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...

Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine

International Nuclear Information System (INIS)

Khatibinia, Mohsen; Javad Fadaee, Mohammad; Salajegheh, Javad; Salajegheh, Eysa

2013-01-01

An efficient metamodeling framework in conjunction with the Monte-Carlo Simulation (MCS) is introduced to reduce the computational cost in seismic reliability assessment of existing RC structures. In order to achieve this purpose, the metamodel is designed by combining weighted least squares support vector machine (WLS-SVM) and a wavelet kernel function, called wavelet weighted least squares support vector machine (WWLS-SVM). In this study, the seismic reliability assessment of existing RC structures with consideration of soil–structure interaction (SSI) effects is investigated in accordance with Performance-Based Design (PBD). This study aims to incorporate the acceptable performance levels of PBD into reliability theory for comparing the obtained annual probability of non-performance with the target values for each performance level. The MCS method as the most reliable method is utilized to estimate the annual probability of failure associated with a given performance level in this study. In WWLS-SVM-based MCS, the structural seismic responses are accurately predicted by WWLS-SVM for reducing the computational cost. To show the efficiency and robustness of the proposed metamodel, two RC structures are studied. Numerical results demonstrate the efficiency and computational advantages of the proposed metamodel for the seismic reliability assessment of structures. Furthermore, the consideration of the SSI effects in the seismic reliability assessment of existing RC structures is compared to the fixed base model. It shows which SSI has the significant influence on the seismic reliability assessment of structures.

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

Science.gov (United States)

Rong, Y; Padron, A V; Hagerty, K J; Nelson, N; Chi, S; Keyhani, N O; Katz, J; Datta, S P A; Gomes, C; McLamore, E S

2018-04-30

Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein-ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra. Here, we show the use of a simple, open source support vector machine learning algorithm for analyzing impedimetric data in lieu of using equivalent circuit analysis. We demonstrate two different protein-based biosensors to show that the tool can be used for various applications. We conclude with a mobile phone-based demonstration focused on the measurement of acetone, an important biomarker related to the onset of diabetic ketoacidosis. In all conditions tested, the open source classifier was capable of performing as well as, or better, than the equivalent circuit analysis for characterizing weak/transient interactions between a model ligand (acetone) and a small chemosensory protein derived from the tsetse fly. In addition, the tool has a low computational requirement, facilitating use for mobile acquisition systems such as mobile phones. The protocol is deployed through Jupyter notebook (an open source computing environment available for mobile phone, tablet or computer use) and the code was written in Python. For each of the applications, we provide step-by-step instructions in English, Spanish, Mandarin and Portuguese to facilitate widespread use. All codes were based on scikit-learn, an open source software machine learning library in the Python language, and were processed in Jupyter notebook, an open-source web application for Python. The tool can easily be integrated with the mobile biosensor equipment for rapid

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)

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

Process Machine Interactions Predicition and Manipulation of Interactions between Manufacturing Processes and Machine Tool Structures

CERN Document Server

Hollmann, Ferdinand

2013-01-01

This contributed volume collects the scientific results of the DFG Priority Program 1180 Prediction and Manipulation of Interactions between Structure and Process. The research program has been conducted during the years 2005 and 2012, whereas the primary goal was the analysis of the interactions between processes and structures in modern production facilities. This book presents the findings of the 20 interdisciplinary subprojects, focusing on different manufacturing processes such as high performance milling, tool grinding or metal forming. It contains experimental investigations as well as mathematical modeling of production processes and machine interactions. New experimental advancements and novel simulation approaches are also included.

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.

Reconfigurable support vector machine classifier with approximate computing

NARCIS (Netherlands)

van Leussen, M.J.; Huisken, J.; Wang, L.; Jiao, H.; De Gyvez, J.P.

2017-01-01

Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and

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.

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...

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.

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...

Probability and sensitivity analysis of machine foundation and soil interaction

Directory of Open Access Journals (Sweden)

Králik J., jr.

2009-06-01

Full Text Available This paper deals with the possibility of the sensitivity and probabilistic analysis of the reliability of the machine foundation depending on variability of the soil stiffness, structure geometry and compressor operation. The requirements to design of the foundation under rotating machines increased due to development of calculation method and computer tools. During the structural design process, an engineer has to consider problems of the soil-foundation and foundation-machine interaction from the safety, reliability and durability of structure point of view. The advantages and disadvantages of the deterministic and probabilistic analysis of the machine foundation resistance are discussed. The sensitivity of the machine foundation to the uncertainties of the soil properties due to longtime rotating movement of machine is not negligible for design engineers. On the example of compressor foundation and turbine fy. SIEMENS AG the affectivity of the probabilistic design methodology was presented. The Latin Hypercube Sampling (LHS simulation method for the analysis of the compressor foundation reliability was used on program ANSYS. The 200 simulations for five load cases were calculated in the real time on PC. The probabilistic analysis gives us more complex information about the soil-foundation-machine interaction as the deterministic analysis.

Man-machine interaction in Canadian nuclear power plants

International Nuclear Information System (INIS)

Olmstead, R.A.

1994-01-01

The design of man-machine interaction in the CANDU plants has evolved considerably over several generations of plants, from the first Douglas Point plant through to the next generation of plants represented by new designs like CANDU 3. In the early plants, the control room configuration was based on designers' projections of control interface requirements. With succeeding generations of designs, there has been an evolution towards a more systematic consideration of human strengths and weaknesses, increasing attention to formal requirements definition, and incorporation of a larger base of operational experience. This paper describes the design of the man-machine interaction for third generation CANDU-3 control rooms for improved operator reliability and reduced costs. (author). 13 refs., 3 figs

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.

Predicting drug-target interactions using restricted Boltzmann machines.

Science.gov (United States)

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

Human and machine perception communication, interaction, and integration

CERN Document Server

Cantoni, Virginio; Setti, Alessandra

2005-01-01

The theme of this book on human and machine perception is communication, interaction, and integration. For each basic topic there are invited lectures, corresponding to approaches in nature and machines, and a panel discussion. The lectures present the state of the art, outlining open questions and stressing synergies among the disciplines related to perception. The panel discussions are forums for open debate. The wide spectrum of topics allows comparison and synergy and can stimulate new approaches.

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.

Advanced man-machine interaction. Fundamentals and implementation

Energy Technology Data Exchange (ETDEWEB)

Kraiss, K.F. (ed.) [Aachen Technische Hochschule (Germany). Lehrstuhl fuer Technische Informatik und Computerwissenschaften

2006-07-01

Man-machine interaction is the gateway providing access to functions and services, which, due to the ever increasing complexity of smart systems, threatens to become a bottleneck. This book therefore introduces not only advanced interfacing concepts, but also gives insight into the related theoretical background.This refers mainly to the realization of video-based multimodal interaction via gesture, mimics, and speech, but also to interacting with virtual object in virtual environments, cooperating with local or remote robots, and user assistance. While most publications in the field of human factors engineering focus on interface design, this book puts special emphasis on implementation aspects. To this end it is accompanied by software development environments for image processing, classification, and virtual environment implementation. In addition a test data base is included for gestures, head pose, facial expressions, full-body person recognition, and people tracking. These data are used for the examples throughout the book, but are also meant to encourage the reader to start experimentation on his own. Thus the book may serve as a self-contained introduction both for researchers and developers of man-machine interfaces. It may also be used for graduate-level university courses. (orig.)

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.

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.

2P2IHUNTER: a tool for filtering orthosteric protein–protein interaction modulators via a dedicated support vector machine

Science.gov (United States)

Hamon, Véronique; Bourgeas, Raphael; Ducrot, Pierre; Theret, Isabelle; Xuereb, Laura; Basse, Marie Jeanne; Brunel, Jean Michel; Combes, Sebastien; Morelli, Xavier; Roche, Philippe

2014-01-01

Over the last 10 years, protein–protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this ‘high-hanging fruit’ is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns. PMID:24196694

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.

Towards Semantic Analysis of Training-Learning Relationships within Human-Machine Interactions

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

In this article First-Order Predicate Logic (FOL) is employed for analysing some relationships between human beings and machines. Based on FOL, I will be conceptually and logically concerned with semantic analysis of training-learning relationships in human-machine interaction. The central focus...

Nanoscale swimmers: hydrodynamic interactions and propulsion of molecular machines

Science.gov (United States)

Sakaue, T.; Kapral, R.; Mikhailov, A. S.

2010-06-01

Molecular machines execute nearly regular cyclic conformational changes as a result of ligand binding and product release. This cyclic conformational dynamics is generally non-reciprocal so that under time reversal a different sequence of machine conformations is visited. Since such changes occur in a solvent, coupling to solvent hydrodynamic modes will generally result in self-propulsion of the molecular machine. These effects are investigated for a class of coarse grained models of protein machines consisting of a set of beads interacting through pair-wise additive potentials. Hydrodynamic effects are incorporated through a configuration-dependent mobility tensor, and expressions for the propulsion linear and angular velocities, as well as the stall force, are obtained. In the limit where conformational changes are small so that linear response theory is applicable, it is shown that propulsion is exponentially small; thus, propulsion is nonlinear phenomenon. The results are illustrated by computations on a simple model molecular machine.

Concept Representation Analysis in the Context of Human-Machine Interactions

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

an inductive machine learning paradigm). The results will support figuring out the most significant key points for constructing a conceptual linkage between a human learning theory and a machine learning paradigm. Accordingly, I will construct a conceptual ground for expressing and analysing concepts......This article attempts to make a conceptual and epistemological junction between human learning and machine learning. I will be concerned with specifying and analysing the structure of concepts in the common ground between a concept-based human learning theory and a concept-based machine learning...... in the common ground of human and informatics sciences and in the context of human-machine interplays....

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.

Modelling of human-machine interaction in equipment design of manufacturing cells

Science.gov (United States)

Cochran, David S.; Arinez, Jorge F.; Collins, Micah T.; Bi, Zhuming

2017-08-01

This paper proposes a systematic approach to model human-machine interactions (HMIs) in supervisory control of machining operations; it characterises the coexistence of machines and humans for an enterprise to balance the goals of automation/productivity and flexibility/agility. In the proposed HMI model, an operator is associated with a set of behavioural roles as a supervisor for multiple, semi-automated manufacturing processes. The model is innovative in the sense that (1) it represents an HMI based on its functions for process control but provides the flexibility for ongoing improvements in the execution of manufacturing processes; (2) it provides a computational tool to define functional requirements for an operator in HMIs. The proposed model can be used to design production systems at different levels of an enterprise architecture, particularly at the machine level in a production system where operators interact with semi-automation to accomplish the goal of 'autonomation' - automation that augments the capabilities of human beings.

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%.

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,.

Deep neural mapping support vector machines.

Science.gov (United States)

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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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

2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine.

Science.gov (United States)

Hamon, Véronique; Bourgeas, Raphael; Ducrot, Pierre; Theret, Isabelle; Xuereb, Laura; Basse, Marie Jeanne; Brunel, Jean Michel; Combes, Sebastien; Morelli, Xavier; Roche, Philippe

2014-01-06

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

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.

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

Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

Science.gov (United States)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

Directory of Open Access Journals (Sweden)

Li Deng

2016-01-01

Full Text Available In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

Science.gov (United States)

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Optimal design method to minimize users' thinking mapping load in human-machine interactions.

Science.gov (United States)

Huang, Yanqun; Li, Xu; Zhang, Jie

2015-01-01

The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today's mental work mastered society. Improving the usability of a product and minimizing user's thinking mapping and interpreting load in human-machine interactions. An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users' intentions and affordance of product interface states. By analyzing the users' thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. Considering multiple factors to minimize users' thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.

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

Study on intelligent processing system of man-machine interactive garment frame model

Science.gov (United States)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

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.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

International Nuclear Information System (INIS)

Guo, Q; Shao, J; Ruiz, V

2005-01-01

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma

Investigation of support vector machine for the detection of architectural distortion in mammographic images

Energy Technology Data Exchange (ETDEWEB)

Guo, Q [Department of Cybernetics, University of Reading, Reading RG6 6AY (United Kingdom); Shao, J [Department of Electronics, University of Kent at Canterbury, Kent CT2 7NT (United Kingdom); Ruiz, V [Department of Cybernetics, University of Reading, Reading RG6 6AY (United Kingdom)

2005-01-01

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

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.

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

Science.gov (United States)

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Direct observation of interaction between plasma ions and grid-excited pulses in a Q-machine

DEFF Research Database (Denmark)

Andersen, S.A.; Jensen, Vagn Orla; Michelsen, Poul

1970-01-01

The change in velocity-distribution function caused by interaction between ions and density pulses in a Q-machine is observed experimentally.......The change in velocity-distribution function caused by interaction between ions and density pulses in a Q-machine is observed experimentally....

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...

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

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.

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.

Beam-machine Interaction at the CERN LHC

CERN Document Server

Boccone, V; Brugger, M; Calviani, M; Cerutti, F; Esposito, L S; Ferrari, A; Lechner, A; Mereghetti, A; Nowak, E; Shetty, N V; Skordis, E; Versaci, R; Vlachoudis, V

2014-01-01

The radiation field generated by a high energy and intensity accelerator is of concern in terms of element functionality threat, component damage, electronics reliability, and material activation, but also provides signatures that allow actual operating conditions to be monitored. The shower initiated by an energetic hadron involves many different physical processes, down to slow neutron interactions and fragment de-excitation, which need to be accurately described for design purposes and to interpret operation events. The experience with the transport and interaction Monte Carlo code FLUKA at the Large Hadron Collider (LHC), operating at CERN with 4 TeV proton beams (and equivalent magnetic rigidity Pb beams) and approaching nominal luminosity and energy, is presented. Design, operation and upgrade challenges are reviewed in the context of beam-machine interaction account and relevant benchmarking examples based on radiation monitor measurements are shown.

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.

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…

Concept Representation Analysis in the Context of Human-Machine Interactions

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

This article attempts to make a conceptual and epistemological junction between human learning and machine learning. I will be concerned with specifying and analysing the structure of concepts in the common ground between a concept-based human learning theory and a concept-based machine learning...... paradigm. I will focus on (i) humans’ conceptual representations in the framework of constructivism (as an educational theory of learning and model of knowing) and constructionism (as a theory for conceptualising learning) and (ii) concept representations in the framework of inductive concept learning (as...... an inductive machine learning paradigm). The results will support figuring out the most significant key points for constructing a conceptual linkage between a human learning theory and a machine learning paradigm. Accordingly, I will construct a conceptual ground for expressing and analysing concepts...

Quantitative chemogenomics: machine-learning models of protein-ligand interaction.

Science.gov (United States)

Andersson, Claes R; Gustafsson, Mats G; Strömbergsson, Helena

2011-01-01

Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed. © 2011 Bentham Science Publishers

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.

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%

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 ...

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)

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...

Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

Directory of Open Access Journals (Sweden)

Julian Zubek

2015-07-01

Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

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

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.

A Study of the Interaction between Batting Cage Baseballs and Pitching Machine

Directory of Open Access Journals (Sweden)

Patrick Drane

2018-02-01

Full Text Available Batting cage pitching machines are widely used across the sports of baseball and softball for training and recreation purposes. The balls are specifically designed for the machines and for the environment to ensure high durability and typically do not have seams. Polymeric foam balls are widely used in these automated pitching machines for batting practice in a cage environment and are similar in weight and size compared with the regulation balls used in leagues. The primary objective of this paper is to characterize the polymeric balls and their interaction with the pitching machine. The paper will present measured ball properties and measured relationships between various pitching machine parameters such as wheel speed, and the ratio of wheel speeds on the ball exit velocity and rotation. This paper will also characterize some of the effects of wear on the baseballs and wheels from their prolonged use.

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.

Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

Directory of Open Access Journals (Sweden)

Seizi Someya

2010-01-01

Full Text Available Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs. We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

Object-Oriented Support for Adaptive Methods on Paranel Machines

Directory of Open Access Journals (Sweden)

Sandeep Bhatt

1993-01-01

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

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.

Man-machine interfaces analysis system based on computer simulation

International Nuclear Information System (INIS)

Chen Xiaoming; Gao Zuying; Zhou Zhiwei; Zhao Bingquan

2004-01-01

The paper depicts a software assessment system, Dynamic Interaction Analysis Support (DIAS), based on computer simulation technology for man-machine interfaces (MMI) of a control room. It employs a computer to simulate the operation procedures of operations on man-machine interfaces in a control room, provides quantified assessment, and at the same time carries out analysis on operational error rate of operators by means of techniques for human error rate prediction. The problems of placing man-machine interfaces in a control room and of arranging instruments can be detected from simulation results. DIAS system can provide good technical supports to the design and improvement of man-machine interfaces of the main control room of a nuclear power plant

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.

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.

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.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

OpenAIRE

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive cha...

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.

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.

Quiescent plasma machine for beam-plasma interaction and wave studies

International Nuclear Information System (INIS)

Ferreira, J.L.

1994-01-01

A quiescent double plasma machine for beam-plasma interaction wave studies is described. A detailed description of several plasma diagnostics used for plasma and wave excitation detection is given. A beam-plasma wave dispersion relation is used to compare theoretical values with the experimentally measured Langmuir wave frequencies and wavelengths. (author). 14 refs, 10 figs

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

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.

DISTRIBUTED SYSTEM FOR HUMAN MACHINE INTERACTION IN VIRTUAL ENVIRONMENTS

Directory of Open Access Journals (Sweden)

Abraham Obed Chan-Canche

2017-07-01

Full Text Available The communication networks built by multiple devices and sensors are becoming more frequent. These device networks allow human-machine interaction development which aims to improve the human performance generating an adaptive environment in response to the information provided by it. The problem of this work is the quick integration of a device network that allows the development of a flexible immersive environment for different uses.

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...

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

Science.gov (United States)

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction.

Science.gov (United States)

Schermer, Maartje

2009-12-01

Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have on our 'symbolic order'-on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility.

Survey of methods for integrated sequence analysis with emphasis on man-machine interaction

Energy Technology Data Exchange (ETDEWEB)

Kahlbom, U; Holmgren, P [RELCON, Stockholm (Sweden)

1995-05-01

This report presents a literature study concerning recently developed monotonic methodologies in the human reliability area. The work was performed by RELCON AB on commission by NKS/RAK-1, subproject 3. The topic of subproject 3 is `Integrated Sequence Analysis with Emphasis on Man-Machine Interaction`. The purpose with the study was to compile recently developed methodologies and to propose some of these methodologies for use in the sequence analysis task. The report describes mainly non-dynamic (monotonic) methodologies. One exception is HITLINE, which is a semi-dynamic method. Reference provides a summary of approaches to dynamic analysis of man-machine-interaction, and explains the differences between monotonic and dynamic methodologies. (au) 21 refs.

Survey of methods for integrated sequence analysis with emphasis on man-machine interaction

International Nuclear Information System (INIS)

Kahlbom, U.; Holmgren, P.

1995-05-01

This report presents a literature study concerning recently developed monotonic methodologies in the human reliability area. The work was performed by RELCON AB on commission by NKS/RAK-1, subproject 3. The topic of subproject 3 is 'Integrated Sequence Analysis with Emphasis on Man-Machine Interaction'. The purpose with the study was to compile recently developed methodologies and to propose some of these methodologies for use in the sequence analysis task. The report describes mainly non-dynamic (monotonic) methodologies. One exception is HITLINE, which is a semi-dynamic method. Reference provides a summary of approaches to dynamic analysis of man-machine-interaction, and explains the differences between monotonic and dynamic methodologies. (au) 21 refs

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%.

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

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.

Patients on weaning trials classified with support vector machines

International Nuclear Information System (INIS)

Garde, Ainara; Caminal, Pere; Giraldo, Beatriz F; Schroeder, Rico; Voss, Andreas; Benito, Salvador

2010-01-01

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h (GSucc); 39 patients who failed the weaning trial (GFail) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (GRein). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients (GSucc, GFail and GRein) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications

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.

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.

Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model

Directory of Open Access Journals (Sweden)

Ji-Long Liu

2015-03-01

Full Text Available Protein-protein interaction (PPI is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR, a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments.

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...

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%.

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.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.

Science.gov (United States)

Manavalan, Balachandran; Shin, Tae H; Lee, Gwang

2018-01-01

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Directory of Open Access Journals (Sweden)

Balachandran Manavalan

2018-03-01

Full Text Available Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

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.

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%.

Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

Science.gov (United States)

Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

2016-10-01

Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

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.

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

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

Energy Technology Data Exchange (ETDEWEB)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

2017-07-14

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

On the Interaction between a Tunnel Boring Machine and the Surrounding Soil

NARCIS (Netherlands)

Festa, D.

2015-01-01

The thesis investigates the mechanical equilibrium of a Tunnel Boring Machine (TBM) driving in soft soil. The interaction between the TBM-shield and the soil is also investigated. The analysis is based on monitoring data gathered during the construction of the Hubertus tunnel in The Hague,

Human-machine interface aspects and use of computer-based operator support systems in control room upgrades and new control room designs for nuclear power plants

International Nuclear Information System (INIS)

Berg, O.

1997-01-01

At the Halden Project efforts are made to explore the possibilities through design, development and validation of Computer-based Operator Support Systems (COSSes) which can assist the operators in different operational situations, ranging from normal operation to disturbance and accident conditions. The programme comprises four main activities: 1) verification and validation of safety critical software systems; 2) man-machine interaction research emphasizing improvements in man-machine interfaces on the basis of human factors studies; 3) computerized operator support systems assisting the operator in fault detection/diagnosis and planning of control actions; and 4) control room development providing a basis for retrofitting of existing control rooms and for the design of advanced concepts. The paper presents the status of this development programme, including descriptions of specific operator support functions implemented in the simulator-based, experimental control room at Halden (HAMMLAB, HAlden Man-Machine LABoratory). These operator aids comprise advanced alarms systems, diagnostic support functions, electronic procedures, critical safety functions surveillance and accident management support systems. The different operator support systems development at the Halden Project are tested and evaluated in HAMMLAB with operators from the Halden Reactor, and occasionally from commercial NPPs, as test subjects. These evaluations provide data on the merits of different operator support systems in an advanced control room setting, as well as on how such systems should be integrated to enhance operator performance. The paper discusses these aspects and the role of computerized operator support systems in plant operation based on the experience from this work at the Halden Project. 15 refs, 5 figs

Human-machine interface aspects and use of computer-based operator support systems in control room upgrades and new control room designs for nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Berg, O [Institutt for Energiteknikk, OECD Halden Reactor Project (Netherlands)

1997-07-01

At the Halden Project efforts are made to explore the possibilities through design, development and validation of Computer-based Operator Support Systems (COSSes) which can assist the operators in different operational situations, ranging from normal operation to disturbance and accident conditions. The programme comprises four main activities: 1) verification and validation of safety critical software systems; 2) man-machine interaction research emphasizing improvements in man-machine interfaces on the basis of human factors studies; 3) computerized operator support systems assisting the operator in fault detection/diagnosis and planning of control actions; and 4) control room development providing a basis for retrofitting of existing control rooms and for the design of advanced concepts. The paper presents the status of this development programme, including descriptions of specific operator support functions implemented in the simulator-based, experimental control room at Halden (HAMMLAB, HAlden Man-Machine LABoratory). These operator aids comprise advanced alarms systems, diagnostic support functions, electronic procedures, critical safety functions surveillance and accident management support systems. The different operator support systems development at the Halden Project are tested and evaluated in HAMMLAB with operators from the Halden Reactor, and occasionally from commercial NPPs, as test subjects. These evaluations provide data on the merits of different operator support systems in an advanced control room setting, as well as on how such systems should be integrated to enhance operator performance. The paper discusses these aspects and the role of computerized operator support systems in plant operation based on the experience from this work at the Halden Project. 15 refs, 5 figs.

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)

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.

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

Support vector machine-based facial-expression recognition method combining shape and appearance

Science.gov (United States)

Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

2010-11-01

Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

Human machine interaction research experience and perspectives as seen from the OECD Halden Reactor Project

International Nuclear Information System (INIS)

Oewre, F.

1999-01-01

In this paper a short review is given on important safety issues in the field of human machine interaction as expressed by important nuclear organisations such as USNRC, IAEA and the OECD NEA. Further on, a presentation is offered of research activities at the OECD Halden Reactor Project in the field of human machine interaction aiming to clarify some of the issues outlined by the above mentioned organisations. The OECD Halden Reactor Project is a joint undertaking of national nuclear organisations in 19 countries sponsoring a jointly financed research programme under the auspices of the OECD - Nuclear Energy Agency. One of the research areas is the man-machine systems research addressing the operator tasks in a control room environment. The overall objective is to provide a basis for improving today's control rooms through introduction of computer-based solutions for effective and safe execution of surveillance and control functions in normal as well as off-normal plant situations. (author)

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

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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.

Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions.

Science.gov (United States)

Agarwal, Shashank; Liu, Feifan; Yu, Hong

2011-10-03

Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm". We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles. Simple Classifier is available at http://sourceforge.net/p/simpleclassify/home/ and OntoNorm at http://sourceforge.net/p/ontonorm/home/.

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.

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)

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

Directory of Open Access Journals (Sweden)

R. Gholami

2012-01-01

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

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...

Bio-Inspired Interaction Control of Robotic Machines for Motor Therapy

OpenAIRE

Zollo, Loredana; Formica, Domenico; Guglielmelli, Eugenio

2007-01-01

In this chapter basic criteria for the design and implementation of interaction control of robotic machines for motor therapy have been briefly introduced and two bio-inspired compliance control laws developed by the authors to address requirements coming from this specific application field have been presented. The two control laws are named the coactivation-based compliance control in the joint space and the torque-dependent compliance control in the joint space, respectively. They try to o...

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.

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.

Explaining Support Vector Machines: A Color Based Nomogram.

Directory of Open Access Journals (Sweden)

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.

Extended functions of the database machine FREND for interactive systems

International Nuclear Information System (INIS)

Hikita, S.; Kawakami, S.; Sano, K.

1984-01-01

Well-designed visual interfaces encourage non-expert users to use relational database systems. In those systems such as office automation systems or engineering database systems, non-expert users interactively access to database from visual terminals. Some users may want to occupy database or other users may share database according to various situations. Because, those jobs need a lot of time to be completed, concurrency control must be well designed to enhance the concurrency. The extended method of concurrency control of FREND is presented in this paper. The authors assume that systems are composed of workstations, a local area network and the database machine FREND. This paper also stresses that those workstations and FREND must cooperate to complete concurrency control for interactive applications

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

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.

Specificity of interactions among the DNA-packaging machine components of T4-related bacteriophages.

Science.gov (United States)

Gao, Song; Rao, Venigalla B

2011-02-04

Tailed bacteriophages use powerful molecular motors to package the viral genome into a preformed capsid. Packaging at a rate of up to ∼2000 bp/s and generating a power density twice that of an automobile engine, the phage T4 motor is the fastest and most powerful reported to date. Central to DNA packaging are dynamic interactions among the packaging components, capsid (gp23), portal (gp20), motor (gp17, large "terminase"), and regulator (gp16, small terminase), leading to precise orchestration of the packaging process, but the mechanisms are poorly understood. Here we analyzed the interactions between small and large terminases of T4-related phages. Our results show that the gp17 packaging ATPase is maximally stimulated by homologous, but not heterologous, gp16. Multiple interaction sites are identified in both gp16 and gp17. The specificity determinants in gp16 are clustered in the diverged N- and C-terminal domains (regions I-III). Swapping of diverged region(s), such as replacing C-terminal RB49 region III with that of T4, switched ATPase stimulation specificity. Two specificity regions, amino acids 37-52 and 290-315, are identified in or near the gp17-ATPase "transmission" subdomain II. gp16 binding at these sites might cause a conformational change positioning the ATPase-coupling residues into the catalytic pocket, triggering ATP hydrolysis. These results lead to a model in which multiple weak interactions between motor and regulator allow dynamic assembly and disassembly of various packaging complexes, depending on the functional state of the packaging machine. This might be a general mechanism for regulation of the phage packaging machine and other complex molecular machines.

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...

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

Science.gov (United States)

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

The simulation of man-machine interaction in NPPs: the system response analyser project

International Nuclear Information System (INIS)

Cacciabue, P.C.

1990-01-01

In this paper, the ongoing research at Joint Research Centre-Ispra on the simulation of man-machine interaction is reviewed with reference to the past experience of system modelling and to the advances of the technological world. These require the coalescence of mixed disciplines covering the fields of engineering, psychology and sociology. In particular, the complexity of man-machine systems with respect to safety analysis is depicted. The developments and issues in modelling humans and machines are discussed: the possibility of combining them through the System Response Analyser methodology is presented as a balanced to be applied when the objective is the study of safety of systems during abnormal sequences. The three analytical tools which constitute the body of system response analysis namely a quasi-classical simulation of the actual plant, a cognitive model of the operator activities and a driver model, are described. (author)

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

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.

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

Human-computer interaction : Guidelines for web animation

OpenAIRE

Galyani Moghaddam, Golnessa; Moballeghi, Mostafa

2006-01-01

Human-computer interaction in the large is an interdisciplinary area which attracts researchers, educators, and practioners from many differenf fields. Human-computer interaction studies a human and a machine in communication, it draws from supporting knowledge on both the machine and the human side. This paper is related to the human side of human-computer interaction and focuses on animations. The growing use of animation in Web pages testifies to the increasing ease with which such multim...

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Science.gov (United States)

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

Technical and Technological Support of Agricultural Enterprises on the Basis of Association Interaction: a Methodical Approach

Directory of Open Access Journals (Sweden)

Pyvovar Petro V.

2017-12-01

Full Text Available The aim of the research is the development of a methodical approach to justifying directions of association interaction of agricultural enterprises for the most effective technical and technological support of their economic activities. It is substantiated that the decision-making regarding the directions of group interaction in the field of forming and using the machine and tractor fleet of agricultural enterprises should be based on results of a strategic analysis of the current level of and trends in the changes in indicators of their technical support. This includes several stages: 1 determining a scenario for the development of the technical base of the economic entity (matrix methods; 2 multifactor grouping taking into account the available equipment and resource potential; 3 identification of potential sources of financing and ways of attracting necessary funds, priority partners and types of equipment and technologies; 4 estimation of a current condition of the enterprise on financial, technical, economic and market criteria; 5 substantiation of directions of group interaction of the enterprise.

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.

Machine Learning.

Science.gov (United States)

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

A user-specific human-machine interaction strategy for a prosthetic shank adapter

Directory of Open Access Journals (Sweden)

Stuhlenmiller Florian

2017-09-01

Full Text Available For people with lower limb amputation, a user-specific human-machine interaction with their prostheses is required to ensure safe and comfortable assistance. Especially during dynamic turning manoeuvres, users experience high loads at the stump, which decreases comfort and may lead to long-term tissue damage. Preliminary experiments with users wearing a configurable, passive torsional adaptor indicate increased comfort and safety achieved by adaptation of torsional stiffness and foot alignment. Moreover, the results show that the individual preference regarding both parameters depend on gait situation and individual preference. Hence, measured loads in the structure of the prosthesis and subjective feedback regarding comfort and safety during different turning motions are considered in a user-specific human-machine interaction strategy for a prosthetic shank adaptor. Therefore, the interrelations of gait parameters with optimal configuration are stored in an individual preference-setting matrix. Stiffness and foot alignment are actively adjusted to the optimal parameters by a parallel elastic actuator. Two subjects reported that they experienced appropriate variation of stiffness and foot alignment, a noticeable reduction of load at the stump and that they could turn with less effort.

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...

Support patient search on pathology reports with interactive online learning based data extraction.

Science.gov (United States)

Zheng, Shuai; Lu, James J; Appin, Christina; Brat, Daniel; Wang, Fusheng

2015-01-01

Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Extracting data from pathology reports could enable

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.

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.

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.

The Double Dance of Agency: a socio-theoretic account of how machines and humans interact

DEFF Research Database (Denmark)

Rose, Jeremy; Jones, Matthew

2005-01-01

The nature of the relationship between information technology (IT) and organizations has been a long-standing debate in the Information Systems literature. Does IT shape organiza-tions, or do people in organisations control how IT is used? To formulate the question a little differently: does agency...... that ma-chines may also be agents. Drawing on critiques of both structuration theory and actor net-work theory, this paper develops a theoretical account of the interaction between human and machine agency: the double dance of agency. The account seeks to contribute to theorisation of the relationship...

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.

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

Masataka Fuchida

2017-01-01

Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

Science.gov (United States)

Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

2018-01-01

Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

International workshop on multimodal analyses enabling artificial agents in human-machine interaction (workshop summary)

NARCIS (Netherlands)

Böck, Ronald; Bonin, Francesca; Campbell, Nick; Poppe, R.W.

2016-01-01

In this paper a brief overview of the third workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction. The paper is focussing on the main aspects intended to be discussed in the workshop reflecting the main scope of the papers presented during the meeting. The MA3HMI

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.

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

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

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.

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.

A machine learning method for fast and accurate characterization of depth-of-interaction gamma cameras

DEFF Research Database (Denmark)

Pedemonte, Stefano; Pierce, Larry; Van Leemput, Koen

2017-01-01

to impose the depth-of-interaction in an experimental set-up. In this article we introduce a machine learning approach for extracting accurate forward models of gamma imaging devices from simple pencil-beam measurements, using a nonlinear dimensionality reduction technique in combination with a finite...

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

Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

Directory of Open Access Journals (Sweden)

Aïsha Sahaï

2017-10-01

Full Text Available Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.

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

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.

Experimental support at proton--proton colliding beam facilities

International Nuclear Information System (INIS)

Potter, K.

1977-01-01

Proton--proton colliding beam facilities have a number of special features which increase the importance of support for experiments when compared to fixed target accelerators: (1) the laboratory system is very close to the center-of-mass system; this affects the geometry and general size of the experiments; (2) the primary p--p interaction is inaccessible, that is, it takes place in an ultrahigh vacuum chamber; and (3) the experiment detection system is necessarily inside the machine structure and becomes very closely linked to it in many respects. An overall picture is given of experimental support based on experience at the CERN ISR under the following headings: Experimental Areas, Scheduling, Intersection Vacuum Chambers, Machine Background, and Magnets for Experiments. The first two of these topics concern the requirements in space and time of an experiment, while the last three are all related to the close interaction between experiment and machine

''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 effective tool for iterative design of human machine interfaces in nuclear power plant

International Nuclear Information System (INIS)

Nakagawa, Takashi; Matsuo, Satoko; Yoshikawa, Hidekazu; Wu, Wei; Kameda, Akiyuki; Fumizawa, Motoo

2000-01-01

The authors have developed SEAMAID, which is a Simulation-based Evaluation and Analysis support system for MAn-machine Interface Design (SEAMAID) in the domain of nuclear power plants. The SEAMAID simulated the interaction between an operator and human machine interfaces (HMI), and supports to evaluate the HMI by using the simulation results. In this paper, a case study of evaluation for conventional center control room design was conducted. The authors were confirmed that SEAMAID is a useful tool for improvements of HMI design (J.P.N.)

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.

LHC Report: machine development

CERN Multimedia

Rogelio Tomás García for the LHC team

2015-01-01

Machine development weeks are carefully planned in the LHC operation schedule to optimise and further study the performance of the machine. The first machine development session of Run 2 ended on Saturday, 25 July. Despite various hiccoughs, it allowed the operators to make great strides towards improving the long-term performance of the LHC.   The main goals of this first machine development (MD) week were to determine the minimum beam-spot size at the interaction points given existing optics and collimation constraints; to test new beam instrumentation; to evaluate the effectiveness of performing part of the beam-squeezing process during the energy ramp; and to explore the limits on the number of protons per bunch arising from the electromagnetic interactions with the accelerator environment and the other beam. Unfortunately, a series of events reduced the machine availability for studies to about 50%. The most critical issue was the recurrent trip of a sextupolar corrector circuit –...

Support patient search on pathology reports with interactive online learning based data extraction

Directory of Open Access Journals (Sweden)

Shuai Zheng

2015-01-01

Full Text Available Background: Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user′s interaction with minimal human effort. Methods : We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system′s data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users′ corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. Results: We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of

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)

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 %

The CANDU man-machine interface and simulator training

International Nuclear Information System (INIS)

Hinchley, E.M.; Yanofsky, N.

1982-09-01

The most significant features of the man-machine interface for CANDU power stations are the extensive use of computer-driven colour graphics displays and the small number of manual controls. The man-machine interface in CANDU stations is designed to present the operator with concise, easy-to-understand information. Future developments in the use of computers in safety shutdown systems, and the use of data highway technologies in plant regulating systems will present special requirements and new opportunities in the application of human factors engineering to the control room. Good man-machine interaction depends on operator training as much as on control room design. In Canada computerized training simulators, which indicate plant response to operator action, are being introducted for operator training. Such simulators support training in normal operation of all plant systems and also in the fault management tasks following malfunctions

13th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2014-01-01

The integrated and advanced science research topic Man-Machine-Environment System Engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”   MMESE primarily focuses on the relationship between man, machines and the environment, studying the optimum combination of man-machine-environment systems. In this system, “man” refers to people in the workplace (e.g. operators, decision-makers); “ machine” is the general name for any object controlled by man (including tools, machinery, computers, systems and technologies), and “environment” describes the specific working conditions under which man and machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of Man-Machine-Environment system...

14th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2015-01-01

The integrated and advanced science research topic man-machine-environment system engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”   MMESE primarily focuses on the relationship between man, machines and the environment, studying the optimum combination of man-machine-environment systems. In this system, “man” refers to people in the workplace (e.g. operators, decision-makers); “ machine” is the general name for any object controlled by man (including tools, machinery, computers, systems and technologies), and “environment” describes the specific working conditions under which man and machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of man-machine-environment system...

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.

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...

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.

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.

Interactive CaringTV® supporting elderly living at home.

Science.gov (United States)

Lehto, Paula

2013-01-01

Interactive CaringTV® is a Finnish innovation that was developed by Laurea University of Applied Sciences in 2006. CaringTV was developed through action research during three research projects. The aim of interactive CaringTV is to support the health and well-being of elderly people living in their own homes. The Safe Home project was based on action research, userdriven methods, and a case study. User-driven methods were applied in planning, implementing and evaluating the programme and eServices e.g. testing and evaluating peer support, including eConsultation as the methods for supporting clients´ coping with life in their own homes. Costeffectiveness and process modelling were studied through the case study. The user-driven approach and the collected data formed the basis for the interactive programme. The online CaringTV programme included content to: support everyday life for the elderly, safety, and activities of daily living, support social relationships, participate in rehabilitation and physical exercises, manage self-care, and health issues. Active participation in the CaringTV programme provided functional ability and everyday coping as well as a meaningful activity in everyday life. CaringTV is an interactive platform to support elderly in their everyday life and help them cope at home. User-driven methods enable participants´ active involvement in planning interactive and online programmes and eServices via CaringTV. The ultimate goal of the CaringTV is to support elderly´s health, wellbeing and interaction. CaringTV empowers elderly people to take responsibility for their own health care as part of healthy ageing.

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

Science.gov (United States)

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance. PMID:26422143

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Science.gov (United States)

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Directory of Open Access Journals (Sweden)

Joachim de Greeff

Full Text Available Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference; the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

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.

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...

On the applicability of brain reading for predictive human-machine interfaces in robotics.

Science.gov (United States)

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

Directory of Open Access Journals (Sweden)

Elsa Andrea Kirchner

Full Text Available The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR, a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

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.

Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

Directory of Open Access Journals (Sweden)

Zhengchao Xie

2012-01-01

Full Text Available Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate (HCO3 -, dissolved oxygen (DO, total nitrogen (TN, UV254, turbidity, conductivity, nitrate, total nitrogen (TN, orthophosphate (PO4 3−, total phosphorus (TP, suspended solid (SS and total organic carbon (TOC selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008 data for training and the most recent 3 years (2009–2011 for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.

Automatically annotating topics in transcripts of patient-provider interactions via machine learning.

Science.gov (United States)

Wallace, Byron C; Laws, M Barton; Small, Kevin; Wilson, Ira B; Trikalinos, Thomas A

2014-05-01

Annotated patient-provider encounters can provide important insights into clinical communication, ultimately suggesting how it might be improved to effect better health outcomes. But annotating outpatient transcripts with Roter or General Medical Interaction Analysis System (GMIAS) codes is expensive, limiting the scope of such analyses. We propose automatically annotating transcripts of patient-provider interactions with topic codes via machine learning. We use a conditional random field (CRF) to model utterance topic probabilities. The model accounts for the sequential structure of conversations and the words comprising utterances. We assess predictive performance via 10-fold cross-validation over GMIAS-annotated transcripts of 360 outpatient visits (>230,000 utterances). We then use automated in place of manual annotations to reproduce an analysis of 116 additional visits from a randomized trial that used GMIAS to assess the efficacy of an intervention aimed at improving communication around antiretroviral (ARV) adherence. With respect to 6 topic codes, the CRF achieved a mean pairwise kappa compared with human annotators of 0.49 (range: 0.47-0.53) and a mean overall accuracy of 0.64 (range: 0.62-0.66). With respect to the RCT reanalysis, results using automated annotations agreed with those obtained using manual ones. According to the manual annotations, the median number of ARV-related utterances without and with the intervention was 49.5 versus 76, respectively (paired sign test P = 0.07). When automated annotations were used, the respective numbers were 39 versus 55 (P = 0.04). While moderately accurate, the predicted annotations are far from perfect. Conversational topics are intermediate outcomes, and their utility is still being researched. This foray into automated topic inference suggests that machine learning methods can classify utterances comprising patient-provider interactions into clinically relevant topics with reasonable accuracy.

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.

Science.gov (United States)

Wang, Yanbin; You, Zhuhong; Li, Xiao; Chen, Xing; Jiang, Tonghai; Zhang, Jingting

2017-05-11

Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori , the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

Modeling Geomagnetic Variations using a Machine Learning Framework

Science.gov (United States)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

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

Human-machine interface for a VR-based medical imaging environment

Science.gov (United States)

Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

1997-05-01

Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

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/.

An Interactive Web-based Learning System for Assisting Machining Technology Education

Directory of Open Access Journals (Sweden)

Min Jou

2008-05-01

Full Text Available The key technique of manufacturing methods is machining. The degree of technique of machining directly affects the quality of the product. Therefore, the machining technique is of primary importance in promoting student practice ability during the training process. Currently, practical training is applied in shop floor to discipline student’s practice ability. Much time and cost are used to teach these techniques. Particularly, computerized machines are continuously increasing in use. The development of educating engineers on computerized machines becomes much more difficult than with traditional machines. This is because of the limitation of the extremely expensive cost of teaching. The quality and quantity of teaching cannot always be promoted in this respect. The traditional teaching methods can not respond well to the needs of the future. Therefore, this research aims to the following topics; (1.Propose the teaching strategies for the students to learning machining processing planning through web-based learning system. (2.Establish on-line teaching material for the computer-aided manufacturing courses including CNC coding method, CNC simulation. (3.Develop the virtual machining laboratory to bring the machining practical training to web-based learning system. (4.Integrate multi-media and virtual laboratory in the developed e-learning web-based system to enhance the effectiveness of machining education through web-based system.

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.

Forecast of hourly global horizontal irradiance based on structured Kernel Support Vector Machine: A case study of Tibet area in China

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao

2017-01-01

Highlights: • The structured variable selection in Kernel SVM is implemented using two ways. • The two-way interaction model is considered to enforce Heredity Principle. • SVMIC is used to select the kernel parameter in proposed approaches. • Simple and fast computations algorithms are derived. - Abstract: Various applications of forecasting effective global horizontal irradiance play increasingly vital role in grid-connected photovoltaic installations, but suffer from forecasting inaccuracy and prohibitively expensive computational cost. Although Support Vector Machine (SVM) is one of the most powerful forecasting approaches, it does not provide an interpretable model. This motivates penalized variable selection methods to be introduced to SVM to select important variables. However, in some forecasting problems, there are some underlying logic or hierarchical structure such as heredity principle among the variables. Penalized Kernel SVM approaches do not take heredity principles into consideration when enforcing sparsity. This paper investigates structural variable selection in Kernel SVM based approach which pursues heredity principle and sparsity simultaneously. To achieve heredity principle, both optimization and procedure based structural variable selection approaches are studied in the Kernel SVM. Computationally, we derive fast and simple-to-implement algorithms to perform structural variable selection and solar irradiance forecasting. Furthermore, Support Vector Machines Information Criterion is utilized to select the kernel parameters to guarantee the model consistency. Real data experiments directly reveal that our proposed KSVM-SVS based approach following heredity principle delivers superior performances in terms of forecasting accuracy comparing with other competitors.

Improving Language Models in Speech-Based Human-Machine Interaction

Directory of Open Access Journals (Sweden)

Raquel Justo

2013-02-01

Full Text Available This work focuses on speech-based human-machine interaction. Specifically, a Spoken Dialogue System (SDS that could be integrated into a robot is considered. Since Automatic Speech Recognition is one of the most sensitive tasks that must be confronted in such systems, the goal of this work is to improve the results obtained by this specific module. In order to do so, a hierarchical Language Model (LM is considered. Different series of experiments were carried out using the proposed models over different corpora and tasks. The results obtained show that these models provide greater accuracy in the recognition task. Additionally, the influence of the Acoustic Modelling (AM in the improvement percentage of the Language Models has also been explored. Finally the use of hierarchical Language Models in a language understanding task has been successfully employed, as shown in an additional series of experiments.

Advanced human machine interaction for an image interpretation workstation

Science.gov (United States)

Maier, S.; Martin, M.; van de Camp, F.; Peinsipp-Byma, E.; Beyerer, J.

2016-05-01

In recent years, many new interaction technologies have been developed that enhance the usability of computer systems and allow for novel types of interaction. The areas of application for these technologies have mostly been in gaming and entertainment. However, in professional environments, there are especially demanding tasks that would greatly benefit from improved human machine interfaces as well as an overall improved user experience. We, therefore, envisioned and built an image-interpretation-workstation of the future, a multi-monitor workplace comprised of four screens. Each screen is dedicated to a complex software product such as a geo-information system to provide geographic context, an image annotation tool, software to generate standardized reports and a tool to aid in the identification of objects. Using self-developed systems for hand tracking, pointing gestures and head pose estimation in addition to touchscreens, face identification, and speech recognition systems we created a novel approach to this complex task. For example, head pose information is used to save the position of the mouse cursor on the currently focused screen and to restore it as soon as the same screen is focused again while hand gestures allow for intuitive manipulation of 3d objects in mid-air. While the primary focus is on the task of image interpretation, all of the technologies involved provide generic ways of efficiently interacting with a multi-screen setup and could be utilized in other fields as well. In preliminary experiments, we received promising feedback from users in the military and started to tailor the functionality to their needs

Interactive Algorithms for Unsupervised Machine Learning

Science.gov (United States)

2015-06-01

in Neural Information Processing Systems, 2013. 14 [3] Louigi Addario-Berry, Nicolas Broutin, Luc Devroye, and Gábor Lugosi. On combinato- rial...Myung Jin Choi, Vincent Y F Tan , Animashree Anandkumar, and Alan S Willsky. Learn- ing Latent Tree Graphical Models. Journal of Machine Learning

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.

16th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2016-01-01

This research topic was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science subject and technology in China!” MMESE primarily focuses on the relationship between Man, Machine and Environment, studying the optimum combination of man-machine-environment systems. In this system, “Man” refers to working people as the subject in the workplace (e.g. operators, decision-makers); “Machine” is the general name for any object controlled by Man (including tools, machinery, computers, systems and technologies), and “Environment” describes the specific working conditions under which Man and Machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization are to ensure "Safety, High efficiency and Economy" of man-machine-environment systems. These...

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.

Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.

Science.gov (United States)

Li, Yang; Wu, Yanbin; Yan, Aixia

2017-07-01

HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replication infection. In this work, 274 LEDGF/p75-IN inhibitors were collected as the dataset. Support Vector Machine (SVM), Decision Tree (DT), Function Tree (FT) and Random Forest (RF) were applied to build several computational models for predicting whether a compound is an active or weakly active LEDGF/p75-IN inhibitor. Each compound is represented by MACCS fingerprints and CORINA Symphony descriptors. The prediction accuracies for the test sets of all the models are over 70 %. The best model Model 3B built by FT obtained a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 81.08 % and 0.62 on test set, respectively. We found that the hydrogen bond and hydrophobic interactions are important for the bioactivity of an inhibitor. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Children's Beliefs about the Fantasy/Reality Status of Hypothesized Machines

Science.gov (United States)

Cook, Claire; Sobel, David M.

2011-01-01

Four-year-olds, 6-year-olds, and adults were asked to make judgments about the reality status of four different types of machines: real machines that children and adults interact with on a daily basis, real machines that children and adults interact with rarely (if at all), and impossible machines that violated a real-world physical or biological…

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.

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...

Effects of digital human-machine interface characteristics on human error in nuclear power plants

International Nuclear Information System (INIS)

Li Pengcheng; Zhang Li; Dai Licao; Huang Weigang

2011-01-01

In order to identify the effects of digital human-machine interface characteristics on human error in nuclear power plants, the new characteristics of digital human-machine interface are identified by comparing with the traditional analog control systems in the aspects of the information display, user interface interaction and management, control systems, alarm systems and procedures system, and the negative effects of digital human-machine interface characteristics on human error are identified by field research and interviewing with operators such as increased cognitive load and workload, mode confusion, loss of situation awareness. As to the adverse effects related above, the corresponding prevention and control measures of human errors are provided to support the prevention and minimization of human errors and the optimization of human-machine interface design. (authors)

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.

Compound induction electric rotating machine

Energy Technology Data Exchange (ETDEWEB)

Decesare, D

1987-07-28

The present invention generally relates to dynamo-electric machines cabable of operating in a generator mode or in a motor mode and more specifically, to increased efficiency compound interaction AC and/or DC dynamo-electric machines. This patent describes such a machine having a distributed armature winding in a cylindrical rotor wound to form axial and substantially radial winding portions and including permanent and/or electromagnets to couple magnetic flux into the peripheral or circumferential surface of the rotor, and to provide interaction between a magnetic field formed beyond the rotor axial surfaces and the rotor to thereby enhance the total induction of flux into the rotor for improved, more efficient operation. 28 figs.,

Protein-Injection Machines in Bacteria.

Science.gov (United States)

Galán, Jorge E; Waksman, Gabriel

2018-03-08

Many bacteria have evolved specialized nanomachines with the remarkable ability to inject multiple bacterially encoded effector proteins into eukaryotic or prokaryotic cells. Known as type III, type IV, and type VI secretion systems, these machines play a central role in the pathogenic or symbiotic interactions between multiple bacteria and their eukaryotic hosts, or in the establishment of bacterial communities in a diversity of environments. Here we focus on recent progress elucidating the structure and assembly pathways of these machines. As many of the interactions shaped by these machines are of medical importance, they provide an opportunity to develop novel therapeutic approaches to combat important human diseases. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Inclusive human machine interaction for India a case study of developing inclusive applications for the Indian population

CERN Document Server

Biswas, Pradipta

2014-01-01

Rapid advancement of interactive technologies during the past two decades has made access to information easier though at the expense of a clear digital divide. There is a generation who grew up with these technologies and another generation who find many modern electronic systems counter intuitive and have no use for them in their daily life. This digital divide becomes more prominent in developing countries as state-of-the-art interactive systems were not and are still not affordable to a large number of users.Inclusive Human Machine Interaction for India presents an end-to-end case study of

A study on the application of voice interaction in automotive human machine interface experience design

Science.gov (United States)

Huang, Zhaohui; Huang, Xiemin

2018-04-01

This paper, firstly, introduces the application trend of the integration of multi-channel interactions in automotive HMI ((Human Machine Interface) from complex information models faced by existing automotive HMI and describes various interaction modes. By comparing voice interaction and touch screen, gestures and other interaction modes, the potential and feasibility of voice interaction in automotive HMI experience design are concluded. Then, the related theories of voice interaction, identification technologies, human beings' cognitive models of voices and voice design methods are further explored. And the research priority of this paper is proposed, i.e. how to design voice interaction to create more humane task-oriented dialogue scenarios to enhance interactive experiences of automotive HMI. The specific scenarios in driving behaviors suitable for the use of voice interaction are studied and classified, and the usability principles and key elements for automotive HMI voice design are proposed according to the scenario features. Then, through the user participatory usability testing experiment, the dialogue processes of voice interaction in automotive HMI are defined. The logics and grammars in voice interaction are classified according to the experimental results, and the mental models in the interaction processes are analyzed. At last, the voice interaction design method to create the humane task-oriented dialogue scenarios in the driving environment is proposed.

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.

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.

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

RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences

Directory of Open Access Journals (Sweden)

Ji-Yong An

2016-05-01

Full Text Available Protein-Protein Interactions (PPIs play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM model and Average Blocks (AB to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed

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

Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach.

Science.gov (United States)

Liu, Henry C; Goldenberg, Anne; Chen, Yuchen; Lun, Christina; Wu, Wei; Bush, Kevin T; Balac, Natasha; Rodriguez, Paul; Abagyan, Ruben; Nigam, Sanjay K

2016-10-01

Statistical analysis was performed on physicochemical descriptors of ∼250 drugs known to interact with one or more SLC22 "drug" transporters (i.e., SLC22A6 or OAT1, SLC22A8 or OAT3, SLC22A1 or OCT1, and SLC22A2 or OCT2), followed by application of machine-learning methods and wet laboratory testing of novel predictions. In addition to molecular charge, organic anion transporters (OATs) were found to prefer interacting with planar structures, whereas organic cation transporters (OCTs) interact with more three-dimensional structures (i.e., greater SP3 character). Moreover, compared with OAT1 ligands, OAT3 ligands possess more acyclic tetravalent bonds and have a more zwitterionic/cationic character. In contrast, OCT1 and OCT2 ligands were not clearly distinquishable form one another by the methods employed. Multiple pharmacophore models were generated on the basis of the drugs and, consistent with the machine-learning analyses, one unique pharmacophore created from ligands of OAT3 possessed cationic properties similar to OCT ligands; this was confirmed by quantitative atomic property field analysis. Virtual screening with this pharmacophore, followed by transport assays, identified several cationic drugs that selectively interact with OAT3 but not OAT1. Although the present analysis may be somewhat limited by the need to rely largely on inhibition data for modeling, wet laboratory/in vitro transport studies, as well as analysis of drug/metabolite handling in Oat and Oct knockout animals, support the general validity of the approach-which can also be applied to other SLC and ATP binding cassette drug transporters. This may make it possible to predict the molecular properties of a drug or metabolite necessary for interaction with the transporter(s), thereby enabling better prediction of drug-drug interactions and drug-metabolite interactions. Furthermore, understanding the overlapping specificities of OATs and OCTs in the context of dynamic transporter tissue

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.

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...

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.

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.

Ghost-in-the-Machine reveals human social signals for human-robot interaction.

Science.gov (United States)

Loth, Sebastian; Jettka, Katharina; Giuliani, Manuel; de Ruiter, Jan P

2015-01-01

We used a new method called "Ghost-in-the-Machine" (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer's requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human-robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience.

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.

Exploring cluster Monte Carlo updates with Boltzmann machines.

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Exploring cluster Monte Carlo updates with Boltzmann machines

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

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.

Analysis of man-machine interaction for control and display system in main control room of light water reactor

International Nuclear Information System (INIS)

Santosa, Kussigit; Supriatna, Piping; Karlina, Itjeu; Widagdo, Suharyo; Darlis; Sudiono, Bambang

1998-01-01

One of potential hazard in Nuclear Power Plant is the failure of its operation. The accident or operation failure in the reactor must be concerned event its probability is low. The important thing should be concerned is 'Analysis of Man-Machine Interaction (MMI) for Control and Display System in Main Control Room (MCR) of Nuclear Power Reactor', especially LWR type. Control and Display System in MCR of Reactor is the main part of MMI link process in Reactor MCR work system. Signal from display system showed performance process in reactor, while this signal will be received by operator. This signal will be described through central nerve for making decision what kind must be done. Then the operator manage the next process of reactor operation through control system. So by knowing Analysis of Man-Machine Interaction for Control and Display System in Main Control Room of Power Reactor, we can understand human error probability of the operator in reactor operation

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

Directory of Open Access Journals (Sweden)

Ji-Yong An

2016-01-01

Full Text Available We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM model and Local Phase Quantization (LPQ to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Interactive visual supports for children with autism

OpenAIRE

Hayes, Gillian R.; Hirano, Sen; Marcu, Gabriela; Monibi, Mohamad; Nguyen, David H.; Yeganyan, Michael

2010-01-01

Interventions to support children with autism often include the use of visual supports, which are cognitive tools to enable learning and the production of language. Although visual supports are effective in helping to diminish many of the challenges of autism, they are difficult and time-consuming to create, distribute, and use. In this paper, we present the results of a qualitative study focused on uncovering design guidelines for interactive visual supports that would address the many chall...

HUMAN-MACHINE INTERACTION IN SOLVING TASKS OF THE PLANNING DEPARTMENT

Directory of Open Access Journals (Sweden)

Boris Alekseyevich Kucherov

2017-12-01

Full Text Available The paper discusses issues of human-machine interaction in solving tasks of the planning department under severe resource restrictions using information technology. The negative factors influencing specialists of the planning department in solving their tasks under the given circumstances are shown. Specific features of designing the user interface in this subject area are noted. Directions to increase the efficiency of reaction of the planning department’s specialists to change the current situation by visual and sound notification of various events are marked. Various ways to develop user interface to generate a conflict-free plan under severe resource restrictions are considered. The variants of informative presentation of operational and statistical information to stakeholders are analyzed. These issues are discussed by the example of the planning department which solves the tasks of allocation of control facilities for spacecraft (a subset of satellite range scheduling problem,

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.

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.

Supporting collaborative computing and interaction

International Nuclear Information System (INIS)

Agarwal, Deborah; McParland, Charles; Perry, Marcia

2002-01-01

To enable collaboration on the daily tasks involved in scientific research, collaborative frameworks should provide lightweight and ubiquitous components that support a wide variety of interaction modes. We envision a collaborative environment as one that provides a persistent space within which participants can locate each other, exchange synchronous and asynchronous messages, share documents and applications, share workflow, and hold videoconferences. We are developing the Pervasive Collaborative Computing Environment (PCCE) as such an environment. The PCCE will provide integrated tools to support shared computing and task control and monitoring. This paper describes the PCCE and the rationale for its design

Man Machine Systems in Education.

Science.gov (United States)

Sall, Malkit S.

This review of the research literature on the interaction between humans and computers discusses how man machine systems can be utilized effectively in the learning-teaching process, especially in secondary education. Beginning with a definition of man machine systems and comments on the poor quality of much of the computer-based learning material…

The use of machine learning and nonlinear statistical tools for ADME prediction.

Science.gov (United States)

Sakiyama, Yojiro

2009-02-01

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

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.

Role of the man-machine interface in accident management strategies

International Nuclear Information System (INIS)

Oewre, Fridtjov

2001-01-01

First, this paper gives a short general review on important safety issues in the field of man-machine interaction as expressed by important nuclear safety organisations. Then follows a summary discussion on what constitutes a modern Man-Machine Interface (MMI) and what is normally meant with accident management and accident management strategies. Furthermore, the paper focuses on three major issues in the context of accident management. First, the need for reliable information in accidents and how this can be obtained by additional computer technology. Second, the use of procedures is discussed, and basic MMI aspects of computer support for procedure presentation are identified followed by a presentation of a new approach on how to computerise procedures. Third, typical information needs for characteristic end-users in accidents, such as the control room operators, technical support staff and plant emergency teams, is discussed. Some ideas on how to apply virtual reality technology in accident management is also presented

A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

Directory of Open Access Journals (Sweden)

Fanping Zhang

2014-01-01

Full Text Available Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds and an approximation (A3 at three resolution levels (21-22-23 using Daubechies (db3 discrete wavelet. Correlation coefficients between each D subtime series and original monthly streamflow time series are calculated. Ds components with high correlation coefficients (D3 are added to the approximation (A3 as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters, C, ε, and σ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines

Science.gov (United States)

Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

2018-01-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership infor- mation and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width E=0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (E=2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (E=0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncon- taminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

The efficacy of support vector machines (SVM)

Indian Academy of Sciences (India)

(2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a ...

Human capabilities in space. [man machine interaction

Science.gov (United States)

Nicogossian, A. E.

1984-01-01

Man's ability to live and perform useful work in space was demonstrated throughout the history of manned space flight. Current planning envisions a multi-functional space station. Man's unique abilities to respond to the unforeseen and to operate at a level of complexity exceeding any reasonable amount of previous planning distinguish him from present day machines. His limitations, however, include his inherent inability to survive without protection, his limited strength, and his propensity to make mistakes when performing repetitive and monotonous tasks. By contrast, an automated system does routine and delicate tasks, exerts force smoothly and precisely, stores, and recalls large amounts of data, and performs deductive reasoning while maintaining a relative insensitivity to the environment. The establishment of a permanent presence of man in space demands that man and machines be appropriately combined in spaceborne systems. To achieve this optimal combination, research is needed in such diverse fields as artificial intelligence, robotics, behavioral psychology, economics, and human factors engineering.

Support vector machine based diagnostic system for breast cancer using swarm intelligence.

Science.gov (United States)

Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

2012-08-01

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

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

Science.gov (United States)

Trochimczuk, R.; Kuźmierowski, T.

2014-11-01

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

Support vector machines and evolutionary algorithms for classification single or together?

CERN Document Server

Stoean, Catalin

2014-01-01

When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this ‘masked hero’ be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.

Man-machine systems research at the OECD Halden reactor project

International Nuclear Information System (INIS)

Owre, F.; Bjorlo, T.J.; Haugset, K.

1994-01-01

The OECD Halden Reactor Project is a jointly financed research programme under the auspices of the OECD - Nuclear Energy Agency with fifteen participating countries. One of the main research topics focuses on man-machine systems. Particular attention is paid to the operator's tasks in the reactor control room environment. The overall objective of the research in this field is to provide a basis for improving today's control rooms through the introduction of computer-based solutions for the effective and safe execution of surveillance and control functions in normal as well as off-normal plant situations. The programme comprises four main activities: the verification and validation of safety critical software systems; man-machine interaction research emphasizing improvements in man-machine interfaces on the basis of human factors studies; computerised operator support systems assisting the operator in fault detection/diagnosis and planning of control actions; and control room development providing a basis for retrofitting of existing control rooms and for the design of advanced concepts

Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

Directory of Open Access Journals (Sweden)

Gusfan Halik

2015-01-01

Full Text Available Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD of General Circulation Model (GCM outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis. A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Directory of Open Access Journals (Sweden)

Jia Uddin

2014-01-01

Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Hybrid genetic algorithm tuned support vector machine regression for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater

Digital Repository Service at National Institute of Oceanography (India)

Patil, S.G.; Mandal, S.; Hegde, A.V.; Muruganandam, A.

Support Vector Machine (SVM) works on structural risk minimization principle that has greater generalization ability and is superior to the empirical risk minimization principle as adopted in conventional neural network models. However...

Supportive and non-supportive interactions in families with a type 2 diabetes patient

DEFF Research Database (Denmark)

Bennich, Birgitte B; Røder, Michael E; Overgaard, Dorthe

2017-01-01

changes and diabetes self-management. The purpose of this integrative review was to summarise and assess published studies on the intra-family perspective of supportive and non-supportive interactions in families with a type 2 diabetes patient. METHODS: Included in the review were published qualitative......BACKGROUND: Type 2 diabetes and its management affect the patient and the close family potentially causing either psychological distress or increased sense of responsibility and collaboration in these families. Interactions between patient and family play an important role in maintaining lifestyle...... of reference lists. Quality assessment, data extraction and analysis were undertaken on all included studies. RESULTS: We identified five eligible research papers. Employing content analysis three categories describing interactions were refined: Impact of practical action, impact of emotional involvement...

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

Science.gov (United States)

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

2010-01-01

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

Preliminary design and manufacturing feasibility study for a machined Zircaloy triangular pitch fuel rod support system (grids) (AWBA development program)

International Nuclear Information System (INIS)

Horwood, W.A.

1981-07-01

General design features and manufacturing operations for a high precision machined Zircaloy fuel rod support grid intended for use in advanced light water prebreeder or breeder reactor designs are described. The grid system consists of a Zircaloy main body with fuel rod and guide tube cells machined using wire EDM, a separate AM-350 stainless steel insert spring which fits into a full length T-slot in each fuel rod cell, and a thin (0.025'' or 0.040'' thick) wire EDM machined Zircaloy coverplate laser welded to each side of the grid body to retain the insert springs. The fuel rods are placed in a triangular pitch array with a tight rod-to-rod spacing of 0.063 inch nominal. Two dimples are positioned at the mid-thickness of the grid (single level) with a 90 0 included angle. Data is provided on the effectiveness of the manufacturing operations chosen for grid machining and assembly

Application of support vector machine for classification of multispectral data

International Nuclear Information System (INIS)

Bahari, Nurul Iman Saiful; Ahmad, Asmala; Aboobaider, Burhanuddin Mohd

2014-01-01

In this paper, support vector machine (SVM) is used to classify satellite remotely sensed multispectral data. The data are recorded from a Landsat-5 TM satellite with resolution of 30x30m. SVM finds the optimal separating hyperplane between classes by focusing on the training cases. The study area of Klang Valley has more than 10 land covers and classification using SVM has been done successfully without any pixel being unclassified. The training area is determined carefully by visual interpretation and with the aid of the reference map of the study area. The result obtained is then analysed for the accuracy and visual performance. Accuracy assessment is done by determination and discussion of Kappa coefficient value, overall and producer accuracy for each class (in pixels and percentage). While, visual analysis is done by comparing the classification data with the reference map. Overall the study shows that SVM is able to classify the land covers within the study area with a high accuracy

Using support vector machines in the multivariate state estimation technique

International Nuclear Information System (INIS)

Zavaljevski, N.; Gross, K.C.

1999-01-01

One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications

Formal modeling of virtual machines

Science.gov (United States)

Cremers, A. B.; Hibbard, T. N.

1978-01-01

Systematic software design can be based on the development of a 'hierarchy of virtual machines', each representing a 'level of abstraction' of the design process. The reported investigation presents the concept of 'data space' as a formal model for virtual machines. The presented model of a data space combines the notions of data type and mathematical machine to express the close interaction between data and control structures which takes place in a virtual machine. One of the main objectives of the investigation is to show that control-independent data type implementation is only of limited usefulness as an isolated tool of program development, and that the representation of data is generally dictated by the control context of a virtual machine. As a second objective, a better understanding is to be developed of virtual machine state structures than was heretofore provided by the view of the state space as a Cartesian product.

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Directory of Open Access Journals (Sweden)

Shuyu Dai

2018-01-01

Full Text Available Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm, SVM (Support Vector

Estimation of loads for the design of support for the rotary machine in nuclear power plant

International Nuclear Information System (INIS)

Gupta, S.K.; Chatterjee, B.; Kushwaha, H.S.; Venkat Raj, V.

2002-01-01

Full text: In a nuclear power plant two major equipment, which have a rotating shaft are pump in the primary heat transport system and turbine in the secondary system. In both cases, the shaft seizure leads to transfer of very large load to the supports. These supports, if not designed for these loads may fail and lead to missile generation. The missile generation should be avoided as it may hit and damage safety related systems. The pump of the primary heat transport system (PHTS) of a nuclear power plant is normally centrifugal type run by an induction motor. If the pump shaft seizes, the seizure load will be experienced by the pump shaft and support structure. Due to the presence of the flywheel, the total moment of inertia of the pump motor assembly is quite high. Hence the resisting torque be many times higher than the motor starting torque. Besides, the electric torque will continue to apply as the motor trip on overload current is delayed by several seconds to avoid inadvertent trip during start up. The electric torque would initially increase and then decrease as the shaft speed decreases. Part of the seizure load will be absorbed by the pump supports passed through the pump shaft. Seizure torque will depend on pump seizure time. Lesser the seizure time, higher would be the load on the pump support. If the pump shaft fails then the supports would see relatively less load. The turbine in the secondary system has a large inertia due to blades. In case of a seizure the generator is tripped in hundreds of milliseconds. The load experienced by supports due to seizure, is significantly enhanced in the first few seconds due to steam supply before it is cut off. These rotating machines are normally not designed for safe shutdown earthquakes (SSE) where integrity of the system is to be ensured. Shaft seizure can be considered as a consequential failure for SSE. In that case, the supports would simultaneously see an earthquake load on supports in addition to seizure

Adapting Virtual Machine Techniques for Seamless Aspect Support

NARCIS (Netherlands)

Bockisch, Christoph; Arnold, Matthew; Dinkelaker, Tom; Mezini, Mira

2006-01-01

Current approaches to compiling aspect-oriented programs are inefficient. This inefficiency has negative effects on the productivity of the development process and is especially prohibitive for dynamic aspect deployment. In this work, we present how well-known virtual machine techniques can be used

Advanced Machining Toolpath for Low Distortion

Science.gov (United States)

2017-02-28

Advanced Machining Toolpath for Low Distortion FINAL STATUS REPORT Prepared by Brian Becker R&D Technology Manager Third Wave Systems, Inc... Machining Toolpath for Low Distortion December 2016 Contract No.: W911W6-16-P-0044 2 Table of Contents 1.0 EXECUTIVE SUMMARY...2 2.1 Task 1: Collect Details of Machining Lab to Support

Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine

Science.gov (United States)

Laib dit Leksir, Y.; Mansour, M.; Moussaoui, A.

2018-03-01

Analysis and processing of databases obtained from infrared thermal inspections made on electrical installations require the development of new tools to obtain more information to visual inspections. Consequently, methods based on the capture of thermal images show a great potential and are increasingly employed in this field. However, there is a need for the development of effective techniques to analyse these databases in order to extract significant information relating to the state of the infrastructures. This paper presents a technique explaining how this approach can be implemented and proposes a system that can help to detect faults in thermal images of electrical installations. The proposed method classifies and identifies the region of interest (ROI). The identification is conducted using support vector machine (SVM) algorithm. The aim here is to capture the faults that exist in electrical equipments during an inspection of some machines using A40 FLIR camera. After that, binarization techniques are employed to select the region of interest. Later the comparative analysis of the obtained misclassification errors using the proposed method with Fuzzy c means and Ostu, has also be addressed.

Collapse moment estimation by support vector machines for wall-thinned pipe bends and elbows

International Nuclear Information System (INIS)

Na, Man Gyun; Kim, Jin Weon; Hwang, In Joon

2007-01-01

The collapse moment due to wall-thinned defects is estimated through support vector machines with parameters optimized by a genetic algorithm. The support vector regression models are developed and applied to numerical data obtained from the finite element analysis for wall-thinned defects in piping systems. The support vector regression models are optimized by using both the data sets (training data and optimization data) prepared for training and optimization, and its performance verification is performed by using another data set (test data) different from the training data and the optimization data. In this work, three support vector regression models are developed, respectively, for three data sets divided into the three classes of extrados, intrados, and crown defects, which is because they have different characteristics. The relative root mean square (RMS) errors of the estimated collapse moment are 0.2333% for the training data, 0.5229% for the optimization data and 0.5011% for the test data. It is known from this result that the support vector regression models are sufficiently accurate to be used in the integrity evaluation of wall-thinned pipe bends and elbows

Support for Multitasking and background Awareness Using Interactive Peripheral Displays

DEFF Research Database (Denmark)

MacIntyre, Blair; Mynatt, Elizabeth Diane; Voida, Stephen

2001-01-01

n this paper, we describe Kimura, an augmented office environment to support common multitasking practices. Previous systems, such as Rooms, limit users by constraining the interaction to the desktop monitor. In Kimura, we leverage interactive projected peripheral displays to support the perusal...

Conceptual models in man-machine design verification

International Nuclear Information System (INIS)

Rasmussen, J.

1985-01-01

The need for systematic methods for evaluation of design concepts for new man-machine systems has been rapidly increasing in consequence of the introduction of modern information technology. Direct empirical methods are difficult to apply when functions during rare conditions and support of operator decisions during emergencies are to be evaluated. In this paper, the problems of analytical evaluations based on conceptual models of the man-machine interaction are discussed, and the relations to system design and analytical risk assessment are considered. Finally, a conceptual framework for analytical evaluation is proposed, including several domains of description: 1. The problem space, in the form of a means-end hierarchy; 2. The structure of the decision process; 3. The mental strategies and heuristics used by operators; 4. The levels of cognitive control and the mechanisms related to human errors. Finally, the need for models representing operators' subjective criteria for choosing among available mental strategies and for accepting advice from intelligent interfaces is discussed

Modeling and prediction of human word search behavior in interactive machine translation

Science.gov (United States)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles.

Science.gov (United States)

Feres, Magda; Louzoun, Yoram; Haber, Simi; Faveri, Marcelo; Figueiredo, Luciene C; Levin, Liran

2018-02-01

The existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machine learning, into generalised chronic periodontitis (ChP), generalised aggressive periodontitis (AgP) and periodontal health (PH). Subgingival biofilm samples were collected from patients with AgP, ChP and PH and analysed for their content of 40 bacterial species using checkerboard DNA-DNA hybridisation. Two stages of machine learning were then performed. First of all, we tested whether there was a difference between the composition of bacterial communities in PH and in disease, and then we tested whether a difference existed in the composition of bacterial communities between ChP and AgP. The data were split in each analysis to 70% train and 30% test. A support vector machine (SVM) classifier was used with a linear kernel and a Box constraint of 1. The analysis was divided into two parts. Overall, 435 patients (3,915 samples) were included in the analysis (PH = 53; ChP = 308; AgP = 74). The variance of the healthy samples in all principal component analysis (PCA) directions was smaller than that of the periodontally diseased samples, suggesting that PH is characterised by a uniform bacterial composition and that the bacterial composition of periodontally diseased samples is much more diverse. The relative bacterial load could distinguish between AgP and ChP. An SVC classifier using a panel of 40 bacterial species was able to distinguish between PH, AgP in young individuals and ChP. © 2017 FDI World Dental Federation.

A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

Science.gov (United States)

Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

2017-09-01

Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Directory of Open Access Journals (Sweden)

Rachid Darnag

2017-02-01

Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

Tool set for distributed real-time machine control

Science.gov (United States)

Carrott, Andrew J.; Wright, Christopher D.; West, Andrew A.; Harrison, Robert; Weston, Richard H.

1997-01-01

Demands for increased control capabilities require next generation manufacturing machines to comprise intelligent building elements, physically located at the point where the control functionality is required. Networks of modular intelligent controllers are increasingly designed into manufacturing machines and usable standards are slowly emerging. To implement a control system using off-the-shelf intelligent devices from multi-vendor sources requires a number of well defined activities, including (a) the specification and selection of interoperable control system components, (b) device independent application programming and (c) device configuration, management, monitoring and control. This paper briefly discusses the support for the above machine lifecycle activities through the development of an integrated computing environment populated with an extendable software toolset. The toolset supports machine builder activities such as initial control logic specification, logic analysis, machine modeling, mechanical verification, application programming, automatic code generation, simulation/test, version control, distributed run-time support and documentation. The environment itself consists of system management tools and a distributed object-oriented database which provides storage for the outputs from machine lifecycle activities and specific target control solutions.

Autocoding State Machine in Erlang

DEFF Research Database (Denmark)

Guo, Yu; Hoffman, Torben; Gunder, Nicholas

2008-01-01

This paper presents an autocoding tool suit, which supports development of state machine in a model-driven fashion, where models are central to all phases of the development process. The tool suit, which is built on the Eclipse platform, provides facilities for the graphical specification...... of a state machine model. Once the state machine is specified, it is used as input to a code generation engine that generates source code in Erlang....

Towards Massive Machine Type Cellular Communications

OpenAIRE

Dawy, Zaher; Saad, Walid; Ghosh, Arunabha; Andrews, Jeffrey G.; Yaacoub, Elias

2015-01-01

Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters - often referred to as machine-to-machine (M2M) or machine-type communications (MTC) - introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently support...

Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines

International Nuclear Information System (INIS)

Li Jiazhong; Liu Huanxiang; Yao Xiaojun; Liu Mancang; Hu Zhide; Fan Botao

2007-01-01

The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors

Shear machines

International Nuclear Information System (INIS)

Astill, M.; Sunderland, A.; Waine, M.G.

1980-01-01

A shear machine for irradiated nuclear fuel elements has a replaceable shear assembly comprising a fuel element support block, a shear blade support and a clamp assembly which hold the fuel element to be sheared in contact with the support block. A first clamp member contacts the fuel element remote from the shear blade and a second clamp member contacts the fuel element adjacent the shear blade and is advanced towards the support block during shearing to compensate for any compression of the fuel element caused by the shear blade (U.K.)

Automated detection of pulmonary nodules in CT images with support vector machines

Science.gov (United States)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

A Semisupervised Support Vector Machines Algorithm for BCI Systems

Science.gov (United States)

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

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.

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.

Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

Science.gov (United States)

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

A SVM bases AI design for interactive gaming

OpenAIRE

Jiang, Yang; Jiang, Jianmin; Palmer, Ian

2008-01-01

Interactive gaming requires automatic processing on large volume of random data produced by players on spot, such as shooting, football kicking, boxing etc. In this paper, we describe an artificial intelligence approach in processing such random data for interactive gaming by using a one-class support vector machine (OC-SVM). In comparison with existing techniques, our OC-SVM based interactive gaming design has the features of: (i): high speed processing, providing instant response to the pla...

Fluid-structure interaction modeling of wind turbines: simulating the full machine

Science.gov (United States)

Hsu, Ming-Chen; Bazilevs, Yuri

2012-12-01

In this paper we present our aerodynamics and fluid-structure interaction (FSI) computational techniques that enable dynamic, fully coupled, 3D FSI simulation of wind turbines at full scale, and in the presence of the nacelle and tower (i.e., simulation of the "full machine"). For the interaction of wind and flexible blades we employ a nonmatching interface discretization approach, where the aerodynamics is computed using a low-order finite-element-based ALE-VMS technique, while the rotor blades are modeled as thin composite shells discretized using NURBS-based isogeometric analysis (IGA). We find that coupling FEM and IGA in this manner gives a good combination of efficiency, accuracy, and flexibility of the computational procedures for wind turbine FSI. The interaction between the rotor and tower is handled using a non-overlapping sliding-interface approach, where both moving- and stationary-domain formulations of aerodynamics are employed. At the fluid-structure and sliding interfaces, the kinematic and traction continuity is enforced weakly, which is a key ingredient of the proposed numerical methodology. We present several simulations of a three-blade 5~MW wind turbine, with and without the tower. We find that, in the case of no tower, the presence of the sliding interface has no effect on the prediction of aerodynamic loads on the rotor. From this we conclude that weak enforcement of the kinematics gives just as accurate results as the strong enforcement, and thus enables the simulation of rotor-tower interaction (as well as other applications involving mechanical components in relative motion). We also find that the blade passing the tower produces a 10-12 % drop (per blade) in the aerodynamic torque. We feel this finding may be important when it comes to the fatigue-life analysis and prediction for wind turbine blades.

Requirements for user interaction support in future CACE environments

DEFF Research Database (Denmark)

Ravn, Ole; Szymkat, M.

1994-01-01

Based on a review of user interaction modes and the specific needs of the CACE domain the paper describes requirements for user interaction in future CACE environments. Taking another look at the design process in CACE key areas in need of more user interaction support are pointed out. Three...

Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

International Nuclear Information System (INIS)

Sun Zhong-Hua; Jiang Fan

2010-01-01

In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

Science.gov (United States)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

Failure and reliability prediction by support vector machines regression of time series data

International Nuclear Information System (INIS)

Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique

2011-01-01

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.

Measurement of the t (bar t) cross section at the Run II Tevatron using Support Vector Machines

International Nuclear Information System (INIS)

Whitehouse, Benjamin Eric

2010-01-01

This dissertation measures the t(bar t) production cross section at the Run II CDF detector using data from early 2001 through March 2007. The Tevatron at Fermilab is a p(bar p) collider with center of mass energy √s = 1.96 TeV. This data composes a sample with a time-integrated luminosity measured at 2.2 ± 0.1 fb -1 . A system of learning machines is developed to recognize t(bar t) events in the 'lepton plus jets' decay channel. Support Vector Machines are described, and their ability to cope with a multi-class discrimination problem is provided. The t(bar t) production cross section is then measured in this framework, and found to be σ t# bar t# = 7.14 ± 0.25 (stat) -0.86 +0.61 (sys) pb.

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

Science.gov (United States)

Xu, Jingting; Hu, Hong; Dai, Yang

The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

Directory of Open Access Journals (Sweden)

Tai-fu Li

2013-01-01

Full Text Available Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s of out-of-control signal and even outperforms the conventional multivariate control scheme.

Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

Directory of Open Access Journals (Sweden)

Nurul Arneida Husin

2012-04-01

Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

Spectral asymptotics of a strong δ′ interaction supported by a surface

International Nuclear Information System (INIS)

Exner, Pavel; Jex, Michal

2014-01-01

Highlights: • Attractive δ ′ interactions supported by a smooth surface are considered. • Surfaces can be either infinite and asymptotically planar, or compact and closed. • Spectral asymptotics is determined by the geometry of the interaction support. - Abstract: We derive asymptotic expansion for the spectrum of Hamiltonians with a strong attractive δ ′ interaction supported by a smooth surface in R 3 , either infinite and asymptotically planar, or compact and closed. Its second term is found to be determined by a Schrödinger type operator with an effective potential expressed in terms of the interaction support curvatures

Introducing Stable Radicals into Molecular Machines.

Science.gov (United States)

Wang, Yuping; Frasconi, Marco; Stoddart, J Fraser

2017-09-27

Ever since their discovery, stable organic radicals have received considerable attention from chemists because of their unique optical, electronic, and magnetic properties. Currently, one of the most appealing challenges for the chemical community is to develop sophisticated artificial molecular machines that can do work by consuming external energy, after the manner of motor proteins. In this context, radical-pairing interactions are important in addressing the challenge: they not only provide supramolecular assistance in the synthesis of molecular machines but also open the door to developing multifunctional systems relying on the various properties of the radical species. In this Outlook, by taking the radical cationic state of 1,1'-dialkyl-4,4'-bipyridinium (BIPY •+ ) as an example, we highlight our research on the art and science of introducing radical-pairing interactions into functional systems, from prototypical molecular switches to complex molecular machines, followed by a discussion of the (i) limitations of the current systems and (ii) future research directions for designing BIPY •+ -based molecular machines with useful functions.

Validating cognitive support for operators of complex human-machine systems

International Nuclear Information System (INIS)

O'Hara, J.; Wachtel, J.

1995-01-01

Modem nuclear power plants (NPPs) are complex systems whose performance is the result of an intricate interaction of human and system control. A complex system may be defined as one which supports a dynamic process involving a large number of elements that interact in many different ways. Safety is addressed through defense-in-depth design and preplanning; i.e., designers consider the types of failures that are most likely to occur and those of high consequence, and design their solutions in advance. However, complex interactions and their failure modes cannot always be anticipated by the designer and may be unfamiliar to plant personnel. These situations may pose cognitive demands on plant personnel, both individually and as a crew. Other factors may contribute to the cognitive challenges of NPP operation as well, including hierarchal processes, dynamic pace, system redundancy and reliability, and conflicting objectives. These factors are discussed in this paper

Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

Energy Technology Data Exchange (ETDEWEB)

Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)

2017-02-15

The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.

SYN Flood Attack Detection in Cloud Computing using Support Vector Machine

Directory of Open Access Journals (Sweden)

Zerina Mašetić

2017-11-01

Full Text Available Cloud computing is a trending technology, as it reduces the cost of running a business. However, many companies are skeptic moving about towards cloud due to the security concerns. Based on the Cloud Security Alliance report, Denial of Service (DoS attacks are among top 12 attacks in the cloud computing. Therefore, it is important to develop a mechanism for detection and prevention of these attacks. The aim of this paper is to evaluate Support Vector Machine (SVM algorithm in creating the model for classification of DoS attacks and normal network behaviors. The study was performed in several phases: a attack simulation, b data collection, cfeature selection, and d classification. The proposedmodel achieved 100% classification accuracy with true positive rate (TPR of 100%. SVM showed outstanding performance in DoS attack detection and proves that it serves as a valuable asset in the network security area.

About the walking machine motion stability

Directory of Open Access Journals (Sweden)

V. V. Lapshin

2014-01-01

Full Text Available The use of legs as propulsive devices of the machine will increase its capability to cross rough and deformable terrain as compared with wheeled and trucked machines. Today it is already possible to speak about design of statically stable walking robots to be used in the certain areas of application. The most promising areas of their application are exploration and emergency-rescue operations in extremely complicated situations (e.g. in the zone of destruction after earthquakes, technogenic catastrophe, etc..In such dangerous situations there is a possibility for the walking machine to be overturned either because of loosing a support to one or several legs or due to significant displacement of the leg support points, which are caused by deformation or destruction of the terrain in the points of the legs support. Therefore, it is necessary to design motion control algorithms that enable teaching the motion control system of a walking robot: How to decrease the possibility of the robot overturning? How to stop the robot as quickly as possible keeping its static stability? What must be done if static stability is lost? Note that the loss of static stability does not inevitably result in the robot falling down. How to fall down better (with minimal robot destruction in inevitable case?This work investigates the first abovementioned problems, i.e. preventing a walking machine from overturning in dangerous situations. For this purpose it suggests to use a special cautious (safe gait, which allows the machine to remain statically stable if it suddenly looses support to its any leg. The natural price for the increased safety to prevent from overturning is the reduced capabilities of robot kinematics and, as a consequence, its capability to cross rough terrain. It is also suggested to reconsider the general definition of a walking machine static stability margin in order to obtain an adequate estimation of the robot overturning possibility

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

Science.gov (United States)

Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang

2014-04-01

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Science.gov (United States)

Chen, Chi-Jim; Pai, Tun-Wen; Cheng, Mox

2015-01-01

A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates. PMID:25835186

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Science.gov (United States)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Directory of Open Access Journals (Sweden)

Chi-Jim Chen

2015-03-01

Full Text Available A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS, successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM, based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

Science.gov (United States)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Ordinary Social Interaction and the Main Effect Between Perceived Support and Affect.

Science.gov (United States)

Lakey, Brian; Vander Molen, Randy J; Fles, Elizabeth; Andrews, Justin

2016-10-01

Relational regulation theory hypothesizes that (a) the main effect between perceived support and mental health primarily reflects ordinary social interaction rather than conversations about stress and how to cope with it, and (b) the extent to which a provider regulates a recipient's mental health primarily reflects the recipient's personal taste (i.e., is relational), rather than the provider's objective supportiveness. In three round-robin studies, participants rated each other on supportiveness and the quality of ordinary social interaction, as well as their own affect when interacting with each other. Samples included marines about to deploy to Afghanistan (N = 100; 150 dyads), students sharing apartments (N = 64; 96 dyads), and strangers (N = 48; 72 dyads). Perceived support and ordinary social interaction were primarily relational, and most of perceived support's main effect on positive affect was redundant with ordinary social interaction. The main effect between perceived support and affect emerged among strangers after brief text conversations, and these links were partially verified by independent observers. Findings for negative affect were less consistent with theory. Ordinary social interaction appears to be able to explain much of the main effect between perceived support and positive affect. © 2015 Wiley Periodicals, Inc.

Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms

Directory of Open Access Journals (Sweden)

Chien-Hung Huang

2015-01-01

Full Text Available Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues’s method by employing the protein-protein interaction (PPI data, domain-domain interaction (DDI data, weighted domain frequency score (DFS, and cancer linker degree (CLD data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I using individual algorithm, (II combining algorithms, and (III combining the same classification types of algorithms. When compared with Aragues’s method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

Science.gov (United States)

Turnip, Arjon; Ilham Rizqywan, M.; Kusumandari, Dwi E.; Turnip, Mardi; Sihombing, Poltak

2018-03-01

An electrocardiogram is a potential bioelectric record that occurs as a result of cardiac activity. QRS Detection with zero crossing calculation is one method that can precisely determine peak R of QRS wave as part of arrhythmia detection. In this paper, two experimental scheme (2 minutes duration with different activities: relaxed and, typing) were conducted. From the two experiments it were obtained: accuracy, sensitivity, and positive predictivity about 100% each for the first experiment and about 79%, 93%, 83% for the second experiment, respectively. Furthermore, the feature set of MIT-BIH arrhythmia using the support vector machine (SVM) method on the WEKA software is evaluated. By combining the available attributes on the WEKA algorithm, the result is constant since all classes of SVM goes to the normal class with average 88.49% accuracy.

Active damage detection method based on support vector machine and impulse response

International Nuclear Information System (INIS)

Taniguchi, Ryuta; Mita, Akira

2004-01-01

An active damage detection method was proposed to characterize damage in bolted joints. The purpose of this study is to propose a damage detection method that can obtain the detailed information of the damage by creating feature vectors for pattern recognition. In the proposed method, the wavelet transform is applied to the sensor signals, and the feature vectors are defined by second power average of the amplitude. The feature vectors generated by experiments were successfully used as the training data for Support Vector Machine (SVM). By applying the wavelet transform to time-frequency analysis, the accuracy of pattern recognition was raised in both correlation coefficient and SVM applications. Moreover, the SVM could identify the damage with very strong discernment capability than others. Applicability of the proposed method was successfully demonstrated. (author)

Prediction on sunspot activity based on fuzzy information granulation and support vector machine

Science.gov (United States)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

Analysis Of Electrical – Thermal Coupling Of Induction Machine ...

African Journals Online (AJOL)

The interaction of the Electrical and mechanical parts of Electrical machines gives rise to the heating of the machine's constituent parts. This consequently leads to an increase in temperature which if not properly monitored may lead to the breakdown of the machine. This paper therefore presents the Electrical and thermal ...

A prospective 5-year study of fixed partial prostheses supported by implants with machined and TiO2-blasted surface

DEFF Research Database (Denmark)

Gotfredsen, K; Karlsson, U

2001-01-01

PURPOSE: The aim of the present study was to evaluate whether there was a difference between machined and TiO(2)-blasted implants regarding survival rate and marginal bone loss during a 5-year observation period. MATERIALS AND METHODS: A total of 133 implants (Astra Tech Dental Implants; Astra Tech...... AB, Mölndal, Sweden) were placed in 50 patients at 6 centers in 4 Scandinavian countries. Forty-eight implants were installed in the maxilla and 85 implants in the mandible. A randomization and a stratification were done, so that each fixed partial prosthesis was supported by at least 1 machined...... and 1 TiO(2)-blasted implant. The implant-supported fixed partial prostheses (ISFPP) were fabricated within 2 months after postoperative healing. A total of 52 ISFPP (17 maxillary, 35 mandibular) were inserted. The patients were clinically examined once a year for 5 years. At the annual follow...

Risk estimation using probability machines

Science.gov (United States)

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

The convergence of parallel Boltzmann machines

NARCIS (Netherlands)

Zwietering, P.J.; Aarts, E.H.L.; Eckmiller, R.; Hartmann, G.; Hauske, G.

1990-01-01

We discuss the main results obtained in a study of a mathematical model of synchronously parallel Boltzmann machines. We present supporting evidence for the conjecture that a synchronously parallel Boltzmann machine maximizes a consensus function that consists of a weighted sum of the regular

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

African Journals Online (AJOL)

... machine (SVM) and its application in microarray experiment of Kashin-Beck disease. ... speed and amount of the corresponding mRNA in gene replication process. ... and revealed that some motifs may be related to the immune reactions.

Support vector machine based estimation of remaining useful life: current research status and future trends

International Nuclear Information System (INIS)

Huang, Hong Zhong; Wang, Hai Kun; Li, Yan Feng; Zhang, Longlong; Liu, Zhiliang

2015-01-01

Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data. Many SVM based methods have been proposed to predict RUL of some key components. We did a literature review related to SVM based RUL estimation within a decade. The references reviewed are classified into two categories: improved SVM algorithms and their applications to RUL estimation. The latter category can be further divided into two types: one, to predict the condition state in the future and then build a relationship between state and RUL; two, to establish a direct relationship between current state and RUL. However, SVM is seldom used to track the degradation process and build an accurate relationship between the current health condition state and RUL. Based on the above review and summary, this paper points out that the ability to continually improve SVM, and obtain a novel idea for RUL prediction using SVM will be future works.

Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

Directory of Open Access Journals (Sweden)

Lei Jiang

2012-01-01

Full Text Available Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.

Distributed interactive graphics applications in computational fluid dynamics

International Nuclear Information System (INIS)

Rogers, S.E.; Buning, P.G.; Merritt, F.J.

1987-01-01

Implementation of two distributed graphics programs used in computational fluid dynamics is discussed. Both programs are interactive in nature. They run on a CRAY-2 supercomputer and use a Silicon Graphics Iris workstation as the front-end machine. The hardware and supporting software are from the Numerical Aerodynamic Simulation project. The supercomputer does all numerically intensive work and the workstation, as the front-end machine, allows the user to perform real-time interactive transformations on the displayed data. The first program was written as a distributed program that computes particle traces for fluid flow solutions existing on the supercomputer. The second is an older post-processing and plotting program modified to run in a distributed mode. Both programs have realized a large increase in speed over that obtained using a single machine. By using these programs, one can learn quickly about complex features of a three-dimensional flow field. Some color results are presented

Thermal expression of intersubjectivity offers new possibilities to human-machine and technologically mediated interactions

Directory of Open Access Journals (Sweden)

Arcangelo eMerla

2014-07-01

Full Text Available The evaluation of the psychophysiological state of the interlocutor is an important element of interpersonal relationships and communication. Thermal infrared imaging has proved to be a reliable tool for non-invasive and contact-less evaluation of vital signs, psychophysiological responses and emotional states. This technique is quickly spreading in many fields, from psychometrics to social and developmental psychology; and from the touch-less monitoring of vital signs and stress, up to the human-machine interaction. In particular, thermal IR imaging promises to be of use for gathering information about affective states in social situations. This paper presents the state of the art of thermal infrared imaging in psychophysiology and in the assessment of affective states. The goal is to provide insights about its potentialities and limits for its use in human-artificial agent interaction in order to contribute to a major issue in the field: the perception by an artificial agent of human psychophysiological and affective states.

Type 2 Diabetes Education and Support in a Virtual Environment: A Secondary Analysis of Synchronously Exchanged Social Interaction and Support.

Science.gov (United States)

Lewinski, Allison A; Anderson, Ruth A; Vorderstrasse, Allison A; Fisher, Edwin B; Pan, Wei; Johnson, Constance M

2018-02-21

Virtual environments (VEs) facilitate interaction and support among individuals with chronic illness, yet the characteristics of these VE interactions remain unknown. The objective of this study was to describe social interaction and support among individuals with type 2 diabetes (T2D) who interacted in a VE. Data included VE-mediated synchronous conversations and text-chat and asynchronous emails and discussion board posts from a study that facilitated interaction among individuals with T2D and diabetes educators (N=24) in 2 types of sessions: education and support. VE interactions consisted of communication techniques (how individuals interact in the VE), expressions of self-management (T2D-related topics), depth (personalization of topics), and breadth (number of topics discussed). Individuals exchanged support more often in the education (723/1170, 61.79%) than in the support (406/1170, 34.70%) sessions or outside session time (41/1170, 3.50%). Of all support exchanges, 535/1170 (45.73%) were informational, 377/1170 (32.22%) were emotional, 217/1170 (18.55%) were appraisal, and 41/1170 (3.50%) were instrumental. When comparing session types, education sessions predominately provided informational support (357/723, 49.4%), and the support sessions predominately provided emotional (159/406, 39.2%) and informational (159/406, 39.2%) support. VE-mediated interactions resemble those in face-to-face environments, as individuals in VEs engage in bidirectional exchanges with others to obtain self-management education and support. Similar to face-to-face environments, individuals in the VE revealed personal information, sought information, and exchanged support during the moderated education sessions and unstructured support sessions. With this versatility, VEs are able to contribute substantially to support for those with diabetes and, very likely, other chronic diseases. ©Allison A Lewinski, Ruth A Anderson, Allison A Vorderstrasse, Edwin B Fisher, Wei Pan, Constance

Singular interactions supported by embedded curves

International Nuclear Information System (INIS)

Kaynak, Burak Tevfik; Turgut, O Teoman

2012-01-01

In this work, singular interactions supported by embedded curves on Riemannian manifolds are discussed from a more direct and physical perspective, via the heat kernel approach. We show that the renormalized problem is well defined, the ground state is finite and the corresponding wavefunction is positive. The renormalization group invariance of the model is also discussed. (paper)

Multi-phase alternative current machine winding design | Khan ...

African Journals Online (AJOL)

... single-phase to 18-phase excitation. Experimental results of a five-phase induction machine supplied from a static five-phase supply are provided to support the proposed design. Keywords: AC machine, Multi-phase machine, Stator winding, Five-phase. International Journal of Engineering, Science and Technology, Vol.

Gaming machine addiction: the role of avoidance, accessibility and social support.

Science.gov (United States)

Thomas, Anna C; Allen, Felicity L; Phillips, James; Karantzas, Gery

2011-12-01

Commonality in etiology and clinical expression plus high comorbidity between pathological gambling and substance use disorders suggest common underlying motives. It is important to understand common motivators and differentiating factors. An overarching framework of addiction was used to examine predictors of problem gambling in current electronic gaming machine (EGM) gamblers. Path analysis was used to examine the relationships between antecedent factors (stressors, coping habits, social support), gambling motivations (avoidance, accessibility, social) and gambling behavior. Three hundred and forty seven (229 females: M = 29.20 years, SD = 14.93; 118 males: M = 29.64 years, SD = 12.49) people participated. Consistent with stress, coping and addiction theory, situational life stressors and general avoidance coping were positively related to avoidance-motivated gambling. In turn, avoidance-motivated gambling was positively related to EGM gambling frequency and problems. Consistent with exposure theory, life stressors were positively related to accessibility-motivated gambling, and accessibility-motivated gambling was positively related to EGM gambling frequency and gambling problems. These findings are consistent with other addiction research and suggest avoidance-motivated gambling is part of a more generalized pattern of avoidance coping with relative accessibility to EGM gambling explaining its choice as a method of avoidance. Findings also showed social support acted as a direct protective factor in relation to gambling frequency and problems and indirectly via avoidance and accessibility gambling motivations. Finally, life stressors were positively related to socially motivated gambling but this motivation was not related to either social support or gambling behavior suggesting it has little direct influence on gambling problems.

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Directory of Open Access Journals (Sweden)

Pontil Massimiliano

2009-10-01

Full Text Available Abstract Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots" at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been

Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

Directory of Open Access Journals (Sweden)

R. Rajesh Sharma

2015-01-01

algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

A support vector machine (SVM) based voltage stability classifier

Energy Technology Data Exchange (ETDEWEB)

Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)

2007-07-01

Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

Science.gov (United States)

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Machine Learning of Musical Gestures

OpenAIRE

Caramiaux, Baptiste; Tanaka, Atau

2013-01-01

We present an overview of machine learning (ML) techniques and theirapplication in interactive music and new digital instruments design. We firstgive to the non-specialist reader an introduction to two ML tasks,classification and regression, that are particularly relevant for gesturalinteraction. We then present a review of the literature in current NIMEresearch that uses ML in musical gesture analysis and gestural sound control.We describe the ways in which machine learning is useful for cre...

A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao

2016-01-01

Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

Science.gov (United States)

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

Science.gov (United States)

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Directory of Open Access Journals (Sweden)

Dongxiao Niu

2018-01-01

Full Text Available Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.

Impact of Health Care Employees’ Job Satisfaction on Organizational Performance Support Vector Machine Approach

Directory of Open Access Journals (Sweden)

CEMIL KUZEY

2018-01-01

Full Text Available This study is undertaken to search for key factors that contribute to job satisfaction among health care workers, and also to determine the impact of these underlying dimensions of employee satisfaction on organizational performance. Exploratory Factor Analysis (EFA is applied to initially uncover the key factors, and then, in the next stage of analysis, a popular data mining technique, Support Vector Machine (SVM is employed on a sample of 249 to determine the impact of job satisfaction factors on organizational performance. According to the proposed model, the main factors are revealed to be management’s attitude, pay/reward, job security and colleagues.

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

Science.gov (United States)

Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

2018-02-01

In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design

International Nuclear Information System (INIS)

Zhao, Hongxia; Zhang, Xiuhui; Ji, Lin; Hu, Haixiang; Li, Qianshu

2014-01-01