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

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

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...

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.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

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...

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

How does Enterprise Architecture support innovation?

DEFF Research Database (Denmark)

Nardello, Marco; Lapalme, James; 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...

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.

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.

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. 

Indonesian Stock Prediction using Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Santoso Murtiyanto

2018-01-01

Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

Architecture for interlock systems: reliability analysis with regard to safety and availability

International Nuclear Information System (INIS)

Wagner, S.; Apollonio, A.; Schmidt, R.; Zerlauth, M.; Vergara-Fernandez, A.

2012-01-01

For particle accelerators like LHC and other large experimental physics facilities like ITER, the machine protection relies on complex interlock systems. In the design of interlock loops for the signal exchange in machine protection systems, the choice of the hardware architecture impacts on machine safety and availability. The reliable performance of a machine stop (leaving the machine in a safe state) in case of an emergency, is an inherent requirement. The constraints in terms of machine availability on the other hand may differ from one facility to another. Spurious machine stops, lowering machine availability, may to a certain extent be tolerated in facilities where they do not cause undue equipment wear-out. In order to compare various interlock loop architectures in terms of safety and availability, the occurrence frequencies of related scenarios have been calculated in a reliability analysis, using a generic analytical model. This paper presents the results and illustrates the potential of the analysis method for supporting the choice of interlock system architectures. The results show the advantages of a 2003 (3 redundant lines with 2-out-of-3 voting) over the 6 architectures under consideration for systems with high requirements in both safety and availability

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...

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.

Architecture Knowledge for Evaluating Scalable Databases

Science.gov (United States)

2015-01-16

Architecture Knowledge for Evaluating Scalable Databases 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Nurgaliev... Scala , Erlang, Javascript Cursor-based queries Supported, Not Supported JOIN queries Supported, Not Supported Complex data types Lists, maps, sets...is therefore needed, using technology such as machine learning to extract content from product documentation. The terminology used in the database

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.

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics

Directory of Open Access Journals (Sweden)

Héctor Herrero

2017-05-01

Full Text Available This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

Support vector machine for diagnosis cancer disease: A comparative study

Directory of Open Access Journals (Sweden)

Nasser H. Sweilam

2010-12-01

Full Text Available 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, Quantum-behave Particle Swarm for training SVM is introduced. Another approach named least square support vector machine (LSSVM and active set strategy are introduced. The obtained results by these methods are tested on a breast cancer dataset and compared with the exact solution model problem.

SANDS: an architecture for clinical decision support in a National Health Information Network.

Science.gov (United States)

Wright, Adam; Sittig, Dean F

2007-10-11

A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics.

Dialogue management in a home machine environment : linguistic components over an agent architecture

OpenAIRE

Quesada Moreno, José Francisco; García, Federico; Sena Pichardo, María Esther; Bernal Bermejo, José Ángel; Amores Carredano, José Gabriel de

2001-01-01

This paper presents the main characteristics of an Agent-based Architecture for the design and implementation of a Spoken Dialogue System. From a theoretical point of view, the system is based on the Information State Update approach, in particular, the system aims at the management of Natural Command Language Dialogue Moves in a Home Machine Environment. Specifically, the paper is focused on the Natural Language Understanding and Dialogue Management Agents...

Systemic Architecture

DEFF Research Database (Denmark)

Poletto, Marco; Pasquero, Claudia

-up or tactical design, behavioural space and the boundary of the natural and the artificial realms within the city and architecture. A new kind of "real-time world-city" is illustrated in the form of an operational design manual for the assemblage of proto-architectures, the incubation of proto-gardens...... and the coding of proto-interfaces. These prototypes of machinic architecture materialize as synthetic hybrids embedded with biological life (proto-gardens), computational power, behavioural responsiveness (cyber-gardens), spatial articulation (coMachines and fibrous structures), remote sensing (FUNclouds...

A novel improved fuzzy support vector machine based stock price trend forecast model

OpenAIRE

Wang, Shuheng; Li, Guohao; Bao, Yifan

2018-01-01

Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support...

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.

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

International Nuclear Information System (INIS)

Ye Meiying; Wang Xiaodong

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 chaotic time series. The effectiveness of the method is demonstrated by applying it to the Henon map. This study also 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.

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

Infinite ensemble of support vector machines for prediction of ...

African Journals Online (AJOL)

user

the support vector machines (SVMs), a machine learning algorithm used ... work designs so that specific, quantitative workplace assessments can be made ... with SVMs can be obtained by embedding the base learners (hypothesis) into a.

Nanorobot architecture for medical target identification

International Nuclear Information System (INIS)

Cavalcanti, Adriano; Shirinzadeh, Bijan; Freita, Robert A Jr; Hogg, Tad

2008-01-01

This work has an innovative approach for the development of nanorobots with sensors for medicine. The nanorobots operate in a virtual environment comparing random, thermal and chemical control techniques. The nanorobot architecture model has nanobioelectronics as the basis for manufacturing integrated system devices with embedded nanobiosensors and actuators, which facilitates its application for medical target identification and drug delivery. The nanorobot interaction with the described workspace shows how time actuation is improved based on sensor capabilities. Therefore, our work addresses the control and the architecture design for developing practical molecular machines. Advances in nanotechnology are enabling manufacturing nanosensors and actuators through nanobioelectronics and biologically inspired devices. Analysis of integrated system modeling is one important aspect for supporting nanotechnology in the fast development towards one of the most challenging new fields of science: molecular machines. The use of 3D simulation can provide interactive tools for addressing nanorobot choices on sensing, hardware architecture design, manufacturing approaches, and control methodology investigation

Nanorobot architecture for medical target identification

Energy Technology Data Exchange (ETDEWEB)

Cavalcanti, Adriano [CAN Center for Automation in Nanobiotech, Melbourne VIC 3168 (Australia); Shirinzadeh, Bijan [Robotics and Mechatronics Research Laboratory, Department of Mechanical Engineering, Monash University, Clayton, Melbourne VIC 3800 (Australia); Freita, Robert A Jr [Institute for Molecular Manufacturing, Pilot Hill, CA 95664 (United States); Hogg, Tad [Hewlett-Packard Laboratories, Palo Alto, CA 94304 (United States)

2008-01-09

This work has an innovative approach for the development of nanorobots with sensors for medicine. The nanorobots operate in a virtual environment comparing random, thermal and chemical control techniques. The nanorobot architecture model has nanobioelectronics as the basis for manufacturing integrated system devices with embedded nanobiosensors and actuators, which facilitates its application for medical target identification and drug delivery. The nanorobot interaction with the described workspace shows how time actuation is improved based on sensor capabilities. Therefore, our work addresses the control and the architecture design for developing practical molecular machines. Advances in nanotechnology are enabling manufacturing nanosensors and actuators through nanobioelectronics and biologically inspired devices. Analysis of integrated system modeling is one important aspect for supporting nanotechnology in the fast development towards one of the most challenging new fields of science: molecular machines. The use of 3D simulation can provide interactive tools for addressing nanorobot choices on sensing, hardware architecture design, manufacturing approaches, and control methodology investigation.

Density Based Support Vector Machines for Classification

OpenAIRE

Zahra Nazari; Dongshik Kang

2015-01-01

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 cl...

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.

Machine learning analysis of binaural rowing sounds

DEFF Research Database (Denmark)

Johard, Leonard; Ruffaldi, Emanuele; Hoffmann, Pablo F.

2011-01-01

Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition metho...... methodology and the evaluation of different machine learning techniques for classifying rowing-sound data. We see that a combination of principal component analysis and shallow networks perform equally well as deep architectures, while being much faster to train.......Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition...

Support Vector Machine and Application in Seizure Prediction

KAUST Repository

Qiu, Simeng

2018-04-01

Nowadays, Machine learning (ML) has been utilized in various kinds of area which across the range from engineering field to business area. In this paper, we first present several kernel machine learning methods of solving classification, regression and clustering problems. These have good performance but also have some limitations. We present examples to each method and analyze the advantages and disadvantages for solving different scenarios. Then we focus on one of the most popular classification methods, Support Vectors Machine (SVM). In addition, we introduce the basic theory, advantages and scenarios of using Support Vector Machine (SVM) deal with classification problems. We also explain a convenient approach of tacking SVM problems which are called Sequential Minimal Optimization (SMO). Moreover, one class SVM can be understood in a different way which is called Support Vector Data Description (SVDD). This is a famous non-linear model problem compared with SVM problems, SVDD can be solved by utilizing Gaussian RBF kernel function combined with SMO. At last, we compared the difference and performance of SVM-SMO implementation and SVM-SVDD implementation. About the application part, we utilized SVM method to handle seizure forecasting in canine epilepsy, after comparing the results from different methods such as random forest, extremely randomized tree, and SVM to classify preictal (pre-seizure) and interictal (interval-seizure) binary data. We draw the conclusion that SVM has the best performance.

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

Evaluating automatically parallelized versions of the support vector machine

NARCIS (Netherlands)

Codreanu, V.; Dröge, B.; Williams, D.; Yasar, B.; Yang, P.; Liu, B.; Dong, F.; Surinta, O.; Schomaker, L.R.B.; Roerdink, J.B.T.M.; Wiering, M.A.

2016-01-01

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

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.

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.

A minimal architecture for joint action

DEFF Research Database (Denmark)

Vesper, Cordula; Butterfill, Stephen; Knoblich, Günther

2010-01-01

What kinds of processes and representations make joint action possible? In this paper we suggest a minimal architecture for joint action that focuses on representations, action monitoring and action prediction processes, as well as ways of simplifying coordination. The architecture spells out...... minimal requirements for an individual agent to engage in a joint action. We discuss existing evidence in support of the architecture as well as open questions that remain to be empirically addressed. In addition, we suggest possible interfaces between the minimal architecture and other approaches...... to joint action. The minimal architecture has implications for theorizing about the emergence of joint action, for human-machine interaction, and for understanding how coordination can be facilitated by exploiting relations between multiple agents’ actions and between actions and the environment....

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...... a poor match for the distributed, loosely-coupled, document-based nature of SOA. The gap is actually increasing. For example, interoperability between different organizations, requires contracts to reduce risks. Thus, high-level models of contracts are making their way into service-oriented architectures......, but application developers are still left to their own devices when it comes to writing code that will comply with a contract. This paper surveys existing and future directions regarding language-based solutions to the above problem....

VLSI and system architecture-the new development of system 5G

Energy Technology Data Exchange (ETDEWEB)

Sakamura, K.; Sekino, A.; Kodaka, T.; Uehara, T.; Aiso, H.

1982-01-01

A research and development proposal is presented for VLSI CAD systems and for a hardware environment called system 5G on which the VLSI CAD systems run. The proposed CAD systems use a hierarchically organized design language to enable design of anything from basic architectures of VLSI to VLSI mask patterns in a uniform manner. The cad systems will eventually become intelligent cad systems that acquire design knowledge and perform automatic design of VLSI chips when the characteristic requirements of VLSI chip is given. System 5G will consist of superinference machines and the 5G communication network. The superinference machine will be built based on a functionally distributed architecture connecting inferommunication network. The superinference machine will be built based on a functionally distributed architecture connecting inference machines and relational data base machines via a high-speed local network. The transfer rate of the local network will be 100 mbps at the first stage of the project and will be improved to 1 gbps. Remote access to the superinference machine will be possible through the 5G communication network. Access to system 5G will use the 5G network architecture protocol. The users will access the system 5G using standardized 5G personal computers. 5G personal logic programming stations, very high intelligent terminals providing an instruction set that supports predicate logic and input/output facilities for audio and graphical information.

Infinite ensemble of support vector machines for prediction of ...

African Journals Online (AJOL)

Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used ...

Canon multifunction copier machines – now with onsite support!

CERN Document Server

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...

A distributed clinical decision support system architecture

Directory of Open Access Journals (Sweden)

Shaker H. El-Sappagh

2014-01-01

Full Text Available This paper proposes an open and distributed clinical decision support system architecture. This technical architecture takes advantage of Electronic Health Record (EHR, data mining techniques, clinical databases, domain expert knowledge bases, available technologies and standards to provide decision-making support for healthcare professionals. The architecture will work extremely well in distributed EHR environments in which each hospital has its own local EHR, and it satisfies the compatibility, interoperability and scalability objectives of an EHR. The system will also have a set of distributed knowledge bases. Each knowledge base will be specialized in a specific domain (i.e., heart disease, and the model achieves cooperation, integration and interoperability between these knowledge bases. Moreover, the model ensures that all knowledge bases are up-to-date by connecting data mining engines to each local knowledge base. These data mining engines continuously mine EHR databases to extract the most recent knowledge, to standardize it and to add it to the knowledge bases. This framework is expected to improve the quality of healthcare, reducing medical errors and guaranteeing the safety of patients by helping clinicians to make correct, accurate, knowledgeable and timely decisions.

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.

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

Science.gov (United States)

Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

2013-01-01

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

Service-Oriented Architecture Approach to MAGTF Logistics Support Systems

Science.gov (United States)

2013-09-01

Support System-Marine Corps IT Information Technology KPI Key Performance Indicators LCE Logistics Command Element ITV In-transit Visibility LCM...building blocks, options, KPI (key performance indicators), design decisions and the corresponding; the physical attributes which is the second attribute... KPI ) that they impact. h. Layer 8 (Information Architecture) The business intelligence layer and information architecture safeguards the inclusion

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

OpenAIRE

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better ...

A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

Science.gov (United States)

Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

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

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.

A network architecture supporting consistent rich behavior in collaborative interactive applications.

Science.gov (United States)

Marsh, James; Glencross, Mashhuda; Pettifer, Steve; Hubbold, Roger

2006-01-01

Network architectures for collaborative virtual reality have traditionally been dominated by client-server and peer-to-peer approaches, with peer-to-peer strategies typically being favored where minimizing latency is a priority, and client-server where consistency is key. With increasingly sophisticated behavior models and the demand for better support for haptics, we argue that neither approach provides sufficient support for these scenarios and, thus, a hybrid architecture is required. We discuss the relative performance of different distribution strategies in the face of real network conditions and illustrate the problems they face. Finally, we present an architecture that successfully meets many of these challenges and demonstrate its use in a distributed virtual prototyping application which supports simultaneous collaboration for assembly, maintenance, and training applications utilizing haptics.

Software and Human-Machine Interface Development for Environmental Controls Subsystem Support

Science.gov (United States)

Dobson, Matthew

2018-01-01

The Space Launch System (SLS) is the next premier launch vehicle for NASA. It is the next stage of manned space exploration from American soil, and will be the platform in which we push further beyond Earth orbit. In preparation of the SLS maiden voyage on Exploration Mission 1 (EM-1), the existing ground support architecture at Kennedy Space Center required significant overhaul and updating. A comprehensive upgrade of controls systems was necessary, including programmable logic controller software, as well as Launch Control Center (LCC) firing room and local launch pad displays for technician use. Environmental control acts as an integral component in these systems, being the foremost system for conditioning the pad and extremely sensitive launch vehicle until T-0. The Environmental Controls Subsystem (ECS) required testing and modification to meet the requirements of the designed system, as well as the human factors requirements of NASA software for Validation and Verification (V&V). This term saw significant strides in the progress and functionality of the human-machine interfaces used at the launch pad, and improved integration with the controller code.

Landslide susceptibility mapping using support vector machine and ...

Indian Academy of Sciences (India)

the prediction rate methods, the validation process was performed by ... support vector machine (SVM); geographical information systems (GIS); ... 2012a), decision tree methods (Akgun .... gence or divergence of water during downhill flow.

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:

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.

Machines and Metaphors

Directory of Open Access Journals (Sweden)

Ángel Martínez García-Posada

2016-10-01

Full Text Available The edition La ley del reloj. Arquitectura, máquinas y cultura moderna (Cátedra, Madrid, 2016 registers the useful paradox of the analogy between architecture and technique. Its author, the architect Eduardo Prieto, also a philosopher, professor and writer, acknowledges the obvious distance from machines to buildings, so great that it can only be solved using strange comparisons, since architecture does not move nor are the machines habitable, however throughout the book, from the origin of the metaphor of the machine, with clarity in his essay and enlightening erudition, he points out with certainty some concomitances of high interest, drawing throughout history a beautiful cartography of the fruitful encounter between organics and mechanics.

Terra Harvest software architecture

Science.gov (United States)

Humeniuk, Dave; Klawon, Kevin

2012-06-01

Under the Terra Harvest Program, the DIA has the objective of developing a universal Controller for the Unattended Ground Sensor (UGS) community. The mission is to define, implement, and thoroughly document an open architecture that universally supports UGS missions, integrating disparate systems, peripherals, etc. The Controller's inherent interoperability with numerous systems enables the integration of both legacy and future UGS System (UGSS) components, while the design's open architecture supports rapid third-party development to ensure operational readiness. The successful accomplishment of these objectives by the program's Phase 3b contractors is demonstrated via integration of the companies' respective plug-'n'-play contributions that include controllers, various peripherals, such as sensors, cameras, etc., and their associated software drivers. In order to independently validate the Terra Harvest architecture, L-3 Nova Engineering, along with its partner, the University of Dayton Research Institute, is developing the Terra Harvest Open Source Environment (THOSE), a Java Virtual Machine (JVM) running on an embedded Linux Operating System. The Use Cases on which the software is developed support the full range of UGS operational scenarios such as remote sensor triggering, image capture, and data exfiltration. The Team is additionally developing an ARM microprocessor-based evaluation platform that is both energy-efficient and operationally flexible. The paper describes the overall THOSE architecture, as well as the design decisions for some of the key software components. Development process for THOSE is discussed as well.

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

Energy Technology Data Exchange (ETDEWEB)

Arumugam, Kamesh [Old Dominion Univ., Norfolk, VA (United States)

2017-05-01

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address

Research on intrusion detection based on Kohonen network and support vector machine

Science.gov (United States)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

LINEAR KERNEL SUPPORT VECTOR MACHINES FOR MODELING PORE-WATER PRESSURE RESPONSES

Directory of Open Access Journals (Sweden)

KHAMARUZAMAN W. YUSOF

2017-08-01

Full Text Available Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

An equation of movement for supporting a drilling machine

Energy Technology Data Exchange (ETDEWEB)

Totev, Sl

1982-01-01

Support of a drilling machine is examined and an equation of movement is written. The equation has an invariant form and may be used for theoretical study of support in order to determine the forces and to study the stability and endurance of the elements as a whole.

Weighted K-means support vector machine for cancer prediction.

Science.gov (United States)

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 techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

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%.

Layered distributed simulation architecture to support the C2 enterprise

CSIR Research Space (South Africa)

Duvenhage, A

2009-09-01

Full Text Available between these systems and that a capability is required to demonstrate, support and evaluate interoperability. This paper discusses the layered software architecture of a C++ software application framework for developing applications that support...

Experimental comparison of support vector machines with random ...

Indian Academy of Sciences (India)

dient 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 ... ping, surveillance system, resource management, tracking ... rocks, water bodies, and anthropogenic elements,.

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.

REDUCING THE LOAD OF THE ELASTIC SUPPORT OF THE RESONANCE VIBRATING CONVEYOR MACHINES

Directory of Open Access Journals (Sweden)

A. I. Afanas'ev

2018-03-01

Full Text Available The relevance of the work is conditioned by the necessity of improving the efficiency of vibrator machines. This is done by means of increasing the reliability of the elastic reference elements. The purpose of the work is to develop a dynamic resonance system of the vibrator machine with a reduced mass of the working body and loads on elastic supports. The resonance vibrator machines appeared in the USSR in the mid-twentieth century. They were used in the coal industry. The machines of foreign production and some of the domestic machines are now produced according to the balanced scheme. Domestic machines of the "PEV" series are made according to the vibro-isolated scheme, and the vibro-exciter is rigidly connected to the box. The resonant oscillation frequency of these machines is 50 Hz, and the maximum acceleration is significantly greater than the one of free fall. These resonant machines operate with the amplitude up to 2.2 mm and they have a ratio mode greater than unity. The practice of running these machines shows their relatively low efficiency when screening thin products. The common disadvantage of unbalanced resonance vibrator machines is a relatively large loading of elastic elements (supports and the presence of a massive frame. The disadvantage of the balanced ones is the reactive mass or several working bodies with the same mass. One of the ways to achieve the goal is to define a rational dynamic scheme of the resonance vibrator machines. The results and their application. The authors proposed to transform a traditional one-mass oscillatory system into a system equivalent to a dynamic vibration dampener. This system can significantly reduce the weight of the machine. It can reduce the rigidity and loading of the elastic supports at a given frequency of oscillations. The upper mass can be reduced by 2 or 3 times, and the lower mass can be several times smaller than the upper one. At the same time, the dynamic loads on the supports

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.

A relational approach to support software architecture analysis

NARCIS (Netherlands)

Feijs, L.M.G.; Krikhaar, R.L.; van Ommering, R.C.

1998-01-01

This paper reports on our experience with a relational approach to support the analysis of existing software architectures. The analysis options provide for visualization and view calculation. The approach has been applied for reverse engineering. It is also possible to check concrete designs

GenSVM: a generalized multiclass support vector machine

NARCIS (Netherlands)

G.J.J. van den Burg (Gertjan); 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

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

Adaptive Training and Collective Decision Support Based on Man-Machine Interface

Science.gov (United States)

2016-03-02

Based on Man -machine Interface The views, opinions and/or findings contained in this report are those of the author(s) and should not contrued as an...ADDRESS (ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 adaptive training, EEG, man -machine interface...non peer-reviewed journals: Final Report: Adaptive Training and Collective Decision Support Based on Man -machine Interface Report Title The existence of

Open architecture CNC system

Energy Technology Data Exchange (ETDEWEB)

Tal, J. [Galil Motion Control Inc., Sunnyvale, CA (United States); Lopez, A.; Edwards, J.M. [Los Alamos National Lab., NM (United States)

1995-04-01

In this paper, an alternative solution to the traditional CNC machine tool controller has been introduced. Software and hardware modules have been described and their incorporation in a CNC control system has been outlined. This type of CNC machine tool controller demonstrates that technology is accessible and can be readily implemented into an open architecture machine tool controller. Benefit to the user is greater controller flexibility, while being economically achievable. PC based, motion as well as non-motion features will provide flexibility through a Windows environment. Up-grading this type of controller system through software revisions will keep the machine tool in a competitive state with minimal effort. Software and hardware modules are mass produced permitting competitive procurement and incorporation. Open architecture CNC systems provide diagnostics thus enhancing maintainability, and machine tool up-time. A major concern of traditional CNC systems has been operator training time. Training time can be greatly minimized by making use of Windows environment features.

Augmenting cognitive architectures to support diagrammatic imagination.

Science.gov (United States)

Chandrasekaran, Balakrishnan; Banerjee, Bonny; Kurup, Unmesh; Lele, Omkar

2011-10-01

Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures--Soar and ACT-R, to name the most prominent--do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive architectures. We discuss the degree to which DRS, our earlier proposal for such an internal representation for diagrams, meets these requirements. In DRS, the diagrams are not raw images, but a composition of objects that can be individuated and thus symbolized, while, unlike traditional symbols, the referent of the symbol is an object that retains its perceptual essence, namely, its spatiality. This duality provides a way to resolve what anti-imagists thought was a contradiction in mental imagery: the compositionality of mental images that seemed to be unique to symbol systems, and their support of a perceptual experience of images and some types of perception on them. We briefly review the use of DRS to augment Soar and ACT-R with a diagrammatic representation component. We identify issues for further research. Copyright © 2011 Cognitive Science Society, Inc.

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

International Nuclear Information System (INIS)

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

Multivariate calibration with least-squares support vector machines.

NARCIS (Netherlands)

Thissen, U.M.J.; Ustun, B.; Melssen, W.J.; Buydens, L.M.C.

2004-01-01

This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and

A comparative analysis of support vector machines and extreme learning machines.

Science.gov (United States)

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

Languages, compilers and run-time environments for distributed memory machines

CERN Document Server

Saltz, J

1992-01-01

Papers presented within this volume cover a wide range of topics related to programming distributed memory machines. Distributed memory architectures, although having the potential to supply the very high levels of performance required to support future computing needs, present awkward programming problems. The major issue is to design methods which enable compilers to generate efficient distributed memory programs from relatively machine independent program specifications. This book is the compilation of papers describing a wide range of research efforts aimed at easing the task of programmin

Support vector machine for the diagnosis of malignant mesothelioma

Science.gov (United States)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

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

High-level language computer architecture

CERN Document Server

Chu, Yaohan

1975-01-01

High-Level Language Computer Architecture offers a tutorial on high-level language computer architecture, including von Neumann architecture and syntax-oriented architecture as well as direct and indirect execution architecture. Design concepts of Japanese-language data processing systems are discussed, along with the architecture of stack machines and the SYMBOL computer system. The conceptual design of a direct high-level language processor is also described.Comprised of seven chapters, this book first presents a classification of high-level language computer architecture according to the pr

Machine vision systems using machine learning for industrial product inspection

Science.gov (United States)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

Performance evaluation of scientific programs on advanced architecture computers

International Nuclear Information System (INIS)

Walker, D.W.; Messina, P.; Baille, C.F.

1988-01-01

Recently a number of advanced architecture machines have become commercially available. These new machines promise better cost-performance then traditional computers, and some of them have the potential of competing with current supercomputers, such as the Cray X/MP, in terms of maximum performance. This paper describes an on-going project to evaluate a broad range of advanced architecture computers using a number of complete scientific application programs. The computers to be evaluated include distributed- memory machines such as the NCUBE, INTEL and Caltech/JPL hypercubes, and the MEIKO computing surface, shared-memory, bus architecture machines such as the Sequent Balance and the Alliant, very long instruction word machines such as the Multiflow Trace 7/200 computer, traditional supercomputers such as the Cray X.MP and Cray-2, and SIMD machines such as the Connection Machine. Currently 11 application codes from a number of scientific disciplines have been selected, although it is not intended to run all codes on all machines. Results are presented for two of the codes (QCD and missile tracking), and future work is proposed

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... Roof Support § 75.205 Installation of roof support using mining machines with integral roof bolters. When roof bolts are installed by a continuous mining machine with intregal roof bolting equipment: (a...

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

HEP specific benchmarks of virtual machines on multi-core CPU architectures

International Nuclear Information System (INIS)

Alef, M; Gable, I

2010-01-01

Virtualization technologies such as Xen can be used in order to satisfy the disparate and often incompatible system requirements of different user groups in shared-use computing facilities. This capability is particularly important for HEP applications, which often have restrictive requirements. The use of virtualization adds flexibility, however, it is essential that the virtualization technology place little overhead on the HEP application. We present an evaluation of the practicality of running HEP applications in multiple Virtual Machines (VMs) on a single multi-core Linux system. We use the benchmark suite used by the HEPiX CPU Benchmarking Working Group to give a quantitative evaluation relevant to the HEP community. Benchmarks are packaged inside VMs and then the VMs are booted onto a single multi-core system. Benchmarks are then simultaneously executed on each VM to simulate highly loaded VMs running HEP applications. These techniques are applied to a variety of multi-core CPU architectures and VM configurations.

An Ensemble of Deep Support Vector Machines for Image Categorization

NARCIS (Netherlands)

Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

2009-01-01

This paper presents the deep support vector machine (D-SVM) inspired by the increasing popularity of deep belief networks for image recognition. Our deep SVM trains an SVM in the standard way and then uses the kernel activations of support vectors as inputs for training another SVM at the next

Progressive Classification Using Support Vector Machines

Science.gov (United States)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user

Rotating electric machine with fluid supported parts

Science.gov (United States)

Smith, Jr., Joseph L.; Kirtley, Jr., James L.

1981-01-01

A rotating electric machine in which the armature winding thereof and other parts are supported by a liquid to withstand the mechanical stresses applied during transient overloads and the like. In particular, a narrow gap is provided between the armature winding and the stator which supports it and this gap is filled with an externally pressurized viscous liquid. The liquid is externally pressurized sufficiently to balance the static loads on the armature winding. Transient mechanical loads which deform the armature winding alter the gap dimensions and thereby additionally pressurize the viscous liquid to oppose the armature winding deformation and more nearly uniformly to distribute the resulting mechanical stresses.

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.

An assessment of support vector machines for land cover classification

Science.gov (United States)

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

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

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...... by being included in ALBidS and then compared with the application of an Artificial Neural Network (ANN), originating promising results: an effective electricity market price forecast in a fast execution time. The proposed approach is tested and validated using real electricity markets data from MIBEL......׳ research group has developed a multi-agent system: Multi-Agent System for Competitive Electricity Markets (MASCEM), which simulates the electricity markets environment. MASCEM is integrated with Adaptive Learning Strategic Bidding System (ALBidS) that works as a decision support system for market players...

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

Science.gov (United States)

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Exploring Hardware Support For Scaling Irregular Applications on Multi-node Multi-core Architectures

Energy Technology Data Exchange (ETDEWEB)

Secchi, Simone; Ceriani, Marco; Tumeo, Antonino; Villa, Oreste; Palermo, Gianluca; Raffo, Luigi

2013-06-05

With the recent emergence of large-scale knowledge dis- covery, data mining and social network analysis, irregular applications have gained renewed interest. Classic cache-based high-performance architectures do not provide optimal performances with such kind of workloads, mainly due to the very low spatial and temporal locality of the irregular control and memory access patterns. In this paper, we present a multi-node, multi-core, fine-grained multi-threaded shared-memory system architecture specifically designed for the execution of large-scale irregular applications, and built on top of three pillars, that we believe are fundamental to support these workloads. First, we offer transparent hardware support for Partitioned Global Address Space (PGAS) to provide a large globally-shared address space with no software library overhead. Second, we employ multi-threaded multi-core processing nodes to achieve the necessary latency tolerance required by accessing global memory, which potentially resides in a remote node. Finally, we devise hardware support for inter-thread synchronization on the whole global address space. We first model the performances by using an analytical model that takes into account the main architecture and application characteristics. We describe the hardware design of the proposed cus- tom architectural building blocks that provide support for the above- mentioned three pillars. Finally, we present a limited-scale evaluation of the system on a multi-board FPGA prototype with typical irregular kernels and benchmarks. The experimental evaluation demonstrates the architecture performance scalability for different configurations of the whole system.

Support Vector Machines: Relevance Feedback and Information Retrieval.

Science.gov (United States)

Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

2002-01-01

Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

OpenAIRE

Ming Cheng; Le Sun; Giuseppe Buja; Lihua Song

2015-01-01

The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs). Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator perm...

High-performance reconfigurable hardware architecture for restricted Boltzmann machines.

Science.gov (United States)

Ly, Daniel Le; Chow, Paul

2010-11-01

Despite the popularity and success of neural networks in research, the number of resulting commercial or industrial applications has been limited. A primary cause for this lack of adoption is that neural networks are usually implemented as software running on general-purpose processors. Hence, a hardware implementation that can exploit the inherent parallelism in neural networks is desired. This paper investigates how the restricted Boltzmann machine (RBM), which is a popular type of neural network, can be mapped to a high-performance hardware architecture on field-programmable gate array (FPGA) platforms. The proposed modular framework is designed to reduce the time complexity of the computations through heavily customized hardware engines. A method to partition large RBMs into smaller congruent components is also presented, allowing the distribution of one RBM across multiple FPGA resources. The framework is tested on a platform of four Xilinx Virtex II-Pro XC2VP70 FPGAs running at 100 MHz through a variety of different configurations. The maximum performance was obtained by instantiating an RBM of 256 × 256 nodes distributed across four FPGAs, which resulted in a computational speed of 3.13 billion connection-updates-per-second and a speedup of 145-fold over an optimized C program running on a 2.8-GHz Intel processor.

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...... the case of Support Vector Machines (SVMS) and we propose a non-parametric scheme to restore proper generalizability. We illustrate the algorithm and its ability to restore performance on a wide range of benchmark data sets....... 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...

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...

Identifying saltcedar with hyperspectral data and support vector machines

Science.gov (United States)

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

Hyperspectral image classification using Support Vector Machine

International Nuclear Information System (INIS)

Moughal, T A

2013-01-01

Classification of land cover hyperspectral images is a very challenging task due to the unfavourable ratio between the number of spectral bands and the number of training samples. The focus in many applications is to investigate an effective classifier in terms of accuracy. The conventional multiclass classifiers have the ability to map the class of interest but the considerable efforts and large training sets are required to fully describe the classes spectrally. Support Vector Machine (SVM) is suggested in this paper to deal with the multiclass problem of hyperspectral imagery. The attraction to this method is that it locates the optimal hyper plane between the class of interest and the rest of the classes to separate them in a new high-dimensional feature space by taking into account only the training samples that lie on the edge of the class distributions known as support vectors and the use of the kernel functions made the classifier more flexible by making it robust against the outliers. A comparative study has undertaken to find an effective classifier by comparing Support Vector Machine (SVM) to the other two well known classifiers i.e. Maximum likelihood (ML) and Spectral Angle Mapper (SAM). At first, the Minimum Noise Fraction (MNF) was applied to extract the best possible features form the hyperspectral imagery and then the resulting subset of the features was applied to the classifiers. Experimental results illustrate that the integration of MNF and SVM technique significantly reduced the classification complexity and improves the classification accuracy.

Modeling Architectural Patterns Using Architectural Primitives

NARCIS (Netherlands)

Zdun, Uwe; Avgeriou, Paris

2005-01-01

Architectural patterns are a key point in architectural documentation. Regrettably, there is poor support for modeling architectural patterns, because the pattern elements are not directly matched by elements in modeling languages, and, at the same time, patterns support an inherent variability that

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

1998-03-19

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

An implementation of support vector machine on sentiment classification of movie reviews

Science.gov (United States)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

CHANGING PARADIGMS IN SPACE THEORIES: Recapturing 20th Century Architectural History

Directory of Open Access Journals (Sweden)

Gül Kaçmaz Erk

2013-03-01

Full Text Available The concept of space entered architectural history as late as 1893. Studies in art opened up the discussion, and it has been studied in various ways in architecture ever since. This article aims to instigate an additional reading to architectural history, one that is not supported by “isms” but based on space theories in the 20th century. Objectives of the article are to bring the concept of space and its changing paradigms to the attention of architectural researchers, to introduce a conceptual framework to classify and clarify theories of space, and to enrich the discussions on the 20th century architecture through theories that are beyond styles. The introduction of space in architecture will revolve around subject-object relationships, three-dimensionality and senses. Modern space will be discussed through concepts such as empathy, perception, abstraction, and geometry. A scientific approach will follow to study the concept of place through environment, event, behavior, and design methods. Finally, the reearch will look at contemporary approaches related to digitally  supported space via concepts like reality-virtuality, mediated experience, and relationship with machines.

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...

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.

Fuzzy-based multi-kernel spherical support vector machine for ...

Indian Academy of Sciences (India)

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 ...

An investigative study towards constructing anthropocentric Man-Machine System design evaluation methodology

International Nuclear Information System (INIS)

Yoshikawa, H.; Gofuku, A.; Itoh, T.; Sasaki, K.

1992-01-01

A methodological investigation has been conducted for evaluating the reliability of man-machine interaction in the total Man-Machine System (MMS) from the view-point of safety maintenance for emergent situations of nuclear power plant. Basic considerations in our study are: (i) what are the MMS design data to be evaluated, (ii) how are those MMS design data should be treated, and (iii) how the introduction effects of various operator support tools can be evaluated. The methods of both qualitative and quantitative MMS design evaluation are summarized in this paper, with the system architecture based on man-machine interaction simulation and the related cognitive human error factor analysis. (author)

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

NARCIS (Netherlands)

Misra, Ankita; Vojinovic, Zoran; Ramakrishnan, Balaji; Luijendijk, Arjen; Ranasinghe, Roshanka

2018-01-01

Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM)

Connection machine: a computer architecture based on cellular automata

Energy Technology Data Exchange (ETDEWEB)

Hillis, W D

1984-01-01

This paper describes the connection machine, a programmable computer based on cellular automata. The essential idea behind the connection machine is that a regular locally-connected cellular array can be made to behave as if the processing cells are connected into any desired topology. When the topology of the machine is chosen to match the topology of the application program, the result is a fast, powerful computing engine. The connection machine was originally designed to implement knowledge retrieval operations in artificial intelligence programs, but the hardware and the programming techniques are apparently applicable to a much larger class of problems. A machine with 100000 processing cells is currently being constructed. 27 references.

Support vector machine: a tool for mapping mineral prospectivity

NARCIS (Netherlands)

Zuo, R.; Carranza, E.J.M

2011-01-01

In this contribution, we describe an application of support vector machine (SVM), a supervised learning algorithm, to mineral prospectivity mapping. The free R package e1071 is used to construct a SVM with sigmoid kernel function to map prospectivity for Au deposits in western Meguma Terrain of Nova

Support vector machines classifiers of physical activities in preschoolers

Science.gov (United States)

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

DC Algorithm for Extended Robust Support Vector Machine.

Science.gov (United States)

Fujiwara, Shuhei; Takeda, Akiko; Kanamori, Takafumi

2017-05-01

Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SVM), a robust variant of E[Formula: see text]-SVM. ER-SVM combines two types of nonconvexity from robust SVMs and E[Formula: see text]-SVM. Because of the two nonconvexities, the existing algorithm we proposed needs to be divided into two parts depending on whether the hyperparameter value is in the extended range or not. The algorithm also heuristically solves the nonconvex problem in the extended range. In this letter, we propose a new, efficient algorithm for ER-SVM. The algorithm deals with two types of nonconvexity while never entailing more computations than either E[Formula: see text]-SVM or robust SVM, and it finds a critical point of ER-SVM. Furthermore, we show that ER-SVM includes the existing robust SVMs as special cases. Numerical experiments confirm the effectiveness of integrating the two nonconvexities.

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...

ATCA for Machines-- Advanced Telecommunications Computing Architecture

Energy Technology Data Exchange (ETDEWEB)

Larsen, R.S.; /SLAC

2008-04-22

The Advanced Telecommunications Computing Architecture is a new industry open standard for electronics instrument modules and shelves being evaluated for the International Linear Collider (ILC). It is the first industrial standard designed for High Availability (HA). ILC availability simulations have shown clearly that the capabilities of ATCA are needed in order to achieve acceptable integrated luminosity. The ATCA architecture looks attractive for beam instruments and detector applications as well. This paper provides an overview of ongoing R&D including application of HA principles to power electronics systems.

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.

ATCA for Machines-- Advanced Telecommunications Computing Architecture

International Nuclear Information System (INIS)

Larsen, R

2008-01-01

The Advanced Telecommunications Computing Architecture is a new industry open standard for electronics instrument modules and shelves being evaluated for the International Linear Collider (ILC). It is the first industrial standard designed for High Availability (HA). ILC availability simulations have shown clearly that the capabilities of ATCA are needed in order to achieve acceptable integrated luminosity. The ATCA architecture looks attractive for beam instruments and detector applications as well. This paper provides an overview of ongoing R and D including application of HA principles to power electronics systems

Supporting Sustainability and Personalization with Product Architecture

DEFF Research Database (Denmark)

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

2011-01-01

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...... 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...

PLM support to architecture based development

DEFF Research Database (Denmark)

Bruun, Hans Peter Lomholt

, 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...... and developing architectures can be difficult to manage, update, and maintain during development. The concept of representing product architectures in computer-based product information tools has though been central in this research, and in the creation of results. A standard PLM tool (Windchill PDMLink...... architectures in computer systems. Presented results build on research literature and experiences from industrial partners. Verification of the theory contributions, approaches, models, and tools, have been carried out in industrial projects, with promising results. This thesis describes the means for: (1...

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

Science.gov (United States)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

Directory of Open Access Journals (Sweden)

Ming Cheng

2015-09-01

Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Consumer support for healthy food and drink vending machines in public places.

Science.gov (United States)

Carrad, Amy M; Louie, Jimmy Chun-Yu; Milosavljevic, Marianna; Kelly, Bridget; Flood, Victoria M

2015-08-01

To investigate the feasibility of introducing vending machines for healthier food into public places, and to examine the effectiveness of two front-of-pack labelling systems in the vending machine context. A survey was conducted with 120 students from a university and 120 employees, patients and visitors of a hospital in regional NSW, Australia. Questions explored vending machine use, attitudes towards healthier snack products and price, and the performance of front-of-pack labelling formats for vending machine products. Most participants viewed the current range of snacks and drinks as "too unhealthy" (snacks 87.5%; drinks 56.7%). Nuts and muesli bars were the most liked healthier vending machine snack. Higher proportions of participants were able to identify the healthier snack in three of the five product comparisons when products were accompanied with any type of front-of-pack label (all pvending machines. Front-of-pack label formats on vending machines may assist consumers to identify healthier products. Public settings, such as universities and hospitals, should support consumers to make healthy dietary choices by improving food environments. © 2015 Public Health Association of Australia.

Support vector machine based battery model for electric vehicles

International Nuclear Information System (INIS)

Wang Junping; Chen Quanshi; Cao Binggang

2006-01-01

The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

Combining extreme learning machines using support vector machines for breast tissue classification.

Science.gov (United States)

Daliri, Mohammad Reza

2015-01-01

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

Development of aerodynamic bearing support for application in air cycle machines

Directory of Open Access Journals (Sweden)

Šimek J.

2014-06-01

Full Text Available Air cycle machines (ACM are used in environmental control system of aircrafts to manage pressurization of the cabin. The aim of this work is to gain theoretical and experimental data enabling replacement of rolling bearings, which require lubrication and have limited operating speed, with aerodynamic bearing support. Aerodynamic bearings do not pollute process air and at the same time allow achieving higher operating speed, thus enabling to reduce machine mass and dimensions. A test stand enabling the verification of aerodynamic bearing support properties for prospective ACM was designed, manufactured and tested with operating speeds up to 65 000 rpm. Some interesting features of the test stand design and the test results are presented. A smaller test stand with operating speed up to 100 000 rpm is in design stage.

Building evolutionary architectures support constant change

CERN Document Server

Ford, Neal; Kua, Patrick

2017-01-01

The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time.

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.

Quantum optimization for training support vector machines.

Science.gov (United States)

Anguita, Davide; Ridella, Sandro; Rivieccio, Fabio; Zunino, Rodolfo

2003-01-01

Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered.

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.

A proposed clinical decision support architecture capable of supporting whole genome sequence information.

Science.gov (United States)

Welch, Brandon M; Loya, Salvador Rodriguez; Eilbeck, Karen; Kawamoto, Kensaku

2014-04-04

Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

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.

Machine Protection

International Nuclear Information System (INIS)

Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

2012-01-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

Machine Protection

Energy Technology Data Exchange (ETDEWEB)

Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

2012-07-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

Machine Protection

CERN Document Server

Zerlauth, Markus; Wenninger, Jörg

2012-01-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

Code-expanded radio access protocol for machine-to-machine communications

DEFF Research Database (Denmark)

Thomsen, Henning; Kiilerich Pratas, Nuno; Stefanovic, Cedomir

2013-01-01

The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated b...... subframes and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the massive support of Machine-Type Communication users that is beyond the reach of current systems.......The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated...... by the random access method employed in LTE, which significantly increases the amount of contention resources without increasing the system resources, such as contention subframes and preambles. This is accomplished by a logical, rather than physical, extension of the access method in which the available system...

Quantum algorithm for support matrix machines

Science.gov (United States)

Duan, Bojia; Yuan, Jiabin; Liu, Ying; Li, Dan

2017-09-01

We propose a quantum algorithm for support matrix machines (SMMs) that efficiently addresses an image classification problem by introducing a least-squares reformulation. This algorithm consists of two core subroutines: a quantum matrix inversion (Harrow-Hassidim-Lloyd, HHL) algorithm and a quantum singular value thresholding (QSVT) algorithm. The two algorithms can be implemented on a universal quantum computer with complexity O[log(npq) ] and O[log(pq)], respectively, where n is the number of the training data and p q is the size of the feature space. By iterating the algorithms, we can find the parameters for the SMM classfication model. Our analysis shows that both HHL and QSVT algorithms achieve an exponential increase of speed over their classical counterparts.

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

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.

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.

Knowledge-based support for design and operational use of human-machine interfaces

International Nuclear Information System (INIS)

Johannsen, G.

1994-01-01

The possibilities for knowledge support of different human user classes, namely operators, operational engineers and designers of human-machine interfaces, are discussed. Several human-machine interface functionalities are briefly explained. The paper deals with such questions as which type of knowledge is needed for design and operation, how to represent it, where to get it from, how to process it, and how to consider and use it. The relationships between design and operational use are thereby emphasised. (author)

Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches

National Research Council Canada - National Science Library

Qi, Yuan

2000-01-01

In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply...

Dual linear structured support vector machine tracking method via scale correlation filter

Science.gov (United States)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Architectures Toward Reusable Science Data Systems

Science.gov (United States)

Moses, John

2015-01-01

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAAs Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience we expect to find architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

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).

Oblique decision trees using embedded support vector machines in classifier ensembles

NARCIS (Netherlands)

Menkovski, V.; Christou, I.; Efremidis, S.

2008-01-01

Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

Support vector machines in analysis of top quark production

International Nuclear Information System (INIS)

Vaiciulis, A.

2003-01-01

The Support Vector Machine (SVM) learning algorithm is a new alternative to multivariate methods such as neural networks. Potential applications of SVMs in high energy physics include the common classification problem of signal/background discrimination as well as particle identification. A comparison of a conventional method and an SVM algorithm is presented here for the case of identifying top quark events in Run II physics at the CDF experiment

PGHPF – An Optimizing High Performance Fortran Compiler for Distributed Memory Machines

Directory of Open Access Journals (Sweden)

Zeki Bozkus

1997-01-01

Full Text Available High Performance Fortran (HPF is the first widely supported, efficient, and portable parallel programming language for shared and distributed memory systems. HPF is realized through a set of directive-based extensions to Fortran 90. It enables application developers and Fortran end-users to write compact, portable, and efficient software that will compile and execute on workstations, shared memory servers, clusters, traditional supercomputers, or massively parallel processors. This article describes a production-quality HPF compiler for a set of parallel machines. Compilation techniques such as data and computation distribution, communication generation, run-time support, and optimization issues are elaborated as the basis for an HPF compiler implementation on distributed memory machines. The performance of this compiler on benchmark programs demonstrates that high efficiency can be achieved executing HPF code on parallel architectures.

Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine

Directory of Open Access Journals (Sweden)

Hosein Nouri-Ahmadabadi

2017-12-01

Full Text Available In this study, an intelligent system based on combined machine vision (MV and Support Vector Machine (SVM was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used to capture images. The images were digitalized by a capture card and transferred to a personal computer for further analysis. Initially, images were converted from RGB color space to HSV color ones. For segmentation of the acquired images, H-component in the HSV color space and Otsu thresholding method were applied. A feature vector containing 30 color features was extracted from the captured images. A feature selection method based on sensitivity analysis was carried out to select superior features. The selected features were presented to SVM classifier. Various SVM models having a different kernel function were developed and tested. The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy (99.17% and then was selected to use in online decision-making unit of the system. By launching the online system, it was found that limiting factors of the system capacity were related to the hardware parts of the system (conveyor belt and pneumatic valves used in the sorting unit. The limiting factors led to a distance of 8 mm between the samples. The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h, respectively. Keywords: Pistachio kernel, Sorting, Machine vision, Sensitivity analysis, Support vector machine

Subspace identification of Hammer stein models using support vector machines

International Nuclear Information System (INIS)

Al-Dhaifallah, Mujahed

2011-01-01

System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.

A Proposed Clinical Decision Support Architecture Capable of Supporting Whole Genome Sequence Information

Directory of Open Access Journals (Sweden)

Brandon M. Welch

2014-04-01

Full Text Available Whole genome sequence (WGS information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR. A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1 each component of the architecture; (2 the interaction of the components; and (3 how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

Architectural Design Support for Composition & Superimposition

NARCIS (Netherlands)

Gurp, Jilles van; Smedinga, Rein; Bosch, Jan

2002-01-01

The ever growing size and complexity of software systems is making it increasingly harder to build systems that both meet current and future requirements. During architecture design, a lot of important design decisions are taken. In this paper, we present an architecture design notation based on

SAM: Support Vector Machine Based Active Queue Management

International Nuclear Information System (INIS)

Shah, M.S.

2014-01-01

Recent years have seen an increasing interest in the design of AQM (Active Queue Management) controllers. The purpose of these controllers is to manage the network congestion under varying loads, link delays and bandwidth. In this paper, a new AQM controller is proposed which is trained by using the SVM (Support Vector Machine) with the RBF (Radial Basis Function) kernal. The proposed controller is called the support vector based AQM (SAM) controller. The performance of the proposed controller has been compared with three conventional AQM controllers, namely the Random Early Detection, Blue and Proportional Plus Integral Controller. The preliminary simulation studies show that the performance of the proposed controller is comparable to the conventional controllers. However, the proposed controller is more efficient in controlling the queue size than the conventional controllers. (author)

A Modified Method Combined with a Support Vector Machine and Bayesian Algorithms in Biological Information

Directory of Open Access Journals (Sweden)

Wen-Gang Zhou

2015-06-01

Full Text Available With the deep research of genomics and proteomics, the number of new protein sequences has expanded rapidly. With the obvious shortcomings of high cost and low efficiency of the traditional experimental method, the calculation method for protein localization prediction has attracted a lot of attention due to its convenience and low cost. In the machine learning techniques, neural network and support vector machine (SVM are often used as learning tools. Due to its complete theoretical framework, SVM has been widely applied. In this paper, we make an improvement on the existing machine learning algorithm of the support vector machine algorithm, and a new improved algorithm has been developed, combined with Bayesian algorithms. The proposed algorithm can improve calculation efficiency, and defects of the original algorithm are eliminated. According to the verification, the method has proved to be valid. At the same time, it can reduce calculation time and improve prediction efficiency.

Extraction of inland Nypa fruticans (Nipa Palm) using Support Vector Machine

Science.gov (United States)

Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Biagtan, A. R.; Panuyas, N. Z.; Quibuyen, J. S.

2017-09-01

Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

Automatic Detection of P and S Phases by Support Vector Machine

Science.gov (United States)

Jiang, Y.; Ning, J.; Bao, T.

2017-12-01

Many methods in seismology rely on accurately picked phases. A well performed program on automatically phase picking will assure the application of these methods. Related researches before mostly focus on finding different characteristics between noise and phases, which are all not enough successful. We have developed a new method which mainly based on support vector machine to detect P and S phases. In it, we first input some waveform pieces into the support vector machine, then employ it to work out a hyper plane which can divide the space into two parts: respectively noise and phase. We further use the same method to find a hyper plane which can separate the phase space into P and S parts based on the three components' cross-correlation matrix. In order to further improve the ability of phase detection, we also employ array data. At last, we show that the overall effect of our method is robust by employing both synthetic and real data.

Mobile virtual synchronous machine for vehicle-to-grid applications

Energy Technology Data Exchange (ETDEWEB)

Pelczar, Christopher

2012-03-20

The Mobile Virtual Synchronous Machine (VISMA) is a power electronics device for Vehicle to Grid (V2G) applications which behaves like an electromechanical synchronous machine and offers the same beneficial properties to the power network, increasing the inertia in the system, stabilizing the grid voltage, and providing a short-circuit current in case of grid faults. The VISMA performs a real-time simulation of a synchronous machine and calculates the phase currents that an electromagnetic synchronous machine would produce under the same local grid conditions. An inverter with a current controller feeds the currents calculated by the VISMA into the grid. In this dissertation, the requirements for a machine model suitable for the Mobile VISMA are set, and a mathematical model suitable for use in the VISMA algorithm is found and tested in a custom-designed simulation environment prior to implementation on the Mobile VISMA hardware. A new hardware architecture for the Mobile VISMA based on microcontroller and FPGA technologies is presented, and experimental hardware is designed, implemented, and tested. The new architecture is designed in such a way that allows reducing the size and cost of the VISMA, making it suitable for installation in an electric vehicle. A simulation model of the inverter hardware and hysteresis current controller is created, and the simulations are verified with various experiments. The verified model is then used to design a new type of PWM-based current controller for the Mobile VISMA. The performance of the hysteresis- and PWM-based current controllers is evaluated and compared for different operational modes of the VISMA and configurations of the inverter hardware. Finally, the behavior of the VISMA during power network faults is examined. A desired behavior of the VISMA during network faults is defined, and experiments are performed which verify that the VISMA, inverter hardware, and current controllers are capable of supporting this

Housing Value Forecasting Based on Machine Learning Methods

Directory of Open Access Journals (Sweden)

Jingyi Mu

2014-01-01

Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

Directory of Open Access Journals (Sweden)

Jian Chai

2015-01-01

Full Text Available This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search, PSO (particle swarm optimization, and GA (genetic algorithm. Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

Central system of Interlock of ITER, high integrity architecture

International Nuclear Information System (INIS)

Prieto, I.; Martinez, G.; Lopez, C.

2014-01-01

The CIS (Central Interlock System), along with the CODAC system and CSS (Central Safety System), form the central I and C systems of ITER. The CIS is responsible for implementing the core functions of protection (Central Interlock Functions) through different systems of plant (Plant Systems) within the overall strategy of investment protection for ITER. IBERDROLA supports engineering to define and develop the control architecture of CIS according to the stringent requirements of integrity, availability and response time. For functions with response times of the order of half a second is selected PLC High availability of industrial range. However, due to the nature of the machine itself, certain functions must be able to act under the millisecond, so it has had to develop a solution based on FPGA (Field Programmable Gate Array) capable of meeting the requirements architecture. In this article CIS architecture is described, as well as the process for the development and validation of the selected platforms. (Author)

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...

Functional language and data flow architectures

Science.gov (United States)

Ercegovac, M. D.; Patel, D. R.; Lang, T.

1983-01-01

This is a tutorial article about language and architecture approaches for highly concurrent computer systems based on the functional style of programming. The discussion concentrates on the basic aspects of functional languages, and sequencing models such as data-flow, demand-driven and reduction which are essential at the machine organization level. Several examples of highly concurrent machines are described.

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%.

A physical implementation of the Turing machine accessed through Web

Directory of Open Access Journals (Sweden)

Marijo Maracic

2008-11-01

Full Text Available A Turing machine has an important role in education in the field of computer science, as it is a milestone in courses related to automata theory, theory of computation and computer architecture. Its value is also recognized in the Computing Curricula proposed by the Association for Computing Machinery (ACM and IEEE Computer Society. In this paper we present a physical implementation of the Turing machine accessed through Web. To enable remote access to the Turing machine, an implementation of the client-server architecture is built. The web interface is described in detail and illustrations of remote programming, initialization and the computation of the Turing machine are given. Advantages of such approach and expected benefits obtained by using remotely accessible physical implementation of the Turing machine as an educational tool in the teaching process are discussed.

Product Quality Modelling Based on Incremental Support Vector Machine

International Nuclear Information System (INIS)

Wang, J; Zhang, W; Qin, B; Shi, W

2012-01-01

Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

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.

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

International Nuclear Information System (INIS)

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

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.

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

Energy Technology Data Exchange (ETDEWEB)

Sahin, M.Ö., E-mail: ozgur.sahin@desy.de; Krücker, D., E-mail: dirk.kruecker@desy.de; Melzer-Pellmann, I.-A., E-mail: isabell.melzer@desy.de

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.

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

International Nuclear Information System (INIS)

Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

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.

Selecting a Benchmark Suite to Profile High-Performance Computing (HPC) Machines

Science.gov (United States)

2014-11-01

architectures. Machines now contain central processing units (CPUs), graphics processing units (GPUs), and many integrated core ( MIC ) architecture all...evaluate the feasibility and applicability of a new architecture just released to the market . Researchers are often unsure how available resources will...architectures. Having a suite of programs running on different architectures, such as GPUs, MICs , and CPUs, adds complexity and technical challenges

Penerapan Support Vector Machine (SVM untuk Pengkategorian Penelitian

Directory of Open Access Journals (Sweden)

Fithri Selva Jumeilah

2017-07-01

Full Text Available Research every college will continue to grow. Research will be stored in softcopy and hardcopy. The preparation of the research should be categorized in order to facilitate the search for people who need reference. To categorize the research, we need a method for text mining, one of them is with the implementation of Support Vector Machines (SVM. The data used to recognize the characteristics of each category then it takes secondary data which is a collection of abstracts of research. The data will be pre-processed with several stages: case folding converts all the letters into lowercase, stop words removal removal of very common words, tokenizing discard punctuation, and stemming searching for root words by removing the prefix and suffix. Further data that has undergone preprocessing will be converted into a numerical form with for the term weighting stage that is the weighting contribution of each word. From the results of term weighting then obtained data that can be used for data training and test data. The training process is done by providing input in the form of text data that is known to the class or category. Then by using the Support Vector Machines algorithm, the input data is transformed into a rule, function, or knowledge model that can be used in the prediction process. From the results of this study obtained that the categorization of research produced by SVM has been very good. This is proven by the results of the test which resulted in an accuracy of 90%.

Architecture is always in the middle…

Directory of Open Access Journals (Sweden)

Tim Gough

2015-12-01

Full Text Available This essay proposes an ontology of architecture that takes its lead from the bread and butter of architecture: a flat ontology opposed to Cartesianism in the sense that no differentiation between realms (body/mind, high/low is accepted. The work of Spinoza and Deleuze is referred to in order to flesh out such an ontology, whose aim is to destroy the very desire for architecture and architectural theory to even pose the question about the difference between bread-and-butter architecture and high architecture. Architecture is shown to be of the nature of an assemblage, of a machine or a haecceity (to use Deleuze and Guattari’s phrase, and the implications of this in relation to the question of composition and reception are outlined.

Open architecture design and approach for the Integrated Sensor Architecture (ISA)

Science.gov (United States)

Moulton, Christine L.; Krzywicki, Alan T.; Hepp, Jared J.; Harrell, John; Kogut, Michael

2015-05-01

Integrated Sensor Architecture (ISA) is designed in response to stovepiped integration approaches. The design, based on the principles of Service Oriented Architectures (SOA) and Open Architectures, addresses the problem of integration, and is not designed for specific sensors or systems. The use of SOA and Open Architecture approaches has led to a flexible, extensible architecture. Using these approaches, and supported with common data formats, open protocol specifications, and Department of Defense Architecture Framework (DoDAF) system architecture documents, an integration-focused architecture has been developed. ISA can help move the Department of Defense (DoD) from costly stovepipe solutions to a more cost-effective plug-and-play design to support interoperability.

A software architecture for adaptive modular sensing systems.

Science.gov (United States)

Lyle, Andrew C; Naish, Michael D

2010-01-01

By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration.

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

International Nuclear Information System (INIS)

Miao, Qiang; Huang, Hong Zhong; Fan, Xianfeng

2007-01-01

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area

Support Vector Machines as tools for mortality graduation

Directory of Open Access Journals (Sweden)

Alberto Olivares

2011-01-01

Full Text Available A topic of interest in demographic and biostatistical analysis as well as in actuarial practice,is the graduation of the age-specific mortality pattern. A classical graduation technique is to fit parametric models. Recently, particular emphasis has been given to graduation using nonparametric techniques. Support Vector Machines (SVM is an innovative methodology that could be utilized for mortality graduation purposes. This paper evaluates SVM techniques as tools for graduating mortality rates. We apply SVM to empirical death rates from a variety of populations and time periods. For comparison, we also apply standard graduation techniques to the same data.

Assessment of modularity architecture for recovery process of electric vehicle in supporting sustainable design

Science.gov (United States)

Baroroh, D. K.; Alfiah, D.

2018-05-01

The electric vehicle is one of the innovations to reduce the pollution of the vehicle. Nevertheless, it still has a problem, especially for disposal stage. In supporting product design and development strategy, which is the idea of sustainable design or problem solving of disposal stage, assessment of modularity architecture from electric vehicle in recovery process needs to be done. This research used Design Structure Matrix (DSM) approach to deciding interaction of components and assessment of modularity architecture using the calculation of value from 3 variables, namely Module Independence (MI), Module Similarity (MS), and Modularity for End of Life Stage (MEOL). The result of this research shows that existing design of electric vehicles has the architectural design which has a high value of modularity for recovery process on disposal stage. Accordingly, so it can be reused and recycled in component level or module without disassembly process to support the product that is environmentally friendly (sustainable design) and able reduce disassembly cost.

Successive overrelaxation for laplacian support vector machine.

Science.gov (United States)

Qi, Zhiquan; Tian, Yingjie; Shi, Yong

2015-04-01

Semisupervised learning (SSL) problem, which makes use of both a large amount of cheap unlabeled data and a few unlabeled data for training, in the last few years, has attracted amounts of attention in machine learning and data mining. Exploiting the manifold regularization (MR), Belkin et al. proposed a new semisupervised classification algorithm: Laplacian support vector machines (LapSVMs), and have shown the state-of-the-art performance in SSL field. To further improve the LapSVMs, we proposed a fast Laplacian SVM (FLapSVM) solver for classification. Compared with the standard LapSVM, our method has several improved advantages as follows: 1) FLapSVM does not need to deal with the extra matrix and burden the computations related to the variable switching, which make it more suitable for large scale problems; 2) FLapSVM’s dual problem has the same elegant formulation as that of standard SVMs. This means that the kernel trick can be applied directly into the optimization model; and 3) FLapSVM can be effectively solved by successive overrelaxation technology, which converges linearly to a solution and can process very large data sets that need not reside in memory. In practice, combining the strategies of random scheduling of subproblem and two stopping conditions, the computing speed of FLapSVM is rigidly quicker to that of LapSVM and it is a valid alternative to PLapSVM.

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

Development of a wearable measurement and control unit for personal customizing machine-supported exercise.

Science.gov (United States)

Wang, Zhihui; Tamura, Naoki; Kiryu, Tohru

2005-01-01

Wearable technology has been used in various health-related fields to develop advanced monitoring solutions. However, the monitoring function alone cannot meet all the requirements of personal customizing machine-supported exercise that have biosignal-based controls. In this paper, we propose a new wearable unit design equipped with measurement and control functions to support the personal customization process. The wearable unit can measure the heart rate and electromyogram signals during exercise and output workload control commands to the exercise machines. We then applied a prototype of the wearable unit to an Internet-based cycle ergometer system. The wearable unit was examined using twelve young people to check its feasibility. The results verified that the unit could successfully adapt to the control of the workload and was effective for continuously supporting gradual changes in physical activities.

A Support Vector Machine Approach to Dutch Part-of-Speech Tagging

NARCIS (Netherlands)

Poel, Mannes; Stegeman, L.; op den Akker, Hendrikus J.A.; Berthold, M.R.; Shawe-Taylor, J.; Lavrac, N.

Part-of-Speech tagging, the assignment of Parts-of-Speech to the words in a given context of use, is a basic technique in many systems that handle natural languages. This paper describes a method for supervised training of a Part-of-Speech tagger using a committee of Support Vector Machines on a

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...... to simulate our self-organisation and self-healing algorithms and the results obtained from this looks promising....

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)

Design Methodology of a Sensor Network Architecture Supporting Urgent Information and Its Evaluation

Science.gov (United States)

Kawai, Tetsuya; Wakamiya, Naoki; Murata, Masayuki

Wireless sensor networks are expected to become an important social infrastructure which helps our life to be safe, secure, and comfortable. In this paper, we propose design methodology of an architecture for fast and reliable transmission of urgent information in wireless sensor networks. In this methodology, instead of establishing single complicated monolithic mechanism, several simple and fully-distributed control mechanisms which function in different spatial and temporal levels are incorporated on each node. These mechanisms work autonomously and independently responding to the surrounding situation. We also show an example of a network architecture designed following the methodology. We evaluated the performance of the architecture by extensive simulation and practical experiments and our claim was supported by the results of these experiments.

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.

Adaptive image denoising based on support vector machine and wavelet description

Science.gov (United States)

An, Feng-Ping; Zhou, Xian-Wei

2017-12-01

Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising.

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. Copyright © 2014 ISA

Specification, Design, and Analysis of Advanced HUMS Architectures

Science.gov (United States)

Mukkamala, Ravi

2004-01-01

During the two-year project period, we have worked on several aspects of domain-specific architectures for HUMS. In particular, we looked at using scenario-based approach for the design and designed a language for describing such architectures. The language is now being used in all aspects of our HUMS design. In particular, we have made contributions in the following areas. 1) We have employed scenarios in the development of HUMS in three main areas. They are: (a) To improve reusability by using scenarios as a library indexing tool and as a domain analysis tool; (b) To improve maintainability by recording design rationales from two perspectives - problem domain and solution domain; (c) To evaluate the software architecture. 2) We have defined a new architectural language called HADL or HUMS Architectural Definition Language. It is a customized version of xArch/xADL. It is based on XML and, hence, is easily portable from domain to domain, application to application, and machine to machine. Specifications written in HADL can be easily read and parsed using the currently available XML parsers. Thus, there is no need to develop a plethora of software to support HADL. 3) We have developed an automated design process that involves two main techniques: (a) Selection of solutions from a large space of designs; (b) Synthesis of designs. However, the automation process is not an absolute Artificial Intelligence (AI) approach though it uses a knowledge-based system that epitomizes a specific HUMS domain. The process uses a database of solutions as an aid to solve the problems rather than creating a new design in the literal sense. Since searching is adopted as the main technique, the challenges involved are: (a) To minimize the effort in searching the database where a very large number of possibilities exist; (b) To develop representations that could conveniently allow us to depict design knowledge evolved over many years; (c) To capture the required information that aid the

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.

A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

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Fuqiang Sun

2017-01-01

Full Text Available Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

Science.gov (United States)

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

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Anang Anggono Lutfi

2018-04-01

Full Text Available The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.

A deep knowledge architecture for intelligent support of nuclear waste transportation decisions

International Nuclear Information System (INIS)

Batra, D.; Bowen, W.M.; Hill, T.R.; Weeks, K.D.

1988-01-01

The concept of intelligent decision support has been discussed and explored in several recent papers, one of which has suggested the use of a Deep Knowledge Architecture. This paper explores this concept through application to a specific decision environment. The complex problems involved in nuclear waste disposal decisions provide an excellent test case. The resulting architecture uses an integrated, multi-level model base to represent the deep knowledge of the problem. Combined with the surface level knowledge represented by the database, the proposed knowledge base complements that of the decision-maker, allowing analysis at a range of levels of decisions which may also occur at a range of levels

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.

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.

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

International Nuclear Information System (INIS)

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

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.

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

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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.

Considering Intermittent Dormancy in an Advanced Life Support Systems Architecture

Science.gov (United States)

Sargusingh, Miriam J.; Perry, Jay L.

2017-01-01

Many advanced human space exploration missions being considered by the National Aeronautics and Space Administration (NASA) include concepts in which in-space systems cycle between inhabited and uninhabited states. Managing the life support system (LSS) may be particularly challenged during these periods of intermittent dormancy. A study to identify LSS management challenges and considerations relating to dormancy is described. The study seeks to define concepts suitable for addressing intermittent dormancy states and to evaluate whether the reference LSS architectures being considered by the Advanced Exploration Systems (AES) Life Support Systems Project (LSSP) are sufficient to support this operational state. The primary focus of the study is the mission concept considered to be the most challenging-a crewed Mars mission with an extensive surface stay. Results from this study are presented and discussed.

Support Vector Machine Based Tool for Plant Species Taxonomic Classification

OpenAIRE

Manimekalai .K; Vijaya.MS

2014-01-01

Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a suitable solution using a supervised learning technique namely support vector machine. Features are extracted from scanned images of plant leaves and trained using SVM. Only class, order, family of plants...

A Software Architecture for Adaptive Modular Sensing Systems

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Andrew C. Lyle

2010-08-01

Full Text Available By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration.

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.

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...

Computerized operator support system with new man-machine interface for BWR power plants

International Nuclear Information System (INIS)

Monta, K.; Naito, N.; Sugawara, M.; Sato, N.; Mori, N.; Tai, I.; Fukumoto, A.; Tsuchida, M.

1984-01-01

Improvement of the man-machine interface of nuclear power plants is an important contribution to the further enhancement of operational safety. In addition, recent advances in computer technology seem to offer the greatest opportunity to date for achieving improvement in the man-machine interface. The development of a computerized operator support system for BWRs has been undertaken since 1980 with the support of the Japanese Government. The conceptual design of this system is based on the role of the operators. The main functions are standby system management, disturbance analysis and post-trip operational guidance. The objective of the standby system management is to monitor the standby status of the engineered safety feature during normal operation to assure its proper functioning at the onset of emergency situations. The disturbance analysis system detects disturbances in the plant in their early stages and informs the plant operators about, for example, the cause of the disturbances, the plant status and possible propagations. Consequently, operators can take corrective actions to prevent unnecessary plant shutdown. The objective of the post trip operational guide is to support operators in diagnosis and corrective action after a plant trip. Its functions are to monitor the performance of the engineered safety feature, to identify the plant status and to guide the appropriate corrective action to achieve safe plant shutdown. The information from the computerized operator support system is supplied to operators through a colour CRT operator console. The authors have evaluated the performance of various new man-machine interfacing tools and proposed a new operator console design. A prototype system has been developed and verification/validation is proceeding with a BWR plant simulator. (author)

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

Science.gov (United States)

Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

Connecting Architecture and Implementation

Science.gov (United States)

Buchgeher, Georg; Weinreich, Rainer

Software architectures are still typically defined and described independently from implementation. To avoid architectural erosion and drift, architectural representation needs to be continuously updated and synchronized with system implementation. Existing approaches for architecture representation like informal architecture documentation, UML diagrams, and Architecture Description Languages (ADLs) provide only limited support for connecting architecture descriptions and implementations. Architecture management tools like Lattix, SonarJ, and Sotoarc and UML-tools tackle this problem by extracting architecture information directly from code. This approach works for low-level architectural abstractions like classes and interfaces in object-oriented systems but fails to support architectural abstractions not found in programming languages. In this paper we present an approach for linking and continuously synchronizing a formalized architecture representation to an implementation. The approach is a synthesis of functionality provided by code-centric architecture management and UML tools and higher-level architecture analysis approaches like ADLs.

Discussion paper for a highly parallel array processor-based machine

International Nuclear Information System (INIS)

Hagstrom, R.; Bolotin, G.; Dawson, J.

1984-01-01

The architectural plant for a quickly realizable implementation of a highly parallel special-purpose computer system with peak performance in the range of 6 billion floating point operations per second is discussed. The architecture is suitable to Lattice Gauge theoretical computations of fundamental physics interest and may be applicable to a range of other problems which deal with numerically intensive computational problems. The plan is quickly realizable because it employs a maximum of commercially available hardware subsystems and because the architecture is software-transparent to the individual processors, allowing straightforward re-use of whatever commercially available operating-systems and support software that is suitable to run on the commercially-produced processors. A tiny prototype instrument, designed along this architecture has already operated. A few elementary examples of programs which can run efficiently are presented. The large machine which the authors would propose to build would be based upon a highly competent array-processor, the ST-100 Array Processor, and specific design possibilities are discussed. The first step toward realizing this plan practically is to install a single ST-100 to allow algorithm development to proceed while a demonstration unit is built using two of the ST-100 Array Processors

Face Recognition in Humans and Machines

Science.gov (United States)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

Explaining Support Vector Machines: A Color Based Nomogram.

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Vanya Van Belle

Full Text Available 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.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.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.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.

T-wave end detection using neural networks and Support Vector Machines.

Science.gov (United States)

Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

2018-05-01

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

Energy Technology Data Exchange (ETDEWEB)

Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P [College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha (China)

2006-10-15

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

International Nuclear Information System (INIS)

Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P

2006-01-01

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method

Automated valve fault detection based on acoustic emission parameters and support vector machine

Directory of Open Access Journals (Sweden)

Salah M. Ali

2018-03-01

Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

Modular Architecture for Integrated Model-Based Decision Support.

Science.gov (United States)

Gaebel, Jan; Schreiber, Erik; Oeser, Alexander; Oeltze-Jafra, Steffen

2018-01-01

Model-based decision support systems promise to be a valuable addition to oncological treatments and the implementation of personalized therapies. For the integration and sharing of decision models, the involved systems must be able to communicate with each other. In this paper, we propose a modularized architecture of dedicated systems for the integration of probabilistic decision models into existing hospital environments. These systems interconnect via web services and provide model sharing and processing capabilities for clinical information systems. Along the lines of IHE integration profiles from other disciplines and the meaningful reuse of routinely recorded patient data, our approach aims for the seamless integration of decision models into hospital infrastructure and the physicians' daily work.

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.

Using support vector machines to identify literacy skills: Evidence from eye movements.

Science.gov (United States)

Lou, Ya; Liu, Yanping; Kaakinen, Johanna K; Li, Xingshan

2017-06-01

Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-topic expository text. Forward fixation time, first-pass rereading time, second-pass fixation time, and regression path reading time on different regions of the text were provided as features. The SVM classification algorithm assisted in distinguishing high-literacy-skilled readers from low-literacy-skilled readers with 80.3 % accuracy. Results demonstrate the effectiveness of combining eye tracking and machine learning techniques to detect readers with low literacy skills, and suggest that such approaches can be potentially used in predicting other cognitive abilities.

Efficient Multiplicative Updates for Support Vector Machines

DEFF Research Database (Denmark)

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

2009-01-01

(NMF) problem. This allows us to derive a novel multiplicative algorithm for solving hard and soft margin SVM. The algorithm follows as a natural extension of the updates for NMF and semi-NMF. No additional parameter setting, such as choosing learning rate, is required. Exploiting the connection......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...... between SVM and NMF formulation, we show how NMF algorithms can be applied to the SVM problem. Multiplicative updates that we derive for SVM problem also represent novel updates for semi-NMF. Further this unified view yields algorithmic insights in both directions: we demonstrate that the Kernel Adatron...

Cognitive Human-Machine Interface Applied in Remote Support for Industrial Robot Systems

Directory of Open Access Journals (Sweden)

Tomasz Kosicki

2013-10-01

Full Text Available An attempt is currently being made to widely introduce industrial robots to Small-Medium Enterprises (SMEs. Since the enterprises usually employ too small number of robot units to afford specialized departments for robot maintenance, they must be provided with inexpensive and immediate support remotely. This paper evaluates whether the support can be provided by means of Cognitive Info-communication – communication in which human cognitive capabilities are extended irrespectively of geographical distances. The evaluations are given with an aid of experimental system that consists of local and remote rooms, which are physically separated – a six-degree-of-freedom NACHI SH133-03 industrial robot is situated in the local room, while the operator, who supervises the robot by means of audio-visual Cognitive Human-Machine Interface, is situated in the remote room. The results of simple experiments show that Cognitive Info-communication is not only efficient mean to provide the support remotely, but is probably also a powerful tool to enhance interaction with any data-rich environment that require good conceptual understanding of system's state and careful attention management. Furthermore, the paper discusses data presentation and reduction methods for data-rich environments, as well as introduces the concepts of Naturally Acquired Data and Cognitive Human-Machine Interfaces.

Space Station data management system architecture

Science.gov (United States)

Mallary, William E.; Whitelaw, Virginia A.

1987-01-01

Within the Space Station program, the Data Management System (DMS) functions in a dual role. First, it provides the hardware resources and software services which support the data processing, data communications, and data storage functions of the onboard subsystems and payloads. Second, it functions as an integrating entity which provides a common operating environment and human-machine interface for the operation and control of the orbiting Space Station systems and payloads by both the crew and the ground operators. This paper discusses the evolution and derivation of the requirements and issues which have had significant effect on the design of the Space Station DMS, describes the DMS components and services which support system and payload operations, and presents the current architectural view of the system as it exists in October 1986; one-and-a-half years into the Space Station Phase B Definition and Preliminary Design Study.

Fast Monte Carlo reliability evaluation using support vector machine

International Nuclear Information System (INIS)

Rocco, Claudio M.; Moreno, Jose Ali

2002-01-01

This paper deals with the feasibility of using support vector machine (SVM) to build empirical models for use in reliability evaluation. The approach takes advantage of the speed of SVM in the numerous model calculations typically required to perform a Monte Carlo reliability evaluation. The main idea is to develop an estimation algorithm, by training a model on a restricted data set, and replace system performance evaluation by a simpler calculation, which provides reasonably accurate model outputs. The proposed approach is illustrated by several examples. Excellent system reliability results are obtained by training a SVM with a small amount of information

Integration issues in virtual enterprises supported by an architectural framework

DEFF Research Database (Denmark)

Zwegers, Arian; Hannus, Matti; Tølle, Martin

2001-01-01

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......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...

Supporting migration to services using software architecture reconstruction

OpenAIRE

O'Brien, Liam; Smith, Dennis; Lewis, Grace

2005-01-01

peer-reviewed There are many good reasons why organizations should perform software architecture reconstructions. However, few organizations are willing to pay for the effort. Software architecture reconstruction must be viewed not as an effort on its own but as a contribution in a broader technical context, such as the streamlining of products into a product line or the modernization of systems that hit their architectural borders, that is require major restructuring. In this paper we ...

FUSION DECISION FOR A BIMODAL BIOMETRIC VERIFICATION SYSTEM USING SUPPORT VECTOR MACHINE AND ITS VARIATIONS

Directory of Open Access Journals (Sweden)

A. Teoh

2017-12-01

Full Text Available This paw presents fusion detection technique comparisons based on support vector machine and its variations for a bimodal biometric verification system that makes use of face images and speech utterances. The system is essentially constructed by a face expert, a speech expert and a fusion decision module. Each individual expert has been optimized to operate in automatic mode and designed for security access application. Fusion decision schemes considered are linear, weighted Support Vector Machine (SVM and linear SVM with quadratic transformation. The conditions tested include the balanced and unbalanced conditions between the two experts in order to obtain the optimum fusion module from  these techniques best suited to the target application.

Enhanced risk management by an emerging multi-agent architecture

Science.gov (United States)

Lin, Sin-Jin; Hsu, Ming-Fu

2014-07-01

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.

Support vector machines for nuclear reactor state estimation

Energy Technology Data Exchange (ETDEWEB)

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.

Support vector machines for nuclear reactor state estimation

International Nuclear Information System (INIS)

Zavaljevski, N.; Gross, K. C.

2000-01-01

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm

Virtual Things for Machine Learning Applications

OpenAIRE

Bovet , Gérôme; Ridi , Antonio; Hennebert , Jean

2014-01-01

International audience; Internet-of-Things (IoT) devices, especially sensors are pro-ducing large quantities of data that can be used for gather-ing knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to ex-ecute machine learning algorithms within the sensor net-work, presenting advantages in terms of privacy and data transfer efficiency. We first argument that some classes of ...

Joint Fire Support in 2020: Development of a Future Joint Fires Systems Architecture for Immediate, Unplanned Targets

National Research Council Canada - National Science Library

Gabriel, J. T; Bartel, Matthew; Dorrough, Grashawn J; Paiz, B. L; Peters, Brian; Savage, Matthew; Nordgran, Spencer

2006-01-01

... in support of the commander. In this context, the Joint Fire Support in 2020 project applied systems engineering procedures and principles to develop functional, physical, and operational architectures that maximize rapid...

The mathematics of the modernist villa architectural analysis using space syntax and isovists

CERN Document Server

Ostwald, Michael J

2018-01-01

This book presents the first detailed mathematical analysis of the social, cognitive and experiential properties of Modernist domestic architecture. The Modern Movement in architecture, which came to prominence during the first half of the twentieth century, may have been famous for its functional forms and machine-made aesthetic, but it also sought to challenge the way people inhabit, understand and experience space. Ludwig Mies van der Rohe’s buildings were not only minimalist and transparent, they were designed to subvert traditional social hierarchies. Frank Lloyd Wright’s organic Modernism not only attempted to negotiate a more responsive relationship between nature and architecture, but also shape the way people experience space. Richard Neutra’s Californian Modernism is traditionally celebrated for its sleek, geometric forms, but his intention was to use design to support a heightened understanding of context. Glenn Murcutt’s pristine pavilions, seemingly the epitome of regional Modernism, actu...

TensorFlow: A system for large-scale machine learning

OpenAIRE

Abadi, Martín; Barham, Paul; Chen, Jianmin; Chen, Zhifeng; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Irving, Geoffrey; Isard, Michael; Kudlur, Manjunath; Levenberg, Josh; Monga, Rajat; Moore, Sherry; Murray, Derek G.

2016-01-01

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexib...

Performance analysis of IMS based LTE and WIMAX integration architectures

Directory of Open Access Journals (Sweden)

A. Bagubali

2016-12-01

Full Text Available In the current networking field many research works are going on regarding the integration of different wireless technologies, with the aim of providing uninterrupted connectivity to the user anywhere, with high data rates due to increased demand. However, the number of objects like smart devices, industrial machines, smart homes, connected by wireless interface is dramatically increasing due to the evolution of cloud computing and internet of things technology. This Paper begins with the challenges involved in such integrations and then explains the role of different couplings and different architectures. This paper also gives further improvement in the LTE and Wimax integration architectures to provide seamless vertical handover and flexible quality of service for supporting voice, video, multimedia services over IP network and mobility management with the help of IMS networks. Evaluation of various parameters like handover delay, cost of signalling, packet loss,, is done and the performance of the interworking architecture is analysed from the simulation results. Finally, it concludes that the cross layer scenario is better than the non cross layer scenario.

Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

Directory of Open Access Journals (Sweden)

Chi-Man Vong

2012-01-01

Full Text Available Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs, a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

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.

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

Science.gov (United States)

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

Guiding Principles for Data Architecture to Support the Pathways Community HUB Model.

Science.gov (United States)

Zeigler, Bernard P; Redding, Sarah; Leath, Brenda A; Carter, Ernest L; Russell, Cynthia

2016-01-01

The Pathways Community HUB Model provides a unique strategy to effectively supplement health care services with social services needed to overcome barriers for those most at risk of poor health outcomes. Pathways are standardized measurement tools used to define and track health and social issues from identification through to a measurable completion point. The HUB use Pathways to coordinate agencies and service providers in the community to eliminate the inefficiencies and duplication that exist among them. Experience with the Model has brought out the need for better information technology solutions to support implementation of the Pathways themselves through decision-support tools for care coordinators and other users to track activities and outcomes, and to facilitate reporting. Here we provide a basis for discussing recommendations for such a data infrastructure by developing a conceptual model that formalizes the Pathway concept underlying current implementations. The main contribution is a set of core recommendations as a framework for developing and implementing a data architecture to support implementation of the Pathways Community HUB Model. The objective is to present a tool for communities interested in adopting the Model to learn from and to adapt in their own development and implementation efforts. Experience with the Community Health Access Project (CHAP) data base system (the core implementation of the Model) has identified several issues and remedies that have been developed to address these issues. Based on analysis of issues and remedies, we present several key features for a data architecture meeting the just mentioned recommendations. Presentation of features is followed by a practical guide to their implementation allowing an organization to consider either tailoring off-the-shelf generic systems to meet the requirements or offerings that are specialized for community-based care coordination. Looking to future extensions, we discuss the

Open Architecture Data System for NASA Langley Combined Loads Test System

Science.gov (United States)

Lightfoot, Michael C.; Ambur, Damodar R.

1998-01-01

The Combined Loads Test System (COLTS) is a new structures test complex that is being developed at NASA Langley Research Center (LaRC) to test large curved panels and cylindrical shell structures. These structural components are representative of aircraft fuselage sections of subsonic and supersonic transport aircraft and cryogenic tank structures of reusable launch vehicles. Test structures are subjected to combined loading conditions that simulate realistic flight load conditions. The facility consists of two pressure-box test machines and one combined loads test machine. Each test machine possesses a unique set of requirements or research data acquisition and real-time data display. Given the complex nature of the mechanical and thermal loads to be applied to the various research test articles, each data system has been designed with connectivity attributes that support both data acquisition and data management functions. This paper addresses the research driven data acquisition requirements for each test machine and demonstrates how an open architecture data system design not only meets those needs but provides robust data sharing between data systems including the various control systems which apply spectra of mechanical and thermal loading profiles.

Improvement of Shade Resilience in Photovoltaic Modules Using Buck Converters in a Smart Module Architecture

Directory of Open Access Journals (Sweden)

S. Zahra Mirbagheri Golroodbari

2018-01-01

Full Text Available Partial shading has a nonlinear effect on the performance of photovoltaic (PV modules. Different methods of optimizing energy harvesting under partial shading conditions have been suggested to mitigate this issue. In this paper, a smart PV module architecture is proposed for improvement of shade resilience in a PV module consisting of 60 silicon solar cells, which compensates the current drops caused by partial shading. The architecture consists of groups of series-connected solar cells in parallel to a DC-DC buck converter. The number of cell groups is optimized with respect to cell and converter specifications using a least-squares support vector machine method. A generic model is developed to simulate the behavior of the smart architecture under different shading patterns, using high time resolution irradiance data. In this research the shading patterns are a combination of random and pole shadows. To investigate the shade resilience, results for the smart architecture are compared with an ideal module, and also ordinary series and parallel connected architectures. Although the annual yield for the smart architecture is 79.5% of the yield of an ideal module, we show that the smart architecture outperforms a standard series connected module by 47%, and a parallel architecture by 13.4%.

ROBUSTNESS OF A FACE-RECOGNITION TECHNIQUE BASED ON SUPPORT VECTOR MACHINES

OpenAIRE

Prashanth Harshangi; Koshy George

2010-01-01

The ever-increasing requirements of security concerns have placed a greater demand for face recognition surveillance systems. However, most current face recognition techniques are not quite robust with respect to factors such as variable illumination, facial expression and detail, and noise in images. In this paper, we demonstrate that face recognition using support vector machines are sufficiently robust to different kinds of noise, does not require image pre-processing, and can be used with...

Architecture and program structures for a special purpose finite element computer

Energy Technology Data Exchange (ETDEWEB)

Norrie, D.H.; Norrie, C.W.

1983-01-01

The development of very large scale integration (VLSI) has made special-purpose computers economically possible. With such a machine, the loss of flexibility compared with a general-purpose computer can be offset by the increased speed which can be obtained by tailoring the architecture to the particular problem or class of problem. The first kind of special-purpose machine has its architecture modelled on the physical structure of the problem and the second kind has its design tailored to the computational algorithm used. The parallel finite element machine (PARFEM) being designed at the University of Calgary for the solution of finite element problems is of the second kind. Its conceptual design is described and progress to date outlined. 14 references.

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.

Architecture independent environment for developing engineering software on MIMD computers

Science.gov (United States)

Valimohamed, Karim A.; Lopez, L. A.

1990-01-01

Engineers are constantly faced with solving problems of increasing complexity and detail. Multiple Instruction stream Multiple Data stream (MIMD) computers have been developed to overcome the performance limitations of serial computers. The hardware architectures of MIMD computers vary considerably and are much more sophisticated than serial computers. Developing large scale software for a variety of MIMD computers is difficult and expensive. There is a need to provide tools that facilitate programming these machines. First, the issues that must be considered to develop those tools are examined. The two main areas of concern were architecture independence and data management. Architecture independent software facilitates software portability and improves the longevity and utility of the software product. It provides some form of insurance for the investment of time and effort that goes into developing the software. The management of data is a crucial aspect of solving large engineering problems. It must be considered in light of the new hardware organizations that are available. Second, the functional design and implementation of a software environment that facilitates developing architecture independent software for large engineering applications are described. The topics of discussion include: a description of the model that supports the development of architecture independent software; identifying and exploiting concurrency within the application program; data coherence; engineering data base and memory management.

Machine Protection: Availability for Particle Accelerators

CERN Document Server

Apollonio, Andrea; Schmidt, Ruediger

2015-03-16

Machine availability is a key indicator for the performance of the next generation of particle accelerators. Availability requirements need to be carefully considered during the design phase to achieve challenging objectives in different fields, as e.g. particle physics and material science. For existing and future High-Power facilities, such as ESS (European Spallation Source) and HL-LHC (High-Luminosity LHC), operation with unprecedented beam power requires highly dependable Machine Protection Systems (MPS) to avoid any damage-induced downtime. Due to the high complexity of accelerator systems, finding the optimal balance between equipment safety and accelerator availability is challenging. The MPS architecture, as well as the choice of electronic components, have a large influence on the achievable level of availability. In this thesis novel methods to address the availability of accelerators and their protection systems are presented. Examples of studies related to dependable MPS architectures are given i...

Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

Directory of Open Access Journals (Sweden)

Qiang Shang

2016-08-01

Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

Neural networks for perception human and machine perception

CERN Document Server

Wechsler, Harry

1991-01-01

Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model ofobject recognition in human vision, the self-organization of functional architecture in t

ADILE: Architecture of a database-supported learning environment

NARCIS (Netherlands)

Hiddink, G.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

Supporting and guiding device that is leak-tight and can be dismantled for the shaft of a rotating machine

International Nuclear Information System (INIS)

Tigoulet, Bernard; Fanchtein, J.P.; Dubost, Rene.

1982-01-01

This device includes a removable bearing casing crossed by at least one shaft of the machine, facilities for guiding this casing in parallel with the axis of the shaft so as to facilitate its removal and refitting, a system for supporting the shaft when the removable casing is not fitted in the machine frame. Application to machines for the extrusion of coating bitumen for radioactive waste [fr

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.

Support vector machines and generalisation in HEP

Science.gov (United States)

Bevan, Adrian; Gamboa Goñi, Rodrigo; Hays, Jon; Stevenson, Tom

2017-10-01

We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for H → τ + τ - at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.

A support vector machine approach for detection of microcalcifications.

Science.gov (United States)

El-Naqa, Issam; Yang, Yongyi; Wernick, Miles N; Galatsanos, Nikolas P; Nishikawa, Robert M

2002-12-01

In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

Science.gov (United States)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Machine en Theater. Ontwerpconcepten van winkelgebouwen

NARCIS (Netherlands)

Kooijman, D.C.

1999-01-01

Machine and Theater, Design Concepts for Shop Buildings is a richly illustrated study of the architectural and urban development of retail buildings, focusing on six essential shop types: the passage and the department store in particular in Germany and France in the nineteenth century; supermarkets

New fuzzy support vector machine for the class imbalance problem in medical datasets classification.

Science.gov (United States)

Gu, Xiaoqing; Ni, Tongguang; Wang, Hongyuan

2014-01-01

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.

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.

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

2015-02-25

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.

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.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

Science.gov (United States)

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

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Yang Sun

2018-01-01

Full Text Available Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM to predict tunnel squeezing based on four parameters, that is, diameter (D, buried depth (H, support stiffness (K, and rock tunneling quality index (Q. We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

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.

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.

Architecture of Institution & Home. Architecture as Cultural Medium

NARCIS (Netherlands)

Robinson, J.W.

2004-01-01

This dissertation addresses how architecture functions as a cultural medium. It does so by by investigating how the architecture of institution and home each construct and support different cultural practices. By studying the design of ordinary settings in terms of how qualitative differences in

SCADA Architecture for Natural Gas plant

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Turc Traian

2009-12-01

Full Text Available The paper describes the Natural Gas Plant SCADA architecture. The main purpose of SCADA system is remote monitoring and controlling of any industrial plant. The SCADA hardware architecture is based on multi-dropping system allowing connecting a large number of different fiels devices. The SCADA Server gathers data from gas plant and stores data to a MtSQL database. The SCADA server is connected to other SCADA client application offers a intuitive and user-friendly HMI. The main benefit of using SCADA is real time displaying of gas plant state. The main contriobution of the authors consists in designing SCADA architecture based on multi-dropping system and Human Machine Interface.

Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

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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%.

Evaluating Space Weather Architecture Options to Support Human Deep Space Exploration of the Moon and Mars

Science.gov (United States)

Parker, L.; Minow, J.; Pulkkinen, A.; Fry, D.; Semones, E.; Allen, J.; St Cyr, C.; Mertens, C.; Jun, I.; Onsager, T.; Hock, R.

2018-02-01

NASA's Engineering and Space Center (NESC) is conducting an independent technical assessment of space environment monitoring and forecasting architecture options to support human and robotic deep space exploration.

Advanced customization in architectural design and construction

CERN Document Server

Naboni, Roberto

2015-01-01

This book presents the state of the art in advanced customization within the sector of architectural design and construction, explaining important new technologies that are boosting design, product and process innovation and identifying the challenges to be confronted as we move toward a mass customization construction industry. Advanced machinery and software integration are discussed, as well as an overview of the manufacturing techniques offered through digital methods that are acquiring particular significance within the field of digital architecture. CNC machining, Robotic Fabrication, and Additive Manufacturing processes are all clearly explained, highlighting their ability to produce personalized architectural forms and unique construction components. Cutting-edge case studies in digitally fabricated architectural realizations are described and, looking towards the future, a new model of 100% customized architecture for design and construction is presented. The book is an excellent guide to the profoun...

Do Performance-Based Codes Support Universal Design in Architecture?

DEFF Research Database (Denmark)

Grangaard, Sidse; Frandsen, Anne Kathrine

2016-01-01

– 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...... 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....

Coal demand prediction based on a support vector machine model

Energy Technology Data Exchange (ETDEWEB)

Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

2007-01-15

A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

Proposed hardware architectures of particle filter for object tracking

Science.gov (United States)

Abd El-Halym, Howida A.; Mahmoud, Imbaby Ismail; Habib, SED

2012-12-01

In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

Lessons from 2011 - Machine protection

International Nuclear Information System (INIS)

Zerlauth, M.; Schmidt, R.; Wenninger, J.

2012-01-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. The LHC Machine Protection and Equipment Systems have been working extremely well during the 2011 run. Ever more failures are captured before effects on the particle beams are seen (i.e. no beam losses or orbit changes are observed). An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

Other programmatic agencies in the metropolis: a machinic approach to urban reterritorialization processes

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Igor Guatelli

2013-06-01

Full Text Available What if the strength of the architectural object were associated with program and spatial strategies engendered at the service of “habitability” and future sociabilities rather than with the building of monumental architectural gadgets and optical events in the landscape? Based on the Deleuzean (from the philosopher Gilles Deleuze machinic phylum as well as concepts associated with it such as “bonding” and “agency,” using the Lacanian approach (from the psychiatrist Jacques Lacan to the gadget concept and the Derridian concept (from the philosopher Jacques Derrida of “supplement,” this article discusses a shift of the most current senses and representations of contemporary urban architectural design historically associated with the notable (meaning the wish to be noticed formal and composite materialization of the artistic object at the service of programmed sociabilities towards nother conceptualization. The building of architectural supports from residual (according to Deleuze, the possibility of producing other wishes, far from the dominant capitalist logic, lies in residues in the residual flows produced by the capital itself programmatic and spatial agencies emerges as a critical path to the categorical imperative of the generalizing global logic. It is a logic based on non-territorial landscapes and centered on investments in the composite view and intentional spatial and programmatic imprisonments in familiar formulae originating from domesticated and standardized prêt-à-utiliser thinking. To think about other architectural spatial and programmatic agencies originating from residues and flows that simultaneously rise from and escape the global logic is to bet on the chance of non-programmed sociabilities taking place. Ceasing to think about architecture as a formal object in its artistic and paradigmatic dimension would mean to conceive it as an urban syntagmatic machine of [de]constructive power

Giro form reading machine

Science.gov (United States)

Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

1995-08-01

Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

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Xiaomin Xu

2015-01-01

Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

Non-linear HVAC computations using least square support vector machines

International Nuclear Information System (INIS)

Kumar, Mahendra; Kar, I.N.

2009-01-01

This paper aims to demonstrate application of least square support vector machines (LS-SVM) to model two complex heating, ventilating and air-conditioning (HVAC) relationships. The two applications considered are the estimation of the predicted mean vote (PMV) for thermal comfort and the generation of psychrometric chart. LS-SVM has the potential for quick, exact representations and also possesses a structure that facilitates hardware implementation. The results show very good agreement between function values computed from conventional model and LS-SVM model in real time. The robustness of LS-SVM models against input noises has also been analyzed.

Single Directional SMO Algorithm for Least Squares Support Vector Machines

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Xigao Shao

2013-01-01

Full Text Available Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs. In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO- type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.

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

Application of support vector machines to breast cancer screening using mammogram and clinical history data

Science.gov (United States)

Land, Walker H., Jr.; McKee, Dan; Velazquez, Roberto; Wong, Lut; Lo, Joseph Y.; Anderson, Francis R.

2003-05-01

The objectives of this paper are to discuss: (1) the development and testing of a new Evolutionary Programming (EP) method to optimally configure Support Vector Machine (SVM) parameters for facilitating the diagnosis of breast cancer; (2) evaluation of EP derived learning machines when the number of BI-RADS and clinical history discriminators are reduced from 16 to 7; (3) establishing system performance for several SVM kernels in addition to the EP/Adaptive Boosting (EP/AB) hybrid using the Digital Database for Screening Mammography, University of South Florida (DDSM USF) and Duke data sets; and (4) obtaining a preliminary evaluation of the measurement of SVM learning machine inter-institutional generalization capability using BI-RADS data. Measuring performance of the SVM designs and EP/AB hybrid against these objectives will provide quantative evidence that the software packages described can generalize to larger patient data sets from different institutions. Most iterative methods currently in use to optimize learning machine parameters are time consuming processes, which sometimes yield sub-optimal values resulting in performance degradation. SVMs are new machine intelligence paradigms, which use the Structural Risk Minimization (SRM) concept to develop learning machines. These learning machines can always be trained to provide global minima, given that the machine parameters are optimally computed. In addition, several system performance studies are described which include EP derived SVM performance as a function of: (a) population and generation size as well as a method for generating initial populations and (b) iteratively derived versus EP derived learning machine parameters. Finally, the authors describe a set of experiments providing preliminary evidence that both the EP/AB hybrid and SVM Computer Aided Diagnostic C++ software packages will work across a large population of patients, based on a data set of approximately 2,500 samples from five different

Support Vector Machines for Hyperspectral Remote Sensing Classification

Science.gov (United States)

Gualtieri, J. Anthony; Cromp, R. F.

1998-01-01

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

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R. Hadapiningradja Kusumodestoni

2017-09-01

Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

DNS Tunneling Detection Method Based on Multilabel Support Vector Machine

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Ahmed Almusawi

2018-01-01

Full Text Available DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an f-measure of 0.80.

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.

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

Science.gov (United States)

Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu

2011-01-01

Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the

Support vector machine incremental learning triggered by wrongly predicted samples

Science.gov (United States)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Large-scale ligand-based predictive modelling using support vector machines.

Science.gov (United States)

Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

2016-01-01

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

Experience with a clustered parallel reduction machine

NARCIS (Netherlands)

Beemster, M.; Hartel, Pieter H.; Hertzberger, L.O.; Hofman, R.F.H.; Langendoen, K.G.; Li, L.L.; Milikowski, R.; Vree, W.G.; Barendregt, H.P.; Mulder, J.C.

A clustered architecture has been designed to exploit divide and conquer parallelism in functional programs. The programming methodology developed for the machine is based on explicit annotations and program transformations. It has been successfully applied to a number of algorithms resulting in a

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

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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.

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

Science.gov (United States)

Yuan, Hua; Huang, Jianping; Cao, Chenzhong

2009-01-01

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. PMID:19742136

The implementation of common object request broker architecture (CORBA) for controlling robot arm via web

International Nuclear Information System (INIS)

Syed Mahamad Zuhdi Amin; Mohd Yazid Idris; Wan Mohd Nasir Wan Kadir

2001-01-01

This paper presents the employment of the Common Object Request Broker Architecture (CORBA) technology in the implementation of our distributed Arm Robot Controller (ARC). CORBA is an industrial standard architecture based on distributed abstract object model, which is developed by Object Management Group (OMG). The architecture consists of five components i.e. Object Request Broker (ORB), Interface Definition Language (IDL), Dynamic Invocation Interface (DII), Interface Repositories (IR) and Object adapter (OA). CORBA objects are different from typical programming objects in three ways i.e. they can be executed on any platform, located anywhere on the network and written in any language that supports IDL mapping. In the implementation of the system, 5 degree of freedom (DOF) arm robot RCS 6.0 and Java as a programming mapping to the CORBA IDL. By implementing this architecture, the objects in the server machine can be distributed over the network in order to run the controller. the ultimate goal for our ARC system is to demonstrate concurrent execution of multiple arm robots through multiple instantiations of distributed object components. (Author)

Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine

OpenAIRE

Chowdhury, Hemayet Ahmed; Nibir, Tanvir Alam; Islam, Md. Saiful

2018-01-01

The Rohingya Movement and Crisis caused a huge uproar in the political and economic state of Bangladesh. Refugee movement is a recurring event and a large amount of data in the form of opinions remains on social media such as Facebook, with very little analysis done on them.To analyse the comments based on all Rohingya related posts, we had to create and modify a classifier based on the Support Vector Machine algorithm. The code is implemented in python and uses scikit-learn library. A datase...

Developing an efficient decision support system for non-traditional machine selection: an application of MOORA and MOOSRA

Directory of Open Access Journals (Sweden)

Asis Sarkar

2015-01-01

Full Text Available The purpose of this paper is to find out an efficient decision support method for non-traditional machine selection. It seeks to analyze potential non-traditional machine selection attributes with a relatively new MCDM approach of MOORA and MOOSRA method. The use of MOORA and MOOSRA method has been adopted to tackle subjective evaluation of information collected from an expert group. An example case study is shown here for better understanding of the said selection module which can be effectively applied to any other decision-making scenario. The method is not only computationally very simple, easily comprehensible, and robust, but also believed to have numerous subjective attributes. The rankings are expected to provide good guidance to the managers of an organization to select a feasible non-traditional machine. It shall also provide a good insight for the non-traditional machine manufacturer who might encourage research work concerning non-traditional machine selection.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

NARCIS (Netherlands)

Pettersson-Yeo, W.; Benetti, S.; Marquand, A.F.; Joules, R.; Catani, M.; Williams, S.C.; Allen, P.; McGuire, P.; Mechelli, A.

2014-01-01

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification

Implementation of a Monte Carlo algorithm for neutron transport on a massively parallel SIMD machine

International Nuclear Information System (INIS)

Baker, R.S.

1992-01-01

We present some results from the recent adaptation of a vectorized Monte Carlo algorithm to a massively parallel architecture. The performance of the algorithm on a single processor Cray Y-MP and a Thinking Machine Corporations CM-2 and CM-200 is compared for several test problems. The results show that significant speedups are obtainable for vectorized Monte Carlo algorithms on massively parallel machines, even when the algorithms are applied to realistic problems which require extensive variance reduction. However, the architecture of the Connection Machine does place some limitations on the regime in which the Monte Carlo algorithm may be expected to perform well

Implementation of a Monte Carlo algorithm for neutron transport on a massively parallel SIMD machine

International Nuclear Information System (INIS)

Baker, R.S.

1993-01-01

We present some results from the recent adaptation of a vectorized Monte Carlo algorithm to a massively parallel architecture. The performance of the algorithm on a single processor Cray Y-MP and a Thinking Machine Corporations CM-2 and CM-200 is compared for several test problems. The results show that significant speedups are obtainable for vectorized Monte Carlo algorithms on massively parallel machines, even when the algorithms are applied to realistic problems which require extensive variance reduction. However, the architecture of the Connection Machine does place some limitations on the regime in which the Monte Carlo algorithm may be expected to perform well. (orig.)

VIBRATIONS MEASUREMENT IN ORDER TO IDENTIFY THE FAULTS TO THE TABLES AND SUPPORTS ON WHICH THE EMBROIDERY MACHINES ARE PLACED

Directory of Open Access Journals (Sweden)

ŞUTEU Marius

2014-05-01

Full Text Available The aim of this paper is to accurately and quickly identify the faults of the tables and supports on which the embroidery machines are placed through vibrations measuring method. Vibrations measurements on Happy embroidery machine were performed at S.C. CONFIDEX S.R.L Oradea. A FFT spectrum analyzer Impaq was used, made by Benstone Instruments Inc –SUA. The measurements were performed in order to seek the role and importance of the rigidity of embroidery machine supports for a better and more efficient performance of the machine. Before performing these measurements was determined the optimal operating mode of the embroidery machine. The vibration measurements were performed in each measuring point, by installing a vibration sensor on the three directions of the Cartesian coordinates system: axial (X, horizontal (Y, vertical (Z. In the present paper is shown only the measuring direction Z (sensor mounting direction and advance of the material on x direction (the embroidery direction this is the most relevant direction, as on this part the embroidery is executed. After performing these vibration measurements on the HAPPY embroidery machine, previously mounted on a big table, after that mounted on a smaller table and a less rigid base. The same vibrations measurements were performed and it was noticed that it is mandatory to position the machine on a big table and a stable base because it will influence both the reliability and the working regime of the machine.

A Smart Gateway Architecture for Improving Efficiency of Home Network Applications

Directory of Open Access Journals (Sweden)

Fei Ding

2016-01-01

Full Text Available A smart home gateway plays an important role in the Internet of Things (IoT system that takes responsibility for the connection between the network layer and the ubiquitous sensor network (USN layer. Even though the home network application is developing rapidly, researches on the home gateway based open development architecture are less. This makes it difficult to extend the home network to support new applications, share service, and interoperate with other home network systems. An integrated access gateway (IAGW is proposed in this paper which upward connects with the operator machine-to-machine platform (M2M P/F. In this home network scheme, the gateway provides standard interfaces for supporting various applications in home environments, ranging from on-site configuration to node and service access. In addition, communication management ability is also provided by M2M P/F. A testbed of a simple home network application system that includes the IAGW prototype is created to test its user interaction capabilities. Experimental results show that the proposed gateway provides significant flexibility for users to configure and deploy a home automation network; it can be applied to other monitoring areas and simultaneously supports a multi-ubiquitous sensor network.

A Machine Learning Concept for DTN Routing

Science.gov (United States)

Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

2017-01-01

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

''Diagonalization'' of a compound Atwood machine

International Nuclear Information System (INIS)

Crawford, F.S.

1987-01-01

We consider a simple Atwood machine consisting of a massless frictionless pulley no. 0 supporting two masses m 1 and m 2 connected by a massless flexible string. We show that the string that supports massless pulley no. 0 ''thinks'' it is simply supporting a mass m 0 , with m 0 = 4m 1 m 2 /(m 1 +m 2 ). This result, together with Einstein's equivalence principle, allows us to solve easily those compound Atwood machines created by replacing one or both of m 1 and m 2 in machine no. 0 by an Atwood machine. We may then replacing the masses in these new machines by machines, etc. The complete solution can be written down immediately, without solving simultaneous equations. Finally we give the effective mass of an Atwood machine whose pulley has nonzero mass and moment of inertia

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.

Development of an evaluation technique for human-machine interface

Energy Technology Data Exchange (ETDEWEB)

Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin [Korea Univ., Seoul (Korea, Republic of)

1997-07-15

The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification.

Development of an evaluation technique for human-machine interface

International Nuclear Information System (INIS)

Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin

1997-07-01

The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

Directory of Open Access Journals (Sweden)

Chih-Feng Chao

2015-01-01

Full Text Available The setting of parameters in the support vector machines (SVMs is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM. This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI, machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM. The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

Research on bearing life prediction based on support vector machine and its application

International Nuclear Information System (INIS)

Sun Chuang; Zhang Zhousuo; He Zhengjia

2011-01-01

Life prediction of rolling element bearing is the urgent demand in engineering practice, and the effective life prediction technique is beneficial to predictive maintenance. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, and is of advantage in prediction. This paper develops SVM-based model for bearing life prediction. The inputs of the model are features of bearing vibration signal and the output is the bearing running time-bearing failure time ratio. The model is built base on a few failed bearing data, and it can fuse information of the predicted bearing. So it is of advantage to bearing life prediction in practice. The model is applied to life prediction of a bearing, and the result shows the proposed model is of high precision.

Automatic Generation of Machine Emulators: Efficient Synthesis of Robust Virtual Machines for Legacy Software Migration

DEFF Research Database (Denmark)

Franz, Michael; Gal, Andreas; Probst, Christian

2006-01-01

As older mainframe architectures become obsolete, the corresponding le- gacy software is increasingly executed via platform emulators running on top of more modern commodity hardware. These emulators are virtual machines that often include a combination of interpreters and just-in-time compilers....... Implementing interpreters and compilers for each combination of emulated and target platform independently of each other is a redundant and error-prone task. We describe an alternative approach that automatically synthesizes specialized virtual-machine interpreters and just-in-time compilers, which...... then execute on top of an existing software portability platform such as Java. The result is a considerably reduced implementation effort....

Normal mammogram detection based on local probability difference transforms and support vector machines

International Nuclear Information System (INIS)

Chiracharit, W.; Kumhom, P.; Chamnongthai, K.; Sun, Y.; Delp, E.J.; Babbs, C.F

2007-01-01

Automatic detection of normal mammograms, as a ''first look'' for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable ''crossed-distributions'' in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity. (author)

HiMoP: A three-component architecture to create more human-acceptable social-assistive robots : Motivational architecture for assistive robots.

Science.gov (United States)

Rodríguez-Lera, Francisco J; Matellán-Olivera, Vicente; Conde-González, Miguel Á; Martín-Rico, Francisco

2018-05-01

Generation of autonomous behavior for robots is a general unsolved problem. Users perceive robots as repetitive tools that do not respond to dynamic situations. This research deals with the generation of natural behaviors in assistive service robots for dynamic domestic environments, particularly, a motivational-oriented cognitive architecture to generate more natural behaviors in autonomous robots. The proposed architecture, called HiMoP, is based on three elements: a Hierarchy of needs to define robot drives; a set of Motivational variables connected to robot needs; and a Pool of finite-state machines to run robot behaviors. The first element is inspired in Alderfer's hierarchy of needs, which specifies the variables defined in the motivational component. The pool of finite-state machine implements the available robot actions, and those actions are dynamically selected taking into account the motivational variables and the external stimuli. Thus, the robot is able to exhibit different behaviors even under similar conditions. A customized version of the "Speech Recognition and Audio Detection Test," proposed by the RoboCup Federation, has been used to illustrate how the architecture works and how it dynamically adapts and activates robots behaviors taking into account internal variables and external stimuli.

OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS

Directory of Open Access Journals (Sweden)

Oman Somantri

2017-01-01

Full Text Available The difficulty in determining the classification of students final project theme often experienced by each college. The purpose of this study is to provide a decision support for policy makers in the study program so that each student can be achieved in accordance with their own competence. From the research that has been done text mining algorithms using Support Vector Machine ( SVM and K -Means as the technology used was produced a better accuracy rate with an accuracy rate of 86.21 % when compared to the SVM without K -Means is 85 , 38 %

Enterprise architecture evaluation using architecture framework and UML stereotypes

Directory of Open Access Journals (Sweden)

Narges Shahi

2014-08-01

Full Text Available There is an increasing need for enterprise architecture in numerous organizations with complicated systems with various processes. Support for information technology, organizational units whose elements maintain complex relationships increases. Enterprise architecture is so effective that its non-use in organizations is regarded as their institutional inability in efficient information technology management. The enterprise architecture process generally consists of three phases including strategic programing of information technology, enterprise architecture programing and enterprise architecture implementation. Each phase must be implemented sequentially and one single flaw in each phase may result in a flaw in the whole architecture and, consequently, in extra costs and time. If a model is mapped for the issue and then it is evaluated before enterprise architecture implementation in the second phase, the possible flaws in implementation process are prevented. In this study, the processes of enterprise architecture are illustrated through UML diagrams, and the architecture is evaluated in programming phase through transforming the UML diagrams to Petri nets. The results indicate that the high costs of the implementation phase will be reduced.

Efficient universal computing architectures for decoding neural activity.

Directory of Open Access Journals (Sweden)

Benjamin I Rapoport

Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion

LHCb experience with running jobs in virtual machines

CERN Document Server

McNab, A; Luzzi, C

2015-01-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites mana...

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

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......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...... 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...

Aging Detection of Electrical Point Machines Based on Support Vector Data Description

Directory of Open Access Journals (Sweden)

Jaewon Sa

2017-11-01

Full Text Available Electrical point machines (EPM must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of “aged” and “not-yet-aged” equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy.

Implementing an Intrusion Detection System in the Mysea Architecture

National Research Council Canada - National Science Library

Tenhunen, Thomas

2008-01-01

.... The objective of this thesis is to design an intrusion detection system (IDS) architecture that permits administrators operating on MYSEA client machines to conveniently view and analyze IDS alerts from the single level networks...

Software architecture evolution

DEFF Research Database (Denmark)

Barais, Olivier; Le Meur, Anne-Francoise; Duchien, Laurence

2008-01-01

Software architectures must frequently evolve to cope with changing requirements, and this evolution often implies integrating new concerns. Unfortunately, when the new concerns are crosscutting, existing architecture description languages provide little or no support for this kind of evolution....... The software architect must modify multiple elements of the architecture manually, which risks introducing inconsistencies. This chapter provides an overview, comparison and detailed treatment of the various state-of-the-art approaches to describing and evolving software architectures. Furthermore, we discuss...... one particular framework named Tran SAT, which addresses the above problems of software architecture evolution. Tran SAT provides a new element in the software architecture descriptions language, called an architectural aspect, for describing new concerns and their integration into an existing...

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.

Peer-to-peer architectures for exascale computing : LDRD final report.

Energy Technology Data Exchange (ETDEWEB)

Vorobeychik, Yevgeniy; Mayo, Jackson R.; Minnich, Ronald G.; Armstrong, Robert C.; Rudish, Donald W.

2010-09-01

The goal of this research was to investigate the potential for employing dynamic, decentralized software architectures to achieve reliability in future high-performance computing platforms. These architectures, inspired by peer-to-peer networks such as botnets that already scale to millions of unreliable nodes, hold promise for enabling scientific applications to run usefully on next-generation exascale platforms ({approx} 10{sup 18} operations per second). Traditional parallel programming techniques suffer rapid deterioration of performance scaling with growing platform size, as the work of coping with increasingly frequent failures dominates over useful computation. Our studies suggest that new architectures, in which failures are treated as ubiquitous and their effects are considered as simply another controllable source of error in a scientific computation, can remove such obstacles to exascale computing for certain applications. We have developed a simulation framework, as well as a preliminary implementation in a large-scale emulation environment, for exploration of these 'fault-oblivious computing' approaches. High-performance computing (HPC) faces a fundamental problem of increasing total component failure rates due to increasing system sizes, which threaten to degrade system reliability to an unusable level by the time the exascale range is reached ({approx} 10{sup 18} operations per second, requiring of order millions of processors). As computer scientists seek a way to scale system software for next-generation exascale machines, it is worth considering peer-to-peer (P2P) architectures that are already capable of supporting 10{sup 6}-10{sup 7} unreliable nodes. Exascale platforms will require a different way of looking at systems and software because the machine will likely not be available in its entirety for a meaningful execution time. Realistic estimates of failure rates range from a few times per day to more than once per hour for these

Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater

Digital Repository Service at National Institute of Oceanography (India)

Harish, N.; Mandal, S.; Rao, S.; Patil, S.G.

breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO...

Architecture Descriptions. A Contribution to Modeling of Production System Architecture

DEFF Research Database (Denmark)

Jepsen, Allan Dam; Hvam, Lars

a proper understanding of the architecture phenomenon and the ability to describe it in a manner that allow the architecture to be communicated to and handled by stakeholders throughout the company. Despite the existence of several design philosophies in production system design such as Lean, that focus...... a diverse set of stakeholder domains and tools in the production system life cycle. To support such activities, a contribution is made to the identification and referencing of production system elements within architecture descriptions as part of the reference architecture framework. The contribution...

Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia

Science.gov (United States)

De Boissieu, Florian; Sevin, Brice; Cudahy, Thomas; Mangeas, Morgan; Chevrel, Stéphane; Ong, Cindy; Rodger, Andrew; Maurizot, Pierre; Laukamp, Carsten; Lau, Ian; Touraivane, Touraivane; Cluzel, Dominique; Despinoy, Marc

2018-02-01

Accurate maps of Earth's geology, especially its regolith, are required for managing the sustainable exploration and development of mineral resources. This paper shows how airborne imaging hyperspectral data collected over weathered peridotite rocks in vegetated, mountainous terrane in New Caledonia were processed using a combination of methods to generate a regolith-geology map that could be used for more efficiently targeting Ni exploration. The image processing combined two usual methods, which are spectral feature extraction and support vector machine (SVM). This rationale being the spectral features extraction can rapidly reduce data complexity by both targeting only the diagnostic mineral absorptions and masking those pixels complicated by vegetation, cloud and deep shade. SVM is a supervised classification method able to generate an optimal non-linear classifier with these features that generalises well even with limited training data. Key minerals targeted are serpentine, which is considered as an indicator for hydrolysed peridotitic rock, and iron oxy-hydroxides (hematite and goethite), which are considered as diagnostic of laterite development. The final classified regolith map was assessed against interpreted regolith field sites, which yielded approximately 70% similarity for all unit types, as well as against a regolith-geology map interpreted using traditional datasets (not hyperspectral imagery). Importantly, the hyperspectral derived mineral map provided much greater detail enabling a more precise understanding of the regolith-geological architecture where there are exposed soils and rocks.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

Science.gov (United States)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

International Nuclear Information System (INIS)

Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

2010-01-01

Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

Directory of Open Access Journals (Sweden)

Alim Samat

2016-03-01

Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

Science.gov (United States)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Enterprise architecture management

DEFF Research Database (Denmark)

Rahimi, Fatemeh; Gøtze, John; Møller, Charles

2017-01-01

Despite the growing interest in enterprise architecture management, researchers and practitioners lack a shared understanding of its applications in organizations. Building on findings from a literature review and eight case studies, we develop a taxonomy that categorizes applications of enterprise...... architecture management based on three classes of enterprise architecture scope. Organizations may adopt enterprise architecture management to help form, plan, and implement IT strategies; help plan and implement business strategies; or to further complement the business strategy-formation process....... The findings challenge the traditional IT-centric view of enterprise architecture management application and suggest enterprise architecture management as an approach that could support the consistent design and evolution of an organization as a whole....

Enterprise architecture management

DEFF Research Database (Denmark)

Rahimi, Fatemeh; Gøtze, John; Møller, Charles

2017-01-01

architecture management based on three classes of enterprise architecture scope. Organizations may adopt enterprise architecture management to help form, plan, and implement IT strategies; help plan and implement business strategies; or to further complement the business strategy-formation process......Despite the growing interest in enterprise architecture management, researchers and practitioners lack a shared understanding of its applications in organizations. Building on findings from a literature review and eight case studies, we develop a taxonomy that categorizes applications of enterprise....... The findings challenge the traditional IT-centric view of enterprise architecture management application and suggest enterprise architecture management as an approach that could support the consistent design and evolution of an organization as a whole....

A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

International Nuclear Information System (INIS)

Baser, Furkan; Demirhan, Haydar

2017-01-01

Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. - Highlights: • A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. • The model is useful under complex terrestrial and climatic conditions.

Bioinspired Architecture Selection for Multitask Learning

Directory of Open Access Journals (Sweden)

Andrés Bueno-Crespo

2017-06-01

Full Text Available Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL, which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method.

Cognitive Architectures and Autonomy: A Comparative Review

Science.gov (United States)

Thórisson, Kristinn; Helgasson, Helgi

2012-05-01

One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

Science.gov (United States)

Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

2017-09-09

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

Light-operated machines based on threaded molecular structures.

Science.gov (United States)

Credi, Alberto; Silvi, Serena; Venturi, Margherita

2014-01-01

Rotaxanes and related species represent the most common implementation of the concept of artificial molecular machines, because the supramolecular nature of the interactions between the components and their interlocked architecture allow a precise control on the position and movement of the molecular units. The use of light to power artificial molecular machines is particularly valuable because it can play the dual role of "writing" and "reading" the system. Moreover, light-driven machines can operate without accumulation of waste products, and photons are the ideal inputs to enable autonomous operation mechanisms. In appropriately designed molecular machines, light can be used to control not only the stability of the system, which affects the relative position of the molecular components but also the kinetics of the mechanical processes, thereby enabling control on the direction of the movements. This step forward is necessary in order to make a leap from molecular machines to molecular motors.

Architectural Engineering to Super-Light Structures

DEFF Research Database (Denmark)

Castberg, Niels Andreas

The increasing global urbanisation creates a great demand for new buildings. In the aim to honour this, a new structural system, offering flexibility and variation at no extra cost appears beneficial. Super-Light Structures constitute such a system. This PhD thesis examines Super-Light Structures...... with architectural engineering as a starting point. The thesis is based on a two stringed hypothesis: Architectural engineering gives rise to better architecture and Super-Light Structures support and enables a static, challenging architecture. The aim of the thesis is to clarify architectural engineering's impact...... on the work process between architects and engineers in the design development. Using architectural engineering, Super-Light Structures are examined in an architectural context, and it is explained how digital tools can support architectural engineering and design of Super-Light Structures. The experiences...

Graduating the age-specific fertility pattern using Support Vector Machines

Directory of Open Access Journals (Sweden)

Anastasia Kostaki

2009-06-01

Full Text Available A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.

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.

Ranking Support Vector Machine with Kernel Approximation

Directory of Open Access Journals (Sweden)

Kai Chen

2017-01-01

Full Text Available 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.

Agent Based Framework Architecture for Supporting Content Adaptation for Mobile Government

Directory of Open Access Journals (Sweden)

Hasan Omar Al-Sakran

2013-01-01

Full Text Available Rapid spread of smart mobile technology that supports internet access is transforming the way governments provide services to their citizens. Mobile devices have different capabilities based on the manufacturers and models. This paper proposes a new framework for adapting the content of M-government services using mobile agent technology. The framework is based on a mediation architecture that uses multiple mobile agents and XML as semi-structure mediation language. The flexibility of the mediation and XML provide an adaptive environment to stream data based on the capabilities of the device sending the query to the system.

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...... is data-based, simulated data from a high fidelity aero-elastic model is used for learning. The model simulates a variable speed horizontal-axis wind turbine composed of three blades and a full converter. Both partial load (blade angles fixed at 0 deg) and full load zones (active pitch actuators...

Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

International Nuclear Information System (INIS)

Liu, Hui; Tian, Hong-qi; Li, Yan-fei

2015-01-01

Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

Comparison of two different running models for the shock wave lithotripsy machine in Taipei City Hospital: self-support versus outsourcing cooperation.

Science.gov (United States)

Huang, Chi-Yi; Chen, Shiou-Sheng; Chen, Li-Kuei

2009-10-01

To compare two different running models including self-support and outsourcing cooperation for the extracorporeal shock wave lithotripsy (SWL) machine in Taipei City Hospital, we made a retrospective study. Self-support means that the hospital has to buy an SWL machine and get all the payment from SWL. In outsourcing cooperation, the cooperative company provides an SWL machine and shares the payment with the hospital. Between January 2002 and December 2006, we used self-support for the SWL machine, and from January 2007 to December 2008, we used outsourcing cooperation. We used the method of full costing to calculate the cost of SWL, and the break-even point was the lowest number of treatment sessions of SWL to make balance of payments every month. Quality parameters including stone-free rate, retreatment rate, additional procedures and complication rate were evaluated. When outsourcing cooperation was used, there were significantly more treatment sessions of SWL every month than when utilizing self-support (36.3 +/- 5.1 vs. 48.1 +/- 8.4, P = 0.03). The cost of SWL for every treatment session was significantly higher using self-support than with outsourcing cooperation (25027.5 +/- 1789.8 NT$ vs. 21367.4 +/- 201.0 NT$). The break-even point was 28.3 (treatment sessions) for self-support, and 28.4 for outsourcing cooperation, when the hospital got 40% of the payment, which would decrease if the percentage increased. No significant differences were noticed for stone-free rate, retreatment rate, additional procedures and complication rate of SWL between the two running models. Besides, outsourcing cooperation had lower cost (every treatment session), but a greater number of treatment sessions of SWL every month than self-support.

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.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

PICNIC Architecture.

Science.gov (United States)

Saranummi, Niilo

2005-01-01

The PICNIC architecture aims at supporting inter-enterprise integration and the facilitation of collaboration between healthcare organisations. The concept of a Regional Health Economy (RHE) is introduced to illustrate the varying nature of inter-enterprise collaboration between healthcare organisations collaborating in providing health services to citizens and patients in a regional setting. The PICNIC architecture comprises a number of PICNIC IT Services, the interfaces between them and presents a way to assemble these into a functioning Regional Health Care Network meeting the needs and concerns of its stakeholders. The PICNIC architecture is presented through a number of views relevant to different stakeholder groups. The stakeholders of the first view are national and regional health authorities and policy makers. The view describes how the architecture enables the implementation of national and regional health policies, strategies and organisational structures. The stakeholders of the second view, the service viewpoint, are the care providers, health professionals, patients and citizens. The view describes how the architecture supports and enables regional care delivery and process management including continuity of care (shared care) and citizen-centred health services. The stakeholders of the third view, the engineering view, are those that design, build and implement the RHCN. The view comprises four sub views: software engineering, IT services engineering, security and data. The proposed architecture is founded into the main stream of how distributed computing environments are evolving. The architecture is realised using the web services approach. A number of well established technology platforms and generic standards exist that can be used to implement the software components. The software components that are specified in PICNIC are implemented in Open Source.

Applications of Case Based Organizational Memory Supported by the PAbMM Architecture

Directory of Open Access Journals (Sweden)

Martín

2017-04-01

Full Text Available In the aim to manage and retrieve the organizational knowledge, in the last years numerous proposals of models and tools for knowledge management and knowledge representation have arisen. However, most of them store knowledge in a non-structured or semi-structured way, hindering the semantic and automatic processing of this knowledge. In this paper we present a more detailed case-based organizational memory ontology, which aims at contributing to the design of an organizational memory based on cases, so that it can be used to learn, reasoning, solve problems, and as support to better decision making as well. The objective of this Organizational Memory is to serve as base for the organizational knowledge exchange in a processing architecture specialized in the measurement and evaluation. In this way, our processing architecture is based on the C-INCAMI framework (Context-Information Need, Concept model, Attribute, Metric and Indicator for defining the measurement projects. Additionally, the proposal architecture uses a big data repository to make available the data for consumption and to manage the Organizational Memory, which allows a feedback mechanism in relation with online processing. In order to illustrate its utility, two practical cases are explained: A pasture predictor system, using the data of the weather radar (WR of the Experimental Agricultural Station (EAS INTA Anguil (La Pampa State, Argentina and an outpatient monitoring scenario. Future trends and concluding remarks are extended.

Second Generation Dutch Pulsar Machine - PuMa-II

NARCIS (Netherlands)

Karuppusamy, Ramesh; Stappers, Ben; Slump, Cornelis H.; van der Klis, Michiel

2004-01-01

The Second Generation Pulsar Machine (PuMa- II) is under development for the Westerbork Synthesis Radio Telescope. This is a summary of th e system design and architecture. We show that state of the art pulsar research is possible with commercially available hardware components. This approach

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

Science.gov (United States)

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

Science.gov (United States)

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Implementation of neural networks on 'Connection Machine'

International Nuclear Information System (INIS)

Belmonte, Ghislain

1990-12-01

This report is a first approach to the notion of neural networks and their possible applications within the framework of artificial intelligence activities of the Department of Applied Mathematics of the Limeil-Valenton Research Center. The first part is an introduction to the field of neural networks; the main neural network models are described in this section. The applications of neural networks in the field of classification have mainly been studied because they could more particularly help to solve some of the decision support problems dealt with by the C.E.A. As the neural networks perform a large number of parallel operations, it was therefore logical to use a parallel architecture computer: the Connection Machine (which uses 16384 processors and is located at E.T.C.A. Arcueil). The second part presents some generalities on the parallelism and the Connection Machine, and two implementations of neural networks on Connection Machine. The first of these implementations concerns one of the most used algorithms to realize the learning of neural networks: the Gradient Retro-propagation algorithm. The second one, less common, concerns a network of neurons destined mainly to the recognition of forms: the Fukushima Neocognitron. The latter is studied by the C.E.A. of Bruyeres-le-Chatel in order to realize an embedded system (including hardened circuits) for the fast recognition of forms [fr

Nonlinear machine learning in soft materials engineering and design

Science.gov (United States)

Ferguson, Andrew

The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable ''digital colloids'' as a novel high-density information storage substrate; probing hierarchically self-assembling onjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. We acknowledge support for this work from a National Science Foundation CAREER Award (Grant No. DMR-1350008) and the Donors of the American Chemical Society Petroleum Research Fund (ACS PRF #54240-DNI6).

Decision support at home (DS@HOME – system architectures and requirements

Directory of Open Access Journals (Sweden)

Marschollek Michael

2012-05-01

Full Text Available Abstract Background Demographic change with its consequences of an aging society and an increase in the demand for care in the home environment has triggered intensive research activities in sensor devices and smart home technologies. While many advanced technologies are already available, there is still a lack of decision support systems (DSS for the interpretation of data generated in home environments. The aim of the research for this paper is to present the state-of-the-art in DSS for these data, to define characteristic properties of such systems, and to define the requirements for successful home care DSS implementations. Methods A literature review was performed along with the analysis of cross-references. Characteristic properties are proposed and requirements are derived from the available body of literature. Results 79 papers were identified and analyzed, of which 20 describe implementations of decision components. Most authors mention server-based decision support components, but only few papers provide details about the system architecture or the knowledge base. A list of requirements derived from the analysis is presented. Among the primary drawbacks of current systems are the missing integration of DSS in current health information system architectures including interfaces, the missing agreement among developers with regard to the formalization and customization of medical knowledge and a lack of intelligent algorithms to interpret data from multiple sources including clinical application systems. Conclusions Future research needs to address these issues in order to provide useful information – and not only large amounts of data – for both the patient and the caregiver. Furthermore, there is a need for outcome studies allowing for identifying successful implementation concepts.

Applying Multi-Class Support Vector Machines for performance assessment of shipping operations: The case of tanker vessels

DEFF Research Database (Denmark)

Pagoropoulos, Aris; Møller, Anders H.; McAloone, Tim C.

2017-01-01

of feature selection algorithms. Afterwards, a model based on Multi- Class Support Vector Machines (SVM) was constructed and the efficacy of the approach is shown through the application of a test set. The results demonstrate the importance and benefits of machine learning algorithms in driving energy....... Identifying the potential of behavioural savings can be challenging, due to the inherent difficulty in analysing the data and operationalizing energy efficiency within the dynamic operating environment of the vessels. This article proposes a supervised learning model for identifying the presence of energy...

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.

Application of support vector machines to breast cancer screening using mammogram and history data

Science.gov (United States)

Land, Walker H., Jr.; Akanda, Anab; Lo, Joseph Y.; Anderson, Francis; Bryden, Margaret

2002-05-01

Support Vector Machines (SVMs) are a new and radically different type of classifiers and learning machines that use a hypothesis space of linear functions in a high dimensional feature space. This relatively new paradigm, based on Statistical Learning Theory (SLT) and Structural Risk Minimization (SRM), has many advantages when compared to traditional neural networks, which are based on Empirical Risk Minimization (ERM). Unlike neural networks, SVM training always finds a global minimum. Furthermore, SVMs have inherent ability to solve pattern classification without incorporating any problem-domain knowledge. In this study, the SVM was employed as a pattern classifier, operating on mammography data used for breast cancer detection. The main focus was to formulate the best learning machine configurations for optimum specificity and positive predictive value at very high sensitivities. Using a mammogram database of 500 biopsy-proven samples, the best performing SVM, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 45.0% and a positive predictive value (PPV) of 50.1% at 100% sensitivity. At 97% sensitivity, a specificity of 55.8% and a PPV of 55.2% were obtained.

A technique to identify some typical radio frequency interference using support vector machine

Science.gov (United States)

Wang, Yuanchao; Li, Mingtao; Li, Dawei; Zheng, Jianhua

2017-07-01

In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%; while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

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.

Operator support systems activities at EPRI

International Nuclear Information System (INIS)

Naser, J.A.

1993-01-01

The integration of operator support systems supports the nuclear power plant goals of improved availability and reliability, enhanced safety, reduced operations and maintenance costs, and improved productivity. Two major aspects which supports this integration are discussed in this paper. The first is the plant communications and computing architecture which provides the infrastructure that allows the integration to exist in a easy to implement manner. Open systems concepts are utilized to guarantee interoperability of systems and interchangeability of equipment. The second is the EPRI Plant-Window System which supplies the interface between the human and the plant systems. It implements common human-machine interfaces amongst systems and supports the implementation of diagnostic and decision aids. Work in both of these areas is being done as part of the EPRI Instrumentation and Control Upgrade Program. A number of operator support systems have been developed and are in various stages of implementation, testing and utilization. Two of these, the RWCU and the EOPTS, are described here. 5 refs, 14 figs

Assessing Implicit Knowledge in BIM Models with Machine Learning

DEFF Research Database (Denmark)

Krijnen, Thomas; Tamke, Martin

2015-01-01

architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models....

Environmental noise forecasting based on support vector machine

Science.gov (United States)

Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

2018-01-01

As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

Software design of the hybrid robot machine for ITER vacuum vessel assembly and maintenance

Energy Technology Data Exchange (ETDEWEB)

Li, Ming, E-mail: Ming.Li@lut.fi [Laboratory of Intelligent Machines, Lappeenranta University of Technology (Finland); Wu, Huapeng; Handroos, Heikki [Laboratory of Intelligent Machines, Lappeenranta University of Technology (Finland); Yang, Guangyou [School of Mechanical Engineering, Hubei University of Technology, Wuhan (China)

2013-10-15

A specific software design is elaborated in this paper for the hybrid robot machine used for the ITER vacuum vessel (VV) assembly and maintenance. In order to provide the multi-machining-function as well as the complicated, flexible and customizable GUI designing satisfying the non-standardized VV assembly process in one hand, and in another hand guarantee the stringent machining precision in the real-time motion control of robot machine, a client–server-control software architecture is proposed, which separates the user interaction, data communication and robot control implementation into different software layers. Correspondingly, three particular application protocols upon the TCP/IP are designed to transmit the data, command and status between the client and the server so as to deal with the abundant data streaming in the software. In order not to be affected by the graphic user interface (GUI) modification process in the future experiment in VV assembly working field, the real-time control system is realized as a stand-alone module in the architecture to guarantee the controlling performance of the robot machine. After completing the software development, a milling operation is tested on the robot machine, and the result demonstrates that both the specific GUI operability and the real-time motion control performance could be guaranteed adequately in the software design.

Software design of the hybrid robot machine for ITER vacuum vessel assembly and maintenance

International Nuclear Information System (INIS)

Li, Ming; Wu, Huapeng; Handroos, Heikki; Yang, Guangyou

2013-01-01

A specific software design is elaborated in this paper for the hybrid robot machine used for the ITER vacuum vessel (VV) assembly and maintenance. In order to provide the multi-machining-function as well as the complicated, flexible and customizable GUI designing satisfying the non-standardized VV assembly process in one hand, and in another hand guarantee the stringent machining precision in the real-time motion control of robot machine, a client–server-control software architecture is proposed, which separates the user interaction, data communication and robot control implementation into different software layers. Correspondingly, three particular application protocols upon the TCP/IP are designed to transmit the data, command and status between the client and the server so as to deal with the abundant data streaming in the software. In order not to be affected by the graphic user interface (GUI) modification process in the future experiment in VV assembly working field, the real-time control system is realized as a stand-alone module in the architecture to guarantee the controlling performance of the robot machine. After completing the software development, a milling operation is tested on the robot machine, and the result demonstrates that both the specific GUI operability and the real-time motion control performance could be guaranteed adequately in the software design

25th Annual International Symposium on Field-Programmable Custom Computing Machines

CERN Document Server

The IEEE Symposium on Field-Programmable Custom Computing Machines is the original and premier forum for presenting and discussing new research related to computing that exploits the unique features and capabilities of FPGAs and other reconfigurable hardware. Over the past two decades, FCCM has been the place to present papers on architectures, tools, and programming models for field-programmable custom computing machines as well as applications that use such systems.

An Enterprise Security Program and Architecture to Support Business Drivers

Directory of Open Access Journals (Sweden)

Brian Ritchot

2013-08-01

Full Text Available This article presents a business-focused approach to developing and delivering enterprise security architecture that is focused on enabling business objectives while providing a sensible and balanced approach to risk management. A balanced approach to enterprise security architecture can create the important linkages between the goals and objectives of a business, and it provides appropriate measures to protect the most critical assets within an organization while accepting risk where appropriate. Through a discussion of information assurance, this article makes a case for leveraging enterprise security architectures to meet an organizations' need for information assurance. The approach is derived from the Sherwood Applied Business Security Architecture (SABSA methodology, as put into practice by Seccuris Inc., an information assurance integrator. An understanding of Seccuris’ approach will illustrate the importance of aligning security activities with high-level business objectives while creating increased awareness of the duality of risk. This business-driven approach to enterprise security architecture can help organizations change the perception of IT security, positioning it as a tool to enable and assure business success, rather than be perceived as an obstacle to be avoided.

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). © 2013 Elsevier B.V. All rights reserved.

New Energy Architecture. Myanmar

Energy Technology Data Exchange (ETDEWEB)

NONE

2013-06-15

A global transition towards a new energy architecture is under way, driven by countries' need to respond to the changing dynamics of economic growth, environmental sustainability and energy security. The World Economic Forum, in collaboration with Accenture, has created the New Energy Architecture Initiative to address and accelerate this transition. The Initiative supports the development of national strategies and policy frameworks as countries seek to achieve the combined goals of energy security and access, sustainability, and economic growth and development. The World Economic Forum has formed a partnership with the Ministry of Energy of Myanmar to help apply the Initiative's approach to this developing and resource-rich nation. The Asian Development Bank and the World Economic Forum's Project Adviser, Accenture, have collaborated with the Forum on this consultation process, and have been supported by relevant government, industry and civil society stakeholders. The consultation process aims to understand the nation's current energy architecture challenges and provide an overview of a path to a New Energy Architecture through a series of insights. These insights could form the basis for a long-term multistakeholder roadmap to build Myanmar's energy sector in a way that is secure and sustainable, and promotes economic growth as the country makes its democratic transition. While not all recommendations can be implemented in the near term, they do provide options for creating a prioritized roadmap for Myanmar's energy transition. This report is the culmination of a nine-month multistakeholder process investigating Myanmar's energy architecture. Over the course of many visits to the country, the team has conducted numerous interviews, multistakeholder workshops, and learning and data-gathering exercises to ensure a comprehensive range of information and views. The team has also engaged with a variety of stakeholders to better inform their findings, which have come

New Energy Architecture. Myanmar

Energy Technology Data Exchange (ETDEWEB)

NONE

2013-06-15

A global transition towards a new energy architecture is under way, driven by countries' need to respond to the changing dynamics of economic growth, environmental sustainability and energy security. The World Economic Forum, in collaboration with Accenture, has created the New Energy Architecture Initiative to address and accelerate this transition. The Initiative supports the development of national strategies and policy frameworks as countries seek to achieve the combined goals of energy security and access, sustainability, and economic growth and development. The World Economic Forum has formed a partnership with the Ministry of Energy of Myanmar to help apply the Initiative's approach to this developing and resource-rich nation. The Asian Development Bank and the World Economic Forum's Project Adviser, Accenture, have collaborated with the Forum on this consultation process, and have been supported by relevant government, industry and civil society stakeholders. The consultation process aims to understand the nation's current energy architecture challenges and provide an overview of a path to a New Energy Architecture through a series of insights. These insights could form the basis for a long-term multistakeholder roadmap to build Myanmar's energy sector in a way that is secure and sustainable, and promotes economic growth as the country makes its democratic transition. While not all recommendations can be implemented in the near term, they do provide options for creating a prioritized roadmap for Myanmar's energy transition. This report is the culmination of a nine-month multistakeholder process investigating Myanmar's energy architecture. Over the course of many visits to the country, the team has conducted numerous interviews, multistakeholder workshops, and learning and data-gathering exercises to ensure a comprehensive range of information and views. The team has also engaged with a variety of stakeholders to better

Machine Phase Fullerene Nanotechnology: 1996

Science.gov (United States)

Globus, Al; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional

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.

Council-supported condom vending machines: are they acceptable to rural communities?

Science.gov (United States)

Tomnay, Jane E; Hatch, Beth

2013-11-01

Twenty-four hour access to condoms for young people living in rural Victoria is problematic for many reasons, including the fact that condom vending machines are often located in venues and places they cannot access. We partnered with three rural councils to install condom vending machines in locations that provided improved access to condoms for local young people. Councils regularly checked the machines, refilled the condoms and retrieved the money. They also managed the maintenance of the machine and provided monthly data. In total, 1153 condoms were purchased over 12 months, with 924 (80%) obtained from male toilets and 69% (801 out of 1153) purchased in the second half of the study. Revenue of $2626.10 (AUD) was generated and no negative feedback from residents was received by any council nor was there any negative reporting by local media. Vandalism, tampering or damage occurred at all sites; however, only two significant episodes of damage required a machine to be sent away for repairs. Condom vending machines installed in rural towns in north-east Victoria are accessible to young people after business hours, are cost-effective for councils and have not generated any complaints from residents. The machines have not suffered unrepairable damage and were used more frequently as the study progressed.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

Science.gov (United States)

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

hERG classification model based on a combination of support vector machine method and GRIND descriptors

DEFF Research Database (Denmark)

Li, Qiyuan; Jorgensen, Flemming Steen; Oprea, Tudor

2008-01-01

and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 mu m and achieved an overall accuracy up...

Fuzzy support vector machine for microarray imbalanced data classification

Science.gov (United States)

Ladayya, Faroh; Purnami, Santi Wulan; Irhamah

2017-11-01

DNA microarrays are data containing gene expression with small sample sizes and high number of features. Furthermore, imbalanced classes is a common problem in microarray data. This occurs when a dataset is dominated by a class which have significantly more instances than the other minority classes. Therefore, it is needed a classification method that solve the problem of high dimensional and imbalanced data. Support Vector Machine (SVM) is one of the classification methods that is capable of handling large or small samples, nonlinear, high dimensional, over learning and local minimum issues. SVM has been widely applied to DNA microarray data classification and it has been shown that SVM provides the best performance among other machine learning methods. However, imbalanced data will be a problem because SVM treats all samples in the same importance thus the results is bias for minority class. To overcome the imbalanced data, Fuzzy SVM (FSVM) is proposed. This method apply a fuzzy membership to each input point and reformulate the SVM such that different input points provide different contributions to the classifier. The minority classes have large fuzzy membership so FSVM can pay more attention to the samples with larger fuzzy membership. Given DNA microarray data is a high dimensional data with a very large number of features, it is necessary to do feature selection first using Fast Correlation based Filter (FCBF). In this study will be analyzed by SVM, FSVM and both methods by applying FCBF and get the classification performance of them. Based on the overall results, FSVM on selected features has the best classification performance compared to SVM.

Understanding and modelling man-machine interaction

International Nuclear Information System (INIS)

Cacciabue, P.C.

1996-01-01

This paper gives an overview of the current state of the art in man-machine system interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to the design and analysis of man-machine interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans an their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (orig.)

Understanding and modelling Man-Machine Interaction

International Nuclear Information System (INIS)

Cacciabue, P.C.

1991-01-01

This paper gives an overview of the current state of the art in man machine systems interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to design and analysis of Man-Machine Interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans and their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (author)

Architectural Theatricality

DEFF Research Database (Denmark)

Tvedebrink, Tenna Doktor Olsen

environments and a knowledge gap therefore exists in present hospital designs. Consequently, the purpose of this thesis has been to investigate if any research-based knowledge exist supporting the hypothesis that the interior architectural qualities of eating environments influence patient food intake, health...... and well-being, as well as outline a set of basic design principles ‘predicting’ the future interior architectural qualities of patient eating environments. Methodologically the thesis is based on an explorative study employing an abductive approach and hermeneutic-interpretative strategy utilizing tactics...... and food intake, as well as a series of references exist linking the interior architectural qualities of healthcare environments with the health and wellbeing of patients. On the basis of these findings, the thesis presents the concept of Architectural Theatricality as well as a set of design principles...

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

Modeling a ground-coupled heat pump system by a support vector machine

Energy Technology Data Exchange (ETDEWEB)

Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

2008-08-15

This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R{sup 2}) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters. The simulation results show that the SVM is a good method for prediction of the COP of the GCHP system. The computation of SVM model is faster compared with other machine learning techniques (artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)); because there are fewer free parameters and only support vectors (only a fraction of all data) are used in the generalization process. (author)

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.

CHRONOS architecture: Experiences with an open-source services-oriented architecture for geoinformatics

Science.gov (United States)

Fils, D.; Cervato, C.; Reed, J.; Diver, P.; Tang, X.; Bohling, G.; Greer, D.

2009-01-01

CHRONOS's purpose is to transform Earth history research by seamlessly integrating stratigraphic databases and tools into a virtual on-line stratigraphic record. In this paper, we describe the various components of CHRONOS's distributed data system, including the encoding of semantic and descriptive data into a service-based architecture. We give examples of how we have integrated well-tested resources available from the open-source and geoinformatic communities, like the GeoSciML schema and the simple knowledge organization system (SKOS), into the services-oriented architecture to encode timescale and phylogenetic synonymy data. We also describe on-going efforts to use geospatially enhanced data syndication and informally including semantic information by embedding it directly into the XHTML Document Object Model (DOM). XHTML DOM allows machine-discoverable descriptive data such as licensing and citation information to be incorporated directly into data sets retrieved by users. ?? 2008 Elsevier Ltd. All rights reserved.

A SUPPORT VECTOR MACHINE APPROACH FOR DEVELOPING TELEMEDICINE SOLUTIONS: MEDICAL DIAGNOSIS

Directory of Open Access Journals (Sweden)

Mihaela GHEORGHE

2015-06-01

Full Text Available Support vector machine represents an important tool for artificial neural networks techniques including classification and prediction. It offers a solution for a wide range of different issues in which cases the traditional optimization algorithms and methods cannot be applied directly due to different constraints, including memory restrictions, hidden relationships between variables, very high volume of computations that needs to be handled. One of these issues relates to medical diagnosis, a subset of the medical field. In this paper, the SVM learning algorithm is tested on a diabetes dataset and the results obtained for training with different kernel functions are presented and analyzed in order to determine a good approach from a telemedicine perspective.

Automatic Task Classification via Support Vector Machine and Crowdsourcing

Directory of Open Access Journals (Sweden)

Hyungsik Shin

2018-01-01

Full Text Available Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

Automatic inspection of textured surfaces by support vector machines

Science.gov (United States)

Jahanbin, Sina; Bovik, Alan C.; Pérez, Eduardo; Nair, Dinesh

2009-08-01

Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

WATERLOPP V2/64: A highly parallel machine for numerical computation

Science.gov (United States)

Ostlund, Neil S.

1985-07-01

Current technological trends suggest that the high performance scientific machines of the future are very likely to consist of a large number (greater than 1024) of processors connected and communicating with each other in some as yet undetermined manner. Such an assembly of processors should behave as a single machine in obtaining numerical solutions to scientific problems. However, the appropriate way of organizing both the hardware and software of such an assembly of processors is an unsolved and active area of research. It is particularly important to minimize the organizational overhead of interprocessor comunication, global synchronization, and contention for shared resources if the performance of a large number ( n) of processors is to be anything like the desirable n times the performance of a single processor. In many situations, adding a processor actually decreases the performance of the overall system since the extra organizational overhead is larger than the extra processing power added. The systolic loop architecture is a new multiple processor architecture which attemps at a solution to the problem of how to organize a large number of asynchronous processors into an effective computational system while minimizing the organizational overhead. This paper gives a brief overview of the basic systolic loop architecture, systolic loop algorithms for numerical computation, and a 64-processor implementation of the architecture, WATERLOOP V2/64, that is being used as a testbed for exploring the hardware, software, and algorithmic aspects of the architecture.

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.

Organizational Learning Supported by Reference Architecture Models

DEFF Research Database (Denmark)

Nardello, Marco; Møller, Charles; Gøtze, John

2017-01-01

of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory...

LHCb experience with running jobs in virtual machines

Science.gov (United States)

McNab, A.; Stagni, F.; Luzzi, C.

2015-12-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites managed using Vcycle/OpenStack, Vac, and HTCondor Vacuum. Finally we show how our use of these resources is monitored using Ganglia and DIRAC.

Precision machining commercialization

International Nuclear Information System (INIS)

1978-01-01

To accelerate precision machining development so as to realize more of the potential savings within the next few years of known Department of Defense (DOD) part procurement, the Air Force Materials Laboratory (AFML) is sponsoring the Precision Machining Commercialization Project (PMC). PMC is part of the Tri-Service Precision Machine Tool Program of the DOD Manufacturing Technology Five-Year Plan. The technical resources supporting PMC are provided under sponsorship of the Department of Energy (DOE). The goal of PMC is to minimize precision machining development time and cost risk for interested vendors. PMC will do this by making available the high precision machining technology as developed in two DOE contractor facilities, the Lawrence Livermore Laboratory of the University of California and the Union Carbide Corporation, Nuclear Division, Y-12 Plant, at Oak Ridge, Tennessee

RBAC Driven Least Privilege Architecture For Control Systems

Energy Technology Data Exchange (ETDEWEB)

Hull, Julie [Honeywell International Inc., Golden Valley, MN (United States); Markham, Mark [Honeywell International Inc., Golden Valley, MN (United States)

2014-01-25

The concept of role based access control (RBAC) within the IT environment has been studied by researchers and was supported by NIST (circa 1992). This earlier work highlighted the benefits of RBAC which include reduced administrative workload and policies which are easier to analyze and apply. The goals of this research were to expand the application of RBAC in the following ways. Apply RBAC to the control systems environment: The typical RBAC model within the IT environment is used to control a user’s access to files. Within the control system environment files are replaced with measurement (e.g., temperature) and control (e.g. valve) points organized as a hierarchy of control assets (e.g. a boiler, compressor, refinery unit). Control points have parameters (e.g., high alarm limit, set point, etc.) associated with them. The RBAC model is extended to support access to points and their parameters based upon roles while at the same time allowing permissions for the points to be defined at the asset level or point level directly. In addition, centralized policy administration with distributed access enforcement mechanisms was developed to support the distributed architecture of distributed control systems and SCADA; Extend the RBAC model to include access control for software and devices: The established RBAC approach is to assign users to roles. This work extends that notion by first breaking the control system down into three layers 1) users, 2) software and 3) devices. An RBAC model is then created for each of these three layers. The result is that RBAC can be used to define machine-to-machine policy enforced via the IP security (IPsec) protocol. This highlights the potential to use RBAC for machine-to-machine connectivity within the internet of things; and Enable dynamic policy based upon the operating mode of the system: The IT environment is generally static with respect to policy. However, large cyber physical systems such as industrial controls have various

Architecture-Level Exploration of Alternative Interconnection Schemes Targeting 3D FPGAs: A Software-Supported Methodology

Directory of Open Access Journals (Sweden)

Kostas Siozios

2008-01-01

Full Text Available In current reconfigurable architectures, the interconnection structures increasingly contribute more to the delay and power consumption. The demand for increased clock frequencies and logic density (smaller area footprint makes the problem even more important. Three-dimensional (3D architectures are able to alleviate this problem by accommodating a number of functional layers, each of which might be fabricated in different technology. However, the benefits of such integration technology have not been sufficiently explored yet. In this paper, we propose a software-supported methodology for exploring and evaluating alternative interconnection schemes for 3D FPGAs. In order to support the proposed methodology, three new CAD tools were developed (part of the 3D MEANDER Design Framework. During our exploration, we study the impact of vertical interconnection between functional layers in a number of design parameters. More specifically, the average gains in operation frequency, power consumption, and wirelength are 35%, 32%, and 13%, respectively, compared to existing 2D FPGAs with identical logic resources. Also, we achieve higher utilization ratio for the vertical interconnections compared to existing approaches by 8% for designing 3D FPGAs, leading to cheaper and more reliable devices.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

Science.gov (United States)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

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

SVM Classifier – a comprehensive java interface for support vector machine classification of microarray data

Science.gov (United States)

Pirooznia, Mehdi; Deng, Youping

2006-01-01

Motivation Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. Results The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1–BRCA2 samples with RBF kernel of SVM. Conclusion We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at . PMID:17217518

Layered Architectures for Quantum Computers and Quantum Repeaters

Science.gov (United States)

Jones, Nathan C.

This chapter examines how to organize quantum computers and repeaters using a systematic framework known as layered architecture, where machine control is organized in layers associated with specialized tasks. The framework is flexible and could be used for analysis and comparison of quantum information systems. To demonstrate the design principles in practice, we develop architectures for quantum computers and quantum repeaters based on optically controlled quantum dots, showing how a myriad of technologies must operate synchronously to achieve fault-tolerance. Optical control makes information processing in this system very fast, scalable to large problem sizes, and extendable to quantum communication.

An Analysis of SE and MBSE Concepts to Support Defence Capability Acquisition

Science.gov (United States)

2014-09-01

Architecture Review Meeting ATM Automatic Teller Machine AUSDAF Australian Defence Architecture Framework (also known as DAF) BOK Body of Knowledge BPMN ...behaviour trees, • business process modelling notation ( BPMN ™), • flowcharts, • IDEFx™ family of diagrams, and • Architecture Description...model diagraming of the ilk of BPMN , UML and SysML33 is heavily rules- 32 Architecture

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.

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.

A Study on Home Based Enterprises in Kampoeng Pandean as Supporting Sustainable Architecture

Directory of Open Access Journals (Sweden)

Safeyah Muchlisiniyati

2016-01-01

Full Text Available Home Based Enterprises (HBEs provide an enormous impact on the lives of the citizens and the environment. The impacts include: increase income and welfare of the family, provide job opportunities, improve the quality of homes and the environment, and ensure life sustainability. The existence of the business leads changes to the house. Those changes that made to the house are often ignore the comfort of home space and the environment as living space. This study aims to look at the development of HBEs performed by community in Kampoeng Pandean. The measurement items used are architectural sustainability factors, ie economical sustainability, social sustainability, and enviromental sustainability. The study is located in Kampoeng Pandean Sidoarjo. The method used is a combination of qualitative and quantitative method. The results show that HBEs in Kampoeng Pandean have not fully supported the sustainable architecture. Environmental sustainability has not been met, due to the density of the environment, the high percentage of building area to land area, and the construction of business space does not consider the comfort factor.

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.

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. PMID:26648660

Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

Energy Technology Data Exchange (ETDEWEB)

Fei, Sheng-wei; Wang, Ming-Jun; Miao, Yu-bin; Tu, Jun; Liu, Cheng-liang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

2009-06-15

Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample. (author)

Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

Energy Technology Data Exchange (ETDEWEB)

Fei Shengwei [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)], E-mail: feishengwei@sohu.com; Wang Mingjun; Miao Yubin; Tu Jun; Liu Chengliang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

2009-06-15

Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

OpenAIRE

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic...

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.

Science.gov (United States)

Mutasa, Simukayi; Chang, Peter D; Ruzal-Shapiro, Carrie; Ayyala, Rama

2018-02-05

-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster

Directory of Open Access Journals (Sweden)

Xia-an Bi

2018-02-01

Full Text Available Autism spectrum disorder (ASD is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC. Resting-state functional magnetic resonance imaging (fMRI data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG (orbital and opercula part, hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.

WWER NPPs fuel handling machine control system

International Nuclear Information System (INIS)

Mini, G.; Rossi, G.; Barabino, M.; Casalini, M.

2001-01-01

In order to increase the safety level of the fuel handling machine on WWER NPPs, Ansaldo Nucleare was asked to design and supply a new Control System. Two FHM Control System units have been already supplied for Temelin NPP and others supplies are in process for the Atommash company, which has in charge the supply of FHMs for NPPs located in Russia, Ukraine and China. The Fuel Handling Machine (FHM) Control System is an integrated system capable of a complete management of nuclear fuel assemblies. The computer-based system takes into account all the operational safety interlocks so that it is able to avoid incorrect and dangerous manoeuvres in the case of operator error. Control system design criteria, hardware and software architecture, and quality assurance control, are in accordance with the most recent international requirements and standards, and in particular for electromagnetic disturbance immunity demands and seismic compatibility. The hardware architecture of the control system is based on ABB INFI 90 system. The microprocessor-based ABB INFI 90 system incorporates and improves upon many of the time proven control capabilities of Bailey Network 90, validated over 14,000 installations world-wide. The control system complies all the former designed sensors and devices of the machine and markedly the angular position measurement sensors named 'selsyn' of Russian design. Nevertheless it is fully compatible with all the most recent sensors and devices currently available on the market (for ex. Multiturn absolute encoders). All control logic components were developed using standard INFI 90 Engineering Work Station, interconnecting blocks extracted from an extensive SAMA library by using a graphical approach (CAD) and allowing an easier intelligibility, more flexibility and updated and coherent documentation. The data acquisition system and the Man Machine Interface are implemented by ABB in co-operation with Ansaldo. The flexible and powerful software structure

VVER NPPs fuel handling machine control system

International Nuclear Information System (INIS)

Mini, G.; Rossi, G.; Barabino, M.; Casalini, M.

2002-01-01

In order to increase the safety level of the fuel handling machine on WWER NPPs, Ansaldo Nucleare was asked to design and supply a new Control System. Two Fuel Handling Machine (FHM) Control System units have been already supplied for Temelin NPP and others supply are in process for the Atommash company, which has in charge the supply of FHMs for NPPs located in Russia, Ukraine and China.The computer-based system takes into account all the operational safety interlocks so that it is able to avoid incorrect and dangerous manoeuvres in the case of operator error. Control system design criteria, hardware and software architecture, and quality assurance control, are in accordance with the most recent international requirements and standards, and in particular for electromagnetic disturbance immunity demands and seismic compatibility. The hardware architecture of the control system is based on ABB INFI 90 system. The microprocessor-based ABB INFI 90 system incorporates and improves upon many of the time proven control capabilities of Bailey Network 90, validated over 14,000 installations world-wide.The control system complies all the former designed sensors and devices of the machine and markedly the angular position measurement sensors named 'selsyn' of Russian design. Nevertheless it is fully compatible with all the most recent sensors and devices currently available on the market (for ex. Multiturn absolute encoders).All control logic were developed using standard INFI 90 Engineering Work Station, interconnecting blocks extracted from an extensive SAMA library by using a graphical approach (CAD) and allowing and easier intelligibility, more flexibility and updated and coherent documentation. The data acquisition system and the Man Machine Interface are implemented by ABB in co-operation with Ansaldo. The flexible and powerful software structure of 1090 Work-stations (APMS - Advanced Plant Monitoring System, or Tenore NT) has been successfully used to interface the

A Support Vector Machine-Based Gender Identification Using Speech Signal

Science.gov (United States)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

Science.gov (United States)

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

Science.gov (United States)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Zio, Enrico

2007-01-01

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project

Support vector machine learning-based fMRI data group analysis.

Science.gov (United States)

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

2007-07-15

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 between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine

Science.gov (United States)

Fu, Yumei; Xiang, Shihan; Chen, Tianyi; Zhou, Ping; Huang, Weiyan

2015-10-01

The fetal electrocardiogram (FECG) signal has important clinical value for diagnosing the fetal heart diseases and choosing suitable therapeutics schemes to doctors. So, the noninvasive extraction of FECG from electrocardiogram (ECG) signals becomes a hot research point. A new method, the Support Vector Machine (SVM) is utilized for the extraction of FECG with limited size of data. Firstly, the theory of the SVM and the principle of the extraction based on the SVM are studied. Secondly, the transformation of maternal electrocardiogram (MECG) component in abdominal composite signal is verified to be nonlinear and fitted with the SVM. Then, the SVM is trained, and the training results are compared with the real data to ensure the effect of the training. Meanwhile, the parameters of the SVM are optimized to achieve the best performance so that the learning machine can be utilized to fit the unknown samples. Finally, the FECG is extracted by removing the optimal estimation of MECG component from the abdominal composite signal. In order to evaluate the performance of FECG extraction based on the SVM, the Signal-to-Noise Ratio (SNR) and the visual test are used. The experimental results show that the FECG with good quality can be extracted, its SNR ratio is significantly increased as high as 9.2349 dB and the time cost is significantly decreased as short as 0.802 seconds. Compared with the traditional method, the noninvasive extraction method based on the SVM has a simple realization, the shorter treatment time and the better extraction quality under the same conditions.

Object Recognition System-on-Chip Using the Support Vector Machines

Directory of Open Access Journals (Sweden)

Houzet Dominique

2005-01-01

Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

Control System Design for Automatic Cavity Tuning Machines

Energy Technology Data Exchange (ETDEWEB)

Carcagno, R.; Khabiboulline, T.; Kotelnikov, S.; Makulski, A.; Nehring, R.; Nogiec, J.; Ross, M.; Schappert, W.; /Fermilab; Goessel, A.; Iversen, J.; Klinke, D.; /DESY

2009-05-01

A series of four automatic tuning machines for 9-cell TESLA-type cavities are being developed and fabricated in a collaborative effort among DESY, FNAL, and KEK. These machines are intended to support high-throughput cavity fabrication for construction of large SRF-based accelerator projects. Two of these machines will be delivered to cavity vendors for the tuning of XFEL cavities. The control system for these machines must support a high level of automation adequate for industrial use by non-experts operators. This paper describes the control system hardware and software design for these machines.

Control System Design for Automatic Cavity Tuning Machines

International Nuclear Information System (INIS)

Carcagno, R.; Khabiboulline, T.; Kotelnikov, S.; Makulski, A.; Nehring, R.; Nogiec, J.; Ross, M.; Schappert, W.; Goessel, A.; Iversen, J.; Klinke, D.

2009-01-01

A series of four automatic tuning machines for 9-cell TESLA-type cavities are being developed and fabricated in a collaborative effort among DESY, FNAL, and KEK. These machines are intended to support high-throughput cavity fabrication for construction of large SRF-based accelerator projects. Two of these machines will be delivered to cavity vendors for the tuning of XFEL cavities. The control system for these machines must support a high level of automation adequate for industrial use by non-experts operators. This paper describes the control system hardware and software design for these machines.

Stable architectures for deep neural networks

Science.gov (United States)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

Directory of Open Access Journals (Sweden)

Utku Kose

2016-03-01

Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors.

Science.gov (United States)

Sato, Tomohiro; Yuki, Hitomi; Takaya, Daisuke; Sasaki, Shunta; Tanaka, Akiko; Honma, Teruki

2012-04-23

In this study, machine learning using support vector machine was combined with three-dimensional (3D) molecular shape overlay, to improve the screening efficiency. Since the 3D molecular shape overlay does not use fingerprints or descriptors to compare two compounds, unlike 2D similarity methods, the application of machine learning to a 3D shape-based method has not been extensively investigated. The 3D similarity profile of a compound is defined as the array of 3D shape similarities with multiple known active compounds of the target protein and is used as the explanatory variable of support vector machine. As the measures of 3D shape similarity for our new prediction models, the prediction performances of the 3D shape similarity metrics implemented in ROCS, such as ShapeTanimoto and ScaledColor, were validated, using the known inhibitors of 15 target proteins derived from the ChEMBL database. The learning models based on the 3D similarity profiles stably outperformed the original ROCS when more than 10 known inhibitors were available as the queries. The results demonstrated the advantages of combining machine learning with the 3D similarity profile to process the 3D shape information of plural active compounds.

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

Science.gov (United States)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine

Science.gov (United States)

Lawi, Armin; Sya'Rani Machrizzandi, M.

2018-03-01

Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person’s mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

Data on Support Vector Machines (SVM model to forecast photovoltaic power

Directory of Open Access Journals (Sweden)

M. Malvoni

2016-12-01

Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

A Systematic Review on Recent Advances in mHealth Systems: Deployment Architecture for Emergency Response.

Science.gov (United States)

Gonzalez, Enrique; Peña, Raul; Avila, Alfonso; Vargas-Rosales, Cesar; Munoz-Rodriguez, David

2017-01-01

The continuous technological advances in favor of mHealth represent a key factor in the improvement of medical emergency services. This systematic review presents the identification, study, and classification of the most up-to-date approaches surrounding the deployment of architectures for mHealth. Our review includes 25 articles obtained from databases such as IEEE Xplore, Scopus, SpringerLink, ScienceDirect, and SAGE. This review focused on studies addressing mHealth systems for outdoor emergency situations. In 60% of the articles, the deployment architecture relied in the connective infrastructure associated with emergent technologies such as cloud services, distributed services, Internet-of-things, machine-to-machine, vehicular ad hoc network, and service-oriented architecture. In 40% of the literature review, the deployment architecture for mHealth considered traditional connective infrastructure. Only 20% of the studies implemented an energy consumption protocol to extend system lifetime. We concluded that there is a need for more integrated solutions specifically for outdoor scenarios. Energy consumption protocols are needed to be implemented and evaluated. Emergent connective technologies are redefining the information management and overcome traditional technologies.

A Systematic Review on Recent Advances in mHealth Systems: Deployment Architecture for Emergency Response

Directory of Open Access Journals (Sweden)

Enrique Gonzalez

2017-01-01

Full Text Available The continuous technological advances in favor of mHealth represent a key factor in the improvement of medical emergency services. This systematic review presents the identification, study, and classification of the most up-to-date approaches surrounding the deployment of architectures for mHealth. Our review includes 25 articles obtained from databases such as IEEE Xplore, Scopus, SpringerLink, ScienceDirect, and SAGE. This review focused on studies addressing mHealth systems for outdoor emergency situations. In 60% of the articles, the deployment architecture relied in the connective infrastructure associated with emergent technologies such as cloud services, distributed services, Internet-of-things, machine-to-machine, vehicular ad hoc network, and service-oriented architecture. In 40% of the literature review, the deployment architecture for mHealth considered traditional connective infrastructure. Only 20% of the studies implemented an energy consumption protocol to extend system lifetime. We concluded that there is a need for more integrated solutions specifically for outdoor scenarios. Energy consumption protocols are needed to be implemented and evaluated. Emergent connective technologies are redefining the information management and overcome traditional technologies.

PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Directory of Open Access Journals (Sweden)

Baskin Berivan

2003-03-01

Full Text Available Abstract Background The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Results Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Conclusions Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

Machine learning methods for planning

CERN Document Server

Minton, Steven

1993-01-01

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

An Efficient Reconfigurable Architecture for Fingerprint Recognition

Directory of Open Access Journals (Sweden)

Satish S. Bhairannawar

2016-01-01

Full Text Available The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate, FAR (False Acceptance Rate, and FRR (False Rejection Rate are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.

Supporting Undergraduate Computer Architecture Students Using a Visual MIPS64 CPU Simulator

Science.gov (United States)

Patti, D.; Spadaccini, A.; Palesi, M.; Fazzino, F.; Catania, V.

2012-01-01

The topics of computer architecture are always taught using an Assembly dialect as an example. The most commonly used textbooks in this field use the MIPS64 Instruction Set Architecture (ISA) to help students in learning the fundamentals of computer architecture because of its orthogonality and its suitability for real-world applications. This…

System architectures for telerobotic research

Science.gov (United States)

Harrison, F. Wallace

1989-01-01

Several activities are performed related to the definition and creation of telerobotic systems. The effort and investment required to create architectures for these complex systems can be enormous; however, the magnitude of process can be reduced if structured design techniques are applied. A number of informal methodologies supporting certain aspects of the design process are available. More recently, prototypes of integrated tools supporting all phases of system design from requirements analysis to code generation and hardware layout have begun to appear. Activities related to system architecture of telerobots are described, including current activities which are designed to provide a methodology for the comparison and quantitative analysis of alternative system architectures.

A Formal Approach to Software Architecture

National Research Council Canada - National Science Library

Allen, Robert

1997-01-01

.... While architectural concepts are often embodied in infrastructure to support specific architectural styles and in the initial conceptualization of a system configuration, the lack of an explicit...

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

Machine learning based switching model for electricity load forecasting

International Nuclear Information System (INIS)

Fan Shu; Chen Luonan; Lee, Weijen

2008-01-01

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

Machine Learning Approach to Deconvolution of Thermal Infrared (TIR) Spectrum of Mercury Supporting MERTIS Onboard ESA/JAXA BepiColombo

Science.gov (United States)

Varatharajan, I.; D'Amore, M.; Maturilli, A.; Helbert, J.; Hiesinger, H.

2018-04-01

Machine learning approach to spectral unmixing of emissivity spectra of Mercury is carried out using endmember spectral library measured at simulated daytime surface conditions of Mercury. Study supports MERTIS payload onboard ESA/JAXA BepiColombo.

Marshall Application Realignment System (MARS) Architecture

Science.gov (United States)

Belshe, Andrea; Sutton, Mandy

2010-01-01

The Marshall Application Realignment System (MARS) Architecture project was established to meet the certification requirements of the Department of Defense Architecture Framework (DoDAF) V2.0 Federal Enterprise Architecture Certification (FEAC) Institute program and to provide added value to the Marshall Space Flight Center (MSFC) Application Portfolio Management process. The MARS Architecture aims to: (1) address the NASA MSFC Chief Information Officer (CIO) strategic initiative to improve Application Portfolio Management (APM) by optimizing investments and improving portfolio performance, and (2) develop a decision-aiding capability by which applications registered within the MSFC application portfolio can be analyzed and considered for retirement or decommission. The MARS Architecture describes a to-be target capability that supports application portfolio analysis against scoring measures (based on value) and overall portfolio performance objectives (based on enterprise needs and policies). This scoring and decision-aiding capability supports the process by which MSFC application investments are realigned or retired from the application portfolio. The MARS Architecture is a multi-phase effort to: (1) conduct strategic architecture planning and knowledge development based on the DoDAF V2.0 six-step methodology, (2) describe one architecture through multiple viewpoints, (3) conduct portfolio analyses based on a defined operational concept, and (4) enable a new capability to support the MSFC enterprise IT management mission, vision, and goals. This report documents Phase 1 (Strategy and Design), which includes discovery, planning, and development of initial architecture viewpoints. Phase 2 will move forward the process of building the architecture, widening the scope to include application realignment (in addition to application retirement), and validating the underlying architecture logic before moving into Phase 3. The MARS Architecture key stakeholders are most

Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

Directory of Open Access Journals (Sweden)

Chen Gao

2017-03-01

Full Text Available Tool fault diagnosis in numerical control (NC machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA and least squares support vector machine (LS-SVM using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis.

Model-centric software architecture reconstruction

NARCIS (Netherlands)

Stoermer, C.; Rowe, A.; O'Brien, L.; Verhoef, C.

2006-01-01

Much progress has been achieved in defining methods, techniques, and tools for software architecture reconstruction (SAR). However, less progress has been achieved in constructing reasoning frameworks from existing systems that support organizations in architecture analysis and design decisions.

Care of architectural archival material

Directory of Open Access Journals (Sweden)

Perović-Ivović Svetlana

2015-01-01

Full Text Available The paper focuses on the complex issue of the preservation and maintenance of architectural records as a distinctive type of archival material, addressing the problems of care and storage of diverse oversize documents done on different supports: architectural drawings on drafting cloth, waxed cloth, paper, plastic film; reproductions such as blueprints, diazo prints, and other types of non-textual documents. Attention is paid to all types of damage caused both by internal factors, i.e. resulting from the nature of the support material, and by external factors involved in the degradation of architectural archival material. The paper also presents comparative conservation methods applied to architectural documents kept in the Archives of Yugoslavia. In conclusion, it points to the importance of preventive care as regards storage conditions, handling and display of this type of archival material.

Machine translation with minimal reliance on parallel resources

CERN Document Server

Tambouratzis, George; Sofianopoulos, Sokratis

2017-01-01

This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.

Towards Horizontal Architecture for Autonomic M2M Service Networks

Directory of Open Access Journals (Sweden)

Juhani Latvakoski

2014-05-01

Full Text Available Today, increasing number of industrial application cases rely on the Machine to Machine (M2M services exposed from physical devices. Such M2M services enable interaction of physical world with the core processes of company information systems. However, there are grand challenges related to complexity and “vertical silos” limiting the M2M market scale and interoperability. It is here expected that horizontal approach for the system architecture is required for solving these challenges. Therefore, a set of architectural principles and key enablers for the horizontal architecture have been specified in this work. A selected set of key enablers called as autonomic M2M manager, M2M service capabilities, M2M messaging system, M2M gateways towards energy constrained M2M asset devices and creation of trust to enable end-to-end security for M2M applications have been developed. The developed key enablers have been evaluated separately in different scenarios dealing with smart metering, car sharing and electric bike experiments. The evaluation results shows that the provided architectural principles, and developed key enablers establish a solid ground for future research and seem to enable communication between objects and applications, which are not initially been designed to communicate together. The aim as the next step in this research is to create a combined experimental system to evaluate the system interoperability and performance in a more detailed manner.

Machine protection: availability for particle accelerators

International Nuclear Information System (INIS)

Apollonio, A.

2015-01-01

Machine availability is a key indicator for the performance of the next generation of particle accelerators. Availability requirements need to be carefully considered during the design phase to achieve challenging objectives in different fields, as e.g. particle physics and material science. For existing and future High-Power facilities, such as ESS (European Spallation Source) and HL-LHC (High-Luminosity LHC), operation with unprecedented beam power requires highly dependable Machine Protection Systems (MPS) to avoid any damage-induced downtime. Due to the high complexity of accelerator systems, finding the optimal balance between equipment safety and accelerator availability is challenging. The MPS architecture, as well as the choice of electronic components, have a large influence on the achievable level of availability. In this thesis novel methods to address the availability of accelerators and their protection systems are presented. Examples of studies related to dependable MPS architectures are given in the thesis, both for Linear accelerators (Linac4, ESS) and circular particle colliders (LHC and HL-LHC). A study of suitable architectures for interlock systems of future availability-critical facilities is presented. Different methods have been applied to assess the anticipated levels of accelerator availability. The thesis presents the prediction of the performance (integrated luminosity for a particle collider) of LHC and future LHC up- grades, based on a Monte Carlo model that allows reproducing a realistic timeline of LHC operation. This model does not only account for the contribution of MPS, but extends to all systems relevant for LHC operation. Results are extrapolated to LHC run 2, run 3 and HL-LHC to derive individual system requirements, based on the target integrated luminosity. (author)

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

Science.gov (United States)

Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

2014-01-01

Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

Neutron–gamma discrimination based on the support vector machine method

International Nuclear Information System (INIS)

Yu, Xunzhen; Zhu, Jingjun; Lin, ShinTed; Wang, Li; Xing, Haoyang; Zhang, Caixun; Xia, Yuxi; Liu, Shukui; Yue, Qian; Wei, Weiwei; Du, Qiang; Tang, Changjian

2015-01-01

In this study, the combination of the support vector machine (SVM) method with the moment analysis method (MAM) is proposed and utilized to perform neutron/gamma (n/γ) discrimination of the pulses from an organic liquid scintillator (OLS). Neutron and gamma events, which can be firmly separated on the scatter plot drawn by the charge comparison method (CCM), are detected to form the training data set and the test data set for the SVM, and the MAM is used to create the feature vectors for individual events in the data sets. Compared to the traditional methods, such as CCM, the proposed method can not only discriminate the neutron and gamma signals, even at lower energy levels, but also provide the corresponding classification accuracy for each event, which is useful in validating the discrimination. Meanwhile, the proposed method can also offer a predication of the classification for the under-energy-limit events

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.

Automatic Detection of Retinal Exudates using a Support Vector Machine

Directory of Open Access Journals (Sweden)

Nualsawat HIRANSAKOLWONG

2013-02-01

Full Text Available Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. Unfortunately, it is a normal situation that retinal images in Thailand are poor quality images. In this paper, we present a series of experiments on feature selection and exudates classification using the support vector machine classifiers. The retinal images are segmented following key preprocessing steps, i.e., color normalization, contrast enhancement, noise removal and color space selection. On data sets of poor quality images, sensitivity, specificity and accuracy is 94.46%, 89.52% and 92.14%, respectively.

Specified international joint research. Report for fiscal 1997 on the result of `Development of Machining Supporting System`; Kokusai tokutei kyodo kenkyu. `Kikai kako shien system no kaihatsu` 1997 nendo seika hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-03-01

On the basis of information obtained from actually performed designing of machines with the aid of computers, researches are conducted for the development of a system that automatically designs required machine tools, machining procedures, machining conditions, and tool paths. The research and development efforts made in fiscal 1997 are enumerated below. In the development of man-machine interfaces, one that integrates a machining procedure designing system, machining condition designing system, and a tool path designing system, all of which are subsystems belonging in a machining supporting system, is developed. In a system evaluation performed through actual machining, an interface between CAD (Computer-Aided Design) technology and a machining supporting system is evaluated, when machining is actually performed for experimentation in an environment in which a machining procedure designing system, machining condition designing system, tool path designing system, and CNC (Computerized Numerical Control) technology collaborate as integrated. As the result, the performance expected to be achieved at the beginning is realized. Two scientists of Russian Academy of Sciences are invited, and researches are conducted concerning knowledge processing technology. 20 refs., 21 figs., 10 tabs.