WorldWideScience

Sample records for machine architecture support

  1. Digital VLSI algorithms and architectures for support vector machines.

    Science.gov (United States)

    Anguita, D; Boni, A; Ridella, S

    2000-06-01

    In this paper, we propose some very simple algorithms and architectures for a digital VLSI implementation of Support Vector Machines. We discuss the main aspects concerning the realization of the learning phase of SVMs, with special attention on the effects of fixed-point math for computing and storing the parameters of the network. Some experiments on two classification problems are described that show the efficiency of the proposed methods in reaching optimal solutions with reasonable hardware requirements.

  2. Biologically relevant neural network architectures for support vector machines.

    Science.gov (United States)

    Jändel, Magnus

    2014-01-01

    Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Bio-signal analysis system design with support vector machines based on cloud computing service architecture.

    Science.gov (United States)

    Shen, Chia-Ping; Chen, Wei-Hsin; Chen, Jia-Ming; Hsu, Kai-Ping; Lin, Jeng-Wei; Chiu, Ming-Jang; Chen, Chi-Huang; Lai, Feipei

    2010-01-01

    Today, many bio-signals such as Electroencephalography (EEG) are recorded in digital format. It is an emerging research area of analyzing these digital bio-signals to extract useful health information in biomedical engineering. In this paper, a bio-signal analyzing cloud computing architecture, called BACCA, is proposed. The system has been designed with the purpose of seamless integration into the National Taiwan University Health Information System. Based on the concept of. NET Service Oriented Architecture, the system integrates heterogeneous platforms, protocols, as well as applications. In this system, we add modern analytic functions such as approximated entropy and adaptive support vector machine (SVM). It is shown that the overall accuracy of EEG bio-signal analysis has increased to nearly 98% for different data sets, including open-source and clinical data sets.

  4. Virtual Machine Support for Many-Core Architectures: Decoupling Abstract from Concrete Concurrency Models

    Directory of Open Access Journals (Sweden)

    Stefan Marr

    2010-02-01

    Full Text Available The upcoming many-core architectures require software developers to exploit concurrency to utilize available computational power. Today's high-level language virtual machines (VMs, which are a cornerstone of software development, do not provide sufficient abstraction for concurrency concepts. We analyze concrete and abstract concurrency models and identify the challenges they impose for VMs. To provide sufficient concurrency support in VMs, we propose to integrate concurrency operations into VM instruction sets. Since there will always be VMs optimized for special purposes, our goal is to develop a methodology to design instruction sets with concurrency support. Therefore, we also propose a list of trade-offs that have to be investigated to advise the design of such instruction sets. As a first experiment, we implemented one instruction set extension for shared memory and one for non-shared memory concurrency. From our experimental results, we derived a list of requirements for a full-grown experimental environment for further research.

  5. Future database machine architectures

    OpenAIRE

    Hsiao, David K.

    1984-01-01

    There are many software database management systems available on many general-purpose computers ranging from micros to super-mainframes. Database machines as backened computers can offload the database management work from the mainframe so that we can retain the same mainframe longer. However, the database backend must also demonstrate lower cost, higher performance, and newer functionality. Some of the fundamental architecture issues in the design of high-performance and great-capacity datab...

  6. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  7. Boosting Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Elkin Eduardo García Díaz

    2006-11-01

    Full Text Available En este artículo, se presenta un algoritmo de clasificación binaria basado en Support Vector Machines (Máquinas de Vectores de Soporte que combinado apropiadamente con técnicas de Boosting consigue un mejor desempeño en cuanto a tiempo de entrenamiento y conserva características similares de generalización con un modelo de igual complejidad pero de representación más compacta./ In this paper we present an algorithm of binary classification based on Support Vector Machines. It is combined with a modified Boosting algorithm. It run faster than the original SVM algorithm with a similar generalization error and equal complexity model but it has more compact representation.

  8. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a

  9. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a centr

  10. Machine-to-machine communications architectures, technology, standards, and applications

    CERN Document Server

    Misic, Vojislav B

    2014-01-01

    With the number of machine-to-machine (M2M)-enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology.Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology.Details a practical scheme for the forward error correction code designInvestigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communicationsIdentifies algorithms that will ensure functionality, performance, reliability, ...

  11. Learning with Support Vector Machines

    CERN Document Server

    Campbell, Colin

    2010-01-01

    Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a

  12. Reversible machine code and its abstract processor architecture

    DEFF Research Database (Denmark)

    Axelsen, Holger Bock; Glück, Robert; Yokoyama, Tetsuo

    2007-01-01

    A reversible abstract machine architecture and its reversible machine code are presented and formalized. For machine code to be reversible, both the underlying control logic and each instruction must be reversible. A general class of machine instruction sets was proven to be reversible, building ...... on our concept of reversible updates. The presentation is abstract and can serve as a guideline for a family of reversible processor designs. By example, we illustrate programming principles for the abstract machine architecture formalized in this paper....

  13. Reversible machine code and its abstract processor architecture

    DEFF Research Database (Denmark)

    Axelsen, Holger Bock; Glück, Robert; Yokoyama, Tetsuo

    2007-01-01

    A reversible abstract machine architecture and its reversible machine code are presented and formalized. For machine code to be reversible, both the underlying control logic and each instruction must be reversible. A general class of machine instruction sets was proven to be reversible, building...

  14. Software Support for LIRAC Architecture

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Memory limitations are always a focus of computer architecture. The live range aware cache(LIRAC) offers a way to reduce memory access using live range information. In the LIRAC system, scratch data need not be written back if the data will no longer be used. Three kinds of software support developed for LIRAC architecture use compiler analyses, binary analyses, and trace analyses. Trace analysis results show that LIRAC can eliminate 29% of cache write-backs on average and up to 83% in the best case for the SPEC CPU 2000 benchmark. These software techniques can show the feasibility and potential benefit of the LIRAC architecture.

  15. Two General Architectures for Intelligent Machine Performance Degradation Assessment

    Directory of Open Access Journals (Sweden)

    Yanwei Xu

    2015-01-01

    Full Text Available Markov model is of good ability to infer random events whose likelihood depends on previous events. Based on this theory, hidden Markov model serves as an extension of Markov model to present an event from observations rather than states in Markov model. Moreover, due to successful applications in speech recognition, it attracts much attention in machine fault diagnosis. This paper presents two architectures for machine performance degradation assessment, which can be used to minimize machine downtime, reduce economic loss, and improve productivity. The major difference between the two architectures is whether historical data are available to build hidden Markov models. In case studies, bearing data as well as available historical data are used to demonstrate the effectiveness of the first architecture. Then, whole life gearbox data without historical data are employed to demonstrate the effectiveness of the second architecture. The results obtained from two case studies show that the presented architectures have good abilities for machine performance degradation assessment.

  16. Differentially Private Support Vector Machines

    CERN Document Server

    Sarwate, Anand; Monteleoni, Claire

    2009-01-01

    This paper addresses the problem of practical privacy-preserving machine learning: how to detect patterns in massive, real-world databases of sensitive personal information, while maintaining the privacy of individuals. Chaudhuri and Monteleoni (2008) recently provided privacy-preserving techniques for learning linear separators via regularized logistic regression. With the goal of handling large databases that may not be linearly separable, we provide privacy-preserving support vector machine algorithms. We address general challenges left open by past work, such as how to release a kernel classifier without releasing any of the training data, and how to tune algorithm parameters in a privacy-preserving manner. We provide general, efficient algorithms for linear and nonlinear kernel SVMs, which guarantee $\\epsilon$-differential privacy, a very strong privacy definition due to Dwork et al. (2006). We also provide learning generalization guarantees. Empirical evaluations reveal promising performance on real and...

  17. Prediction of Machine Tool Condition Using Support Vector Machine

    Science.gov (United States)

    Wang, Peigong; Meng, Qingfeng; Zhao, Jian; Li, Junjie; Wang, Xiufeng

    2011-07-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  18. Open architecture controller solution for custom machine systems

    Science.gov (United States)

    Anderson, Ronald L.; Reagin, J. M.; Garner, T. D.; Sweeny, T. E.

    1997-01-01

    In today's marketplace, product quality and price have become requirements for entry and are no longer sufficient to differentiate one's product and gain a competitive advantage. A key to competition in the future will be a company's ability to respond quickly to a rapidly-changing global marketplace. Developers of manufacturing equipment must play a role in the reduction of the product development cycle time by increasing the flexibility of their equipment and decreasing its cost and time to market. This paper will discuss the implementation of an open-architecture machine controller on a flip-chip placement machine and how this implementation supports the goals of reduced development time and increased equipment flexibility. The following subjects are discussed: 1) Issues related to the selection of a standard operating system, including real-time performance, preemptive multi-tasking, multi-threaded applications, and development tools. 2) The use of a common API for motion, and I/O. 3) Use of a rapid application development and object-oriented programming techniques on the machine controller to shorten development time and support code reuse. 4) Specific hardware and software issues related to the implementation of the flip chip controller. This includes hardware and software implementation details, controller performance, and human interface issues.

  19. A New Incremental Support Vector Machine Algorithm

    Directory of Open Access Journals (Sweden)

    Wenjuan Zhao

    2012-10-01

    Full Text Available Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vector machine. The goal concept is updated by incremental learning. Each training procedure only includes new training data. The time complexity is independent of whole training set. Compared with the other incremental version, the training speed of this approach is improved and the change of hyperplane is reduced.

  20. Evaluation of tool support for architectural evolution

    NARCIS (Netherlands)

    Jansen, A; Bosch, J

    2004-01-01

    Evolution of software architectures is, different from architectural design, an area that only few tools have covered. We claim this is due to the lack of support for an important concept of architectural evolution: the notion of architectural design decisions. The absence of this concept in archite

  1. Evaluation of tool support for architectural evolution

    NARCIS (Netherlands)

    Jansen, A; Bosch, J

    2004-01-01

    Evolution of software architectures is, different from architectural design, an area that only few tools have covered. We claim this is due to the lack of support for an important concept of architectural evolution: the notion of architectural design decisions. The absence of this concept in

  2. New approach to training support vector machine

    Institute of Scientific and Technical Information of China (English)

    Tang Faming; Chen Mianyun; Wang Zhongdong

    2006-01-01

    Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.

  3. Deep Support Vector Machines for Regression Problems

    NARCIS (Netherlands)

    Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus

    2013-01-01

    In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain su

  4. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

    2017-01-01

    The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

  5. Deep Support Vector Machines for Regression Problems

    NARCIS (Netherlands)

    Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus

    2013-01-01

    In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain

  6. Logical Evaluation of Consciousness: For Incorporating Consciousness into Machine Architecture

    OpenAIRE

    2010-01-01

    Machine Consciousness is the study of consciousness in a biological, philosophical, mathematical and physical perspective and designing a model that can fit into a programmable system architecture. Prime objective of the study is to make the system architecture behave consciously like a biological model does. Present work has developed a feasible definition of consciousness, that characterizes consciousness with four parameters i.e., parasitic, symbiotic, self referral and reproduction. Prese...

  7. Dynamic Analysis of Foundation Supporting Rotary Machine

    Directory of Open Access Journals (Sweden)

    Utkarsh S. Patel

    2015-08-01

    Full Text Available With the advancement of technology in the field of industry, high speed machinery has been developed. As the speed of machinery has increased, vibrations also increased. Machines transmit vibrations to the structure supporting them. Hence, it is important to design and develop such structure which sustains the vibrations of machinery. Hence, in this study it has been aimed to execute the study on foundations supporting rotary type of machine like blower. In this paper, the most important parameters like frequency and amplitude are considered while execution of analysis of machine foundation supporting blower type machine. This paper shows, better interface between foundation designer and machine manufacturer for better performance of machine. The design aids/approaches for foundation design is also described in this paper and an attempt has been made to study the dynamic behaviour of a foundation structure for blower type machine subjected to forces due to operation of blower machine. Two different types of foundations for Rotary type Machine that is Blower have been studied in this paper

  8. A NEW HYPERSPHERE SUPPORT VECTOR MACHINE ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    Zhang Xinfeng; Shen Lansun

    2006-01-01

    The hypersphere support vector machine is a new algorithm in pattern recognition. By studying three kinds ofhypersphere support vector machines, it is found that their solutions are identical and the margin between two classes of samples is zero or is not unique. In this letter, a new kind ofhypersphere support vector machine is proposed. By introducing a parameter n(n>l), a unique solution of the margin can be obtained.Theoretical analysis and experimental results show that the proposed algorithm can achieve better generalization performance.

  9. Novel cascade FPGA accelerator for support vector machines classification.

    Science.gov (United States)

    Papadonikolakis, Markos; Bouganis, Christos-Savvas

    2012-07-01

    Support vector machines (SVMs) are a powerful machine learning tool, providing state-of-the-art accuracy to many classification problems. However, SVM classification is a computationally complex task, suffering from linear dependencies on the number of the support vectors and the problem's dimensionality. This paper presents a fully scalable field programmable gate array (FPGA) architecture for the acceleration of SVM classification, which exploits the device heterogeneity and the dynamic range diversities among the dataset attributes. An adaptive and fully-customized processing unit is proposed, which utilizes the available heterogeneous resources of a modern FPGA device in efficient way with respect to the problem's characteristics. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 2-3 orders of magnitude, compared to the CPU implementation. The proposed architecture outperforms other proposed FPGA and graphic processor unit approaches by more than seven times. Furthermore, based on the special properties of the heterogeneous architecture, this paper introduces the first FPGA-oriented cascade SVM classifier scheme, which exploits the FPGA reconfigurability and intensifies the custom-arithmetic properties of the heterogeneous architecture. The results show that the proposed cascade scheme is able to increase the heterogeneous classifier throughput even further, without introducing any penalty on the resource utilization.

  10. Logical Evaluation of Consciousness: For Incorporating Consciousness into Machine Architecture

    CERN Document Server

    Padhy, C N

    2010-01-01

    Machine Consciousness is the study of consciousness in a biological, philosophical, mathematical and physical perspective and designing a model that can fit into a programmable system architecture. Prime objective of the study is to make the system architecture behave consciously like a biological model does. Present work has developed a feasible definition of consciousness, that characterizes consciousness with four parameters i.e., parasitic, symbiotic, self referral and reproduction. Present work has also developed a biologically inspired consciousness architecture that has following layers: quantum layer, cellular layer, organ layer and behavioral layer and traced the characteristics of consciousness at each layer. Finally, the work has estimated physical and algorithmic architecture to devise a system that can behave consciously.

  11. Software architecture for time-constrained machine vision applications

    Science.gov (United States)

    Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

    2013-01-01

    Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

  12. SIMULATION OF MULTI-SUPPORT MACHINE VIBRATIONS

    Directory of Open Access Journals (Sweden)

    N. N. Hurski

    2009-01-01

    Full Text Available The paper considers problems pertaining to simulation of multi-support machine vibrations by means of  ADMOS programming product. A mathematical model of the multi-support machine is presented and its main geometric and physical and mechanical parameters are given in the paper. The paper reveals analysis results of the vibration processes and observes variables in time and frequency areas. The investigations have made it possible to obtain amplitude and frequency and statistical characteristics  of  the machine mass centre acceleration during its motion along various types of roads

  13. How Does Enterprise Architecture Support Innovation?

    DEFF Research Database (Denmark)

    Nardello, Marco; Lapalme, James S.; Toppenberg, Gustav

    2015-01-01

    Innovation is becoming increasingly important for Enterprise Architecture (EA) teams. Consequently, it is crucial that tools be developed to assist Enterprise Architecture teams when evaluating how (and how well) they are supporting innovation within the context of their enterprise. To date very...

  14. Cascade Support Vector Machines with Dimensionality Reduction

    Directory of Open Access Journals (Sweden)

    Oliver Kramer

    2015-01-01

    Full Text Available Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

  15. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. PLM support to architecture based development

    DEFF Research Database (Denmark)

    Bruun, Hans Peter Lomholt

    Designers doing product architecture based development look to convert desired behaviour to solutions for a portfolio of products, and through modularisation pursue commonality among different variants without increasing the internal task proportional to handling variety. To develop product...... architectures for a portfolio of products that support the right balance between commonality and variety is today a foremost part of most large companies’ development operations. A challenge is that product architectures are influencing external and internal performance of markets, production, technology......, organisation, processes, etc. To identify, evaluate, and align aspects of these domains are necessary for developing the optimal layout of product architectures. It is stated in this thesis that architectures describe building principles for products, product families, and product programs, where this project...

  17. Adaptive compensation of sculptured surface machining errors by open architecture manufacturing system

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Presents the adaptive compensation of sculptured surfacemachining errors by using the open architecture intelligent manufacturing system to ensure real-time high-precision machining of sculptured surface, and the tool deflection model constructed for prediction of machining errors to be compensated and analysis of the effect of tool deflection on machining errors, and concludes from experimental results that the open architecture intelligent manufacturing system can effectively improve the machining precision and reduce the machining errors by 30%.

  18. Architectural Support for Global Smart Spaces

    CERN Document Server

    Dearle, Alan; Morrison, Ron; McCarthy, Andrew; Mullen, Kevin; Yang, Yanyan; Connor, Richard; Welen, Paula; Wilson, Andy; 10.1007/3-540-36389-0_11

    2010-01-01

    A GLObal Smart Space (GLOSS) provides support for interaction amongst people, artefacts and places while taking account of both context and movement on a global scale. Crucial to the definition of a GLOSS is the provision of a set of location-aware services that detect, convey, store and exploit location information. We use one of these services, hearsay, to illustrate the implementation dimensions of a GLOSS. The focus of the paper is on both local and global software architecture to support the implementation of such services. The local architecture is based on XML pipe-lines and is used to construct location-aware components. The global architecture is based on a hybrid peer-to-peer routing scheme and provides the local architectures with the means to communicate in the global context.

  19. Support vector machine applied in QSAR modelling

    Institute of Scientific and Technical Information of China (English)

    MEI Hu; ZHOU Yuan; LIANG Guizhao; LI Zhiliang

    2005-01-01

    Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural network (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) regression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel function. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.

  20. The Specification of a Data Base Machine Architecture Development Facility and a Methodology for Developing Special Purpose Function Architectures,

    Science.gov (United States)

    1980-07-01

    LIUZ ZII UNCLASSIF71ED RAUC -TR-80-26 N RADC-TR40-256 In-Moure Report July 1980 THE SPECIFICATION OF A DATA BASE MACHINE ARCHITECTURE DEVELOPMENT...ROME AIR DEVELOPME14T CENTER GRIFFISS AFB NY F/6 9/2 THE SPECIFICATION OF A DATA BASE MACHINE ARCHITECTURE DEVELOPME--ETC(U) r UNCLASSIFIED RAUC -TB BR

  1. Weighted Twin Support Vector Machine with Universum

    Directory of Open Access Journals (Sweden)

    Shuxia Lu

    Full Text Available Universum is a new concept proposed recently, which is defined to be the sample that does not belong to any classes concerned. Support Vector Machine with Universum (..-SVM is a new algorithm, which can exploit Universum samples to improve the classifica ...

  2. Efficient Multiplicative Updates for Support Vector Machines

    DEFF Research Database (Denmark)

    Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten

    2009-01-01

    The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...

  3. Efficient Multiplicative Updates for Support Vector Machines

    DEFF Research Database (Denmark)

    Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten

    2009-01-01

    The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...

  4. Assured Mission Support Space Architecture (AMSSA) study

    Science.gov (United States)

    Hamon, Rob

    1993-01-01

    The assured mission support space architecture (AMSSA) study was conducted with the overall goal of developing a long-term requirements-driven integrated space architecture to provide responsive and sustained space support to the combatant commands. Although derivation of an architecture was the focus of the study, there are three significant products from the effort. The first is a philosophy that defines the necessary attributes for the development and operation of space systems to ensure an integrated, interoperable architecture that, by design, provides a high degree of combat utility. The second is the architecture itself; based on an interoperable system-of-systems strategy, it reflects a long-range goal for space that will evolve as user requirements adapt to a changing world environment. The third product is the framework of a process that, when fully developed, will provide essential information to key decision makers for space systems acquisition in order to achieve the AMSSA goal. It is a categorical imperative that military space planners develop space systems that will act as true force multipliers. AMSSA provides the philosophy, process, and architecture that, when integrated with the DOD requirements and acquisition procedures, can yield an assured mission support capability from space to the combatant commanders. An important feature of the AMSSA initiative is the participation by every organization that has a role or interest in space systems development and operation. With continued community involvement, the concept of the AMSSA will become a reality. In summary, AMSSA offers a better way to think about space (philosophy) that can lead to the effective utilization of limited resources (process) with an infrastructure designed to meet the future space needs (architecture) of our combat forces.

  5. Assured Mission Support Space Architecture (AMSSA) study

    Science.gov (United States)

    Hamon, Rob

    1993-01-01

    The assured mission support space architecture (AMSSA) study was conducted with the overall goal of developing a long-term requirements-driven integrated space architecture to provide responsive and sustained space support to the combatant commands. Although derivation of an architecture was the focus of the study, there are three significant products from the effort. The first is a philosophy that defines the necessary attributes for the development and operation of space systems to ensure an integrated, interoperable architecture that, by design, provides a high degree of combat utility. The second is the architecture itself; based on an interoperable system-of-systems strategy, it reflects a long-range goal for space that will evolve as user requirements adapt to a changing world environment. The third product is the framework of a process that, when fully developed, will provide essential information to key decision makers for space systems acquisition in order to achieve the AMSSA goal. It is a categorical imperative that military space planners develop space systems that will act as true force multipliers. AMSSA provides the philosophy, process, and architecture that, when integrated with the DOD requirements and acquisition procedures, can yield an assured mission support capability from space to the combatant commanders. An important feature of the AMSSA initiative is the participation by every organization that has a role or interest in space systems development and operation. With continued community involvement, the concept of the AMSSA will become a reality. In summary, AMSSA offers a better way to think about space (philosophy) that can lead to the effective utilization of limited resources (process) with an infrastructure designed to meet the future space needs (architecture) of our combat forces.

  6. Software system architecture for corporate user support

    Science.gov (United States)

    Sukhopluyeva, V. S.; Kuznetsov, D. Y.

    2017-01-01

    In this article, several existing ready-to-use solutions for the HelpDesk are reviewed. Advantages and disadvantages of these systems are identified. Architecture of software solution for a corporate user support system is presented in a form of the use case, state, and component diagrams described by using a unified modeling language (UML).

  7. Learning from Distributions via Support Measure Machines

    CERN Document Server

    Muandet, Krikamol; Fukumizu, Kenji; Dinuzzo, Francesco

    2012-01-01

    This paper presents a kernel-based discriminative learning framework on probability measures. Rather than relying on large collections of vectorial training examples, our framework learns using a collection of probability distributions that have been constructed to meaningfully represent training data. By representing these probability distributions as mean embeddings in the reproducing kernel Hilbert space (RKHS), we are able to apply many standard kernel-based learning techniques in straightforward fashion. To accomplish this, we construct a generalization of the support vector machine (SVM) called a support measure machine (SMM). Our analyses of SMMs provides several insights into their relationship to traditional SVMs. Based on such insights, we propose a flexible SVM (Flex-SVM) that places different kernel functions on each training example. Experimental results on both synthetic and real-world data demonstrate the effectiveness of our proposed framework.

  8. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  9. Computerized Interactive Gaming via Supporting Vector Machines

    OpenAIRE

    Jiang, Yang; Jiang, Jianmin; Palmer, Ian

    2008-01-01

    Computerized interactive gaming requires automatic processing of large volume of random data produced by players on spot, such as shooting, football kicking, and boxing. This paper describes a supporting vector machine-based artificial intelligence algorithm as one of the possible solutions to the problem of random data processing and the provision of interactive indication for further actions. In comparison with existing techniques, such as rule-based and neural networks, and so forth, our S...

  10. Image Segmentation Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    XU Hai-xiang; ZHU Guang-xi; TIAN Jin-wen; ZHANG Xiang; PENG Fu-yuan

    2005-01-01

    Image segmentation is a necessary step in image analysis. Support vector machine (SVM) approach is proposed to segment images and its segmentation performance is evaluated.Experimental results show that: the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron (BP-MLP) approach and fuzzy c-means (FCM) approach.

  11. Supporting shared data structures on distributed memory architectures

    Science.gov (United States)

    Koelbel, Charles; Mehrotra, Piyush; Vanrosendale, John

    1990-01-01

    Programming nonshared memory systems is more difficult than programming shared memory systems, since there is no support for shared data structures. Current programming languages for distributed memory architectures force the user to decompose all data structures into separate pieces, with each piece owned by one of the processors in the machine, and with all communication explicitly specified by low-level message-passing primitives. A new programming environment is presented for distributed memory architectures, providing a global name space and allowing direct access to remote parts of data values. The analysis and program transformations required to implement this environment are described, and the efficiency of the resulting code on the NCUBE/7 and IPSC/2 hypercubes are described.

  12. Object-Oriented Support for Adaptive Methods on Paranel Machines

    Directory of Open Access Journals (Sweden)

    Sandeep Bhatt

    1993-01-01

    Full Text Available This article reports on experiments from our ongoing project whose goal is to develop a C++ library which supports adaptive and irregular data structures on distributed memory supercomputers. We demonstrate the use of our abstractions in implementing "tree codes" for large-scale N-body simulations. These algorithms require dynamically evolving treelike data structures, as well as load-balancing, both of which are widely believed to make the application difficult and cumbersome to program for distributed-memory machines. The ease of writing the application code on top of our C++ library abstractions (which themselves are application independent, and the low overhead of the resulting C++ code (over hand-crafted C code supports our belief that object-oriented approaches are eminently suited to programming distributed-memory machines in a manner that (to the applications programmer is architecture-independent. Our contribution in parallel programming methodology is to identify and encapsulate general classes of communication and load-balancing strategies useful across applications and MIMD architectures. This article reports experimental results from simulations of half a million particles using multiple methods.

  13. Support Vector Machine%支持向量机

    Institute of Scientific and Technical Information of China (English)

    张浩然; 韩正之; 李昌刚

    2002-01-01

    This paper gives a introduction of the basic ideas, basic theory, key techniques, and application of the sup-port vector machine (SVM), and indicates the similarities and differences between support vector machines and neuralnetworks.

  14. Mechanical Fault Diagnosis Using Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    LI Ling-jun; ZHANG Zhou-suo; HE Zheng-jia

    2003-01-01

    The Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory ( SLT) , which can get good classification effects even with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents a SVM-based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearings is conducted. The vibration signals acquired from the bearings are used directly in the calculating without the preprocessing of extracting its features. Compared with the methods based on Artificial Neural Network (ANN), the SVM-based meth-od has desirable advantages. It is applicable for on-line diagnosis of mechanical systems.

  15. Modular particle filtering FPGA hardware architecture for brain machine interfaces.

    Science.gov (United States)

    Mountney, John; Obeid, Iyad; Silage, Dennis

    2011-01-01

    As the computational complexities of neural decoding algorithms for brain machine interfaces (BMI) increase, their implementation through sequential processors becomes prohibitive for real-time applications. This work presents the field programmable gate array (FPGA) as an alternative to sequential processors for BMIs. The reprogrammable hardware architecture of the FPGA provides a near optimal platform for performing parallel computations in real-time. The scalability and reconfigurability of the FPGA accommodates diverse sets of neural ensembles and a variety of decoding algorithms. Throughput is significantly increased by decomposing computations into independent parallel hardware modules on the FPGA. This increase in throughput is demonstrated through a parallel hardware implementation of the auxiliary particle filtering signal processing algorithm.

  16. Integrated quality control architecture for multistage machining processes

    Science.gov (United States)

    Yang, Jie; Liu, Guixiong

    2010-12-01

    To solve problems concerning the process quality prediction control for the multistage machining processes, a integrated quality control architecture is proposed in this paper. First, a hierarchical multiple criteria decision model is established for the key process and the weight matrix method stratified is discussed. Predictive control of the manufacturing quality is not just for on-site monitoring and control layer, control layer in the enterprise, remote monitoring level of quality exists a variety of target predictive control demand, therefore, based on XML to achieve a unified description of manufacturing quality information, and in different source of quality information between agencies to achieve the transfer and sharing. This will predict complex global quality control, analysis and diagnosis data to lay a good foundation to achieve a more practical, open and standardized manufacturing quality with higher levels of information integration system.

  17. Modular analytics management architecture for interoperability and decision support

    Science.gov (United States)

    Marotta, Stephen; Metzger, Max; Gorman, Joe; Sliva, Amy

    2016-05-01

    The Dual Node Decision Wheels (DNDW) architecture is a new approach to information fusion and decision support systems. By combining cognitive systems engineering organizational analysis tools, such as decision trees, with the Dual Node Network (DNN) technical architecture for information fusion, the DNDW can align relevant data and information products with an organization's decision-making processes. In this paper, we present the Compositional Inference and Machine Learning Environment (CIMLE), a prototype framework based on the principles of the DNDW architecture. CIMLE provides a flexible environment so heterogeneous data sources, messaging frameworks, and analytic processes can interoperate to provide the specific information required for situation understanding and decision making. It was designed to support the creation of modular, distributed solutions over large monolithic systems. With CIMLE, users can repurpose individual analytics to address evolving decision-making requirements or to adapt to new mission contexts; CIMLE's modular design simplifies integration with new host operating environments. CIMLE's configurable system design enables model developers to build analytical systems that closely align with organizational structures and processes and support the organization's information needs.

  18. TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

    Institute of Scientific and Technical Information of China (English)

    Zhang Xinsheng; Gao Xinbo; Wang Ying

    2009-01-01

    Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper,we generalize the vector-based learning algorithm TWin Support Vector Machine (TWSVM)to the tensor-based method TWin Support Tensor Machines(TWSTM),which accepts general tensors as input.To examine the effectiveness of TWSTM,we implement the TWSTM method for Microcalcification Clusters (MCs) detection.In the tensor subspace domain,the MCs detection procedure is formulated as a supervised learning and classification problem.and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM,the tensor version reduces the overfitting problem.

  19. Density Based Support Vector Machines for Classification

    Directory of Open Access Journals (Sweden)

    Zahra Nazari

    2015-04-01

    Full Text Available Support Vector Machines (SVM is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used classification algorithm is very sensitive to these outliers and lacks the ability to discard them. Many research results prove this sensitivity which is a weak point for SVM. Different approaches are proposed to reduce the effect of outliers but no method is suitable for all types of data sets. In this paper, the new method of Density Based SVM (DBSVM is introduced. Population Density is the basic concept which is used in this method for both linear and non-linear SVM to detect outliers. Experiments on artificial data sets, real high-dimensional benchmark data sets of Liver disorder and Heart disease, and data sets of new and fatigued banknotes’ acoustic signals can prove the efficiency of this method on noisy data classification and the better generalization that it can provide compared to the standard SVM.

  20. Support Vector Machine for mechanical faults classification

    Institute of Scientific and Technical Information of China (English)

    JIANG Zhi-qiang; FU Han-guang; LI Ling-jun

    2005-01-01

    Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for online diagnosis for mechanical system.

  1. Supernova Recognition using Support Vector Machines

    Energy Technology Data Exchange (ETDEWEB)

    Romano, Raquel A.; Aragon, Cecilia R.; Ding, Chris

    2006-10-01

    We introduce a novel application of Support Vector Machines(SVMs) to the problem of identifying potential supernovae usingphotometric and geometric features computed from astronomical imagery.The challenges of this supervised learning application are significant:1) noisy and corrupt imagery resulting in high levels of featureuncertainty,2) features with heavy-tailed, peaked distributions,3)extremely imbalanced and overlapping positiveand negative data sets, and4) the need to reach high positive classification rates, i.e. to find allpotential supernovae, while reducing the burdensome workload of manuallyexamining false positives. High accuracy is achieved viaasign-preserving, shifted log transform applied to features with peaked,heavy-tailed distributions. The imbalanced data problem is handled byoversampling positive examples,selectively sampling misclassifiednegative examples,and iteratively training multiple SVMs for improvedsupernovarecognition on unseen test data. We present crossvalidationresults and demonstrate the impact on a largescale supernova survey thatcurrently uses the SVM decision value to rank-order 600,000 potentialsupernovae each night.

  2. Support vector machines with a reject option

    CERN Document Server

    Wegkamp, Marten; 10.3150/10-BEJ320

    2012-01-01

    This paper studies $\\ell_1$ regularization with high-dimensional features for support vector machines with a built-in reject option (meaning that the decision of classifying an observation can be withheld at a cost lower than that of misclassification). The procedure can be conveniently implemented as a linear program and computed using standard software. We prove that the minimizer of the penalized population risk favors sparse solutions and show that the behavior of the empirical risk minimizer mimics that of the population risk minimizer. We also introduce a notion of classification complexity and prove that our minimizers adapt to the unknown complexity. Using a novel oracle inequality for the excess risk, we identify situations where fast rates of convergence occur.

  3. Support Vector Machines and Generalisation in HEP

    Science.gov (United States)

    Bethani, A.; Bevan, A. J.; Hays, J.; Stevenson, T. J.

    2016-10-01

    We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.

  4. Color Image Classification Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    冯霞

    2003-01-01

    An efficient method using various histogram-based (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categories is presented. Principal component analysis(PCA) is used to project the original high dimensional histograms onto their eigenspaees. Lower dimensional eigenfeatures are then used to train support vector machines(SVMs) to classify images into their categories. Experimental results show that even though different descriptors perform differently,they are all highly redundant. It is shown that the dimensionality of all these descriptors,regardless of their performances,can be significantly reduced without affecting classification accuracy, Such scheme would be useful when it is used in an interactive setting for relevant feedback in content-based image retrieval,where low dimensional content descriptors will enable fast online learning and reclassification of results.

  5. Support Vector Machines and Generalisation in HEP

    CERN Document Server

    Bethani, A; Hays, J; Stevenson, T J

    2016-01-01

    We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.

  6. Applications of Support Vector Machines in Astronomy

    Science.gov (United States)

    Zhang, Y.; Zhao, Y.

    2014-05-01

    We review Support Vector Machines (SVMs) as applied in astronomy. SVMs are mainly used for solving the and regression issues. Take classification for example, selecting of cataclysmic variables from large spectroscopic survey, detecting quasar candidates from multiwavelength photometric data, identification of blue horizontal branch stars from photometric data, classification of galactic spectra, supernova search; for regression problem, photometric redshift estimation of galaxies and quasars, physical parameter measurement (metallicity, gravity, effective temperature) of stars. Comparatively, SVMs show better performance in classification than in regression. Nevertheless, SVMs has its disadvantages, which needs large computation cost on training. Based on this problem, CUDA-Accelerated SVMs is put forward. As for accuracy of SVMs, SVMs combined with other algorithms has further improvement, such as SVM-KNN.

  7. Supporting Sustainability and Personalization with Product Architecture

    DEFF Research Database (Denmark)

    Nielsen, Kjeld; Jørgensen, Kaj Asbjørn; Taps, Stig B.

    2011-01-01

    is a driver for MCPC and earlier research within product architecture has indicated that modularization could support sustainability. In this paper, work on the drivers for modularization with focus on sustainability and MCPC, will be presented. Several modularization methods and drivers are analyzed......Mass Customization, Personalization and Co-creation (MCPC) are continuously being adopted as a competitive business strategy. Consumers as well as governments are at the same time applying pressure on companies to adopt a more sustainable strategy, consumers request greener products and governments...... apply rules for reuse and more eco-friendly manufacturing. There are several factors which could indicate that MCPC would not unify the support of a strategy for sustainability, however there are also factors which could increase the sustainability of products designed for MCPC. Modularization...

  8. Supporting Sustainability and Personalization with Product Architecture

    DEFF Research Database (Denmark)

    Nielsen, Kjeld; Jørgensen, Kaj Asbjørn; Taps, Stig B.;

    2011-01-01

    is a driver for MCPC and earlier research within product architecture has indicated that modularization could support sustainability. In this paper, work on the drivers for modularization with focus on sustainability and MCPC, will be presented. Several modularization methods and drivers are analyzed......Mass Customization, Personalization and Co-creation (MCPC) are continuously being adopted as a competitive business strategy. Consumers as well as governments are at the same time applying pressure on companies to adopt a more sustainable strategy, consumers request greener products and governments...... apply rules for reuse and more eco-friendly manufacturing. There are several factors which could indicate that MCPC would not unify the support of a strategy for sustainability, however there are also factors which could increase the sustainability of products designed for MCPC. Modularization...

  9. Functional Interface Considerations within an Exploration Life Support System Architecture

    Science.gov (United States)

    Perry, Jay L.; Sargusingh, Miriam J.; Toomarian, Nikzad

    2016-01-01

    As notional life support system (LSS) architectures are developed and evaluated, myriad options must be considered pertaining to process technologies, components, and equipment assemblies. Each option must be evaluated relative to its impact on key functional interfaces within the LSS architecture. A leading notional architecture has been developed to guide the path toward realizing future crewed space exploration goals. This architecture includes atmosphere revitalization, water recovery and management, and environmental monitoring subsystems. Guiding requirements for developing this architecture are summarized and important interfaces within the architecture are discussed. The role of environmental monitoring within the architecture is described.

  10. Scalable software architectures for decision support.

    Science.gov (United States)

    Musen, M A

    1999-12-01

    Interest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods--domain-independent algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

  11. SUPPORT VECTOR MACHINE METHOD FOR PREDICTING INVESTMENT MEASURES

    Directory of Open Access Journals (Sweden)

    Olga V. Kitova

    2016-01-01

    Full Text Available Possibilities of applying intelligent machine learning technique based on support vectors for predicting investment measures are considered in the article. The base features of support vector method over traditional econometric techniques for improving the forecast quality are described. Computer modeling results in terms of tuning support vector machine models developed with programming language Python for predicting some investment measures are shown.

  12. Model-driven Migration of Supervisory Machine Control Architectures

    NARCIS (Netherlands)

    Graaf, B.; Weber, S.; Van Deursen, A.

    2006-01-01

    Supervisory machine control is the high-level control in advanced manufacturing machines that is responsible for the coordination of manufacturing activities. Traditionally, the design of such control systems is based on finite state machines. An alternative, more flexible approach is based on

  13. Scaling Support Vector Machines On Modern HPC Platforms

    Energy Technology Data Exchange (ETDEWEB)

    You, Yang; Fu, Haohuan; Song, Shuaiwen; Randles, Amanda; Kerbyson, Darren J.; Marquez, Andres; Yang, Guangwen; Hoisie, Adolfy

    2015-02-01

    We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.

  14. L'orthoglide : une machine-outil rapide d'architecture parall\\`ele isotrope

    CERN Document Server

    Wenger, Philippe; Majou, Félix

    2007-01-01

    This article presents the Orthoglide project. The purpose of this project is the realization of a prototype of machine tool to three degrees of translation. The characteristic of this machine is a parallel kinematic architecture optimized to obtain a compact workspace with homogeneous performance. For that, the principal criterion of design which was used is the isotropy.

  15. Using Multiple FPGA Architectures for Real-time Processing of Low-level Machine Vision Functions

    Science.gov (United States)

    Thomas H. Drayer; William E. King; Philip A. Araman; Joseph G. Tront; Richard W. Conners

    1995-01-01

    In this paper, we investigate the use of multiple Field Programmable Gate Array (FPGA) architectures for real-time machine vision processing. The use of FPGAs for low-level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented...

  16. Ranking Support Vector Machine with Kernel Approximation.

    Science.gov (United States)

    Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

    2017-01-01

    Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  17. SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    LI Hong-shuang; L(U) Zhen-zhou; YUE Zhu-feng

    2006-01-01

    Support vector machine (SVM) was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method (FORM) and the Monte Carlo simulation (MCS). As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence,two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method (RSM) and the artificial neural network (ANN), which are widely used to replace the implicit state function for alleviating the computation cost,the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the "curse of dimensionality". The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis (usually finite element analysis), and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.

  18. Face Behavior Recognition Through Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Haval A. Ahmed

    2016-01-01

    Full Text Available Communication between computers and humans has grown to be a major field of research. Facial Behavior Recognition through computer algorithms is a motivating and difficult field of research for establishing emotional interactions between humans and computers. Although researchers have suggested numerous methods of emotion recognition within the literature of this field, as yet, these research works have mainly focused on one method for their system output i.e. used one facial database for assessing their works. This may diminish the generalization method and additionally it might shrink the comparability range. A proposed technique for recognizing emotional expressions that are expressed through facial aspects of still images is presented. This technique uses the Support Vector Machines (SVM as a classifier of emotions. Substantive problems are considered such as diversity in facial databases, the samples included in each database, the number of facial expressions experienced an accurate method of extracting facial features, and the variety of structural models. After many experiments and the results of different models being compared, it is determined that this approach produces high recognition rates.

  19. Recursive support vector machines for dimensionality reduction.

    Science.gov (United States)

    Tao, Qing; Chu, Dejun; Wang, Jue

    2008-01-01

    The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.

  20. From scientific instrument to industrial machine : Coping with architectural stress in embedded systems

    NARCIS (Netherlands)

    Doornbos, R.; Loo, S. van

    2012-01-01

    Architectural stress is the inability of a system design to respond to new market demands. It is an important yet often concealed issue in high tech systems. In From scientific instrument to industrial machine, we look at the phenomenon of architectural stress in embedded systems in the context of a

  1. Prediction in Marketing Using the Support Vector Machine

    OpenAIRE

    Dapeng Cui; David Curry

    2005-01-01

    Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining. The support vector machine (SVM) is a semiparametric technique with origins in the machine-learning literature of computer science. Its approach to prediction...

  2. Software Architecture Patterns for System Administration Support

    NARCIS (Netherlands)

    Bijvank, Ronald; Wiersema, Wiebe; Köppe, Christian

    2013-01-01

    Many quality aspects of software systems are addressed in the existing literature on software architecture patterns. But the aspect of system administration seems to be a bit overlooked, even though it is an important aspect too. In this work we present three software architecture patterns that, whe

  3. Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

    Institute of Scientific and Technical Information of China (English)

    TANG Xian-lun; ZHUANG Ling; QIU Guo-qing; CAI Jun

    2009-01-01

    The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

  4. Introducing Tool Support for Managing Architectural Knowledge: An Experience Report

    Energy Technology Data Exchange (ETDEWEB)

    Babar, Muhammad A.; Northway, Andrew; Gorton, Ian; Heuer, Paul; Nguyen, Thong

    2008-04-04

    Management of software architecture knowledge is vital for improving an organisation’s architectural capabilities. Despite the recognition of the importance of capturing and reusing software architecture knowledge, there is currently no suitable support mechanism available. To address this issue, we have developed a conceptual framework for managing architecture design knowledge. A web-based knowledge management tool, Process-based Architecture Knowledge Management Environment (PAKME), has been developed to support that framework. PAKME is being trialled to help systematise the architecture knowledge management and evaluation process of an industrial collaborator. This paper reports the objectives, logistics and initial findings of this project, Specifically we have deployed and used PAKME in an Australian Defence acquisition environment for evaluating architecture of a aircraft system.

  5. NC flame pipe cutting machine tool based on open architecture CNC system

    Institute of Scientific and Technical Information of China (English)

    Xiaogen NIE; Yanbing LIU

    2009-01-01

    Based on the analysis of the principle and flame movement of a pipe cutting machine tool, a retrofit NC flame pipe cutting machine tool (NFPCM) that can meet the demands of cutting various pipes is proposed. The paper deals with the design and implementation of an open architecture CNC system for the NFPCM, many of whose aspects are similar to milling machines; however, different from their machining processes and control strategies. The paper emphasizes on the NC system structure and the method for directly creating the NC file according to the cutting type and parameters. Further, the paper develops the program and sets up the open and module NC system.

  6. Modelling of internal architecture of kinesin nanomotor as a machine language.

    Science.gov (United States)

    Khataee, H R; Ibrahim, M Y

    2012-09-01

    Kinesin is a protein-based natural nanomotor that transports molecular cargoes within cells by walking along microtubules. Kinesin nanomotor is considered as a bio-nanoagent which is able to sense the cell through its sensors (i.e. its heads and tail), make the decision internally and perform actions on the cell through its actuator (i.e. its motor domain). The study maps the agent-based architectural model of internal decision-making process of kinesin nanomotor to a machine language using an automata algorithm. The applied automata algorithm receives the internal agent-based architectural model of kinesin nanomotor as a deterministic finite automaton (DFA) model and generates a regular machine language. The generated regular machine language was acceptable by the architectural DFA model of the nanomotor and also in good agreement with its natural behaviour. The internal agent-based architectural model of kinesin nanomotor indicates the degree of autonomy and intelligence of the nanomotor interactions with its cell. Thus, our developed regular machine language can model the degree of autonomy and intelligence of kinesin nanomotor interactions with its cell as a language. Modelling of internal architectures of autonomous and intelligent bio-nanosystems as machine languages can lay the foundation towards the concept of bio-nanoswarms and next phases of the bio-nanorobotic systems development.

  7. Support vector machines optimization based theory, algorithms, and extensions

    CERN Document Server

    Deng, Naiyang; Zhang, Chunhua

    2013-01-01

    Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

  8. Modular reconfigurable machines incorporating modular open architecture control

    CSIR Research Space (South Africa)

    Padayachee, J

    2008-01-01

    Full Text Available degrees of freedom on a single platform. A corresponding modular Open Architecture Control (OAC) system is presented. OAC overcomes the inflexibility of fixed proprietary automation, ensuring that MRMs provide the reconfigurability and extensibility...

  9. Supporting visual quality assessment with machine learning

    NARCIS (Netherlands)

    Gastaldo, P.; Zunino, R.; Redi, J.

    2013-01-01

    Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality

  10. Weighted K-means support vector machine for cancer prediction

    OpenAIRE

    Kim, Sunghwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble tec...

  11. Support Vector Machine Optimized by Improved Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiang Chang Sheng

    2013-07-01

    Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.

  12. Evaluating automatically parallelized versions of the support vector machine

    NARCIS (Netherlands)

    Codreanu, Valeriu; Droge, Bob; Williams, David; Yasar, Burhan; Yang, Fo; Liu, Baoquan; Dong, Feng; Surinta, Olarik; Schomaker, Lambertus; Roerdink, Jos; Wiering, Marco

    2014-01-01

    The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the

  13. DNA regulatory motif selection based on support vector machine ...

    African Journals Online (AJOL)

    DNA regulatory motif selection based on support vector machine (SVM) and its application in microarray ... African Journal of Biotechnology ... experiments to explore the underlying relationships between motif types and gene functions.

  14. An Introduction to Support Vector Machines: A Review

    OpenAIRE

    Chen, Yiling; Councill, Isaac G.

    2003-01-01

    Review of "An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Nello Cristianini and John Shawe-Taylor, New York, Cambridge University Press, 2000, 189 pp., $45, ISBN 0-521-78019-5.

  15. A support vector machine approach to the development of an ...

    African Journals Online (AJOL)

    PROMOTING ACCESS TO AFRICAN RESEARCH ... Abstract. This paper demonstrated the use of support vector machine (SVM) model to develop an ... system application and implementation was carried out with java programming language.

  16. Support Vector Machines for Pattern Classification

    CERN Document Server

    Abe, Shigeo

    2010-01-01

    A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empir

  17. Exploration Clinical Decision Support System: Medical Data Architecture

    Science.gov (United States)

    Lindsey, Tony; Shetye, Sandeep; Shaw, Tianna (Editor)

    2016-01-01

    The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear

  18. Language-based support for service oriented architectures

    DEFF Research Database (Denmark)

    Giambiagi, Pablo; Owe, Olaf; Ravn, Anders Peter

    2006-01-01

    The fast evolution of the Internet has popularized service-oriented architectures (SOA) with their promise of dynamic IT-supported inter-business collaborations. Yet this popularity does not reflect on the number of actual applications using the architecture. Programming models in use today make...

  19. Twin support vector machines models, extensions and applications

    CERN Document Server

    Jayadeva; Chandra, Suresh

    2017-01-01

    This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

  20. Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

    Science.gov (United States)

    Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

    2012-08-07

    Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

  1. Diagnosis of Acute Coronary Syndrome with a Support Vector Machine.

    Science.gov (United States)

    Berikol, Göksu Bozdereli; Yildiz, Oktay; Özcan, I Türkay

    2016-04-01

    Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data.

  2. Architectural Support for Detection and Recovery using Hardware Wrappers

    Science.gov (United States)

    2013-04-01

    SECRYPT 2011, Seville, Spain 2011. 5. A. Baumgarten, M. Steffen, M. Clausman, and J. Zambreno. "A Case Study in Hardware Trojan Design and...AFRL-OSR-VA-TR-2013-0204 Architectural Support for Detection and Recovery using Hardware Wrappers Bhagirath Narahari Rahul...Include area code) 02-26-2013 FINAL REPORT March 1, 2009 - Nov 30, 2012 Architectural Support for Detection and Recovery using Hardware Wrappers

  3. Design of a real-time open architecture controller for reconfigurable machine tool

    CSIR Research Space (South Africa)

    Masekamela, I

    2008-06-01

    Full Text Available modular structure in form of modular machines and open architecture controllers that can quickly change the physical structure and appropriately adjust the control system to adapt to the new production requirements. The paper aims to present the design...

  4. Architecture and life support systems for a rotating space habitat

    Science.gov (United States)

    Misra, Gaurav

    Life Support Systems are critical to sustain human habitation of space over long time periods. As orbiting space habitats become operational in the future, support systems such as atmo-sphere, food, water etc. will play a very pivotal role in sustaining life. To design a long-duration space habitat, it's important to consider the full gamut of human experience of the environment. Long-term viability depends on much more than just the structural or life support efficiency. A space habitat isn't just a machine; it's a life experience. To be viable, it needs to keep the inhabitants satisfied with their condition. This paper provides conceptual research on several key factors that influence the growth and sustainability of humans in a space habitat. Apart from the main life support system parameters, the architecture (both interior and exterior) of the habitat will play a crucial role in influencing the liveability in the space habitat. In order to ensure the best possible liveability for the inhabitants, a truncated (half cut) torus is proposed as the shape of the habitat. This structure rotating at an optimum rpm will en-sure 1g pseudo gravity to the inhabitants. The truncated torus design has several advantages over other proposed shapes such as a cylinder or a sphere. The design provides minimal grav-ity variation (delta g) in the living area, since its flat outer pole ensures a constant gravity. The design is superior in economy of structural and atmospheric mass. Interior architecture of the habitat addresses the total built environment, drawing from diverse disciplines includ-ing physiology, psychology, and sociology. Furthermore, factors such as line of sight, natural sunlight and overhead clearance have been discussed in the interior architecture. Substantial radiation shielding is also required in order to prevent harmful cosmic radiations and solar flares from causing damage to inhabitants. Regolith shielding of 10 tons per meter square is proposed for the

  5. An Architecture for Nested Transaction Support on Standard Database Systems

    NARCIS (Netherlands)

    Boertjes, E.M.; Grefen, P.W.P.J.; Vonk, J.; Apers, Peter M.G.

    Many applications dealing with complex processes require database support for nested transactions. Current commercial database systems lack this kind of support, offering flat, non-nested transactions only. This paper presents a three-layer architecture for implementing nested transaction support on

  6. Time-triggered State-machine Reliable Software Architecture for Micro Turbine Engine Control

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qi; XU Guoqiang; DING Shuiting

    2012-01-01

    Time-triggered (TT) embedded software pattern is well accepted in aerospace industry for its high reliability.Finite-state-machine (FSM) design method is widely used for its high efficiency and predictable behavior.In this paper,the time-triggered and state-machine combination software architecture is implemented for a 25 kg thrust micro turbine engine (MTE) used for unmanned aerial vehicle (UAV) system; also model-based-design development workflow for airworthiness software directive DO-178B is utilized.Experimental results show that time-triggered state-machine software architecture and development method could shorten the system development time,reduce the system test cost and make the turbine engine easily comply with the airworthiness rules.

  7. Image Reconstruction Using Pixel Wise Support Vector Machine SVM Classification.

    Directory of Open Access Journals (Sweden)

    Mohammad Mahmudul Alam Mia

    2015-02-01

    Full Text Available Abstract Image reconstruction using support vector machine SVM has been one of the major parts of image processing. The exactness of a supervised image classification is a function of the training data used in its generation. In this paper we studied support vector machine for classification aspects and reconstructed an image using support vector machine. Firstly value of the random pixels is used as the SVM classifier. Then the SVM classifier is trained by using those values of the random pixels. Finally the image is reconstructed after cross-validation with the trained SVM classifier. Matlab result shows that training with support vector machine produce better results and great computational efficiency with only a few minutes of runtime is necessary for training. Support vector machine have high classification accuracy and much faster convergence. Overall classification accuracy is 99.5. From our experiment It can be seen that classification accuracy mostly depends on the choice of the kernel function and best estimation of parameters for kernel is critical for a given image.

  8. Masquerade Detection Using Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    YANG Min; WANG Li-na; ZHANG Huan-guo; CHEN Wei

    2005-01-01

    A new method using support vector data description (SVDD) to distinguish legitimate users from masqueraders based on UNIX user command sequences is proposed. Sliding windows are used to get low detection delay.Experiments demonstrate that the detection effect using en riched sequences is better than that of using truncated sequences. As a SVDD profile is composed of a small amount of support vectors, our SVDD-based method can achieve computation and storage advantage when the detection performance is similar to existing method.

  9. Dynamic Modeling and Analysis of the Large-Scale Rotary Machine with Multi-Supporting

    Directory of Open Access Journals (Sweden)

    Xuejun Li

    2011-01-01

    Full Text Available The large-scale rotary machine with multi-supporting, such as rotary kiln and rope laying machine, is the key equipment in the architectural, chemistry, and agriculture industries. The body, rollers, wheels, and bearings constitute a chain multibody system. Axis line deflection is a vital parameter to determine mechanics state of rotary machine, thus body axial vibration needs to be studied for dynamic monitoring and adjusting of rotary machine. By using the Riccati transfer matrix method, the body system of rotary machine is divided into many subsystems composed of three elements, namely, rigid disk, elastic shaft, and linear spring. Multiple wheel-bearing structures are simplified as springs. The transfer matrices of the body system and overall transfer equation are developed, as well as the response overall motion equation. Taken a rotary kiln as an instance, natural frequencies, modal shape, and response vibration with certain exciting axis line deflection are obtained by numerical computing. The body vibration modal curves illustrate the cause of dynamical errors in the common axis line measurement methods. The displacement response can be used for further measurement dynamical error analysis and compensation. The response overall motion equation could be applied to predict the body motion under abnormal mechanics condition, and provide theory guidance for machine failure diagnosis.

  10. Virtual Class Support at the Virtual Machine Level

    DEFF Research Database (Denmark)

    Nielsen, Anders Bach; Ernst, Erik

    2009-01-01

    This paper describes how virtual classes can be supported in a virtual machine.  Main-stream virtual machines such as the Java Virtual Machine and the .NET platform dominate the world today, and many languages are being executed on these virtual machines even though their embodied design choices...... conflict with the design choices of the virtual machine.  For instance, there is a non-trivial mismatch between the main-stream virtual machines mentioned above and dynamically typed languages.  One language concept that creates an even greater mismatch is virtual classes, in particular because fully...... general support for virtual classes requires generation of new classes at run-time by mixin composition.  Languages like CaesarJ and ObjectTeams can express virtual classes restricted to the subset that does not require run-time generation of classes, because of the restrictions imposed by the Java...

  11. An Architecture for Hybrid Manufacturing Combining 3D Printing and CNC Machining

    Directory of Open Access Journals (Sweden)

    Marcel Müller

    2016-01-01

    Full Text Available Additive manufacturing is one of the key technologies of the 21st century. Additive manufacturing processes are often combined with subtractive manufacturing processes to create hybrid manufacturing because it is useful for manufacturing complex parts, for example, 3D printed sensor systems. Currently, several CNC machines are required for hybrid manufacturing: one machine is required for additive manufacturing and one is required for subtractive manufacturing. Disadvantages of conventional hybrid manufacturing methods are presented. Hybrid manufacturing with one CNC machine offers many advantages. It enables manufacturing of parts with higher accuracy, less production time, and lower costs. Using the example of fused layer modeling (FLM, we present a general approach for the integration of additive manufacturing processes into a numerical control for machine tools. The resulting CNC architecture is presented and its functionality is demonstrated. Its application is beyond the scope of this paper.

  12. Enhance the Performance of Virtual Machines by Using Cluster Computing Architecture

    Directory of Open Access Journals (Sweden)

    Chia-Ying Tseng

    2013-05-01

    Full Text Available Virtualization is a very important technology in the IaaS of the cloud computing. User uses computing resource as a virtual machine (VM provided from the system provider. The VM's performance is depended on physical machine. A VM should be deployed all required resources when it is created. If there is no more resource could be deployed, the VM should be move to another physical machine for getting higher performance by using VM's live migration. The overhead of a VM's live migration is 30 to 90 seconds. If there are many virtual machines which need live migration, the cost of overhead will be very much. This paper presents how to use cluster computing architecture to improve the VM's performance. It will enhance 15% of per-formance compared with VM's live migration.  

  13. Supporting crosscutting concern modelling in software architecture design

    Institute of Scientific and Technical Information of China (English)

    CAO Donggang; MEI Hong; ZHOU Minghui

    2007-01-01

    Crosscutting concerns such as logging,security,and transaction,are well supported in the programming level by aspect-oriented programming technologies.However,addressing these issues in the high-level architecture design still remains open.This paper presents a novel approach to supporting crosscutting concern modelling in the software architecture design of component-based systems.We introduce a new element named "Aspect"into our architecture description language,ABC/ADL,to clearly model the behavior of crosscutting concerns.Aspect is the first class entity as Component and Connector in ABC/ADL.ABC/ADL Connectors provide the weaving points where the component and aspect crosscut.This approach effectively enables "separation of concerns" in high-level architecture design,and facilitates black-box reuse of COTS components.

  14. MULTI SUPPORT VECTOR MACHINES DECISION MODEL AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    阎威武; 陈治纲; 邵惠鹤

    2002-01-01

    Support Vector Machines (SVM) is a powerful machine learning method developed from statistical learning theory and is currently an active field in artificial intelligent technology. SVM is sensitive to noise vectors near hyperplane since it is determined only by few support vectors. In this paper, Multi SVM decision model(MSDM)was proposed. MSDM consists of multiple SVMs and makes decision by synthetic information based on multi SVMs. MSDM is applied to heart disease diagnoses based on UCI benchmark data set. MSDM somewhat inproves the robust of decision system.

  15. Image denoising using least squares wavelet support vector machines

    Institute of Scientific and Technical Information of China (English)

    Guoping Zeng; Ruizhen Zhao

    2007-01-01

    We propose a new method for image denoising combining wavelet transform and support vector machines (SVMs). A new image filter operator based on the least squares wavelet support vector machines (LSWSVMs) is presented. Noisy image can be denoised through this filter operator and wavelet thresholding technique. Experimental results show that the proposed method is better than the existing SVM regression with the Gaussian radial basis function (RBF) and polynomial RBF. Meanwhile, it can achieve better performance than other traditional methods such as the average filter and median filter.

  16. An extended Lagrangian support vector machine for classifications

    Institute of Scientific and Technical Information of China (English)

    YANG Xiaowei; SHU Lei; HAO Zhifeng; LIANG Yanchun; LIU Guirong; HAN Xu

    2004-01-01

    Lagrangian support vector machine (LSVM) cannot solve large problems for nonlinear kernel classifiers. In order to extend the LSVM to solve very large problems, an extended Lagrangian support vector machine (ELSVM) for classifications based on LSVM and SVMlight is presented in this paper. Our idea for the ELSVM is to divide a large quadratic programming problem into a series of subproblems with small size and to solve them via LSVM. Since the LSVM can solve small and medium problems for nonlinear kernel classifiers, the proposed ELSVM can be used to handle large problems very efficiently. Numerical experiments on different types of problems are performed to demonstrate the high efficiency of the ELSVM.

  17. Classification using least squares support vector machine for reliability analysis

    Institute of Scientific and Technical Information of China (English)

    Zhi-wei GUO; Guang-chen BAI

    2009-01-01

    In order to improve the efficiency of the support vector machine (SVM) for classification to deal with a large amount of samples,the least squares support vector machine (LSSVM) for classification methods is introduced into the reliability analysis.To reduce the computational cost,the solution of the SVM is transformed from a quadratic programming to a group of linear equations.The numerical results indicate that the reliability method based on the LSSVM for classification has higher accuracy and requires less computational cost than the SVM method.

  18. WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION

    Institute of Scientific and Technical Information of China (English)

    Tong Yubing; Yang Dongkai; Zhang Qishan

    2006-01-01

    Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target funciton with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.

  19. Adjustable entropy function method for support vector machine

    Institute of Scientific and Technical Information of China (English)

    Wu Qing; Liu Sanyang; Zhang Leyou

    2008-01-01

    Based on KKT complementary condition in optimization theory,an unconstrained non-differential optimization model for support vector machine is proposed.An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution.The proposed method can find an optimal solution with a relatively small parameter p,which avoids the numerical overflow in the traditional entropy function methods.It is a new approach to solve support vector machine.The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.

  20. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  1. Clustering-based support for software architecture restructuring

    CERN Document Server

    Streekmann, Niels

    2011-01-01

    The maintenance of long-living software systems is an essential topic in today's software engineering practice and research. Software Architecture Restructuring is an important task to adjust these systems to current requirements and to keep them maintainable. Niels Streekmann introduces an approach to Software Architecture Restructuring that semi-automates this task by introducing graph clustering. The approach provides an iterative process that systematically incorporates human architectural knowledge for the improvement of the restructuring result. Thus, it supports the task of planning the

  2. A Novel Support Vector Machine with Globality-Locality Preserving

    Directory of Open Access Journals (Sweden)

    Cheng-Long Ma

    2014-01-01

    Full Text Available Support vector machine (SVM is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM, is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.

  3. Monitoring Grinding Wheel Redress-life Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Xun Chen; Thitikorn Limchimchol

    2006-01-01

    Condition monitoring is a very important aspect in automated manufacturing processes. Any malfunction of a machining process will deteriorate production quality and efficiency. This paper presents an application of support vector machines in grinding process monitoring. The paper starts with an overview of grinding behaviour. Grinding force is analysed through a Short Time Fourier Transform (STFT) to identify features for condition monitoring. The Support Vector Machine (SVM) methodology is introduced as a powerful tool for the classification of different wheel wear situations.After training with available signal data, the SVM is able to identify the state of a grinding process. The requirement and strategy for using SVM for grinding process monitoring is discussed, while the result of the example illustrates how effective SVMs can be in determining wheel redress-life.

  4. Flexible architecture of data acquisition firmware based on multi-behaviors finite state machine

    Science.gov (United States)

    Arpaia, Pasquale; Cimmino, Pasquale

    2016-11-01

    A flexible firmware architecture for different kinds of data acquisition systems, ranging from high-precision bench instruments to low-cost wireless transducers networks, is presented. The key component is a multi-behaviors finite state machine, easily configurable to both low- and high-performance requirements, to diverse operating systems, as well as to on-line and batch measurement algorithms. The proposed solution was validated experimentally on three case studies with data acquisition architectures: (i) concentrated, in a high-precision instrument for magnetic measurements at CERN, (ii) decentralized, for telemedicine remote monitoring of patients at home, and (iii) distributed, for remote monitoring of building's energy loss.

  5. River Flow Estimation from Upstream Flow Records Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Halil Karahan

    2014-01-01

    Full Text Available A novel architecture for flood routing model has been proposed and its efficiency is validated on several problems by employing support vector machines. The architecture is designed by including the inputs and observed and calculated outflows from the previous time step output. Whole observed data have been used for determining the model parameters in the heuristic methods given in the literature, which constitutes the major disadvantage of the existing approaches. Moreover, using the whole data for training may lead to overtraining problem that causes overfitting of estimations and data. Therefore, in this study, 60–90% of the data are randomly selected for training and then the remaining data are used for validation. In order to take the effects of the measurement errors into consideration, the data are corrupted by some additive noise. The results show that the proposed architecture improves the model performance under noisy and missing data conditions and that support vector machines can be powerful alternative in flood routing modeling.

  6. Team Machine: A Decision Support System for Team Formation

    Science.gov (United States)

    Bergey, Paul; King, Mark

    2014-01-01

    This paper reports on the cross-disciplinary research that resulted in a decision-support tool, Team Machine (TM), which was designed to create maximally diverse student teams. TM was used at a large United States university between 2004 and 2012, and resulted in significant improvement in the performance of student teams, superior overall balance…

  7. Analog neural network for support vector machine learning.

    Science.gov (United States)

    Perfetti, Renzo; Ricci, Elisa

    2006-07-01

    An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

  8. Predicting post-translational lysine acetylation using support vector machines

    DEFF Research Database (Denmark)

    Gnad, Florian; Ren, Shubin; Choudhary, Chunaram

    2010-01-01

    spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico...

  9. GenSVM: a generalized multiclass support vector machine

    NARCIS (Netherlands)

    G.J.J. van den Burg (Gerrit); P.J.F. Groenen (Patrick)

    2016-01-01

    textabstractTraditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM is proposed called GenSVM. In this method classification boundaries for a K-class proble

  10. Support Vector Machine-Based Nonlinear System Modeling and Control

    Institute of Scientific and Technical Information of China (English)

    张浩然; 韩正之; 冯瑞; 于志强

    2003-01-01

    This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM.At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.

  11. From scientific instrument to industrial machine coping with architectural stress in embedded systems

    CERN Document Server

    Doornbos, Richard

    2012-01-01

    Architectural stress is the inability of a system design to respond to new market demands. It is an important yet often concealed issue in high tech systems. In From scientific instrument to industrial machine, we look at the phenomenon of architectural stress in embedded systems in the context of a transmission electron microscope system built by FEI Company. Traditionally, transmission electron microscopes are manually operated scientific instruments, but they also have enormous potential for use in industrial applications. However, this new market has quite different characteristics. There are strong demands for cost-effective analysis, accurate and precise measurements, and ease-of-use. These demands can be translated into new system qualities, e.g. reliability, predictability and high throughput, as well as new functions, e.g. automation of electron microscopic analyses, automated focusing and positioning functions. From scientific instrument to industrial machine takes a pragmatic approach to the proble...

  12. Support vector machine classifiers for large data sets.

    Energy Technology Data Exchange (ETDEWEB)

    Gertz, E. M.; Griffin, J. D.

    2006-01-31

    This report concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. Several methods are proposed based on interior point methods for convex quadratic programming. Software implementations are developed by adapting the object-oriented packaging OOQP to the problem structure and by using the software package PETSc to perform time-intensive computations in a distributed setting. Linear systems arising from classification problems with moderately large numbers of features are solved by using two techniques--one a parallel direct solver, the other a Krylov-subspace method incorporating novel preconditioning strategies. Numerical results are provided, and computational experience is discussed.

  13. Quantum support vector machine for big data classification.

    Science.gov (United States)

    Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

    2014-09-26

    Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

  14. Twin Support Vector Machine: A review from 2007 to 2014

    Directory of Open Access Journals (Sweden)

    Divya Tomar

    2015-03-01

    Full Text Available Twin Support Vector Machine (TWSVM is an emerging machine learning method suitable for both classification and regression problems. It utilizes the concept of Generalized Eigen-values Proximal Support Vector Machine (GEPSVM and finds two non-parallel planes for each class by solving a pair of Quadratic Programming Problems. It enhances the computational speed as compared to the traditional Support Vector Machine (SVM. TWSVM was initially constructed to solve binary classification problems; later researchers successfully extended it for multi-class problem domain. TWSVM always gives promising empirical results, due to which it has many attractive features which enhance its applicability. This paper presents the research development of TWSVM in recent years. This study is divided into two main broad categories - variant based and multi-class based TWSVM methods. The paper primarily discusses the basic concept of TWSVM and highlights its applications in recent years. A comparative analysis of various research contributions based on TWSVM is also presented. This is helpful for researchers to effectively utilize the TWSVM as an emergent research methodology and encourage them to work further in the performance enhancement of TWSVM.

  15. Packet Classification using Support Vector Machines with String Kernels

    Directory of Open Access Journals (Sweden)

    Sarthak Munshi

    2016-08-01

    Full Text Available Since the inception of internet many methods have been devised to keep untrusted and malicious packets away from a user’s system . The traffic / packet classification can be used as an important tool to detect intrusion in the system. Using Machine Learning as an efficient statistical based approach for classifying packets is a novel method in practice today . This paper emphasizes upon using an advanced string kernel method within a support vector machine to classify packets .There exists a paper related to a similar problem using Machine Learning [2]. But the researches mentioned in their paper are not up-to date and doesn’t account for modern day string kernels that are much more efficient . My work extends their research by introducing different approaches to classify encrypted / unencrypted traffic / packets .

  16. OLLAF: A Fine Grained Dynamically Reconfigurable Architecture for OS Support

    Directory of Open Access Journals (Sweden)

    Garcia Samuel

    2009-01-01

    Full Text Available Fine Grained Dynamically Reconfigurable Architecture (FGDRA offers a flexibility for embedded systems with a great power processing efficiency by exploiting optimizations opportunities at architectural level thanks to their fine configuration granularity. But this increase design complexity that should be abstracted by tools and operating system. In order to have a usable solution, a good inter-overlapping between tools, OS, and platform must exist. In this paper we present OLLAF, an FGDRA specially designed to efficiently support an OS. The studies presented here show the contribution of this architecture in terms of hardware context management and preemption support. Studies presented here show the gain that can be obtained, by using OLLAF instead of a classical FPGA, in terms of context management and preemption overhead.

  17. Optical Access Multiservice Architecture with Support to Smart Grid

    DEFF Research Database (Denmark)

    Gómez-Martínez, Alejandro; Amaya-Fernández, Ferney; Hincapié, Roberto

    2013-01-01

    The increasing demand of fixed and mobile applications, and considering that smart grid imposes new requirements to the access networks, in this paper we present an optical access architecture to support home multiservice including smart grid applications. We propose a migration path based in a WDM...

  18. Deep learning of support vector machines with class probability output networks.

    Science.gov (United States)

    Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

    2015-04-01

    Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated.

  19. Canon multifunction copier machines – now with onsite support!

    CERN Multimedia

    2013-01-01

    Following a retendering process in 2012, the IT Department is pleased to announce that leased multifunction copier machines are now covered by onsite support, provided by Canon technicians via the CERN Service Desk support system.   You can now contact the Service Desk regarding any problems or requests for toner: Telephone: 77777 Email: Service-Desk@cern.ch Please remember to quote the machine printer name and/or serial number (marked on the side of the machine). The following submission forms are available online: Report a failure with a printer or copier Request for network printer or copier installation or move Request toner/ink for my printer or copier The website below details the range of models available, all of which include print, photocopy and scan-to-mail functions as standard. These multifunction copier machines are leased subject to a monthly charge (minimum of 48 months) plus a “per click” charge to cover consumables (except staples), leaving you noth...

  20. Least squares support vector machine for short-term prediction of meteorological time series

    Science.gov (United States)

    Mellit, A.; Pavan, A. Massi; Benghanem, M.

    2013-01-01

    The prediction of meteorological time series plays very important role in several fields. In this paper, an application of least squares support vector machine (LS-SVM) for short-term prediction of meteorological time series (e.g. solar irradiation, air temperature, relative humidity, wind speed, wind direction and pressure) is presented. In order to check the generalization capability of the LS-SVM approach, a K-fold cross-validation and Kolmogorov-Smirnov test have been carried out. A comparison between LS-SVM and different artificial neural network (ANN) architectures (recurrent neural network, multi-layered perceptron, radial basis function and probabilistic neural network) is presented and discussed. The comparison showed that the LS-SVM produced significantly better results than ANN architectures. It also indicates that LS-SVM provides promising results for short-term prediction of meteorological data.

  1. Robust support vector machine-trained fuzzy system.

    Science.gov (United States)

    Forghani, Yahya; Yazdi, Hadi Sadoghi

    2014-02-01

    Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.

  2. ARCHITECTURE SOFTWARE SOLUTION TO SUPPORT AND DOCUMENT MANAGEMENT QUALITY SYSTEM

    Directory of Open Access Journals (Sweden)

    Milan Eric

    2010-12-01

    Full Text Available One of the basis of a series of standards JUS ISO 9000 is quality system documentation. An architecture of the quality system documentation depends on the complexity of business system. An establishment of an efficient management documentation of system of quality is of a great importance for the business system, as well as in the phase of introducing the quality system and in further stages of its improvement. The study describes the architecture and capability of software solutions to support and manage the quality system documentation in accordance with the requirements of standards ISO 9001:2001, ISO 14001:2005 HACCP etc.

  3. The Use of Open Source Software for Open Architecture System on CNC Milling Machine

    Directory of Open Access Journals (Sweden)

    Dalmasius Ganjar Subagio

    2012-03-01

    Full Text Available Computer numerical control (CNC milling machine system cannot be separated from the software required to follow the provisions of the Open Architecture capabilities that have portability, extend ability, interoperability, and scalability. When a prescribed period of a CNC milling machine has passed and the manufacturer decided to discontinue it, then the user will have problems for maintaining the performance of the machine. This paper aims to show that the using of open source software (OSS is the way out to maintain engine performance. With the use of OSS, users no longer depend on the software built by the manufacturer because OSS is open and can be developed independently. In this paper, USBCNC V.3.42 is used as an alternative OSS. The test result shows that the work piece is in match with the desired pattern. The test result shows that the performance of machines using OSS has similar performance with the machine using software from the manufacturer. 

  4. Study on Support Vector Machine Based on 1-Norm

    Institute of Scientific and Technical Information of China (English)

    PAN Mei-qin; HE Guo-ping; HAN Cong-ying; XUE Xin; SHI You-qun

    2006-01-01

    The model of optimization problem for Support Vector Machine(SVM) is provided, which based on the definitions of the dual norm and the distance between a point and its projection onto a given plane. The model of improved Support Vector Machine based on 1-norm (1 - SVM) is provided from the optimization problem, yet it is a discrete programming. With the smoothing technique and optimality knowledge, the discrete programming is changed into a continuous programming. Experimental results show that the algorithm is easy to implement and this method can select and suppress the problem features more efficiently.Illustrative examples show that the 1 - SVM deal with the linear or nonlinear classification well.

  5. Support vector machine-based multi-model predictive control

    Institute of Scientific and Technical Information of China (English)

    Zhejing BA; Youxian SUN

    2008-01-01

    In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression.At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control(SVMN-MPC)algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions.As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN.MPCcontroller is activated at each sampling instant.The proposed modeling,switching and controller design is demonstrated in simulation results.

  6. Novel algorithm for constructing support vector machine regression ensemble

    Institute of Scientific and Technical Information of China (English)

    Li Bo; Li Xinjun; Zhao Zhiyan

    2006-01-01

    A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.

  7. Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Wahyudi Setiawan

    2014-02-01

    Full Text Available Diabetic Retinopathy is a complication of Diabetes Melitus. It can be a blindness if untreated settled as early as possible. System created in this thesis is the detection of diabetic retinopathy level of the image obtained from fundus photographs. There are three main steps to resolve the problems, preprocessing, feature extraction and classification. Preprocessing methods that used in this system are Grayscale Green Channel, Gaussian Filter, Contrast Limited Adaptive Histogram Equalization and Masking. Two Dimensional Linear Discriminant Analysis (2DLDA is used for feature extraction. Support Vector Machine (SVM is used for classification. The test result performed by taking a dataset of MESSIDOR with number of images that vary for the training phase, otherwise is used for the testing phase. Test result show the optimal accuracy are 84% .   Keywords : Diabetic Retinopathy, Support Vector Machine, Two Dimensional Linear Discriminant Analysis, MESSIDOR

  8. Evolutionary Support Vector Machines for Transient Stability Monitoring

    Science.gov (United States)

    Dora Arul Selvi, B.; Kamaraj, N.

    2012-03-01

    Currently, power systems are in the need of fast and reliable contingency monitoring systems for the purpose of maintaining stability in the presence of deregulated and open market environment. In this paper, a quick and unfailing transient stability monitoring algorithm that considers both the symmetrical and unsymmetrical faults is presented. support vector machines (SVMs) are employed as pattern classifiers so as to construct fast relation mappings between the transient stability results and the selected input attributes using mutual information. The type of fault is recognized by a SVM classifier and the critical clearing time of the fault is estimated by a support vector regression machine. The SVM parameters are tuned by an elitist multi-objective non-dominated sorting genetic algorithm in such a manner that the best classification and regression performance are accomplished. To demonstrate the good potential of the scheme, IEEE 3 generator system and a South Indian Grid are utilized.

  9. Estimating coal reserves using a support vector machine

    Institute of Scientific and Technical Information of China (English)

    LIU Wen-kai; WANG Rui-fang; ZHENG Xiao-juan

    2008-01-01

    The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau's as the input data. Then coal reserves within a particular region were calculated. These cal-culated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.

  10. Saudi License Plate Recognition Algorithm Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Khaled Suwais; Rana Al-Otaibi; Ali Alshahrani

    2013-01-01

    License plate recognition (LPR) is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine (SVM) algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

  11. Chord Recognition Based on Temporal Correlation Support Vector Machine

    OpenAIRE

    Zhongyang Rao; Xin Guan; Jianfu Teng

    2016-01-01

    In this paper, we propose a method called temporal correlation support vector machine (TCSVM) for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and...

  12. Inverse Learning Control of Nonlinear Systems Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    HU Zhong-hui; LI Yuan-gui; CAI Yun-ze; XU Xiao-ming

    2005-01-01

    An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverselearning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.

  13. Reliability Impacts in Life Support Architecture and Technology Selection

    Science.gov (United States)

    Lange Kevin E.; Anderson, Molly S.

    2012-01-01

    Quantitative assessments of system reliability and equivalent system mass (ESM) were made for different life support architectures based primarily on International Space Station technologies. The analysis was applied to a one-year deep-space mission. System reliability was increased by adding redundancy and spares, which added to the ESM. Results were thus obtained allowing a comparison of the ESM for each architecture at equivalent levels of reliability. Although the analysis contains numerous simplifications and uncertainties, the results suggest that achieving necessary reliabilities for deep-space missions will add substantially to the life support ESM and could influence the optimal degree of life support closure. Approaches for reducing reliability impacts were investigated and are discussed.

  14. Characterization of digital medical images utilizing support vector machines

    Directory of Open Access Journals (Sweden)

    Zafiropoulos Elias P

    2004-03-01

    Full Text Available Abstract Background In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. Methods The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. Results The SVM (Support Vector Machines algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi, while the neural networks performed approximately the same. Conclusion The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis.

  15. Approximate entropy and support vector machines for electroencephalogram signal classification*****

    Institute of Scientific and Technical Information of China (English)

    Zhen Zhang; Yi Zhou; Ziyi Chen; Xianghua Tian; Shouhong Du; Ruimei Huang

    2013-01-01

    The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index-approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epi-leptic seizures were included in this study. They were al diagnosed with neocortex localized epi-lepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was con-structed with the approximate entropy extracted from one epileptic case, and then electroence-phalogram waves of the other three cases were classified, reaching a 93.33%accuracy rate. Our findings suggest that the use of approximate entropy al ows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

  16. Quintic spline smooth semi-supervised support vector classification machine

    Institute of Scientific and Technical Information of China (English)

    Xiaodan Zhang; Jinggai Ma; Aihua Li; Ang Li

    2015-01-01

    A semi-supervised vector machine is a relatively new learning method using both labeled and unlabeled data in classifi-cation. Since the objective function of the model for an unstrained semi-supervised vector machine is not smooth, many fast opti-mization algorithms cannot be applied to solve the model. In order to overcome the difficulty of dealing with non-smooth objective functions, new methods that can solve the semi-supervised vector machine with desired classification accuracy are in great demand. A quintic spline function with three-times differentiability at the ori-gin is constructed by a general three-moment method, which can be used to approximate the symmetric hinge loss function. The approximate accuracy of the quintic spline function is estimated. Moreover, a quintic spline smooth semi-support vector machine is obtained and the convergence accuracy of the smooth model to the non-smooth one is analyzed. Three experiments are performed to test the efficiency of the model. The experimental results show that the new model outperforms other smooth models, in terms of classification performance. Furthermore, the new model is not sensitive to the increasing number of the labeled samples, which means that the new model is more efficient.

  17. Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines.

    Science.gov (United States)

    Kyrkou, Christos; Bouganis, Christos-Savvas; Theocharides, Theocharis; Polycarpou, Marios M

    2016-01-01

    Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the majority of the data belong to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, SVM classification is a computationally demanding task and existing hardware architectures for SVMs only consider monolithic classifiers. This paper proposes the acceleration of cascade SVMs through a hybrid processing hardware architecture optimized for the cascade SVM classification flow, accompanied by a method to reduce the required hardware resources for its implementation, and a method to improve the classification speed utilizing cascade information to further discard data samples. The proposed SVM cascade architecture is implemented on a Spartan-6 field-programmable gate array (FPGA) platform and evaluated for object detection on 800×600 (Super Video Graphics Array) resolution images. The proposed architecture, boosted by a neural network that processes cascade information, achieves a real-time processing rate of 40 frames/s for the benchmark face detection application. Furthermore, the hardware-reduction method results in the utilization of 25% less FPGA custom-logic resources and 20% peak power reduction compared with a baseline implementation.

  18. Lunar Surface Architecture Utilization and Logistics Support Assessment

    Science.gov (United States)

    Bienhoff, Dallas; Findiesen, William; Bayer, Martin; Born, Andrew; McCormick, David

    2008-01-01

    Crew and equipment utilization and logistics support needs for the point of departure lunar outpost as presented by the NASA Lunar Architecture Team (LAT) and alternative surface architectures were assessed for the first ten years of operation. The lunar surface architectures were evaluated and manifests created for each mission. Distances between Lunar Surface Access Module (LSAM) landing sites and emplacement locations were estimated. Physical characteristics were assigned to each surface element and operational characteristics were assigned to each surface mobility element. Stochastic analysis was conducted to assess probable times to deploy surface elements, conduct exploration excursions, and perform defined crew activities. Crew time is divided into Outpost-related, exploration and science, overhead, and personal activities. Outpost-related time includes element deployment, EVA maintenance, IVA maintenance, and logistics resupply. Exploration and science activities include mapping, geological surveys, science experiment deployment, sample analysis and categorizing, and physiological and biological tests in the lunar environment. Personal activities include sleeping, eating, hygiene, exercising, and time off. Overhead activities include precursor or close-out tasks that must be accomplished but don't fit into the other three categories such as: suit donning and doffing, airlock cycle time, suit cleaning, suit maintenance, post-landing safing actions, and pre-departure preparations. Equipment usage time, spares, maintenance actions, and Outpost consumables are also estimated to provide input into logistics support planning. Results are normalized relative to the NASA LAT point of departure lunar surface architecture.

  19. A supportive architecture for CFD-based design optimisation

    Science.gov (United States)

    Li, Ni; Su, Zeya; Bi, Zhuming; Tian, Chao; Ren, Zhiming; Gong, Guanghong

    2014-03-01

    Multi-disciplinary design optimisation (MDO) is one of critical methodologies to the implementation of enterprise systems (ES). MDO requiring the analysis of fluid dynamics raises a special challenge due to its extremely intensive computation. The rapid development of computational fluid dynamic (CFD) technique has caused a rise of its applications in various fields. Especially for the exterior designs of vehicles, CFD has become one of the three main design tools comparable to analytical approaches and wind tunnel experiments. CFD-based design optimisation is an effective way to achieve the desired performance under the given constraints. However, due to the complexity of CFD, integrating with CFD analysis in an intelligent optimisation algorithm is not straightforward. It is a challenge to solve a CFD-based design problem, which is usually with high dimensions, and multiple objectives and constraints. It is desirable to have an integrated architecture for CFD-based design optimisation. However, our review on existing works has found that very few researchers have studied on the assistive tools to facilitate CFD-based design optimisation. In the paper, a multi-layer architecture and a general procedure are proposed to integrate different CFD toolsets with intelligent optimisation algorithms, parallel computing technique and other techniques for efficient computation. In the proposed architecture, the integration is performed either at the code level or data level to fully utilise the capabilities of different assistive tools. Two intelligent algorithms are developed and embedded with parallel computing. These algorithms, together with the supportive architecture, lay a solid foundation for various applications of CFD-based design optimisation. To illustrate the effectiveness of the proposed architecture and algorithms, the case studies on aerodynamic shape design of a hypersonic cruising vehicle are provided, and the result has shown that the proposed architecture

  20. A new support vector machine based multiuser detection scheme

    Institute of Scientific and Technical Information of China (English)

    WANG Yong-jian; ZHAO Hong-lin

    2008-01-01

    In order to suppress the multiple access interference(MAI)in 3G,which limits the capacity of a CDMA communication system,a fast relevance vector machine(FRVM)is employed in the muhinser detection (MUD)scheme.This method aims to overcome the shortcomings of many ordinary support vector machine (SVM)based MUD schemes,such as the long training time and the inaccuracy of the decision data,and enhance the performance of a CDMA communication system.Computer simulation results demonstrate that the proposed FRVM based muhiuser detection has lower bit error rate,costs short training time,needs fewer kernel functions and possesses better near-far resistance.

  1. Support Vector Machines for decision support in electricity markets׳ strategic bidding

    DEFF Research Database (Denmark)

    Pinto, Tiago; Sousa, Tiago M.; Praça, Isabel

    2015-01-01

    . The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated...... – Iberian market operator....

  2. Integration issues in virtual enterprises supported by an architectural framework

    DEFF Research Database (Denmark)

    Zwegers, Arian; Hannus, Matti; Tølle, Martin

    2001-01-01

    Nowadays, enterprises cooperate more extensively with other enterprises during the entire product life cycle. Temporary alliances between various enterprises emerge such as those in Virtual Enterprises. However, many enterprises experience difficulties in the formation and operation of virtual...... enterprises, especially concerning integration issues. This paper lays down an architectural framework, called VERAM, which aims to support the set-up and operation of virtual enterprises. Five different levels of integration are identified. They should all be addressed during the formation of a virtual...

  3. Decision Support System for Diabetes Mellitus through Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Tarik A. Rashid

    2016-07-01

    Full Text Available recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM patients good health treatments and recommendations.

  4. Optimization of Support Vector Machine (SVM) for Object Classification

    Science.gov (United States)

    Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

    2012-01-01

    The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

  5. MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM.Combined the LS-SVM with the Multi-Resolution Analysis (MRA), this letter proposes the Multi-resolution LS-SVM (MLS-SVM). The proposed algorithm has the same theoretical framework as MRA but with better approximation ability. At a fixed scale MLS-SVM is a classical LS-SVM, but MLS-SVM can gradually approximate the target function at different scales. In experiments, the MLS-SVM is used for nonlinear system identification, and achieves better identification accuracy.

  6. Debris Flow Hazard Assessment Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    YUAN Lifeng; ZHANG Youshui

    2006-01-01

    Seven factors, including the maximum volume of once flow , occurrence frequency of debris flow , watershed area , main channel length , watershed relative height difference , valley incision density and the length ratio of sediment supplement are chosen as evaluation factors of debris flow hazard degree. Using support vector machine (SVM) theory, we selected 259 basic data of 37 debris flow channels in Yunnan Province as learning samples in this study. We create a debris flow hazard assessment model based on SVM. The model was validated though instance applications and showed encouraging results.

  7. Support vector machine for predicting protein interactions using domain scores

    Institute of Scientific and Technical Information of China (English)

    PENG Xin-jun; WANG Yi-fei

    2009-01-01

    Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.

  8. Estimation of underdetermined mixing matrix based on support vector machine

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In underdetermined blind source separation (BSS), a novel algorithm based on extended support vector machine(SVM) is proposed to estimate the mixing matrix in this paper, including the number of the active sources. Instead of traditional clustering algorithms, it mainly takes the modulus of observations and the number in each direction of arrival, without any prior knowledge about the sources except for sparsity, and it is not sensitive to the initial values. Simulations are given to illustrate availability and robustness of our algorithm.

  9. Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-hua; YAN Xue-mei; WANG Xiao-guang

    2007-01-01

    To improve the training speed of support vector machine (SVM), a method called improved center distance ratio method (ICDRM) with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental results show that the improved SVM method-ICDRM does well at identification rate and training speed.

  10. Cross-Validation, Bootstrap, and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Masaaki Tsujitani

    2011-01-01

    Full Text Available This paper considers the applications of resampling methods to support vector machines (SVMs. We take into account the leaving-one-out cross-validation (CV when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs. The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. We analyze the data from a mackerel-egg survey and a liver-disease study.

  11. Hybrid Optimization of Support Vector Machine for Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

    XI Fu-li; YU Song-nian; HAO Wei

    2005-01-01

    Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it's an effective method and can improve the perfornance of SVM-based intrusion detection system further.

  12. Probability output of multi-class support vector machines

    Institute of Scientific and Technical Information of China (English)

    忻栋; 吴朝晖; 潘云鹤

    2002-01-01

    A novel approach to interpret the outputs of multi-class support vector machines is proposed in this paper. Using the geometrical interpretation of the classifying heperplane and the distance of the pattern from the hyperplane, one can calculate the posterior probability in binary classification case. This paper focuses on the probability output in multi-class phase where both the one-against-one and one-against-rest strategies are considered. Experiment on the speaker verification showed that this method has high performance.

  13. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  14. Do Performance-Based Codes Support Universal Design in Architecture?

    Science.gov (United States)

    Grangaard, Sidse; Frandsen, Anne Kathrine

    2016-01-01

    The research project 'An analysis of the accessibility requirements' studies how Danish architectural firms experience the accessibility requirements of the Danish Building Regulations and it examines their opinions on how future regulative models can support innovative and inclusive design - Universal Design (UD). The empirical material consists of input from six workshops to which all 700 Danish Architectural firms were invited, as well as eight group interviews. The analysis shows that the current prescriptive requirements are criticized for being too homogenous and possibilities for differentiation and zoning are required. Therefore, a majority of professionals are interested in a performance-based model because they think that such a model will support 'accessibility zoning', achieving flexibility because of different levels of accessibility in a building due to its performance. The common understanding of accessibility and UD is directly related to buildings like hospitals and care centers. When the objective is both innovative and inclusive architecture, the request of a performance-based model should be followed up by a knowledge enhancement effort in the building sector. Bloom's taxonomy of educational objectives is suggested as a tool for such a boost. The research project has been financed by the Danish Transport and Construction Agency.

  15. Information Architecture for Quality Management Support in Hospitals.

    Science.gov (United States)

    Rocha, Álvaro; Freixo, Jorge

    2015-10-01

    Quality Management occupies a strategic role in organizations, and the adoption of computer tools within an aligned information architecture facilitates the challenge of making more with less, promoting the development of a competitive edge and sustainability. A formal Information Architecture (IA) lends organizations an enhanced knowledge but, above all, favours management. This simplifies the reinvention of processes, the reformulation of procedures, bridging and the cooperation amongst the multiple actors of an organization. In the present investigation work we planned the IA for the Quality Management System (QMS) of a Hospital, which allowed us to develop and implement the QUALITUS (QUALITUS, name of the computer application developed to support Quality Management in a Hospital Unit) computer application. This solution translated itself in significant gains for the Hospital Unit under study, accelerating the quality management process and reducing the tasks, the number of documents, the information to be filled in and information errors, amongst others.

  16. Ecological Footprint Model Using the Support Vector Machine Technique

    Science.gov (United States)

    Ma, Haibo; Chang, Wenjuan; Cui, Guangbai

    2012-01-01

    The per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It aims to quantify the Earth's biological resources required to support human activity. In this paper, we summarize relevant previous literature, and present five factors that influence per capita EF. These factors are: National gross domestic product (GDP), urbanization (independent of economic development), distribution of income (measured by the Gini coefficient), export dependence (measured by the percentage of exports to total GDP), and service intensity (measured by the percentage of service to total GDP). A new ecological footprint model based on a support vector machine (SVM), which is a machine-learning method based on the structural risk minimization principle from statistical learning theory was conducted to calculate the per capita EF of 24 nations using data from 123 nations. The calculation accuracy was measured by average absolute error and average relative error. They were 0.004883 and 0.351078% respectively. Our results demonstrate that the EF model based on SVM has good calculation performance. PMID:22291949

  17. Chord Recognition Based on Temporal Correlation Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongyang Rao

    2016-05-01

    Full Text Available In this paper, we propose a method called temporal correlation support vector machine (TCSVM for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and the sparse component to contain the vocal signals. Then, we extract a new logarithmic pitch class profile (LPCP feature called enhanced LPCP from the low-rank part. To exploit the temporal correlation among the LPCP features of chords, we propose an improved support vector machine algorithm called TCSVM. We perform this study using the MIREX’09 (Music Information Retrieval Evaluation eXchange Audio Chord Estimation dataset. Furthermore, we conduct comprehensive experiments using different pitch class profile feature vectors to examine the performance of TCSVM. The results of our method are comparable to the state-of-the-art methods that entered the MIREX in 2013 and 2014 for the MIREX’09 Audio Chord Estimation task dataset.

  18. Least squares weighted twin support vector machines with local information

    Institute of Scientific and Technical Information of China (English)

    花小朋; 徐森; 李先锋

    2015-01-01

    A least squares version of the recently proposed weighted twin support vector machine with local information (WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information (LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time.

  19. Clifford support vector machines for classification, regression, and recurrence.

    Science.gov (United States)

    Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

    2010-11-01

    This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

  20. A Novel Kernel for Least Squares Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    FENG Wei; ZHAO Yong-ping; DU Zhong-hua; LI De-cai; WANG Li-feng

    2012-01-01

    Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.

  1. Reducing Support Vector Machine Classification Error by Implementing Kalman Filter

    Directory of Open Access Journals (Sweden)

    Muhsin Hassan

    2013-08-01

    Full Text Available The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques.

  2. Support Vector Machine Diagnosis of Acute Abdominal Pain

    Science.gov (United States)

    Björnsdotter, Malin; Nalin, Kajsa; Hansson, Lars-Erik; Malmgren, Helge

    This study explores the feasibility of a decision-support system for patients seeking care for acute abdominal pain, and, specifically the diagnosis of acute diverticulitis. We used a linear support vector machine (SVM) to separate diverticulitis from all other reported cases of abdominal pain and from the important differential diagnosis non-specific abdominal pain (NSAP). On a database containing 3337 patients, the SVM obtained results comparable to those of the doctors in separating diverticulitis or NSAP from the remaining diseases. The distinction between diverticulitis and NSAP was, however, substantially improved by the SVM. For this patient group, the doctors achieved a sensitivity of 0.714 and a specificity of 0.963. When adjusted to the physicians' results, the SVM sensitivity/specificity was higher at 0.714/0.985 and 0.786/0.963 respectively. Age was found as the most important discriminative variable, closely followed by C-reactive protein level and lower left side pain.

  3. Estimation of sand liquefaction based on support vector machines

    Institute of Scientific and Technical Information of China (English)

    苏永华; 马宁; 胡检; 杨小礼

    2008-01-01

    The origin and influence factors of sand liquefaction were analyzed, and the relation between liquefaction and its influence factors was founded. A model based on support vector machines (SVM) was established whose input parameters were selected as following influence factors of sand liquefaction: magnitude (M), the value of SPT, effective pressure of superstratum, the content of clay and the average of grain diameter. Sand was divided into two classes: liquefaction and non-liquefaction, and the class label was treated as output parameter of the model. Then the model was used to estimate sand samples, 20 support vectors and 17 borderline support vectors were gotten, then the parameters were optimized, 14 support vectors and 6 borderline support vectors were gotten, and the prediction precision reaches 100%. In order to verify the generalization of the SVM method, two other practical samples’ data from two cities, Tangshan of Hebei province and Sanshui of Guangdong province, were dealt with by another more intricate model for polytomies, which also considered some influence factors of sand liquefaction as the input parameters and divided sand into four liquefaction grades: serious liquefaction, medium liquefaction, slight liquefaction and non-liquefaction as the output parameters. The simulation results show that the latter model has a very high precision, and using SVM model to estimate sand liquefaction is completely feasible.

  4. A support vector machine approach to detect financial statement fraud in South Africa: A first look

    CSIR Research Space (South Africa)

    Moepya, SO

    2014-04-01

    Full Text Available Auditors face the difficult task of detecting companies that issue manipulated financial statements. In recent years, machine learning methods have provided a feasible solution to this task. This study develops support vector machine (SVM) models...

  5. A comparative study of slope failure prediction using logistic regression, support vector machine and least square support vector machine models

    Science.gov (United States)

    Zhou, Lim Yi; Shan, Fam Pei; Shimizu, Kunio; Imoto, Tomoaki; Lateh, Habibah; Peng, Koay Swee

    2017-08-01

    A comparative study of logistic regression, support vector machine (SVM) and least square support vector machine (LSSVM) models has been done to predict the slope failure (landslide) along East-West Highway (Gerik-Jeli). The effects of two monsoon seasons (southwest and northeast) that occur in Malaysia are considered in this study. Two related factors of occurrence of slope failure are included in this study: rainfall and underground water. For each method, two predictive models are constructed, namely SOUTHWEST and NORTHEAST models. Based on the results obtained from logistic regression models, two factors (rainfall and underground water level) contribute to the occurrence of slope failure. The accuracies of the three statistical models for two monsoon seasons are verified by using Relative Operating Characteristics curves. The validation results showed that all models produced prediction of high accuracy. For the results of SVM and LSSVM, the models using RBF kernel showed better prediction compared to the models using linear kernel. The comparative results showed that, for SOUTHWEST models, three statistical models have relatively similar performance. For NORTHEAST models, logistic regression has the best predictive efficiency whereas the SVM model has the second best predictive efficiency.

  6. Using support vector machines for anomalous change detonation

    Energy Technology Data Exchange (ETDEWEB)

    Theiler, James P [Los Alamos National Laboratory; Steinwart, Ingo [UNIV STUTTGART; Llamocca, Daniel [UNM

    2010-01-01

    We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit. Results on real and simulated anomalous changes are used to compare performance to algorithms which effectively assume a Gaussian distribution. In this paper, we investigate the use of support vector machines (SVMs) with radial basis kernels for finding anomalous changes. Compared to typical applications of SVMs, we are operating in a regime of very low false alarm rate. This means that even for relatively large training sets, the data are quite meager in the regime of operational interest. This drives us to use larger training sets, which in turn places more of a computational burden on the SVM. We initially considered three different approaches to to address the need to work in the very low false alarm rate regime. The first is a standard SVM which is trained at one threshold (where more reliable estimates of false alarm rates are possible) and then re-thresholded for the low false alarm rate regime. The second uses the same thresholding approach, but employs a so-called least squares SVM; here a quadratic (instead of a hinge-based) loss function is employed, and for this model, there are good theoretical arguments in favor of adjusting the threshold in a straightforward manner. The third approach employs a weighted support vector machine, where the weights for the two types of errors (false alarm and missed detection) are automatically adjusted to achieve the desired false alarm rate. We have found in previous experiments (not shown here) that the first two types can in some cases work well, while in other cases they do not. This renders both approaches unreliable for automated change detection. By contrast, the third approach reliably produces good results, but at

  7. Fast and Accurate Support Vector Machines on Large Scale Systems

    Energy Technology Data Exchange (ETDEWEB)

    Vishnu, Abhinav; Narasimhan, Jayenthi; Holder, Larry; Kerbyson, Darren J.; Hoisie, Adolfy

    2015-09-08

    Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

  8. Hybrid Support Vector Machines-Based Multi-fault Classification

    Institute of Scientific and Technical Information of China (English)

    GAO Guo-hua; ZHANG Yong-zhong; ZHU Yu; DUAN Guang-huang

    2007-01-01

    Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 (one versus one) and 1-v-r (one versus rest), are respectively adopted at different classification levels. At the parallel classification level, using 1-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

  9. Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

    Science.gov (United States)

    Sun, Shiliang; Xie, Xijiong

    2016-09-01

    Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

  10. Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    R. Gholami

    2012-01-01

    Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

  11. Fuzzy support vector machines based on linear clustering

    Science.gov (United States)

    Xiong, Shengwu; Liu, Hongbing; Niu, Xiaoxiao

    2005-10-01

    A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.

  12. Detection of Splice Sites Using Support Vector Machine

    Science.gov (United States)

    Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

    Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

  13. Data filtering with support vector machines in geometric camera calibration.

    Science.gov (United States)

    Ergun, B; Kavzoglu, T; Colkesen, I; Sahin, C

    2010-02-01

    The use of non-metric digital cameras in close-range photogrammetric applications and machine vision has become a popular research agenda. Being an essential component of photogrammetric evaluation, camera calibration is a crucial stage for non-metric cameras. Therefore, accurate camera calibration and orientation procedures have become prerequisites for the extraction of precise and reliable 3D metric information from images. The lack of accurate inner orientation parameters can lead to unreliable results in the photogrammetric process. A camera can be well defined with its principal distance, principal point offset and lens distortion parameters. Different camera models have been formulated and used in close-range photogrammetry, but generally sensor orientation and calibration is performed with a perspective geometrical model by means of the bundle adjustment. In this study, support vector machines (SVMs) using radial basis function kernel is employed to model the distortions measured for Olympus Aspherical Zoom lens Olympus E10 camera system that are later used in the geometric calibration process. It is intended to introduce an alternative approach for the on-the-job photogrammetric calibration stage. Experimental results for DSLR camera with three focal length settings (9, 18 and 36 mm) were estimated using bundle adjustment with additional parameters, and analyses were conducted based on object point discrepancies and standard errors. Results show the robustness of the SVMs approach on the correction of image coordinates by modelling total distortions on-the-job calibration process using limited number of images.

  14. A support vector machine approach for classification of welding defects from ultrasonic signals

    Science.gov (United States)

    Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

    2014-07-01

    Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

  15. Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling

    Institute of Scientific and Technical Information of China (English)

    Yong Mao; Xiao-Bo Zhou; Dao-Ying Pi; You-Xian Sun

    2005-01-01

    In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.

  16. An implantable VLSI architecture for real time spike sorting in cortically controlled Brain Machine Interfaces.

    Science.gov (United States)

    Aghagolzadeh, Mehdi; Zhang, Fei; Oweiss, Karim

    2010-01-01

    Brain Machine Interface (BMI) systems demand real-time spike sorting to instantaneously decode the spike trains of simultaneously recorded cortical neurons. Real-time spike sorting, however, requires extensive computational power that is not feasible to implement in implantable BMI architectures, thereby requiring transmission of high-bandwidth raw neural data to an external computer. In this work, we describe a miniaturized, low power, programmable hardware module capable of performing this task within the resource constraints of an implantable chip. The module computes a sparse representation of the spike waveforms followed by "smart" thresholding. This cascade restricts the sparse representation to a subset of projections that preserve the discriminative features of neuron-specific spike waveforms. In addition, it further reduces telemetry bandwidth making it feasible to wirelessly transmit only the important biological information to the outside world, thereby improving the efficiency, practicality and viability of BMI systems in clinical applications.

  17. Support vector machine approach for protein subcellular localization prediction.

    Science.gov (United States)

    Hua, S; Sun, Z

    2001-08-01

    Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.

  18. Finger vein image quality evaluation using support vector machines

    Science.gov (United States)

    Yang, Lu; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

    2013-02-01

    In an automatic finger-vein recognition system, finger-vein image quality is significant for segmentation, enhancement, and matching processes. In this paper, we propose a finger-vein image quality evaluation method using support vector machines (SVMs). We extract three features including the gradient, image contrast, and information capacity from the input image. An SVM model is built on the training images with annotated quality labels (i.e., high/low) and then applied to unseen images for quality evaluation. To resolve the class-imbalance problem in the training data, we perform oversampling for the minority class with random-synthetic minority oversampling technique. Cross-validation is also employed to verify the reliability and stability of the learned model. Our experimental results show the effectiveness of our method in evaluating the quality of finger-vein images, and by discarding low-quality images detected by our method, the overall finger-vein recognition performance is considerably improved.

  19. Application of Support Vector Machine to Ship Steering

    Institute of Scientific and Technical Information of China (English)

    LUO Wei-lin; ZOU Zao-jian; LI Tie-shan

    2009-01-01

    System identification is an effective way for modeling ship manoeuvring motion and ship manoeuvrability prediction. Support vector machine is proposed to identify the manoeuvring indices in four different response models of ship steering motion, including the first order linear, the first order nonlinear, the second order linear and the second order nonlinear models. Predictions of manoeuvres including trained samples by using the identified parameters are compared with the results of free-running model tests. It is discussed that the different four categories are consistent with each other both analytically and numerically. The generalization of the identified model is verified by predicting different untrained manoeuvres. The simulations and comparisons demonstrate the validity of the proposed method.

  20. BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    V. Dheepa

    2012-07-01

    Full Text Available Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.

  1. Hybrid Neural Network and Support Vector Machine Method for Optimization

    Science.gov (United States)

    Rai, Man Mohan (Inventor)

    2007-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  2. Support Vector Machine active learning for 3D model retrieval

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user's semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

  3. Scorebox extraction from mobile sports videos using Support Vector Machines

    Science.gov (United States)

    Kim, Wonjun; Park, Jimin; Kim, Changick

    2008-08-01

    Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.

  4. Temperature prediction control based on least squares support vector machines

    Institute of Scientific and Technical Information of China (English)

    Bin LIU; Hongye SU; Weihua HUANG; Jian CHU

    2004-01-01

    A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity.The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel.In the process of system running,the off-line model is linearized at each sampling instant,and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant.The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay.The results of the experiment verify the effectiveness and merit of the algorithm.

  5. Reinforced Angle-based Multicategory Support Vector Machines

    Science.gov (United States)

    Zhang, Chong; Liu, Yufeng; Wang, Junhui; Zhu, Hongtu

    2015-01-01

    The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of k classification functions for a k-class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with k − 1 functions directly. We prove that RAMSVMs can enjoy Fisher consistency. Moreover, we show that the RAMSVM can be implemented using the very efficient coordinate descent algorithm on its dual problem. Numerical experiments demonstrate that our method is highly competitive in terms of computational speed, as well as classification prediction performance. Supplemental materials for the article are available online. PMID:27891045

  6. Application of support vector machine to synthetic earthquake prediction

    Institute of Scientific and Technical Information of China (English)

    Chun Jiang; Xueli Wei; Xiaofeng Cui; Dexiang You

    2009-01-01

    This paper introduces the method of support vector machine (SVM) into the field of synthetic earthquake prediction, which is a non-linear and complex seismogenic system. As an example, we apply this method to predict the largest annual magnitude for the North China area (30°E-42°E, 108°N-125°N) and the capital region (38°E-41.5°E, 114°N-120°N) on the basis of seismicity parameters and observed precursory data. The corresponding prediction rates for the North China area and the capital region are 64.1% and 75%, respectively, which shows that the method is feasible.

  7. SENSITIVITY ANALYSIS FOR ROLLING PROCESS BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    Huang Yanwei; Wu Tihua; Zhao Jingyi; Wang Yiqun

    2005-01-01

    A method for the calculation of the sensitivity factors of the rolling process has been obtained by differentiating the roll force model based on support vector machine. It can eliminate the algebraic loop of the analytical model of the rolling process. The simulations in the first stand of five stand cold tandem rolling mill indicate that the calculation for sensitivities by this proposed method can obtain a good accuracy, and an appropriate adjustment on the control variables determined directly by the sensitivity has an excellent compensation accuracy. Moreover, the roll gap has larger effect on the exit thickness than both front tension and back tension, and it is more efficient to select the roll gap as the controlvariable of the thickness control system in the first stand.

  8. Application of Support Vector Machine to Forex Monitoring

    Science.gov (United States)

    Kamruzzaman, Joarder; Sarker, Ruhul A.

    Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

  9. Speaker Identification using MFCC-Domain Support Vector Machine

    CERN Document Server

    Kamruzzaman, S M; Islam, Md Saiful; Haque, Md Emdadul; 10.3923/ijepe.2007.274.278

    2010-01-01

    Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). In this work, melfrequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network. This work firstly used sequential minimum optimization (SMO) learning technique for SVM that improve performance over traditional techniques Chunking, Osuna. The cepstrum coefficients representing the speaker characteristics of a speech segment are computed by nonlinear filter bank analysis and discrete cosine transform. The speaker identification ability and convergence speed of the SVMs are investigated for different combinations of features. Extensive experimental results on several sam...

  10. Mandarin Digits Speech Recognition Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    XIE Xiang; KUANG Jing-ming

    2005-01-01

    A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33%, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.

  11. The seam offset identification based on support vector regression machines

    Institute of Scientific and Technical Information of China (English)

    Zeng Songsheng; Shi Yonghua; Wang Guorong; Huang Guoxing

    2009-01-01

    The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filtering from the longitudinal direction.

  12. Threat Assessment of Targets Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    CAI Huai-ping; LIU Jing-xu; CHEN Ying-wu

    2006-01-01

    In the context of cooperative engagement of armored vehicles, the threat factors of offensive targets are analyzed, and a threat assessment (TA) model is built based on a support v.ector machine (SVM) method. The SVM-based model has some advantages over the traditional method-based models: the complex factors of threat are considered in the cooperative engagement; the shortcomings of neural networks, such as local minimum and "over fitting", are overcome to improve the generalization ability; its operation speed is high and meets the needs of real time C2 of cooperative engagement; the assessment results could be more reasonable because of its self-learning capability. The analysis and simulation indicate that the SVM method is an effective method to resolve the TA problems.

  13. FORECASTING NIKKEI 225 INDEX WITH SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    HUANG Wei; Yoshiteru Nakamori; WANG Shouyang; YU Lean

    2003-01-01

    Support Vector Machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare the performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms other classification methods. Furthermore, we propose a combining model by integrating SVM with other classification methods. The combining model performs the best among the forecasting methods.

  14. Sensitivity of Support Vector Machine Classification to Various Training Features

    Directory of Open Access Journals (Sweden)

    Fuling Bian

    2013-07-01

    Full Text Available Remote sensing image classification is one of the most important techniques in image interpretation, which can be used for environmental monitoring, evaluation and prediction. Many algorithms have been developed for image classification in the literature. Support vector machine (SVM is a kind of supervised classification that has been widely used recently. The classification accuracy produced by SVM may show variation depending on the choice of training features. In this paper, SVM was used for land cover classification using Quickbird images. Spectral and textural features were extracted for the classification and the results were analyzed thoroughly. Results showed that the number of features employed in SVM was not the more the better. Different features are suitable for different type of land cover extraction. This study verifies the effectiveness and robustness of SVM in the classification of high spatial resolution remote sensing images.    

  15. Virtual screening with support vector machines and structure kernels

    CERN Document Server

    Mahé, Pierre

    2007-01-01

    Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and computationally efficient framework to include relevant information and prior knowledge about the data and problems to be handled. In particular, with kernel methods molecules do not need to be represented and stored explicitly as vectors or fingerprints, but only to be compared to each other through a comparison function technically called a kernel. While classical kernels can be used to compare vector or fingerprint representations of molecules, completely new kernels were developed in the recent years to directly compare the 2D or 3D structures of molecules, without the need for an explicit vectorization step through the extraction of molecular descriptors. While still in their infancy, these approaches have already demonstrated their relevance on several toxicity prediction and s...

  16. Support vector machine ensemble using rough sets theory

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A support vector machine (SVM) ensemble classifier is proposed. Performance of SVM trained in an input space consisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decisionmaking of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.

  17. An Efficient Audio Classification Approach Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Lhoucine Bahatti

    2016-05-01

    Full Text Available In order to achieve an audio classification aimed to identify the composer, the use of adequate and relevant features is important to improve performance especially when the classification algorithm is based on support vector machines. As opposed to conventional approaches that often use timbral features based on a time-frequency representation of the musical signal using constant window, this paper deals with a new audio classification method which improves the features extraction according the Constant Q Transform (CQT approach and includes original audio features related to the musical context in which the notes appear. The enhancement done by this work is also lay on the proposal of an optimal features selection procedure which combines filter and wrapper strategies. Experimental results show the accuracy and efficiency of the adopted approach in the binary classification as well as in the multi-class classification.

  18. Estimating Military Aircraft Cost Using Least Squares Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    ZHU Jia-yuan; ZHANG Xi-bin; ZHANG Heng-xi; REN Bo

    2004-01-01

    A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines(LS-SVM),and a military aircraft life-cycle-cost(LCC)intelligent estimation model is proposed based on the improved LS-SVM.The intelligent cost estimation process is divided into three steps in the model.In the first step,a cost-drive-factor needs to be selected,which is significant for cost estimation.In the second step,military aircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM.Then the model can be used for new type aircraft cost estimation.Chinese military aircraft costs are estimated in the paper.The results show that the estimated costs by the new model are closer to the true costs than that of the traditionally used methods.

  19. TYRE DYNAMICS MODELLING OF VEHICLE BASED ON SUPPORT VECTOR MACHINES

    Institute of Scientific and Technical Information of China (English)

    ZHENG Shuibo; TANG Houjun; HAN Zhengzhi; ZHANG Yong

    2006-01-01

    Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines (SVMs). The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation(BP) neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMs-tyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simulation.

  20. Fit-for-Purpose Visualisation of Architecture to Support Defence Capability Decision-Making

    Science.gov (United States)

    2012-07-01

    has included software support for naval tactical systems, intelligence analysis, and research into systems of systems and architecture practice ...UNCLASSIFIED Fit-for-Purpose Visualisation of Architecture to support Defence Capability Decision-Making Kevin O’Shea, Peter Pong and...architecture development approach to capture capability development information with an emphasis on developing a fit-for-purpose visualisation to

  1. Explaining Support Vector Machines: A Color Based Nomogram

    Science.gov (United States)

    Van Belle, Vanya; Van Calster, Ben; Van Huffel, Sabine; Suykens, Johan A. K.; Lisboa, Paulo

    2016-01-01

    Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811

  2. A Java-based enterprise system architecture for implementing a continuously supported and entirely Web-based exercise solution.

    Science.gov (United States)

    Wang, Zhihui; Kiryu, Tohru

    2006-04-01

    Since machine-based exercise still uses local facilities, it is affected by time and place. We designed a web-based system architecture based on the Java 2 Enterprise Edition that can accomplish continuously supported machine-based exercise. In this system, exercise programs and machines are loosely coupled and dynamically integrated on the site of exercise via the Internet. We then extended the conventional health promotion model, which contains three types of players (users, exercise trainers, and manufacturers), by adding a new player: exercise program creators. Moreover, we developed a self-describing strategy to accommodate a variety of exercise programs and provide ease of use to users on the web. We illustrate our novel design with examples taken from our feasibility study on a web-based cycle ergometer exercise system. A biosignal-based workload control approach was introduced to ensure that users performed appropriate exercise alone.

  3. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Science.gov (United States)

    Khawaja, Taimoor Saleem

    and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

  4. Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression

    Institute of Scientific and Technical Information of China (English)

    YE Mei-Ying; WANG Xiao-Dong

    2005-01-01

    A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.

  5. Perichromosomal protein Ki67 supports mitotic chromosome architecture.

    Science.gov (United States)

    Takagi, Masatoshi; Natsume, Toyoaki; Kanemaki, Masato T; Imamoto, Naoko

    2016-10-01

    Although the condensin complexes and topoisomerase IIα (TopoIIα) are the central players in mitotic chromosome formation, they are insufficient for its completion, and additional factors involved in the process have been extensively sought. In this study, we examined the possibility that Ki67, a perichromosomal protein widely used as a cell proliferation marker, is one such factor. Using a combination of auxin-inducible degron and CRISPR-Cas9-based gene editing technologies, we generated a human HCT116 cell line in which Ki67 is rapidly depleted in a few hours. The removal of Ki67 before mitotic entry did not impact the early mitotic chromosome assembly observed in prophase but subsequently resulted in the formation of misshapen mitotic chromosomes. When Ki67 was removed after mitotic entry, preassembled rod-shaped mitotic chromosomes became disorganized. In addition, we show that Ki67 and TopoIIα are reciprocally coimmunoprecipitated from mitotic cell extracts. These observations indicate that Ki67 aids the finalization of mitotic chromosome formation and helps maintain rod-shaped chromosome architecture, likely in collaboration with TopoIIα. Together, these findings represent a new model in which mitotic chromosome architecture is supported both internally and externally. © 2016 Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.

  6. Support vector machine for day ahead electricity price forecasting

    Science.gov (United States)

    Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

    2015-05-01

    Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

  7. Prediction of cell penetrating peptides by support vector machines.

    Directory of Open Access Journals (Sweden)

    William S Sanders

    2011-07-01

    Full Text Available Cell penetrating peptides (CPPs are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs. We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

  8. 30 CFR 75.205 - Installation of roof support using mining machines with integral roof bolters.

    Science.gov (United States)

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Installation of roof support using mining machines with integral roof bolters. 75.205 Section 75.205 Mineral Resources MINE SAFETY AND HEALTH... Roof Support § 75.205 Installation of roof support using mining machines with integral roof bolters...

  9. Support Vector Machine Classification of Drunk Driving Behaviour

    Science.gov (United States)

    Chen, Huiqin; Chen, Lei

    2017-01-01

    Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN), the root mean square value of the difference of the adjacent R–R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  10. Support Vector Machine Classification of Drunk Driving Behaviour

    Directory of Open Access Journals (Sweden)

    Huiqin Chen

    2017-01-01

    Full Text Available Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN, the root mean square value of the difference of the adjacent R–R interval series (RMSSD, low frequency (LF, high frequency (HF, the ratio of the low and high frequencies (LF/HF, and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  11. Support Vector Machine Classification of Drunk Driving Behaviour.

    Science.gov (United States)

    Chen, Huiqin; Chen, Lei

    2017-01-23

    Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R-R intervals (SDNN), the root mean square value of the difference of the adjacent R-R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  12. Research on Application of Regression Least Squares Support Vector Machine on Performance Prediction of Hydraulic Excavator

    Directory of Open Access Journals (Sweden)

    Zhan-bo Chen

    2014-01-01

    Full Text Available In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the algorithm of the regression least squares support vector machine is designed. Finally, the performance prediction simulation of hydraulic excavator based on regression least squares support vector machine is carried out, and simulation results show that this method can predict the performance changing rules of hydraulic excavator correctly.

  13. ADILE: Architecture of a Database-Supported Learning Environment

    NARCIS (Netherlands)

    Hiddink, Gerrit W.

    2001-01-01

    This article proposes an architecture for distributed learning environments that use databases to store learning material. As the layout of learning material can inhibit reuse, the ar-chitecture implements the notion of "separation of layout and structure" using XML technology. Also, the architectur

  14. Implementing Scientific Simulation Codes Highly Tailored for Vector Architectures Using Custom Configurable Computing Machines

    Science.gov (United States)

    Rutishauser, David

    2006-01-01

    The motivation for this work comes from an observation that amidst the push for Massively Parallel (MP) solutions to high-end computing problems such as numerical physical simulations, large amounts of legacy code exist that are highly optimized for vector supercomputers. Because re-hosting legacy code often requires a complete re-write of the original code, which can be a very long and expensive effort, this work examines the potential to exploit reconfigurable computing machines in place of a vector supercomputer to implement an essentially unmodified legacy source code. Custom and reconfigurable computing resources could be used to emulate an original application's target platform to the extent required to achieve high performance. To arrive at an architecture that delivers the desired performance subject to limited resources involves solving a multi-variable optimization problem with constraints. Prior research in the area of reconfigurable computing has demonstrated that designing an optimum hardware implementation of a given application under hardware resource constraints is an NP-complete problem. The premise of the approach is that the general issue of applying reconfigurable computing resources to the implementation of an application, maximizing the performance of the computation subject to physical resource constraints, can be made a tractable problem by assuming a computational paradigm, such as vector processing. This research contributes a formulation of the problem and a methodology to design a reconfigurable vector processing implementation of a given application that satisfies a performance metric. A generic, parametric, architectural framework for vector processing implemented in reconfigurable logic is developed as a target for a scheduling/mapping algorithm that maps an input computation to a given instance of the architecture. This algorithm is integrated with an optimization framework to arrive at a specification of the architecture parameters

  15. A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

    OpenAIRE

    Qingsong Xu

    2014-01-01

    Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have b...

  16. Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

    Science.gov (United States)

    Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

    2016-07-01

    Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification

  17. NESVM: a Fast Gradient Method for Support Vector Machines

    CERN Document Server

    Zhou, Tianyi; Wu, Xindong

    2010-01-01

    Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models, e.g., classical SVM, linear programming SVM and least square SVM. Compared against SVM-Perf \\cite{SVM_Perf}\\cite{PerfML} (its convergence rate in solving the dual SVM is upper bounded by $\\mathcal O(1/\\sqrt{k})$, wherein $k$ is the number of iterations.) and Pegasos \\cite{Pegasos} (online SVM that converges at rate $\\mathcal O(1/k)$ for the primal SVM), NESVM achieves the optimal convergence rate at $\\mathcal O(1/k^{2})$ and a linear time complexity. In particular, NESVM smoothes the non-differentiable hinge loss and $\\ell_1$-norm in the primal SVM. Then the optimal gradient method without any line search is ado...

  18. Incremental Support Vector Machine Framework for Visual Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yuichi Motai

    2007-01-01

    Full Text Available Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

  19. Spatio-temporal avalanche forecasting with Support Vector Machines

    Directory of Open Access Journals (Sweden)

    A. Pozdnoukhov

    2011-02-01

    Full Text Available This paper explores the use of the Support Vector Machine (SVM as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches. Based on the historical observations of avalanche activity, meteorological conditions and snowpack observations in the field, an SVM is used to build a data-driven spatio-temporal forecast for the local mountain region. It incorporates the outputs of simple physics-based and statistical approaches used to interpolate meteorological and snowpack-related data over a digital elevation model of the region. The interpretation of the produced forecast is discussed, and the quality of the model is validated using observations and avalanche bulletins of the recent years. The insight into the model behaviour is presented to highlight the interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.

  20. Cavitation detection of butterfly valve using support vector machines

    Science.gov (United States)

    Yang, Bo-Suk; Hwang, Won-Woo; Ko, Myung-Han; Lee, Soo-Jong

    2005-10-01

    Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur, resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, monitoring of cavitation is of economic interest and is very important in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals acquired from butterfly valves in the pumping stations. And the classification success rate is compared with that of self-organizing feature map neural network (SOFM).

  1. Nonlinear structural damage detection using support vector machines

    Science.gov (United States)

    Xiao, Li; Qu, Wenzhong

    2012-04-01

    An actual structure including connections and interfaces may exist nonlinear. Because of many complicated problems about nonlinear structural health monitoring (SHM), relatively little progress have been made in this aspect. Statistical pattern recognition techniques have been demonstrated to be competitive with other methods when applied to real engineering datasets. When a structure existing 'breathing' cracks that open and close under operational loading may cause a linear structural system to respond to its operational and environmental loads in a nonlinear manner nonlinear. In this paper, a vibration-based structural health monitoring when the structure exists cracks is investigated with autoregressive support vector machine (AR-SVM). Vibration experiments are carried out with a model frame. Time-series data in different cases such as: initial linear structure; linear structure with mass changed; nonlinear structure; nonlinear structure with mass changed are acquired.AR model of acceleration time-series is established, and different kernel function types and corresponding parameters are chosen and compared, which can more accurate, more effectively locate the damage. Different cases damaged states and different damage positions have been recognized successfully. AR-SVM method for the insufficient training samples is proved to be practical and efficient on structure nonlinear damage detection.

  2. River flow time series using least squares support vector machines

    Science.gov (United States)

    Samsudin, R.; Saad, P.; Shabri, A.

    2011-06-01

    This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

  3. Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    R. Johny Elton

    2016-08-01

    Full Text Available This paper proposes support vector machine (SVM based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD uses fuzzy entropy (FuzzyEn as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various experiments by adding real background noises of different signal-to-noise ratios (SNR ranging from −10 dB to 10 dB to actual speech signals collected from the TIMIT database. The analysis proves that FuzzyEn feature shows better results in discriminating noise and corrupted noisy speech. The efficacy of the SVM classifier was validated using 10-fold cross validation. Furthermore, the results obtained by the proposed method was compared with those of previous standardized VAD algorithms as well as recently developed methods. Performance comparison suggests that the proposed method is proven to be more efficient in detecting speech under various noisy environments with an accuracy of 93.29%, and the FuzzyEn feature detects speech efficiently even at low SNR levels.

  4. Support Vector Machine Ensemble Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Ye; YIN Ru-po; CAI Yun-ze; XU Xiao-ming

    2006-01-01

    Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

  5. A Semisupervised Support Vector Machines Algorithm for BCI Systems

    Directory of Open Access Journals (Sweden)

    Jianzhao Qin

    2007-07-01

    Full Text Available As an emerging technology, brain-computer interfaces (BCIs bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM algorithm for brain-computer interface (BCI systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.

  6. A Reformulation of Support Vector Machines for General Confidence Functions

    Science.gov (United States)

    Guo, Yuhong; Schuurmans, Dale

    We present a generalized view of support vector machines that does not rely on a Euclidean geometric interpretation nor even positive semidefinite kernels. We base our development instead on the confidence matrix—the matrix normally determined by the direct (Hadamard) product of the kernel matrix with the label outer-product matrix. It turns out that alternative forms of confidence matrices are possible, and indeed useful. By focusing on the confidence matrix instead of the underlying kernel, we can derive an intuitive principle for optimizing example weights to yield robust classifiers. Our principle initially recovers the standard quadratic SVM training criterion, which is only convex for kernel-derived confidence measures. However, given our generalized view, we are then able to derive a principled relaxation of the SVM criterion that yields a convex upper bound. This relaxation is always convex and can be solved with a linear program. Our new training procedure obtains similar generalization performance to standard SVMs on kernel-derived confidence functions, but achieves even better results with indefinite confidence functions.

  7. Incremental support vector machines for fast reliable image recognition

    Energy Technology Data Exchange (ETDEWEB)

    Makili, L., E-mail: makili_le@yahoo.com [Instituto Superior Politécnico da Universidade Katyavala Bwila, Benguela (Angola); Vega, J. [Asociación EURATOM/CIEMAT para Fusión, Madrid (Spain); Dormido-Canto, S. [Dpto. Informática y Automática – UNED, Madrid (Spain)

    2013-10-15

    Highlights: ► A conformal predictor using SVM as the underlying algorithm was implemented. ► It was applied to image recognition in the TJ–II's Thomson Scattering Diagnostic. ► To improve time efficiency an approach to incremental SVM training has been used. ► Accuracy is similar to the one reached when standard SVM is used. ► Computational time saving is significant for large training sets. -- Abstract: This paper addresses the reliable classification of images in a 5-class problem. To this end, an automatic recognition system, based on conformal predictors and using Support Vector Machines (SVM) as the underlying algorithm has been developed and applied to the recognition of images in the Thomson Scattering Diagnostic of the TJ–II fusion device. Using such conformal predictor based classifier is a computationally intensive task since it implies to train several SVM models to classify a single example and to perform this training from scratch takes a significant amount of time. In order to improve the classification time efficiency, an approach to the incremental training of SVM has been used as the underlying algorithm. Experimental results show that the overall performance of the new classifier is high, comparable to the one corresponding to the use of standard SVM as the underlying algorithm and there is a significant improvement in time efficiency.

  8. Classification of Cotton Leaf Spot Disease Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Prof. Sonal P. Patil

    2014-05-01

    Full Text Available In order to obtain more value added products, a product quality control is essentially required Many studies show that quality of agriculture products may be reduced from many causes. One of the most important factors of such quality plant diseases. Consequently, minimizing plant diseases allows substantially improving quality of the product Suitable diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian cotton regions. In this paper I express Technological Strategies uses mobile captured symptoms of Cotton Leaf Spot images and categorize the diseases using support vector machine. The classifier is being trained to achieve intelligent farming, including early detection of disease in the groves, selective fungicide application, etc. This proposed work is based on Segmentation techniques in which, the captured images are processed for enrichment first. Then texture and color Feature extraction techniques are used to extract features such as boundary, shape, color and texture for the disease spots to recognize diseases.

  9. New architectures support for ALMA common software: lessons learned

    Science.gov (United States)

    Menay, Camilo E.; Zamora, Gabriel A.; Tobar, Rodrigo J.; Avarias, Jorge A.; Dahl-skog, Kevin R.; von Brand, Horst H.; Chiozzi, Gianluca

    2010-07-01

    ALMA Common Software (ACS) is a distributed control framework based on CORBA that provides communication between distributed pieces of software. Because of its size and complexity it provides its own compilation system, a mix of several technologies. The current ACS compilation process depends on specific tools, compilers, code generation, and a strict dependency model induced by the large number of software components. This document presents a summary of several porting and compatibility attempts at using ACS on platforms other than the officially supported one. A porting of ACS to the Microsoft Windows Platform and to the ARM processor architecture were attempted, with different grades of success. Also, support for LINUX-PREEMPT (a set of real-time patches for the Linux kernel) using a new design for real-time services was implemented. These efforts were integrated with the ACS building and compilation system, while others were included in its design. Lessons learned in this process are presented, and a general approach is extracted from them.

  10. A New Multithreaded Architecture Supporting Direct Execution of Esterel

    Directory of Open Access Journals (Sweden)

    Yuan Simon

    2009-01-01

    Full Text Available Abstract We propose a fully pipelined, multithreaded, reactive processor called STARPro for direct execution of Esterel. STARPro provides native support for Esterel threads and their scheduling. In addition, it also natively supports Esterel's preemption constructs, instructions for signal manipulation, and a notion of logical ticks for synchronous execution. In addition to the reactive processors, we propose a new intermediate format called unrolled concurrent control-flow graph with surface and depth ( that closely resembles the Esterel source. A compiler, based on , has been developed for code generation. We have synthesized STARPro and have carried out a range of benchmarking experiments. Experimental results reveal substantial improvement in performance and code size compared to software compilers. We also excel in comparison to recent reactive architectures, by achieving an average speed-up of 37% in worst-case reaction times and a speed-up of 38% in average-case reaction times. This has been achieved by utilizing fewer hardware resources, while incurring an average code size increase of 40%.

  11. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  12. Kernel Learning in Support Vector Machines using Dual-Objective Optimization

    NARCIS (Netherlands)

    Pietersma, Auke-Dirk; Schomaker, Lambertus; Wiering, Marco

    2011-01-01

    Support vector machines (SVMs) are very popular methods for solving classification problems that require mapping input features to target labels. When dealing with real-world data sets, the different classes are usually not linearly separable, and therefore support vector machines employ a particula

  13. Profiled support vector machines for antisense oligonucleotide efficacy prediction

    Directory of Open Access Journals (Sweden)

    Martín-Guerrero José D

    2004-09-01

    Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

  14. CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    K.-Y. Lee

    2016-06-01

    Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate

  15. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    Science.gov (United States)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  16. Architecture For The Optimization Of A Machining Process In Real Time Through Rule-Based Expert System

    Science.gov (United States)

    Serrano, Rafael; González, Luis Carlos; Martín, Francisco Jesús

    2009-11-01

    Under the project SENSOR-IA which has had financial funding from the Order of Incentives to the Regional Technology Centers of the Counsil of Innovation, Science and Enterprise of Andalusia, an architecture for the optimization of a machining process in real time through rule-based expert system has been developed. The architecture consists of an acquisition system and sensor data processing engine (SATD) from an expert system (SE) rule-based which communicates with the SATD. The SE has been designed as an inference engine with an algorithm for effective action, using a modus ponens rule model of goal-oriented rules.The pilot test demonstrated that it is possible to govern in real time the machining process based on rules contained in a SE. The tests have been done with approximated rules. Future work includes an exhaustive collection of data with different tool materials and geometries in a database to extract more precise rules.

  17. SANDS: a service-oriented architecture for clinical decision support in a National Health Information Network.

    Science.gov (United States)

    Wright, Adam; Sittig, Dean F

    2008-12-01

    In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are:

  18. Fast Training of Support Vector Machines Using Error-Center-Based Optimization

    Institute of Scientific and Technical Information of China (English)

    L. Meng; Q. H. Wu

    2005-01-01

    This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments withvarious training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard quadratic programming (QP) techniques.

  19. Support vector machines for TEC seismo-ionospheric anomalies detection

    Directory of Open Access Journals (Sweden)

    M. Akhoondzadeh

    2013-02-01

    Full Text Available Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011, Haiti (12 January 2010 and Samoa (29 September 2009. The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012 obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method

  20. Smart dynamic software components enabling decision support in Machine-to-machine networks

    Directory of Open Access Journals (Sweden)

    Alexander Dannies

    2013-01-01

    Full Text Available The future Internet of Things will be extended by machine-to-machine communication technologies in order to include sensor information. The overwhelming amount of data will require autonomous decision making processes which are directly executed at the location where data is generated or measured. An intelligent sensor system needs to be able to adapt to new parameters in its surrounding unknown at the time of deployment. In our paper we show that Java enables software updates on mobile devices and also that it is possible to run algorithms required for decision making processes on wireless sensor platforms based on Java.

  1. A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

    Science.gov (United States)

    Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

    2015-02-01

    We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

  2. Knowledge Network Architecture in Support of International Science

    Science.gov (United States)

    Hugo, Wim

    2015-04-01

    ICSU (The International Council for Science) created the World Data System (WDS) as an interdisciplinary body at its General Assembly in Maputo in 2008, and since then the membership of the WDS has grown to include 86 members, of whom 56 are institutions or data centres focused on providing quality-assured data and services to the scientific community. In addition to its objective of providing universal and equitable access to such data and services, WDS is also active in promoting stewardship, standards and conventions, and improved access to products derived from data and services. Whereas WDS is in process of aggregating and harmonizing the meta-data collections of its membership, it is clear that additional benefits can be obtained by supplementing such traditional meta-data sources with information about members, authors, and the coverages of the data, as well as metrics such as citation indices, quality indicators, and usability. Moreover, the relationships between the actors and systems that populate this meta-data landscape can be seen as a knowledge network that describes a sub-set of global scientific endeavor. Such a knowledge network is useful in many ways, supporting both machine-based and human requests for contextual information related to a specific data set, institution, author, topic, or other entities in the network. Specific use cases that can be realised include decision and policy support for funding agencies, identification of collaborators, ranking of data sources, availability of data for specific coverages, and many more. The paper defines the scope of and conceptual background to such a knowledge network, discusses some initial work done by WDS to establish the network, and proposes an implementation model for rapid operationalisation. In this model, established interests such as DataCITE, ORCID, and CrossRef have well-defined roles, and the standards, services, and registries required to build a community-maintained, scalable knowledge

  3. WDS Knowledge Network Architecture in Support of International Science

    Science.gov (United States)

    Mokrane, M.; Minster, J. B. H.; Hugo, W.

    2014-12-01

    ICSU (International Council for Science) created the World Data System (WDS) as an interdisciplinary body at its General Assembly in Maputo in 2008, and since then the membership of the WDS has grown to include 86 members, of whom 56 are institutions or data centers focused on providing quality-assured data and services to the scientific community, and 10 more are entire networks of such data facilities and services. In addition to its objective of providing universal and equitable access to scientific data and services, WDS is also active in promoting stewardship, standards and conventions, and improved access to products derived from data and services. Whereas WDS is in process of aggregating and harmonizing the metadata collections of its membership, it is clear that additional benefits can be obtained by supplementing such traditional metadata sources with information about members, authors, and the coverages of the data, as well as metrics such as citation indices, quality indicators, and usability. Moreover, the relationships between the actors and systems that populate this metadata landscape can be seen as a knowledge network that describes a subset of global scientific endeavor. Such a knowledge network is useful in many ways, supporting both machine-based and human requests for contextual information related to a specific data set, institution, author, topic, or other entities in the network. Specific use cases that can be realized include decision and policy support for funding agencies, identification of collaborators, ranking of data sources, availability of data for specific coverages, and many more. The paper defines the scope of and conceptual background to such a knowledge network, discusses some initial work done by WDS to establish the network, and proposes an implementation model for rapid operationalization. In this model, established interests such as DataCite, ORCID, and CrossRef have well-defined roles, and the standards, services, and

  4. Towards a Tool for Computer Supported Configuring of Machine Systems

    DEFF Research Database (Denmark)

    Hansen, Claus Thorp

    1996-01-01

    An engineering designer designing a product determines not only the product's component structure, but also a set of different structures which carry product behaviour and performance and make the product suited for its life phases. Whereas the nature of the elements of a machine system is fairly...

  5. Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

    Directory of Open Access Journals (Sweden)

    Ralph Olusola Aluko

    2016-12-01

    Full Text Available In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

  6. Prediction of protein binding sites in protein structures using hidden Markov support vector machine

    Directory of Open Access Journals (Sweden)

    Lin Lei

    2009-11-01

    Full Text Available Abstract Background Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance. Results In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods. Conclusion The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.

  7. CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Gears alternately mesh and detach in driving process, and then working conditions of gears are alternately changing, so they are easy to be spalled and worn. But because of the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; their fault features are difficult to extract. This study aims to propose an approach of gear faults classification,using the cumulants and support vector machines. The cumulants can eliminate the additive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vector machines as classifier, which is employed structural risk minimisation principle, is superior to that of conventional neural networks, which is employed traditional empirical risk minimisation principle. Support vector machines as the classifier, and the third and fourth order cumulants as input, gears faults are successfully recognized. The experimental results show that the method of fault classification combining cumulants with support vector machines is very effective.

  8. Protein domain boundary prediction by combining support vector machine and domain guess by size algorithm

    Institute of Scientific and Technical Information of China (English)

    Dong Qiwen; Wang Xiaolong; Lin Lei

    2007-01-01

    Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. A novel method for domain boundary prediction has been presented, which combines the support vector machine with domain guess by size algorithm. Since the evolutional information of multiple domains can be detected by position specific score matrix, the support vector machine method is trained and tested using the values of position specific score matrix generated by PSI-BLAST. The candidate domain boundaries are selected from the output of support vector machine, and are then inputted to domain guess by size algorithm to give the final results of domain boundary prediction. The experimental results show that the combined method outperforms the individual method of both support vector machine and domain guess by size.

  9. Mass detection algorithm based on support vector machine and relevance feedback

    Institute of Scientific and Technical Information of China (English)

    Ying WANG; Xinbo GAO

    2008-01-01

    To improve the detection of mass with appearance that borders on the similarity between mass and density tissues in the breast,an support vector machine classifier based on typical features iS designed to classify the region of interest(ROI).Furthermore,relevance feedback is introduced to improve the performance of support vector machines.A new mass detection scheme based on the support vector machine and the relevance feedback is proposed.Simulation experiments on mammograms illustrate that the novel support vector machine classifier based on typical features can improve the detection performance of the featureless classifier by 5%,while the introduction of relevance feedback can further improve the detection performance to about 90%.

  10. Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation

    OpenAIRE

    Indah Agustien; Muhammad Rahmat Widyanto; Sukmawati Endah; Tarzan Basaruddin

    2010-01-01

    Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine is proposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonized using thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose, whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce t...

  11. Do Performance-Based Codes Support Universal Design in Architecture?

    DEFF Research Database (Denmark)

    Grangaard, Sidse; Frandsen, Anne Kathrine

    2016-01-01

    understanding of accessibility and UD is directly related to buildings like hospitals and care centers. When the objective is both innovative and inclusive architecture, the request of a performance-based model should be followed up by a knowledge enhancement effort in the building sector. Bloom´s taxonomy...

  12. An Information Architecture To Support the Visualization of Personal Histories.

    Science.gov (United States)

    Plaisant, Catherine; Shneiderman, Ben; Mushlin, Rich

    1998-01-01

    Proposes an information architecture for personal-history data and describes how the data model can be extended to a runtime model for a compact visualization using graphical timelines. The model groups personal-history events into aggregates that are contained in facets, crosslinks are made, and data attributes are mapped. (Author/LRW)

  13. Do Performance-Based Codes Support Universal Design in Architecture?

    DEFF Research Database (Denmark)

    Grangaard, Sidse; Frandsen, Anne Kathrine

    2016-01-01

    understanding of accessibility and UD is directly related to buildings like hospitals and care centers. When the objective is both innovative and inclusive architecture, the request of a performance-based model should be followed up by a knowledge enhancement effort in the building sector. Bloom´s taxonomy...

  14. Space Network IP Services (SNIS): An Architecture for Supporting Low Earth Orbiting IP Satellite Missions

    Science.gov (United States)

    Israel, David J.

    2005-01-01

    The NASA Space Network (SN) supports a variety of missions using the Tracking and Data Relay Satellite System (TDRSS), which includes ground stations in White Sands, New Mexico and Guam. A Space Network IP Services (SNIS) architecture is being developed to support future users with requirements for end-to-end Internet Protocol (IP) communications. This architecture will support all IP protocols, including Mobile IP, over TDRSS Single Access, Multiple Access, and Demand Access Radio Frequency (RF) links. This paper will describe this architecture and how it can enable Low Earth Orbiting IP satellite missions.

  15. On the Use of Machine Learning Methods for Characterization of Contaminant Source Zone Architecture

    Science.gov (United States)

    Zhang, H.; Mendoza-Sanchez, I.; Christ, J.; Miller, E. L.; Abriola, L. M.

    2011-12-01

    Recent research has identified the importance of DNAPL mass distribution in the evolution of down-gradient contaminant plumes and the control of source zone remediation effectiveness. Advances in the management of sites containing DNAPL source zones, however, are currently limited by the difficulty associated with characterizing subsurface DNAPL source zone 'architecture'. Specifically, knowledge of the ganglia to pool ratio (GTP) has been demonstrated useful in the assessment and prediction of system behavior. In this paper, we present an approach to the estimation of a quantity related to GTP, the pool fraction (PF), defined as the percentage of the source zone volume occupied by pools, based on observations of plume concentrations. Here we discuss the development and initial validation of an approach for PF estimation based on machine learning method. The algorithm is constructed in a way that, when given new concentration data, prediction of the PF of the associated source zone is attained. An ideal solution would make use of the concentration signals to estimate a single value for PF. Unfortunately, this problem is not well-posed given the data at our disposal. Thus, we relax the regression approach to one of classification. We quantize pool fraction (i.e., the interval between zero and one) into a number of intervals and employ machine learning methods to use the concentration data to determine the interval containing the PF for a given set of data. This approach is predicated on the assumption that quantities (i.e., features) derived from the concentration data of evolving plumes with similar source zone PFs will in fact be similar to one another. Thus, within the training process we must determine a suitable collection of features and build methods for evaluating and optimizing similarity in features space that results in high accuracy in terms of predicting the correct PF interval. Moreover, the number and boundaries of these intervals must also be

  16. Chaotic time series prediction using mean-field theory for support vector machine

    Institute of Scientific and Technical Information of China (English)

    Cui Wan-Zhao; Zhu Chang-Chun; Bao Wen-Xing; Liu Jun-Hua

    2005-01-01

    This paper presents a novel method for predicting chaotic time series which is based on the support vector machines approach, and it uses the mean-field theory for developing an easy and efficient learning procedure for the support vector machine. The proposed method approximates the distribution of the support vector machine parameters to a Gaussian process and uses the mean-field theory to estimate these parameters easily, and select the weights of the mixture of kernels used in the support vector machine estimation more accurately and faster than traditional quadratic programming-based algorithms. Finally, relationships between the embedding dimension and the predicting performance of this method are discussed, and the Mackey-Glass equation is applied to test this method. The stimulations show that the mean-field theory for support vector machine can predict chaotic time series accurately, and even if the embedding dimension is unknown, the predicted results are still satisfactory. This result implies that the mean-field theory for support vector machine is a good tool for studying chaotic time series.

  17. Capturing Business Strategy and Value in Enterprise Architecture to Support Portfolio Valuation

    NARCIS (Netherlands)

    Iacob, Maria Eugenia; Quartel, Dick; Jonkers, H.

    2012-01-01

    This paper investigates and enhances the suitability of the Archi Mate enterprise architecture modeling language to support the modeling of business strategy concepts and architecture-based approaches to IT portfolio valuation. It gives an overview of existing strategy and valuation concepts and

  18. A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

    Science.gov (United States)

    Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

    2017-03-01

    The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

  19. Coordinated machine learning and decision support for situation awareness.

    Energy Technology Data Exchange (ETDEWEB)

    Draelos, Timothy John; Zhang, Peng-Chu.; Wunsch, Donald C. (University of Missouri, Rolla, MO); Seiffertt, John (University of Missouri, Rolla, MO); Conrad, Gregory N.; Brannon, Nathan Gregory

    2007-09-01

    For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

  20. Execution time support for scientific programs on distributed memory machines

    Science.gov (United States)

    Berryman, Harry; Saltz, Joel; Scroggs, Jeffrey

    1990-01-01

    Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated.

  1. Support vector machine method for forecasting future strong earthquakes in Chinese mainland

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization (forecasting) ability than that of artificial neural networks. The strong earthquakes in Chinese mainland are related to a certain extent to the intensive seismicity along the main plate boundaries in the world,however, the relation is nonlinear. In the paper, we have studied this unclear relation by the support vector machine method for the purpose of forecasting strong earthquakes in Chinese mainland.

  2. Semigroup based neural network architecture for extrapolation of mass unbalance for rotating machines in power plants

    Energy Technology Data Exchange (ETDEWEB)

    Kim, B.H.; Velas, J.P.; Lee, K.Y [Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering

    2006-07-01

    This paper presented a mathematical method that power plant operators can use to estimate rotational mass unbalance, which is the most common source of vibration in turbine generators. An unbalanced rotor or driveshaft causes vibration and stress in the rotating part and in its supporting structure. As such, balancing the rotating part is important to minimize structural stress, minimize operator annoyance and fatigue, increase bearing life, or minimize power loss. The newly proposed method for estimating vibration on a turbine generator uses mass unbalance extrapolation based on a modified system-type neural network architecture, notably the semigroup theory used to study differential equations, partial differential equations and their combinations. Rather than relying on inaccurate vibration measurements, this method extrapolates a set of reliable mass unbalance readings from a common source of vibration. Given a set of empirical data with no analytic expression, the authors first developed an analytic description and then extended that model along a single axis. The algebraic decomposition which was used to obtain the analytic description of empirical data in the semigroup form involved the product of a coefficient vector and a basis set of vectors. The proposed approach was simulated on empirical data. The concept can also be tested in many other engineering and non-engineering problems. 23 refs., 11 figs.

  3. Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

    Science.gov (United States)

    Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

    Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

  4. [Comparative Efficiency of Algorithms Based on Support Vector Machines for Regression].

    Science.gov (United States)

    Kadyrova, N O; Pavlova, L V

    2015-01-01

    Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of support vector machines for regression is presented. The comparative efficiency of a number of support-vector-algorithms for regression is investigated. A thorough analysis of the study results found the most efficient support vector algorithms for regression. The description of the presented algorithms, sufficient for their practical implementation is given.

  5. Variance inflation in high dimensional Support Vector Machines

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2013-01-01

    Many important machine learning models, supervised and unsupervised, are based on simple Euclidean distance or orthogonal projection in a high dimensional feature space. When estimating such models from small training sets we face the problem that the span of the training data set input vectors...... is not the full input space. Hence, when applying the model to future data the model is effectively blind to the missed orthogonal subspace. This can lead to an inflated variance of hidden variables estimated in the training set and when the model is applied to test data we may find that the hidden variables...... follow a different probability law with less variance. While the problem and basic means to reconstruct and deflate are well understood in unsupervised learning, the case of supervised learning is less well understood. We here investigate the effect of variance inflation in supervised learning including...

  6. Foundations of microprogramming architecture, software and applications

    CERN Document Server

    Agrawala, Ashok K

    1976-01-01

    Foundations of Microprogramming: Architecture, Software, and Applications discusses the foundations and trends in microprogramming, focusing on the architectural, software, and application aspects of microprogramming. The book reviews microprocessors, microprogramming concepts, and characteristics, as well as the architectural features in microprogrammed computers. The text explains support software and the different hierarchies or levels of languages. These include assembler languages which are mnemonic or symbolic representation of machine commands; the procedure oriented machine-dependent;

  7. Design and Development of Decision Support System for Equipping Farm Machines

    Directory of Open Access Journals (Sweden)

    Ying Wang

    2014-06-01

    Full Text Available Equipping farm machines is the key link of agricultural production process. The decision support system of equipping farm machines is able to aid managers to make scientific and effective decision. In this paper, the decision support system of equipping farm machines is designed and developed based on the related theories and the thought of prototype. The system chooses Delphi 7.0 as development language, and uses three classic equipping methods to establish system models. For the complex linear programming model, firstly it is established by M-file of Matlab, then COM components are generated; finally Delphi calls the COM components to solve. The database of the system is established and managed by SQL Server 2005. It can be seen from the result of the system application study that the system could assist users to equip farm machines more scientifically and dynamically

  8. An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis

    Directory of Open Access Journals (Sweden)

    Sandeep Chaurasia

    2013-09-01

    Full Text Available Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of machine learning. In this paper we have used an approach by using support vector machine classifier to construct a model that is useful for the breast cancer survivability prediction. We have used both 5 cross and 10 cross validation of variable selection on input feature vectors and the performance measurement through bio-learning class performance while measuring AUC, specificity and sensitivity. The performance of the SVM is much better than the other machine learning classifier.

  9. An Architecture for Hybrid Manufacturing Combining 3D Printing and CNC Machining

    OpenAIRE

    Marcel Müller; Elmar Wings

    2016-01-01

    Additive manufacturing is one of the key technologies of the 21st century. Additive manufacturing processes are often combined with subtractive manufacturing processes to create hybrid manufacturing because it is useful for manufacturing complex parts, for example, 3D printed sensor systems. Currently, several CNC machines are required for hybrid manufacturing: one machine is required for additive manufacturing and one is required for subtractive manufacturing. Disadvantages of conventional h...

  10. An Architecture for Hybrid Manufacturing Combining 3D Printing and CNC Machining

    OpenAIRE

    Marcel Müller; Elmar Wings

    2016-01-01

    Additive manufacturing is one of the key technologies of the 21st century. Additive manufacturing processes are often combined with subtractive manufacturing processes to create hybrid manufacturing because it is useful for manufacturing complex parts, for example, 3D printed sensor systems. Currently, several CNC machines are required for hybrid manufacturing: one machine is required for additive manufacturing and one is required for subtractive manufacturing. Disadvantages of conventional h...

  11. Design of a real-time open architecture controller for a reconfigurable machine tool

    CSIR Research Space (South Africa)

    Masekamela, I

    2008-11-01

    Full Text Available The paper presents the design and the development of a real-time, open architecture controller that is used for control of reconfigurable manufacturing tools (RMTs) in reconfigurable manufacturing systems (RMS). The controller that is presented can...

  12. Performance and optimization of support vector machines in high-energy physics classification problems

    Science.gov (United States)

    Sahin, M. Ö.; Krücker, D.; Melzer-Pellmann, I.-A.

    2016-12-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.

  13. Support Vector Machine Learning-based fMRI Data Group Analysis*

    OpenAIRE

    Wang, Ze; Childress, Anna R.; Wang, Jiongjiong; Detre, John A.

    2007-01-01

    To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference be...

  14. A novel EPON architecture for supporting direct communication between ONUs

    Science.gov (United States)

    Wang, Liqian; Chen, Xue; Wang, Zhen

    2008-11-01

    In the traditional EPON network, optical signal from one ONU can not reach other ONUs. So ONUs can not directly transmit packets to other ONUs .The packets must be transferred by the OLT and it consumes both upstream bandwidth and downstream bandwidth. The bandwidth utilization is low and becomes lower when there are more packets among ONUs. When the EPON network carries P2P (Peer-to-Peer) applications and VPN applications, there would be a great lot of packets among ONUs and the traditional EPON network meets the problem of low bandwidth utilization. In the worst situation the bandwidth utilization of traditional EPON only is 50 percent. This paper proposed a novel EPON architecture and a novel medium access control protocol to realize direct packets transmission between ONUs. In the proposed EPON we adopt a novel circled architecture in the splitter. Due to the circled-splitter, optical signals from an ONU can reach the other ONUs and packets could be directly transmitted between two ONUs. The traffic between two ONUs only consumes upstream bandwidth and the bandwidth cost is reduced by 50 percent. Moreover, this kind of directly transmission reduces the packet's latency.

  15. [Comparative efficiency of algorithms based on support vector machines for binary classification].

    Science.gov (United States)

    Kadyrova, N O; Pavlova, L V

    2015-01-01

    Methods of construction of support vector machines require no further a priori infoimation and provide big data processing, what is especially important for various problems in computational biology. The question of the quality of learning algorithms is considered. The main algorithms of support vector machines for binary classification are reviewed and they were comparatively explored for their efficiencies. The critical analysis of the results of this study revealed the most effective support-vector-classifiers. The description of the recommended algorithms, sufficient for their practical implementation, is presented.

  16. Strategic Bidding for Electri city Markets Negotiation Using Support Vector Machines

    DEFF Research Database (Denmark)

    Pereira, Rafael; Sousa, Tiago; Pinto, Tiago

    2014-01-01

    . The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated...

  17. Data fusion for fault diagnosis using multi-class Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space.Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are processed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

  18. Simulation and Prediction of Alkalinity in Sintering Process Based on Grey Least Squares Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    SONG Qiang; WANG Ai-min

    2009-01-01

    The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.

  19. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

    Directory of Open Access Journals (Sweden)

    Wang Lily

    2008-07-01

    Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

  20. Balance in machine architecture: Bandwidth on board and offboard, integer/control speed and flops versus memory

    Energy Technology Data Exchange (ETDEWEB)

    Fischler, M.

    1992-04-01

    The issues to be addressed here are those of ``balance`` in machine architecture. By this, we mean how much emphasis must be placed on various aspects of the system to maximize its usefulness for physics. There are three components that contribute to the utility of a system: How the machine can be used, how big a problem can be attacked, and what the effective capabilities (power) of the hardware are like. The effective power issue is a matter of evaluating the impact of design decisions trading off architectural features such as memory bandwidth and interprocessor communication capabilities. What is studied is the effect these machine parameters have on how quickly the system can solve desired problems. There is a reasonable method for studying this: One selects a few representative algorithms and computes the impact of changing memory bandwidths, and so forth. The only room for controversy here is in the selection of representative problems. The issue of how big a problem can be attacked boils down to a balance of memory size versus power. Although this is a balance issue it is very different than the effective power situation, because no firm answer can be given at this time. The power to memory ratio is highly problem dependent, and optimizing it requires several pieces of physics input, including: how big a lattice is needed for interesting results; what sort of algorithms are best to use; and how many sweeps are needed to get valid results. We seem to be at the threshold of learning things about these issues, but for now, the memory size issue will necessarily be addressed in terms of best guesses, rules of thumb, and researchers` opinions.

  1. Balance in machine architecture: Bandwidth on board and offboard, integer/control speed and flops versus memory

    Energy Technology Data Exchange (ETDEWEB)

    Fischler, M.

    1992-04-01

    The issues to be addressed here are those of balance'' in machine architecture. By this, we mean how much emphasis must be placed on various aspects of the system to maximize its usefulness for physics. There are three components that contribute to the utility of a system: How the machine can be used, how big a problem can be attacked, and what the effective capabilities (power) of the hardware are like. The effective power issue is a matter of evaluating the impact of design decisions trading off architectural features such as memory bandwidth and interprocessor communication capabilities. What is studied is the effect these machine parameters have on how quickly the system can solve desired problems. There is a reasonable method for studying this: One selects a few representative algorithms and computes the impact of changing memory bandwidths, and so forth. The only room for controversy here is in the selection of representative problems. The issue of how big a problem can be attacked boils down to a balance of memory size versus power. Although this is a balance issue it is very different than the effective power situation, because no firm answer can be given at this time. The power to memory ratio is highly problem dependent, and optimizing it requires several pieces of physics input, including: how big a lattice is needed for interesting results; what sort of algorithms are best to use; and how many sweeps are needed to get valid results. We seem to be at the threshold of learning things about these issues, but for now, the memory size issue will necessarily be addressed in terms of best guesses, rules of thumb, and researchers' opinions.

  2. A Simple Network Management Architecture for Supporting Network Administrator and QoS Requirements

    CERN Document Server

    Phonphoem, Anan

    2011-01-01

    In this paper, a simple network management architecture for supporting both QoS requirements and organization network management policies is purposed. By grouping the traffic flows according to the QoS requirements or certain network management policies, the network resources are effectively controlled. The purposed architecture is easy to deploy; the gateway is the only equipment that needs installation, leaving the rest of the system untouched. The architecture has not significantly degraded the overall system utilization when applying it to the outgoing bound of the gateway. The architecture can also be implemented on the wireless LAN at the access point because the architecture is designed in such the way that it is independent to both the lower and upper protocol layers.

  3. Ultrasonic fluid quantity measurement in dynamic vehicular applications a support vector machine approach

    CERN Document Server

    Terzic, Jenny; Nagarajah, Romesh; Alamgir, Muhammad

    2013-01-01

    Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is a signal classification technique which has far greater accuracy than conventional signal averaging methods. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach describes the research and development of a fluid level measurement system for dynamic environments. The measurement system is based on a single ultrasonic sensor. A Support Vector Machines (SVM) based signal characterization and processing system has been developed to compensate for the effects of slosh and temperature variation in fluid level measurement systems used in dynamic environments including automotive applications. It has been demonstrated that a simple ν-SVM model with Radial Basis Function (RBF) Kernel with the inclusion of a Moving Median filter could be used to achieve the high levels...

  4. Breast cancer diagnosis using level-set statistics and support vector machines.

    Science.gov (United States)

    Liu, Jianguo; Yuan, Xiaohui; Buckles, Bill P

    2008-01-01

    Breast cancer diagnosis based on microscopic biopsy images and machine learning has demonstrated great promise in the past two decades. Various feature selection (or extraction) and classification algorithms have been attempted with success. However, some feature selection processes are complex and the number of features used can be quite large. We propose a new feature selection method based on level-set statistics. This procedure is simple and, when used with support vector machines (SVM), only a small number of features is needed to achieve satisfactory accuracy that is comparable to those using more sophisticated features. Therefore, the classification can be completed in much shorter time. We use multi-class support vector machines as the classification tool. Numerical results are reported to support the viability of this new procedure.

  5. A Support Vector Machine-Based Dynamic Network for Visual Speech Recognition Applications

    Directory of Open Access Journals (Sweden)

    Mihaela Gordan

    2002-11-01

    Full Text Available Visual speech recognition is an emerging research field. In this paper, we examine the suitability of support vector machines for visual speech recognition. Each word is modeled as a temporal sequence of visemes corresponding to the different phones realized. One support vector machine is trained to recognize each viseme and its output is converted to a posterior probability through a sigmoidal mapping. To model the temporal character of speech, the support vector machines are integrated as nodes into a Viterbi lattice. We test the performance of the proposed approach on a small visual speech recognition task, namely the recognition of the first four digits in English. The word recognition rate obtained is at the level of the previous best reported rates.

  6. Application of support vector machine in the prediction of mechanical property of steel materials

    Institute of Scientific and Technical Information of China (English)

    Ling Wang; Zhichun Mu; Hui Guo

    2006-01-01

    The investigation of the influences of important parameters including steel chemical composition and hot rolling parameters on the mechanical properties of steel is a key for the systems that are used to predict mechanical properties. To improve the prediction accuracy, support vector machine was used to predict the mechanical properties of hot-rolled plain carbon steel Q235B. Support vector machine is a novel machine learning method, which is a powerful tool used to solve the problem characterized by small sample, nonlinearity, and high dimension with a good generalization performance. On the basis of the data collected from the supervisor of hotrolling process, the support vector regression algorithm was used to build prediction models, and the off-line simulation indicates that predicted and measured results are in good agreement.

  7. Small-time scale network traffic prediction based on a local support vector machine regression model

    Institute of Scientific and Technical Information of China (English)

    Meng Qing-Fang; Chen Yue-Hui; Peng Yu-Hua

    2009-01-01

    In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

  8. DDoS detection based on wavelet kernel support vector machine

    Institute of Scientific and Technical Information of China (English)

    YANG Ming-hui; WANG Ru-chuan

    2008-01-01

    To enhance the detection accuracy and deduce false positive rate of distributed denial of service (DDoS) attack detection, a new machine learning method was proposed. With the analysis of support vector machine (SVM) and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine (WSVM) is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.

  9. Lithium-ion battery remaining useful life prediction based on grey support vector machines

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2015-12-01

    Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.

  10. A reliability assessment method based on support vector machines for CNC equipment

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.

  11. A reliability assessment method based on support vector machines for CNC equipment

    Institute of Scientific and Technical Information of China (English)

    WU Jun; DENG Chao; SHAO XinYu; XIE S Q

    2009-01-01

    With the applications of high technology, a catastrophic failure of CNC equipment rarely occurs at normal operation conditions. So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level. This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available. The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment. A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built. A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.

  12. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  13. Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.

  14. Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    TIAN Huifang; SUN Shanxia

    2006-01-01

    By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.

  15. Flexure bearing support, with particular application to stirling machines

    Science.gov (United States)

    Beckett, Carl D.; Lauhala, Victor C.; Neely, Ron; Penswick, Laurence B.; Ritter, Darren C.; Nelson, Richard L.; Wimer, Burnell P.

    1996-01-01

    The use of flexures in the form of flat spiral springs cut from sheet metal materials provides support for coaxial nonrotating linear reciprocating members in power conversion machinery, such as Stirling cycle engines or heat pumps. They permit operation with little or no rubbing contact or other wear mechanisms. The relatively movable members include one member having a hollow interior structure within which the flexures are located. The flexures permit limited axial movement between the interconnected members, but prevent adverse rotational movement and radial displacement from their desired coaxial positions.

  16. Computer Security Primer: Systems Architecture, Special Ontology and Cloud Virtual Machines

    Science.gov (United States)

    Waguespack, Leslie J.

    2014-01-01

    With the increasing proliferation of multitasking and Internet-connected devices, security has reemerged as a fundamental design concern in information systems. The shift of IS curricula toward a largely organizational perspective of security leaves little room for focus on its foundation in systems architecture, the computational underpinnings of…

  17. A support architecture for reliable distributed computing systems

    Science.gov (United States)

    Dasgupta, Partha; Leblanc, Richard J., Jr.

    1988-01-01

    The Clouds project is well underway to its goal of building a unified distributed operating system supporting the object model. The operating system design uses the object concept of structuring software at all levels of the system. The basic operating system was developed and work is under progress to build a usable system.

  18. Physical activity support community togetheractive - architecture, implementation and evaluation

    NARCIS (Netherlands)

    Elloumi, Lamia; van Beijnum, Bernhard J.F.; Hermens, Hermanus J.

    Reducing sedentary lifestyle and physical inactivity is getting an increased attention of researchers and health organizations due to its significant benefits on health. In the same direction we are proposing a virtual community system, TogetherActive, which supports people in their daily physical

  19. Service-Oriented Architecture Approach to MAGTF Logistics Support Systems

    Science.gov (United States)

    2013-09-01

    me her unconditional love and support throughout my tenure at NPS as well as the past 17 years of my Marine Corps career . Her unselfish sacrifice and... indecisiveness the inability to effectively perform LCM systems integration will continue to trouble the Marine Corps. In order to meaningfully

  20. Physical activity support community togetheractive - architecture, implementation and evaluation

    NARCIS (Netherlands)

    Elloumi, Lamia; Beijnum, van Bert-Jan; Hermens, Hermie

    2015-01-01

    Reducing sedentary lifestyle and physical inactivity is getting an increased attention of researchers and health organizations due to its significant benefits on health. In the same direction we are proposing a virtual community system, TogetherActive, which supports people in their daily physical a

  1. On-line least squares support vector machine algorithm in gas prediction

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xiao-hu; WANG Gang; ZHAO Ke-ke; TAN De-jian

    2009-01-01

    Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions. The Support Vector Machine (SVM) is a new machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. But the common LS-SVM algorithm, used directly in safety predictions, has some problems. We have first studied gas prediction problems and the basic theory of LS-SVM. Given these problems, we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm, based on LS-SVM. Finally, given our observed data, we used the on-line algorithm to predict gas emissions and used other related algorithm to com- pare its performance. The simulation results have verified the validity of the new algorithm.

  2. Loi de commande prédictive pour le positionement des axes d'une machine outil à architecture ouverte

    OpenAIRE

    Susanu, Mara; Dumur, Didier; Tournier, Christophe; Lartigue, Claire

    2004-01-01

    National audience; Résumé : Concevoir une CN pour machine-outil selon une structure à architecture ouverte permet d'en améliorer la flexibilité en autorisant l'intégration de modules spécifiques. Dans ce contexte, l'article envisage en premier lieu l'ajout d'un module de commande d'axe basé sur une stratégie prédictive avancée. Cette stratégie incluant une anticipation en boucle fermée s'avère particulièrement performante en termes de suivi de consigne et de facilité d'implantation. Considéra...

  3. Interpreting linear support vector machine models with heat map molecule coloring

    Directory of Open Access Journals (Sweden)

    Rosenbaum Lars

    2011-03-01

    Full Text Available Abstract Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.

  4. A Software Architecture for Simulation Support in Building Automation

    Directory of Open Access Journals (Sweden)

    Sergio Leal

    2014-07-01

    Full Text Available Building automation integrates the active components in a building and, thus, has to connect components of different industries. The goal is to provide reliable and efficient operation. This paper describes how simulation can support building automation and how the deployment process of simulation assisted building control systems can be structured. We look at the process as a whole and map it to a set of formally described workflows that can partly be automated. A workbench environment supports the process execution by means of improved planning, collaboration and deployment. This framework allows integration of existing tools, as well as manual tasks, and is, therefore, many more intricate than regular software deployment tools. The complex environment of building commissioning requires expertise in different domains, especially lighting, heating, ventilation, air conditioning, measurement and control technology, as well as energy efficiency; therefore, we present a framework for building commissioning and describe a deployment process that is capable of supporting the various phases of this approach.

  5. Scaling Impacts in Life Support Architecture and Technology Selection

    Science.gov (United States)

    Lange, Kevin

    2016-01-01

    For long-duration space missions outside of Earth orbit, reliability considerations will drive higher levels of redundancy and/or on-board spares for life support equipment. Component scaling will be a critical element in minimizing overall launch mass while maintaining an acceptable level of system reliability. Building on an earlier reliability study (AIAA 2012-3491), this paper considers the impact of alternative scaling approaches, including the design of technology assemblies and their individual components to maximum, nominal, survival, or other fractional requirements. The optimal level of life support system closure is evaluated for deep-space missions of varying duration using equivalent system mass (ESM) as the comparative basis. Reliability impacts are included in ESM by estimating the number of component spares required to meet a target system reliability. Common cause failures are included in the analysis. ISS and ISS-derived life support technologies are considered along with selected alternatives. This study focusses on minimizing launch mass, which may be enabling for deep-space missions.

  6. An open-source highly scalable web service architecture for the Apertium machine translation engine

    OpenAIRE

    Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio

    2009-01-01

    Some machine translation services like Google Ajax Language API have become very popular as they make the collaboratively created contents of the web 2.0 available to speakers of many languages. One of the keys of its success is its clear and easy-to-use application programming interface (API) and a scalable and reliable service. This paper describes a highly scalable implementation of an Apertium-based translation web service, that aims to make contents available to speakers of lesser resour...

  7. Executable Architecture of Net Enabled Operations: State Machine of Federated Nodes

    Science.gov (United States)

    2009-11-01

    améliorations. Il est ressorti d’un des ateliers sur le développement d’une machine à états de nœuds fédérés une question importante et c’est la...processus serait plus facile à comprendre s’il était possible de présenter un ou deux modèles durant l’atelier. Les participants de cet atelier ont

  8. Physicists purchase materials testing machine in support of pioneering particle physics experiments

    CERN Multimedia

    Sharpe, Suzanne

    2007-01-01

    "The particle physics group at Liverpool University has purchased an LRXPlus singlecolumn materials testing machine from Lloyd Instruments, which will be used to help characterise the carbon-fibre support frames for detectors used for state-of-the-art particle physics experiments." (1 page)

  9. SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

    NARCIS (Netherlands)

    P.J.F. Groenen (Patrick); G.I. Nalbantov (Georgi); J.C. Bioch (Cor)

    2007-01-01

    textabstractSupport vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal suppor

  10. Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

    Indian Academy of Sciences (India)

    B T Abe; O O Olugbara; T Marwala

    2014-06-01

    The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

  11. Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

    Science.gov (United States)

    Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

  12. Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

    LENUS (Irish Health Repository)

    Mourao-Miranda, J

    2012-05-01

    To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

  13. Estimation of the wind turbine yaw error by support vector machines

    DEFF Research Database (Denmark)

    Sheibat-Othman, Nida; Othman, Sami; Tayari, Raoaa

    2015-01-01

    Wind turbine yaw error information is of high importance in controlling wind turbine power and structural load. Normally used wind vanes are imprecise. In this work, the estimation of yaw error in wind turbines is studied using support vector machines for regression (SVR). As the methodology...

  14. Alcohols' Classification by Infrared Spectra Segment Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Wei XIE; Fu Sheng NIE; Meng Long LI; Guang Ming LI; Min Chun LU

    2006-01-01

    This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines (SVMs). The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.

  15. Online Handwritten Character Recognition of Devanagari and Telugu Characters using Support Vector Machines

    OpenAIRE

    Swethalakshmi, H.; Jayaraman, Anitha; Chakravarthy, V. Srinivasa; Sekhar, C. Chandra

    2006-01-01

    http://www.suvisoft.com; A system for recognition of online handwritten characters has been presented for Indian writing systems. A handwritten character is represented as a sequence of strokes whose features are extracted and classied. Support vector machines have been used for constructing the stroke recognition engine. The results have been presented after testing the system on Devanagari and Telugu scripts.

  16. Support Vector Machine for Discrimination Between Fault and Magnetizing Inrush Current in Power Transformer

    Directory of Open Access Journals (Sweden)

    V. Malathi

    2007-01-01

    Full Text Available This study presents a novel technique based on Support Vector Machine (SVM for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena in power transformer.

  17. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods

    OpenAIRE

    Zhang, Tong

    2001-01-01

    This book is an introduction to support vector machines and related kernel methods in supervised learning, whose task is to estimate an input-output functional relationship from a training set of examples. A learning problem is referred to as classification if its output take discrete values in a set of possible categories and regression if it has continuous real-valued output.

  18. Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    XU Rui-Rui; BIAN Guo-Xing; GAO Chen-Feng; CHEN Tian-Lun

    2005-01-01

    The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction.First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

  19. A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge.

    Science.gov (United States)

    Hatsek, Avner; Shahar, Yuval; Taieb-Maimon, Meirav; Shalom, Erez; Klimov, Denis; Lunenfeld, Eitan

    2010-01-01

    Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians' assessment was significantly lower than the assessment of the knowledge engineers.

  20. Performance and optimization of support vector machines in high-energy physics classification problems

    CERN Document Server

    Sahin, Mehmet Özgür; Melzer-Pellmann, Isabell-Alissandra

    2016-01-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

  1. PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    Ye Feng; Song Yonglun; Li Di; Lai Yizong

    2003-01-01

    A quality monitoring method by means of support vector machines (SVM) for robotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the welding process signal,a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. Under the samples obtained from auto parts welding production line, the learning machine with a radial basis function kernel shows good performance. And this method can be feasible to identify defect online in welding production.

  2. Applying the Support Vector Machine Method to Matching IRAS and SDSS Catalogues

    Directory of Open Access Journals (Sweden)

    Chen Cao

    2007-10-01

    Full Text Available This paper presents results of applying a machine learning technique, the Support Vector Machine (SVM, to the astronomical problem of matching the Infra-Red Astronomical Satellite (IRAS and Sloan Digital Sky Survey (SDSS object catalogues. In this study, the IRAS catalogue has much larger positional uncertainties than those of the SDSS. A model was constructed by applying the supervised learning algorithm (SVM to a set of training data. Validation of the model shows a good identification performance (∼ 90% correct, better than that derived from classical cross-matching algorithms, such as the likelihood-ratio method used in previous studies.

  3. Performance and optimization of support vector machines in high-energy physics classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, M.Oe.; Kruecker, D.; Melzer-Pellmann, I.A.

    2016-01-15

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

  4. Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.

    Science.gov (United States)

    Guo, Qi; Shao, Jiaqing; Ruiz, Virginie F

    2009-01-01

    This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A ( z ) = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A ( z ) value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A ( z ) value. FBM texture model is the

  5. Modelling habitat requirements of white-clawed crayfish (Austropotamobius pallipes using support vector machines

    Directory of Open Access Journals (Sweden)

    Favaro L.

    2011-07-01

    Full Text Available The white-clawed crayfish’s habitat has been profoundly modified in Piedmont (NW Italy due to environmental changes caused by human impact. Consequently, native populations have decreased markedly. In this research project, support vector machines were tested as possible tools for evaluating the ecological factors that determine the presence of white-clawed crayfish. A system of 175 sites was investigated, 98 of which recorded the presence of Austropotamobius pallipes. At each site 27 physical-chemical, environmental and climatic variables were measured according to their importance to A. pallipes. Various feature selection methods were employed. These yielded three subsets of variables that helped build three different types of models: (1 models with no variable selection; (2 models built by applying Goldberg’s genetic algorithm after variable selection; (3 models built by using a combination of four supervised-filter evaluators after variable selection. These different model types helped us realise how important it was to select the right features if we wanted to build support vector machines that perform as well as possible. In addition, support vector machines have a high potential for predicting indigenous crayfish occurrence, according to our findings. Therefore, they are valuable tools for freshwater management, tools that may prove to be much more promising than traditional and other machine-learning techniques.

  6. Reducing U2R and R2L category false negative rates with support vector machines

    Directory of Open Access Journals (Sweden)

    Maček Nemanja

    2014-01-01

    Full Text Available The KDD Cup '99 is commonly used dataset for training and testing IDS machine learning algorithms. Some of the major downsides of the dataset are the distribution and the proportions of U2R and R2L instances, which represent the most dangerous attack types, as well as the existence of R2L attack instances identical to normal traffic. This enforces minor category detection complexity and causes problems while building a machine learning model capable of detecting these attacks with sufficiently low false negative rate. This paper presents a new support vector machine based intrusion detection system that classifies unknown data instances according both to the feature values and weight factors that represent importance of features towards the classification. Increased detection rate and significantly decreased false negative rate for U2R and R2L categories, that have a very few instances in the training set, have been empirically proven.

  7. Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems

    Science.gov (United States)

    Kuusisto, Finn; Dutra, Inês; Elezaby, Mai; Mendonça, Eneida A.; Shavlik, Jude; Burnside, Elizabeth S.

    2015-01-01

    While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy. PMID:26306246

  8. Support vector machine used to diagnose the fault of rotor broken bars of induction motors

    DEFF Research Database (Denmark)

    Zhitong, Cao; Jiazhong, Fang; Hongpingn, Chen

    2003-01-01

    The data-based machine learning is an important aspect of modern intelligent technology, while statistical learning theory (SLT) is a new tool that studies the machine learning methods in the case of a small number of samples. As a common learning method, support vector machine (SVM) is derived...... for the SVM. After a SVM is trained with learning sample vectors, so each kind of the rotor broken bar faults of induction motors can be classified. Finally the retest is demonstrated, which proves that the SVM really has preferable ability of classification. In this paper we tried applying the SVM...... from the SLT. Here we were done some analogical experiments of the rotor broken bar faults of induction motors used, analyzed the signals of the sample currents with Fourier transform, and constructed the spectrum characteristics from low frequency to high frequency used as learning sample vectors...

  9. Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine

    Science.gov (United States)

    Sheng, Hanmin; Xiao, Jian

    2015-05-01

    The aim of this study is to estimate the state of charge (SOC) of the lithium iron phosphate (LiFePO4) battery pack by applying machine learning strategy. To reduce the noise sensitive issue of common machine learning strategies, a kind of SOC estimation method based on fuzzy least square support vector machine is proposed. By applying fuzzy inference and nonlinear correlation measurement, the effects of the samples with low confidence can be reduced. Further, a new approach for determining the error interval of regression results is proposed to avoid the control system malfunction. Tests are carried out on modified COMS electric vehicles, with two battery packs each consists of 24 50 Ah LiFePO4 batteries. The effectiveness of the method is proven by the test and the comparison with other popular methods.

  10. PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.

    Science.gov (United States)

    Liu, Xiaoyong; Fu, Hui

    2014-01-01

    Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

  11. PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

    Directory of Open Access Journals (Sweden)

    Xiaoyong Liu

    2014-01-01

    Full Text Available Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM, particle swarm optimization (PSO, and cuckoo search (CS. The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

  12. Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems.

    Science.gov (United States)

    Kuusisto, Finn; Dutra, Inês; Elezaby, Mai; Mendonça, Eneida A; Shavlik, Jude; Burnside, Elizabeth S

    2015-01-01

    While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy.

  13. A Numerical Comparison of Rule Ensemble Methods and Support Vector Machines

    Energy Technology Data Exchange (ETDEWEB)

    Meza, Juan C.; Woods, Mark

    2009-12-18

    Machine or statistical learning is a growing field that encompasses many scientific problems including estimating parameters from data, identifying risk factors in health studies, image recognition, and finding clusters within datasets, to name just a few examples. Statistical learning can be described as 'learning from data' , with the goal of making a prediction of some outcome of interest. This prediction is usually made on the basis of a computer model that is built using data where the outcomes and a set of features have been previously matched. The computer model is called a learner, hence the name machine learning. In this paper, we present two such algorithms, a support vector machine method and a rule ensemble method. We compared their predictive power on three supernova type 1a data sets provided by the Nearby Supernova Factory and found that while both methods give accuracies of approximately 95%, the rule ensemble method gives much lower false negative rates.

  14. Automated Classification of Epiphyses in the Distal Radius and Ulna using a Support Vector Machine.

    Science.gov (United States)

    Wang, Ya-hui; Liu, Tai-ang; Wei, Hua; Wan, Lei; Ying, Chong-liang; Zhu, Guang-you

    2016-03-01

    The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X-ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross-validation test and independent test for accuracy (PA ). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models.

  15. A Supporting Architecture for Generic Service Integration in IMS Learning Design

    NARCIS (Netherlands)

    de la Fuente Valentin, Luis; Miao, Yongwu; Pardo, Abelardo; Delgado Kloos, Carlos

    2008-01-01

    De la Fuente Valentin, L., Miao, Y., Pardo, A., & Delgado Kloos, C. (2008). A Supporting Architecture for Generic Service Integration in IMS Learning Design. In P. Dillenbourg & M. Specht (Eds.), Times of Convergence. Technologies Across Learning Contexts - Proceedings of the Third European

  16. Web-service architecture for tools supporting life-long e-Learning platforms

    NARCIS (Netherlands)

    Dimov, Alexander; Stefanov, Krassen

    2009-01-01

    Dimov, A., & Stefanov, K. (2008). Web-service architecture for tools supporting life-long e-Learning platforms. In R. Koper, K. Stefanov & D. Dicheva (Eds.), Proceedings of the 5th International TENCompetence Open Workshop "Stimulating Personal Development and Knowledge Sharing" (pp. 67-71).

  17. PlanetDR, a scalable architecture for federated repositories supporting IMS Learning Design

    NARCIS (Netherlands)

    Blat, Josep; Griffiths, David; Navarrete, Toni; Santos, José Luis; García, Pedro; Pujol, Jordi

    2006-01-01

    This paper discusses PlanetDR, whose architecture supports very large federated educational digital repositories. It is based on the implementation of current open specifications for interoperability (such as IEEE Learning Object Metadata and IMS Digital Repositories Interoperability, in its Edusour

  18. Web-service architecture for tools supporting life-long e-Learning platforms

    NARCIS (Netherlands)

    Dimov, Alexander; Stefanov, Krassen

    2009-01-01

    Dimov, A., & Stefanov, K. (2008). Web-service architecture for tools supporting life-long e-Learning platforms. In R. Koper, K. Stefanov & D. Dicheva (Eds.), Proceedings of the 5th International TENCompetence Open Workshop "Stimulating Personal Development and Knowledge Sharing" (pp. 67-71). October

  19. A Supporting Architecture for Generic Service Integration in IMS Learning Design

    NARCIS (Netherlands)

    de la Fuente Valentin, Luis; Miao, Yongwu; Pardo, Abelardo; Delgado Kloos, Carlos

    2008-01-01

    De la Fuente Valentin, L., Miao, Y., Pardo, A., & Delgado Kloos, C. (2008). A Supporting Architecture for Generic Service Integration in IMS Learning Design. In P. Dillenbourg & M. Specht (Eds.), Times of Convergence. Technologies Across Learning Contexts - Proceedings of the Third European Conferen

  20. Architecture, Design, and Development of an HTML/JavaScript Web-Based Group Support System.

    Science.gov (United States)

    Romano, Nicholas C., Jr.; Nunamaker, Jay F., Jr.; Briggs, Robert O.; Vogel, Douglas R.

    1998-01-01

    Examines the need for virtual workspaces and describes the architecture, design, and development of GroupSystems for the World Wide Web (GSWeb), an HTML/JavaScript Web-based Group Support System (GSS). GSWeb, an application interface similar to a Graphical User Interface (GUI), is currently used by teams around the world and relies on user…

  1. Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

    Science.gov (United States)

    Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

    2010-01-01

    Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a

  2. Managing magnetic resonance imaging machines: support tools for scheduling and planning.

    Science.gov (United States)

    Carpenter, Adam P; Leemis, Lawrence M; Papir, Alan S; Phillips, David J; Phillips, Grace S

    2011-06-01

    We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning.

  3. The Model of Information Support for Management of Investment Attractiveness of Machine-Building Enterprises

    Directory of Open Access Journals (Sweden)

    Chernetska Olga V.

    2016-11-01

    Full Text Available The article discloses the content of the definition of “information support”, identifies basic approaches to the interpretation of this economic category. The main purpose of information support for management of enterprise investment attractiveness is determined. The key components of information support for management of enterprise investment attractiveness are studied. The main types of automated information systems for management of the investment attractiveness of enterprises are identified and characterized. The basic computer programs for assessing the level of investment attractiveness of enterprises are considered. A model of information support for management of investment attractiveness of machine-building enterprises is developed.

  4. A Distributed Architecture for Tsunami Early Warning and Collaborative Decision-support in Crises

    Science.gov (United States)

    Moßgraber, J.; Middleton, S.; Hammitzsch, M.; Poslad, S.

    2012-04-01

    The presentation will describe work on the system architecture that is being developed in the EU FP7 project TRIDEC on "Collaborative, Complex and Critical Decision-Support in Evolving Crises". The challenges for a Tsunami Early Warning System (TEWS) are manifold and the success of a system depends crucially on the system's architecture. A modern warning system following a system-of-systems approach has to integrate various components and sub-systems such as different information sources, services and simulation systems. Furthermore, it has to take into account the distributed and collaborative nature of warning systems. In order to create an architecture that supports the whole spectrum of a modern, distributed and collaborative warning system one must deal with multiple challenges. Obviously, one cannot expect to tackle these challenges adequately with a monolithic system or with a single technology. Therefore, a system architecture providing the blueprints to implement the system-of-systems approach has to combine multiple technologies and architectural styles. At the bottom layer it has to reliably integrate a large set of conventional sensors, such as seismic sensors and sensor networks, buoys and tide gauges, and also innovative and unconventional sensors, such as streams of messages from social media services. At the top layer it has to support collaboration on high-level decision processes and facilitates information sharing between organizations. In between, the system has to process all data and integrate information on a semantic level in a timely manner. This complex communication follows an event-driven mechanism allowing events to be published, detected and consumed by various applications within the architecture. Therefore, at the upper layer the event-driven architecture (EDA) aspects are combined with principles of service-oriented architectures (SOA) using standards for communication and data exchange. The most prominent challenges on this layer

  5. The Setting is the Service: How the Architecture of Sober Living Residences Supports Community Based Recovery.

    Science.gov (United States)

    Wittman, Fried; Jee, Babette; Polcin, Douglas L; Henderson, Diane

    2014-07-01

    The architecture of residential recovery settings is an important silent partner in the alcohol/drug recovery field. The settings significantly support or hinder recovery experiences of residents, and shape community reactions to the presence of sober living houses (SLH) in ordinary neighborhoods. Grounded in the principles of Alcoholics Anonymous, the SLH provides residents with settings designed to support peer based recovery; further, these settings operate in a community context that insists on sobriety and strongly encourages attendance at 12-step meetings. Little formal research has been conducted to show how architectural features of the recovery setting - building appearance, spatial layouts, furnishings and finishes, policies for use of the facilities, physical care and maintenance of the property, neighborhood features, aspects of location in the city - function to promote (or retard) recovery, and to build (or detract from) community support. This paper uses a case-study approach to analyze the architecture of a community-based residential recovery service that has demonstrated successful recovery outcomes for its residents, is popular in its community, and has achieved state-wide recognition. The Environmental Pattern Language (Alexander, Ishikawa, & Silverstein, 1977) is used to analyze its architecture in a format that can be tested, critiqued, and adapted for use by similar programs in many communities, providing a model for replication and further research.

  6. Context-aware adaptation for group communication support applications with dynamic architecture

    CERN Document Server

    Rodriguez, Ismael Bouassida; Chassot, Christophe; Jmaiel, Mohamed

    2008-01-01

    In this paper, we propose a refinement-based adaptation approach for the architecture of distributed group communication support applications. Unlike most of previous works, our approach reaches implementable, context-aware and dynamically adaptable architectures. To model the context, we manage simultaneously four parameters that influence Qos provided by the application. These parameters are: the available bandwidth, the exchanged data communication priority, the energy level and the available memory for processing. These parameters make it possible to refine the choice between the various architectural configurations when passing from a given abstraction level to the lower level which implements it. Our approach allows the importance degree associated with each parameter to be adapted dynamically. To implement adaptation, we switch between the various configurations of the same level, and we modify the state of the entities of a given configuration when necessary. We adopt the direct and mediated Producer-...

  7. Service Oriented Architecture for Clinical Decision Support: A Systematic Review and Future Directions

    Science.gov (United States)

    Loya, Salvador Rodriguez; Kawamoto, Kensaku; Chatwin, Chris; Huser, Vojtech

    2017-01-01

    The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems’ performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS PMID:25325996

  8. Service oriented architecture for clinical decision support: a systematic review and future directions.

    Science.gov (United States)

    Loya, Salvador Rodriguez; Kawamoto, Kensaku; Chatwin, Chris; Huser, Vojtech

    2014-12-01

    The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems' performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS.

  9. Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

    Indian Academy of Sciences (India)

    Liu Zhong-bao; Song Wen-ai; Zhang Jing; Zhao Wen-juan

    2017-06-01

    Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher’s Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

  10. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

    Directory of Open Access Journals (Sweden)

    Jian Shi

    2016-11-01

    Full Text Available Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.

  11. Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Teng Wang

    2017-03-01

    Full Text Available In order to improve the prediction ability of welding quality during high-power disk laser welding, a new approach was proposed and applied in the classification of the dynamic features of metal vapor plume. Six features were extracted through the color image processing method. Three features, including the area of plume, number of spatters, and horizontal coordinate of plume centroid, were selected based on the classification accuracy rates and Pearson product-moment correlation coefficients. A support vector machine model was adopted to classify the welding quality status into two categories, good or poor. The results demonstrated that the support vector machine model established according to the selected features had satisfactory prediction and generalization ability. The classification accuracy rate was higher than 90%, and the model could be applied in the prediction of welding quality during high-power disk laser welding.

  12. A Parallel Decision Model Based on Support Vector Machines and Its Application to Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu(阎威武); Shao Huihe

    2004-01-01

    Many industrial process systems are becoming more and more complex and are characterized by distributed features. To ensure such a system to operate under working order, distributed parameter values are often inspected from subsystems or different points in order to judge working conditions of the system and make global decisions. In this paper, a parallel decision model based on Support Vector Machine (PDMSVM) is introduced and applied to the distributed fault diagnosis in industrial process. PDMSVM is convenient for information fusion of distributed system and it performs well in fault diagnosis with distributed features. PDMSVM makes decision based on synthetic information of subsystems and takes the advantage of Support Vector Machine. Therefore decisions made by PDMSVM are highly reliable and accurate.

  13. Evaluation and recognition of skin images with aging by support vector machine

    Science.gov (United States)

    Hu, Liangjun; Wu, Shulian; Li, Hui

    2016-10-01

    Aging is a very important issue not only in dermatology, but also cosmetic science. Cutaneous aging involves both chronological and photoaging aging process. The evaluation and classification of aging is an important issue with the medical cosmetology workers nowadays. The purpose of this study is to assess chronological-age-related and photo-age-related of human skin. The texture features of skin surface skin, such as coarseness, contrast were analyzed by Fourier transform and Tamura. And the aim of it is to detect the object hidden in the skin texture in difference aging skin. Then, Support vector machine was applied to train the texture feature. The different age's states were distinguished by the support vector machine (SVM) classifier. The results help us to further understand the mechanism of different aging skin from texture feature and help us to distinguish the different aging states.

  14. Metabolic changes in rat urine after acute paraquat poisoning and discriminated by support vector machine.

    Science.gov (United States)

    Wen, Congcong; Wang, Zhiyi; Zhang, Meiling; Wang, Shuanghu; Geng, Peiwu; Sun, Fa; Chen, Mengchun; Lin, Guanyang; Hu, Lufeng; Ma, Jianshe; Wang, Xianqin

    2016-01-01

    Paraquat is quick-acting and non-selective, killing green plant tissue on contact; it is also toxic to human beings and animals. In this study, we developed a urine metabonomic method by gas chromatography-mass spectrometry to evaluate the effect of acute paraquat poisoning on rats. Pattern recognition analysis, including both partial least squares discriminate analysis and principal component analysis revealed that acute paraquat poisoning induced metabolic perturbations. Compared with the control group, the levels of benzeneacetic acid and hexadecanoic acid of the acute paraquat poisoning group (intragastric administration 36 mg/kg) increased, while the levels of butanedioic acid, pentanedioic acid, altronic acid decreased. Based on these urinary metabolomics data, support vector machine was applied to discriminate the metabolomic change of paraquat groups from the control group, which achieved 100% classification accuracy. In conclusion, metabonomic method combined with support vector machine can be used as a useful diagnostic tool in paraquat-poisoned rats.

  15. Support vector machine classification trees based on fuzzy entropy of classification.

    Science.gov (United States)

    de Boves Harrington, Peter

    2017-02-15

    The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). This classifier partitioned the data by finding gaps in the data space. For large and complex datasets, there may be no gaps in the data space confounding this type of classifier. A novel algorithm was devised that uses fuzzy entropy to find optimal partitions for situations when clusters of data are overlapped in the data space. Also, a kernel version of the fuzzy entropy algorithm was devised. A fast support vector machine implementation is used that has no cost C or slack variables to optimize. Statistical comparisons using bootstrapped Latin partitions among the tree classifiers were made using a synthetic XOR data set and validated with ten prediction sets comprised of 50,000 objects and a data set of NMR spectra obtained from 12 tea sample extracts.

  16. Modelling of chaotic systems based on modified weighted recurrent least squares support vector machines

    Institute of Scientific and Technical Information of China (English)

    Sun Jian-Cheng; Zhang Tai-Yi; Liu Feng

    2004-01-01

    Positive Lyapunov exponents cause the errors in modelling of the chaotic time series to grow exponentially. In this paper, we propose the modified version of the support vector machines (SVM) to deal with this problem. Based on recurrent least squares support vector machines (RLS-SVM), we introduce a weighted term to the cost function to compensate the prediction errors resulting from the positive global Lyapunov exponents. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Santa Fe competition time series. The simulation results shows that the proposed method can capture the dynamics of the chaotic time series effectively.

  17. A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Feng Rui(冯瑞); Song Chunlin; Zhang Yanzhu; Shao Huihe

    2004-01-01

    Artificial Neural Networks (ANNs) such as radial basis function neural networks (RBFNNs) have been successfully used in soft sensor modeling. However, the generalization ability of conventional ANNs is not very well. For this reason, we present a novel soft sensor modeling approach based on Support Vector Machines (SVMs). Since standard SVMs have the limitation of speed and size in training large data set, we hereby propose Least Squares Support Vector Machines (LS_SVMs) and apply it to soft sensor modeling. Systematic analysis is performed and the result indicates that the proposed method provides satisfactory performance with excellent approximation and generalization property. Monte Carlo simulations show that our soft sensor modeling approach achieves performance superior to the conventional method based on RBFNNs.

  18. New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

    Directory of Open Access Journals (Sweden)

    Xiaoqing Gu

    2014-01-01

    Full Text Available In medical datasets classification, support vector machine (SVM is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM for the class imbalance problem (called FSVM-CIP is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

  19. Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method

    Institute of Scientific and Technical Information of China (English)

    GAO Hai-feng; BAI Guang-chen

    2015-01-01

    To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method (SRSM) is proposed. SRSM integrates the advantages of support vector machine (SVM) and traditional response surface method (RSM), and utilizes experimental samples to construct a suitable response surface function (RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method (MCM); while SRSM (17.296 s) needs far less running time than MCM (10958 s) and RSM (9840 s). Therefore, under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.

  20. Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

    CERN Document Server

    Yang, Xin-She; Fong, Simon

    2012-01-01

    Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.

  1. Suspended Sediment Load Prediction Using Support Vector Machines in the Goodwin Creek Experimental Watershed

    Science.gov (United States)

    Chiang, Jie-Lun; Tsai, Kuang-Jung; Chen, Yie-Ruey; Lee, Ming-Hsi; Sun, Jai-Wei

    2014-05-01

    Strong correlation exists between river discharge and suspended sediment load. The relationship of discharge and suspended sediment load was used to estimate suspended sediment load by using regression model, artificial neural network and support vector machine in this study. Records of river discharges and suspended sediment loads in the Goodwin Creek Experimental Watershed in United States were investigated as a case study. Seventy percent of the records were used as training data set to develop prediction models. The other thirty percent records were used as verification data set. The performances of those models were evaluated by mean absolute percentage error (MAPE). The MAPEs show that support vector machine outperforms the artificial neural network and regression model. The results show that the MAPE of the proposed SVM can achieve less than 14% for 120 minutes prediction (four time steps). As a result, we believe that the proposed SVM model has high potential for predicting suspended sediment load.

  2. Application of support vector machine and particle swarm optimization in micro near infrared spectrometer

    Science.gov (United States)

    Xiong, Yuhong; Liu, Yunxiang; Shu, Minglei

    2016-10-01

    In the process of actual measurement and analysis of micro near infrared spectrometer, genetic algorithm is used to select the wavelengths and then partial least square method is used for modeling and analyzing. Because genetic algorithm has the disadvantages of slow convergence and difficult parameter setting, and partial least square method in dealing with nonlinear data is far from being satisfactory, the practical application effect of partial least square method based on genetic algorithm is severely affected negatively. The paper introduces the fundamental principles of particle swarm optimization and support vector machine, and proposes a support vector machine method based on particle swarm optimization. The method can overcome the disadvantage of partial least squares method based on genetic algorithm to a certain extent. Finally, the method is tested by an example, and the results show that the method is effective.

  3. A Supply Chain Architecture Based on Multi-agent Systems to Support Decentralized Collaborative Processes

    Science.gov (United States)

    Hernández, Jorge E.; Poler, Raúl; Mula, Josefa

    In a supply chain management context, the enterprise architecture concept to efficiently support the collaborative processes among the supply chain members involved has been evolving. Each supply chain has an organizational structure that describes the hierarchical relationships among its members, ranging from centralized to decentralized organizations. From a decentralized perspective, each supply chain member is able to identify collaborative and non collaborative partners and the kind of information to be exchanged to support negotiation processes. The same concepts of organizational structure and negotiation rules can be applied to a multi-agent system. This paper proposes a novel supply chain architecture to support decentralized collaborative processes in supply chains by considering a multi-agent-based system modeling approach.

  4. Integrated application of uniform design and least-squares support vector machines to transfection optimization

    Directory of Open Access Journals (Sweden)

    Pan Jin-Shui

    2009-05-01

    Full Text Available Abstract Background Transfection in mammalian cells based on liposome presents great challenge for biological professionals. To protect themselves from exogenous insults, mammalian cells tend to manifest poor transfection efficiency. In order to gain high efficiency, we have to optimize several conditions of transfection, such as amount of liposome, amount of plasmid, and cell density at transfection. However, this process may be time-consuming and energy-consuming. Fortunately, several mathematical methods, developed in the past decades, may facilitate the resolution of this issue. This study investigates the possibility of optimizing transfection efficiency by using a method referred to as least-squares support vector machine, which requires only a few experiments and maintains fairly high accuracy. Results A protocol consists of 15 experiments was performed according to the principle of uniform design. In this protocol, amount of liposome, amount of plasmid, and the number of seeded cells 24 h before transfection were set as independent variables and transfection efficiency was set as dependent variable. A model was deduced from independent variables and their respective dependent variable. Another protocol made up by 10 experiments was performed to test the accuracy of the model. The model manifested a high accuracy. Compared to traditional method, the integrated application of uniform design and least-squares support vector machine greatly reduced the number of required experiments. What's more, higher transfection efficiency was achieved. Conclusion The integrated application of uniform design and least-squares support vector machine is a simple technique for obtaining high transfection efficiency. Using this novel method, the number of required experiments would be greatly cut down while higher efficiency would be gained. Least-squares support vector machine may be applicable to many other problems that need to be optimized.

  5. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.

  6. TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification

    OpenAIRE

    Bresson, Xavier; Zhang, Ruiliang

    2012-01-01

    We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV classification perform significantly better when the number of labeled data is small.

  7. A NOVEL MULTICLASS SUPPORT VECTOR MACHINE ALGORITHM USING MEAN REVERSION AND COEFFICIENT OF VARIANCE

    Directory of Open Access Journals (Sweden)

    Bhusana Premanode

    2013-01-01

    Full Text Available Inaccuracy of a kernel function used in Support Vector Machine (SVM can be found when simulated with nonlinear and stationary datasets. To minimise the error, we propose a new multiclass SVM model using mean reversion and coefficient of variance algorithm to partition and classify imbalance in datasets. By introducing a series of test statistic, simulations of the proposed algorithm outperformed the performance of the SVM model without using multiclass SVM model.

  8. Support Vector Machines and Kd-tree for Separating Quasars from Large Survey Databases

    OpenAIRE

    2008-01-01

    We compare the performance of two automated classification algorithms: k-dimensional tree (kd-tree) and support vector machines (SVMs), to separate quasars from stars in the databases of the Sloan Digital Sky Survey (SDSS) and the Two Micron All Sky Survey (2MASS) catalogs. The two algorithms are trained on subsets of SDSS and 2MASS objects whose nature is known via spectroscopy. We choose different attribute combination as input patterns to train the classifier using photometric data only an...

  9. Discussion of Some Problems About Nonlinear Time Series Prediction Using v-Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    GAO Cheng-Feng; CHEN Tian-Lun; NAN Tian-Shi

    2007-01-01

    Some problems in using v-support vector machine (v-SVM) for the prediction of nonlinear time series are discussed. The problems include selection of various net parameters, which affect the performance of prediction, mixture of kernels, and decomposition cooperation linear programming v-SVM regression, which result in improvements of the algorithm. Computer simulations in the prediction of nonlinear time series produced by Mackey-Glass equation and Lorenz equation provide some improved results.

  10. An improved method of support vector machine and its applications to financial time series forecasting

    Institute of Scientific and Technical Information of China (English)

    LIANG Yanchun; SUN Yanfeng

    2003-01-01

    A novel method for kernel function of support vector machine is presented based on the information geometry theory. The kernel function is modified using a conformal mapping to make the kernel data-dependent so as to increase the ability of predicting high noise data of the method. Numerical simulations demonstrate the effectiveness of the method. Simulated results on the prediction of the stock price show that the improved approach possesses better forecasting precision and ability of generalization than the conventional models.

  11. A Support Vector Machine-based Evaluation Model of Customer Satisfaction Degree in Logistics

    Institute of Scientific and Technical Information of China (English)

    SUN Hua-li; XIE Jian-ying

    2007-01-01

    This paper pressnts a novel evaluation model of the customer satisfaction degree (CSD) in logistics based on support vector machine (SVM). Firstly, the relation between the suppliers and the customers is analyzed. Secondly, the evaluation index system and fuzzy quantitative methods are provided. Thirdly, the CSD evaluation system including eight indexes and three ranks rinsed on one-against-one mode of SVM is built. Last simulation experiment is presented to illustrate the theoretical results.

  12. A Multiple Model Approach to Modeling Based on Fuzzy Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    冯瑞; 张艳珠; 宋春林; 邵惠鹤

    2003-01-01

    A new multiple models(MM) approach was proposed to model complex industrial process by using Fuzzy Support Vector Machines (F SVMs). By applying the proposed approach to a pH neutralization titration experi-ment, F_SVMs MM not only provides satisfactory approximation and generalization property, but also achieves superior performance to USOCPN multiple modeling method and single modeling method based on standard SVMs.

  13. Drifting model approach to modeling based on weighted support vector machines

    Institute of Scientific and Technical Information of China (English)

    冯瑞; 宋春林; 邵惠鹤

    2004-01-01

    This paper proposes a novel drifting modeling (DM) method. Briefly, we first employ an improved SVMs algorithm named weighted support vector machines (W_SVMs), which is suitable for locally learning, and then the DM method using the algorithm is proposed. By applying the proposed modeling method to Fluidized Catalytic Cracking Unit (FCCU), the simulation results show that the property of this proposed approach is superior to global modeling method based on standard SVMs.

  14. Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    JIA Jiong; ZHANG Hao-ran

    2006-01-01

    This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR,then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.

  15. Novel Method of Predicting Network Bandwidth Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    沈伟; 冯瑞; 邵惠鹤

    2004-01-01

    In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.

  16. A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines

    OpenAIRE

    2015-01-01

    Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algori...

  17. Kinematic Analysis of Cpm Machine Supporting to Rehabilitation Process after Surgical Knee Arthroscopy and Arthroplasty

    Science.gov (United States)

    Trochimczuk, R.; Kuźmierowski, T.

    2014-11-01

    Existing commercial solutions of the CPM (Continuous Passive Motion) machines are described in the paper. Based on the analysis of existing solutions we present our conceptual solution to support the process of rehabilitation of the knee joint which is necessary after arthroscopic surgery. For a given novel structure we analyze and present proprietary algorithms and the computer application to simulate the operation of our PCM device. In addition, we suggest directions for further research.

  18. Optical diagnosis of colon and cervical cancer by support vector machine

    Science.gov (United States)

    Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Dey, Rajib; Das, Nandan K.; Pradhan, Sanjay; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.; Mohanty, Samarendra

    2016-05-01

    A probabilistic robust diagnostic algorithm is very much essential for successful cancer diagnosis by optical spectroscopy. We report here support vector machine (SVM) classification to better discriminate the colon and cervical cancer tissues from normal tissues based on elastic scattering spectroscopy. The efficacy of SVM based classification with different kernel has been tested on multifractal parameters like Hurst exponent, singularity spectrum width in order to classify the cancer tissues.

  19. Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

    OpenAIRE

    de Morsier, Frank; Borgeaud, Maurice; Gass, Volker; Küchler, Christoph; Thiran, Jean-Philippe

    2012-01-01

    Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using th...

  20. Distributed Sensor Architecture for Intelligent Control that Supports Quality of Control and Quality of Service

    Directory of Open Access Journals (Sweden)

    Jose-Luis Poza-Lujan

    2015-02-01

    Full Text Available This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS parameters and the optimization of control using Quality of Control (QoC parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS communication standard as proposed by the Object Management Group (OMG. As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems.

  1. Distributed Sensor Architecture for Intelligent Control that Supports Quality of Control and Quality of Service

    Science.gov (United States)

    Poza-Lujan, Jose-Luis; Posadas-Yagüe, Juan-Luis; Simó-Ten, José-Enrique; Simarro, Raúl; Benet, Ginés

    2015-01-01

    This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS) parameters and the optimization of control using Quality of Control (QoC) parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS) communication standard as proposed by the Object Management Group (OMG). As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl) has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC) system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems. PMID:25723145

  2. Support vector machine based fault classification and location of a long transmission line

    Directory of Open Access Journals (Sweden)

    Papia Ray

    2016-09-01

    Full Text Available This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21% and least fault distance estimation error (0.29%.

  3. Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

    Science.gov (United States)

    Luo, Wei-Ping; Li, Hong-Qi; Shi, Ning

    2016-06-01

    At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

  4. A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

    Science.gov (United States)

    Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

    2012-01-01

    A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

  5. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  6. Active Learning for Transductive Support Vector Machines with Applications to Text Classification

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    This paper presents a novel active learning approach for transductive support vector machines with applications to text classification. The concept of the centroid of the support vectors is proposed so that the selective sampling based on measuring the distance from the unlabeled samples to the centroid is feasible and simple to compute. With additional hypothesis, active learning offers better performance with comparison to regular inductive SVMs and transductive SVMs with random sampling,and it is even competitive to transductive SVMs on all available training data. Experimental results prove that our approach is efficient and easy to implement.

  7. Effective software design and development for the new graph architecture HPC machines.

    Energy Technology Data Exchange (ETDEWEB)

    Dechev, Damian

    2012-03-01

    Software applications need to change and adapt as modern architectures evolve. Nowadays advancement in chip design translates to increased parallelism. Exploiting such parallelism is a major challenge in modern software engineering. Multicore processors are about to introduce a significant change in the way we design and use fundamental data structures. In this work we describe the design and programming principles of a software library of highly concurrent scalable and nonblocking data containers. In this project we have created algorithms and data structures for handling fundamental computations in massively multithreaded contexts, and we have incorporated these into a usable library with familiar look and feel. In this work we demonstrate the first design and implementation of a wait-free hash table. Our multiprocessor data structure design allows a large number of threads to concurrently insert, remove, and retrieve information. Non-blocking designs alleviate the problems traditionally associated with the use of mutual exclusion, such as bottlenecks and thread-safety. Lock-freedom provides the ability to share data without some of the drawbacks associated with locks, however, these designs remain susceptible to starvation. Furthermore, wait-freedom provides all of the benefits of lock-free synchronization with the added assurance that every thread makes progress in a finite number of steps. This implies deadlock-freedom, livelock-freedom, starvation-freedom, freedom from priority inversion, and thread-safety. The challenges of providing the desirable progress and correctness guarantees of wait-free objects makes their design and implementation difficult. There are few wait-free data structures described in the literature. Using only standard atomic operations provided by the hardware, our design is portable; therefore, it is applicable to a variety of data-intensive applications including the domains of embedded systems and supercomputers.Our experimental

  8. Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

    Science.gov (United States)

    Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

    2017-04-01

    As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

  9. A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

    2014-06-01

    To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).

  10. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.

    Science.gov (United States)

    Gaonkar, Bilwaj; T Shinohara, Russell; Davatzikos, Christos

    2015-08-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification.

  11. Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chun-Chieh Wang

    2013-01-01

    Full Text Available The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE, multi-scale permutation entropy (MPE, multi-scale root-mean-square (MSRMS and multi-band spectrum entropy (MBSE. Some of the features are then selected as the inputs of the support vector machine (SVM classifier through the Fisher score (FS as well as the Mahalanobis distance (MD evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.

  12. Virtual and Augmented Reality in Architectural Design and Education : An Immersive Multimodal Platform to Support Architectural Pedagogy

    OpenAIRE

    Milovanovic, Julie; Moreau, Guillaume; Siret, Daniel; Miguet, Francis

    2017-01-01

    International audience; Virtual Reality and Augmented Reality research in the architecture field show a variety of possible uses of systems to accompany designers, laymen and decision makers in their architectural design process. This article provides a survey of VR and AR devices among a corpus of papers selected from conferences and journals on CAAD (Computer Aided Architectural Design). A closer look at some specific research projects highlights their potentials and limits, which formalize...

  13. A Framework for Evaluating Computer Architectures to Support Systems with Security Requirements, with Applications.

    Science.gov (United States)

    1987-11-05

    develops a set of criteria for evaluating computer architectures that are to support sy’stemns v% ith securit % requirements. Central to these criteria is the...M.. u Fu ’VMR Appendix B DEC VAX-11/780 OVERVIEW The VAX-I1/780 is a 32-bit computer with a virtual memory space of up to 4G -bytes IBI]. The

  14. Earth Orbiting Support Systems for commercial low Earth orbit data relay: Assessing architectures through tradespace exploration

    Science.gov (United States)

    Palermo, Gianluca; Golkar, Alessandro; Gaudenzi, Paolo

    2015-06-01

    As small satellites and Sun Synchronous Earth Observation systems are assuming an increased role in nowadays space activities, including commercial investments, it is of interest to assess how infrastructures could be developed to support the development of such systems and other spacecraft that could benefit from having a data relay service in Low Earth Orbit (LEO), as opposed to traditional Geostationary relays. This paper presents a tradespace exploration study of the architecture of such LEO commercial satellite data relay systems, here defined as Earth Orbiting Support Systems (EOSS). The paper proposes a methodology to formulate architectural decisions for EOSS constellations, and enumerate the corresponding tradespace of feasible architectures. Evaluation metrics are proposed to measure benefits and costs of architectures; lastly, a multicriteria Pareto criterion is used to downselect optimal architectures for subsequent analysis. The methodology is applied to two case studies for a set of 30 and 100 customer-spacecraft respectively, representing potential markets for LEO services in Exploration, Earth Observation, Science, and CubeSats. Pareto analysis shows how increased performance of the constellation is always achieved by an increased node size, as measured by the gain of the communications antenna mounted on EOSS spacecraft. On the other hand, nonlinear trends in optimal orbital altitude, number of satellites per plane, and number of orbital planes, are found in both cases. An upward trend in individual node memory capacity is found, although never exceeding 256 Gbits of onboard memory for both cases that have been considered, assuming the availability of a polar ground station for EOSS data downlink. System architects can use the proposed methodology to identify optimal EOSS constellations for a given service pricing strategy and customer target, thus identifying alternatives for selection by decision makers.

  15. Secure Service Oriented Architectures (SOA) Supporting NEC [Architecture orientée service (SOA) gérant la NEC

    NARCIS (Netherlands)

    Meiler, P.P.; Schmeing, M.

    2009-01-01

    Combined scenario ; Data management ; Data processing ; Demonstrator ; Information systems ; Integrated systems ; Interoperability ; Joint scenario ; Network Enabled Capability (NEC) ; Operational effectiveness ; Operations research ; Scenarios ; Secure communication ; Service Oriented Architecture

  16. Support vector machine-based feature extractor for L/H transitions in JETa)

    Science.gov (United States)

    González, S.; Vega, J.; Murari, A.; Pereira, A.; Ramírez, J. M.; Dormido-Canto, S.; Jet-Efda Contributors

    2010-10-01

    Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.

  17. Splicing-site recognition of rice (Oryza sativa L.)DNA sequences by support vector machines

    Institute of Scientific and Technical Information of China (English)

    彭司华; 樊龙江; 彭小宁; 庄树林; 杜维; 陈良标

    2003-01-01

    Motivation: It was found that high accuracy splicing-site recognition of rice (Oryza sativa L.) DNA sequence is especially difficult. We described a new method for the splicing-site recognition of rice DNA sequences. Method: Based on the intron in eukaryotic organisms conforming to the principle of GT-AG, we used support vector machines (SVM) to predict the splicing sites. By machine learning, we built a model and used it to test the effect of the test data set of true and pseudo splicing sites. Results: The prediction accuracy we obtained was 87.53% at the true 5' end splicing site and 87.37% at the true 3' end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches.

  18. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

    Science.gov (United States)

    Khan, Saranjam; Ullah, Rahat; Khan, Asifullah; Wahab, Noorul; Bilal, Muhammad; Ahmed, Mushtaq

    2016-06-01

    The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

  19. A font and size-independent OCR system for printed Kannada documents using support vector machines

    Indian Academy of Sciences (India)

    T V Ashwin; P S Sastry

    2002-02-01

    This paper describes an OCR system for printed text documents in Kannada, a South Indian language. The input to the system would be the scanned image of a page of text and the output is a machine editable file compatible with most typesetting software. The system first extracts words from the document image and then segments the words into sub-character level pieces. The segmentation algorithm is motivated by the structure of the script. We propose a novel set of features for the recognition problem which are computationally simple to extract. The final recognition is achieved by employing a number of 2-class classifiers based on the Support Vector Machine (SVM) method. The recognition is independent of the font and size of the printed text and the system is seen to deliver reasonable performance.

  20. Support vector machines for prediction of protein signal sequences and their cleavage sites.

    Science.gov (United States)

    Cai, Yu-Dong; Lin, Shuo-liang; Chou, Kuo-Chen

    2003-01-01

    Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is an important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy (see, e.g. K.C. Chou, Current Protein and Peptide Science 2002;3:615-22). In this paper, support vector machines (SVMs), a new machine learning method, is applied to approach this problem. The overall rate of correct prediction for 1939 secretary proteins and 1440 nonsecretary proteins was over 91%. It has not escaped our attention that the new method may also serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells. Copyright 2002 Elsevier Science Inc.

  1. Comparison on neural networks and support vector machines in suppliers' selection

    Institute of Scientific and Technical Information of China (English)

    Hu Guosheng; Zhang Guohong

    2008-01-01

    Suppliers' selection in supply chain management (SCM) has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization,and uncontrolled convergence. A relatively new machine learning technique, support vector machine (SVM), which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers' samples are very insufficient. SVMs are adaptive to deal with small samples' training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.

  2. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

  3. Application of neural networks and support vector machine for significant wave height prediction

    Directory of Open Access Journals (Sweden)

    Jadran Berbić

    2017-07-01

    Full Text Available For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN and support vector machine (SVM. The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.

  4. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  5. Blind multiuser detector for chaos-based CDMA using support vector machine.

    Science.gov (United States)

    Kao, Johnny Wei-Hsun; Berber, Stevan Mirko; Kecman, Vojislav

    2010-08-01

    The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results.

  6. Operator functional state classification using least-square support vector machine based recursive feature elimination technique.

    Science.gov (United States)

    Yin, Zhong; Zhang, Jianhua

    2014-01-01

    This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized.

  7. SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu; Shao Huihe; Wang Xiaofan

    2004-01-01

    Soft sensor is widely used in industrial process control.It plays an important role to improve the quality of product and assure safety in production.The core of soft sensor is to construct soft sensing model.A new soft sensing modeling method based on support vector machine (SVM) is proposed.SVM is a new machine learning method based on statistical learning theory and is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima.The proposed methods are applied to the estimation of frozen point of light diesel oil in distillation column.The estimated outputs of soft sensing model based on SVM match the real values of frozen point and follow varying trend of frozen point very well.Experiment results show that SVM provides a new effective method for soft sensing modeling and has promising application in industrial process applications.

  8. Parameter selection of support vector machine for function approximation based on chaos optimization

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The support vector machine (SVM) is a novel machine learning method,which has the ability to approximate nonlinear functions with arbitrary accuracy.Setting parameters well is very crucial for SVM learning results and generalization ability,and now there is no systematic,general method for parameter selection.In this article,the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values.The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy.Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.

  9. Signal Detection for QPSK Based Cognitive Radio Systems using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    M. T. Mushtaq

    2015-04-01

    Full Text Available Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying based signals propagating through an AWGN (Additive White Gaussian Noise channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR values up to -50 dB.

  10. Web Page Classification using an ensemble of support vector machine classifiers

    Directory of Open Access Journals (Sweden)

    Shaobo Zhong

    2011-11-01

    Full Text Available Web Page Classification (WPC is both an important and challenging topic in data mining. The knowledge of WPC can help users to obtain useable information from the huge internet dataset automatically and efficiently. Many efforts have been made to WPC. However, there is still room for improvement of current approaches. One particular challenge in training classifiers comes from the fact that the available dataset is usually unbalanced. Standard machine learning algorithms tend to be overwhelmed by the major class and ignore the minor one and thus lead to high false negative rate. In this paper, a novel approach for Web page classification was proposed to address this problem by using an ensemble of support vector machine classifiers to perform this work. Principal Component Analysis (PCA is used for feature reduction and Independent Component Analysis (ICA for feature selection. The experimental results indicate that the proposed approach outperforms other existing classifiers widely used in WPC.

  11. Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification

    Science.gov (United States)

    Ding, Yifu; Tavolara, Thomas; Cheng, Keith

    2016-03-01

    Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

  12. A divide-and-combine method for large scale nonparallel support vector machines.

    Science.gov (United States)

    Tian, Yingjie; Ju, Xuchan; Shi, Yong

    2016-03-01

    Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming.

  13. Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

    Directory of Open Access Journals (Sweden)

    Liao Li

    2010-10-01

    Full Text Available Abstract Background Protein-protein interaction (PPI plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs, based on domains represented as interaction profile hidden Markov models (ipHMM where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB. Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD. Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure, an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on

  14. Mining protein function from text using term-based support vector machines

    Science.gov (United States)

    Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

    2005-01-01

    Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

  15. Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Ali Asghar Besalatpour

    2016-02-01

    stability. Conclusion: The pixel-scale soil aggregate stability predicted that using the developed SVM and MLR models demonstrates the usefulness of incorporating topographic and vegetation information along with the soil properties as predictors. However, the SVM model achieved more accuracy in predicting soil aggregate stability compared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some soil physical properties such as geometric mean diameter of soil aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmed by the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectiveness with respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate soil scientists to work on it further in the future.

  16. 基于支持向量机的分段线性学习方法%A Subsection Learning Algorithm Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    杨强; 吴中福; 王茜

    2003-01-01

    In this paper, we discuss drawback of traditional subsection learning algorithm in pattern recognition and exiting support vector machines (including kernel functions), the necessity of using subsection learning algorithm based on support vector machines as well as. In turn, a subsection learning algorithm based on support vector machines, is proposed in this paper.

  17. A P2P Architecture to Support Mobile Real-Time Multimedia Communications

    Directory of Open Access Journals (Sweden)

    Daniel G. Costa

    2010-10-01

    Full Text Available A new SIP standard based on peer to peer paradigm allows the keeping of user localization information in a decentralized way, adding flexibility to real-time mobile communications. Despite specification of terminal mobility in P2PSIP, handover procedures in transport-layer have better performance when compared with solutions in application-layer of Internet. SCTP transport protocol supports handover and a partial reliable communication service. Putting together P2PSIP and SCTP extensions, an end-to-end architecture can be proposed, aimed at the support of real-time multimedia applications with mobility requirements.

  18. Big Data Architectures for Operationalized Seismic and Subsurface Monitoring and Decision Support Workflows

    Science.gov (United States)

    Irving, D. H.; Rasheed, M.; Hillman, C.; O'Doherty, N.

    2012-12-01

    Oilfield management is moving to a more operational footing with near-realtime seismic and sensor monitoring governing drilling, fluid injection and hydrocarbon extraction workflows within safety, productivity and profitability constraints. To date, the geoscientific analytical architectures employed are configured for large volumes of data, computational power or analytical latency and compromises in system design must be made to achieve all three aspects. These challenges are encapsulated by the phrase 'Big Data' which has been employed for over a decade in the IT industry to describe the challenges presented by data sets that are too large, volatile and diverse for existing computational architectures and paradigms. We present a data-centric architecture developed to support a geoscientific and geotechnical workflow whereby: ●scientific insight is continuously applied to fresh data ●insights and derived information are incorporated into engineering and operational decisions ●data governance and provenance are routine within a broader data management framework Strategic decision support systems in large infrastructure projects such as oilfields are typically relational data environments; data modelling is pervasive across analytical functions. However, subsurface data and models are typically non-relational (i.e. file-based) in the form of large volumes of seismic imaging data or rapid streams of sensor feeds and are analysed and interpreted using niche applications. The key architectural challenge is to move data and insight from a non-relational to a relational, or structured, data environment for faster and more integrated analytics. We describe how a blend of MapReduce and relational database technologies can be applied in geoscientific decision support, and the strengths and weaknesses of each in such an analytical ecosystem. In addition we discuss hybrid technologies that use aspects of both and translational technologies for moving data and analytics

  19. Design and Parametric Sizing of Deep Space Habitats Supporting NASA'S Human Space Flight Architecture Team

    Science.gov (United States)

    Toups, Larry; Simon, Matthew; Smitherman, David; Spexarth, Gary

    2012-01-01

    NASA's Human Space Flight Architecture Team (HAT) is a multi-disciplinary, cross-agency study team that conducts strategic analysis of integrated development approaches for human and robotic space exploration architectures. During each analysis cycle, HAT iterates and refines the definition of design reference missions (DRMs), which inform the definition of a set of integrated capabilities required to explore multiple destinations. An important capability identified in this capability-driven approach is habitation, which is necessary for crewmembers to live and work effectively during long duration transits to and operations at exploration destinations beyond Low Earth Orbit (LEO). This capability is captured by an element referred to as the Deep Space Habitat (DSH), which provides all equipment and resources for the functions required to support crew safety, health, and work including: life support, food preparation, waste management, sleep quarters, and housekeeping.The purpose of this paper is to describe the design of the DSH capable of supporting crew during exploration missions. First, the paper describes the functionality required in a DSH to support the HAT defined exploration missions, the parameters affecting its design, and the assumptions used in the sizing of the habitat. Then, the process used for arriving at parametric sizing estimates to support additional HAT analyses is detailed. Finally, results from the HAT Cycle C DSH sizing are presented followed by a brief description of the remaining design trades and technological advancements necessary to enable the exploration habitation capability.

  20. Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masayuki Yarimizu

    2015-01-01

    Full Text Available Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs, and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.

  1. Inferring the location of buried UXO using a support vector machine

    Science.gov (United States)

    Fernández, Juan Pablo; Sun, Keli; Barrowes, Benjamin; O'Neill, Kevin; Shamatava, Irma; Shubitidze, Fridon; Paulsen, Keith D.

    2007-04-01

    The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined, before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation. In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer program by feeding it features of representative examples, and the machine, in turn, can generalize this information by finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in search of an optimal predictive configuration.

  2. Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results.

    Science.gov (United States)

    Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

    2015-01-01

    Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

  3. Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

    Directory of Open Access Journals (Sweden)

    Antonio Cerasa

    2015-01-01

    Full Text Available Presently, there are no valid biomarkers to identify individuals with eating disorders (ED. The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa were compared against 17 body mass index-matched healthy controls (HC. Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

  4. Support vector machine for classification of walking conditions using miniature kinematic sensors.

    Science.gov (United States)

    Lau, Hong-Yin; Tong, Kai-Yu; Zhu, Hailong

    2008-06-01

    A portable gait analysis and activity-monitoring system for the evaluation of activities of daily life could facilitate clinical and research studies. This current study developed a small sensor unit comprising an accelerometer and a gyroscope in order to detect shank and foot segment motion and orientation during different walking conditions. The kinematic data obtained in the pre-swing phase were used to classify five walking conditions: stair ascent, stair descent, level ground, upslope and downslope. The kinematic data consisted of anterior-posterior acceleration and angular velocity measured from the shank and foot segments. A machine learning technique known as support vector machine (SVM) was applied to classify the walking conditions. SVM was also compared with other machine learning methods such as artificial neural network (ANN), radial basis function network (RBF) and Bayesian belief network (BBN). The SVM technique was shown to have a higher performance in classification than the other three methods. The results using SVM showed that stair ascent and stair descent could be distinguished from each other and from the other walking conditions with 100% accuracy by using a single sensor unit attached to the shank segment. For classification results in the five walking conditions, performance improved from 78% using the kinematic signals from the shank sensor unit to 84% by adding signals from the foot sensor unit. The SVM technique with the portable kinematic sensor unit could automatically recognize the walking condition for quantitative analysis of the activity pattern.

  5. Clustering technique-based least square support vector machine for EEG signal classification.

    Science.gov (United States)

    Siuly; Li, Yan; Wen, Peng Paul

    2011-12-01

    This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

  6. Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation

    Directory of Open Access Journals (Sweden)

    Indah Agustien

    2010-10-01

    Full Text Available Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine is proposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonized using thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose, whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce the computational time. The modified particle filter consists of four main phases. First, particles are generated to predict target’s location. Second, weight of certain particles is calculated and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, update particles based on each weight. The modified particle filter could reduce computational time of object tracking since this method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method builds Gaussian distribution and calculates particle’s weight using this distribution. Through experiment using video data taken in front of cashier of convenient store, the proposed method reduced computational time in tracking process until 68.34% in average compare to the conventional one, meanwhile the accuracy of tracking with this new method is comparable with particle filter method i.e. 90.3%. Combination particle filter with binary Gaussian weighting and support vector machine is promising for advanced early crime scene investigation.

  7. eDNA: A Bio-Inspired Reconfigurable Hardware Cell Architecture Supporting Self-organisation and Self-healing

    DEFF Research Database (Denmark)

    Boesen, Michael Reibel; Madsen, Jan

    2009-01-01

    This paper presents the concept of a biological inspired reconfigurable hardware cell architecture which supports self-organisation and self-healing. Two fundamental processes in biology, namely fertilization-to-birth and cell self-healing have inspired the development of this cell architecture. ...

  8. Support vector echo-state machine for chaotic time-series prediction.

    Science.gov (United States)

    Shi, Zhiwei; Han, Min

    2007-03-01

    A novel chaotic time-series prediction method based on support vector machines (SVMs) and echo-state mechanisms is proposed. The basic idea is replacing "kernel trick" with "reservoir trick" in dealing with nonlinearity, that is, performing linear support vector regression (SVR) in the high-dimension "reservoir" state space, and the solution benefits from the advantages from structural risk minimization principle, and we call it support vector echo-state machines (SVESMs). SVESMs belong to a special kind of recurrent neural networks (RNNs) with convex objective function, and their solution is global, optimal, and unique. SVESMs are especially efficient in dealing with real life nonlinear time series, and its generalization ability and robustness are obtained by regularization operator and robust loss function. The method is tested on the benchmark prediction problem of Mackey-Glass time series and applied to some real life time series such as monthly sunspots time series and runoff time series of the Yellow River, and the prediction results are promising.

  9. Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Jin Weidong; Zhang Gexiang; Hu Laizhao

    2006-01-01

    This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform (WPT) is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines (SVMs) are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

  10. Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Yun-Fei Wang; Huan Chen; Yan-Hong Zhou

    2005-01-01

    A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRsand non-GPCRs has also been exploited to improve the prediction performance.The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.

  11. Biometric gait recognition for mobile devices using wavelet transform and support vector machines

    DEFF Research Database (Denmark)

    Hestbek, Martin Reese; Nickel, C.; Busch, C.

    2012-01-01

    The ever growing number of mobile devices has turned the attention to security and usability. If a mobile device is lost or stolen this can lead to loss of personal information and the possibility of identity theft. People often tend not to use passwords which leads to lack of personal security...... obtained from mobile devices. Gait templates were constructed of Bark-frequency cepstral coefficients (BFCC) from the wavelet coefficients and these were arranged to train a support vector machine (SVM). A cross-day scenario demonstrates that the proposed approach shows competitive recognition performance...

  12. Thrust estimator design based on least squares support vector regression machine

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yong-ping; SUN Jian-guo

    2010-01-01

    In order to realize direct thrust control instead of traditional sensor-based control for nero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem.Furthermore,numerical simulations confirm the effectiveness of our presented scheme.During the process of estimator design,a wrap per criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.

  13. Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

    Science.gov (United States)

    Zhang, Qigui; Deng, Kai

    2016-12-01

    As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

  14. An Auto-flag Method of Radio Visibility Data Based on Support Vector Machine

    Science.gov (United States)

    Hui-mei, Dai; Ying, Mei; Wei, Wang; Hui, Deng; Feng, Wang

    2017-01-01

    The Mingantu Ultrawide Spectral Radioheliograph (MUSER) has entered a test observation stage. After the construction of the data acquisition and storage system, it is urgent to automatically flag and eliminate the abnormal visibility data so as to improve the imaging quality. In this paper, according to the observational records, we create a credible visibility set, and further obtain the corresponding flag model of visibility data by using the support vector machine (SVM) technique. The results show that the SVM is a robust approach to flag the MUSER visibility data, and can attain an accuracy of about 86%. Meanwhile, this method will not be affected by solar activities, such as flare eruptions.

  15. Performance and optimization of support vector machines in high-energy physics classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, Mehmet Oezguer; Kruecker, Dirk; Melzer-Pellmann, Isabell [DESY, Hamburg (Germany)

    2016-07-01

    In this talk, the use of Support Vector Machines (SVM) is promoted for new-physics searches in high-energy physics. We developed an interface, called SVM HEP Interface (SVM-HINT), for a popular SVM library, LibSVM, and introduced a statistical-significance based hyper-parameter optimization algorithm for the new-physics searches. As example case study, a search for Supersymmetry at the Large Hadron Collider is given to demonstrate the capabilities of SVM using SVM-HINT.

  16. Ozone Monitoring Using Support Vector Machine and K-Nearest Neighbors Methods

    Directory of Open Access Journals (Sweden)

    FALEH Rabeb

    2017-05-01

    Full Text Available Due to health impacts caused by the pollutant gases, monitoring and controlling air quality is an important field of interest. This paper deals with ozone monitoring in four stations measuring air quality located in many Tunisian cities using numerous measuring instruments and polluting gas analyzers. Prediction of ozone concentrations in two Tunisian cities, Tunis and Sfax is screened based on supervised classification models. The K -Nearest neighbors results reached 98.7 % success rate in the recognition and ozone identification. Support Vector Machines (SVM with the linear, polynomial and RBF kernel were applied to build a classifier and full accuracy (100% was again achieved with the RBF kernel.

  17. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    Directory of Open Access Journals (Sweden)

    R. Rajesh Sharma

    2015-01-01

    algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

  18. Analysis and prediction of nutritional requirements using structural properties of metabolic networks and support vector machines.

    Science.gov (United States)

    Tamura, Takeyuki; Christian, Nils; Takemoto, Kazuhiro; Ebenhöh, Oliver; Akutsu, Tatsuya

    2010-01-01

    Properties of graph representation of genome scale metabolic networks have been extensively studied. However, the relationship between these structural properties and functional properties of the networks are still very unclear. In this paper, we focus on nutritional requirements of organisms as a functional property and study the relationship with structural properties of a graph representation of metabolic networks. In order to examine the relationship, we study to what extent the nutritional requirements can be predicted by using support vector machines from structural properties, which include degree exponent, edge density, clustering coefficient, degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. Furthermore, we study which properties are influential to the nutritional requirements.

  19. Application of support vector machine and quantum genetic algorithm in infrared target recognition

    Science.gov (United States)

    Wang, Hongliang; Huang, Yangwen; Ding, Haifei

    2010-08-01

    In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.

  20. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jian-Jiun Ding

    2012-07-01

    Full Text Available Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.

  1. Impact of Health Care Employees’ Job Satisfaction On Organizational Performance Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    Cemil Kuzey

    2012-05-01

    Full Text Available This study was undertaken to search for key factors that contribute to job satisfaction among health care workers, and also to determine the impact of these underlying dimensions of employee satisfaction on organizational performance. Exploratory Factor Analysis (EFA was applied to initially uncover the key factors, and then, in the next stage of analysis, a popular data mining technique, Support Vector Machine (SVM was employed on a sample of 249 to determine the impact of job satisfaction factors on organizational performance. According to the proposed model, the main factors were revealed to be management’s attitude, pay/reward, job security and colleagues.

  2. Study on flaw identification of ultrasonic signal for large shafts based on optimal support vector machine

    Institute of Scientific and Technical Information of China (English)

    Zhao Xiufen; Yin Guofu; Tian Guiyun; Yin Ying

    2008-01-01

    Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.

  3. Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine

    Science.gov (United States)

    Kim, Sang-Kyun; Chang, Joon-Hyuk

    In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.

  4. Hepatic CT image retrieval based on the combination of Gabor filters and support vector machine

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Content-based image retrieval has been an active area of research for more than ten years.Gabor schemes and support vector machine (SVM) method have been proven effective in image representation and classification. In this paper,we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography (CT) images query.In our experiments,a batch of hepatic CT images containing several types of CT findings are used for the retrieval test.Precision comparison between our scheme and existing methods is presented.

  5. Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

    Science.gov (United States)

    Chen, S; Samingan, A K; Hanzo, L

    2001-01-01

    The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

  6. Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

    Institute of Scientific and Technical Information of China (English)

    WANG Yong; GUO Hui

    2014-01-01

    Support vector machines(SVM) received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.

  7. Mixture gas component concentration analysis based on support vector machine and infrared spectrum

    Institute of Scientific and Technical Information of China (English)

    Peng Bai; Junhua Liu

    2006-01-01

    @@ A novel quantitative analysis method of multi-component mixture gas concentration based on support vector machine (SVM) and spectroscopy is proposed. Through transformation of the kernel function, the seriously overlapped and nonlinear spectrum data are transformed in high-dimensional space, but the highdimensional data can be processed in the original space. Some factors, such as kernel function, range of the wavelength, and penalty coefficient, are discussed. This method is applied to the quantitative analysis of natural gas components concentration, and the component concentration maximal deviation is 2.28%.

  8. PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

    Directory of Open Access Journals (Sweden)

    Yukai Yao

    2015-01-01

    Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

  9. Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

    Institute of Scientific and Technical Information of China (English)

    张燕昆; 刘重庆

    2003-01-01

    Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an algorithm by combining SVM classifier with NNC to improve the correct recognition rate. We conduct the experiment on the Cambridge ORL face database. The result shows that our approach outperforms the standard eigenface approach and some other approaches.

  10. Accurate performance estimators for information retrieval based on span bound of support vector machines

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed,these only focus on accuracy which is based on the leave-one-out cross validation procedure. Information-retrieval-related performance measures are always neglected in a kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the leave-one-out procedure. Experiments have proven that our proposed estimators are both effective and stable.

  11. Feature Subset Selection for Hot Method Prediction using Genetic Algorithm wrapped with Support Vector Machines

    Directory of Open Access Journals (Sweden)

    S. Johnson

    2011-01-01

    Full Text Available Problem statement: All compilers have simple profiling-based heuristics to identify and predict program hot methods and also to make optimization decisions. The major challenge in the profile-based optimization is addressing the problem of overhead. The aim of this work is to perform feature subset selection using Genetic Algorithms (GA to improve and refine the machine learnt static hot method predictive technique and to compare the performance of the new models against the simple heuristics. Approach: The relevant features for training the predictive models are extracted from an initial set of randomly selected ninety static program features, with the help of the GA wrapped with the predictive model using the Support Vector Machine (SVM, a Machine Learning (ML algorithm. Results: The GA-generated feature subsets containing thirty and twenty nine features respectively for the two predictive models when tested on MiBench predict Long Running Hot Methods (LRHM and frequently called hot methods (FCHM with the respective accuracies of 71% and 80% achieving an increase of 19% and 22%. Further, inlining of the predicted LRHM and FCHM improve the program performance by 3% and 5% as against 4% and 6% with Low Level Virtual Machines (LLVM default heuristics. When intra-procedural optimizations (IPO are performed on the predicted hot methods, this system offers a performance improvement of 5% and 4% as against 0% and 3% by LLVM default heuristics on LRHM and FCHM respectively. However, we observe an improvement of 36% in certain individual programs. Conclusion: Overall, the results indicate that the GA wrapped with SVM derived feature reduction improves the hot method prediction accuracy and that the technique of hot method prediction based optimization is potentially useful in selective optimization.

  12. Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

    Directory of Open Access Journals (Sweden)

    Licandro Francesco

    2007-01-01

    Full Text Available Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated.

  13. Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

    Directory of Open Access Journals (Sweden)

    Francesco Licandro

    2007-03-01

    Full Text Available Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated.

  14. Exploring Life Support Architectures for Evolution of Deep Space Human Exploration

    Science.gov (United States)

    Anderson, Molly S.; Stambaugh, Imelda C.

    2015-01-01

    Life support system architectures for long duration space missions are often explored analytically in the human spaceflight community to find optimum solutions for mass, performance, and reliability. But in reality, many other constraints can guide the design when the life support system is examined within the context of an overall vehicle, as well as specific programmatic goals and needs. Between the end of the Constellation program and the development of the "Evolvable Mars Campaign", NASA explored a broad range of mission possibilities. Most of these missions will never be implemented but the lessons learned during these concept development phases may color and guide future analytical studies and eventual life support system architectures. This paper discusses several iterations of design studies from the life support system perspective to examine which requirements and assumptions, programmatic needs, or interfaces drive design. When doing early concept studies, many assumptions have to be made about technology and operations. Data can be pulled from a variety of sources depending on the study needs, including parametric models, historical data, new technologies, and even predictive analysis. In the end, assumptions must be made in the face of uncertainty. Some of these may introduce more risk as to whether the solution for the conceptual design study will still work when designs mature and data becomes available.

  15. Simulating Turing machines on Maurer machines

    NARCIS (Netherlands)

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

    2008-01-01

    In a previous paper, we used Maurer machines to model and analyse micro-architectures. In the current paper, we investigate the connections between Turing machines and Maurer machines with the purpose to gain an insight into computability issues relating to Maurer machines. We introduce ways to

  16. A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support.

    Science.gov (United States)

    Connolly, Brian; Cohen, K Bretonnel; Santel, Daniel; Bayram, Ulya; Pestian, John

    2017-08-07

    Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models' ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed.

  17. Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chenzhong Cao

    2009-07-01

    Full Text Available Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT and murine local lymph node assay (LLNA are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.

  18. Application of Support Vector Machine to Reliability Analysis of Engine Systems

    Directory of Open Access Journals (Sweden)

    Zhang Xinfeng

    2013-07-01

    Full Text Available Reliability analysis plays a very important role for assessing the performance and making maintenance plans of engine systems. This research presents a comparative study of the predictive performances of support vector machines (SVM , least square support vector machine (LSSVM and neural network time series models for forecasting failures and reliability in engine systems. Further, the reliability indexes of engine systems are computed by the weibull probability paper programmed with Matlab. The results shows that the probability distribution of the forecasting outcomes is consistent to the distribution of the actual data, which all follow weibull distribution and the predictions by SVM and LSSVM can provide accurate predictions of the characteristic life. So SVM and LSSVM are both another choice of engine system reliability analysis. Moreover, the predictive precise of the method based on LSSVM is higher than that of SVM. In small samples, the prediction by LSSVM will be more popular, because its compution cost is lower and the precise can be more satisfied.

  19. Using support vector machines to improve elemental ion identification in macromolecular crystal structures

    Energy Technology Data Exchange (ETDEWEB)

    Morshed, Nader [University of California, Berkeley, CA 94720 (United States); Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Echols, Nathaniel, E-mail: nechols@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Adams, Paul D., E-mail: nechols@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); University of California, Berkeley, CA 94720 (United States)

    2015-05-01

    A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

  20. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masataka Fuchida

    2017-01-01

    Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

  1. Human action recognition with group lasso regularized-support vector machine

    Science.gov (United States)

    Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

    2016-05-01

    The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

  2. CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2010-12-01

    Full Text Available Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic programs.

  3. [NIR spectroscopy based on least square support vector machines for quality prediction of tomato juice].

    Science.gov (United States)

    Huang, Kang; Wang, Hui-jun; Xu, Hui-rong; Wang, Jian-ping; Ying, Yi-bin

    2009-04-01

    The application of least square support vector machines (LS-SVM) regression method based on statistics study theory to the analysis with near infrared (NIR) spectra of tomato juice was introduced in the present paper. In this method, LS-SVM was used for establishing model of spectral analysis, and was applied to predict the sugar contents (SC) and available acid (VA) in tomato juice samples. NIR transmission spectra of tomato juice were measured in the spectral range of 800-2,500 nm using InGaAs detector. The radial basis function (RBF) was adopted as a kernel function of LS-SVM. Sixty seven tomato juice samples were used as calibration set, and thirty three samples were used as validation set. The results of the method for sugar contents (SC) and available acid (VA) prediction were: a high correlation coefficient of 0.9903 and 0.9675, and a low root mean square error of prediction (RMSEP) of 0.0056 degree Brix and 0.0245, respectively. And compared to PLS and PCR methods, the performance of the LSSVM method was better. The results indicated that it was possible to built statistic models to quantify some common components in tomato juice using near-infrared (NIR) spectroscopy and least square support vector machines (LS-SVM) regression method as a nonlinear multivariate calibration procedure, and LS-SVM could be a rapid and accurate method for juice components determination based on NIR spectra.

  4. Fuzzy nonlinear proximal support vector machine for land extraction based on remote sensing image.

    Directory of Open Access Journals (Sweden)

    Xiaomei Zhong

    Full Text Available Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM by basing on ETM(+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da'an in northern China. Two multi-category strategies, namely "one-against-one" and "one-against-rest" for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient, stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC, back propagation neural network (BPN, and the proximal support vector machine (PSVM under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments.

  5. Fuzzy Support Vector Machine-based Multi-agent Optimal Path

    Directory of Open Access Journals (Sweden)

    Gireesh Kumar T

    2010-07-01

    Full Text Available A mobile robot to navigate purposefully from a start location to a target location, needs three basic requirements: sensing, learning, and reasoning. In the existing system, the mobile robot navigates in a known environment on a predefined path. However, the pervasive presence of uncertainty in sensing and learning, makes the choice of a suitable tool of reasoning and decision-making that can deal with incomplete information, vital to ensure a robust control system. This problem can be overcome by the proposed navigation method using fuzzy support vector machine (FSVM. It proposes a fuzzy logic-based support vector machine (SVM approach to secure a collision-free path avoiding multiple dynamic obstacles. The navigator consists of an FSVM-based collision avoidance. The decisions are taken at each step for the mobile robot to attain the goal position without collision. Fuzzy-SVM rule bases are built, which require simple evaluation data rather than thousands of input-output training data. The effectiveness of the proposed method is verified by a series of simulations and implemented with a microcontroller for navigation.Defence Science Journal, 2010, 60(4, pp.387-391, DOI:http://dx.doi.org/10.14429/dsj.60.496

  6. Fuzzy-based multi-kernel spherical support vector machine for effective handwritten character recognition

    Indian Academy of Sciences (India)

    A K SAMPATH; N GOMATHI

    2017-09-01

    Due to constant advancement of computer tools, automated conversion of images of typed,handwritten and printed text is important for various applications, which has led to intense research for several years in the field of offline handwritten character recognition. Handwritten character recognition is complex because characters differ by writing style, shapes and writing devices. To resolve this problem, we propose a fuzzy-based multi-kernel spherical support vector machine. Initially, the input image is fed into the pre-processing step to acquire suitable images. Then, histogram of oriented gradient (HOG) descriptor is utilised forfeature extraction. The HOG descriptor constitutes a histogram estimation and normalisation computation. The features are then classified using the proposed classifier for character recognition. In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is analysed by metrics such as false acceptance rate, false rejection rate and accuracy, which is implemented in MATLAB. Then, the performance is compared with existing systems based on the percentage of training data samples. Thus, the outcome of our proposed system attains 99% higher accuracy, which ensures efficient recognition performance

  7. Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization

    Directory of Open Access Journals (Sweden)

    Yogameena, B.

    2010-01-01

    Full Text Available Problem statement: Detection of individual’s abnormal human behaviors in the crowd has become a critical problem because in the event of terror strikes. This study presented a real-time video surveillance system which classifies normal and abnormal behaviors in crowds. The aim of this research was to provide a system which can aid in monitoring crowded urban environments. Approach: The proposed behaviour classification was through projection which separated individuals and using star skeletonization the features like body posture and the cyclic motion cues were obtained. Using these cues the Support Vector Machine (SVM classified the normal and abnormal behaviors of human. Results: Experimental results demonstrated the method proposed was robust and efficient in the classification of normal and abnormal human behaviors. A comparative study of classification accuracy between principal component analysis and Support Vector Machine (SVM classification was also presented. Conclusion: The proposed method classified the behavior such as running people in a crowded environment, bending down movement while most are walking or standing, a person carrying a long bar and a person waving hand in the crowd is classified.

  8. Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings.

    Science.gov (United States)

    Khandoker, Ahsan H; Palaniswami, Marimuthu; Karmakar, Chandan K

    2009-01-01

    Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.

  9. Least squares twin support vector machine with Universum data for classification

    Science.gov (United States)

    Xu, Yitian; Chen, Mei; Li, Guohui

    2016-11-01

    Universum, a third class not belonging to either class of the classification problem, allows to incorporate the prior knowledge into the learning process. A lot of previous work have demonstrated that the Universum is helpful to the supervised and semi-supervised classification. Moreover, Universum has already been introduced into the support vector machine (SVM) and twin support vector machine (TSVM) to enhance the generalisation performance. To further increase the generalisation performance, we propose a least squares TSVM with Universum data (?-TSVM) in this paper. Our ?-TSVM possesses the following advantages: first, it exploits Universum data to improve generalisation performance. Besides, it implements the structural risk minimisation principle by adding a regularisation to the objective function. Finally, it costs less computing time by solving two small-sized systems of linear equations instead of a single larger-sized quadratic programming problem. To verify the validity of our proposed algorithm, we conduct various experiments around the size of labelled samples and the number of Universum data on data-sets including seven benchmark data-sets, Toy data, MNIST and Face images. Empirical experiments indicate that Universum contributes to making prediction accuracy improved even stable. Especially when fewer labelled samples given, ?-TSVM is far superior to the improved LS-TSVM (ILS-TSVM), and slightly superior to the ?-TSVM.

  10. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jia Uddin

    2014-01-01

    Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

  11. The generalized architecture of the decision support system on the basis of Business Intelligence in an equitable construction

    OpenAIRE

    Круковський, Ігор Анатолійович

    2016-01-01

    On the basis of retrospective analysis of Business Intelligence the generalized architecture of the Decision Support System, marked as DSS ВІ 2.0 (Decision Support System, Business Intelligence 2.0) has been worked out. Its differs in including of facilities from Text Mining and programmatic agents with the functions of expertmini systems; by the selection of subsystems of complex visualization; including of facilities of making videos; possibility of adaptation of module architecture in acco...

  12. Technology Roadmap Instrumentation, Control, and Human-Machine Interface to Support DOE Advanced Nuclear Energy Programs

    Energy Technology Data Exchange (ETDEWEB)

    Donald D Dudenhoeffer; Burce P Hallbert

    2007-03-01

    Instrumentation, Controls, and Human-Machine Interface (ICHMI) technologies are essential to ensuring delivery and effective operation of optimized advanced Generation IV (Gen IV) nuclear energy systems. In 1996, the Watts Bar I nuclear power plant in Tennessee was the last U.S. nuclear power plant to go on line. It was, in fact, built based on pre-1990 technology. Since this last U.S. nuclear power plant was designed, there have been major advances in the field of ICHMI systems. Computer technology employed in other industries has advanced dramatically, and computing systems are now replaced every few years as they become functionally obsolete. Functional obsolescence occurs when newer, more functional technology replaces or supersedes an existing technology, even though an existing technology may well be in working order.Although ICHMI architectures are comprised of much of the same technology, they have not been updated nearly as often in the nuclear power industry. For example, some newer Personal Digital Assistants (PDAs) or handheld computers may, in fact, have more functionality than the 1996 computer control system at the Watts Bar I plant. This illustrates the need to transition and upgrade current nuclear power plant ICHMI technologies.

  13. Clinical Decision Support for Whole Genome Sequence Information Leveraging a Service-Oriented Architecture: a Prototype

    Science.gov (United States)

    Welch, Brandon M.; Rodriguez-Loya, Salvador; Eilbeck, Karen; Kawamoto, Kensaku

    2014-01-01

    Whole genome sequence (WGS) information could soon be routinely available to clinicians to support the personalized care of their patients. At such time, clinical decision support (CDS) integrated into the clinical workflow will likely be necessary to support genome-guided clinical care. Nevertheless, developing CDS capabilities for WGS information presents many unique challenges that need to be overcome for such approaches to be effective. In this manuscript, we describe the development of a prototype CDS system that is capable of providing genome-guided CDS at the point of care and within the clinical workflow. To demonstrate the functionality of this prototype, we implemented a clinical scenario of a hypothetical patient at high risk for Lynch Syndrome based on his genomic information. We demonstrate that this system can effectively use service-oriented architecture principles and standards-based components to deliver point of care CDS for WGS information in real-time. PMID:25954430

  14. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  15. Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

    Science.gov (United States)

    Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

    2010-12-01

    We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

  16. A one-layer recurrent neural network for support vector machine learning.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2004-04-01

    This paper presents a one-layer recurrent neural network for support vector machine (SVM) learning in pattern classification and regression. The SVM learning problem is first converted into an equivalent formulation, and then a one-layer recurrent neural network for SVM learning is proposed. The proposed neural network is guaranteed to obtain the optimal solution of support vector classification and regression. Compared with the existing two-layer neural network for the SVM classification, the proposed neural network has a low complexity for implementation. Moreover, the proposed neural network can converge exponentially to the optimal solution of SVM learning. The rate of the exponential convergence can be made arbitrarily high by simply turning up a scaling parameter. Simulation examples based on benchmark problems are discussed to show the good performance of the proposed neural network for SVM learning.

  17. The new interpretation of support vector machines on statistical learning theory

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    This paper is concerned with the theoretical foundation of support vector machines (SVMs). The purpose is to develop further an exact relationship between SVMs and the statistical learning theory (SLT). As a representative, the standard C-support vector classification (C-SVC) is considered here. More precisely, we show that the decision function obtained by C-SVC is just one of the decision functions obtained by solving the optimization problem derived directly from the structural risk minimization principle. In addition, an interesting meaning of the parameter C in C-SVC is given by showing that C corresponds to the size of the decision function candidate set in the structural risk minimization principle.

  18. A Gaussian Belief Propagation Solver for Large Scale Support Vector Machines

    CERN Document Server

    Bickson, Danny; Dolev, Danny

    2008-01-01

    Support vector machines (SVMs) are an extremely successful type of classification and regression algorithms. Building an SVM entails solving a constrained convex quadratic programming problem, which is quadratic in the number of training samples. We introduce an efficient parallel implementation of an support vector regression solver, based on the Gaussian Belief Propagation algorithm (GaBP). In this paper, we demonstrate that methods from the complex system domain could be utilized for performing efficient distributed computation. We compare the proposed algorithm to previously proposed distributed and single-node SVM solvers. Our comparison shows that the proposed algorithm is just as accurate as these solvers, while being significantly faster, especially for large datasets. We demonstrate scalability of the proposed algorithm to up to 1,024 computing nodes and hundreds of thousands of data points using an IBM Blue Gene supercomputer. As far as we know, our work is the largest parallel implementation of bel...

  19. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    Science.gov (United States)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  20. Multiple support vector machines for land cover change detection: An application for mapping urban extensions

    Science.gov (United States)

    Nemmour, Hassiba; Chibani, Youcef

    The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted.