are compared across GPUs and CPUs. Through the analysis, it will be demonstrated that GPGPUs (General Purpose GPU) real-time processing of the array radar data is possible with relatively low-cost commercial GPUs.
An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques
Directory of Open Access Journals (Sweden)
Elid Rubio
2017-01-01
Full Text Available In this work an extension of the Fuzzy Possibilistic C-Means (FPCM algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM algorithm to observe if the proposed approach has better performance than this algorithm.
Directory of Open Access Journals (Sweden)
Kateryna Novokhatska
2016-03-01
Full Text Available In recent years, materialized views (MVs are widely used to enhance the database performance by storing pre-calculated results of resource-intensive queries in the physical memory. In order to identify which queries may be potentially materialized, database transaction log for a long period of time should be analyzed. The goal of analysis is to distinguish resource-intensive and frequently used queries collected from database log, and optimize these queries by implementation of MVs. In order to achieve greater efficiency of MVs, they were used not only for the optimization of single queries, but also for entire groups of queries that are similar in syntax and execution results. Thus, the problem stated in this article is the development of approach that will allow forming groups of queries with similar syntax around the most resource-intensive queries in order to identify the list of potential candidates for materialization. For solving this problem, we have applied the algorithm of categorical data clustering to the query grouping problem on the step of database log analysis and searching candidates for materialization. In the current work CLOPE algorithm was modified to cover the introduced problem. Statistical and timing indicators were taken into account in order to form the clusters around the most resource intensive queries. Application of modified algorithm CLOPE allowed to decrease calculable complexity of clustering and to enhance the quality of formed groups.
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Energy Technology Data Exchange (ETDEWEB)
Sjenitzer, Bart L.; Hoogenboom, J. Eduard, E-mail: B.L.Sjenitzer@TUDelft.nl, E-mail: J.E.Hoogenboom@TUDelft.nl [Delft University of Technology (Netherlands)
2011-07-01
A new Dynamic Monte Carlo method is implemented in the general purpose Monte Carlo code Tripoli 4.6.1. With this new method incorporated, a general purpose code can be used for safety transient analysis, such as the movement of a control rod or in an accident scenario. To make the Tripoli code ready for calculating on dynamic systems, the Tripoli scheme had to be altered to incorporate time steps, to include the simulation of delayed neutron precursors and to simulate prompt neutron chains. The modified Tripoli code is tested on two sample cases, a steady-state system and a subcritical system and the resulting neutron fluxes behave just as expected. The steady-state calculation has a constant neutron flux over time and this result shows the stability of the calculation. The neutron flux stays constant with acceptable variance. This also shows that the starting conditions are determined correctly. The sub-critical case shows that the code can also handle dynamic systems with a varying neutron flux. (author)
International Nuclear Information System (INIS)
Sjenitzer, Bart L.; Hoogenboom, J. Eduard
2011-01-01
A new Dynamic Monte Carlo method is implemented in the general purpose Monte Carlo code Tripoli 4.6.1. With this new method incorporated, a general purpose code can be used for safety transient analysis, such as the movement of a control rod or in an accident scenario. To make the Tripoli code ready for calculating on dynamic systems, the Tripoli scheme had to be altered to incorporate time steps, to include the simulation of delayed neutron precursors and to simulate prompt neutron chains. The modified Tripoli code is tested on two sample cases, a steady-state system and a subcritical system and the resulting neutron fluxes behave just as expected. The steady-state calculation has a constant neutron flux over time and this result shows the stability of the calculation. The neutron flux stays constant with acceptable variance. This also shows that the starting conditions are determined correctly. The sub-critical case shows that the code can also handle dynamic systems with a varying neutron flux. (author)
Power performance of the general-purpose heat source radioisotope thermoelectric generator
International Nuclear Information System (INIS)
Bennett, G.L.; Lombardo, J.J.; Rock, B.J.
1986-01-01
The General-Purpose Heat Source Radioisotope Thermoelectric Generator (GRHS-RTG) has been developed under the sponsorship of the Department of Energy (DOE) to provide electrical power for the National Aeronautics and Space Administration (NASA) Galileo mission to Jupiter and the joint NASA/European Space Agency (ESA) Ulysses mission to study the polar regions of the sun. A total of five nuclear-heated generators and one electrically heated generator have been built and tested, proving out the design concept and meeting the specification requirements. The GPHS-RTG design is built upon the successful-technology used in the RTGs flown on the two NASA Voyager spacecraft and two US Air Force communications satellites. THe GPHS-RTG converts about 4400 W(t) from the nuclear heat source into at least 285 W(e) at beginning of mission (BOM). The GPHS-RTG consists of two major components: the General-Purpose Heat Source (GPHS) and the Converter. A conceptual drawing of the GPHs-RTG is presented and its design and performance are described
Economic selection index development for Beefmaster cattle II: General-purpose breeding objective.
Ochsner, K P; MacNeil, M D; Lewis, R M; Spangler, M L
2017-05-01
An economic selection index was developed for Beefmaster cattle in a general-purpose production system in which bulls are mated to a combination of heifers and mature cows, with resulting progeny retained as replacements or sold at weaning. National average prices from 2010 to 2014 were used to establish income and expenses for the system. Genetic parameters were obtained from the literature. Economic values were estimated by simulating 100,000 animals and approximating the partial derivatives of the profit function by perturbing traits 1 at a time, by 1 unit, while holding the other traits constant at their respective means. Relative economic values for the objective traits calving difficultly direct (CDd), calving difficulty maternal (CDm), weaning weight direct (WWd), weaning weight maternal (WWm), mature cow weight (MW), and heifer pregnancy (HP) were -2.11, -1.53, 18.49, 11.28, -33.46, and 1.19, respectively. Consequently, under the scenario assumed herein, the greatest improvements in profitability could be made by decreasing maintenance energy costs associated with MW followed by improvements in weaning weight. The accuracy of the index lies between 0.218 (phenotypic-based index selection) and 0.428 (breeding values known without error). Implementation of this index would facilitate genetic improvement and increase profitability of Beefmaster cattle operations with a general-purpose breeding objective when replacement females are retained and with weaned calves as the sale end point.
General-purpose readout electronics for white neutron source at China Spallation Neutron Source.
Wang, Q; Cao, P; Qi, X; Yu, T; Ji, X; Xie, L; An, Q
2018-01-01
The under-construction White Neutron Source (WNS) at China Spallation Neutron Source is a facility for accurate measurements of neutron-induced cross section. Seven spectrometers are planned at WNS. As the physical objectives of each spectrometer are different, the requirements for readout electronics are not the same. In order to simplify the development of the readout electronics, this paper presents a general method for detector signal readout. This method has advantages of expansibility and flexibility, which makes it adaptable to most detectors at WNS. In the WNS general-purpose readout electronics, signals from any kinds of detectors are conditioned by a dedicated signal conditioning module corresponding to this detector, and then digitized by a common waveform digitizer with high speed and high precision (1 GSPS at 12-bit) to obtain the full waveform data. The waveform digitizer uses a field programmable gate array chip to process the data stream and trigger information in real time. PXI Express platform is used to support the functionalities of data readout, clock distribution, and trigger information exchange between digitizers and trigger modules. Test results show that the performance of the WNS general-purpose readout electronics can meet the requirements of the WNS spectrometers.
General Purpose Data-Driven Online System Health Monitoring with Applications to Space Operations
Iverson, David L.; Spirkovska, Lilly; Schwabacher, Mark
2010-01-01
Modern space transportation and ground support system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional parameter limit checking, or model-based or rule-based methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults, failures, or precursors of significant failures. The Inductive Monitoring System (IMS) is a general purpose, data-driven system health monitoring software tool that has been successfully applied to several aerospace applications and is under evaluation for anomaly detection in vehicle and ground equipment for next generation launch systems. After an introduction to IMS application development, we discuss these NASA online monitoring applications, including the integration of IMS with complementary model-based and rule-based methods. Although the examples presented in this paper are from space operations applications, IMS is a general-purpose health-monitoring tool that is also applicable to power generation and transmission system monitoring.
Operation of general purpose stepping motor controllers at the National Synchrotron Light Source
International Nuclear Information System (INIS)
Stubblefield, F.W.
1986-10-01
A prototype and four copies of a general purpose subsystem for mechanical positioning of detectors, samples, and beam line optical elements which constitute experiments at the National Synchrotron Light Source facility of Brookhaven National Laboratory have been constructed and placed into operation. Construction of a sixth subsystem is nearing completion. The subsystems effect mechanical positioning by controlling a set of stepping motors and their associated position encoders. The units are general purpose in the sense that they receive commands over a standard 9600 baud asynchronous serial line compatible with the RS-232-C electrical signal standard, generate TTL-compatible streams of stepping pulses which can be used with a wide variety of stepping motors, and read back position values from a number of different types and models of position encoder. The basic structure of the motor controller subsystem will be briefly reviewed. Short descriptions of the positioning apparatus actuated at each of the test and experiment stations employing a motor control unit are given. Additions and enhancements to the subsystem made in response to problems indicated by actual operation of the four installed units are described in more detail
Operation of general purpose stepping motor controllers at the National Synchrotron Light Source
International Nuclear Information System (INIS)
Stubblefield, F.W.
1987-01-01
A prototype and four copies of a general purpose subsystem for mechanical positioning of detectors, samples, and beam line optical elements which constitute experiments at the National Synchrotron Light Source facility of Brookhaven National Laboratory have been constructed and placed into operation. Construction of a sixth subsystem is nearing completion. The subsystems effect mechanical positioning by controlling a set of stepping motors and their associated position encoders. The units are general purpose in the sense that they receive commands over a standard 9600 baud asynchronous serial line compatible with the RS-232-C electrical signal standard, generate TTL-compatible streams of stepping pulses which can be used with a wide variety of stepping motors, and read back position values from a number of different types and models of position encoder. The basic structure of the motor controller subsystem is briefly reviewed. Short descriptions of the positioning apparatus actuated at each of the test and experiment stations employing a motor control unit are given. Additions and enhancements to the sub-system made in response to problems indicated by actual operation of the four installed units are described in more detail
Directory of Open Access Journals (Sweden)
Ulrich Schmitt
2015-10-01
Full Text Available As drivers of human civilization, Knowledge Management (KM processes have co-evolved in line with General-Purpose-Technologies (GPT, such as writing, printing, and information and communication systems. As evidenced by the recent shift from information scarcity to abundance, GPTs are capable of drastically altering societies due to their game-changing impact on our spheres of work and personal development. This paper looks at the prospect of whether a novel Personal Knowledge Management (PKM concept supported by a prototype system has got what it takes to grow into a transformative General-Purpose-Technology. Following up on a series of papers, the KM scenario of a decentralizing revolution where individuals and self-organized groups yield more power and autonomy is examined according to a GPT's essential characteristics, including a wide scope for improvement and elaboration (in people's private, professional and societal life, applicability across a broad range of uses in a wide variety of products and processes (in multi-disciplinary educational and work contexts, and strong complementarities with existing or potential new technologies (like organizational KM Systems and a proposed World Heritage of Memes Repository. The result portrays the PKM concept as a strong candidate due to its personal, autonomous, bottom-up, collaborative, interdisciplinary, and creativity-supporting approach destined to advance the availability, quantity, and quality of the world extelligence and to allow for a wider sharing and faster diffusion of ideas across current disciplinary and opportunity divides.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Chen, Pin-Yu; Hero, Alfred O.
2017-01-01
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a multilayer spectral graph clustering (SGC) framework that performs convex layer aggregation. Under a multilayer signal plus noise model, we provide a phase transition analys...
Bhattacharya, Anindya; De, Rajat K
2010-08-01
Distance based clustering algorithms can group genes that show similar expression values under multiple experimental conditions. They are unable to identify a group of genes that have similar pattern of variation in their expression values. Previously we developed an algorithm called divisive correlation clustering algorithm (DCCA) to tackle this situation, which is based on the concept of correlation clustering. But this algorithm may also fail for certain cases. In order to overcome these situations, we propose a new clustering algorithm, called average correlation clustering algorithm (ACCA), which is able to produce better clustering solution than that produced by some others. ACCA is able to find groups of genes having more common transcription factors and similar pattern of variation in their expression values. Moreover, ACCA is more efficient than DCCA with respect to the time of execution. Like DCCA, we use the concept of correlation clustering concept introduced by Bansal et al. ACCA uses the correlation matrix in such a way that all genes in a cluster have the highest average correlation values with the genes in that cluster. We have applied ACCA and some well-known conventional methods including DCCA to two artificial and nine gene expression datasets, and compared the performance of the algorithms. The clustering results of ACCA are found to be more significantly relevant to the biological annotations than those of the other methods. Analysis of the results show the superiority of ACCA over some others in determining a group of genes having more common transcription factors and with similar pattern of variation in their expression profiles. Availability of the software: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to
A neural network clustering algorithm for the ATLAS silicon pixel detector
Aad, Georges; Abdallah, Jalal; Abdel Khalek, Samah; Abdinov, Ovsat; Aben, Rosemarie; Abi, Babak; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Agustoni, Marco; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexandre, Gauthier; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Allbrooke, Benedict; Allison, Lee John; Allport, Phillip; Almond, John; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Alviggi, Mariagrazia; Amako, Katsuya; Amaral Coutinho, Yara; Amelung, Christoph; Amidei, Dante; Amor Dos Santos, Susana Patricia; Amorim, Antonio; Amoroso, Simone; Amram, Nir; Amundsen, Glenn; Anastopoulos, Christos; Ancu, Lucian Stefan; Andari, Nansi; Andeen, Timothy; Anders, Christoph Falk; Anders, Gabriel; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Anduaga, Xabier; Angelidakis, Stylianos; Angelozzi, Ivan; Anger, Philipp; Angerami, Aaron; Anghinolfi, Francis; Anisenkov, Alexey; Anjos, Nuno; Annovi, Alberto; Antonaki, Ariadni; Antonelli, Mario; Antonov, Alexey; Antos, Jaroslav; Anulli, Fabio; Aoki, Masato; Aperio Bella, Ludovica; Apolle, Rudi; Arabidze, Giorgi; Aracena, Ignacio; Arai, Yasuo; Araque, Juan Pedro; Arce, Ayana; Arguin, Jean-Francois; Argyropoulos, Spyridon; Arik, Metin; Armbruster, Aaron James; Arnaez, Olivier; Arnal, Vanessa; Arnold, Hannah; Arratia, Miguel; Arslan, Ozan; Artamonov, Andrei; Artoni, Giacomo; Asai, Shoji; Asbah, Nedaa; Ashkenazi, Adi; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astalos, Robert; Atkinson, Markus; Atlay, Naim Bora; Auerbach, Benjamin; Augsten, Kamil; Aurousseau, Mathieu; Avolio, Giuseppe; Azuelos, Georges; Azuma, Yuya; Baak, Max; Baas, Alessandra; Bacci, Cesare; Bachacou, Henri; Bachas, Konstantinos; Backes, Moritz; Backhaus, Malte; Backus Mayes, John; Badescu, Elisabeta; Bagiacchi, Paolo; Bagnaia, Paolo; Bai, Yu; Bain, Travis; Baines, John; Baker, Oliver Keith; Balek, Petr; Balli, Fabrice; Banas, Elzbieta; Banerjee, Swagato; Bannoura, Arwa A E; Bansal, Vikas; Bansil, Hardeep Singh; Barak, Liron; Baranov, Sergei; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnett, Bruce; Barnett, Michael; Barnovska, Zuzana; Baroncelli, Antonio; Barone, Gaetano; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Bartoldus, Rainer; Barton, Adam Edward; Bartos, Pavol; Bartsch, Valeria; Bassalat, Ahmed; Basye, Austin; Bates, Richard; Batkova, Lucia; Batley, Richard; Battaglia, Marco; Battistin, Michele; Bauer, Florian; Bawa, Harinder Singh; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Becker, Anne Kathrin; Becker, Sebastian; Beckingham, Matthew; Becot, Cyril; Beddall, Andrew; Beddall, Ayda; Bedikian, Sourpouhi; Bednyakov, Vadim; Bee, Christopher; Beemster, Lars; Beermann, Thomas; Begel, Michael; Behr, Katharina; Belanger-Champagne, Camille; Bell, Paul; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellerive, Alain; Bellomo, Massimiliano; Belotskiy, Konstantin; Beltramello, Olga; Benary, Odette; Benchekroun, Driss; Bendtz, Katarina; Benekos, Nektarios; Benhammou, Yan; Benhar Noccioli, Eleonora; Benitez Garcia, Jorge-Armando; Benjamin, Douglas; Bensinger, James; Benslama, Kamal; Bentvelsen, Stan; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Beringer, Jürg; Bernard, Clare; Bernat, Pauline; Bernius, Catrin; Bernlochner, Florian Urs; Berry, Tracey; Berta, Peter; Bertella, Claudia; Bertoli, Gabriele; Bertolucci, Federico; Bertsche, David; Besana, Maria Ilaria; Besjes, Geert-Jan; Bessidskaia, Olga; Bessner, Martin Florian; Besson, Nathalie; Betancourt, Christopher; Bethke, Siegfried; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianchini, Louis; Bianco, Michele; Biebel, Otmar; Bieniek, Stephen Paul; Bierwagen, Katharina; Biesiada, Jed; Biglietti, Michela; Bilbao De Mendizabal, Javier; Bilokon, Halina; Bindi, Marcello; Binet, Sebastien; Bingul, Ahmet; Bini, Cesare; Black, Curtis; Black, James; Black, Kevin; Blackburn, Daniel; Blair, Robert; Blanchard, Jean-Baptiste; Blazek, Tomas; Bloch, Ingo; Blocker, Craig; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocchetta, Simona Serena; Bocci, Andrea; Bock, Christopher; Boddy, Christopher Richard; Boehler, Michael; Boek, Thorsten Tobias; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bogouch, Andrei; Bohm, Christian; Bohm, Jan; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boldyrev, Alexey; Bomben, Marco; Bona, Marcella; Boonekamp, Maarten; Borisov, Anatoly; Borissov, Guennadi; Borri, Marcello; Borroni, Sara; Bortfeldt, Jonathan; Bortolotto, Valerio; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boterenbrood, Hendrik; Boudreau, Joseph; Bouffard, Julian; Bouhova-Thacker, Evelina Vassileva; Boumediene, Djamel Eddine; Bourdarios, Claire; Bousson, Nicolas; Boutouil, Sara; Boveia, Antonio; Boyd, James; Boyko, Igor; Bracinik, Juraj; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brazzale, Simone Federico; Brelier, Bertrand; Brendlinger, Kurt; Brennan, Amelia Jean; Brenner, Richard; Bressler, Shikma; Bristow, Kieran; Bristow, Timothy Michael; Britton, Dave; Brochu, Frederic; Brock, Ian; Brock, Raymond; Bromberg, Carl; Bronner, Johanna; Brooijmans, Gustaaf; Brooks, Timothy; Brooks, William; Brosamer, Jacquelyn; Brost, Elizabeth; Brown, Jonathan; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Brunet, Sylvie; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Bryngemark, Lene; Buanes, Trygve; Buat, Quentin; Bucci, Francesca; Buchholz, Peter; Buckingham, Ryan; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Buehrer, Felix; Bugge, Lars; Bugge, Magnar Kopangen; Bulekov, Oleg; Bundock, Aaron Colin; Burckhart, Helfried; Burdin, Sergey; Burghgrave, Blake; Burke, Stephen; Burmeister, Ingo; Busato, Emmanuel; Büscher, Daniel; Büscher, Volker; Bussey, Peter; Buszello, Claus-Peter; Butler, Bart; Butler, John; Butt, Aatif Imtiaz; Buttar, Craig; Butterworth, Jonathan; Butti, Pierfrancesco; Buttinger, William; Buzatu, Adrian; Byszewski, Marcin; Cabrera Urbán, Susana; Caforio, Davide; Cakir, Orhan; Calafiura, Paolo; Calandri, Alessandro; Calderini, Giovanni; Calfayan, Philippe; Calkins, Robert; Caloba, Luiz; Calvet, David; Calvet, Samuel; Camacho Toro, Reina; Camarda, Stefano; Cameron, David; Caminada, Lea Michaela; Caminal Armadans, Roger; Campana, Simone; Campanelli, Mario; Campoverde, Angel; Canale, Vincenzo; Canepa, Anadi; Cano Bret, Marc; Cantero, Josu; Cantrill, Robert; Cao, Tingting; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capua, Marcella; Caputo, Regina; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Sascha; Carquin, Edson; Carrillo-Montoya, German D; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Casolino, Mirkoantonio; Castaneda-Miranda, Elizabeth; Castelli, Angelantonio; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Catastini, Pierluigi; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Cattani, Giordano; Caughron, Seth; Cavaliere, Viviana; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Ceradini, Filippo; Cerio, Benjamin; Cerny, Karel; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cerv, Matevz; Cervelli, Alberto; Cetin, Serkant Ali; Chafaq, Aziz; Chakraborty, Dhiman; Chalupkova, Ina; Chang, Philip; Chapleau, Bertrand; Chapman, John Derek; Charfeddine, Driss; Charlton, Dave; Chau, Chav Chhiv; Chavez Barajas, Carlos Alberto; Cheatham, Susan; Chegwidden, Andrew; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chelstowska, Magda Anna; Chen, Chunhui; Chen, Hucheng; Chen, Karen; Chen, Liming; Chen, Shenjian; Chen, Xin; Chen, Yujiao; Cheng, Hok Chuen; Cheng, Yangyang; Cheplakov, Alexander; Cherkaoui El Moursli, Rajaa; Chernyatin, Valeriy; Cheu, Elliott; Chevalier, Laurent; Chiarella, Vitaliano; Chiefari, Giovanni; Childers, John Taylor; Chilingarov, Alexandre; Chiodini, Gabriele; Chisholm, Andrew; Chislett, Rebecca Thalatta; Chitan, Adrian; Chizhov, Mihail; Chouridou, Sofia; Chow, Bonnie Kar Bo; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Chwastowski, Janusz; Chytka, Ladislav; Ciapetti, Guido; Ciftci, Abbas Kenan; Ciftci, Rena; Cinca, Diane; Cindro, Vladimir; Ciocio, Alessandra; Cirkovic, Predrag; Citron, Zvi Hirsh; Citterio, Mauro; Ciubancan, Mihai; Clark, Allan G; Clark, Philip James; Clarke, Robert; Cleland, Bill; Clemens, Jean-Claude; Clement, Christophe; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coffey, Laurel; Cogan, Joshua Godfrey; Coggeshall, James; Cole, Brian; Cole, Stephen; Colijn, Auke-Pieter; Collot, Johann; Colombo, Tommaso; Colon, German; Compostella, Gabriele; Conde Muiño, Patricia; Coniavitis, Elias; Conidi, Maria Chiara; Connell, Simon Henry; Connelly, Ian; Consonni, Sofia Maria; Consorti, Valerio; Constantinescu, Serban; Conta, Claudio; Conti, Geraldine; Conventi, Francesco; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cooper-Smith, Neil; Copic, Katherine; Cornelissen, Thijs; Corradi, Massimo; Corriveau, Francois; Corso-Radu, Alina; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Côté, David; Cottin, Giovanna; Cowan, Glen; Cox, Brian; Cranmer, Kyle; Cree, Graham; Crépé-Renaudin, Sabine; Crescioli, Francesco; Cribbs, Wayne Allen; Crispin Ortuzar, Mireia; Cristinziani, Markus; Croft, Vince; Crosetti, Giovanni; Cuciuc, Constantin-Mihai; Cuhadar Donszelmann, Tulay; Cummings, Jane; Curatolo, Maria; Cuthbert, Cameron; Czirr, Hendrik; Czodrowski, Patrick; Czyczula, Zofia; D'Auria, Saverio; D'Onofrio, Monica; Da Cunha Sargedas De Sousa, Mario Jose; Da Via, Cinzia; Dabrowski, Wladyslaw; Dafinca, Alexandru; Dai, Tiesheng; Dale, Orjan; Dallaire, Frederick; Dallapiccola, Carlo; Dam, Mogens; Daniells, Andrew Christopher; Dano Hoffmann, Maria; Dao, Valerio; Darbo, Giovanni; Darmora, Smita; Dassoulas, James; Dattagupta, Aparajita; Davey, Will; David, Claire; Davidek, Tomas; Davies, Eleanor; Davies, Merlin; Davignon, Olivier; Davison, Adam; Davison, Peter; Davygora, Yuriy; Dawe, Edmund; Dawson, Ian; Daya-Ishmukhametova, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; De Groot, Nicolo; de Jong, Paul; De la Torre, Hector; De Lorenzi, Francesco; De Nooij, Lucie; De Pedis, Daniele; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dearnaley, William James; Debbe, Ramiro; Debenedetti, Chiara; Dechenaux, Benjamin; Dedovich, Dmitri; Deigaard, Ingrid; Del Peso, Jose; Del Prete, Tarcisio; Deliot, Frederic; Delitzsch, Chris Malena; Deliyergiyev, Maksym; Dell'Acqua, Andrea; Dell'Asta, Lidia; Dell'Orso, Mauro; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delsart, Pierre-Antoine; Deluca, Carolina; Demers, Sarah; Demichev, Mikhail; Demilly, Aurelien; Denisov, Sergey; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Deterre, Cecile; Deviveiros, Pier-Olivier; Dewhurst, Alastair; Dhaliwal, Saminder; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Domenico, Antonio; Di Donato, Camilla; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Mattia, Alessandro; Di Micco, Biagio; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Di Valentino, David; Dias, Flavia; Diaz, Marco Aurelio; Diehl, Edward; Dietrich, Janet; Dietzsch, Thorsten; Diglio, Sara; Dimitrievska, Aleksandra; Dingfelder, Jochen; Dionisi, Carlo; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djobava, Tamar; Barros do Vale, Maria Aline; Do Valle Wemans, André; Doan, Thi Kieu Oanh; Dobos, Daniel; Doglioni, Caterina; Doherty, Tom; Dohmae, Takeshi; Dolejsi, Jiri; Dolezal, Zdenek; Dolgoshein, Boris; Donadelli, Marisilvia; Donati, Simone; Dondero, Paolo; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dova, Maria-Teresa; Doyle, Tony; Dris, Manolis; Dubbert, Jörg; Dube, Sourabh; Dubreuil, Emmanuelle; Duchovni, Ehud; Duckeck, Guenter; Ducu, Otilia Anamaria; Duda, Dominik; Dudarev, Alexey; Dudziak, Fanny; Duflot, Laurent; Duguid, Liam; Dührssen, Michael; Dunford, Monica; Duran Yildiz, Hatice; Düren, Michael; Durglishvili, Archil; Dwuznik, Michal; Dyndal, Mateusz; Ebke, Johannes; Edson, William; Edwards, Nicholas Charles; Ehrenfeld, Wolfgang; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Engelmann, Roderich; Erdmann, Johannes; Ereditato, Antonio; Eriksson, Daniel; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Ernwein, Jean; Errede, Deborah; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Fehling-Kaschek, Mirjam; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Perez, Sonia; Ferrag, Samir; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Adam; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Florez Bustos, Andres Carlos; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fortin, Dominique; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Franchino, Silvia; Francis, David; Franklin, Melissa; Franz, Sebastien; Fraternali, Marco; French, Sky; Friedrich, Conrad; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallo, Valentina Santina; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gandrajula, Reddy Pratap; Gao, Jun; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Gellerstedt, Karl; Gemme, Claudia; Gemmell, Alistair; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Ghodbane, Nabil; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gianotti, Fabiola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giordano, Raffaele; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Glonti, George; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godfrey, Jennifer; Godlewski, Jan; Goeringer, Christian; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gomez Fajardo, Luz Stella; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Gouighri, Mohamed; Goujdami, Driss; Goulette, Marc Phillippe; Goussiou, Anna; Goy, Corinne; Gozpinar, Serdar; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Grebenyuk, Oleg; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grishkevich, Yaroslav; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Groth-Jensen, Jacob; Grout, Zara Jane; Guan, Liang; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guicheney, Christophe; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Gunther, Jaroslav; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guttman, Nir; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Hall, David; Halladjian, Garabed; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamilton, Samuel; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harper, Devin; Harrington, Robert; Harris, Orin; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauschild, Michael; Hauser, Reiner; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Hejbal, Jiri; Helary, Louis; Heller, Claudio; Heller, Matthieu; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Heng, Yang; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Hensel, Carsten; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillert, Sonja; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoffman, Julia; Hoffmann, Dirk; Hofmann, Julia Isabell; Hohlfeld, Marc; Holmes, Tova Ray; Hong, Tae Min; Hooft van Huysduynen, Loek; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hsu, Catherine; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Hurwitz, Martina; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansen, Hendrik; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Ji, Weina; Jia, Jiangyong; Jiang, Yi; Jimenez Belenguer, Marcos; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Erik; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jungst, Ralph Markus; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kamenshchikov, Andrey; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karastathis, Nikolaos; Karnevskiy, Mikhail; Karpov, Sergey; Karpova, Zoya; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kasieczka, Gregor; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kaushik, Venkatesh; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keil, Markus; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Kessoku, Kohei; Keung, Justin; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Khodinov, Alexander; Khomich, Andrei; Khoo, Teng Jian; Khoriauli, Gia; Khoroshilov, Andrey; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kimura, Naoki; Kind, Oliver; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kittelmann, Thomas; Kiuchi, Kenji; Kladiva, Eduard; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Klok, Peter; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Dai; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koevesarki, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Koll, James; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Korotkov, Vladislav; Kortner, Oliver; Kortner, Sandra; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kral, Vlastimil; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasny, Mieczyslaw Witold; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Kruker, Tobias; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunkle, Joshua; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; La Rosa, Alessandro; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Laier, Heiko; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Le, Bao Tran; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Hurng-Chun; Lee, Jason; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmacher, Marc; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzen, Georg; Lenzi, Bruno; Leone, Robert; Leone, Sandra; Leonhardt, Kathrin; Leonidopoulos, Christos; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Lester, Christopher Michael; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Lewis, George; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Bo; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Livermore, Sarah; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loddenkoetter, Thomas; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Loh, Chang Wei; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Lombardo, Vincenzo Paolo; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lowe, Andrew; Lu, Feng; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lungwitz, Matthias; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeno, Mayuko; Maeno, Tadashi; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Mahmoud, Sara; Maiani, Camilla; Maidantchik, Carmen; Maier, Andreas Alexander; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Mal, Prolay; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany Andreina; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mapelli, Livio; March, Luis; Marchand, Jean-Francois; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marques, Carlos; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Homero; Martinez, Mario; Martin-Haugh, Stewart; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Mättig, Peter; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Meade, Andrew; Mechnich, Joerg; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Melachrinos, Constantinos; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Meric, Nicolas; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Merritt, Hayes; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Migas, Sylwia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Milic, Adriana; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Milstein, Dmitry; Minaenko, Andrey; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mirabelli, Giovanni; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Mitsui, Shingo; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Moraes, Arthur; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Morvaj, Ljiljana; Moser, Hans-Guenther; Mosidze, Maia; Moss, Josh; Motohashi, Kazuki; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Thibaut; Mueller, Timo; Muenstermann, Daniel; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagel, Martin; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Nanava, Gizo; Narayan, Rohin; Nattermann, Till; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Nef, Pascal Daniel; Negri, Andrea; Negri, Guido; Negrini, Matteo; Nektarijevic, Snezana; Nelson, Andrew; Nelson, Timothy Knight; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolics, Katalin; Nikolopoulos, Konstantinos; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nodulman, Lawrence; Nomachi, Masaharu; Nomidis, Ioannis; Norberg, Scarlet; Nordberg, Markus; Novgorodova, Olga; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Ohshima, Takayoshi; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olchevski, Alexander; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; Oreglia, Mark; Oren, Yona; Orestano, Domizia; Orlando, Nicola; Oropeza Barrera, Cristina; Orr, Robert; Osculati, Bianca; Ospanov, Rustem; Otero y Garzon, Gustavo; Otono, Hidetoshi; Ouchrif, Mohamed; Ouellette, Eric; Ould-Saada, Farid; Ouraou, Ahmimed; Oussoren, Koen Pieter; Ouyang, Qun; Ovcharova, Ana; Owen, Mark; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pachal, Katherine; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Pagáčová, Martina; Pagan Griso, Simone; Paganis, Efstathios; Pahl, Christoph; Paige, Frank; Pais, Preema; Pajchel, Katarina; Palacino, Gabriel; Palestini, Sandro; Palka, Marek; Pallin, Dominique; Palma, Alberto; Palmer, Jody; Pan, Yibin; Panagiotopoulou, Evgenia; Panduro Vazquez, William; Pani, Priscilla; Panikashvili, Natalia; Panitkin, Sergey; Pantea, Dan; Paolozzi, Lorenzo; Papadopoulou, Theodora; Papageorgiou, Konstantinos; Paramonov, Alexander; Paredes Hernandez, Daniela; Parker, Michael Andrew; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passaggio, Stefano; Passeri, Antonio; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Patricelli, Sergio; Pauly, Thilo; Pearce, James; Pedersen, Maiken; Pedraza Lopez, Sebastian; Pedro, Rute; Peleganchuk, Sergey; Pelikan, Daniel; Peng, Haiping; Penning, Bjoern; Penwell, John; Perepelitsa, Dennis; Perez Codina, Estel; Pérez García-Estañ, María Teresa; Perez Reale, Valeria; Perini, Laura; Pernegger, Heinz; Perrino, Roberto; Peschke, Richard; Peshekhonov, Vladimir; Peters, Krisztian; Peters, Yvonne; Petersen, Brian; Petersen, Troels; Petit, Elisabeth; Petridis, Andreas; Petridou, Chariclia; Petrolo, Emilio; Petrucci, Fabrizio; Pettersson, Nora Emilia; Pezoa, Raquel; Phillips, Peter William; Piacquadio, Giacinto; Pianori, Elisabetta; Picazio, Attilio; Piccaro, Elisa; Piccinini, Maurizio; Piegaia, Ricardo; Pignotti, David; Pilcher, James; Pilkington, Andrew; Pina, João Antonio; Pinamonti, Michele; Pinder, Alex; Pinfold, James; Pingel, Almut; Pinto, Belmiro; Pires, Sylvestre; Pitt, Michael; Pizio, Caterina; Plazak, Lukas; Pleier, Marc-Andre; Pleskot, Vojtech; Plotnikova, Elena; Plucinski, Pawel; Poddar, Sahill; Podlyski, Fabrice; Poettgen, Ruth; Poggioli, Luc; Pohl, David-leon; Pohl, Martin; Polesello, Giacomo; Policicchio, Antonio; Polifka, Richard; Polini, Alessandro; Pollard, Christopher Samuel; Polychronakos, Venetios; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Portell Bueso, Xavier; Pospisil, Stanislav; Potamianos, Karolos; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Pozdnyakov, Valery; Pralavorio, Pascal; Pranko, Aliaksandr; Prasad, Srivas; Pravahan, Rishiraj; Prell, Soeren; Price, Darren; Price, Joe; Price, Lawrence; Prieur, Damien; Primavera, Margherita; Proissl, Manuel; Prokofiev, Kirill; Prokoshin, Fedor; Protopapadaki, Eftychia-sofia; Protopopescu, Serban; Proudfoot, James; Przybycien, Mariusz; Przysiezniak, Helenka; Ptacek, Elizabeth; Puddu, Daniele; Pueschel, Elisa; Puldon, David; Purohit, Milind; Puzo, Patrick; Qian, Jianming; Qin, Gang; Qin, Yang; Quadt, Arnulf; Quarrie, David; Quayle, William; Queitsch-Maitland, Michaela; Quilty, Donnchadha; Qureshi, Anum; Radeka, Veljko; Radescu, Voica; Radhakrishnan, Sooraj Krishnan; Radloff, Peter; Rados, Pere; Ragusa, Francesco; Rahal, Ghita; Rajagopalan, Srinivasan; Rammensee, Michael; Randle-Conde, Aidan Sean; Rangel-Smith, Camila; Rao, Kanury; Rauscher, Felix; Rave, Tobias Christian; Ravenscroft, Thomas; Raymond, Michel; Read, Alexander Lincoln; Readioff, Nathan Peter; Rebuzzi, Daniela; Redelbach, Andreas; Redlinger, George; Reece, Ryan; Reeves, Kendall; Rehnisch, Laura; Reisin, Hernan; Relich, Matthew; Rembser, Christoph; Ren, Huan; Ren, Zhongliang; Renaud, Adrien; Rescigno, Marco; Resconi, Silvia; Rezanova, Olga; Reznicek, Pavel; Rezvani, Reyhaneh; Richter, Robert; Ridel, Melissa; Rieck, Patrick; Rieger, Julia; Rijssenbeek, Michael; Rimoldi, Adele; Rinaldi, Lorenzo; Ritsch, Elmar; Riu, Imma; Rizatdinova, Flera; Rizvi, Eram; Robertson, Steven; Robichaud-Veronneau, Andree; Robinson, Dave; Robinson, James; Robson, Aidan; Roda, Chiara; Rodrigues, Luis; Roe, Shaun; Røhne, Ole; Rolli, Simona; Romaniouk, Anatoli; Romano, Marino; Romero Adam, Elena; Rompotis, Nikolaos; Roos, Lydia; Ros, Eduardo; Rosati, Stefano; Rosbach, Kilian; Rose, Matthew; Rosendahl, Peter Lundgaard; Rosenthal, Oliver; Rossetti, Valerio; Rossi, Elvira; Rossi, Leonardo Paolo; Rosten, Rachel; Rotaru, Marina; Roth, Itamar; Rothberg, Joseph; Rousseau, David; Royon, Christophe; Rozanov, Alexandre; Rozen, Yoram; Ruan, Xifeng; Rubbo, Francesco; Rubinskiy, Igor; Rud, Viacheslav; Rudolph, Christian; Rudolph, Matthew Scott; Rühr, Frederik; Ruiz-Martinez, Aranzazu; Rurikova, Zuzana; Rusakovich, Nikolai; Ruschke, Alexander; Rutherfoord, John; Ruthmann, Nils; Ryabov, Yury; Rybar, Martin; Rybkin, Grigori; Ryder, Nick; Saavedra, Aldo; Sacerdoti, Sabrina; Saddique, Asif; Sadeh, Iftach; Sadrozinski, Hartmut; Sadykov, Renat; Safai Tehrani, Francesco; Sakamoto, Hiroshi; Sakurai, Yuki; Salamanna, Giuseppe; Salamon, Andrea; Saleem, Muhammad; Salek, David; Sales De Bruin, Pedro Henrique; Salihagic, Denis; Salnikov, Andrei; Salt, José; Salvachua Ferrando, Belén; Salvatore, Daniela; Salvatore, Pasquale Fabrizio; Salvucci, Antonio; Salzburger, Andreas; Sampsonidis, Dimitrios; Sanchez, Arturo; Sánchez, Javier; Sanchez Martinez, Victoria; Sandaker, Heidi; Sandbach, Ruth Laura; Sander, Heinz Georg; Sanders, Michiel; Sandhoff, Marisa; Sandoval, Tanya; Sandoval, Carlos; Sandstroem, Rikard; Sankey, Dave; Sansoni, Andrea; Santoni, Claudio; Santonico, Rinaldo; Santos, Helena; Santoyo Castillo, Itzebelt; Sapp, Kevin; Sapronov, Andrey; Saraiva, João; Sarrazin, Bjorn; Sartisohn, Georg; Sasaki, Osamu; Sasaki, Yuichi; Sauvage, Gilles; Sauvan, Emmanuel; Savard, Pierre; Savu, Dan Octavian; Sawyer, Craig; Sawyer, Lee; Saxon, David; Saxon, James; Sbarra, Carla; Sbrizzi, Antonio; Scanlon, Tim; Scannicchio, Diana; Scarcella, Mark; Scarfone, Valerio; Schaarschmidt, Jana; Schacht, Peter; Schaefer, Douglas; Schaefer, Ralph; Schaepe, Steffen; Schaetzel, Sebastian; Schäfer, Uli; Schaffer, Arthur; Schaile, Dorothee; Schamberger, R. Dean; Scharf, Veit; Schegelsky, Valery; Scheirich, Daniel; Schernau, Michael; Scherzer, Max; Schiavi, Carlo; Schieck, Jochen; Schillo, Christian; Schioppa, Marco; Schlenker, Stefan; Schmidt, Evelyn; Schmieden, Kristof; Schmitt, Christian; Schmitt, Christopher; Schmitt, Sebastian; Schneider, Basil; Schnellbach, Yan Jie; Schnoor, Ulrike; Schoeffel, Laurent; Schoening, Andre; Schoenrock, Bradley Daniel; Schorlemmer, Andre Lukas; Schott, Matthias; Schouten, Doug; Schovancova, Jaroslava; Schramm, Steven; Schreyer, Manuel; Schroeder, Christian; Schuh, Natascha; Schultens, Martin Johannes; Schultz-Coulon, Hans-Christian; Schulz, Holger; Schumacher, Markus; Schumm, Bruce; Schune, Philippe; Schwanenberger, Christian; Schwartzman, Ariel; Schwegler, Philipp; Schwemling, Philippe; Schwienhorst, Reinhard; Schwindling, Jerome; Schwindt, Thomas; Schwoerer, Maud; Sciacca, Gianfranco; Scifo, Estelle; Sciolla, Gabriella; Scott, Bill; Scuri, Fabrizio; Scutti, Federico; Searcy, Jacob; Sedov, George; Sedykh, Evgeny; Seidel, Sally; Seiden, Abraham; Seifert, Frank; Seixas, José; Sekhniaidze, Givi; Sekula, Stephen; Selbach, Karoline Elfriede; Seliverstov, Dmitry; Sellers, Graham; Semprini-Cesari, Nicola; Serfon, Cedric; Serin, Laurent; Serkin, Leonid; Serre, Thomas; Seuster, Rolf; Severini, Horst; Sfiligoj, Tina; Sforza, Federico; Sfyrla, Anna; Shabalina, Elizaveta; Shamim, Mansoora; Shan, Lianyou; Shang, Ruo-yu; Shank, James; Shapiro, Marjorie; Shatalov, Pavel; Shaw, Kate; Shehu, Ciwake Yusufu; Sherwood, Peter; Shi, Liaoshan; Shimizu, Shima; Shimmin, Chase Owen; Shimojima, Makoto; Shiyakova, Mariya; Shmeleva, Alevtina; Shochet, Mel; Short, Daniel; Shrestha, Suyog; Shulga, Evgeny; Shupe, Michael; Shushkevich, Stanislav; Sicho, Petr; Sidiropoulou, Ourania; Sidorov, Dmitri; Sidoti, Antonio; Siegert, Frank; Sijacki, Djordje; Silva, José; Silver, Yiftah; Silverstein, Daniel; Silverstein, Samuel; Simak, Vladislav; Simard, Olivier; Simic, Ljiljana; Simion, Stefan; Simioni, Eduard; Simmons, Brinick; Simoniello, Rosa; Simonyan, Margar; Sinervo, Pekka; Sinev, Nikolai; Sipica, Valentin; Siragusa, Giovanni; Sircar, Anirvan; Sisakyan, Alexei; Sivoklokov, Serguei; Sjölin, Jörgen; Sjursen, Therese; Skottowe, Hugh Philip; Skovpen, Kirill; Skubic, Patrick; Slater, Mark; Slavicek, Tomas; Sliwa, Krzysztof; Smakhtin, Vladimir; Smart, Ben; Smestad, Lillian; Smirnov, Sergei; Smirnov, Yury; Smirnova, Lidia; Smirnova, Oxana; Smith, Kenway; Smizanska, Maria; Smolek, Karel; Snesarev, Andrei; Snidero, Giacomo; Snyder, Scott; Sobie, Randall; Socher, Felix; Soffer, Abner; Soh, Dart-yin; Solans, Carlos; Solar, Michael; Solc, Jaroslav; Soldatov, Evgeny; Soldevila, Urmila; Solfaroli Camillocci, Elena; Solodkov, Alexander; Soloshenko, Alexei; Solovyanov, Oleg; Solovyev, Victor; Sommer, Philip; Song, Hong Ye; Soni, Nitesh; Sood, Alexander; Sopczak, Andre; Sopko, Bruno; Sopko, Vit; Sorin, Veronica; Sosebee, Mark; Soualah, Rachik; Soueid, Paul; Soukharev, Andrey; South, David; Spagnolo, Stefania; Spanò, Francesco; Spearman, William Robert; Spettel, Fabian; Spighi, Roberto; Spigo, Giancarlo; Spousta, Martin; Spreitzer, Teresa; Spurlock, Barry; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stanecka, Ewa; Stanek, Robert; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; Stonjek, Stefan; Stradling, Alden; Straessner, Arno; Stramaglia, Maria Elena; Strandberg, Jonas; Strandberg, Sara; Strandlie, Are; Strauss, Emanuel; Strauss, Michael; Strizenec, Pavol; Ströhmer, Raimund; Strom, David; Stroynowski, Ryszard; Stucci, Stefania Antonia; Stugu, Bjarne; Styles, Nicholas Adam; Su, Dong; Su, Jun; Subramania, Halasya Siva; Subramaniam, Rajivalochan; Succurro, Antonella; Sugaya, Yorihito; Suhr, Chad; Suk, Michal; Sulin, Vladimir; Sultansoy, Saleh; Sumida, Toshi; Sun, Xiaohu; Sundermann, Jan Erik; Suruliz, Kerim; Susinno, Giancarlo; Sutton, Mark; Suzuki, Yu; Svatos, Michal; Swedish, Stephen; Swiatlowski, Maximilian; Sykora, Ivan; Sykora, Tomas; Ta, Duc; Taccini, Cecilia; Tackmann, Kerstin; Taenzer, Joe; Taffard, Anyes; Tafirout, Reda; Taiblum, Nimrod; Takahashi, Yuta; Takai, Helio; Takashima, Ryuichi; Takeda, Hiroshi; Takeshita, Tohru; Takubo, Yosuke; Talby, Mossadek; Talyshev, Alexey; Tam, Jason; Tan, Kong Guan; Tanaka, Junichi; Tanaka, Reisaburo; Tanaka, Satoshi; Tanaka, Shuji; Tanasijczuk, Andres Jorge; Tannenwald, Benjamin Bordy; Tannoury, Nancy; Tapprogge, Stefan; Tarem, Shlomit; Tarrade, Fabien; Tartarelli, Giuseppe Francesco; Tas, Petr; Tasevsky, Marek; Tashiro, Takuya; Tassi, Enrico; Tavares Delgado, Ademar; Tayalati, Yahya; Taylor, Frank; Taylor, Geoffrey; Taylor, Wendy; Teischinger, Florian Alfred; Teixeira Dias Castanheira, Matilde; Teixeira-Dias, Pedro; Temming, Kim Katrin; Ten Kate, Herman; Teng, Ping-Kun; Teoh, Jia Jian; Terada, Susumu; Terashi, Koji; Terron, Juan; Terzo, Stefano; Testa, Marianna; Teuscher, Richard; Therhaag, Jan; Theveneaux-Pelzer, Timothée; Thomas, Juergen; Thomas-Wilsker, Joshuha; Thompson, Emily; Thompson, Paul; Thompson, Peter; Thompson, Stan; Thomsen, Lotte Ansgaard; Thomson, Evelyn; Thomson, Mark; Thong, Wai Meng; Thun, Rudolf; Tian, Feng; Tibbetts, Mark James; Tikhomirov, Vladimir; Tikhonov, Yury; Timoshenko, Sergey; Tiouchichine, Elodie; Tipton, Paul; Tisserant, Sylvain; Todorov, Theodore; Todorova-Nova, Sharka; Toggerson, Brokk; Tojo, Junji; Tokár, Stanislav; Tokushuku, Katsuo; Tollefson, Kirsten; Tomlinson, Lee; Tomoto, Makoto; Tompkins, Lauren; Toms, Konstantin; Topilin, Nikolai; Torrence, Eric; Torres, Heberth; Torró Pastor, Emma; Toth, Jozsef; Touchard, Francois; Tovey, Daniel; Tran, Huong Lan; Trefzger, Thomas; Tremblet, Louis; Tricoli, Alessandro; Trigger, Isabel Marian; Trincaz-Duvoid, Sophie; Tripiana, Martin; Triplett, Nathan; Trischuk, William; Trocmé, Benjamin; Troncon, Clara; Trottier-McDonald, Michel; Trovatelli, Monica; True, Patrick; Trzebinski, Maciej; Trzupek, Adam; Tsarouchas, Charilaos; Tseng, Jeffrey; Tsiareshka, Pavel; Tsionou, Dimitra; Tsipolitis, Georgios; Tsirintanis, Nikolaos; Tsiskaridze, Shota; Tsiskaridze, Vakhtang; Tskhadadze, Edisher; Tsukerman, Ilya; Tsulaia, Vakhtang; Tsuno, Soshi; Tsybychev, Dmitri; Tudorache, Alexandra; Tudorache, Valentina; Tuna, Alexander Naip; Tupputi, Salvatore; Turchikhin, Semen; Turecek, Daniel; Turk Cakir, Ilkay; Turra, Ruggero; Tuts, Michael; Tykhonov, Andrii; Tylmad, Maja; Tyndel, Mike; Uchida, Kirika; Ueda, Ikuo; Ueno, Ryuichi; Ughetto, Michael; Ugland, Maren; Uhlenbrock, Mathias; Ukegawa, Fumihiko; Unal, Guillaume; Undrus, Alexander; Unel, Gokhan; Ungaro, Francesca; Unno, Yoshinobu; Urbaniec, Dustin; Urquijo, Phillip; Usai, Giulio; Usanova, Anna; Vacavant, Laurent; Vacek, Vaclav; Vachon, Brigitte; Valencic, Nika; Valentinetti, Sara; Valero, Alberto; Valery, Loic; Valkar, Stefan; Valladolid Gallego, Eva; Vallecorsa, Sofia; Valls Ferrer, Juan Antonio; Van Den Wollenberg, Wouter; Van Der Deijl, Pieter; van der Geer, Rogier; van der Graaf, Harry; Van Der Leeuw, Robin; van der Ster, Daniel; van Eldik, Niels; van Gemmeren, Peter; Van Nieuwkoop, Jacobus; van Vulpen, Ivo; van Woerden, Marius Cornelis; Vanadia, Marco; Vandelli, Wainer; Vanguri, Rami; Vaniachine, Alexandre; Vankov, Peter; Vannucci, Francois; Vardanyan, Gagik; Vari, Riccardo; Varnes, Erich; Varol, Tulin; Varouchas, Dimitris; Vartapetian, Armen; Varvell, Kevin; Vazeille, Francois; Vazquez Schroeder, Tamara; Veatch, Jason; Veloso, Filipe; Veneziano, Stefano; Ventura, Andrea; Ventura, Daniel; Venturi, Manuela; Venturi, Nicola; Venturini, Alessio; Vercesi, Valerio; Verducci, Monica; Verkerke, Wouter; Vermeulen, Jos; Vest, Anja; Vetterli, Michel; Viazlo, Oleksandr; Vichou, Irene; Vickey, Trevor; Vickey Boeriu, Oana Elena; Viehhauser, Georg; Viel, Simon; Vigne, Ralph; Villa, Mauro; Villaplana Perez, Miguel; Vilucchi, Elisabetta; Vincter, Manuella; Vinogradov, Vladimir; Virzi, Joseph; Vivarelli, Iacopo; Vives Vaque, Francesc; Vlachos, Sotirios; Vladoiu, Dan; Vlasak, Michal; Vogel, Adrian; Vogel, Marcelo; Vokac, Petr; Volpi, Guido; Volpi, Matteo; von der Schmitt, Hans; von Radziewski, Holger; von Toerne, Eckhard; Vorobel, Vit; Vorobev, Konstantin; Vos, Marcel; Voss, Rudiger; Vossebeld, Joost; Vranjes, Nenad; Vranjes Milosavljevic, Marija; Vrba, Vaclav; Vreeswijk, Marcel; Vu Anh, Tuan; Vuillermet, Raphael; Vukotic, Ilija; Vykydal, Zdenek; Wagner, Peter; Wagner, Wolfgang; Wahlberg, Hernan; Wahrmund, Sebastian; Wakabayashi, Jun; Walder, James; Walker, Rodney; Walkowiak, Wolfgang; Wall, Richard; Waller, Peter; Walsh, Brian; Wang, Chao; Wang, Chiho; Wang, Fuquan; Wang, Haichen; Wang, Hulin; Wang, Jike; Wang, Jin; Wang, Kuhan; Wang, Rui; Wang, Song-Ming; Wang, Tan; Wang, Xiaoxiao; Wanotayaroj, Chaowaroj; Warburton, Andreas; Ward, Patricia; Wardrope, David Robert; Warsinsky, Markus; Washbrook, Andrew; Wasicki, Christoph; Watkins, Peter; Watson, Alan; Watson, Ian; Watson, Miriam; Watts, Gordon; Watts, Stephen; Waugh, Ben; Webb, Samuel; Weber, Michele; Weber, Stefan Wolf; Webster, Jordan S; Weidberg, Anthony; Weigell, Philipp; Weinert, Benjamin; Weingarten, Jens; Weiser, Christian; Weits, Hartger; Wells, Phillippa; Wenaus, Torre; Wendland, Dennis; Weng, Zhili; Wengler, Thorsten; Wenig, Siegfried; Wermes, Norbert; Werner, Matthias; Werner, Per; Wessels, Martin; Wetter, Jeffrey; Whalen, Kathleen; White, Andrew; White, Martin; White, Ryan; White, Sebastian; Whiteson, Daniel; Wicke, Daniel; Wickens, Fred; Wiedenmann, Werner; Wielers, Monika; Wienemann, Peter; Wiglesworth, Craig; Wiik-Fuchs, Liv Antje Mari; Wijeratne, Peter Alexander; Wildauer, Andreas; Wildt, Martin Andre; Wilkens, Henric George; Will, Jonas Zacharias; Williams, Hugh; Williams, Sarah; Willis, Christopher; Willocq, Stephane; Wilson, Alan; Wilson, John; Wingerter-Seez, Isabelle; Winklmeier, Frank; Winter, Benedict Tobias; Wittgen, Matthias; Wittig, Tobias; Wittkowski, Josephine; Wollstadt, Simon Jakob; Wolter, Marcin Wladyslaw; Wolters, Helmut; Wosiek, Barbara; Wotschack, Jorg; Woudstra, Martin; Wozniak, Krzysztof; Wright, Michael; Wu, Mengqing; Wu, Sau Lan; Wu, Xin; Wu, Yusheng; Wulf, Evan; Wyatt, Terry Richard; Wynne, Benjamin; Xella, Stefania; Xiao, Meng; Xu, Da; Xu, Lailin; Yabsley, Bruce; Yacoob, Sahal; Yamada, Miho; Yamaguchi, Hiroshi; Yamaguchi, Yohei; Yamamoto, Akira; Yamamoto, Kyoko; Yamamoto, Shimpei; Yamamura, Taiki; Yamanaka, Takashi; Yamauchi, Katsuya; Yamazaki, Yuji; Yan, Zhen; Yang, Haijun; Yang, Hongtao; Yang, Un-Ki; Yang, Yi; Yanush, Serguei; Yao, Liwen; Yao, Weiming; Yasu, Yoshiji; Yatsenko, Elena; Yau Wong, Kaven Henry; Ye, Jingbo; Ye, Shuwei; Yen, Andy L; Yildirim, Eda; Yilmaz, Metin; Yoosoofmiya, Reza; Yorita, Kohei; Yoshida, Rikutaro; Yoshihara, Keisuke; Young, Charles; Young, Christopher John; Youssef, Saul; Yu, David Ren-Hwa; Yu, Jaehoon; Yu, Jiaming; Yu, Jie; Yuan, Li; Yurkewicz, Adam; Yusuff, Imran; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zevi della Porta, Giovanni; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Huaqiao; Zhang, Jinlong; Zhang, Lei; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Lei; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Robert; Zimmermann, Simone; Zimmermann, Stephanie; Zinonos, Zinonas; Ziolkowski, Michael; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zutshi, Vishnu; Zwalinski, Lukasz
2014-09-15
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
A Real-Time Programmer's Tour of General-Purpose L4 Microkernels
Directory of Open Access Journals (Sweden)
Ruocco Sergio
2008-01-01
Full Text Available Abstract L4-embedded is a microkernel successfully deployed in mobile devices with soft real-time requirements. It now faces the challenges of tightly integrated systems, in which user interface, multimedia, OS, wireless protocols, and even software-defined radios must run on a single CPU. In this paper we discuss the pros and cons of L4-embedded for real-time systems design, focusing on the issues caused by the extreme speed optimisations it inherited from its general-purpose ancestors. Since these issues can be addressed with a minimal performance loss, we conclude that, overall, the design of real-time systems based on L4-embedded is possible, and facilitated by a number of design features unique to microkernels and the L4 family.
General purpose dynamic Monte Carlo with continuous energy for transient analysis
Energy Technology Data Exchange (ETDEWEB)
Sjenitzer, B. L.; Hoogenboom, J. E. [Delft Univ. of Technology, Dept. of Radiation, Radionuclide and Reactors, Mekelweg 15, 2629JB Delft (Netherlands)
2012-07-01
For safety assessments transient analysis is an important tool. It can predict maximum temperatures during regular reactor operation or during an accident scenario. Despite the fact that this kind of analysis is very important, the state of the art still uses rather crude methods, like diffusion theory and point-kinetics. For reference calculations it is preferable to use the Monte Carlo method. In this paper the dynamic Monte Carlo method is implemented in the general purpose Monte Carlo code Tripoli4. Also, the method is extended for use with continuous energy. The first results of Dynamic Tripoli demonstrate that this kind of calculation is indeed accurate and the results are achieved in a reasonable amount of time. With the method implemented in Tripoli it is now possible to do an exact transient calculation in arbitrary geometry. (authors)
Generic functional requirements for a NASA general-purpose data base management system
Lohman, G. M.
1981-01-01
Generic functional requirements for a general-purpose, multi-mission data base management system (DBMS) for application to remotely sensed scientific data bases are detailed. The motivation for utilizing DBMS technology in this environment is explained. The major requirements include: (1) a DBMS for scientific observational data; (2) a multi-mission capability; (3) user-friendly; (4) extensive and integrated information about data; (5) robust languages for defining data structures and formats; (6) scientific data types and structures; (7) flexible physical access mechanisms; (8) ways of representing spatial relationships; (9) a high level nonprocedural interactive query and data manipulation language; (10) data base maintenance utilities; (11) high rate input/output and large data volume storage; and adaptability to a distributed data base and/or data base machine configuration. Detailed functions are specified in a top-down hierarchic fashion. Implementation, performance, and support requirements are also given.
A General Purpose Feature Extractor for Light Detection and Ranging Data
Directory of Open Access Journals (Sweden)
Edwin B. Olson
2010-11-01
Full Text Available Feature extraction is a central step of processing Light Detection and Ranging (LIDAR data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.
A general purpose feature extractor for light detection and ranging data.
Li, Yangming; Olson, Edwin B
2010-01-01
Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.
A General Purpose Connections type CTI Server Based on SIP Protocol and Its Implementation
Watanabe, Toru; Koizumi, Hisao
In this paper, we propose a general purpose connections type CTI (Computer Telephony Integration) server that provides various CTI services such as voice logging where the CTI server communicates with IP-PBX using the SIP (Session Initiation Protocol), and accumulates voice packets of external line telephone call flowing between an IP telephone for extension and a VoIP gateway connected to outside line networks. The CTI server realizes CTI services such as voice logging, telephone conference, or IVR (interactive voice response) with accumulating and processing voice packets sampled. Furthermore, the CTI server incorporates a web server function which can provide various CTI services such as a Web telephone directory via a Web browser to PCs, cellular telephones or smart-phones in mobile environments.
International Nuclear Information System (INIS)
Bennett, G.L.; Lombardo, J.J.; Hemler, R.J.; Peterson, J.R.
1986-01-01
Electrical power for NASA's Galileo mission to Jupiter and ESA's Ulysses mission to explore the polar regions of the Sun will be provided by General-Purpose Heat Source Radioisotope Thermo-electric Generators (GPHS-RTGs). Building upon the successful RTG technology used in the Voyager program, each GPHS-RTG will provide at least 285 W(e) at beginning-of-mission. The design concept has been proven through extensive tests of an electrically heated Engineering Unit and a nuclear-heated Qualification Unit. Four flight generators have been successfully assembled and tested for use on the Galileo and Ulysses spacecraft. All indications are that the GPHS-RTGs will meet or exceed the power requirement of the missions
INGEN: a general-purpose mesh generator for finite element codes
International Nuclear Information System (INIS)
Cook, W.A.
1979-05-01
INGEN is a general-purpose mesh generator for two- and three-dimensional finite element codes. The basic parts of the code are surface and three-dimensional region generators that use linear-blending interpolation formulas. These generators are based on an i, j, k index scheme that is used to number nodal points, construct elements, and develop displacement and traction boundary conditions. This code can generate truss elements (2 modal points); plane stress, plane strain, and axisymmetry two-dimensional continuum elements (4 to 8 nodal points); plate elements (4 to 8 nodal points); and three-dimensional continuum elements (8 to 21 nodal points). The traction loads generated are consistent with the element generated. The expansion--contraction option is of special interest. This option makes it possible to change an existing mesh such that some regions are refined and others are made coarser than the original mesh. 9 figures
The ICVSIE: A General Purpose Integral Equation Method for Bio-Electromagnetic Analysis.
Gomez, Luis J; Yucel, Abdulkadir C; Michielssen, Eric
2018-03-01
An internally combined volume surface integral equation (ICVSIE) for analyzing electromagnetic (EM) interactions with biological tissue and wide ranging diagnostic, therapeutic, and research applications, is proposed. The ICVSIE is a system of integral equations in terms of volume and surface equivalent currents in biological tissue subject to fields produced by externally or internally positioned devices. The system is created by using equivalence principles and solved numerically; the resulting current values are used to evaluate scattered and total electric fields, specific absorption rates, and related quantities. The validity, applicability, and efficiency of the ICVSIE are demonstrated by EM analysis of transcranial magnetic stimulation, magnetic resonance imaging, and neuromuscular electrical stimulation. Unlike previous integral equations, the ICVSIE is stable regardless of the electric permittivities of the tissue or frequency of operation, providing an application-agnostic computational framework for EM-biomedical analysis. Use of the general purpose and robust ICVSIE permits streamlining the development, deployment, and safety analysis of EM-biomedical technologies.
Real-time traffic sign recognition based on a general purpose GPU and deep-learning.
Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran
2017-01-01
We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).
Interfacing a General Purpose Fluid Network Flow Program with the SINDA/G Thermal Analysis Program
Schallhorn, Paul; Popok, Daniel
1999-01-01
A general purpose, one dimensional fluid flow code is currently being interfaced with the thermal analysis program Systems Improved Numerical Differencing Analyzer/Gaski (SINDA/G). The flow code, Generalized Fluid System Simulation Program (GFSSP), is capable of analyzing steady state and transient flow in a complex network. The flow code is capable of modeling several physical phenomena including compressibility effects, phase changes, body forces (such as gravity and centrifugal) and mixture thermodynamics for multiple species. The addition of GFSSP to SINDA/G provides a significant improvement in convective heat transfer modeling for SINDA/G. The interface development is conducted in multiple phases. This paper describes the first phase of the interface which allows for steady and quasi-steady (unsteady solid, steady fluid) conjugate heat transfer modeling.
A low cost general purpose portable programmable master/slave manipulative appliance
International Nuclear Information System (INIS)
Cameron, W.
1984-01-01
The TRIUMF 100 μA 500 MeV cyclotron, located at the University of British Columbia, required a low cost, portable master/slave manipulative capability for experimental beam line servicing. A programmable capability was also required for the hot cell manipulators. A general purpose unit was developed that might also have applications in light manufacturing and medical rehabilitation. The project now in prototype testing represents a modular portable robot costing less than $5000 that is lead-through-teach programmable by either a master controller or hands-on lead-through. Task programs are stored and retrieved on any 32 k personal computer. An on-board proportional integral derivative controller (Motorola 6809 based) gives discrete positioning of the six degrees of freedom 2 kg capacity end effector
Simrank: Rapid and sensitive general-purpose k-mer search tool
Energy Technology Data Exchange (ETDEWEB)
DeSantis, T.Z.; Keller, K.; Karaoz, U.; Alekseyenko, A.V; Singh, N.N.S.; Brodie, E.L; Pei, Z.; Andersen, G.L; Larsen, N.
2011-04-01
Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project (http://nihroadmap.nih.gov/hmp). Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available. Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset. Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity.
A Real-Time Programmer's Tour of General-Purpose L4 Microkernels
Directory of Open Access Journals (Sweden)
Sergio Ruocco
2008-02-01
Full Text Available L4-embedded is a microkernel successfully deployed in mobile devices with soft real-time requirements. It now faces the challenges of tightly integrated systems, in which user interface, multimedia, OS, wireless protocols, and even software-defined radios must run on a single CPU. In this paper we discuss the pros and cons of L4-embedded for real-time systems design, focusing on the issues caused by the extreme speed optimisations it inherited from its general-purpose ancestors. Since these issues can be addressed with a minimal performance loss, we conclude that, overall, the design of real-time systems based on L4-embedded is possible, and facilitated by a number of design features unique to microkernels and the L4 family.
International Nuclear Information System (INIS)
Stubblefield, F.W.; Alberi, J.L.
1982-01-01
A general-purpose mechanical positioning subsystem for open-loop control of experiment devices which have their positions established and read out by stepping motor-encoder combinations has been developed. The subsystem is to be used mainly for experiments to be conducted at the National Synchrotron Light Source at Brookhaven National Laboratory. The subsystem unit has been designed to be compatible with a wide variety of stepping motor and encoder types. The unit may be operated by any device capable of driving a standard RS-232-C asynchronous serial communication line. An informal survey has shown that several experiments at the Light Source will use one particular type of computer, operating system, and programming language. Accordingly, a library of subroutines compatible with this combination of computer system elements has been written to facilitate driving the positioning subsystem unit
International Nuclear Information System (INIS)
Chung, Youngjoo; Kim, Keeman.
1991-01-01
An operating system shell GPDAS (General Purpose Data Acquisition Shell) on MS-DOS-based microcomputers has been developed to provide flexibility in data acquisition and device control for magnet measurements at the Advanced Photon Source. GPDAS is both a command interpreter and an integrated script-based programming environment. It also incorporates the MS-DOS shell to make use of the existing utility programs for file manipulation and data analysis. Features include: alias definition, virtual memory, windows, graphics, data and procedure backup, background operation, script programming language, and script level debugging. Data acquisition system devices can be controlled through IEEE488 board, multifunction I/O board, digital I/O board and Gespac crate via Euro G-64 bus. GPDAS is now being used for diagnostics R ampersand D and accelerator physics studies as well as for magnet measurements. Their hardware configurations will also be discussed. 3 refs., 3 figs
Literature Review: Weldability of Iridium DOP-26 Alloy for General Purpose Heat Source
Energy Technology Data Exchange (ETDEWEB)
Burgardt, Paul [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Pierce, Stanley W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-10-19
The basic purpose of this paper is to provide a literature review relative to fabrication of the General Purpose Heat Source (GPHS) that is used to provide electrical power for deep space missions of NASA. The particular fabrication operation to be addressed here is arc welding of the GPHS encapsulation. A considerable effort was made to optimize the fabrication of the fuel pellets and of other elements of the encapsulation; that work will not be directly addressed in this paper. This report consists of three basic sections: 1) a brief description of the GPHS will be provided as background information for the reader; 2) mechanical properties and the optimization thereof as relevant to welding will be discussed; 3) a review of the arc welding process development and optimization will be presented. Since the welding equipment must be upgraded for future production, some discussion of the historical establishment of relevant welding variables and possible changes thereto will also be discussed.
Developing wearable bio-feedback systems: a general-purpose platform.
Bianchi, Luigi; Babiloni, Fabio; Cincotti, Febo; Arrivas, Marco; Bollero, Patrizio; Marciani, Maria Grazia
2003-06-01
Microprocessors, even those in PocketPCs, have adequate power for many real-time biofeedback applications for disabled people. This power allows design of portable or wearable devices that are smaller and lighter, and that have longer battery life compared to notebook-based systems. In this paper, we discuss a general-purpose hardware/software solution based on industrial or consumer devices and a C++ framework. Its flexibility and modularity make it adaptable to a wide range of situations. Moreover, its design minimizes system requirements and programming effort, thus allowing efficient systems to be built quickly and easily. Our design has been used to build two brain computer interface systems that were easily ported from the Win32 platform.
General-purpose heat source safety verification test series: SVT-11 through SVT-13
International Nuclear Information System (INIS)
George, T.G.; Pavone, D.
1986-05-01
The General-Purpose Heat Source (GPHS) is a modular component of the radioisotope thermoelectric generator that will provide power for the Galileo and Ulysses (formerly ISPM) space missions. The GPHS provides power by transmitting the heat of 238 Pu α-decay to an array of thermoelectric elements. Because the possibility of an orbital abort always exists, the heat source was designed and constructed to minimize plutonia release in any accident environment. The Safety Verification Test (SVT) series was formulated to evaluate the effectiveness of GPHS plutonia containment after atmospheric reentry and Earth impact. The first two reports (covering SVT-1 through SVT-10) described the results of flat, side-on, and angular module impacts against steel targets at 54 m/s. This report describes flat-on module impacts against concrete and granite targets, at velocities equivalent to or higher than previous SVTs
''Sheiva'' : a general purpose multi-parameter data acquisition and processing system at VECC
International Nuclear Information System (INIS)
Viyogi, Y.P.; Ganguly, N.K.
1982-01-01
A general purpose interactive software to be used with the PDP-15/76 on-line computer at VEC Centre for the acquisition and processing of data in nuclear physics experiments is described. The program can accommodate a maximum of thirty two inputs although the present hardware limits the number of inputs to eight. Particular emphasis is given to the problems of flexibility and ease of operation, memory optimisation and techniques dealing with experimenter-computer interaction. Various graphical methods for one- and two-dimensional data presentation are discussed. Specific problems of particle identification using detector telescopes have been dealt with carefully to handle experiments using several detector telescopes and those involving light particle-heavy particle coincidence studies. Steps needed to tailor this program towards utilisation for special experiments are also described. (author)
A general purpose subroutine for fast fourier transform on a distributed memory parallel machine
Dubey, A.; Zubair, M.; Grosch, C. E.
1992-01-01
One issue which is central in developing a general purpose Fast Fourier Transform (FFT) subroutine on a distributed memory parallel machine is the data distribution. It is possible that different users would like to use the FFT routine with different data distributions. Thus, there is a need to design FFT schemes on distributed memory parallel machines which can support a variety of data distributions. An FFT implementation on a distributed memory parallel machine which works for a number of data distributions commonly encountered in scientific applications is presented. The problem of rearranging the data after computing the FFT is also addressed. The performance of the implementation on a distributed memory parallel machine Intel iPSC/860 is evaluated.
ABAQUS-EPGEN: a general-purpose finite-element code. Volume 1. User's manual
International Nuclear Information System (INIS)
Hibbitt, H.D.; Karlsson, B.I.; Sorensen, E.P.
1982-10-01
This document is the User's Manual for ABAQUS/EPGEN, a general purpose finite element computer program, designed specifically to serve advanced structural analysis needs. The program contains very general libraries of elements, materials and analysis procedures, and is highly modular, so that complex combinations of features can be put together to model physical problems. The program is aimed at production analysis needs, and for this purpose aspects such as ease-of-use, reliability, flexibility and efficiency have received maximum attention. The input language is designed to make it straightforward to describe complicated models; the analysis procedures are highly automated with the program choosing time or load increments based on user supplied tolerances and controls; and the program offers a wide range of post-processing options for display of the analysis results
Energy Technology Data Exchange (ETDEWEB)
Li, Weizhong
2011-10-12
San Diego Supercomputer Center's Weizhong Li on "Effective Analysis of NGS Metagenomic Data with Ultra-fast Clustering Algorithms" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.
A general-purpose process modelling framework for marine energy systems
International Nuclear Information System (INIS)
Dimopoulos, George G.; Georgopoulou, Chariklia A.; Stefanatos, Iason C.; Zymaris, Alexandros S.; Kakalis, Nikolaos M.P.
2014-01-01
Highlights: • Process modelling techniques applied in marine engineering. • Systems engineering approaches to manage the complexity of modern ship machinery. • General purpose modelling framework called COSSMOS. • Mathematical modelling of conservation equations and related chemical – transport phenomena. • Generic library of ship machinery component models. - Abstract: High fuel prices, environmental regulations and current shipping market conditions impose ships to operate in a more efficient and greener way. These drivers lead to the introduction of new technologies, fuels, and operations, increasing the complexity of modern ship energy systems. As a means to manage this complexity, in this paper we present the introduction of systems engineering methodologies in marine engineering via the development of a general-purpose process modelling framework for ships named as DNV COSSMOS. Shifting the focus from components – the standard approach in shipping- to systems, widens the space for optimal design and operation solutions. The associated computer implementation of COSSMOS is a platform that models, simulates and optimises integrated marine energy systems with respect to energy efficiency, emissions, safety/reliability and costs, under both steady-state and dynamic conditions. DNV COSSMOS can be used in assessment and optimisation of design and operation problems in existing vessels, new builds as well as new technologies. The main features and our modelling approach are presented and key capabilities are illustrated via two studies on the thermo-economic design and operation optimisation of a combined cycle system for large bulk carriers, and the transient operation simulation of an electric marine propulsion system
Directory of Open Access Journals (Sweden)
Gao Qingji
2014-04-01
Full Text Available Airport passengers are required to put only one baggage each time in the check-in self-service so that the baggage can be detected and identified successfully. In order to automatically get the number of baggage that had been put on the conveyor belt, dual laser rangefinders are used to scan the outer contour of luggage in this paper. The algorithm based on hierarchical clustering and cube-fitting is proposed to inspect the number and dimension of airline luggage. Firstly, the point cloud is projected to vertical direction. By the analysis of one-dimensional clustering, the number and height of luggage will be quickly computed. Secondly, the method of nearest hierarchical clustering is applied to divide the point cloud if the above cannot be distinguished. It can preferably solve the difficult issue like crossing or overlapping pieces of baggage. Finally, the point cloud is projected to the horizontal plane. By rotating point cloud based on the centre, its minimum bounding rectangle (MBR is obtained. The length and width of luggage are got form MBR. Many experiments in different cases have been done to verify the effectiveness of the algorithm.
Directory of Open Access Journals (Sweden)
Mingshan Xie
2017-01-01
Full Text Available In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange.
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.
Digital Signal Processing Based on a Clustering Algorithm for Ir/Au TES Microcalorimeter
Zen, N.; Kunieda, Y.; Takahashi, H.; Hiramoto, K.; Nakazawa, M.; Fukuda, D.; Ukibe, M.; Ohkubo, M.
2006-02-01
In recent years, cryogenic microcalorimeters using their superconducting transition edge have been under development for possible application to the research for astronomical X-ray observations. To improve the energy resolution of superconducting transition edge sensors (TES), several correction methods have been developed. Among them, a clustering method based on digital signal processing has recently been proposed. In this paper, we applied the clustering method to Ir/Au bilayer TES. This method resulted in almost a 10% improvement in the energy resolution. Conversely, from the point of view of imaging X-ray spectroscopy, we applied the clustering method to pixellated Ir/Au-TES devices. We will thus show how a clustering method which sorts signals by their shapes is also useful for position identification
The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure
Euá n, Carolina; Ombao, Hernando; Ortega, Joaquí n
2018-01-01
We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms
International Nuclear Information System (INIS)
Zio, E.; Bazzo, R.
2010-01-01
In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into 'families'. On the basis of the decision maker's preferences, each family is then synthetically represented by a 'head of the family' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions