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Sample records for machine svm-based approach

  1. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

  2. Power quality events recognition using a SVM-based method

    Energy Technology Data Exchange (ETDEWEB)

    Cerqueira, Augusto Santiago; Ferreira, Danton Diego; Ribeiro, Moises Vidal; Duque, Carlos Augusto [Department of Electrical Circuits, Federal University of Juiz de Fora, Campus Universitario, 36036 900, Juiz de Fora MG (Brazil)

    2008-09-15

    In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost. (author)

  3. Automatic Detection of Tumor in Wireless Capsule Endoscopy Images Using Energy Based Textural Features and SVM Based RFE Approach

    Directory of Open Access Journals (Sweden)

    B. Ashokkumar

    2014-04-01

    Full Text Available This paper deals with processing of wireless capsule endoscopy (WCE images from gastrointestinal tract, by extracting textural features and developing a suitable classifier to recognize as a normal or abnormal /tumor image. Images obtained from WCE are prone to noise. To reduce the noise, filtration technique is used. The quality of the filtered image is degraded, so to enhance the quality of the image, discrete wavelet transform (DWT is used. The textural features (average, energy are obtained from DWT for three color spaces (RGB, HSI, Lab. Feature selection is based on support vector machine- recursive feature elimination approach.

  4. Generalized SMO algorithm for SVM-based multitask learning.

    Science.gov (United States)

    Cai, Feng; Cherkassky, Vladimir

    2012-06-01

    Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

  5. An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

    Directory of Open Access Journals (Sweden)

    Ruben Ruiz-Gonzalez

    2014-11-01

    Full Text Available The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

  6. SVM-based glioma grading: Optimization by feature reduction analysis.

    Science.gov (United States)

    Zöllner, Frank G; Emblem, Kyrre E; Schad, Lothar R

    2012-09-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity=89%, specificity=84%) when reducing the feature vector from 101 (100-bins rCBV histogram+age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∼87%) while reducing the number of features by up to 98%.

  7. Detection of subsurface metallic utilities by means of a SAP technique: Comparing MUSIC- and SVM-based approaches

    Science.gov (United States)

    Meschino, Simone; Pajewski, Lara; Pastorino, Matteo; Randazzo, Andrea; Schettini, Giuseppe

    2013-10-01

    The identification of buried cables, pipes, conduits, and other cylindrical utilities is a very important task in civil engineering. In the last years, several methods have been proposed in the literature for tackling this problem. Most commonly employed approaches are based on the use of Ground Penetrating Radars, i.e., they extract the needed information about the unknown scenario starting from the electromagnetic field collected by a set of antennas. In the present paper, a statistical method, based on the use of smart antenna techniques, is used for the localization of a single buried object. In particular, two efficient algorithms for the estimation of the directions of arrival of the electromagnetic waves scattered by the targets, namely the MUltiple SIgnal Classification and the Support Vector Regression, are considered and their performances are compared.

  8. Efficient SVM-based Recognition of Chinese Personal Names

    Institute of Scientific and Technical Information of China (English)

    Yu Ying(宇缨); Wang Xiaolong; Liu Bingquan; Wang Hui

    2004-01-01

    This paper provides a flexible and efficient method to identify Chinese personal names based on SVM (Support Vector Machines). In its approach, forming rules of personal name is employed to select candidate set, then SVM based identification strategies is used to recognize real personal name in the candidate set. Basic semanteme of word in context and frequency information of word inside candidate are selected as features in its methodology, which reduce the feature space scale dramatically and calculate more efficiently. Results of open testing achieved F-measure 90.59% in 2 million words news and F-measure 86.67% in 16.17 million words news based on this project.

  9. An S-Transform and Support Vector Machine (SVM-Based Online Method for Diagnosing Broken Strands in Transmission Lines

    Directory of Open Access Journals (Sweden)

    Caxin Sun

    2011-08-01

    Full Text Available During their long-term outdoor field service, overhead transmission lines will be exposed to strikes by lightning, corrosion by chemical contaminants, ice-shedding, wind vibration of conductors, line galloping, external destructive forces and so on, which will generally cause a series of latent faults such as aluminum strand fracture. This may lead to broken transmission lines which will have a very strong impact on the safe operation of power grids that if the latent faults cannot be recognized and fixed as soon as possible. The detection of broken strands in transmission lines using inspection robots equipped with suitable detectors is a method with good prospects. In this paper, a method for detecting broken strands in transmission lines using an eddy current transducer (ECT carried by a robot is developed, and an approach for identifying broken strands in transmission lines based on an S-transform is proposed. The proposed approach utilizes the S-transform to extract the module and phase information at each frequency point from detection signals. Through module phase and comparison, the characteristic frequency points are ascertained, and the fault information of the detection signal is constructed. The degree of confidence of broken strand identification is defined by the Shannon fuzzy entropy (SFE-BSICD. The proposed approach combines module information while utilizing phase information, SFE-BSICD, and the energy, so the reliability is greatly improved. These characteristic qualities of broken strands in transmission lines are used as the input of a multi-classification SVM, allowing the number of broken strands to be determined. Through experimental field verification, it can be shown that the proposed approach displays high accuracy and the SFE-BSICD is defined reasonably.

  10. SVM-based glioma grading. Optimization by feature reduction analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

    2012-11-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

  11. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

  12. SVM-based prediction of caspase substrate cleavage sites

    Directory of Open Access Journals (Sweden)

    Ranganathan Shoba

    2006-12-01

    Full Text Available Abstract Background Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. Results A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i the tetrapeptide cleavage sites, (ii the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. Conclusion This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

  13. SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

    Science.gov (United States)

    Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

    2013-03-01

    Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors.

  14. 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...

  15. Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction.

    Science.gov (United States)

    Li, Liqi; Yu, Sanjiu; Xiao, Weidong; Li, Yongsheng; Hu, Wenjuan; Huang, Lan; Zheng, Xiaoqi; Zhou, Shiwen; Yang, Hua

    2015-01-01

    Mitochondrion, a tiny energy factory, plays an important role in various biological processes of most eukaryotic cells. Mitochondrial defection is associated with a series of human diseases. Knowledge of the submitochondrial locations of proteins can help to reveal the biological functions of novel proteins, and understand the mechanisms underlying various biological processes occurring in the mitochondrion. However, experimental methods to determine protein submitochondrial locations are costly and time consuming. Thus it is essential to develop a fast and reliable computational method to predict protein submitochondrial locations. Here, we proposed a support vector machine (SVM) based approach for predicting protein submitochondrial locations. Information from the position-specific score matrix (PSSM), gene ontology (GO) and the protein feature (PROFEAT) was integrated into the principal features of this model. Then a recursive feature selection scheme was employed to select the optimal features. Finally, an SVM module was used to predict protein submitochondrial locations based on the optimal features. Through the jackknife cross-validation test, our method achieved an accuracy of 99.37% on benchmark dataset M317, and 100% on the other two datasets, M1105 and T86. These results indicate that our method is economic and effective for accurate prediction of the protein submitochondrial location.

  16. SVM based layout retargeting for fast and regularized inverse lithography

    Institute of Scientific and Technical Information of China (English)

    Kai-sheng LUO; Zheng SHI; Xiao-lang YAN; Zhen GENG

    2014-01-01

    Inverse lithography technology (ILT), also known as pixel-based optical proximity correction (PB-OPC), has shown promising capability in pushing the current 193 nm lithography to its limit. By treating the mask optimization process as an inverse problem in lithography, ILT provides a more complete exploration of the solution space and better pattern fidelity than the tradi-tional edge-based OPC. However, the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process. To address this issue, in this paper we propose a support vector machine (SVM) based layout retargeting method for ILT, which is designed to generate a good initial input mask for the optimization process and promote the convergence speed. Supervised by optimized masks of training layouts generated by conventional ILT, SVM models are learned and used to predict the initial pixel values in the‘undefined areas’ of the new layout. By this process, an initial input mask close to the final optimized mask of the new layout is generated, which reduces iterations needed in the following optimization process. Manu-facturability is another critical issue in ILT;however, the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models. To compensate for this drawback, a spatial filter is employed to regularize the retargeted mask for complexity reduction. We implemented our layout retargeting method with a regularized level-set based ILT (LSB-ILT) algorithm under partially coherent illumination conditions. Experimental results show that with an initial input mask generated by our layout retargeting method, the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%, respectively.

  17. The Database State Machine Approach

    OpenAIRE

    1999-01-01

    Database replication protocols have historically been built on top of distributed database systems, and have consequently been designed and implemented using distributed transactional mechanisms, such as atomic commitment. We present the Database State Machine approach, a new way to deal with database replication in a cluster of servers. This approach relies on a powerful atomic broadcast primitive to propagate transactions between database servers, and alleviates the need for atomic comm...

  18. A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

    Science.gov (United States)

    Zhan, Liwei; Li, Chengwei

    2017-02-01

    A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

  19. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions

    Science.gov (United States)

    Yoon, Heesung; Hyun, Yunjung; Ha, Kyoochul; Lee, Kang-Kun; Kim, Gyoo-Bum

    2016-05-01

    The prediction of long-term groundwater level fluctuations is necessary to effectively manage groundwater resources and to assess the effects of changes in rainfall patterns on groundwater resources. In the present study, a weighted error function approach was utilised to improve the performance of artificial neural network (ANN)- and support vector machine (SVM)-based recursive prediction models for the long-term prediction of groundwater levels in response to rainfall. The developed time series models were applied to groundwater level data from 5 groundwater-monitoring stations in South Korea. The results demonstrated that the weighted error function approach can improve the stability and accuracy of recursive prediction models, especially for ANN models. The comparison of the model performance showed that the recursive prediction performance of the SVM was superior to the performance of the ANN in this case study.

  20. Setup reduction approaches for machining

    Energy Technology Data Exchange (ETDEWEB)

    Gillespie, L.K.

    1997-04-01

    Rapid setup is a common improvement approach in press working operations such as blanking and shearing. It has paid major dividends in the sheet metal industry. It also has been a major improvement thrust for high-production machining operations. However, the literature does not well cover all the setup operations and constraints for job shop work. This review provides some insight into the issues involved. It highlights the floor problems and provides insights for further improvement. The report is designed to provide a quick understanding of the issues.

  1. Advances in SVM-Based System Using GMM Super Vectors for Text-Independent Speaker Verification

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jian; DONG Yuan; ZHAO Xianyu; YANG Hao; LU Liang; WANG Haila

    2008-01-01

    For text-independent speaker verification,the Gaussian mixture model (GMM) using a universal background model strategy and the GMM using support vector machines are the two most commonly used methodologies.Recently,a new SVM-based speaker verification method using GMM super vectors has been proposed.This paper describes the construction of a new speaker verification system and investigates the use of nuisance attribute projection and test normalization to further enhance performance.Experiments were conducted on the core test of the 2006 NIST speaker recognition evaluation corpus.The experimental results indicate that an SVM-based speaker verification system using GMM super vectors can achieve ap-pealing performance.With the use of nuisance attribute projection and test normalization,the system per-formance can be significantly improved,with improvements in the equal error rate from 7.78% to 4.92% and detection cost function from 0.0376 to 0.0251.

  2. Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

    Directory of Open Access Journals (Sweden)

    Zhou Wan

    2015-01-01

    Full Text Available Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM. After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV. After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

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

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

    DEFF Research Database (Denmark)

    Pereira, Rafael; Sousa, Tiago; Pinto, Tiago

    2014-01-01

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

  5. New approach to training support vector machine

    Institute of Scientific and Technical Information of China (English)

    Tang Faming; Chen Mianyun; Wang Zhongdong

    2006-01-01

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

  6. Machine learning approaches in medical image analysis

    DEFF Research Database (Denmark)

    de Bruijne, Marleen

    2016-01-01

    Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols......, learning from weak labels, and interpretation and evaluation of results....

  7. Automatic Parameters Selection for SVM Based on PSO

    Institute of Scientific and Technical Information of China (English)

    ZHANG Mingfeng; ZHU Yinghua; ZHENG Xu; LIU Yu

    2007-01-01

    Motivated by the fact that automatic parameters selection for Support Vector Machine (SVM) is an important issue to make SVM practically useful and the common used Leave-One-Out (LOO) method is complex calculation and time consuming,an effective strategy for automatic parameters selection for SVM is proposed by using the Particle Swarm Optimization (PSO) in this paper.Simulation results of practice data model demonstrate the effectiveness and high efficiency of the proposed approach.

  8. Machine learning an artificial intelligence approach

    CERN Document Server

    Banerjee, R; Bradshaw, Gary; Carbonell, Jaime Guillermo; Mitchell, Tom Michael; Michalski, Ryszard Spencer

    1983-01-01

    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV a

  9. Mechanical Fault Diagnosis Using Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

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

    2003-01-01

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

  10. Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data

    Science.gov (United States)

    Nieto, Paulino José García; García-Gonzalo, Esperanza; Vilán, José Antonio Vilán; Robleda, Abraham Segade

    2015-12-01

    The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-SVM-based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, this hybrid PSO-SVM-based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

  11. Support vector machine based fault detection approach for RFT-30 cyclotron

    Science.gov (United States)

    Kong, Young Bae; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-01

    An RFT-30 is a 30 MeV cyclotron used for radioisotope applications and radiopharmaceutical researches. The RFT-30 cyclotron is highly complex and includes many signals for control and monitoring of the system. It is quite difficult to detect and monitor the system failure in real time. Moreover, continuous monitoring of the system is hard and time-consuming work for human operators. In this paper, we propose a support vector machine (SVM) based fault detection approach for the RFT-30 cyclotron. The proposed approach performs SVM learning with training samples to construct the classification model. To compensate the system complexity due to the large-scale accelerator, we utilize the principal component analysis (PCA) for transformation of the original data. After training procedure, the proposed approach detects the system faults in real time. We analyzed the performance of the proposed approach utilizing the experimental data of the RFT-30 cyclotron. The performance results show that the proposed SVM approach can provide an efficient way to control the cyclotron system.

  12. A Machine Learning Approach to Automated Negotiation

    Institute of Scientific and Technical Information of China (English)

    Zhang Huaxiang(张化祥); Zhang Liang; Huang Shangteng; Ma Fanyuan

    2004-01-01

    Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents' average Q values in each negotiation stage. The delayed reward is used to generate agents' offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.

  13. Integrated approach to advanced machining

    Energy Technology Data Exchange (ETDEWEB)

    LeSar, R.A.; Bourke, M.A.M.; Rangaswamy, P.; Day, R.D.; Hatch, D.J.

    1997-08-01

    The residual stress state induced by machining in a Ti alloy as function of cutting tool sharpness and depth of cut was predicted and measured. Residual stresses were greater for the dull tool than for the sharp tool. XRD was used to measure the residual stress state of the material; these measurements revealed that the hoop stress increased with depth of cut; however the radial stress decreased with depth of cut. An elastic-plastic model provided a possible explanation for this behavior in that, for small depths of cut, the tool makes multiple passes through the damage subsurface layer. This causes both residual stress components to increase, but the radial stress increases by a much greater amount than the hoop stress.

  14. 基于支持向量机无限集成学习方法的遥感图像分类%Remotely sensed imagery classification by SVM-based Infinite Ensemble Learning method

    Institute of Scientific and Technical Information of China (English)

    杨娜; 秦志远; 张俊

    2013-01-01

    基于支持向量机的无限集成学习方法(SVM-based IEL)是机器学习领域新兴起的一种集成学习方法.本文将SVM-based IEL引入遥感图像的分类领域,并同时将SVM、Bagging、AdaBoost和SVM-based IEL等方法应用于遥感图像分类.实验表明:Bagging方法可以提高遥感图像的分类精度,而AdaBoost却降低了遥感图像的分类精度;同时,与SVM、有限集成的学习方法相比,SVM-based IEL方法具有可以显著地提高遥感图像的分类精度、分类效率的优势.%Support-vector-machines-based Infinite Ensemble Learning method ( SVM-based IEL) is one of the ensemble learning methods in the field of machine learning. In this paper, the SVM-based IEL was applied to the classification of remotely sensed imagery besides classic ensemble learning methods such as Bagging, AdaBoost and SVM etc. SVM was taken as the base classifier in Bagging, AdaBoost The experiments showed that the classic ensemble learning methods have different performances compared to SVM. In detail , the Bagging was capable of enhancing the classification accuracy but the AdaBoost was decreasing the classification accuracy. Furthermore, the experiments suggested that compared to SVM and classic ensemble learning methods, SVM-based IEL has many merits such as increasing both of the classification accuracy and classification efficiency.

  15. 基于支持向量机MPLS的间歇过程故障诊断方法%On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes

    Institute of Scientific and Technical Information of China (English)

    李运锋; 汪志锋; 袁景淇

    2006-01-01

    In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

  16. Face Detection Using Adaboosted SVM-Based Component Classifier

    CERN Document Server

    Valiollahzadeh, Seyyed Majid; Nazari, Mohammad

    2008-01-01

    Recently, Adaboost has been widely used to improve the accuracy of any given learning algorithm. In this paper we focus on designing an algorithm to employ combination of Adaboost with Support Vector Machine as weak component classifiers to be used in Face Detection Task. To obtain a set of effective SVM-weaklearner Classifier, this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one. Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of classification accuracy, to other commonly used Adaboost methods, such as Decision Trees and Neural Networks, on CMU+MIT face database. Results indicate that the performance of the proposed method is overall superior to previous Adaboost approaches.

  17. EFICAz2: enzyme function inference by a combined approach enhanced by machine learning

    Directory of Open Access Journals (Sweden)

    Skolnick Jeffrey

    2009-04-01

    Full Text Available Abstract Background We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs. FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest or heterofunctional (annotated with another EC number or lacking enzymatic activity. Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI is lower than 30%. To improve EFICAz's performance in this regime, we: i increased the number of predictive components and ii took advantage of consensual information from the different components to make the final EC number assignment. Results We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i the new SVM-based components outperform their FDR-based counterparts, and ii both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz2, exhibits a highly improved prediction precision at MTTSI 2 and KEGG shows that: i when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii EFICAz2 generates considerably more unique assignments than KEGG. Conclusion Performance benchmarks and the

  18. A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features.

    Science.gov (United States)

    Li, Liqi; Luo, Qifa; Xiao, Weidong; Li, Jinhui; Zhou, Shiwen; Li, Yongsheng; Zheng, Xiaoqi; Yang, Hua

    2017-02-01

    Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

  19. A machine learning approach for classification of anatomical coverage in CT

    Science.gov (United States)

    Wang, Xiaoyong; Lo, Pechin; Ramakrishna, Bharath; Goldin, Johnathan; Brown, Matthew

    2016-03-01

    Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

  20. A SVM-based quantitative fMRI method for resting-state functional network detection.

    Science.gov (United States)

    Song, Xiaomu; Chen, Nan-kuei

    2014-09-01

    Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.

  1. LDA-SVM-based EGFR mutation model for NSCLC brain metastases: an observational study.

    Science.gov (United States)

    Hu, Nan; Wang, Ge; Wu, Yu-Hao; Chen, Shi-Feng; Liu, Guo-Dong; Chen, Chuan; Wang, Dong; He, Zhong-Shi; Yang, Xue-Qin; He, Yong; Xiao, Hua-Liang; Huang, Ding-De; Xiong, Kun-Lin; Wu, Yan; Huang, Ming; Yang, Zhen-Zhou

    2015-02-01

    Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non-small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this small cohort of

  2. LDA-SVM-Based EGFR Mutation Model for NSCLC Brain Metastases

    Science.gov (United States)

    Hu, Nan; Wang, Ge; Wu, Yu-Hao; Chen, Shi-Feng; Liu, Guo-Dong; Chen, Chuan; Wang, Dong; He, Zhong-Shi; Yang, Xue-Qin; He, Yong; Xiao, Hua-Liang; Huang, Ding-De; Xiong, Kun-Lin; Wu, Yan; Huang, Ming; Yang, Zhen-Zhou

    2015-01-01

    Abstract Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non–small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this

  3. SVM-Based Control System for a Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Foudil Abdessemed

    2012-12-01

    Full Text Available Real systems are usually non‐linear, ill‐defined, have variable parameters and are subject to external disturbances. Modelling these systems is often an approximation of the physical phenomena involved. However, it is from this approximate system of representation that we propose ‐ in this paper ‐ to build a robust control, in the sense that it must ensure low sensitivity towards parameters, uncertainties, variations and external disturbances. The computed torque method is a well‐established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose the estimation of the dynamics model of the nonlinear system with a machine learning regression method. The output of this regressor is used in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator. In cases where some of the parameters of the plant undergo a change in their values, poor performance may result. To cope with this drawback, a fuzzy precompensator is inserted to reinforce the SVM computed torque‐based controller and avoid any deterioration. The theory is developed and the simulation results are carried out on a two‐degree of freedom robot manipulator to demonstrate the validity of the proposed approach.

  4. SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

    Science.gov (United States)

    Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

    2017-03-03

    Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects' hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

  5. SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

    Science.gov (United States)

    Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

    2017-01-01

    Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise. PMID:28273818

  6. SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

    Directory of Open Access Journals (Sweden)

    Jiping Xiong

    2017-03-01

    Full Text Available Although wrist-type photoplethysmographic (hereafter referred to as WPPG sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

  7. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

    Science.gov (United States)

    Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

    2016-09-02

    Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

  8. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

    Directory of Open Access Journals (Sweden)

    Yi Long

    2016-09-01

    Full Text Available Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM optimized by particle swarm optimization (PSO to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz, a three-layer wavelet packet analysis (WPA is used for feature extraction, after which, the kernel principal component analysis (kPCA is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

  9. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

    Science.gov (United States)

    Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

    2016-01-01

    Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance. PMID:27598160

  10. SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS

    Institute of Scientific and Technical Information of China (English)

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

    2006-01-01

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

  11. Modelling and Simulation of SVM Based DVR System for Voltage Sag Mitigation

    Directory of Open Access Journals (Sweden)

    S. Leela

    2013-12-01

    Full Text Available The aim of this study is to design and simulate three phase DVR system using MATLAB simulink. SVM based DVR is proposed to reduce the sag on the transmission line. The SVM based DVR injects voltage into the line to compensate the voltage drop. Sag is created by connecting a heavy load in parallel with the existing system. This sag will be compensated by injecting the inverter output through an injection transformer. The results of simulation are compared with the theoretical results.

  12. A NEW SVM BASED EMOTIONAL CLASSIFICATION OF IMAGE

    Institute of Scientific and Technical Information of China (English)

    Wang Weining; Yu Yinglin; Zhang Jianchao

    2005-01-01

    How high-level emotional representation of art paintings can be inferred from percep tual level features suited for the particular classes (dynamic vs. static classification)is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.

  13. Incremental Training for SVM-Based Classification with Keyword Adjusting

    Institute of Scientific and Technical Information of China (English)

    SUN Jin-wen; YANG Jian-wu; LU Bin; XIAO Jian-guo

    2004-01-01

    This paper analyzed the theory of incremental learning of SVM (support vector machine) and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning.According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the difference between incremental learning and batch learning through the keyword adjusting.The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the batch method.

  14. In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing.

    Directory of Open Access Journals (Sweden)

    Christian Weis

    Full Text Available The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm3, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

  15. In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing.

    Science.gov (United States)

    Weis, Christian; Hess, Andreas; Budinsky, Lubos; Fabry, Ben

    2015-01-01

    The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI) in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM) based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm3, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

  16. Modeling software with finite state machines a practical approach

    CERN Document Server

    Wagner, Ferdinand; Wagner, Thomas; Wolstenholme, Peter

    2006-01-01

    Modeling Software with Finite State Machines: A Practical Approach explains how to apply finite state machines to software development. It provides a critical analysis of using finite state machines as a foundation for executable specifications to reduce software development effort and improve quality. This book discusses the design of a state machine and of a system of state machines. It also presents a detailed analysis of development issues relating to behavior modeling with design examples and design rules for using finite state machines. This volume describes a coherent and well-tested fr

  17. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  18. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

    Science.gov (United States)

    Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

    2015-07-05

    Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

  19. A multi-class SVM based on FCOWA-ER%一种基于FCOWA-ER的SVM多分类方法

    Institute of Scientific and Technical Information of China (English)

    刘卫兵; 杨艺; 韩德强

    2015-01-01

    支持向量机(SVM)在处理多分类问题时,需要综合利用多个二分类SVM,以获得多分类判决结果。传统多分类拓展方法使用的是SVM的硬输出,在一定程度上造成了信息的丢失。为了更加充分地利用信息,提出一种基于证据推理-多属性决策方法的SVM多分类算法,将多分类问题视为一个多属性决策问题,使用证据推理-模糊谨慎有序加权平均方法(FCOWA-ER)实现SVM的多分类判决。实验结果表明,所提出方法可以获得更高的分类精度。%Multiple bi-class SVMs are used together to obtain the final decision when the support vector machine(SVM) is applied to multi-class classification problems. The conventional methods of applying the SVM to multiple classification tasks are all based on the hard output of SVM, which can bring the loss of information to some extent. Therefore, a multi-class SVM based on an evidential reasoning based multiple attribute decision approach is proposed to use more information. The multi-class classification problem is modelled as a multi-criteria decision making problem. Then a fuzzy-cautious OWA(ordered weighted averaging) approach with evidential reasoning(FCOWA-ER) is used to implement multi-class classification and obtain the final decision. The simulation results show that the method proposed has better accuracy compared with conventional methods.

  20. On the effect of subliminal priming on subjective perception of images: a machine learning approach.

    Science.gov (United States)

    Kumar, Parmod; Mahmood, Faisal; Mohan, Dhanya Menoth; Wong, Ken; Agrawal, Abhishek; Elgendi, Mohamed; Shukla, Rohit; Dauwels, Justin; Chan, Alice H D

    2014-01-01

    The research presented in this article investigates the influence of subliminal prime words on peoples' judgment about images, through electroencephalograms (EEGs). In this cross domain priming paradigm, the participants are asked to rate how much they like the stimulus images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words, with EEG recorded simultaneously. Statistical analysis tools are used to analyze the effect of priming on behavior, and machine learning techniques to infer the primes from EEGs. The experiment reveals strong effects of subliminal priming on the participants' explicit rating of images. The subjective judgment affected by the priming makes visible change in event-related potentials (ERPs); results show larger ERP amplitude for the negative primes compared with positive and neutral primes. In addition, Support Vector Machine (SVM) based classifiers are proposed to infer the prime types from the average ERPs, which yields a classification rate of 70%.

  1. The cognitive approach to conscious machines

    CERN Document Server

    Haikonen, Pentti O

    2003-01-01

    Could a machine have an immaterial mind? The author argues that true conscious machines can be built, but rejects artificial intelligence and classical neural networks in favour of the emulation of the cognitive processes of the brain-the flow of inner speech, inner imagery and emotions. This results in a non-numeric meaning-processing machine with distributed information representation and system reactions. It is argued that this machine would be conscious; it would be aware of its own existence and its mental content and perceive this as immaterial. Novel views on consciousness and the mind-

  2. Intelligent Machine Learning Approaches for Aerospace Applications

    Science.gov (United States)

    Sathyan, Anoop

    Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire

  3. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

    Science.gov (United States)

    Hu, Yongli; Hase, Takeshi; Li, Hui Peng; Prabhakar, Shyam; Kitano, Hiroaki; Ng, See Kiong; Ghosh, Samik; Wee, Lawrence Jin Kiat

    2016-12-22

    The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)). Thirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases. This novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

  4. LS-SVM Based AGC of an Asynchronous Power System with Dynamic Participation from DFIG Based Wind Turbines

    Directory of Open Access Journals (Sweden)

    Gulshan Sharma

    2014-08-01

    Full Text Available Modern power systems are large and interconnected with growing trends to integrate wind energy to the power system and meet the ever rising energy demand in an economical manner. The penetration of wind energy has motivated power engineers and researchers to investigate the dynamic participation of Doubly Fed Induction Generators (DFIG based wind turbines in Automatic Generation Control (AGC services. However, with dynamic participation of DFIG, the AGC problem becomes more complex and under these conditions classical AGC are not suitable. Therefore, a new non-linear Least Squares Support Vector Machines (LS-SVM based regulator for solution of AGC problem is proposed in this study. The proposed AGC regulator is trained for a wide range of operating conditions and load changes using an off-line data set generated from the robust control technique. A two-area power system connected via parallel AC/DC tie-lines with DFIG based wind turbines in each area is considered to demonstrate the effectiveness of the proposed AGC regulator and compared with results obtained using Multi-Layer Perceptron (MLP neural networks and conventional PI regulators under various operating conditions and load changes.

  5. FaaPred: a SVM-based prediction method for fungal adhesins and adhesin-like proteins.

    Directory of Open Access Journals (Sweden)

    Jayashree Ramana

    Full Text Available Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.

  6. SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids.

    Science.gov (United States)

    Mohammad, Tabrez Anwar Shamim; Nagarajaram, Hampapathalu Adimurthy

    2011-08-01

    The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.

  7. Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

    Science.gov (United States)

    Macesic, Nenad; Polubriaginof, Fernanda; Tatonetti, Nicholas P

    2017-09-12

    Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

  8. A Stock Market Prediction Method Based on Support Vector Machines (SVM and Independent Component Analysis (ICA

    Directory of Open Access Journals (Sweden)

    Hakob GRIGORYAN

    2016-08-01

    Full Text Available The research presented in this work focuses on financial time series prediction problem. The integrated prediction model based on support vector machines (SVM with independent component analysis (ICA (called SVM-ICA is proposed for stock market prediction. The presented approach first uses ICA technique to extract important features from the research data, and then applies SVM technique to perform time series prediction. The results obtained from the SVM-ICA technique are compared with the results of SVM-based model without using any pre-processing step. In order to show the effectiveness of the proposed methodology, two different research data are used as illustrative examples. In experiments, the root mean square error (RMSE measure is used to evaluate the performance of proposed models. The comparative analysis leads to the conclusion that the proposed SVM-ICA model outperforms the simple SVM-based model in forecasting task of nonstationary time series.

  9. Support Vector Machine for mechanical faults classification

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  10. Reliability Approach for Machine Protection Design in Particle Accelerators

    CERN Document Server

    Apollonio, A; Mikulec, B; Puccio, B; Sanchez Alvarez, J L; Schmidt, R; Wagner, S

    2013-01-01

    Particle accelerators require Machine Protection Systems (MPS) to prevent beam-induced damage of equipment in case of failures. This becomes increasingly important for proton colliders with large energy stored in the beam such as LHC, for high power accelerators with a beam power of up to 10 MW, such as the European Spallation Source (ESS), and for linear colliders with high beam power and very small beam size. The reliability of Machine Protection Systems is crucial for safe machine operation; all possible sources of risk need to be taken into account in the early design stage. This paper presents a systematic approach to classify failures and to assess the associated risk, and discusses the impact of such considerations on the design of Machine Protection Systems. The application of this approach will be illustrated using the new design of the MPS for LINAC4, a linear accelerator under construction at CERN.

  11. 基于信息熵的SVM入侵检测技术%Exploring SVM-based intrusion detection through information entropy theory

    Institute of Scientific and Technical Information of China (English)

    朱文杰; 王强; 翟献军

    2013-01-01

    在传统基于SVM的入侵检测中,核函数构造和特征选择采用先验知识,普遍存在准确度不高、效率低下的问题.通过信息熵理论与SVM算法相结合的方法改进为基于信息熵的SVM入侵检测算法,可以提高入侵检测的准确性,提升入侵检测的效率.基于信息熵的SVM入侵检测算法包括两个方面:一方面,根据样本包含的用户信息熵和方差,将样本特征统一,以特征是否属于置信区间来度量.将得到的样本特征置信向量作为SVM核函数的构造参数,既可保证训练样本集与最优分类面之间的对应关系,又可得到入侵检测需要的最大分类间隔;另一方面,将样本包含的用户信息量作为度量大幅度约简样本特征子集,不但降低了样本计算规模,而且提高了分类器的训练速度.实验表明,该算法在入侵检测系统中的应用优于传统的SVM算法.%In traditional SVM based intrusion detection approaches,both core function construction and feature selection use prior knowdege.Due to this,they are not only inefficient but also inaccurate.It is observed that integrating information entropy theory into SVM-based intrusion detection can enhance both the precision and the speed.Concludely speaking,SVM-based entropy intrusion detection algorithms are made up of two aspects:on one hand,setting sample confidence vector as core function's constructor of SVM algorithm can guarantee the mapping relationship between training sample and optimization classification plane.Also,the intrusion detection's maximum interval can be acquired.On the other hand,simplifying feature subset with samples's entropy as metric standard can not only shrink the computing scale but also improve the speed.Experiments prove that the SVM based entropy intrusion detection algoritm outperfomrs other tradional algorithms.

  12. Machine Learning Approaches for Modeling Spammer Behavior

    CERN Document Server

    Islam, Md Saiful; Islam, Md Rafiqul

    2010-01-01

    Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Na\\"ive Bayesian classifier (Na\\"ive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.

  13. Remote leak localization approach for fusion machines

    Energy Technology Data Exchange (ETDEWEB)

    Durocher, Au., E-mail: aurelien.durocher@cea.fr [CEA-IRFM, F-13108 Saint Paul-Lez-Durance (France); Bruno, V.; Chantant, M.; Gargiulo, L. [CEA-IRFM, F-13108 Saint Paul-Lez-Durance (France); Gherman, T. [Floralis UJF Filiale, F-38610 Gières (France); Hatchressian, J.-C.; Houry, M.; Le, R.; Mouyon, D. [CEA-IRFM, F-13108 Saint Paul-Lez-Durance (France)

    2013-10-15

    Highlights: ► Description of leaks issue. ► Selection of leak localization concepts. ► Qualification of leak localization concepts. -- Abstract: Fusion machine operation requires high-vacuum conditions and does not tolerate water or gas leak in the vacuum vessels, even if they are micrometric. Tore Supra, as a fully actively cooled tokamak, has got a large leak management experience; 34 water leaks occurred since the beginning of its operation in 1988. To handle this issue, after preliminary machine protection phases, the current process for leak localization is based on water or helium pressurization network by network. It generally allows the identification of a set of components where the leakage element is located. However, the unique background of CEA-IRFM laboratory points needs of accuracy and promptness out in the leak localization process. Moreover, in-vessel interventions have to be performed trying to minimize time and risks for the persons. They are linked to access conditions, radioactivity, tracer gas high pressure and vessel conditioning. Remote operation will be one of the ways to improve these points on future fusion machines. In this case, leak sensors would have to be light weight devices in order to be integrated on a carrier or to be located outside with a sniffing process set up. A leak localization program is on-going at CEA-IRFM Laboratory with the first goal of identifying and characterizing relevant concepts to localize helium or water leaks on ITER. In the same time, CEA has developed robotic carrier for effective in-vessel intervention in a hostile environment. Three major tests campaigns with the goal to identify leak sensors have been achieved on several CEA test-beds since 2010. Very promising results have been obtained: relevant scenario of leak localization performed, concepts tested in a high volume test-bed called TITAN, and, in several conditions of pressure and temperature (ultrahigh vacuum to atmospheric pressure and 20

  14. ABOUT COMPLEX APPROACH TO MODELLING OF TECHNOLOGICAL MACHINES FUNCTIONING

    Directory of Open Access Journals (Sweden)

    A. A. Honcharov

    2015-01-01

    Full Text Available Problems arise in the process of designing, production and investigation of a complicated technological machine. These problems concern not only properties of some types of equipment but they have respect to regularities of control object functioning as a whole. A technological machine is thought of as such technological complex where it is possible to lay emphasis on a control system (or controlling device and a controlled object. The paper analyzes a number of existing approaches to construction of models for controlling devices and their functioning. A complex model for a technological machine operation has been proposed in the paper; in other words it means functioning of a controlling device and a controlled object of the technological machine. In this case models of the controlling device and the controlled object of the technological machine can be represented as aggregate combination (elements of these models. The paper describes a conception on realization of a complex model for a technological machine as a model for interaction of units (elements in the controlling device and the controlled object. When a control activation is given to the controlling device of the technological machine its modelling is executed at an algorithmic or logic level and the obtained output signals are interpreted as events and information about them is transferred to executive mechanisms.The proposed scheme of aggregate integration considers element models as object classes and the integration scheme is presented as a combination of object property values (combination of a great many input and output contacts and combination of object interactions (in the form of an integration operator. Spawn of parent object descendants of the technological machine model and creation of their copies in various project parts is one of the most important means of the distributed technological machine modelling that makes it possible to develop complicated models of

  15. An Approach for Implementing State Machines with Online Testability

    Directory of Open Access Journals (Sweden)

    P. K. Lala

    2010-01-01

    Full Text Available During the last two decades, significant amount of research has been performed to simplify the detection of transient or soft errors in VLSI-based digital systems. This paper proposes an approach for implementing state machines that uses 2-hot code for state encoding. State machines designed using this approach allow online detection of soft errors in registers and output logic. The 2-hot code considerably reduces the number of required flip-flops and leads to relatively straightforward implementation of next state and output logic. A new way of designing output logic for online fault detection has also been presented.

  16. Understanding bimolecular machines: Theoretical and experimental approaches

    Science.gov (United States)

    Goler, Adam Scott

    This dissertation concerns the study of two classes of molecular machines from a physical perspective: enzymes and membrane proteins. Though the functions of these classes of proteins are different, they each represent important test-beds from which new understanding can be developed by the application of different techniques. HIV1 Reverse Transcriptase is an enzyme that performs multiple functions, including reverse transcription of RNA into an RNA/DNA duplex, RNA degradation by the RNaseH domain, and synthesis of dsDNA. These functions allow for the incorporation of the retroviral genes into the host genome. Its catalytic cycle requires repeated large-scale conformational changes fundamental to its mechanism. Motivated by experimental work, these motions were studied theoretically by the application of normal mode analysis. It was observed that the lowest order modes correlate with largest amplitude (low-frequency) motion, which are most likely to be catalytically relevant. Comparisons between normal modes obtained via an elastic network model to those calculated from the essential dynamics of a series of all-atom molecular dynamics simulations show the self-consistency between these calculations. That similar conformational motions are seen between independent theoretical methods reinforces the importance of large-scale subdomain motion for the biochemical action of DNA polymerases in general. Moreover, it was observed that the major subunits of HIV1 Reverse Transcriptase interact quasi-harmonically. The 5HT3A Serotonin receptor and P2X1 receptor, by contrast, are trans-membrane proteins that function as ligand gated ion channels. Such proteins feature a central pore, which allows for the transit of ions necessary for cellular function across a membrane. The pore is opened by the ligation of binding sites on the extracellular portion of different protein subunits. In an attempt to resolve the individual subunits of these membrane proteins beyond the diffraction

  17. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2014-01-01

    Full Text Available A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM, has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC curve (AUC, f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

  18. A GA-Based Approach for FMS Machine Loading Planing

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The machine loading problem in flexible manufacturing system isaddressed in this paper. The problem is modelled as a mixed integer program. A Genetic Algorithm (GA) approach is developed to yield an optimal solution. In the genetic algorithm, chromosomes are encoded in term of operation routes. A point-to-point crossover search operator together with a Cyclic Shifting Mutation (CSM) operator is designed to adapt to the problem. At last computational experience with the model is presented, and the results show that our genetic algorithms are very powerful and suitable to machine loading problems.

  19. 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.

  20. 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

  1. A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

  2. Application of TRIZ approach to machine vibration condition monitoring problems

    Science.gov (United States)

    Cempel, Czesław

    2013-12-01

    Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

  3. Practical approach to apply range image sensors in machine automation

    Science.gov (United States)

    Moring, Ilkka; Paakkari, Jussi

    1993-10-01

    In this paper we propose a practical approach to apply range imaging technology in machine automation. The applications we are especially interested in are industrial heavy-duty machines like paper roll manipulators in harbor terminals, harvesters in forests and drilling machines in mines. Characteristic of these applications is that the sensing system has to be fast, mid-ranging, compact, robust, and relatively cheap. On the other hand the sensing system is not required to be generic with respect to the complexity of scenes and objects or number of object classes. The key in our approach is that just a limited range data set or as we call it, a sparse range image is acquired and analyzed. This makes both the range image sensor and the range image analysis process more feasible and attractive. We believe that this is the way in which range imaging technology will enter the large industrial machine automation market. In the paper we analyze as a case example one of the applications mentioned and, based on that, we try to roughly specify the requirements for a range imaging based sensing system. The possibilities to implement the specified system are analyzed based on our own work on range image acquisition and interpretation.

  4. Quantum Machine and SR Approach: a Unified Model

    CERN Document Server

    Garola, C; Sozzo, S; Garola, Claudio; Pykacz, Jaroslav; Sozzo, Sandro

    2005-01-01

    The Geneva-Brussels approach to quantum mechanics (QM) and the semantic realism (SR) nonstandard interpretation of QM exhibit some common features and some deep conceptual differences. We discuss in this paper two elementary models provided in the two approaches as intuitive supports to general reasonings and as a proof of consistency of general assumptions, and show that Aerts' quantum machine can be embodied into a macroscopic version of the microscopic SR model, overcoming the seeming incompatibility between the two models. This result provides some hints for the construction of a unified perspective in which the two approaches can be properly placed.

  5. A control approach for plasma density in tokamak machines

    Energy Technology Data Exchange (ETDEWEB)

    Boncagni, Luca, E-mail: luca.boncagni@enea.it [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Pucci, Daniele; Piesco, F.; Zarfati, Emanuele [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy); Mazzitelli, G. [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Monaco, S. [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy)

    2013-10-15

    Highlights: •We show a control approach for line plasma density in tokamak. •We show a control approach for pressure in a tokamak chamber. •We show experimental results using one valve. -- Abstract: In tokamak machines, chamber pre-fill is crucial to attain plasma breakdown, while plasma density control is instrumental for several tasks such as machine protection and achievement of desired plasma performances. This paper sets the principles of a new control strategy for attaining both chamber pre-fill and plasma density regulation. Assuming that the actuation mean is a piezoelectric valve driven by a varying voltage, the proposed control laws ensure convergence to reference values of chamber pressure during pre-fill, and of plasma density during plasma discharge. Experimental results at FTU are presented to discuss weaknesses and strengths of the proposed control strategy. The whole system has been implemented by using the MARTe framework [1].

  6. English to Sanskrit Machine Translation Using Transfer Based approach

    Science.gov (United States)

    Pathak, Ganesh R.; Godse, Sachin P.

    2010-11-01

    Translation is one of the needs of global society for communicating thoughts and ideas of one country with other country. Translation is the process of interpretation of text meaning and subsequent production of equivalent text, also called as communicating same meaning (message) in another language. In this paper we gave detail information on how to convert source language text in to target language text using Transfer Based Approach for machine translation. Here we implemented English to Sanskrit machine translator using transfer based approach. English is global language used for business and communication but large amount of population in India is not using and understand the English. Sanskrit is ancient language of India most of the languages in India are derived from Sanskrit. Sanskrit can be act as an intermediate language for multilingual translation.

  7. Evaluating machine learning classification for financial trading: An empirical approach

    OpenAIRE

    Gerlein, EA; McGinnity, M; Belatreche, A; Coleman, S.

    2016-01-01

    Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models s...

  8. ARABIC-MALAY MACHINE TRANSLATION USING RULE-BASED APPROACH

    Directory of Open Access Journals (Sweden)

    Ahmed Jumaa Alsaket

    2014-01-01

    Full Text Available Arabic machine translation has been taking place in machine translation projects in recent years. This study concentrates on the translation of Arabic text to its equivalent in Malay language. The problem of this research is the syntactic and morphological differences between Arabic and Malay adjective sentences. The main aim of this study is to design and develop Arabic-Malay machine translation model. First, we analyze the adjective role in the Arabic and Malay languages. Based on this analysis, we identify the transfer bilingual rules form source language to target language so that the translation of source language to target language can be performed by computers successfully. Then, we build and implement a machine translation prototype called AMTS to translate from Arabic to Malay based on rule based approach. The system is evaluated on set of simple Arabic sentences. The techniques used to evaluate the correctness of the system translation are the BLEU metric algorithm and the human judgment. The results of the BLEU algorithm show that the AMTS system performs better than Google in the translation of Arabic sentences into Malay. In addition, the average accuracy given by human judges is 92.3% for our system and 75.3% for Google.

  9. Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    Merfat.M. Altawaier

    2016-12-01

    Full Text Available With the dramatic expansion of information over internet, users around the world express their opinion daily on the social network such as Facebook and Twitter. Large corporations nowadays invest on analyzing these opinions in order to assess their products or services by knowing the people feedback toward such business. The process of knowing users’ opinions toward particular product or services whether positive or negative is called sentiment analysis. Arabic is one of the common languages that have been addressed regarding sentiment analysis. In the literature, several approaches have been proposed for Arabic sentiment analysis and most of these approaches are using machine learning techniques. Machine learning techniques are various and have different performances. Therefore, in this study, we try to identifying a simple, but workable approach for Arabic sentiment analysis on Twitter. Hence, this study aims to investigate the machine learning technique in terms of Arabic sentiment analysis on Twitter. Three techniques have been used including Naïve Bayes, Decision Tree (DT and Support Vector Machine (SVM. In addition, two simple sub-tasks pre-processing have been also used; Term Frequency-Inverse Document Frequency (TF-IDF and Arabic stemming to get the heaviest weight term as the feature for tweet classification. TF-IDF aims to identify the most frequent words, whereas stemming aims to retrieve the stem of the word by removing the inflectional derivations. The dataset that has been used is Modern Arabic Corpus which consists of Arabic tweets. The performance of classification has been evaluated based on the information retrieval metrics precision, recall and f-measure. The experimental results have shown that DT has outperformed the other techniques by obtaining 78% of f-measure.

  10. Relevance vector machine technique for the inverse scattering problem

    Institute of Scientific and Technical Information of China (English)

    Wang Fang-Fang; Zhang Ye-Rong

    2012-01-01

    A novel method based on the relevance vector machine(RVM)for the inverse scattering problem is presented in this paper.The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered.The nonlinearity is embodied in the relation between the scattered field and the target property,which can be obtained through the RVM training process.Besides,rather than utilizing regularization,the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output.Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy,convergence,robustness,generalization,and improved performance in terms of sparse property in comparison with the support vector machine(SVM)based approach.

  11. MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins.

    Science.gov (United States)

    Tamanna; Ramana, Jayashree

    2015-10-01

    The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pneumonia etc. is becoming more difficult. Hence, it is crucial to understand the underlying mechanism of drug resistance mechanism and devising novel solution to address this problem. Multidrug And Toxin Extrusion (MATE) proteins, first characterized as bacterial drug transporters, are present in almost all species. It plays a very important function in the secretion of cationic drugs across the cell membrane. In this work, we propose SVM based method for prediction of MATE proteins. The data set employed for training consists of 189 non-redundant protein sequences, that are further classified as positive (63 sequences) set comprising of sequences from MATE family, and negative (126 sequences) set having protein sequences from other transporters families proteins and random protein sequences taken from NCBI while in the test set, there are 120 protein sequences in all (8 in positive and 112 in negative set). The model was derived using Position Specific Scoring Matrix (PSSM) composition and achieved an overall accuracy 92.06%. The five-fold cross validation was used to optimize SVM parameter and select the best model. The prediction algorithm presented here is implemented as a freely available web server MATEPred, which will assist in rapid identification of MATE proteins.

  12. DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

    Science.gov (United States)

    Kim, Sang-Kyun; Nam, SeJin; Kim, SangHyun

    2017-01-28

    Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE. This research established an SVM-based classifier model to identify articles on traditional medicine. The TAK + HM classifier, trained with the features of title, abstract, keywords, herbal data, and MeSH, has a precision of 0.954 and a recall of 0.902. In particular, the feature of herbal data significantly increased the performance of the classifier. By using the TAK + HM classifier, a total of about 108,000 articles were discriminated as articles on traditional medicine from among all articles in MEDLINE. We also built a web server called DisArticle ( http://informatics.kiom.re.kr/disarticle ), in which users can search for the articles and obtain statistical data. Because much evidence-based research on traditional medicine has been published in recent years, it has become necessary to search for articles on traditional medicine exclusively in literature databases. DisArticle can help users to search for and analyze the research trends in traditional medicine.

  13. Machine Learning Approaches: From Theory to Application in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Elisa Veronese

    2013-01-01

    Full Text Available In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

  14. TSMC: A Novel Approach for Live Virtual Machine Migration

    Directory of Open Access Journals (Sweden)

    Jiaxing Song

    2014-01-01

    Full Text Available Cloud computing attracted more and more attention in recent years, and virtualization technology is the key point for deploying infrastructure services in cloud environment. It allows application isolation and facilitates server consolidation, load balancing, fault management, and power saving. Live virtual machine migration can effectively relocate virtual resources and it has become an important management method in clusters and data centers. Existing precopy live migration approach has to iteratively copy redundant memory pages; another postcopy live migration approach would lead to a lot of page faults and application degradation. In this paper, we present a novel approach called TSMC (three-stage memory copy for live virtual machine migration. In TSMC, memory pages only need to be transmitted twice at most and page fault just occurred in small part of dirty pages. We implement it in Xen and compare it with Xen’s original precopy approach. The experimental results under various memory workloads show that TSMC approach can significantly reduce the cumulative migration time and total pages transferred and achieve better network IO performance in the same time.

  15. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  16. Amp: A modular approach to machine learning in atomistic simulations

    Science.gov (United States)

    Khorshidi, Alireza; Peterson, Andrew A.

    2016-10-01

    Electronic structure calculations, such as those employing Kohn-Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties-namely that they are noiseless and targeted training data can be produced on-demand-that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which

  17. Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

    Directory of Open Access Journals (Sweden)

    Ruizhi Chen

    2012-05-01

    Full Text Available The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.

  18. Using LS-SVM based motion recognition for smartphone indoor wireless positioning.

    Science.gov (United States)

    Pei, Ling; Liu, Jingbin; Guinness, Robert; Chen, Yuwei; Kuusniemi, Heidi; Chen, Ruizhi

    2012-01-01

    The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in "Static Tests" and a 3.53 m in "Stop-Go Tests".

  19. Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems.

    Science.gov (United States)

    Yin, Zhendong; Cui, Kai; Wu, Zhilu; Yin, Liang

    2015-05-21

    The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

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

    Directory of Open Access Journals (Sweden)

    Lhoucine Bahatti

    2016-05-01

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

  1. Geminivirus data warehouse: a database enriched with machine learning approaches.

    Science.gov (United States)

    Silva, Jose Cleydson F; Carvalho, Thales F M; Basso, Marcos F; Deguchi, Michihito; Pereira, Welison A; Sobrinho, Roberto R; Vidigal, Pedro M P; Brustolini, Otávio J B; Silva, Fabyano F; Dal-Bianco, Maximiller; Fontes, Renildes L F; Santos, Anésia A; Zerbini, Francisco Murilo; Cerqueira, Fabio R; Fontes, Elizabeth P B

    2017-05-05

    The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics. Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes. The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.

  2. Urdu to Punjabi Machine Translation: An Incremental Training Approach

    Directory of Open Access Journals (Sweden)

    Umrinderpal Singh

    2016-04-01

    Full Text Available The statistical machine translation approach is highly popular in automatic translation research area and promising approach to yield good accuracy. Efforts have been made to develop Urdu to Punjabi statistical machine translation system. The system is based on an incremental training approach to train the statistical model. In place of the parallel sentences corpus has manually mapped phrases which were used to train the model. In preprocessing phase, various rules were used for tokenization and segmentation processes. Along with these rules, text classification system was implemented to classify input text to predefined classes and decoder translates given text according to selected domain by the text classifier. The system used Hidden Markov Model(HMM for the learning process and Viterbi algorithm has been used for decoding. Experiment and evaluation have shown that simple statistical model like HMM yields good accuracy for a closely related language pair like Urdu-Punjabi. The system has achieved 0.86 BLEU score and in manual testing and got more than 85% accuracy.

  3. A Simple and General Approach to Determination of Self and Mutual Inductances for AC machines

    DEFF Research Database (Denmark)

    Lu, Kaiyuan; Rasmussen, Peter Omand; Ritchie, Ewen

    2011-01-01

    Modelling of AC electrical machines plays an important role in electrical engineering education related to electrical machine design and control. One of the fundamental requirements in AC machine modelling is to derive the self and mutual inductances, which could be position dependant. Theories...... developed so far for inductance determination are often associated with complicated machine magnetic field analysis, which exhibits a difficulty for most students. This paper describes a simple and general approach to the determination of self and mutual inductances of different types of AC machines. A new...... determination are given for a 3-phase, salient-pole synchronous machine, and an induction machine....

  4. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    Science.gov (United States)

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  5. Predicting DPP-IV inhibitors with machine learning approaches

    Science.gov (United States)

    Cai, Jie; Li, Chanjuan; Liu, Zhihong; Du, Jiewen; Ye, Jiming; Gu, Qiong; Xu, Jun

    2017-04-01

    Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV inhibitors are structurally diversified. This makes it difficult to build virtual screening models based upon the known DPP-IV inhibitor libraries using conventional QSAR approaches. In this paper, we report a new strategy to predict DPP-IV inhibitors with machine learning approaches involving naïve Bayesian (NB) and recursive partitioning (RP) methods. We built 247 machine learning models based on 1307 known DPP-IV inhibitors with optimized molecular properties and topological fingerprints as descriptors. The overall predictive accuracies of the optimized models were greater than 80%. An external test set, composed of 65 recently reported compounds, was employed to validate the optimized models. The results demonstrated that both NB and RP models have a good predictive ability based on different combinations of descriptors. Twenty "good" and twenty "bad" structural fragments for DPP-IV inhibitors can also be derived from these models for inspiring the new DPP-IV inhibitor scaffold design.

  6. Predicting DPP-IV inhibitors with machine learning approaches

    Science.gov (United States)

    Cai, Jie; Li, Chanjuan; Liu, Zhihong; Du, Jiewen; Ye, Jiming; Gu, Qiong; Xu, Jun

    2017-02-01

    Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV inhibitors are structurally diversified. This makes it difficult to build virtual screening models based upon the known DPP-IV inhibitor libraries using conventional QSAR approaches. In this paper, we report a new strategy to predict DPP-IV inhibitors with machine learning approaches involving naïve Bayesian (NB) and recursive partitioning (RP) methods. We built 247 machine learning models based on 1307 known DPP-IV inhibitors with optimized molecular properties and topological fingerprints as descriptors. The overall predictive accuracies of the optimized models were greater than 80%. An external test set, composed of 65 recently reported compounds, was employed to validate the optimized models. The results demonstrated that both NB and RP models have a good predictive ability based on different combinations of descriptors. Twenty "good" and twenty "bad" structural fragments for DPP-IV inhibitors can also be derived from these models for inspiring the new DPP-IV inhibitor scaffold design.

  7. A machine learning approach to nonlinear modal analysis

    Science.gov (United States)

    Worden, K.; Green, P. L.

    2017-02-01

    Although linear modal analysis has proved itself to be the method of choice for the analysis of linear dynamic structures, its extension to nonlinear structures has proved to be a problem. A number of competing viewpoints on nonlinear modal analysis have emerged, each of which preserves a subset of the properties of the original linear theory. From the geometrical point of view, one can argue that the invariant manifold approach of Shaw and Pierre is the most natural generalisation. However, the Shaw-Pierre approach is rather demanding technically, depending as it does on the analytical construction of a mapping between spaces, which maps physical coordinates into invariant manifolds spanned by independent subsets of variables. The objective of the current paper is to demonstrate a data-based approach motivated by Shaw-Pierre method which exploits the idea of statistical independence to optimise a parametric form of the mapping. The approach can also be regarded as a generalisation of the Principal Orthogonal Decomposition (POD). A machine learning approach to inversion of the modal transformation is presented, based on the use of Gaussian processes, and this is equivalent to a nonlinear form of modal superposition. However, it is shown that issues can arise if the forward transformation is a polynomial and can thus have a multi-valued inverse. The overall approach is demonstrated using a number of case studies based on both simulated and experimental data.

  8. A new DFM approach to combine machining and additive manufacturing

    CERN Document Server

    Kerbrat, Olivier; Hascoët, Jean-Yves; 10.1016/j.compind.2011.04.003

    2011-01-01

    Design For Manufacturing (DFM) approaches aim to integrate manufacturability aspects during the design stage. Most of DFM approaches usually consider only one manufacturing process, but products competitiveness may be improved by designing hybrid modular products, in which products are seen as 3-D puzzles with modules realized aside by the best manufacturing process and further gathered. A new DFM system is created in order to give quantitative information during the product design stage of which modules will benefit in being machined and which ones will advantageously be realized by an additive process (such as Selective Laser Sintering or laser deposition). A methodology for a manufacturability evaluation in case of a subtractive or an additive manufacturing process is developed and implemented in a CAD software. Tests are carried out on industrial products from automotive industry.

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

    Science.gov (United States)

    Hua, S; Sun, Z

    2001-08-01

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

  10. AREA DETERMINATION OF DIABETIC FOOT ULCER IMAGES USING A CASCADED TWO-STAGE SVM BASED CLASSIFICATION.

    Science.gov (United States)

    Wang, Lei; Pedersen, Peder; Agu, Emmanuel; Strong, Diane; Tulu, Bengisu

    2016-11-23

    It is standard practice for clinicians and nurses to primarily assess patients' wounds via visual examination. This subjective method can be inaccurate in wound assessment and also represents a significant clinical workload. Hence, computer-based systems, especially implemented on mobile devices, can provide automatic, quantitative wound assessment and can thus be valuable for accurately monitoring wound healing status. Out of all wound assessment parameters, the measurement of the wound area is the most suitable for automated analysis. Most of the current wound boundary determination methods only process the image of the wound area along with a small amount of surrounding healthy skin. In this paper, we present a novel approach that uses Support Vector Machine (SVM) to determine the wound boundary on a foot ulcer image captured with an image capture box, which provides controlled lighting, angle and range conditions. The Simple Linear Iterative Clustering (SLIC) method is applied for effective super-pixel segmentation. A cascaded two-stage classifier is trained as follows: in the first stage a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and a set of incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from super-pixels that are used as input for each stage in the classifier training. Specifically, we apply the color and Bag-of-Word (BoW) representation of local Dense SIFT features (DSIFT) as the descriptor for ruling out irrelevant regions (first stage), and apply color and wavelet based features as descriptors for distinguishing healthy tissue from wound regions (second stage). Finally, the detected wound boundary is refined by applying a Conditional Random Field (CRF) image processing technique. We have implemented the wound classification on a Nexus

  11. A Cooperative Approach to Virtual Machine Based Fault Injection

    Energy Technology Data Exchange (ETDEWEB)

    Naughton III, Thomas J [ORNL; Engelmann, Christian [ORNL; Vallee, Geoffroy R [ORNL; Aderholdt, William Ferrol [ORNL; Scott, Stephen L [Tennessee Technological University (TTU)

    2017-01-01

    Resilience investigations often employ fault injection (FI) tools to study the effects of simulated errors on a target system. It is important to keep the target system under test (SUT) isolated from the controlling environment in order to maintain control of the experiement. Virtual machines (VMs) have been used to aid these investigations due to the strong isolation properties of system-level virtualization. A key challenge in fault injection tools is to gain proper insight and context about the SUT. In VM-based FI tools, this challenge of target con- text is increased due to the separation between host and guest (VM). We discuss an approach to VM-based FI that leverages virtual machine introspection (VMI) methods to gain insight into the target s context running within the VM. The key to this environment is the ability to provide basic information to the FI system that can be used to create a map of the target environment. We describe a proof- of-concept implementation and a demonstration of its use to introduce simulated soft errors into an iterative solver benchmark running in user-space of a guest VM.

  12. Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach.

    Science.gov (United States)

    García-Nieto, P J; García-Gonzalo, E; Alonso Fernández, J R; Díaz Muñiz, C

    2017-07-15

    Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes and deterioration of water quality and all its uses in general, when the production of algae and other aquatic vegetations are increased. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on support vector machines (SVM) approach in combination with the particle swarm optimization (PSO) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the SVM training procedure, which significantly influences the regression accuracy. The results of the present study are twofold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.90 for the Total phosphorus estimation and 0.92 for the Chlorophyll concentration were obtained when this hybrid PSO-SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

  13. Detection of Alzheimer's disease using group lasso SVM-based region selection

    Science.gov (United States)

    Sun, Zhuo; Fan, Yong; Lelieveldt, Boudewijn P. F.; van de Giessen, Martijn

    2015-03-01

    Alzheimer's disease (AD) is one of the most frequent forms of dementia and an increasing challenging public health problem. In the last two decades, structural magnetic resonance imaging (MRI) has shown potential in distinguishing patients with Alzheimer's disease and elderly controls (CN). To obtain AD-specific biomarkers, previous research used either statistical testing to find statistically significant different regions between the two clinical groups, or l1 sparse learning to select isolated features in the image domain. In this paper, we propose a new framework that uses structural MRI to simultaneously distinguish the two clinical groups and find the bio-markers of AD, using a group lasso support vector machine (SVM). The group lasso term (mixed l1- l2 norm) introduces anatomical information from the image domain into the feature domain, such that the resulting set of selected voxels are more meaningful than the l1 sparse SVM. Because of large inter-structure size variation, we introduce a group specific normalization factor to deal with the structure size bias. Experiments have been performed on a well-designed AD vs. CN dataset1 to validate our method. Comparing to the l1 sparse SVM approach, our method achieved better classification performance and a more meaningful biomarker selection. When we vary the training set, the selected regions by our method were more stable than the l1 sparse SVM. Classification experiments showed that our group normalization lead to higher classification accuracy with fewer selected regions than the non-normalized method. Comparing to the state-of-art AD vs. CN classification methods, our approach not only obtains a high accuracy with the same dataset, but more importantly, we simultaneously find the brain anatomies that are closely related to the disease.

  14. Extracting meaning from audio signals - a machine learning approach

    DEFF Research Database (Denmark)

    Larsen, Jan

    2007-01-01

    * Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression......* Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression...

  15. Extracting meaning from audio signals - a machine learning approach

    DEFF Research Database (Denmark)

    Larsen, Jan

    2007-01-01

    * Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression......* Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression...

  16. Topic-aware pivot language approach for statistical machine translation

    Institute of Scientific and Technical Information of China (English)

    Jin-song SU; Xiao-dong SHI; Yan-zhou HUANG; Yang LIU; Qing-qiang WU; Yi-dong CHEN; Huai-lin DONG

    2014-01-01

    The pivot language approach for statistical machine translation (SMT) is a good method to break the resource bottleneck for certain language pairs. However, in the implementation of conventional approaches, pivot-side context information is far from fully utilized, resulting in erroneous estimations of translation probabilities. In this study, we propose two topic-aware pivot language approaches to use different levels of pivot-side context. The fi rst method takes advantage of document-level context by assuming that the bridged phrase pairs should be similar in the document-level topic distributions. The second method focuses on the effect of local context. Central to this approach are that the phrase sense can be refl ected by local context in the form of probabilistic topics, and that bridged phrase pairs should be compatible in the latent sense distributions. Then, we build an interpolated model bringing the above methods together to further enhance the system performance. Experimental results on French-Spanish and French-German translations using English as the pivot language demonstrate the effectiveness of topic-based context in pivot-based SMT.

  17. A discourse based approach in text-based machine translation

    CERN Document Server

    Ullah, Sana; Kwak, Kyung Sup

    2009-01-01

    This paper presents a theoretical research based approach to ellipsis resolution in machine translation. Moreover, the formula of discourse is applied in order to resolve ellipses. The validity of the discourse formula is analyzed by applying it to the real world text i.e. newspaper fragments. The source text is converted into mono-sentential discourses where complex discourses require further dissection either directly into primitive discourses or first into compound discourses and later into primitive ones. The procedure of dissection needs further improvement i.e. discovering as many primitive discourse forms as possible [1]. This work is further improvement to the concepts presented by Khan (Khan, 1995). Likewise, an attempt has been made to investigate new primitive discourses i.e. patterns from the given text.

  18. Machine Learning Approaches for Predicting Protein Complex Similarity.

    Science.gov (United States)

    Farhoodi, Roshanak; Akbal-Delibas, Bahar; Haspel, Nurit

    2017-01-01

    Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g., Van der Waals, electrostatic, and desolvation forces) and the similarity of a conformation to its native structure, the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods, producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods, we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network, a multilayer neural network, and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small, often less than 1.5 Å, error margin when trained with structures that have RMSD values of up to 7 Å. In our most recent experiments with the protein samples having RMSD values up to 27 Å, the average prediction error was still relatively small, attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.

  19. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

    OpenAIRE

    Cho, Kyunghyun; van Merrienboer, Bart; Bahdanau, Dzmitry; Bengio, Yoshua

    2014-01-01

    Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and...

  20. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  1. Linear SVM-Based Android Malware Detection for Reliable IoT Services

    Directory of Open Access Journals (Sweden)

    Hyo-Sik Ham

    2014-01-01

    Full Text Available Current many Internet of Things (IoT services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

  2. A Vectorial modeling for the pentaphase Permanent Magnet Synchronous Machine based on multimachine approach

    Directory of Open Access Journals (Sweden)

    Abdelkrim Sellam

    2012-12-01

    Full Text Available The polyphase [1] machines are developed mainly in the field of variable speed drives of high power because increasing the number of phases on the one hand allows to reduce the dimensions of the components in power modulators energy and secondly to improve the operating safety. By a vector approach (vector space, it is possible to find a set of single-phase machine and / or two-phase fictitious equivalent to polyphase synchronous machine.These fictitious machines are coupled electrically and mechanically but decoupled magnetically. This approach leads to introduce the concept of the equivalent machine (multimachine multiconverter system MMS which aims to analyze systems composed of multiple machines (or multiple converters in electric drives. A first classification multimachine multiconverter system follows naturally from MMS formalism. We present an example of a synchronous machine pent phase.

  3. A Simple and General Approach to Determination of Self and Mutual Inductances for AC machines

    DEFF Research Database (Denmark)

    Lu, Kaiyuan; Rasmussen, Peter Omand; Ritchie, Ewen

    2011-01-01

    Modelling of AC electrical machines plays an important role in electrical engineering education related to electrical machine design and control. One of the fundamental requirements in AC machine modelling is to derive the self and mutual inductances, which could be position dependant. Theories...... developed so far for inductance determination are often associated with complicated machine magnetic field analysis, which exhibits a difficulty for most students. This paper describes a simple and general approach to the determination of self and mutual inductances of different types of AC machines. A new...

  4. SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

    Science.gov (United States)

    Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

  5. Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    for Support Vector Machines. However, good generalization may be recovered in part by a simple renormalization procedure. We show that with proper renormalization, cross-validation based parameter optimization leads to the acceptance of more non-linearity in neuroimage classifiers than would have been...

  6. PRISMA database machine: A distributed, main-memory approach

    NARCIS (Netherlands)

    Schmidt, J.W.; Apers, Peter M.G.; Ceri, S.; Kersten, Martin L.; Oerlemans, Hans C.M.; Missikoff, M.

    1988-01-01

    The PRISMA project is a large-scale research effort in the design and implementation of a highly parallel machine for data and knowledge processing. The PRISMA database machine is a distributed, main-memory database management system implemented in an object-oriented language that runs on top of a m

  7. PRISMA database machine: A distributed, main-memory approach

    NARCIS (Netherlands)

    Apers, Peter M.G.; Kersten, Martin L.; Oerlemans, Hans C.M.; Schmidt, J.W.; Ceri, S.; Missikoff, M.

    1988-01-01

    The PRISMA project is a large-scale research effort in the design and implementation of a highly parallel machine for data and knowledge processing. The PRISMA database machine is a distributed, main-memory database management system implemented in an object-oriented language that runs on top of a m

  8. SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis

    CERN Document Server

    Kisku, Dakshina Ranjan; Sing, Jamuna Kanta; Gupta, Phalguni

    2010-01-01

    Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set o...

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

    African Journals Online (AJOL)

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

  10. Machine learning for adaptive many-core machines a practical approach

    CERN Document Server

    Lopes, Noel

    2015-01-01

    The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.

  11. Dual Numbers Approach in Multiaxis Machines Error Modeling

    Directory of Open Access Journals (Sweden)

    Jaroslav Hrdina

    2014-01-01

    Full Text Available Multiaxis machines error modeling is set in the context of modern differential geometry and linear algebra. We apply special classes of matrices over dual numbers and propose a generalization of such concept by means of general Weil algebras. We show that the classification of the geometric errors follows directly from the algebraic properties of the matrices over dual numbers and thus the calculus over the dual numbers is the proper tool for the methodology of multiaxis machines error modeling.

  12. EQUIVALENT NORMAL CURVATURE APPROACH MILLING MODEL OF MACHINING FREEFORM SURFACES

    Institute of Scientific and Technical Information of China (English)

    YI Xianzhong; MA Weiguo; QI Haiying; YAN Zesheng; GAO Deli

    2008-01-01

    A new milling methodology with the equivalent normal curvature milling model machining freeform surfaces is proposed based on the normal curvature theorems on differential geometry. Moreover, a specialized whirlwind milling tool and a 5-axis CNC horizontal milling machine are introduced. This new milling model can efficiently enlarge the material removal volume at the tip of the whirlwind milling tool and improve the producing capacity. The machining strategy of this model is to regulate the orientation of the whirlwind milling tool relatively to the principal directions of the workpiece surface at the point of contact, so as to create a full match with collision avoidance between the workpiece surface and the symmetric rotational surface of the milling tool. The practical results show that this new milling model is an effective method in machining complex three- dimensional surfaces. This model has a good improvement on finishing machining time and scallop height in machining the freeform surfaces over other milling processes. Some actual examples for manufacturing the freeform surfaces with this new model are given.

  13. Crack identification for rotating machines based on a nonlinear approach

    Science.gov (United States)

    Cavalini, A. A., Jr.; Sanches, L.; Bachschmid, N.; Steffen, V., Jr.

    2016-10-01

    In a previous contribution, a crack identification methodology based on a nonlinear approach was proposed. The technique uses external applied diagnostic forces at certain frequencies attaining combinational resonances, together with a pseudo-random optimization code, known as Differential Evolution, in order to characterize the signatures of the crack in the spectral responses of the flexible rotor. The conditions under which combinational resonances appear were determined by using the method of multiple scales. In real conditions, the breathing phenomenon arises from the stress and strain distribution on the cross-sectional area of the crack. This mechanism behavior follows the static and dynamic loads acting on the rotor. Therefore, the breathing crack can be simulated according to the Mayes' model, in which the crack transition from fully opened to fully closed is described by a cosine function. However, many contributions try to represent the crack behavior by machining a small notch on the shaft instead of the fatigue process. In this paper, the open and breathing crack models are compared regarding their dynamic behavior and the efficiency of the proposed identification technique. The additional flexibility introduced by the crack is calculated by using the linear fracture mechanics theory (LFM). The open crack model is based on LFM and the breathing crack model corresponds to the Mayes' model, which combines LFM with a given breathing mechanism. For illustration purposes, a rotor composed by a horizontal flexible shaft, two rigid discs, and two self-aligning ball bearings is used to compose a finite element model of the system. Then, numerical simulation is performed to determine the dynamic behavior of the rotor. Finally, the results of the inverse problem conveyed show that the methodology is a reliable tool that is able to estimate satisfactorily the location and depth of the crack.

  14. Classification of EEG data using FHT and SVM based on Bayesian Network

    Directory of Open Access Journals (Sweden)

    V. Baby Deepa

    2011-09-01

    Full Text Available Brain Computer Interface (BCI enables the capturing and processing of motor imagery related brain signals which can be interpreted by computers. BCI systems capture the motor imagery signals via Electroencephalogram or Electrocorticogram. The processing of the signal is usually attempted by extracting feature vectors in the frequency domain and using classification algorithms to interpret the motor imagery action. In this paper we investigate the motor imagery signals obtained from BCI competition dataset IVA using the Fast Hartley Transform (FHT for feature vector extraction and feature reduction using support vector machine. The processed data is trained and classified using the Bayes Net.

  15. Methodology for selection of attributes and operating conditions for SVM-Based fault locator's

    Directory of Open Access Journals (Sweden)

    Debbie Johan Arredondo Arteaga

    2017-01-01

    Full Text Available Context: Energy distribution companies must employ strategies to meet their timely and high quality service, and fault-locating techniques represent and agile alternative for restoring the electric service in the power distribution due to the size of distribution services (generally large and the usual interruptions in the service. However, these techniques are not robust enough and present some limitations in both computational cost and the mathematical description of the models they use. Method: This paper performs an analysis based on a Support Vector Machine for the evaluation of the proper conditions to adjust and validate a fault locator for distribution systems; so that it is possible to determine the minimum number of operating conditions that allow to achieve a good performance with a low computational effort. Results: We tested the proposed methodology in a prototypical distribution circuit, located in a rural area of Colombia. This circuit has a voltage of 34.5 KV and is subdivided in 20 zones. Additionally, the characteristics of the circuit allowed us to obtain a database of 630.000 records of single-phase faults and different operating conditions. As a result, we could determine that the locator showed a performance above 98% with 200 suitable selected operating conditions. Conclusions: It is possible to improve the performance of fault locators based on Support Vector Machine. Specifically, these improvements are achieved by properly selecting optimal operating conditions and attributes, since they directly affect the performance in terms of efficiency and the computational cost.

  16. An SVM-based solution for fault detection in wind turbines.

    Science.gov (United States)

    Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

    2015-03-09

    Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

  17. An SVM-Based Solution for Fault Detection in Wind Turbines

    Directory of Open Access Journals (Sweden)

    Pedro Santos

    2015-03-01

    Full Text Available Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

  18. Data Triage of Astronomical Transients: A Machine Learning Approach

    Science.gov (United States)

    Rebbapragada, U.

    This talk presents real-time machine learning systems for triage of big data streams generated by photometric and image-differencing pipelines. Our first system is a transient event detection system in development for the Palomar Transient Factory (PTF), a fully-automated synoptic sky survey that has demonstrated real-time discovery of optical transient events. The system is tasked with discriminating between real astronomical objects and bogus objects, which are usually artifacts of the image differencing pipeline. We performed a machine learning forensics investigation on PTF’s initial system that led to training data improvements that decreased both false positive and negative rates. The second machine learning system is a real-time classification engine of transients and variables in development for the Australian Square Kilometre Array Pathfinder (ASKAP), an upcoming wide-field radio survey with unprecedented ability to investigate the radio transient sky. The goal of our system is to classify light curves into known classes with as few observations as possible in order to trigger follow-up on costlier assets. We discuss the violation of standard machine learning assumptions incurred by this task, and propose the use of ensemble and hierarchical machine learning classifiers that make predictions most robustly.

  19. Machine learning approaches in medical image analysis: From detection to diagnosis

    NARCIS (Netherlands)

    M. de Bruijne (Marleen)

    2016-01-01

    textabstractMachine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging

  20. Machine learning approaches in medical image analysis: From detection to diagnosis.

    Science.gov (United States)

    de Bruijne, Marleen

    2016-10-01

    Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

  1. A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

    Directory of Open Access Journals (Sweden)

    Hongzhuan Zhao

    2016-04-01

    Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

  2. Component Content Soft-Sensor of SVM Based on Ions Color Characteristics

    Directory of Open Access Journals (Sweden)

    Zhang Kunpeng

    2012-10-01

    Full Text Available In consideration of different characteristic colors of Ions in the P507-HCL Pr/Nd extraction separation system, ions color image feature H, S, I that closely related to the element component contents are extracted by using image processing method. Principal Component Analysis algorithm is employed to determine statistics mean of H, S, I which has the stronger correlation with element component content and the auxiliary variables are obtained. With the algorithm of support vector machine, a component contents soft-sensor model in Pr/Nd extraction process is established. Finally, simulations and tests verify the rationality and feasibility of the proposed method. The research results provide theoretical foundation for the online measurement of the component content in Pr/Nd countercurrent extraction separation process.

  3. A SVM-based method for sentiment analysis in Persian language

    Science.gov (United States)

    Hajmohammadi, Mohammad Sadegh; Ibrahim, Roliana

    2013-03-01

    Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

  4. Feature Selection By KDDA For SVM-Based MultiView Face Recognition

    CERN Document Server

    Valiollahzadeh, Seyyed Majid; Nazari, Mohammad

    2008-01-01

    Applications such as face recognition that deal with high-dimensional data need a mapping technique that introduces representation of low-dimensional features with enhanced discriminatory power and a proper classifier, able to classify those complex features. Most of traditional Linear Discriminant Analysis suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" problem which is often encountered in FR tasks. In this short paper, we combine nonlinear kernel based mapping of data called KDDA with Support Vector machine classifier to deal with both of the shortcomings in an efficient and cost effective manner. The proposed here method is compared, in terms of classification accuracy, to other commonly used FR methods on UMIST face database. Results indicate that the performance of the proposed method is overall superior to those of trad...

  5. Machine Vision for Relative Spacecraft Navigation During Approach to Docking

    Science.gov (United States)

    Chien, Chiun-Hong; Baker, Kenneth

    2011-01-01

    This paper describes a machine vision system for relative spacecraft navigation during the terminal phase of approach to docking that: 1) matches high contrast image features of the target vehicle, as seen by a camera that is bore-sighted to the docking adapter on the chase vehicle, to the corresponding features in a 3d model of the docking adapter on the target vehicle and 2) is robust to on-orbit lighting. An implementation is provided for the case of the Space Shuttle Orbiter docking to the International Space Station (ISS) with quantitative test results using a full scale, medium fidelity mock-up of the ISS docking adapter mounted on a 6-DOF motion platform at the NASA Marshall Spaceflight Center Flight Robotics Laboratory and qualitative test results using recorded video from the Orbiter Docking System Camera (ODSC) during multiple orbiter to ISS docking missions. The Natural Feature Image Registration (NFIR) system consists of two modules: 1) Tracking which tracks the target object from image to image and estimates the position and orientation (pose) of the docking camera relative to the target object and 2) Acquisition which recognizes the target object if it is in the docking camera Field-of-View and provides an approximate pose that is used to initialize tracking. Detected image edges are matched to the 3d model edges whose predicted location, based on the pose estimate and its first time derivative from the previous frame, is closest to the detected edge1 . Mismatches are eliminated using a rigid motion constraint. The remaining 2d image to 3d model matches are used to make a least squares estimate of the change in relative pose from the previous image to the current image. The changes in position and in attitude are used as data for two Kalman filters whose outputs are smoothed estimate of position and velocity plus attitude and attitude rate that are then used to predict the location of the 3d model features in the next image.

  6. Extracting Information from Spoken User Input. A Machine Learning Approach

    NARCIS (Netherlands)

    Lendvai, P.K.

    2004-01-01

    We propose a module that performs automatic analysis of user input in spoken dialogue systems using machine learning algorithms. The input to the module is material received from the speech recogniser and the dialogue manager of the spoken dialogue system, the output is a four-level

  7. A New Approach For FEM Simulation of NC Machining Processes

    Science.gov (United States)

    Wang, Sheng Ping; Padmanaban, Shivakumar

    2004-06-01

    The paper describes a new method for a finite element based pseudo-simulation of Numerically Controlled (NC) machining (material removal) processes. Industrial machining of a component usually results in warping or distortion due to the re-establishment of equilibrium in the retained part along with the relief of the insitu residual stresses in the removed part. In many cases, these distortions can be so large that the part may no longer be able to serve its designated functionality. Considering that the machining process is fundamentally a material removal process, a new method based on an automated removal of finite elements in the cutting area has been developed in the finite element analysis (FEA) software MSC.Marc to conduct pseudo-simulation of the NC machining process., A number of key software enhancements have been made to facilitate the pseudo-simulation of the NC machining process. First, a seamless interface has been developed to import APT/CL data generated by CAD/CAM systems. Then, the cutting paths have been generated based on information in the APT/CL files and used for the automatic detection of the intersection between the cutter and the finite element mesh. With each incremental motion of the cutter, the FEA solver detects all the elements that are located within the cutting path. Such elements are then deactivated in a step-by-step manner that is consistent with the actual machining process. In order to improve the fidelity of the cut area, local adaptive mesh refinement in the vicinity of the cutting tool is undertaken. This enables relatively coarser meshes away from the cut area and provides more accurate representation of the actual volume that is removed. As demonstrated by an industrial example, the enhanced software features in MSC.Marc have made it possible to practically and efficiently analyze complex machining processes of 3D production parts and provide an elegant tool for predicting distortions in large structures due to the relief

  8. A Review on Approaches for Condition Based Maintenance in Applications with Induction Machines located Offshore

    Directory of Open Access Journals (Sweden)

    J. Cibulka

    2012-04-01

    Full Text Available This paper presents a review of different approaches for Condition Based Maintenance (CBM of induction machines and drive trains in offshore applications. The paper contains an overview of common failure modes, monitoring techniques, approaches for diagnostics, and an overview of typical maintenance actions. Although many papers have been written in this area before, this paper puts an emphasis on recent developments and limits the scope to induction machines and drive trains applied in applications located offshore.

  9. A Machine Learning Approach for the Identification of Bengali Noun-Noun Compound Multiword Expressions

    OpenAIRE

    Gayen, Vivekananda; Sarkar, Kamal

    2014-01-01

    This paper presents a machine learning approach for identification of Bengali multiword expressions (MWE) which are bigram nominal compounds. Our proposed approach has two steps: (1) candidate extraction using chunk information and various heuristic rules and (2) training the machine learning algorithm called Random Forest to classify the candidates into two groups: bigram nominal compound MWE or not bigram nominal compound MWE. A variety of association measures, syntactic and linguistic clue...

  10. Online Adaptive Error Compensation SVM-Based Sliding Mode Control of an Unmanned Aerial Vehicle

    Directory of Open Access Journals (Sweden)

    Kaijia Xue

    2016-01-01

    Full Text Available Unmanned Aerial Vehicle (UAV is a nonlinear dynamic system with uncertainties and noises. Therefore, an appropriate control system has an obligation to ensure the stabilization and navigation of UAV. This paper mainly discusses the control problem of quad-rotor UAV system, which is influenced by unknown parameters and noises. Besides, a sliding mode control based on online adaptive error compensation support vector machine (SVM is proposed for stabilizing quad-rotor UAV system. Sliding mode controller is established through analyzing quad-rotor dynamics model in which the unknown parameters are computed by offline SVM. During this process, the online adaptive error compensation SVM method is applied in this paper. As modeling errors and noises both exist in the process of flight, the offline SVM one-time mode cannot predict the uncertainties and noises accurately. The control law is adjusted in real-time by introducing new training sample data to online adaptive SVM in the control process, so that the stability and robustness of flight are ensured. It can be demonstrated through the simulation experiments that the UAV that joined online adaptive SVM can track the changing path faster according to its dynamic model. Consequently, the proposed method that is proved has the better control effect in the UAV system.

  11. EHPred: an SVM-based method for epoxide hydrolases recognition and classification

    Institute of Scientific and Technical Information of China (English)

    JIA Jia; YANG Liang; ZHANG Zi-zhang

    2006-01-01

    A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM classifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew,s correlation coefficient (MCC) of 0.84.Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.

  12. SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

    Science.gov (United States)

    Suresh, V; Parthasarathy, S

    2014-01-01

    We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

  13. Deriving statistical significance maps for SVM based image classification and group comparisons.

    Science.gov (United States)

    Gaonkar, Bilwaj; Davatzikos, Christos

    2012-01-01

    Population based pattern analysis and classification for quantifying structural and functional differences between diverse groups has been shown to be a powerful tool for the study of a number of diseases, and is quite commonly used especially in neuroimaging. The alternative to these pattern analysis methods, namely mass univariate methods such as voxel based analysis and all related methods, cannot detect multivariate patterns associated with group differences, and are not particularly suitable for developing individual-based diagnostic and prognostic biomarkers. A commonly used pattern analysis tool is the support vector machine (SVM). Unlike univariate statistical frameworks for morphometry, analytical tools for statistical inference are unavailable for the SVM. In this paper, we show that null distributions ordinarily obtained by permutation tests using SVMs can be analytically approximated from the data. The analytical computation takes a small fraction of the time it takes to do an actual permutation test, thereby rendering it possible to quickly create statistical significance maps derived from SVMs. Such maps are critical for understanding imaging patterns of group differences and interpreting which anatomical regions are important in determining the classifier's decision.

  14. SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy.

    Directory of Open Access Journals (Sweden)

    Suhang Chen

    Full Text Available Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs. In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.

  15. SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy.

    Science.gov (United States)

    Chen, Suhang; Chang, Sheng; Huang, Qijun; He, Jin; Wang, Hao; Huang, Qiangui

    2014-01-01

    Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.

  16. On the Use of Time–Frequency Reassignment and SVM-Based Classifier for Audio Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Souli S. Sameh

    2014-11-01

    Full Text Available In this paper, we propose a robust environmental sound spectrogram classification approach. Its purpose is surveillance and security applications based on the reassignment method and log-Gabor filters. Besides, the reassignment method is applied to the spectrogram to improve the readability of the time-frequency representation, and to assure a better localization of the signal components. Our approach includes three methods. In the first two methods, the reassigned spectrograms are passed through appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criterion. The third method uses the same steps but applied only to three patches extracted from each reassigned spectrogram. The proposed approach is tested on a large database consists of 1000 sounds belonging to ten classes. The recognition is based on Multiclass Support Vector Machines.

  17. Time-series prediction and applications a machine intelligence approach

    CERN Document Server

    Konar, Amit

    2017-01-01

    This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

  18. Machine Learning Approaches for analysis of League of Legends

    OpenAIRE

    Janežič, Simon

    2016-01-01

    Our goal is to use machine learning for predicting winners of League of Legends matches. League of Legends is a multiplayer game that combines elements from strategic and action games. Every year, multiple professional League of Legends competitions are being held acros the globe. We try to predict both professional and non-professional matches. Getting data for both types of matches is already a challenge. For non-professional matches official application programming interface is used, whil...

  19. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

    OpenAIRE

    Shi, Xingjian; Chen, Zhourong; Wang, Hao; Yeung, Dit-Yan; Wong, Wai-Kin; Woo, Wang-chun

    2015-01-01

    The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (F...

  20. Approaches to handle scarce resources for Bengali statistical machine translation

    OpenAIRE

    Roy, Maxim

    2010-01-01

    Machine translation (MT) is a hard problem because of the highly complex, irregular and diverse nature of natural language. MT refers to computerized systems that utilize software to translate text from one natural language into another with or without human assistance. It is impossible to accurately model all the linguistic rules and relationships that shape the translation process, and therefore MT has to make decisions based on incomplete data. In order to handle this incomplete data, a pr...

  1. SVM-based base-metal prospectivity modeling of the Aravalli Orogen, Northwestern India

    Science.gov (United States)

    Porwal, Alok; Yu, Le; Gessner, Klaus

    2010-05-01

    The Proterozoic Aravalli orogen in the state of Rajasthan, northwestern India, constitutes the most important metallogenic province for base-metal deposits in India and hosts the entire economically viable lead-zinc resource-base of the country. The orogen evolved through near-orderly Wilson cycles of repeated extensional and compressional tectonics resulting in sequential opening and closing of intracratonic rifts and amalgamation of crustal domains during a circa 1.0-Ga geological history from 2.2 Ga to 1.0 Ga. This study develops a conceptual tectonostratigraphic model of the orogen based on a synthesis of the available geological, geophysical and geochronological data followed by deep-seismic-reflectivity-constrained 2-D forward gravity modeling, and links it to the Proterozoic base-metal metallogeny in the orogen in order to identify key geological controls on the base-metal mineralization. These controls are translated into exploration criteria for base-metal deposits, validated using empirical spatial analysis, and used to derive input spatial variables for model-based base-metal prospectivity mapping of the orogen. A support vector machine (SVM) algorithm augmented by incorporating a feature selection procedure is used in a GIS environment to implement the prospectivity mapping. A comparison of the SVM-derived prospectivity map with the ones derived using other established models such as neural-networks, logistic regression, and Bayesian weights-of-evidence indicates that the SVM outperforms other models, which is attributed to the capability of the SVM to return robust classification based on small training datasets.

  2. SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE

    Science.gov (United States)

    Mantilla, Juan; Garreau, Mireille; Bellanger, Jean-Jacques; Paredes, José Luis

    2015-01-01

    In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI), taking as reference, strain information obtained from 2D Speckle Tracking Echocardiography (STE). Without the need of pre-processing and by exploiting all the images acquired during a cardiac cycle, spatio-temporal profiles are extracted from a subset of radial lines from the ventricle centroid to points outside the epicardial border. Classical Support Vector Machines (SVM) are used to classify features extracted from gray levels of the spatio-temporal profile as well as their representations in the Wavelet domain under the assumption that the data may be sparse in that domain. Based on information obtained from radial strain curves in 2D-STE studies, we label all the spatio-temporal profiles that belong to a particular segment as normal if the peak systolic radial strain curve of this segment presents normal kinesis, or abnormal if the peak systolic radial strain curve presents hypokinesis or akinesis. For this study, short-axis cine- MR images are collected from 9 patients with cardiac dyssynchrony for which we have the radial strain tracings at the mid-papilary muscle obtained by 2D STE; and from one control group formed by 9 healthy subjects. The best classification performance is obtained with the gray level information of the spatio-temporal profiles using a RBF kernel with 91.88% of accuracy, 92.75% of sensitivity and 91.52% of specificity.

  3. SVM-Based CAC System for B-Mode Kidney Ultrasound Images.

    Science.gov (United States)

    Subramanya, M B; Kumar, Vinod; Mukherjee, Shaktidev; Saini, Manju

    2015-08-01

    The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

  4. [SVM-based spectral recognition of corn and weeds at seedling stage in fields].

    Science.gov (United States)

    Deng, Wei; Zhang, Lu-Da; He, Xiong-Kui; Mueller, J; Zeng, Ai-Jun; Song, Jian-Li; Liu, Ya-Jia; Zhou, Ji-Zhong; Chen, Ji; Wang, Xu

    2009-07-01

    A handheld FieldSpec 3 Spectroradiometer manufactured by ASD Incorporated Company in USA was used to measure the spectroscopic data of canopies of seedling corns, Dchinochloa crasgalli, and Echinochloa crusgalli weeds within the 350-2 500 nm wavelength range in the field. Each canopy was measured five times continuously. The five original spectroscopic data were averaged over the whole wavelength range in order to eliminate random noise. Then the averaged original data were converted into reflectance data, and the unsmooth parts of reflectance spectral curves with large noise were removed. The effective wavelength range for spectral data process was selected as 350-1 300 and 1 400-1 800 nm. Support vector machine (SVM) was chosen as a method of pattern recognition in this paper. SVM has the advantages of solving the problem of small sample size, being able to reach a global optimization, minimization of structure risk, and having higher generalization capability. Two classes of classifier SVM models were built up respectively using "linear", "polynomial", "RBF"(radial basis function), and "mlp (multilayer perception)" kernels. Comparison of different kernel functions for SVM shows that higher precision can be obtained by using "polynomial" kernel function with 3 orders. The accuracy can be above 80%, but the SV ratio is relatively low. On the basis of two-class classification model, taking use of voting procedure, a model based on one-against-one-algorithm multi-class classification SVM was set up. The accuracy reaches 80%. Although the recognition accuracy of the model based on SVM algorithm is not above 90%, the authors still think that the research on weeds recognition using spectrum technology combining SVM method discussed in this paper is tremendously significant. Because the data used in this study were measured over plant canopies outdoor in the field, the measurement is affected by illumination intensity, soil background, atmosphere temperature and

  5. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...... system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here...

  6. Machine Translation Approaches and Survey for Indian Languages

    OpenAIRE

    Khan, Nadeem Jadoon; Anwar, Waqas; Durrani, Nadir

    2017-01-01

    In this study, we present an analysis regarding the performance of the state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple Indian languages. We report baseline systems on several language pairs. The motivation of this study is to promote the development of SMT and linguistic resources for these language pairs, as the current state-of-the-art is quite bleak due to sparse data resources. The success of an SMT system is contingent on the availability of a large parallel c...

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

  8. Finding New Perovskite Halides via Machine learning

    Directory of Open Access Journals (Sweden)

    Ghanshyam ePilania

    2016-04-01

    Full Text Available Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning via building a support vector machine (SVM based classifier that uses elemental features (or descriptors to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

  9. Finding New Perovskite Halides via Machine learning

    Science.gov (United States)

    Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

    2016-04-01

    Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

  10. Filter Feeding Mechanism Simulated Machine Paradigms – A Theoretical Approach

    Directory of Open Access Journals (Sweden)

    Channaveerappa. H,

    2014-01-01

    Full Text Available Bionics is the emerging branch of bio engineering where in the structures and functions of organism are utilized to construct a gadget that resembles the structure and performs similar function. The functional principles are also used to construct special gadgets to perform functions in the form of simulated robots. Animal models have also been used in creation of many structures/machines, for example the organization and flight mechanism of birds, echolocation in bats, and internal ear of mammals have been taken as blue prints to design aero planes, radars and telegraphic systems respectively. Here we are using ciliary feeding mechanisms in animals to create a machine that can be used for a particular purpose. Cilia are minute finger like protoplasmic extensions serve different functions like movement, creation of water current propelling and filter feeding in animals. In many invertebrates and lower chordates rotor movements of cilia create whirl pool of water current to obtain food material. Animals those use cilia for feeding are referred to ciliary feeders or filter feeders. The filter feeders are highly diverse in their habit but share common requirements. The filter feeders may be sessile or free swimming forms but the principles of feeding remains the same. In lower chordates the pharngometry of pharynx plays a decisive role in filter feeding. The filter feeding mechanism is highly evolved in animals through well designed evolutionary paradigms.

  11. A Vectorial modeling for the pentaphase Permanent Magnet Synchronous Machine based on multimachine approach

    Directory of Open Access Journals (Sweden)

    Abdelkrim Sellam,Boubakeur Dehiba,Mohamed B. Benabdallah,Mohamed Abid,Nacéra Bachir Bouiadjra,Boubakeur Bensaid,Mustapha Djouhri

    2012-12-01

    Full Text Available The polyphase [1] machines are developed mainly inthe field of variable speed drives of high powerbecause increasing the number of phases on the onehand allows to reduce the dimensions of thecomponents in power modulators energy and secondlyto improve the operating safety. By a vector approach(vector space, it is possible to find a set of singlephasemachine and / or two-phase fictitious equivalentto polyphase synchronous machine.These fictitiousmachines are coupled electrically and mechanicallybut decoupled magnetically. This approach leads tointroduce the concept of the equivalent machine(multimachine multiconverter system MMS whichaims to analyze systems composed of mul tiplemachines (or multiple converters in electric drives. Afirst classification multimachine multiconvertersystem follows naturally from MMS formalism. Wepresent an example of a synchronous machine pent

  12. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

    Directory of Open Access Journals (Sweden)

    Sandeep Chaurasia

    2013-09-01

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

  14. Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

    Science.gov (United States)

    Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

    2017-08-01

    We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

  15. An SVM-based method for predicting cigarette sales volume%一种基于支持向量机的卷烟销量预测方法

    Institute of Scientific and Technical Information of China (English)

    武牧; 林慧苹; 李素科; 吴明治; 王治国; 吴高峰

    2016-01-01

    为解决现有线性回归方法对市级卷烟销量预测研究效果不佳等问题,基于支持向量机(SVM,Support vector machine)设计并实现了一种市级卷烟销量预测方法.以湖南中烟工业有限责任公司卷烟销量为研究对象,将支持向量机(SVM)方法应用到卷烟销量预测中,提出了基于SVM的卷烟销量预测混合方法(SHPM,SVM-based hybrid prediction method).将SHPM与线性回归方法、ARIMA(Autoregressive integrated moving average)方法、SVM方法进行了市级卷烟销量预测的对比实验,结果表明:将SVM方法应用到卷烟销量预测中是可行的.在市级卷烟销量预测上,SHPM预测结果误差相比SVM方法降低9.58%,比线性回归方法降低11.83%,比ARIMA方法降低45.79%.因此,SHPM是一种有效的市级卷烟销量预测方法.%Not satisfied with the accuracy of cigarette sales volume prediction with linear regression method, an SHPM (SVM-based hybrid prediction method) was proposed based on SVM (Support vector machine) by taking the sales volume of China Tobacco Hunan Industrial Company Limited as objects. Municipal level cigarette sales volume predicted separately by SHPM, linear regression, ARIMA (autoregressive integrated moving average) and SVM were compared and analyzed. The results showed that it was feasible to predict cigarette sales volumes with SVM method. The prediction errors against SVM, linear regression and ARIMA reduced by 9.58%, 11.83% and 45.79%, respectively; SHPM prediction method was more effective.

  16. A New Approach on the Design and Optimization of Brushless Doubly-Fed Reluctance Machines

    OpenAIRE

    Staudt, Tiago; Wurtz, Frédéric; Gerbaud, Laurent; Batistela, Nelson Jhoe; Kuo-Peng, Patrick

    2014-01-01

    International audience; The Brushless Doubly-Fed Reluctance Machine (BDFRM) is being considered as a viable generator alternative to be used in wind turbines. A literature review shows that there is still a lack of researches to define a design procedure to make this machine widely used in such application. This paper aims to address this issue by considering a new BDFRM design method using a reluctance network approach and the concepts of sizing and optimization models. It also presents a ca...

  17. A rule-based approach to model checking of UML state machines

    Science.gov (United States)

    Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

    2016-12-01

    In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

  18. Rule based systems for big data a machine learning approach

    CERN Document Server

    Liu, Han; Cocea, Mihaela

    2016-01-01

    The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

  19. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by {\\em ad hoc......} techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have...... been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...

  20. Effective and efficient optics inspection approach using machine learning algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Abdulla, G; Kegelmeyer, L; Liao, Z; Carr, W

    2010-11-02

    The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

  1. SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA.

    Science.gov (United States)

    López, M M; Ramírez, J; Górriz, J M; Alvarez, I; Salas-Gonzalez, D; Segovia, F; Chaves, R

    2009-10-30

    Single-photon emission tomography (SPECT) imaging has been widely used to guide clinicians in the early Alzheimer's disease (AD) diagnosis challenge. However, AD detection still relies on subjective steps carried out by clinicians, which entail in some way subjectivity to the final diagnosis. In this work, kernel principal component analysis (PCA) and linear discriminant analysis (LDA) are applied on functional images as dimension reduction and feature extraction techniques, which are subsequently used to train a supervised support vector machine (SVM) classifier. The complete methodology provides a kernel-based computer-aided diagnosis (CAD) system capable to distinguish AD from normal subjects with 92.31% accuracy rate for a SPECT database consisting of 91 patients. The proposed methodology outperforms voxels-as-features (VAF) that was considered as baseline approach, which yields 80.22% for the same SPECT database.

  2. An Approach to the Classification of Cutting Vibration on Machine Tools

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2016-02-01

    Full Text Available Predictions of cutting vibrations are necessary for improving the operational efficiency, product quality, and safety in the machining process, since the vibration is the main factor for resulting in machine faults. “Cutting vibration” may be caused by setting incorrect parameters before machining is commenced and may affect the precision of the machined work piece. This raises the need to have an effective model that can be used to predict cutting vibrations. In this study, an artificial neural network (ANN model to forecast and classify the cutting vibration of the intelligent machine tool is presented. The factors that may cause cutting vibrations is firstly identified and a dataset for the research purpose is constructed. Then, the applicability of the model is illustrated. Based on the results in the comparative analysis, the artificial neural network approach performed better than the others. Because the vibration can be forecasted and classified, the product quality can be managed. This work may help new workers to avoid operating machine tools incorrectly, and hence can decrease manufacturing costs. It is expected that this study can enhance the performance of machine tools in metalworking sectors.

  3. A tabu search approach for a single-machine batching problem using an efficient method to calculate a best neighbour

    NARCIS (Netherlands)

    Hurink, Johann L.

    1998-01-01

    In this paper we present a tabu search approach for a single-machine batching problem. A set of jobs has to be scheduled on a batching machine. This machine is able to handle several jobs simultaneously. The time for processing a subset of jobs simultaneously is equal to the sum of the processing

  4. Faster and better: a machine learning approach to corner detection

    CERN Document Server

    Rosten, Edward; Drummond, Tom

    2008-01-01

    The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations [Schmid et al 2000]. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection, and using machine learning we derive a feature detector from this which can fully process live PAL video using less than 5% of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion a...

  5. A new DFM approach to combine machining and additive manufacturing

    OpenAIRE

    Kerbrat, Olivier; MOGNOL, Pascal; Hascoët, Jean-Yves

    2011-01-01

    International audience; Design For Manufacturing (DFM) approaches aim to integrate manufacturability aspects during the design stage. Most of DFM approaches usually consider only one manufacturing process, but products competitiveness may be improved by designing hybrid modular products, in which products are seen as 3-D puzzles with modules realized aside by the best manufacturing process and further gathered. A new DFM system is created in order to give quantitative information during the p...

  6. A new DFM approach to combine machining and additive manufacturing

    OpenAIRE

    Kerbrat, Olivier; Mognol, Pascal; Hascoët, Jean-Yves

    2011-01-01

    International audience; Design For Manufacturing (DFM) approaches aim to integrate manufacturability aspects during the design stage. Most of DFM approaches usually consider only one manufacturing process, but products competitiveness may be improved by designing hybrid modular products, in which products are seen as 3-D puzzles with modules realized aside by the best manufacturing process and further gathered. A new DFM system is created in order to give quantitative information during the p...

  7. Support-Vector-Machine-Based False Alarm Filter of Mechatronic Built-in Test

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Diagnosing intermittent fault is an important approach to reduce built-in test (BIT) false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines ( SVM) which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate(AR) model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.

  8. Machine Learning Approaches for Predicting Human Skin Sensitization Hazard

    Science.gov (United States)

    One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single in chemico, in vit...

  9. Machine Learning Approaches for Predicting Human Skin Sensitization Hazard

    Science.gov (United States)

    One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single in chemico, in vit...

  10. PLS-LS-SVM based modeling of ATR-IR as a robust method in detection and qualification of alprazolam

    Science.gov (United States)

    Parhizkar, Elahehnaz; Ghazali, Mohammad; Ahmadi, Fatemeh; Sakhteman, Amirhossein

    2017-02-01

    According to the United States pharmacopeia (USP), Gold standard technique for Alprazolam determination in dosage forms is HPLC, an expensive and time-consuming method that is not easy to approach. In this study chemometrics assisted ATR-IR was introduced as an alternative method that produce similar results in fewer time and energy consumed manner. Fifty-eight samples containing different concentrations of commercial alprazolam were evaluated by HPLC and ATR-IR method. A preprocessing approach was applied to convert raw data obtained from ATR-IR spectra to normal matrix. Finally, a relationship between alprazolam concentrations achieved by HPLC and ATR-IR data was established using PLS-LS-SVM (partial least squares least squares support vector machines). Consequently, validity of the method was verified to yield a model with low error values (root mean square error of cross validation equal to 0.98). The model was able to predict about 99% of the samples according to R2 of prediction set. Response permutation test was also applied to affirm that the model was not assessed by chance correlations. At conclusion, ATR-IR can be a reliable method in manufacturing process in detection and qualification of alprazolam content.

  11. PLS-LS-SVM based modeling of ATR-IR as a robust method in detection and qualification of alprazolam.

    Science.gov (United States)

    Parhizkar, Elahehnaz; Ghazali, Mohammad; Ahmadi, Fatemeh; Sakhteman, Amirhossein

    2017-02-15

    According to the United States pharmacopeia (USP), Gold standard technique for Alprazolam determination in dosage forms is HPLC, an expensive and time-consuming method that is not easy to approach. In this study chemometrics assisted ATR-IR was introduced as an alternative method that produce similar results in fewer time and energy consumed manner. Fifty-eight samples containing different concentrations of commercial alprazolam were evaluated by HPLC and ATR-IR method. A preprocessing approach was applied to convert raw data obtained from ATR-IR spectra to normal matrix. Finally, a relationship between alprazolam concentrations achieved by HPLC and ATR-IR data was established using PLS-LS-SVM (partial least squares least squares support vector machines). Consequently, validity of the method was verified to yield a model with low error values (root mean square error of cross validation equal to 0.98). The model was able to predict about 99% of the samples according to R(2) of prediction set. Response permutation test was also applied to affirm that the model was not assessed by chance correlations. At conclusion, ATR-IR can be a reliable method in manufacturing process in detection and qualification of alprazolam content. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Opinion Mining in Latvian Text Using Semantic Polarity Analysis and Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Gatis Špats

    2016-07-01

    Full Text Available In this paper we demonstrate approaches for opinion mining in Latvian text. Authors have applied, combined and extended results of several previous studies and public resources to perform opinion mining in Latvian text using two approaches, namely, semantic polarity analysis and machine learning. One of the most significant constraints that make application of opinion mining for written content classification in Latvian text challenging is the limited publicly available text corpora for classifier training. We have joined several sources and created a publically available extended lexicon. Our results are comparable to or outperform current achievements in opinion mining in Latvian. Experiments show that lexicon-based methods provide more accurate opinion mining than the application of Naive Bayes machine learning classifier on Latvian tweets. Methods used during this study could be further extended using human annotators, unsupervised machine learning and bootstrapping to create larger corpora of classified text.

  13. Creating Situational Awareness in Spacecraft Operations with the Machine Learning Approach

    Science.gov (United States)

    Li, Z.

    2016-09-01

    This paper presents a machine learning approach for the situational awareness capability in spacecraft operations. There are two types of time dependent data patterns for spacecraft datasets: the absolute time pattern (ATP) and the relative time pattern (RTP). The machine learning captures the data patterns of the satellite datasets through the data training during the normal operations, which is represented by its time dependent trend. The data monitoring compares the values of the incoming data with the predictions of machine learning algorithm, which can detect any meaningful changes to a dataset above the noise level. If the difference between the value of incoming telemetry and the machine learning prediction are larger than the threshold defined by the standard deviation of datasets, it could indicate the potential anomaly that may need special attention. The application of the machine-learning approach to the Advanced Himawari Imager (AHI) on Japanese Himawari spacecraft series is presented, which has the same configuration as the Advanced Baseline Imager (ABI) on Geostationary Environment Operational Satellite (GOES) R series. The time dependent trends generated by the data-training algorithm are in excellent agreement with the datasets. The standard deviation in the time dependent trend provides a metric for measuring the data quality, which is particularly useful in evaluating the detector quality for both AHI and ABI with multiple detectors in each channel. The machine-learning approach creates the situational awareness capability, and enables engineers to handle the huge data volume that would have been impossible with the existing approach, and it leads to significant advances to more dynamic, proactive, and autonomous spacecraft operations.

  14. An optimal setup planning selection approach in a complex product machining process

    Science.gov (United States)

    Zhu, Fang

    2011-10-01

    Setup planning has very important influence on the product quality in a Complex Product Machining Process (CPMP). Part production in a CPMP involves multiple setup plans, which will lead into variation propagation and lead to extreme complexity in final product quality. Current approaches of setup planning in a CPMP are experience-based that lead to adopt higher machining process cost to ensure the final product quality, and most approaches are used for a single machining process. This work attempts to solve those challenging problems and aims to develop a method to obtain an optimal setup planning in a CPMP, which can satisfies the quality specifications and minimizes the expected value of the sum of machining costs. To this end, a machining process model is established to describe the variation propagation effect of setup plan throughout all stages in a CPMP firstly and then a quantitative setup plan evaluation methods driven by cost constraint is proposed to clarify what is optimality of setup plans. Based on the above procedures, an optimal setup planning is obtained through a dynamic programming solver. At last, a case study is provided to illustrate the validity and the significance of the proposed setup planning selective method.

  15. Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

    Directory of Open Access Journals (Sweden)

    Yuliang Su

    2015-04-01

    Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

  16. Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar

    1999-01-01

    Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...

  17. Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar

    1999-01-01

    Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...

  18. An efficient fusion approach for combining human and machine decisions

    Science.gov (United States)

    Lee, Hyungtae; Kwon, Heesung; Robinson, Ryan M.; Nothwang, William D.; Marathe, Amar R.

    2016-05-01

    A novel approach for the fusion of heterogeneous object classification methods is proposed. In order to effectively integrate the outputs of multiple classifiers, the level of ambiguity in each individual classification score is estimated using the precision/recall relationship of the corresponding classifier. The main contribution of the proposed work is a novel fusion method, referred to as Dynamic Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses (target, non-target, intermediate state (target or non-target) based on confidence levels in the classification results conditioned on the prior performance of individual classifiers. In DBF, a joint basic probability assignment, which is obtained from optimally fusing information from all classifiers, is determined by the Dempster's combination rule, and is easily reduced to a single fused classification score. Experiments on RSVP dataset demonstrates that the recognition accuracy of DBF is considerably greater than that of the conventional naive Bayesian fusion as well as individual classifiers used for the fusion.

  19. MACHINE LEARNING APPROACH FOR AUTOMATIC SEASONAL TOUR PACKAGE

    Directory of Open Access Journals (Sweden)

    Biswamayee M

    2015-10-01

    Full Text Available The online travel data imposes associate increasing difficult for tourists United Nations agency need to choose between sizable amount of accessible package for satisfying their customized desires. This TAST model will represent travel packages and tourists by totally different topics distribution, wherever the topics extraction is conditioned on each the tourists and also the intrinsic options like location , travel seasons of the landscapes. supported this subject model illustration we have a tendency to planned a cocktail approaches to come up with the list for customized travel package recommendation. we have a tendency to extend the TAST model to the tourist-relation-area-season topic (TRASTmodel for capturing the latent relationships among the tourists in every travel cluster.

  20. Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

    DEFF Research Database (Denmark)

    Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna

    2013-01-01

    machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region......-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods...

  1. A Machine Approach for Field Weakening of Permanent-Magnet Motors

    Energy Technology Data Exchange (ETDEWEB)

    Hsu, J.S.

    2000-04-02

    The commonly known technology of field weakening for permanent-magnet (PM) motors is achieved by controlling the direct-axis current component through an inverter, without using mechanical variation of the air gap, a new machine approach for field weakening of PM machines by direct control of air-gap fluxes is introduced. The demagnetization situation due to field weakening is not an issue with this new method. In fact, the PMs are strengthened at field weakening. The field-weakening ratio can reach 1O:1 or higher. This technology is particularly useful for the PM generators and electric vehicle drives.

  2. Nonlinear Modeling of a High Precision Servo Injection Molding Machine Including Novel Molding Approach

    Institute of Scientific and Technical Information of China (English)

    何雪松; 王旭永; 冯正进; 章志新; 杨钦廉

    2003-01-01

    A nonlinear mathematical model of the injection molding process for electrohydraulic servo injection molding machine (IMM) is developed.It was found necessary to consider the characteristics of asymmetric cylinder for electrohydraulic servo IMM.The model is based on the dynamics of the machine including servo valve,asymmetric cylinder and screw,and the non-Newtonian flow behavior of polymer melt in injection molding is also considered.The performance of the model was evaluated based on novel approach of molding - injection and compress molding,and the results of simulation and experimental data demonstrate the effectiveness of the model.

  3. Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

    DEFF Research Database (Denmark)

    Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna;

    2013-01-01

    machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region......-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods...

  4. Machine Learning Approaches for High-resolution Urban Land Cover Classification: A Comparative Study

    Energy Technology Data Exchange (ETDEWEB)

    Vatsavai, Raju [ORNL; Chandola, Varun [ORNL; Cheriyadat, Anil M [ORNL; Bright, Eddie A [ORNL; Bhaduri, Budhendra L [ORNL; Graesser, Jordan B [ORNL

    2011-01-01

    The proliferation of several machine learning approaches makes it difficult to identify a suitable classification technique for analyzing high-resolution remote sensing images. In this study, ten classification techniques were compared from five broad machine learning categories. Surprisingly, the performance of simple statistical classification schemes like maximum likelihood and Logistic regression over complex and recent techniques is very close. Given that these two classifiers require little input from the user, they should still be considered for most classification tasks. Multiple classifier systems is a good choice if the resources permit.

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

    Institute of Scientific and Technical Information of China (English)

    冯瑞; 张艳珠; 宋春林; 邵惠鹤

    2003-01-01

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

  6. Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification

    CERN Document Server

    Devine, Thomas; McLaughlin, Maura

    2016-01-01

    Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a new, two-stage approach for identifying and classifying dispersed pulse groups (DPGs) in single-pulse search output. The first stage identified DPGs and extracted features to characterize them using a new peak identification algorithm which tracks sloping tendencies around local maxima in plots of signal-to-noise ratio vs. dispersion measure. The second stage used supervised machine learning to classify DPGs. We created four benchmark data sets: one unbalanced and three balanced versions using three different imbalance treatments.We empirically evaluated 48 classifiers by training and testing binary and multiclass versions of six machine learning algorithms on each of the four benchmark versions. While each classifier had advantages and disadvantages, all classifiers with im...

  7. A new damage diagnosis approach for NC machine tools based on hybrid Stationary subspace analysis

    Science.gov (United States)

    Gao, Chen; Zhou, Yuqing; Ren, Yan

    2017-05-01

    This paper focused on the damage diagnosis for NC machine tools and put forward a damage diagnosis method based on hybrid Stationary subspace analysis (SSA), for improving the accuracy and visibility of damage identification. First, the observed single sensor signal was reconstructed to multi-dimensional signals by the phase space reconstruction technique, as the inputs of SSA. SSA method was introduced to separate the reconstructed data into stationary components and non-stationary components without the need for independency and prior information of the origin signals. Subsequently, the selected non-stationary components were analysed for training LS-SVM (Least Squares Support Vector Machine) classifier model, in which several statistic parameters in the time and frequency domains were exacted as the sample of LS-SVM. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy of the proposed approach.

  8. A three-stage expert system based on support vector machines for thyroid disease diagnosis.

    Science.gov (United States)

    Chen, Hui-Ling; Yang, Bo; Wang, Gang; Liu, Jie; Chen, Yi-Dong; Liu, Da-You

    2012-06-01

    In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

  9. Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

    Science.gov (United States)

    Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

    2015-01-01

    Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

  10. Classification of follicular lymphoma images: a holistic approach with symbol-based machine learning methods.

    Science.gov (United States)

    Zorman, Milan; Sánchez de la Rosa, José Luis; Dinevski, Dejan

    2011-12-01

    It is not very often to see a symbol-based machine learning approach to be used for the purpose of image classification and recognition. In this paper we will present such an approach, which we first used on the follicular lymphoma images. Lymphoma is a broad term encompassing a variety of cancers of the lymphatic system. Lymphoma is differentiated by the type of cell that multiplies and how the cancer presents itself. It is very important to get an exact diagnosis regarding lymphoma and to determine the treatments that will be most effective for the patient's condition. Our work was focused on the identification of lymphomas by finding follicles in microscopy images provided by the Laboratory of Pathology in the University Hospital of Tenerife, Spain. We divided our work in two stages: in the first stage we did image pre-processing and feature extraction, and in the second stage we used different symbolic machine learning approaches for pixel classification. Symbolic machine learning approaches are often neglected when looking for image analysis tools. They are not only known for a very appropriate knowledge representation, but also claimed to lack computational power. The results we got are very promising and show that symbolic approaches can be successful in image analysis applications.

  11. Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

    Science.gov (United States)

    DeFelipe-Oroquieta, Jesús; Kastanauskaite, Asta; de Sola, Rafael G.; DeFelipe, Javier; Bielza, Concha; Larrañaga, Pedro

    2013-01-01

    Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery. PMID:23646148

  12. Machining Performance Study on Metal Matrix Composites-A Response Surface Methodology Approach

    Directory of Open Access Journals (Sweden)

    A. Srinivasan

    2012-01-01

    Full Text Available Problem statement: Metal Matrix Composites (MMC have become a leading material among composite materials and in particular, particle reinforced aluminum MMCs have received considerable attention due to their excellent engineering properties. These materials are known as the difficult-to-machine materials because of the hardness and abrasive nature of reinforcement element-like Alumina (Al2O3. Approach: In this study, an attempt has been made to model the machinability evaluation through the response surface methodology in machining of homogenized 10% micron Al2O3 LM25 Al MMC manufactured through stir casting method. Results: The combined effects of three machining parameters including cutting speed (s, feed rate (f and depth of cut (d on the basis of three performance characteristics of tool wear (VB, surface Roughness (Ra and cutting Force (Fz were investigated. The contour plots were generated to study the effect of process parameters as well as their interactions. Conclusion: The process parameters are optimized using desirability-based approach response surface methodology.

  13. A New Approach of Error Compensation on NC Machining Based on Memetic Computation

    Directory of Open Access Journals (Sweden)

    Huanglin Zeng

    2013-04-01

    Full Text Available This paper is a study of the application of Memetic computation integrating and coordinating intelligence algorithms to solve the problems of error compensation for a high-precision numeral control machining system. The primary focus is on development of integrated intelligent computation approach to set up an error compensation system of a numeral control machine tool based on a dynamic feedback neural network. Optimization of error measurement points of a numeral control machine tool is realized by way of application of error variable attribute reduction on rough set theory. A principal component analysis is used for data compression and feature extraction to reduce the input dimension of a dynamic feedback neural network. A dynamic feedback neural network is trained on ant colony algorithm so that network can converge to get a global optimum. Positioning error caused in thermal deformation compensation capabilities were tested using industry standard equipment and procedures. The results obtained shows that this approach can effectively improve compensation precision and real time of error compensation on machine tools.

  14. Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.

    Directory of Open Access Journals (Sweden)

    Rubén Armañanzas

    Full Text Available Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE. Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

  15. Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines

    Science.gov (United States)

    Er, Poi Voon; Teo, Chek Sing; Tan, Kok Kiong

    2016-02-01

    Moving mechanical parts in a machine will inevitably generate vibration profiles reflecting its operating conditions. Vibration profile analysis is a useful tool for real-time condition monitoring to avoid loss of performance and unwanted machine downtime. In this paper, we propose and validate an approach for sensor placement, selection and fusion for continuous machine condition monitoring. The main idea is to use a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it is not suitable to mount a sensor. The locations for sensors' mountings which are subsequently used for vibration inference are identified based on sensitivity calibration at these locations moderated with normalized Fisher Information (NFI) associated with the measurement quality of the sensor at that location. Each of the identified sensor placement location is associated with one or more sensitive frequencies for which it ranks top in terms of the moderated sensitivities calibrated. A set of Radial Basis Function (RBF), each of them associated with a range of sensitive frequencies, is used to infer the vibration at the critical point for that frequency. The overall vibration spectrum of the critical point is then fused from these components. A comprehensive set of experimental results for validation of the proposed approach is provided in the paper.

  16. Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification.

    Directory of Open Access Journals (Sweden)

    Anirban Mukhopadhyay

    Full Text Available With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes.

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

    Institute of Scientific and Technical Information of China (English)

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

    2004-01-01

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

  18. Machine learning approaches to analysing textual injury surveillance data: a systematic review.

    Science.gov (United States)

    Vallmuur, Kirsten

    2015-06-01

    To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality

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

    Institute of Scientific and Technical Information of China (English)

    冯瑞; 宋春林; 邵惠鹤

    2004-01-01

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

  20. Aspects of Production Ecologization of Machine-Building Enterprises as Part of the System Approach

    Science.gov (United States)

    Lazutina, T. V.; Tempel, Yu A.; Tempel, O. A.; Lazutin, N. К

    2017-05-01

    The paper considers the role of the machine-building industry in Russia and the impact of its activities on the ecological situation in the country. As part of the problem we identified areas of environmental pollution from different production industries, including foundry, power, metal working and welding. In addition, the paper presents a strategy for production ecologization, based on a system approach viewed as a set of measures aimed at reducing the danger of technological processes for the environment and people.

  1. Design Optimization And Thermal Analysis Of Multipurpose Refrigerant Gas Recovery Machine: An Innovative Approach

    Science.gov (United States)

    Jirapure, Sagar C.

    2010-10-01

    Today on the global scene, world will see a definite impact of global temperatures, coupled with burgeoning population, will make society more vulnerable to climatic disorder, drought, famines, and flood longer heat waves spreading to newer areas. Tropical islands and low-lying coastal areas will face the treat of being submerged. Hence, it is important to reduce refrigerant emission and the approaching and with approaching phase out date for CFC elimination. It is important to recover as much of refrigerant as possible. Likewise, HFCs should also be recovered as they have high GWP. For such recovery of refrigerant, a recovery & recycling machine is required. The machine is usually comprising a hermetic compressor, air-cooled condenser, distilator, Oil separator, filters and Electric motor. The refrigerant from the appliances is drown through the filters by compressor and then discharged into recovery cylinder. The recovery cylinder should just be used for recovered refrigerant. This is because oil will usually be covered with refrigerant and this will contaminate the cylinder. It is therefore very important to treat the recovery of refrigerant at most accuracy, which require close optimization of design and further analysis of gas behavior in the system. This project aims to provide optimum solution as regards to thermal analysis of gas recovery machine. For getting insight the parameters like Specific heat of vapor at constant pressure, Density of saturated vapor, Latent heat of vaporization at boiling point, Thermal conductivity of vapor, Surface tension, etc. will require critical approach. To impart the thermal analysis, ANSYS software is proposed. It in turn provides the details of temperatures through out the piping of machine, pressure and velocity of at various locations of different components as well as the thermal zones near to high pressure and temperatures in the machine, which gives better approach for design of future gas recovery machines. It is

  2. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

    Science.gov (United States)

    Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

    2015-01-01

    Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

  3. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.

    Science.gov (United States)

    Itu, Lucian; Rapaka, Saikiran; Passerini, Tiziano; Georgescu, Bogdan; Schwemmer, Chris; Schoebinger, Max; Flohr, Thomas; Sharma, Puneet; Comaniciu, Dorin

    2016-07-01

    Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor. Copyright © 2016 the American Physiological Society.

  4. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

    Directory of Open Access Journals (Sweden)

    Huu-Tho Nguyen

    Full Text Available Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process and a fuzzy COmplex PRoportional ASsessment (COPRAS for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

  5. PREDICTION OF TOOL CONDITION DURING TURNING OF ALUMINIUM/ALUMINA/GRAPHITE HYBRID METAL MATRIX COMPOSITES USING MACHINE LEARNING APPROACH

    Directory of Open Access Journals (Sweden)

    N. RADHIKA

    2015-10-01

    Full Text Available Aluminium/alumina/graphite hybrid metal matrix composites manufactured using stir casting technique was subjected to machining studies to predict tool condition during machining. Fresh tool as well as tools with specific amount of wear deliberately created prior to machining experiments was used. Vibration signals were acquired using an accelerometer for each tool condition. These signals were then processed to extract statistical and histogram features to predict the tool condition during machining. Two classifiers namely, Random Forest and Classification and Regression Tree (CART were used to classify the tool condition. Results showed that histogram features with Random Forest classifier yielded maximum efficiency in predicting the tool condition. This machine learning approach enables the prediction of tool failure in advance, thereby minimizing the unexpected breakdown of tool and machine.

  6. An intelligent approach to machine component health prognostics by utilizing only truncated histories

    Science.gov (United States)

    Lu, Chen; Tao, Laifa; Fan, Huanzhen

    2014-01-01

    Numerous techniques and methods have been proposed to reduce the production downtime, spare-part inventory, maintenance cost, and safety hazards of machineries and equipment. Prognostics are regarded as a significant and promising tool for achieving these benefits for machine maintenance. However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances. In practice, engineering assets are rarely being permitted to run to failure. Many studies have reported valuable models and methods that engage in maximizing both truncated and failure data. However, limited studies have focused on cases where only truncated data are available, which is common in machine condition monitoring. Therefore, this study develops an intelligent machine component prognostics system by utilizing only truncated histories. First, the truncated Minimum Quantization Error (MQE) histories were obtained by Self-organizing Map network after feature extraction. The chaos-based parallel multilayer perceptron network and polynomial fitting for residual errors were adopted to generate the predicted MQEs and failure times following the truncation times. The feed-forward neural network (FFNN) was trained with inputs both from the truncated MQE histories and from the predicted MQEs. The target vectors of survival probabilities were estimated by intelligent product limit estimator using the truncation times and generated failure times. After validation, the FFNN was applied to predict the machine component health of individual units. To validate the proposed method, two cases were considered by using the degradation data generated by bearing testing rig. Results demonstrate that the proposed method is a promising intelligent prognostics approach for machine component health.

  7. Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

    Directory of Open Access Journals (Sweden)

    Roland Zemp

    2016-01-01

    Full Text Available Occupational musculoskeletal disorders, particularly chronic low back pain (LBP, are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user’s sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest. Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples. Sixteen force sensor values and the backrest angle were used as the explanatory variables (features for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

  8. Support-Vector-Machine-Based Reduced-Order Model for Limit Cycle Oscillation Prediction of Nonlinear Aeroelastic System

    Directory of Open Access Journals (Sweden)

    Gang Chen

    2012-01-01

    Full Text Available It is not easy for the system identification-based reduced-order model (ROM and even eigenmode based reduced-order model to predict the limit cycle oscillation generated by the nonlinear unsteady aerodynamics. Most of these traditional ROMs are sensitive to the flow parameter variation. In order to deal with this problem, a support vector machine- (SVM- based ROM was investigated and the general construction framework was proposed. The two-DOF aeroelastic system for the NACA 64A010 airfoil in transonic flow was then demonstrated for the new SVM-based ROM. The simulation results show that the new ROM can capture the LCO behavior of the nonlinear aeroelastic system with good accuracy and high efficiency. The robustness and computational efficiency of the SVM-based ROM would provide a promising tool for real-time flight simulation including nonlinear aeroelastic effects.

  9. A machine learning approach to extract spinal column centerline from three-dimensional CT data

    Science.gov (United States)

    Wang, Caihua; Li, Yuanzhong; Ito, Wataru; Shimura, Kazuo; Abe, Katsumi

    2009-02-01

    The spinal column is one of the most important anatomical structures in the human body and its centerline, that is, the centerline of vertebral bodies, is a very important feature used by many applications in medical image processing. In the past, some approaches have been proposed to extract the centerline of spinal column by using edge or region information of vertebral bodies. However, those approaches may suffer from difficulties in edge detection or region segmentation of vertebral bodies when there exist vertebral diseases such as osteoporosis, compression fracture. In this paper, we propose a novel approach based on machine learning to robustly extract the centerline of the spinal column from threedimensional CT data. Our approach first applies a machine learning algorithm, called AdaBoost, to detect vertebral cord regions, which have a S-shape similar to and close to, but can be detected more stably than, the spinal column. Then a centerline of detected vertebral cord regions is obtained by fitting a spline curve to their central points, using the associated AdaBoost scores as weights. Finally, the obtained centerline of vertebral cord is linearly deformed and translated in the sagittal direction to fit the top and bottom boundaries of the vertebral bodies and then a centerline of the spinal column is obtained. Experimental results on a large CT data set show the effectiveness of our approach.

  10. Analysis of machining characteristics in drilling of GFRP composite with application of fuzzy logic approach

    Directory of Open Access Journals (Sweden)

    B.C. Routar

    2013-10-01

    Full Text Available This paper discusses the application of the Taguchi method to optimize the machining parameters for machining of GFRP composite in drilling for individual responses such as thrust force and delamination factor. Moreover, a multi-response performance characteristic is used for optimization of process parameters with application of grey relational analysis. An orthogonal array (L9, grey relational generation, grey relational coefficient and grey – fuzzy grade obtained from the grey relational analysis applied as performance index to solve the optimization problem of drilling parameters for GFRP composite. Taguchi orthogonal array, the signal-to-noise ratio, and the analysis of variance are used to investigate the optimal levels of cutting parameters. The confirmation tests are conducted to verify the results and it is observed that grey-fuzzy approach is efficient in determining the optimal cutting parameters.

  11. Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

    Science.gov (United States)

    Abellán-Nebot, J. V.; Liu, J.; Romero, F.

    2009-11-01

    The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

  12. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

    Science.gov (United States)

    Ramakrishnan, Raghunathan; Dral, Pavlo O; Rupp, Matthias; von Lilienfeld, O Anatole

    2015-05-12

    Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

  13. Big Data meets Quantum Chemistry Approximations: The $\\Delta$-Machine Learning Approach

    CERN Document Server

    Ramakrishnan, Raghunathan; Rupp, Matthias; von Lilienfeld, O Anatole

    2015-01-01

    Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k constitutional isomers of C$_7$H$_{10}$O$_2$ we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semi-empirical quantum chemistry and machine learning models trained on 1 and 10\\% of 134k organ...

  14. Two Approaches for the Management of Virtual Machines on Grid Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Tapiador, D.; Rubio-Montero, A. J.; Juedo, E.; Montero, R. S.; Llorente, I. M.

    2007-07-01

    Virtual machines are a promising technology to overcome some of the problems found in current Grid infrastructures, like heterogeneity, performance partitioning or application isolation. This work shows a comparison between two strategies to manage virtual machines in Globus Grids. The first alternative is a straightforward deployment that does not require additional middle ware to be installed. It is only based on standard Grid services and is not bound to a given virtualization technology. Although this option is fully functional, it is only suitable for single process batch jobs. The second solution makes use of the Virtual Workspace Service which allows a remote client to securely negotiate and manage a virtual resource. This approach better exploits the potential benefits offered by the virtualization technology and provides a wider application range. (Author)

  15. A state machine approach in modelling the heating process of a building

    Energy Technology Data Exchange (ETDEWEB)

    Pakanen, Jouko [Helsinki University of Technology, P.O. Box 3300, FI-02015 TKK Espoo (Finland); Karjalainen, Sami [VTT, P.O. Box 1000, FI-02044 VTT Espoo (Finland)

    2009-05-15

    Process models and their applications have gradually become an integral part of the design, maintenance and automation of modern buildings. The following state machine model outlines a new approach in this area. The heating power described by the model is based on the recent inputs as well as on the past inputs and outputs of the process, thus also representing the states of the system. Identifying the model means collecting, assorting and storing observations, but also effectively utilizing their inherent relationships and nearest neighbours. The last aspect enables to create a uniform set of data, which forms the characteristic, dynamic behaviour of the HVAC process. The state machine model is non-parametric and needs no sophisticated algorithm for identification. It is therefore suitable for small microprocessor devices equipped with a larger memory capacity. The first test runs, performed in a simulated environment, were encouraging and showed good prediction capability. (author)

  16. A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

    Energy Technology Data Exchange (ETDEWEB)

    Bramer, Lisa M.; Chatterjee, Samrat; Holmes, Aimee E.; Robinson, Sean M.; Bradley, Steven F.; Webb-Robertson, Bobbie-Jo M.

    2015-09-30

    Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used to develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.

  17. Candidate gene prioritization by network analysis of differential expression using machine learning approaches

    Directory of Open Access Journals (Sweden)

    Nitsch Daniela

    2010-09-01

    Full Text Available Abstract Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking. Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we

  18. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    Science.gov (United States)

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  19. A hybrid multi-objective evolutionary algorithm approach for handling sequence- and machine-dependent set-up times in unrelated parallel machine scheduling problem

    Indian Academy of Sciences (India)

    V K MANUPATI; G RAJYALAKSHMI; FELIX T S CHAN; J J THAKKAR

    2017-03-01

    This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time,the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine scheduling (UPMS) problem. The above-mentioned multi-objectives were considered based on nonzero ready times, machine- and sequence-dependent set-up times and secondary resource constraints for jobs.The proposed approach considers unrelated parallel machines with inherent uncertainty in processing times and due dates. Since the problem is shown to be NP-hard in nature, it is a challenging task to find the optimal/nearoptimal solutions for conflicting objectives simultaneously in a reasonable time. Therefore, we introduced a new multi-objective-based evolutionary artificial immune non-dominated sorting genetic algorithm (AI-NSGA-II) to resolve the above-mentioned complex problem. The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventionalnon-dominated sorting genetic algorithm (CNSGA-II), and it is found that the proposed multi-objective-based hybrid meta-heuristic produces high-quality solutions. Finally, the results obtained from benchmark instances and randomly generated instances as test problems evince the robust performance of the proposed multiobjective algorithm.

  20. Reliability Analysis of a 3-Machine Power Station Using State Space Approach

    Directory of Open Access Journals (Sweden)

    WasiuAkande Ahmed

    2014-07-01

    Full Text Available With the advent of high-integrity fault-tolerant systems, the ability to account for repairs of partially failed (but still operational systems become increasingly important. This paper presents a systemic method of determining the reliability of a 3-machine electric power station, taking into consideration the failure rates and repair rates of the individual component (machine that make up the system. A state-space transition process for a 3-machine with 23 states was developed and consequently, steady state equations were generated based on Markov mathematical modeling of the power station. Important reliability components were deduced from this analysis. This research simulation was achieved with codes written in Excel® -VBA programming environment. System reliability using state space approach proofs to be a viable and efficient technique of reliability prediction as it is able to predict the state of the system under consideration. For the purpose of neatness and easy entry of data, Graphic User Interface (GUI was designed.

  1. Biosimilarity Assessments of Model IgG1-Fc Glycoforms Using a Machine Learning Approach.

    Science.gov (United States)

    Kim, Jae Hyun; Joshi, Sangeeta B; Tolbert, Thomas J; Middaugh, C Russell; Volkin, David B; Smalter Hall, Aaron

    2016-02-01

    Biosimilarity assessments are performed to decide whether 2 preparations of complex biomolecules can be considered "highly similar." In this work, a machine learning approach is demonstrated as a mathematical tool for such assessments using a variety of analytical data sets. As proof-of-principle, physical stability data sets from 8 samples, 4 well-defined immunoglobulin G1-Fragment crystallizable glycoforms in 2 different formulations, were examined (see More et al., companion article in this issue). The data sets included triplicate measurements from 3 analytical methods across different pH and temperature conditions (2066 data features). Established machine learning techniques were used to determine whether the data sets contain sufficient discriminative power in this application. The support vector machine classifier identified the 8 distinct samples with high accuracy. For these data sets, there exists a minimum threshold in terms of information quality and volume to grant enough discriminative power. Generally, data from multiple analytical techniques, multiple pH conditions, and at least 200 representative features were required to achieve the highest discriminative accuracy. In addition to classification accuracy tests, various methods such as sample space visualization, similarity analysis based on Euclidean distance, and feature ranking by mutual information scores are demonstrated to display their effectiveness as modeling tools for biosimilarity assessments.

  2. Characterization of the Montane Huntington Wildlife Forest Ecosystem Using Machine Learning Approaches from Remote Sensing Data

    Science.gov (United States)

    Li, Manqi

    Montane forests are susceptible to various stressors such as land use and climate change. Consequently, research on characterizing montane forest ecosystems should be conducted on a continuous basis for sustainable forest management. In this research, forest type mapping and change analysis, and biomass/carbon stock quantification were performed over a mountainous forest located in the central Adirondack Park, NY, by employing machine learning techniques at the plot level. Multi-temporal Landsat TM data were used to classify forest type cover and to detect forest cover changes for the past 20 years. Forest biomass and carbon stock quantification was then performed using full waveform LiDAR data collected in September 2011. Accuracies from the two case studies were in support of the versatility of machine learning approaches for forest and ecological investigation. Topographic characteristics affected the classification accuracy as well as the forest type change for the past 20 years. LiDAR-derived metrics, especially height-based ones, proved useful for quantifying biomass/carbon stock. Keywords: Landsat TM, full waveform LiDAR, forest classification, forest change analysis, biomass, carbon stock, machine learning

  3. A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing

    Directory of Open Access Journals (Sweden)

    Xiaoying Wang

    2013-01-01

    Full Text Available As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically.

  4. Designing “Theory of Machines and Mechanisms” course on Project Based Learning approach

    DEFF Research Database (Denmark)

    Shinde, Vikas

    2013-01-01

    by the industry and the learning outcomes specified by the National Board of Accreditation (NBA), India; this course is restructured on Project Based Learning approach. A mini project is designed to suit course objectives. An objective of this paper is to discuss the rationale of this course design......Theory of Machines and Mechanisms course is one of the essential courses of Mechanical Engineering undergraduate curriculum practiced at Indian Institute. Previously, this course was taught by traditional instruction based pedagogy. In order to achieve profession specific skills demanded...

  5. Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning

    CERN Document Server

    Mordohai, Philippos

    2006-01-01

    This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organiza

  6. Machine-learning approach for local classification of crystalline structures in multiphase systems

    Science.gov (United States)

    Dietz, C.; Kretz, T.; Thoma, M. H.

    2017-07-01

    Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.

  7. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula.

    Directory of Open Access Journals (Sweden)

    Emily S W Wong

    Full Text Available Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree and developed an algorithm (SpiderP for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html, a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from

  8. Classification of Convective and Stratiform Cells in Meteorological Radar Images Using SVM Based on a Textural Analysis

    Institute of Scientific and Technical Information of China (English)

    Abdenasser Djafri; Boualem Haddad

    2014-01-01

    This contribution deals with the discrimination between stratiform and convective cells in meteorological radar images. This study is based on a textural analysis of the latter and their classification using a support vector machine (SVM). First, we apply different textural parameters such as energy, entropy, inertia, and local homogeneity. Through this experience, we identify the different textural features of both the stratiform and convective cells. Then, we use an SVM to find the best discriminating parameter between the two types of clouds. The main goal of this work is to better apply the Palmer and Marshall Z-R relations specific to each type of precipitation.

  9. DTC-SVM Based on PI Torque and PI Flux Controllers to Achieve High Performance of Induction Motor

    Directory of Open Access Journals (Sweden)

    Hassan Farhan Rashag

    2014-01-01

    Full Text Available The fundamental idea of direct torque control of induction machines is investigated in order to emphasize the property produced by a given voltage vector on stator flux and torque variations. The proposed control system is based on Space Vector Modulation (SVM of electrical machines, Improvement model reference adaptive system, real time of stator resistance and estimation of stator flux. The purpose of this control is to minimize electromagnetic torque and flux ripple and minimizing distortion of stator current. In this proposed method, PI torque and PI flux controller are designed to achieve estimated torque and flux with good tracking and fast response with reference torque and there is no steady state error. In addition, design of PI torque and PI flux controller are used to optimize voltages in d-q reference frame that applied to SVM. The simulation Results of proposed DTC-SVM have complete excellent performance in steady and transient states as compared with classical DTC-SVM.

  10. SVM-BASED MULTI-AGENT NEGOTIATION PARTNER SELECTION%基于SVM的多Agent协商伙伴选择

    Institute of Scientific and Technical Information of China (English)

    谷琦松; 刘胜全

    2012-01-01

    According to the interactive features of Multi-agent negotiation problem, SVM (Support Vector Machine) classification method is involved in to study the Agent' s negotiation history information, extract samples from the Agent' s negotiation history information to train SVM, and combine the simulated negotiation process with one's decision-making information to predict possible results when negotiating with a particular partner and the corresponding negotiation revenue. Thus, depending on the Agent's self-interest principle, the most appropriate negotiation partner is selected. Finally, the effectiveness and superiority of the method presented in this paper are verified through simulation experiments.%根据多Agent协商问题的交互特点,引入SVM(Support Vector Machine)分类方法对Agent的协商历史信息进行学习,从Agent的协商历史信息中提取样本来训练SVM,结合模拟协商过程和己方的决策信息,预测与特定伙伴协商时可能出现的结果以及相应的协商收益,根据Agent的自利性原则,选择最合适的协商伙伴.最后,通过仿真实验验证了所提出方法的有效性和优越性.

  11. Full-polarization radar remote sensing and data mining for tropical crops mapping: a successful SVM-based classification model

    Science.gov (United States)

    Denize, J.; Corgne, S.; Todoroff, P.; LE Mezo, L.

    2015-12-01

    In Reunion, a tropical island of 2,512 km², 700 km east of Madagascar in the Indian Ocean, constrained by a rugged relief, agricultural sectors are competing in highly fragmented agricultural land constituted by heterogeneous farming systems from corporate to small-scale farming. Policymakers, planners and institutions are in dire need of reliable and updated land use references. Actually conventional land use mapping methods are inefficient under the tropic with frequent cloud cover and loosely synchronous vegetative cycles of the crops due to a constant temperature. This study aims to provide an appropriate method for the identification and mapping of tropical crops by remote sensing. For this purpose, we assess the potential of polarimetric SAR imagery associated with associated with machine learning algorithms. The method has been developed and tested on a study area of 25*25 km thanks to 6 RADARSAT-2 images in 2014 in full-polarization. A set of radar indicators (backscatter coefficient, bands ratios, indices, polarimetric decompositions (Freeman-Durden, Van zyl, Yamaguchi, Cloude and Pottier, Krogager), texture, etc.) was calculated from the coherency matrix. A random forest procedure allowed the selection of the most important variables on each images to reduce the dimension of the dataset and the processing time. Support Vector Machines (SVM), allowed the classification of these indicators based on a learning database created from field observations in 2013. The method shows an overall accuracy of 88% with a Kappa index of 0.82 for the identification of four major crops.

  12. Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

    Directory of Open Access Journals (Sweden)

    Niu AQ

    2016-07-01

    Full Text Available Ai-qin Niu,1 Liang-jun Xie,2 Hui Wang,1 Bing Zhu,1 Sheng-qi Wang3 1Department of Gynecology, the First People’s Hospital of Shangqiu, Shangqiu, Henan, People’s Republic of China; 2Department of Image Diagnoses, the Third Hospital of Jinan, Jinan, Shandong, People’s Republic of China; 3Department of Mammary Disease, Guangdong Provincial Hospital of Chinese Medicine, the Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China Background: Estrogen receptors (ERs are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects. Methods: Herein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML methods. Results: The chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior

  13. Clinical utility of machine learning approaches in schizophrenia: Improving diagnostic confidence for translational neuroimaging

    Directory of Open Access Journals (Sweden)

    Sarina eIwabuchi

    2013-08-01

    Full Text Available Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3-Tesla and 7-Tesla to discriminate patients with schizophrenia (n=19 from healthy controls (n=20. Grey (GM and white matter (WM images were used as inputs into a support vector machine (SVM to classify patients and control subjects. 7T classifiers outperformed the 3T classifiers with accuracy reaching as high as 77% for the 7T GM classifier compared to 66.6% for the 3T GM classifier. Furthermore, diagnostic odds ratio (a measure that is not affected by variations in sample characteristics and number needed to predict (a measure based on Bayesian certainty of a test result indicated superior performance of the 7T classifiers, whereby for each correct diagnosis made, the number of patients that need to be examined using the 7T GM classifier was one less than the number that need to be examined if a different classifier was used. Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making. We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches. This will not only promote the search for an optimum diagnostic tool but also aid in the translation of neuroimaging to clinical use.

  14. A Cognitive Systems Engineering Approach to Developing Human Machine Interface Requirements for New Technologies

    Science.gov (United States)

    Fern, Lisa Carolynn

    This dissertation examines the challenges inherent in designing and regulating to support human-automation interaction for new technologies that will be deployed into complex systems. A key question for new technologies with increasingly capable automation, is how work will be accomplished by human and machine agents. This question has traditionally been framed as how functions should be allocated between humans and machines. Such framing misses the coordination and synchronization that is needed for the different human and machine roles in the system to accomplish their goals. Coordination and synchronization demands are driven by the underlying human-automation architecture of the new technology, which are typically not specified explicitly by designers. The human machine interface (HMI), which is intended to facilitate human-machine interaction and cooperation, typically is defined explicitly and therefore serves as a proxy for human-automation cooperation requirements with respect to technical standards for technologies. Unfortunately, mismatches between the HMI and the coordination and synchronization demands of the underlying human-automation architecture can lead to system breakdowns. A methodology is needed that both designers and regulators can utilize to evaluate the predicted performance of a new technology given potential human-automation architectures. Three experiments were conducted to inform the minimum HMI requirements for a detect and avoid (DAA) system for unmanned aircraft systems (UAS). The results of the experiments provided empirical input to specific minimum operational performance standards that UAS manufacturers will have to meet in order to operate UAS in the National Airspace System (NAS). These studies represent a success story for how to objectively and systematically evaluate prototype technologies as part of the process for developing regulatory requirements. They also provide an opportunity to reflect on the lessons learned in order

  15. A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

    Directory of Open Access Journals (Sweden)

    Vaishnavi Ravindran

    2016-11-01

    Full Text Available In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG and Gray level co-occurrence matrix (GLCM features. Since damaged car detection has not been attempted, two datasets of damaged cars - Damaged Cars Dataset-1 (DCD-1 and Damaged Cars Dataset-2 (DCD-2 – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality.

  16. Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

    Science.gov (United States)

    Kagawa, Rina; Kawazoe, Yoshimasa; Ida, Yusuke; Shinohara, Emiko; Tanaka, Katsuya; Imai, Takeshi; Ohe, Kazuhiko

    2017-07-01

    Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users' objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

  17. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

    Science.gov (United States)

    Liu, Xiaoming; Mei, Ming; Liu, Jun; Hu, Wei

    2015-12-01

    Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

  18. A Machine Learning Approach to Recovery of Scene Geometry from Images

    CERN Document Server

    Trinh, Hoang

    2010-01-01

    Recovering the 3D structure of the scene from images yields useful information for tasks such as shape and scene recognition, object detection, or motion planning and object grasping in robotics. In this thesis, we introduce a general machine learning approach called unsupervised CRF learning based on maximizing the conditional likelihood. We apply our approach to computer vision systems that recover the 3-D scene geometry from images. We focus on recovering 3D geometry from single images, stereo pairs and video sequences. Building these systems requires algorithms for doing inference as well as learning the parameters of conditional Markov random fields (MRF). Our system is trained unsupervisedly without using ground-truth labeled data. We employ a slanted-plane stereo vision model in which we use a fixed over-segmentation to segment the left image into coherent regions called superpixels, then assign a disparity plane for each superpixel. Plane parameters are estimated by solving an MRF labelling problem, t...

  19. Fully nonlinear statistical and machine-learning approaches for hydrological frequency estimation at ungauged sites

    Science.gov (United States)

    Ouali, D.; Chebana, F.; Ouarda, T. B. M. J.

    2017-06-01

    The high complexity of hydrological systems has long been recognized. Despite the increasing number of statistical techniques that aim to estimate hydrological quantiles at ungauged sites, few approaches were designed to account for the possible nonlinear connections between hydrological variables and catchments characteristics. Recently, a number of nonlinear machine-learning tools have received attention in regional frequency analysis (RFA) applications especially for estimation purposes. In this paper, the aim is to study nonlinearity-related aspects in the RFA of hydrological variables using statistical and machine-learning approaches. To this end, a variety of combinations of linear and nonlinear approaches are considered in the main RFA steps (delineation and estimation). Artificial neural networks (ANNs) and generalized additive models (GAMs) are combined to a nonlinear ANN-based canonical correlation analysis (NLCCA) procedure to ensure an appropriate nonlinear modeling of the complex processes involved. A comparison is carried out between classical linear combinations (CCAs combined with linear regression (LR) model), semilinear combinations (e.g., NLCCA with LR) and fully nonlinear combinations (e.g., NLCCA with GAM). The considered models are applied to three different data sets located in North America. Results indicate that fully nonlinear models (in both RFA steps) are the most appropriate since they provide best performances and a more realistic description of the physical processes involved, even though they are relatively more complex than linear ones. On the other hand, semilinear models which consider nonlinearity either in the delineation or estimation steps showed little improvement over linear models. The linear approaches provided the lowest performances.

  20. An integrated two-stage support vector machine approach to forecast inundation maps during typhoons

    Science.gov (United States)

    Jhong, Bing-Chen; Wang, Jhih-Huang; Lin, Gwo-Fong

    2017-04-01

    During typhoons, accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation. Due to the lack of observed data of inundation maps, sufficient observed data are not available for developing inundation forecasting models. In this paper, the inundation depths, which are simulated and validated by a physically based two-dimensional model (FLO-2D), are used as a database for inundation forecasting. A two-stage inundation forecasting approach based on Support Vector Machine (SVM) is proposed to yield 1- to 6-h lead-time inundation maps during typhoons. In the first stage (point forecasting), the proposed approach not only considers the rainfall intensity and inundation depth as model input but also simultaneously considers cumulative rainfall and forecasted inundation depths. In the second stage (spatial expansion), the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps. The results clearly indicate that the proposed approach effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time. Moreover, the proposed approach is capable of providing accurate inundation maps for 1- to 6-h lead times. In conclusion, the proposed two-stage forecasting approach is suitable and useful for improving the inundation forecasting during typhoons, especially for long lead times.

  1. 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.

  2. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  3. SVM-based multimodal classification of activities of daily living in Health Smart Homes: sensors, algorithms, and first experimental results.

    Science.gov (United States)

    Fleury, Anthony; Vacher, Michel; Noury, Norbert

    2010-03-01

    By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.

  4. A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

    Directory of Open Access Journals (Sweden)

    B. Yekkehkhany

    2014-10-01

    Full Text Available In this paper, a framework is developed based on Support Vector Machines (SVM for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF. The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  5. Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

    Science.gov (United States)

    Poos, Alexandra M; Maicher, André; Dieckmann, Anna K; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer

    2016-06-02

    Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

  6. Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

    Science.gov (United States)

    Poos, Alexandra M.; Maicher, André; Dieckmann, Anna K.; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer

    2016-01-01

    Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments. PMID:26908654

  7. A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

    CERN Document Server

    Ntampaka, Michelle; Sutherland, Dougal J; Battaglia, Nicholas; Poczos, Barnabas; Schneider, Jeff

    2014-01-01

    We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different methods are tested against a mock cluster catalog constructed using halos with mass >= 10^14 Msolar/h from Multidark's publicly-available N-body MDPL halo catalog. In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v. The resulting fractional mass error distribution is broad, with width = 0.86 (68% scatter), and has extended high-error tails. The standard scaling relation can be simply enhanced by including higher-order moments of the LOS velocity distribution. Applying the kurtosis as a linear correction term to log(sigma_v) reduces the width of the error distribution to 0.74 (15% improvement). Machine learning can be used to take full advantage of all the information in the velocity distribution. We employ the Support ...

  8. Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

    Science.gov (United States)

    Wang, Jian-Xun; Wu, Jin-Long; Xiao, Heng

    2017-03-01

    Turbulence modeling is a critical component in numerical simulations of industrial flows based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of efforts in the turbulence modeling community, universally applicable RANS models with predictive capabilities are still lacking. Large discrepancies in the RANS-modeled Reynolds stresses are the main source that limits the predictive accuracy of RANS models. Identifying these discrepancies is of significance to possibly improve the RANS modeling. In this work, we propose a data-driven, physics-informed machine learning approach for reconstructing discrepancies in RANS modeled Reynolds stresses. The discrepancies are formulated as functions of the mean flow features. By using a modern machine learning technique based on random forests, the discrepancy functions are trained by existing direct numerical simulation (DNS) databases and then used to predict Reynolds stress discrepancies in different flows where data are not available. The proposed method is evaluated by two classes of flows: (1) fully developed turbulent flows in a square duct at various Reynolds numbers and (2) flows with massive separations. In separated flows, two training flow scenarios of increasing difficulties are considered: (1) the flow in the same periodic hills geometry yet at a lower Reynolds number and (2) the flow in a different hill geometry with a similar recirculation zone. Excellent predictive performances were observed in both scenarios, demonstrating the merits of the proposed method.

  9. A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

    Science.gov (United States)

    Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez-Benitez, Gabriela; Vock, David M; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E; O'Connor, Patrick J

    2015-09-20

    Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system.

  10. A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

    2015-12-01

    Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

  11. Global assessment of soil organic carbon stocks and spatial distribution of histosols: the Machine Learning approach

    Science.gov (United States)

    Hengl, Tomislav

    2016-04-01

    Preliminary results of predicting distribution of soil organic soils (Histosols) and soil organic carbon stock (in tonnes per ha) using global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) are presented. We focus on using a data-driven approach i.e. Machine Learning techniques that often require no knowledge about the distribution of the target variable or knowledge about the possible relationships. Other advantages of using machine learning are (DOI: 10.1371/journal.pone.0125814): All rules required to produce outputs are formalized. The whole procedure is documented (the statistical model and associated computer script), enabling reproducible research. Predicted surfaces can make use of various information sources and can be optimized relative to all available quantitative point and covariate data. There is more flexibility in terms of the spatial extent, resolution and support of requested maps. Automated mapping is also more cost-effective: once the system is operational, maintenance and production of updates are an order of magnitude faster and cheaper. Consequently, prediction maps can be updated and improved at shorter and shorter time intervals. Some disadvantages of automated soil mapping based on Machine Learning are: Models are data-driven and any serious blunders or artifacts in the input data can propagate to order-of-magnitude larger errors than in the case of expert-based systems. Fitting machine learning models is at the order of magnitude computationally more demanding. Computing effort can be even tens of thousands higher than if e.g. linear geostatistics is used. Many machine learning models are fairly complex often abstract and any interpretation of such models is not trivial and require special multidimensional / multivariable plotting and data mining

  12. 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...

  13. Thermo-energetic design of machine tools a systemic approach to solve the conflict between power efficiency, accuracy and productivity demonstrated at the example of machining production

    CERN Document Server

    2015-01-01

    The approach to the solution within the CRC/TR 96 financed by the German Research Foundation DFG aims at measures that will allow manufacturing accuracy to be maintained under thermally unstable conditions with increased productivity, without an additional demand for energy for tempering. The challenge of research in the CRC/TR 96 derives from the attempt to satisfy the conflicting goals of reducing energy consumption and increasing accuracy and productivity in machining. In the current research performed in 19 subprojects within the scope of the CRC/TR 96, correction and compensation solutions that influence the thermo-elastic machine tool behaviour efficiently and are oriented along the thermo-elastic functional chain are explored and implemented. As part of this general objective, the following issues must be researched and engineered in an interdisciplinary setting and brought together into useful overall solutions:   1.  Providing the modelling fundamentals to calculate the heat fluxes and the resulti...

  14. Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

    Science.gov (United States)

    Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

    2017-07-28

    There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

  15. An Approach to the Classification of Cutting Vibration on Machine Tools

    OpenAIRE

    Jeng-Fung Chen; Shih-Kuei Lo; Quang Hung Do

    2016-01-01

    Predictions of cutting vibrations are necessary for improving the operational efficiency, product quality, and safety in the machining process, since the vibration is the main factor for resulting in machine faults. “Cutting vibration” may be caused by setting incorrect parameters before machining is commenced and may affect the precision of the machined work piece. This raises the need to have an effective model that can be used to predict cutting vibrations. In this study, an artificial neu...

  16. 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.

  17. A hybrid least squares support vector machines and GMDH approach for river flow forecasting

    Science.gov (United States)

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

    2010-06-01

    This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

  18. Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

    CERN Document Server

    Sasdelli, Michele; Vilalta, R; Aguena, M; Busti, V C; Camacho, H; Trindade, A M M; Gieseke, F; de Souza, R S; Fantaye, Y T; Mazzali, P A

    2015-01-01

    The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate the automatic discovery of sub-populations of SNIa; to that end we introduce the DRACULA Python package (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy). Our approach is divided in three steps: (i) Transfer Learning, which takes advantage of all available spectra (even those without an epoch estimate) as an information source, (ii) dimensionality reduction through Deep Learning and (iii) unsupervised learning (clustering) using K-Means. Results match a previously suggested classification scheme, showing that the proposed method is able to grasp the main spectral features behind the definition of such subclasses. Moreover, our methodo...

  19. A MACHINE LEARNING APPROACH TO ANOMALY-BASED DETECTION ON ANDROID PLATFORMS

    Directory of Open Access Journals (Sweden)

    Joshua Abah

    2015-11-01

    Full Text Available The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these mobile devices have led to increasing danger associated with malware targeted at these devices. Detecting such malware presents inimitable challenges as signature-based detection techniques available today are becoming inefficient in detecting new and unknown malware. In this research, a machine learning approach for the detection of malware on Android platforms is presented. The detection system monitors and extracts features from the applications while in execution and uses them to perform in-device detection using a trained K-Nearest Neighbour classifier. Results shows high performance in the detection rate of the classifier with accuracy of 93.75%, low error rate of 6.25% and low false positive rate with ability of detecting real Android malware.

  20. Using a symbiotic man/machine approach to evaluating visual clinical research data.

    Science.gov (United States)

    Long, J M; Irani, E A; Hunter, D W; Slagle, J R; Matts, J P; Castaneda, W; Pearce, M; Bissett, J; Sawin, H; Edmiston, A

    1988-10-01

    Some candidate medical expert system applications have a significant visual component. Knowledge engineers usually dismiss such task domains as potential expert systems applications. Our success in developing ESCA, a system for evaluating serial coronary angiograms, shows that such task domains should not be dismissed so quickly. We used a symbiotic approach between man and machine, where technologists provide the visual skills with an expert system imitating the conceptual skills of the expert, to produce a partially automated system that is more consistent and cost effective than one that is fully manual. The agreement between the system's conclusions and that of a panel of experts is good. The expert system actually has a slightly higher agreement rate with the expert panel than the agreement rate between two expert panel teams evaluating the same film pair.

  1. A comparative analysis of machine learning approaches for plant disease identification

    Directory of Open Access Journals (Sweden)

    Hidayat ur Rahman

    2017-08-01

    Full Text Available Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages. Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG have been used. Results: 382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves. Conclusion: From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants.

  2. Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

    Directory of Open Access Journals (Sweden)

    Yinghai Ke

    2016-03-01

    Full Text Available This study presented a MODIS 8-day 1 km evapotranspiration (ET downscaling method based on Landsat 8 data (30 m and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST, and vegetation indices (VIs derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR, Cubist, and Random Forest (RF were used to model the relationship between the Landsat indicators and MODIS 8-day 1 km ET. The models were then used to predict 30 m ET based on Landsat 8 indicators. A total of thirty-two pairs of Landsat 8 images/MODIS ET data were evaluated at four study sites including two in United States and two in South Korea. Among the three models, RF produced the lowest error, with relative Root Mean Square Error (rRMSE less than 20%. Vegetation greenness related indicators such as Normalized Difference Vegetation Index (NDVI, Enhanced Vegetation Index (EVI, Soil Adjusted Vegetation Index (SAVI, and vegetation moisture related indicators such as Normalized Difference Infrared Index—Landsat 8 OLI band 7 (NDIIb7 and Normalized Difference Water Index (NDWI were the five most important features used in RF model. Temperature-based indicators were less important than vegetation greenness and moisture-related indicators because LST could have considerable variation during each 8-day period. The predicted Landsat downscaled ET had good overall agreement with MODIS ET (average rRMSE = 22% and showed a similar temporal trend as MODIS ET. Compared to the MODIS ET product, the downscaled product demonstrated more spatial details, and had better agreement with in situ ET observations (R2 = 0.56. However, we found that the accuracy of MODIS ET was the main control factor of the accuracy of the downscaled product. Improved coarse-resolution ET estimation would result in better finer-resolution estimation. This study proved the potential of using machine learning

  3. Study on Approach for Computer-Aided Design and Machining of General Cylindrical Cam Using Relative Velocity and Inverse Kinematics

    Institute of Scientific and Technical Information of China (English)

    Se-Hwan; Park; Byong-Kook; Gu; Joong-Ho; Shin; Geun-Jong; Yoo

    2002-01-01

    Cylindrical Cam Mechanism which is one of the best eq uipments to accomplish an accurate motion transmission is widely used in the fie lds of industries, such as machine tool exchangers, textile machinery and automa tic transfer equipments. This paper proposes a new approach for the shape design and manufacturing of the cylindrical cam. The design approach uses the relative velocity concept and the manufacturing approach uses the inverse kinematics concept. For the shape desig n, the contact points betw...

  4. An Approach to Realizing Process Control for Underground Mining Operations of Mobile Machines.

    Directory of Open Access Journals (Sweden)

    Zhen Song

    Full Text Available The excavation and production in underground mines are complicated processes which consist of many different operations. The process of underground mining is considerably constrained by the geometry and geology of the mine. The various mining operations are normally performed in series at each working face. The delay of a single operation will lead to a domino effect, thus delay the starting time for the next process and the completion time of the entire process. This paper presents a new approach to the process control for underground mining operations, e.g. drilling, bolting, mucking. This approach can estimate the working time and its probability for each operation more efficiently and objectively by improving the existing PERT (Program Evaluation and Review Technique and CPM (Critical Path Method. If the delay of the critical operation (which is on a critical path inevitably affects the productivity of mined ore, the approach can rapidly assign mucking machines new jobs to increase this amount at a maximum level by using a new mucking algorithm under external constraints.

  5. A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

    Directory of Open Access Journals (Sweden)

    Gaochao Xu

    2013-01-01

    Full Text Available Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

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

    Science.gov (United States)

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

    2016-05-01

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

  7. A semi-analytical approach for stiffness modeling of PKM by considering compliance of machine frame with complex geometry

    Institute of Scientific and Technical Information of China (English)

    WANG YouYu; HUANG Tian; ZHAO XueMan; MEI JiangPing; Derek G CHETWYND

    2008-01-01

    Stiffness modeling is one of the most significant issues in the design of parallel kinematic machine (PKM).This paper presents a semi-analytical approach that enables the stiffness of PKM with complex machine frame geometry to be estimated effectively.This approach can be implemented by three steps:(i) decomposition of the entire system into two sub-systems associated with the parallel mechanism and the machine frame respectively;(ii) stiffness modeling of each sub-system using the analytical approach and the finite element analysis;and (iii) generation of the stiffness model of the entire system by means of linear superposition.In the modeling process of each sub-system,the virtual work princi-ple and overall deflection Jacobian are employed with special attention to the bending rigidity of the constrained passive limb and the interface stiffness of the machine frame.The stiffness distribution of a 5-DOF hybrid robot named TriVariant-B is investigated as an example to illustrate the effectivaness of this approach.The contributions of component rigidities to that of the system are evaluated using global indices.It shows that the results achieved by this approach have a good match to those obtained through finite element analysis and experiments.

  8. Icing detection from Communication, Ocean and Meteorological Satellite and Himawari-8 data using machine learning approaches

    Science.gov (United States)

    Sim, S.; Park, H.; Im, J.; Park, S.

    2016-12-01

    Aircraft icing is a hazardous phenomenon which has potential to cause fatalities and socioeconomic losses. It is caused by super-cooled droplets (SCDs) colliding on the surface of aircraft frame when an aircraft flies through SCD rich clouds. When icing occurs, the aerodynamic balance of the aircraft is disturbed, resulting in a potential problem in aircraft operation. Thus, identification of potential icing clouds is crucial for aviation. Satellite remote sensing data such as Geostationary Operational Environmental Satellite (GOES) series have been widely used to detect potential icing clouds. An icing detection algorithm, operationally used in the US, consists of several thresholds of cloud optical depth, effective radius, and liquid water path based on the physical properties of icing. On the other hand, there is no operational icing detection algorithm in Asia, although there are several geostationary meteorological satellite sensors. In this study, we proposed machine learning-based models to detect icing over East Asia focusing on the Korean Peninsula using two geostationary satellite sensors—Meteorological Imager (MI) onboard Communication, Ocean and Meteorological Satellite (COMS) and Advanced Himawari Imager (AHI) onboard Himawari-8. While COMS MI provides data at 5 channels, Himawari-8 AHI has advanced capability of data collection, providing data at 16 channels. Instead of simple thresholding approaches used in the literature, we adopted two machine learning algorithms—decision trees (DT) and random forest (RF) to develop icing detection models based on Pilot REPorts (PIREPs) as reference data. Results show that the COMS icing detection model by RF produced a detection rate of 88.67% and a false alarm rate of 14.42%, which were improved when compared with the result of the direct application of the GOES algorithm to the COMS MI data (a detection rate of 20.83% and a false alarm rate of 25.44%). Although much higher accuracy (a detection rate > 95

  9. Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

    Science.gov (United States)

    Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

    2017-01-01

    Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99

  10. VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

    Institute of Scientific and Technical Information of China (English)

    Sudipto Saha; G.P.S. Raghava

    2006-01-01

    In this study, an attempt has been made to predict the major functions of gramnegative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins (255 ofcellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors). First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins (http://www.imtech.res.in/raghava/vicmpred/).

  11. Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

    Science.gov (United States)

    Cui, Zaixu; Xia, Zhichao; Su, Mengmeng; Shu, Hua; Gong, Gaolang

    2016-04-01

    Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals.

  12. A Novel Approach for English to South Dravidian Language Statistical Machine Translation System

    Directory of Open Access Journals (Sweden)

    Unnikrishnan P

    2010-11-01

    Full Text Available Development of a well fledged bilingual machine translation (MT system for any two natural languages with limited electronic resources and tools is a challenging and demanding task. This paper presents the development of a statistical machine translation (SMT system for English to South Dravidian languages like Malayalam and Kannada by incorporating syntactic and morphological information. SMT is a data oriented statistical framework for translating text from one natural language to another based on the knowledge extracted from bilingual corpus. Even though there are efforts towards building such an English to South Dravidian translation system ,unfortunately we do not have an efficient translation system till now. The first and most important step in SMT is creating a well aligned parallel corpus for training the system. Experimental research shows that the existing methodology for bilingual parallel corpus creation is not efficient for English to South Dravidian language in the SMT system. In order toincrease the performance of the translation system, we have introduced a new approach in creating parallel corpus. The mainideas which we have implemented and proven very effective forEnglish to south Dravidian languages SMT system are: (i reordering the English source sentence according to Dravidian syntax, (ii using the root suffix separation on both English and Dravidian words and iii use of morphological information which substantially reduce the corpus size required for training the system. Since the navailability of full fledged parsing and morphological tools for Malayalam and Kannada languages, sentence synthesis was done both manually and existing morph analyzer created by Amrita university. From the experiment we found that the performance of our systems are significantly well and achieves a very competitive accuracy for small sized bilingual corpora. The proposed ideas can be directly used for other south Dravidian languages like Tamil

  13. Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

    Directory of Open Access Journals (Sweden)

    David Burstein

    2009-07-01

    Full Text Available A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine

  14. Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

    Science.gov (United States)

    Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

    2017-01-01

    Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NBSW=NBBI-GRAM=SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we

  15. A novel approach to predict surface roughness in machining operations using fuzzy set theory

    Directory of Open Access Journals (Sweden)

    Tzu-Liang (Bill Tseng

    2016-01-01

    Full Text Available The increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness.

  16. The brain as a flexible task machine: implications for visual rehabilitation using noninvasive vs. invasive approaches.

    Science.gov (United States)

    Reich, Lior; Maidenbaum, Shachar; Amedi, Amir

    2012-02-01

    The exciting view of our brain as highly flexible task-based and not sensory-based raises the chances for visual rehabilitation, long considered unachievable, given adequate training in teaching the brain how to see. Recent advances in rehabilitation approaches, both noninvasive, like sensory substitution devices (SSDs) which present visual information using sound or touch, and invasive, like visual prosthesis, may potentially be used to achieve this goal, each alone, and most preferably together. Visual impairments and said solutions are being used as a model for answering fundamental questions ranging from basic cognitive neuroscience, showing that several key visual brain areas are actually highly flexible, modality-independent and, as was recently shown, even visual experience-independent task machines, to technological and behavioral developments, allowing blind persons to 'see' using SSDs and other approaches. SSDs can be potentially used as a research tool for assessing the brain's functional organization; as an aid for the blind in daily visual tasks; to visually train the brain prior to invasive procedures, by taking advantage of the 'visual' cortex's flexibility and task specialization even in the absence of vision; and to augment postsurgery functional vision using a unique SSD-prostheses hybrid. Taken together the reviewed results suggest a brighter future for visual neuro-rehabilitation.

  17. CoSpa: A Co-training Approach for Spam Review Identification with Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Wen Zhang

    2016-03-01

    Full Text Available Spam reviews are increasingly appearing on the Internet to promote sales or defame competitors by misleading consumers with deceptive opinions. This paper proposes a co-training approach called CoSpa (Co-training for Spam review identification to identify spam reviews by two views: one is the lexical terms derived from the textual content of the reviews and the other is the PCFG (Probabilistic Context-Free Grammars rules derived from a deep syntax analysis of the reviews. Using SVM (Support Vector Machine as the base classifier, we develop two strategies, CoSpa-C and CoSpa-U, embedded within the CoSpa approach. The CoSpa-C strategy selects unlabeled reviews classified with the largest confidence to augment the training dataset to retrain the classifier. The CoSpa-U strategy randomly selects unlabeled reviews with a uniform distribution of confidence. Experiments on the spam dataset and the deception dataset demonstrate that both the proposed CoSpa algorithms outperform the traditional SVM with lexical terms and PCFG rules in spam review identification. Moreover, the CoSpa-U strategy outperforms the CoSpa-C strategy when we use the absolute value of decision function of SVM as the confidence.

  18. Learning Pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data

    NARCIS (Netherlands)

    Di Mitri, Daniele; Scheffel, Maren; Drachsler, Hendrik; Börner, Dirk; Ternier, Stefaan; Specht, Marcus

    2017-01-01

    Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving Ph

  19. 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…

  20. Coastal water quality estimation from Geostationary Ocean Color Imager (GOCI) satellite data using machine learning approaches

    Science.gov (United States)

    Im, Jungho; Ha, Sunghyun; Kim, Yong Hoon; Ha, Hokyung; Choi, Jongkuk; Kim, Miae

    2014-05-01

    It is important to monitor coastal water quality using key parameters such as chlorophyll-a concentration and suspended sediment to better manage coastal areas as well as to better understand the nature of biophysical processes in coastal seawater. Remote sensing technology has been commonly used to monitor coastal water quality due to its ability of covering vast areas at high temporal resolution. While it is relatively straightforward to estimate water quality in open ocean (i.e., Case I water) using remote sensing, coastal water quality estimation is still challenging as many factors can influence water quality, including various materials coming from inland water systems and tidal circulation. There are continued efforts to accurately estimate water quality parameters in coastal seawater from remote sensing data in a timely manner. In this study, two major water quality indicators, chlorophyll-a concentration and the amount of suspended sediment, were estimated using Geostationary Ocean Color Imager (GOCI) satellite data. GOCI, launched in June 2010, is the first geostationary ocean color observation satellite in the world. GOCI collects data hourly for 8 hours a day at 6 visible and 2 near-infrared bands at a 500 m resolution with 2,500 x 2,500 km square around Korean peninsula. Along with conventional statistical methods (i.e., various linear and non-linear regression), three machine learning approaches such as random forest, Cubist, and support vector regression were evaluated for coastal water quality estimation. In situ measurements (63 samples; including location, two water quality parameters, and the spectra of surface water using a hand-held spectroradiometer) collected during four days between 2011 and 2012 were used as reference data. Due to the small sample size, leave-one-out cross validation was used to assess the performance of the water quality estimation models. Atmospherically corrected radiance data and selected band-ratioed images were used

  1. Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

    Directory of Open Access Journals (Sweden)

    Simpson David

    2006-03-01

    Full Text Available Abstract Background Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. Results Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE, this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re

  2. Self-commissioning of permanent magnet synchronous machine drives using hybrid approach

    DEFF Research Database (Denmark)

    Basar, M. Sertug; Bech, Michael Møller; Andersen, Torben Ole

    2014-01-01

    Self-commissioning of permanent-magnet (PM) synchronous machines (PMSMs) is of prime importance in an industrial drive system because control performance and system stability depend heavily on the accurate machine parameter information. This article focuses on a combination of offline and online ...

  3. On Heuristic Approach for Solution of Scheduling Problem Involving Transportation Time and Break-down Times for Three Machines

    Directory of Open Access Journals (Sweden)

    A. Khodadadi

    2014-04-01

    Full Text Available In most manufacturing and distribution systems, semi-finished jobs are transferred from one processing facility to another by transporters such as automated guided vehicles and conveyors and finished jobs are delivered to customers or warehouses by vehicles such as trucks. Most machine scheduling models assume either that there are a finite number of transporters for delivering jobs or that jobs are delivered instantaneously from one location to another without transportation time involved. In this study we study a new simple heuristic algorithm for a ‘3-machine, n-job’ flow shop scheduling problem in which transportation time and break down times of machines are considered. A heuristic approach method to find optimal and near optimal sequence minimizing the total elapsed time.

  4. CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

    Directory of Open Access Journals (Sweden)

    Jihong Chen

    2015-06-01

    Full Text Available Building cyber-physical system (CPS models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control (CNC system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control (NC processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

  5. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Hong-Hai Tran

    2014-01-01

    Full Text Available Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM algorithm is utilized to classify grouting activities into two classes: success and  failure. Meanwhile, the differential evolution (DE optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

  6. Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

    Science.gov (United States)

    Komasi, Mehdi; Sharghi, Soroush

    2016-01-01

    Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

  7. Machine learning approach identifies new pathways associated with demyelination in a viral model of multiple sclerosis

    Science.gov (United States)

    Ulrich, Reiner; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang

    2010-01-01

    Abstract Theiler’s murine encephalomyelitis is an experimentally virus-induced inflammatory demyelinating disease of the spinal cord, displaying clinical and pathological similarities to chronic progressive multiple sclerosis. The aim of this study was to identify pathways associated with chronic demyelination using an assumption-free combined microarray and immunohistology approach. Movement control as determined by rotarod assay significantly worsened in Theiler’s murine encephalomyelitis -virus-infected SJL/J mice from 42 to 196 days after infection (dpi). In the spinal cords, inflammatory changes were detected 14 to 196 dpi, and demyelination progressively increased from 42 to 196 dpi. Microarray analysis revealed 1001 differentially expressed genes over the study period. The dominating changes as revealed by k-means and functional annotation clustering included up-regulations related to intrathecal antibody production and antigen processing and presentation via major histocompatibility class II molecules. A random forest machine learning algorithm revealed that down-regulated lipid and cholesterol biosynthesis, differentially expressed neurite morphogenesis and up-regulated toll-like receptor-4-induced pathways were intimately associated with demyelination as measured by immunohistology. Conclusively, although transcriptional changes were dominated by the adaptive immune response, the main pathways associated with demyelination included up-regulation of toll-like receptor 4 and down-regulation of cholesterol biosynthesis. Cholesterol biosynthesis is a rate limiting step of myelination and its down-regulation is suggested to be involved in chronic demyelination by an inhibition of remyelination. PMID:19183246

  8. Intelligent Video Object Classification Scheme using Offline Feature Extraction and Machine Learning based Approach

    Directory of Open Access Journals (Sweden)

    Chandra Mani Sharma

    2012-01-01

    Full Text Available Classification of objects in video stream is important because of its application in many emerging areas such as visual surveillance, content based video retrieval and indexing etc. The task is far more challenging because the video data is of heavy and highly variable nature. The processing of video data is required to be in real-time. This paper presents a multiclass object classification technique using machine learning approach. Haar-like features are used for training the classifier. The feature calculation is performed using Integral Image representation and we train the classifier offline using a Stage-wise Additive Modeling using a Multiclass Exponential loss function (SAMME. The validity of the method has been verified from the implementation of a real-time human-car detector. Experimental results show that the proposed method can accurately classify objects, in video, into their respective classes. The proposed object classifier works well in outdoor environment in presence of moderate lighting conditions and variable scene background. The proposed technique is compared, with other object classification techniques, based on various performance parameters.

  9. A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

    Science.gov (United States)

    Gelfusa, M.; Murari, A.; Lungaroni, M.; Malizia, A.; Parracino, S.; Peluso, E.; Cenciarelli, O.; Carestia, M.; Pizzoferrato, R.; Vega, J.; Gaudio, P.

    2016-10-01

    Two of the major new concerns of modern societies are biosecurity and biosafety. Several biological agents (BAs) such as toxins, bacteria, viruses, fungi and parasites are able to cause damage to living systems either humans, animals or plants. Optical techniques, in particular LIght Detection And Ranging (LIDAR), based on the transmission of laser pulses and analysis of the return signals, can be successfully applied to monitoring the release of biological agents into the atmosphere. It is well known that most of biological agents tend to emit specific fluorescence spectra, which in principle allow their detection and identification, if excited by light of the appropriate wavelength. For these reasons, the detection of the UVLight Induced Fluorescence (UV-LIF) emitted by BAs is particularly promising. On the other hand, the stand-off detection of BAs poses a series of challenging issues; one of the most severe is the automatic discrimination between various agents which emit very similar fluorescence spectra. In this paper, a new data analysis method, based on a combination of advanced filtering techniques and Support Vector Machines, is described. The proposed approach covers all the aspects of the data analysis process, from filtering and denoising to automatic recognition of the agents. A systematic series of numerical tests has been performed to assess the potential and limits of the proposed methodology. The first investigations of experimental data have already given very encouraging results.

  10. A Machine-learning approach to classification of X-ray sources

    Science.gov (United States)

    Hare, Jeremy; Kargaltsev, Oleg; Rangelov, Blagoy; Pavlov, George; Posselt, Bettina; Volkov, Igor

    2017-08-01

    Chandra and XMM-Newton X-ray observatories have serendipitously detected a large number of Galactic sources. Although their properties are automatically extracted and stored in catalogs, most of these sources remain unexplored. Classifying these sources can enable population studies on much larger scales and may also reveal new types of X-ray sources. For most of these sources the X-ray data alone are not enough to identify their nature, and multiwavelength data must be used. We developed a multiwavelength classification pipeline (MUWCLASS), which relies on supervised machine learning and a rich training dataset. We describe the training dataset, the pipeline and its testing, and will show/discuss how the code performs in different example environments, such as unidentified gamma-ray sources, supernova remnants, dwarf galaxies, stellar clusters, and the inner Galactic plane. We also discuss the application of this approach to the data from upcoming new X-ray observatories (e.g., eROSITA, Athena).

  11. A machine learning approach to automated structural network analysis: application to neonatal encephalopathy.

    Directory of Open Access Journals (Sweden)

    Etay Ziv

    Full Text Available Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopathy. In this manuscript we use diffusion tractography to construct structural networks for a cohort of patients with neonatal encephalopathy. We systematically map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds. Our algorithm offers a novel tool to evaluate neonates at risk for developing neurological deficit. The described approach can be applied to any brain pathology that affects structural connectivity.

  12. Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches.

    Science.gov (United States)

    Papa, E; Doucet, J P; Sangion, A; Doucet-Panaye, A

    2016-07-01

    The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on the surface of nanoparticles represents a key step that influences nanoparticle behaviour. In this study, the quantitative description of the composition of the protein corona was used to study the effect on cell association induced by 84 surface-modified gold nanoparticles of different sizes. Quantitative relationships between the protein corona and the activity of the gold nanoparticles were modelled by using several machine learning-based linear and non-linear approaches. Models based on a selection of only six serum proteins had robust and predictive results. The Projection Pursuit Regression method had the best performances (r(2) = 0.91; Q(2)loo = 0.81; r(2)ext = 0.79). The present study confirmed the utility of protein corona composition to predict the bioactivity of gold nanoparticles and identified the main proteins that act as promoters or inhibitors of cell association. In addition, the comparison of several techniques showed which strategies offer the best results in prediction and could be used to support new toxicological studies on gold-based nanomaterials.

  13. A microscopy approach for in situ inspection of micro-coordinate measurement machine styli for contamination

    Science.gov (United States)

    Feng, Xiaobing; Pascal, Jonathan; Lawes, Simon

    2017-09-01

    During the process of measurement using a micro-coordinate measurement machine (µCMM) contamination gradually builds up on the surface of the stylus tip and affects the dimensional accuracy of the measurement. Regular inspection of the stylus for contamination is essential to determine the appropriate cleaning interval and prevent the dimensional error from becoming significant. However, in situ inspection of a µCMM stylus is challenging due to the size, spherical shape, material and surface properties of a typical stylus. To address this challenge, this study evaluates several non-contact measurement technologies for in situ stylus inspection and, based on those findings, proposes a cost-effective microscopy approach. The operational principle is then demonstrated by an automated prototype, coordinated directly by the CMM software MCOSMOS, with an effective threshold of detection as low as 400 nm and a large field of view and depth of field. The level of contamination on the stylus has been found to increase steadily with the number of measurement contacts made. Once excessive contamination is detected on the stylus, measurement should be stopped and a stylus cleaning procedure should be performed to avoid affecting measurement accuracy.

  14. Alternating current multi-circuit electric machines a new approach to the steady-state parameter determination

    CERN Document Server

    Asanbayev, Valentin

    2015-01-01

    This book details an approach for realization of the field decomposition concept. The book presents the  methods as well as techniques and procedures for establishing electric machine circuit-loops and determining their parameters. The methods developed have been realized using the models of machines with laminated and solid rotor having classical structure. The use of such models are well recognized and simplifies practical implementation of the obtained results. This book also: ·         Includes methods for a construction of electric machine equivalent circuits that allows the replacement of the field models of the machine with simple circuit models ·         Demonstrates the practical implementation of the proposed techniques and procedures ·         Presents parameters of the circuit-loops in the form most convenient for practical implementation ·         Uses methods based on machine models widely used in practice

  15. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

    Science.gov (United States)

    Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

    2016-12-01

    Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

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

    Directory of Open Access Journals (Sweden)

    William S Sanders

    2011-07-01

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

  17. Optimizing machining parameters of wire-EDM process to cut Al7075/SiCp composites using an integrated statistical approach

    Institute of Scientific and Technical Information of China (English)

    Thella Babu Rao

    2016-01-01

    Metal matrix composites (MMCs) as advanced materials,while producing the components with high dimensional accuracy and intricate shapes,are more complex and cost effective for machining than conventional alloys.It is due to the presence of discontinuously distributed hard ceramic with the MMCs and involvement of a large number of machining control variables.However,determination of optimal machining conditions helps the process engineer to make the process efficient and effective.In the present investigation a novel hybrid multi-response optimization approach is proposed to derive the economic machining conditions for MMCs.This hybrid approach integrates the concepts of grey relational analysis (GRA),principal component analysis (PCA) and Taguchi method (TM) to derive the optimal machining conditions.The machining experiments are planned to machine A17075/SiCp MMCs using wire-electrical discharge machining (WEDM) process.SiC particulate size and its weight percentage are explicitly considered here as the process variables along with the WEDM input variables.The derived optimal process responses are confirmed by the experimental validation tests and the results show satisfactory.The practical possibility of the derived optimal machining conditions is also analyzed and presented using scanning electron microscope (SEM) examinations.According to the growing industrial need of making high performance,low cost components,this investigation provides a simple and sequential approach to enhance the WEDM performance while machining MMCs.

  18. Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

    Science.gov (United States)

    Cheng, Kai; Niu, Zhi-Chao; Wang, Robin C.; Rakowski, Richard; Bateman, Richard

    2017-09-01

    Smart machining has tremendous potential and is becoming one of new generation high value precision manufacturing technologies in line with the advance of Industry 4.0 concepts. This paper presents some innovative design concepts and, in particular, the development of four types of smart cutting tools, including a force-based smart cutting tool, a temperature-based internally-cooled cutting tool, a fast tool servo (FTS) and smart collets for ultraprecision and micro manufacturing purposes. Implementation and application perspectives of these smart cutting tools are explored and discussed particularly for smart machining against a number of industrial application requirements. They are contamination-free machining, machining of tool-wear-prone Si-based infra-red devices and medical applications, high speed micro milling and micro drilling, etc. Furthermore, implementation techniques are presented focusing on: (a) plug-and-produce design principle and the associated smart control algorithms, (b) piezoelectric film and surface acoustic wave transducers to measure cutting forces in process, (c) critical cutting temperature control in real-time machining, (d) in-process calibration through machining trials, (e) FE-based design and analysis of smart cutting tools, and (f) application exemplars on adaptive smart machining.

  19. Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.

    Science.gov (United States)

    Grouin, Cyril; Zweigenbaum, Pierre

    2013-01-01

    In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.

  20. Identification of asteroids trapped inside three-body mean motion resonances: a machine-learning approach

    Science.gov (United States)

    Smirnov, Evgeny A.; Markov, Alexey B.

    2017-08-01

    In this paper, we apply the following machine learning methods that do not require numerical integration - namely, k-nearest neighbours, decision tree, gradient boosting and logistic regression - for identifying three-body resonant asteroids in the main belt. It is shown that the results of the identification by machine learning methods are accurate and take significantly less time than numerical integration (seconds versus days). We have identified 404 new asteroids subjected to the three-body resonance 4J-2S-1 using a machine learning methodology.

  1. Machine Learning

    CERN Document Server

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  2. Novel approach of crater detection by crater candidate region selection and matrix-pattern-oriented least squares support vector machine

    Institute of Scientific and Technical Information of China (English)

    Ding Meng; Cao Yunfeng; Wu Qingxian

    2013-01-01

    Impacted craters are commonly found on the surface of planets,satellites,asteroids and other solar system bodies.In order to speed up the rate of constructing the database of craters,it is important to develop crater detection algorithms.This paper presents a novel approach to automatically detect craters on planetary surfaces.The approach contains two parts:crater candidate region selection and crater detection.In the first part,crater candidate region selection is achieved by Kanade-Lucas-Tomasi (KLT) detector.Matrix-pattern-oriented least squares support vector machine (MatLSSVM),as the matrixization version of least square support vector machine (SVM),inherits the advantages of least squares support vector machine (LSSVM),reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM.The second part of the approach employs MatLSSVM to design classifier for crater detection.Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%.In addition,the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection.The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.

  3. A geometric approach for fault detection and isolation of stator short circuit failure in a single asynchronous machine

    KAUST Repository

    Khelouat, Samir

    2012-06-01

    This paper deals with the problem of detection and isolation of stator short-circuit failure in a single asynchronous machine using a geometric approach. After recalling the basis of the geometric approach for fault detection and isolation in nonlinear systems, we will study some structural properties which are fault detectability and isolation fault filter existence. We will then design filters for residual generation. We will consider two approaches: a two-filters structure and a single filter structure, both aiming at generating residuals which are sensitive to one fault and insensitive to the other faults. Some numerical tests will be presented to illustrate the efficiency of the method.

  4. Parallel Machine Scheduling Models with Fuzzy Parameters and Precedence Constraints: A Credibility Approach

    Institute of Scientific and Technical Information of China (English)

    HOU Fu-jun; WU Qi-zong

    2007-01-01

    A method for modeling the parallel machine scheduling problems with fuzzy parameters and precedence constraints based on credibility measure is provided.For the given n jobs to be processed on m machines, it is assumed that the processing times and the due dates are nonnegative fuzzy numbers and all the weights are positive, crisp numbers.Based on credibility measure, three parallel machine scheduling problems and a goal-programming model are formulated.Feasible schedules are evaluated not only by their objective values but also by the credibility degree of satisfaction with their precedence constraints.The genetic algorithm is utilized to find the best solutions in a short period of time.An illustrative numerical example is also given.Simulation results show that the proposed models are effective, which can deal with the parallel machine scheduling problems with fuzzy parameters and precedence constraints based on credibility measure.

  5. Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

    Science.gov (United States)

    Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

    2016-11-16

    Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

  6. Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining

    Directory of Open Access Journals (Sweden)

    Qiaokang Liang

    2016-11-01

    Full Text Available Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

  7. An approach to error elimination for multi-axis CNC machining and robot manipulation

    Institute of Scientific and Technical Information of China (English)

    XIONG; CaiHua

    2007-01-01

    The geometrical accuracy of a machined feature on a workpiece during machining processes is mainly affected by the kinematic chain errors of multi-axis CNC machines and robots, locating precision of fixtures, and datum errors on the workpiece. It is necessary to find a way to minimize the feature errors on the workpiece. In this paper, the kinematic chain errors are transformed into the displacements of the workpiece. The relationship between the kinematic chain errors and the displacements of the position and orientation of the workpiece is developed. A mapping model between the displacements of workpieces and the datum errors, and adjustments of fixtures is established. The suitable sets of unit basis twists for each of the commonly encountered types of feature and the corresponding locating directions are analyzed, and an error elimination (EE) method of the machined feature is formulated. A case study is given to verify the EE method.

  8. The art and science of rotating field machines design a practical approach

    CERN Document Server

    Ostović, Vlado

    2017-01-01

    This book highlights procedures utilized by the design departments of leading global manufacturers, offering readers essential insights into the electromagnetic and thermal design of rotating field (induction and synchronous) electric machines. Further, it details the physics of the key phenomena involved in the machines’ operation, conducts a thorough analysis and synthesis of polyphase windings, and presents the tools and methods used in the evaluation of winding performance. The book develops and solves the machines’ magnetic circuits, and determines their electromagnetic forces and torques. Special attention is paid to thermal problems in electrical machines, along with fluid flow computations. With a clear emphasis on the practical aspects of electric machine design and synthesis, the author applies his nearly 40 years of professional experience with electric machine manufacturers – both as an employee and consultant – to provide readers with the tools they need to determine fluid flow parameters...

  9. Processing outcomes of the AFM probe-based machining approach with different feed directions

    OpenAIRE

    2016-01-01

    We present experimental and theoretical results to describe and explain processing outcomes when producing nanochannels that are a few times wider than the atomic force microscope (AFM) probe using an AFM. This is achieved when AFM tip-based machining is performed with reciprocating motion of the tip of the AFM probe. In this case, different feed directions with respect to the orientation of the AFM probe can be used. The machining outputs of interest are the chip formation process, obtained ...

  10. A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

    OpenAIRE

    2010-01-01

    A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific p...

  11. Hybrid Optimization of Support Vector Machine for Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  12. Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

    Science.gov (United States)

    Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

    2015-08-01

    Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

  13. A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

    Science.gov (United States)

    Giraldo, Sergio I.; Ramirez, Rafael

    2016-01-01

    Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules

  14. Designing Green Stormwater Infrastructure for Hydrologic and Human Benefits: An Image Based Machine Learning Approach

    Science.gov (United States)

    Rai, A.; Minsker, B. S.

    2014-12-01

    Urbanization over the last century has degraded our natural water resources by increasing storm-water runoff, reducing nutrient retention, and creating poor ecosystem health downstream. The loss of tree canopy and expansion of impervious area and storm sewer systems have significantly decreased infiltration and evapotranspiration, increased stream-flow velocities, and increased flood risk. These problems have brought increasing attention to catchment-wide implementation of green infrastructure (e.g., decentralized green storm water management practices such as bioswales, rain gardens, permeable pavements, tree box filters, cisterns, urban wetlands, urban forests, stream buffers, and green roofs) to replace or supplement conventional storm water management practices and create more sustainable urban water systems. Current green infrastructure (GI) practice aims at mitigating the negative effects of urbanization by restoring pre-development hydrology and ultimately addressing water quality issues at an urban catchment scale. The benefits of green infrastructure extend well beyond local storm water management, as urban green spaces are also major contributors to human health. Considerable research in the psychological sciences have shown significant human health benefits from appropriately designed green spaces, yet impacts on human wellbeing have not yet been formally considered in GI design frameworks. This research is developing a novel computational green infrastructure (GI) design framework that integrates hydrologic requirements with criteria for human wellbeing. A supervised machine learning model is created to identify specific patterns in urban green spaces that promote human wellbeing; the model is linked to RHESSYS model to evaluate GI designs in terms of both hydrologic and human health benefits. An application of the models to Dead Run Watershed in Baltimore showed that image mining methods were able to capture key elements of human preferences that could

  15. Exploring the spectroscopic diversity of Type Ia supernovae with DRACULA: a machine learning approach

    Science.gov (United States)

    Sasdelli, M.; Ishida, E. E. O.; Vilalta, R.; Aguena, M.; Busti, V. C.; Camacho, H.; Trindade, A. M. M.; Gieseke, F.; de Souza, R. S.; Fantaye, Y. T.; Mazzali, P. A.

    2016-09-01

    The existence of multiple subclasses of Type Ia supernovae (SNe Ia) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNe Ia through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using deep learning, we were capable of performing such identification in a four-dimensional feature space (+1 for time evolution), while the standard principal component analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first-order effect in the determination of SN Ia subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNe Ia subtypes (and outliers). All tools used in this work were made publicly available in the PYTHON package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).

  16. A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

    Directory of Open Access Journals (Sweden)

    Sergio Ivan Giraldo

    2016-12-01

    Full Text Available Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1 quantitatively evaluate the accuracy of the induced models, (2 analyse the relative importance of the considered musical features, (3 discuss some of the learnt expressive performance rules in the context of previous work, and (4 assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules’ performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the

  17. A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

    Science.gov (United States)

    Giraldo, Sergio I; Ramirez, Rafael

    2016-01-01

    Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

  18. Diagnostic classification of specific phobia subtypes using structural MRI data: a machine-learning approach.

    Science.gov (United States)

    Lueken, Ulrike; Hilbert, Kevin; Wittchen, Hans-Ulrich; Reif, Andreas; Hahn, Tim

    2015-01-01

    While neuroimaging research has advanced our knowledge about fear circuitry dysfunctions in anxiety disorders, findings based on diagnostic groups do not translate into diagnostic value for the individual patient. Machine-learning generates predictive information that can be used for single subject classification. We applied Gaussian process classifiers to a sample of patients with specific phobia as a model disorder for pathological forms of anxiety to test for classification based on structural MRI data. Gray (GM) and white matter (WM) volumetric data were analyzed in 33 snake phobics (SP; animal subtype), 26 dental phobics (DP; blood-injection-injury subtype) and 37 healthy controls (HC). Results showed good accuracy rates for GM and WM data in predicting phobia subtypes (GM: 62 % phobics vs. HC, 86 % DP vs. HC, 89 % SP vs. HC, 89 % DP vs. SP; WM: 88 % phobics vs. HC, 89 % DP vs. HC, 79 % SP vs. HC, 79 % DP vs. HC). Regarding GM, classification improved when considering the subtype compared to overall phobia status. The discriminatory brain pattern was not solely based on fear circuitry structures but included widespread cortico-subcortical networks. Results demonstrate that multivariate pattern recognition represents a promising approach for the development of neuroimaging-based diagnostic markers that could support clinical decisions. Regarding the increasing number of fMRI studies on anxiety disorders, researchers are encouraged to use functional and structural data not only for studying phenotype characteristics on a group level, but also to evaluate their incremental value for diagnostic or prognostic purposes.

  19. A comparison of rule-based and machine learning approaches for classifying patient portal messages.

    Science.gov (United States)

    Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

    2017-09-01

    Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard

  20. A Runway Incursion Detection Approach Based on Multiple Protected Area and Flight Status Machine for A-SMGCS

    Directory of Open Access Journals (Sweden)

    Li Jing

    2016-01-01

    Full Text Available A-SMGCS is a modular system defined in the ICAO (International Civil Aviation Organization Manual on Advanced Surface Movement Guidance and Control System (A-SMGCS .One of A-SMGCS goals is to provide enhanced safety and protection of the runway. This paper presents a novel runway incursion detection approach for A-SMGCS, in which a multiple protected area is proposed to decrease complexity of pre-treatment for incursion judgment, and a flight status machine is designed to specify the transitions of one flight from one target status to another. Additionally, an HMI (Human Machine Interface independently developed by the Second Research Institute of CAAC (Civil Aviation Administration of China was designed in order to validate the runway incursion detection approach, the result shows that the algorithm has the potential to significantly improve runway safety by early detection and alerting of runway incursions.

  1. Developing the enzyme-machine analogy: a non-mathematical approach to teaching Michaelis-Menten kinetics

    Directory of Open Access Journals (Sweden)

    Simon Brown

    2010-06-01

    Full Text Available The behavior of enzyme-catalyzed reactions is not made clear to many students by the standard mathematical description of enzyme kinetics. An enzyme-machine analogy is described that has made the details of the Michaelis-Menten mechanism and the associated kinetics more accessible with minimal use of mathematics. Students taught using the analogy appear to have fewer of the misconceptions than those taught using a more mathematical approach.

  2. Developing the enzyme-machine analogy: a non-mathematical approach to teaching Michaelis-Menten kinetics

    OpenAIRE

    Simon Brown

    2010-01-01

    The behavior of enzyme-catalyzed reactions is not made clear to many students by the standard mathematical description of enzyme kinetics. An enzyme-machine analogy is described that has made the details of the Michaelis-Menten mechanism and the associated kinetics more accessible with minimal use of mathematics. Students taught using the analogy appear to have fewer of the misconceptions than those taught using a more mathematical approach.

  3. 基于支持向量机方法的股票预测模型%Forecast Models for Stocks Based on the Support Vector Machine Approach

    Institute of Scientific and Technical Information of China (English)

    马耀兰

    2013-01-01

      利用支持向量机方法建立股票投资预测模型,经过与多项式函数及Sigmoid核函数的对比,选用Gauss径向基函数作为SVM核函数;抽取223支上市公司的股票作为研究样本,并选取对股票投资影响显著的财务指标构造样本数据集,代入支持向量机模型进行实证测算;实验表明,与BP神经网络模型相比,在样本有限的情况下,基支持向量机的股票投资模型预测精度更高。%With the SVM approach , a forecast model of stock investment value was built .By comparing with polynomial function and sigmoid function, radial basic function was selected as the kernel function of SVM .223 stocks of Listed Compa-nies was selected as research sample , and the financial data which influenced the stock investment value was selected to con -struct the sample feature set which is put into the SVM model for empirical calculation .Experimental results show that SVM -based model performed significantly better than the neural network based model in both prediction precision and speed , espe-cially under the condition of limited training samples .

  4. Self-commissioning of permanent magnet synchronous machine drives using hybrid approach

    DEFF Research Database (Denmark)

    Basar, Mehmet Sertug

    2014-01-01

    Self-commissioning of permanent-magnet (PM) synchronous machines (PMSMs) is of prime importance in an industrial drive system because control performance and system stability depend heavily on the accurate machine parameter information. This article focuses on a combination of offline and online...... parameter estimation for a non-salient pole PMSM which eliminates the need for any prior knowledge on machine parameters. Stator resistance and inductance are first identified at standstill utilising fundamental and high-frequency excitation signals, respectively. A novel method has been developed...... and employed for inductance estimation. Then, stator resistance, inductance and PM flux are updated online using a recursive least-squares (RLS) algorithm. The proposed controllers are designed using MATLAB/Simulink® and implemented on d-Space® real-time system incorporating a commercially available PMSM drive....

  5. Bayesian analysis of rotating machines - A statistical approach to estimate and track the fundamental frequency

    DEFF Research Database (Denmark)

    Pedersen, Thorkild Find

    2003-01-01

    Rotating and reciprocating mechanical machines emit acoustic noise and vibrations when they operate. Typically, the noise and vibrations are concentrated in narrow frequency bands related to the running speed of the machine. The frequency of the running speed is referred to as the fundamental...... of an adaptive comb filter is derived for tracking non-stationary signals. The estimation problem is then rephrased in terms of the Bayesian statistical framework. In the Bayesian framework both parameters and observations are considered stochastic processes. The result of the estimation is an expression...

  6. Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Iness Ahriz

    2010-01-01

    Full Text Available Indoor handset localization in an urban apartment setting is studied using GSM trace mobile measurements. Nearest-neighbor, Support Vector Machine, Multilayer Perceptron, and Gaussian Process classifiers are compared. The linear Support Vector Machine provides mean room classification accuracy of almost 98% when all GSM carriers are used. To our knowledge, ours is the first study to use fingerprints containing all GSM carriers, as well as the first to suggest that GSM can be useful for localization of very high performance.

  7. An information-theoretic machine learning approach to expression QTL analysis.

    Directory of Open Access Journals (Sweden)

    Tao Huang

    Full Text Available Expression Quantitative Trait Locus (eQTL analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL, and on other chromosomes (trans-eQTL. Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What's more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR and Incremental Feature Selection (IFS, were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA. In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.

  8. Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

    Directory of Open Access Journals (Sweden)

    Ioannis Matthaiou

    2017-09-01

    Full Text Available In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine’s operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be non-stationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the non-stationary parts. The problem of high dimensionality of the features is addressed by “compressing” them using the kernel principal component analysis so that more meaningful, lower-dimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine (OCSVM algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine’s “normal” condition correspond to fuels and air-to-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the

  9. Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

    Science.gov (United States)

    Yamamoto, Yoichiro; Saito, Akira; Tateishi, Ayako; Shimojo, Hisashi; Kanno, Hiroyuki; Tsuchiya, Shinichi; Ito, Ken-ichi; Cosatto, Eric; Graf, Hans Peter; Moraleda, Rodrigo R.; Eils, Roland; Grabe, Niels

    2017-01-01

    Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression. PMID:28440283

  10. Introduction to special issue on machine learning approaches to shallow parsing

    NARCIS (Netherlands)

    Hammerton, J; Osborne, M; Armstrong, S; Daelemans, W

    2002-01-01

    This article introduces the problem of partial or shallow parsing (assigning partial syntactic structure to sentences) and explains why it is an important natural language processing (NLP) task. The complexity of the task makes Machine Learning an attractive option in comparison to the handcrafting

  11. A novel approach on parameter identification for inverter driven induction machines

    NARCIS (Netherlands)

    Koning, R.F.F.; Chou, C.T.; Verhaegen, M.H.G.; Klaassens, J.B.; Uittenbogaart, J.R.

    2000-01-01

    AC machines are applied in actuator systems for many applications. The development of fast processors open up the possibilities to apply new techniques for identification and control of electrical drives in real-time, so as to improve and optimize their performances. The paper presents a systematic

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

    NARCIS (Netherlands)

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

    2007-01-01

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

  13. A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions

    DEFF Research Database (Denmark)

    Petersen, Joan P; Winther, Ole; Jacobsen, Daniel J

    2012-01-01

    The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive...

  14. Ranking chemical structures for drug discovery: a new machine learning approach.

    Science.gov (United States)

    Agarwal, Shivani; Dugar, Deepak; Sengupta, Shiladitya

    2010-05-24

    With chemical libraries increasingly containing millions of compounds or more, there is a fast-growing need for computational methods that can rank or prioritize compounds for screening. Machine learning methods have shown considerable promise for this task; indeed, classification methods such as support vector machines (SVMs), together with their variants, have been used in virtual screening to distinguish active compounds from inactive ones, while regression methods such as partial least-squares (PLS) and support vector regression (SVR) have been used in quantitative structure-activity relationship (QSAR) analysis for predicting biological activities of compounds. Recently, a new class of machine learning methods - namely, ranking methods, which are designed to directly optimize ranking performance - have been developed for ranking tasks such as web search that arise in information retrieval (IR) and other applications. Here we report the application of these new ranking methods in machine learning to the task of ranking chemical structures. Our experiments show that the new ranking methods give better ranking performance than both classification based methods in virtual screening and regression methods in QSAR analysis. We also make some interesting connections between ranking performance measures used in cheminformatics and those used in IR studies.

  15. Mathematical programming approach to multidimensional mechanism design for single machine scheduling

    NARCIS (Netherlands)

    Duives, Jelle; Uetz, Marc

    2011-01-01

    We consider an optimal mechanism design problem for single machine scheduling that has been proposed by Heydenreich et al. in 2008. There, an example was presented to show that the 2-dimensional mechanism design problem does not satisfy a condition called IIA - independence of irrelevant alternative

  16. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    NARCIS (Netherlands)

    Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D G; Cordeiro, Maria Natália D S; Gümüş, Zeynep H; Costa, Joaquim; Bonvin, Alexandre M J J|info:eu-repo/dai/nl/113691238; de Sousa Moreira, Irina|info:eu-repo/dai/nl/412025000

    2016-01-01

    Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model

  17. A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions

    DEFF Research Database (Denmark)

    Petersen, Joan P; Winther, Ole; Jacobsen, Daniel J

    2012-01-01

    The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive sett...

  18. A suction detection system for rotary blood pumps based on the Lagrangian support vector machine algorithm.

    Science.gov (United States)

    Wang, Yu; Simaan, Marwan A

    2013-05-01

    The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided.

  19. Predictions of hot spot residues at protein-protein interfaces using support vector machines.

    Directory of Open Access Journals (Sweden)

    Stefano Lise

    Full Text Available Protein-protein interactions are critically dependent on just a few 'hot spot' residues at the interface. Hot spots make a dominant contribution to the free energy of binding and they can disrupt the interaction if mutated to alanine. Here, we present HSPred, a support vector machine(SVM-based method to predict hot spot residues, given the structure of a complex. HSPred represents an improvement over a previously described approach (Lise et al, BMC Bioinformatics 2009, 10:365. It achieves higher accuracy by treating separately predictions involving either an arginine or a glutamic acid residue. These are the amino acid types on which the original model did not perform well. We have therefore developed two additional SVM classifiers, specifically optimised for these cases. HSPred reaches an overall precision and recall respectively of 61% and 69%, which roughly corresponds to a 10% improvement. An implementation of the described method is available as a web server at http://bioinf.cs.ucl.ac.uk/hspred. It is free to non-commercial users.

  20. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  1. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

    Science.gov (United States)

    Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

    2017-03-01

    In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

  2. Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach.

    Science.gov (United States)

    Ding, Fangyu; Ge, Quansheng; Jiang, Dong; Fu, Jingying; Hao, Mengmeng

    2017-01-01

    Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.

  3. Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors

    KAUST Repository

    Warriach, Ehsan Ullah

    2013-01-01

    This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN-SVM significantly outperforms other algorithms in terms of classification accuracy. We also show that some of these algorithms could run in real time on the prototype system. Copyright © 2013 ACM.

  4. Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.

    Science.gov (United States)

    Kang, John; Schwartz, Russell; Flickinger, John; Beriwal, Sushil

    2015-12-01

    Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, "spam" filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the "barrier to entry" for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods--logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)--and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. A continuous speech recognition approach for the design of a dictation machine

    OpenAIRE

    Smaïli, Kamel; Charpillet, François; Pierrel, Jean-Mari; Haton, Jean-Paul

    1991-01-01

    International audience; The oral entry of texts (dictation machine) remains an important potential field of application for automatic speech recognition. The RFIA group of CRIN/INRIA has been investigating this research area for the french language during the past ten years. We propose in this paper a general presentation of the present state of our MAUD system which is based upon four major interacting components: an acoustic phonetic decoder, a lexical component, a linguistic model and a us...

  6. A novel approach for lie detection based on F-score and extreme learning machine.

    Directory of Open Access Journals (Sweden)

    Junfeng Gao

    Full Text Available A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.

  7. "Machine" consciousness and "artificial" thought: an operational architectonics model guided approach.

    Science.gov (United States)

    Fingelkurts, Andrew A; Fingelkurts, Alexander A; Neves, Carlos F H

    2012-01-05

    Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical operational architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis of the phenomenal level of brain organization. In this context the problem of producing man-made "machine" consciousness and "artificial" thought is a matter of duplicating all levels of the operational architectonics hierarchy (with its inherent rules and mechanisms) found in the brain electromagnetic field. We hope that the conceptual-theoretical framework described in this paper will stimulate the interest of mathematicians and/or computer scientists to abstract and formalize principles of hierarchy of brain operations which are the building blocks for phenomenal consciousness and thought. Copyright © 2010 Elsevier B.V. All rights reserved.

  8. New approach for the diagnosis of extractions with neural network machine learning.

    Science.gov (United States)

    Jung, Seok-Ki; Kim, Tae-Woo

    2016-01-01

    The decision to extract teeth for orthodontic treatment is important and difficult because it tends to be based on the practitioner's experiences. The purposes of this study were to construct an artificial intelligence expert system for the diagnosis of extractions using neural network machine learning and to evaluate the performance of this model. The subjects included 156 patients. Input data consisted of 12 cephalometric variables and an additional 6 indexes. Output data consisted of 3 bits to divide the extraction patterns. Four neural network machine learning models for the diagnosis of extractions were constructed using a back-propagation algorithm and were evaluated. The success rates of the models were 93% for the diagnosis of extraction vs nonextraction and 84% for the detailed diagnosis of the extraction patterns. This study suggests that artificial intelligence expert systems with neural network machine learning could be useful in orthodontics. Improved performance was achieved by components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization. Copyright © 2016 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

  9. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

    Science.gov (United States)

    Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

    2017-01-01

    Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

  10. Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach

    Science.gov (United States)

    Yadav, Basant; Ch, Sudheer; Mathur, Shashi; Adamowski, Jan

    2016-12-01

    In-situ bioremediation is the most common groundwater remediation procedure used for treating organically contaminated sites. A simulation-optimization approach, which incorporates a simulation model for groundwaterflow and transport processes within an optimization program, could help engineers in designing a remediation system that best satisfies management objectives as well as regulatory constraints. In-situ bioremediation is a highly complex, non-linear process and the modelling of such a complex system requires significant computational exertion. Soft computing techniques have a flexible mathematical structure which can generalize complex nonlinear processes. In in-situ bioremediation management, a physically-based model is used for the simulation and the simulated data is utilized by the optimization model to optimize the remediation cost. The recalling of simulator to satisfy the constraints is an extremely tedious and time consuming process and thus there is need for a simulator which can reduce the computational burden. This study presents a simulation-optimization approach to achieve an accurate and cost effective in-situ bioremediation system design for groundwater contaminated with BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) compounds. In this study, the Extreme Learning Machine (ELM) is used as a proxy simulator to replace BIOPLUME III for the simulation. The selection of ELM is done by a comparative analysis with Artificial Neural Network (ANN) and Support Vector Machine (SVM) as they were successfully used in previous studies of in-situ bioremediation system design. Further, a single-objective optimization problem is solved by a coupled Extreme Learning Machine (ELM)-Particle Swarm Optimization (PSO) technique to achieve the minimum cost for the in-situ bioremediation system design. The results indicate that ELM is a faster and more accurate proxy simulator than ANN and SVM. The total cost obtained by the ELM-PSO approach is held to a minimum

  11. Two computational approaches for Monte Carlo based shutdown dose rate calculation with applications to the JET fusion machine

    Energy Technology Data Exchange (ETDEWEB)

    Petrizzi, L.; Batistoni, P.; Migliori, S. [Associazione EURATOM ENEA sulla Fusione, Frascati (Roma) (Italy); Chen, Y.; Fischer, U.; Pereslavtsev, P. [Association FZK-EURATOM Forschungszentrum Karlsruhe (Germany); Loughlin, M. [EURATOM/UKAEA Fusion Association, Culham Science Centre, Abingdon, Oxfordshire, OX (United Kingdom); Secco, A. [Nice Srl Via Serra 33 Camerano Casasco AT (Italy)

    2003-07-01

    In deuterium-deuterium (D-D) and deuterium-tritium (D-T) fusion plasmas neutrons are produced causing activation of JET machine components. For safe operation and maintenance it is important to be able to predict the induced activation and the resulting shut down dose rates. This requires a suitable system of codes which is capable of simulating both the neutron induced material activation during operation and the decay gamma radiation transport after shut-down in the proper 3-D geometry. Two methodologies to calculate the dose rate in fusion devices have been developed recently and applied to fusion machines, both using the MCNP Monte Carlo code. FZK has developed a more classical approach, the rigorous 2-step (R2S) system in which MCNP is coupled to the FISPACT inventory code with an automated routing. ENEA, in collaboration with the ITER Team, has developed an alternative approach, the direct 1 step method (D1S). Neutron and decay gamma transport are handled in one single MCNP run, using an ad hoc cross section library. The intention was to tightly couple the neutron induced production of a radio-isotope and the emission of its decay gammas for an accurate spatial distribution and a reliable calculated statistical error. The two methods have been used by the two Associations to calculate the dose rate in five positions of JET machine, two inside the vacuum chamber and three outside, at cooling times between 1 second and 1 year after shutdown. The same MCNP model and irradiation conditions have been assumed. The exercise has been proposed and financed in the frame of the Fusion Technological Program of the JET machine. The scope is to supply the designers with the most reliable tool and data to calculate the dose rate on fusion machines. Results showed that there is a good agreement: the differences range between 5-35%. The next step to be considered in 2003 will be an exercise in which the comparison will be done with dose-rate data from JET taken during and

  12. A least square support vector machine-based approach for contingency classification and ranking in a large power system

    Directory of Open Access Journals (Sweden)

    Bhanu Pratap Soni

    2016-12-01

    Full Text Available This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs: line MVA performance index (PIMVA and Voltage-reactive power performance index (PIVQ. SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system. The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

  13. Imputation And Classification Of Missing Data Using Least Square Support Vector Machines – A New Approach In Dementia Diagnosis

    Directory of Open Access Journals (Sweden)

    T R Sivapriya

    2012-07-01

    Full Text Available This paper presents a comparison of different data imputation approaches used in filling missing data and proposes a combined approach to estimate accurately missing attribute values in a patient database. The present study suggests a more robust technique that is likely to supply a value closer to the one that is missing for effective classification and diagnosis. Initially data is clustered and z-score method is used to select possible values of an instance with missing attribute values. Then multiple imputation method using LSSVM (Least Squares Support Vector Machine is applied to select the most appropriate values for the missing attributes. Five imputed datasets have been used to demonstrate the performance of the proposed method. Experimental results show that our method outperforms conventional methods of multiple imputation and mean substitution. Moreover, the proposed method CZLSSVM (Clustered Z-score Least Square Support Vector Machine has been evaluated in two classification problems for incomplete data. The efficacy of the imputation methods have been evaluated using LSSVM classifier. Experimental results indicate that accuracy of the classification is increases with CZLSSVM in the case of missing attribute value estimation. It is found that CZLSSVM outperforms other data imputation approaches like decision tree, rough sets and artificial neural networks, K-NN (K-Nearest Neighbour and SVM. Further it is observed that CZLSSVM yields 95 per cent accuracy and prediction capability than other methods included and tested in the study.

  14. Profiled support vector machines for antisense oligonucleotide efficacy prediction

    Directory of Open Access Journals (Sweden)

    Martín-Guerrero José D

    2004-09-01

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

  15. Novel Approach for Automatic Detection of Atrial Fibrillation Based on Inter Beat Intervals and Support Vector Machine

    DEFF Research Database (Denmark)

    Andersen, Rasmus S.; Poulsen, Erik S.; Puthusserypady, Sadasivan

    2017-01-01

    for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classification. The results are compared to a state of the art algorithm based on raw ECG....... Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classification performance (Sensitivity≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for significantly reduced computation time without loss of performance....

  16. Support Vector Machine Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier Recognition System

    Directory of Open Access Journals (Sweden)

    Ghulam M. Hassan

    2012-01-01

    Full Text Available Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma σ while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for σ value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of σ for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed systems success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having σ value of 10 is optimal in identifying RPW from an image. The optimal input

  17. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2015-12-01

    Full Text Available Mechanical faults of high voltage circuit breakers (HVCBs are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE and one-class support vector machine (OCSVM is proposed. In this method; the S-transform (ST is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach.

  18. Impact of Health Care Employees’ Job Satisfaction On Organizational Performance Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    Cemil Kuzey

    2012-05-01

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

  19. [Fragments Woven into a Whole: A New Approach to the Diagnosis of Cancer through Mass Spectrometry and Machine-Learning].

    Science.gov (United States)

    Takeda, Sen; Yoshimura, Kentaro; Tanabe, Kunio

    2015-09-01

    Conventionally, a definitive diagnosis of cancer is derived from histopathological diagnostics based on morphological criteria that are difficult to standardize on a quantifiable basis. On the other hand, while molecular tumor markers and blood biochemical profiles give quantitative values evaluated by objective criteria, these parameters are usually generated by deductive methods such as peak extraction. Therefore, some of the data that may contain useful information on specimens are discarded. To overcome the disadvantages of these methods, we have developed a new approach by employing both mass spectrometry and machine-learning for cancer diagnosis. Probe electrospray ionization (PESI) is a derivative of electrospray ionization that uses a fine acupuncture needle as a sample picker as well as an ion emitter for mass spectrometry. This method enables us to ionize very small tissue samples up to a few pico liters in the presence of physiological concentrations of inorganic salts, without the need for any sample pretreatment. Moreover, as this technique makes it possible to ionize all components with minimal suppression effects, we can retrieve much more molecular information from specimens. To make the most of data enriched with lipid compounds and substances with lower molecular weights such as carbohydrates, we employed machine-learning named the dual penalized logistic regression machine (dPLRM). This method is completely different from pattern-matching in that it discriminates categories by projecting the spectral data into a mathematical space with very high dimensions, where final judgment is made. We are now approaching the final clinical trial to validate the usefulness of our system.

  20. 一种基于 QBC 的 SVM 主动学习算法%Active learning algorithm for SVM based on QBC

    Institute of Scientific and Technical Information of China (English)

    徐海龙; 别晓峰; 冯卉; 吴天爱

    2015-01-01

    To the problem that large-scale labeled samples is not easy to acquire and the class-unbalanced dataset in the course of souport vector machine (SVM)training,an active learning algorithm based on query by committee (QBC)for SVM(QBC-ASVM)is proposed,which efficiently combines the improved QBC active learning and the weighted SVM.In this method,QBC active learning is used to select the samples which are the most valuable to the current SVM classifier,and the weighted SVM is used to reduce the impact of the unba-lanced data set on SVMs active learning.The experimental results show that the proposed approach can consid-erably reduce the labeled samples and costs compared with the passive SVM,and at the same time,it can ensure that the accurate classification performance is kept as the passive SVM,and the proposed method improves gen-eralization performance and also expedites the SVM training.%针对支持向量机(souport vector machine,SVM)训练学习过程中样本分布不均衡、难以获得大量带有类标注样本的问题,提出一种基于委员会投票选择(query by committee,QBC)的 SVM 主动学习算法 QBC-AS-VM,将改进的 QBC 主动学习方法与加权 SVM 方法有机地结合应用于 SVM 训练学习中,通过改进的 QBC 主动学习,主动选择那些对当前 SVM 分类器最有价值的样本进行标注,在 SVM 主动学习中应用改进的加权 SVM,减少了样本分布不均衡对 SVM 主动学习性能的影响,实验结果表明在保证不影响分类精度的情况下,所提出的算法需要标记的样本数量大大少于随机采样法需要标记的样本数量,降低了学习的样本标记代价,提高了 SVM 泛化性能而且训练速度同样有所提高。

  1. Applying machine learning approaches to improving the accuracy of breast-tumour diagnosis via fine needle aspiration

    Institute of Scientific and Technical Information of China (English)

    YUAN Qian-fei; CAI Cong-zhong; XIAO Han-guang; LIU Xing-hua

    2007-01-01

    Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnosing method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumour through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according to signal-to-noise ratio to identify determinants distinguishing benign breast tumours from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04%, a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5-fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumours and thus reduce the incidence of misdiagnosis.

  2. An Approach to automatically optimize the Hydraulic performance of Blade System for Hydraulic Machines using Multi-objective Genetic Algorithm

    Science.gov (United States)

    Lai, Xide; Chen, Xiaoming; Zhang, Xiang; Lei, Mingchuan

    2016-11-01

    This paper presents an approach to automatic hydraulic optimization of hydraulic machine's blade system combining a blade geometric modeller and parametric generator with automatic CFD solution procedure and multi-objective genetic algorithm. In order to evaluate a plurality of design options and quickly estimate the blade system's hydraulic performance, the approximate model which is able to substitute for the original inside optimization loop has been employed in the hydraulic optimization of blade by using function approximation. As the approximate model is constructed through the database samples containing a set of blade geometries and their resulted hydraulic performances, it can ensure to correctly imitate the real blade's performances predicted by the original model. As hydraulic machine designers are accustomed to do design with 2D blade profiles on stream surface that are then stacked to 3D blade geometric model in the form of NURBS surfaces, geometric variables to be optimized were defined by a series profiles on stream surfaces. The approach depends on the cooperation between a genetic algorithm, a database and user defined objective functions and constraints which comprises hydraulic performances, structural and geometric constraint functions. Example covering optimization design of a mixed-flow pump impeller is presented.

  3. On Internet Traffic Classification: A Two-Phased Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Taimur Bakhshi

    2016-01-01

    Full Text Available Traffic classification utilizing flow measurement enables operators to perform essential network management. Flow accounting methods such as NetFlow are, however, considered inadequate for classification requiring additional packet-level information, host behaviour analysis, and specialized hardware limiting their practical adoption. This paper aims to overcome these challenges by proposing two-phased machine learning classification mechanism with NetFlow as input. The individual flow classes are derived per application through k-means and are further used to train a C5.0 decision tree classifier. As part of validation, the initial unsupervised phase used flow records of fifteen popular Internet applications that were collected and independently subjected to k-means clustering to determine unique flow classes generated per application. The derived flow classes were afterwards used to train and test a supervised C5.0 based decision tree. The resulting classifier reported an average accuracy of 92.37% on approximately 3.4 million test cases increasing to 96.67% with adaptive boosting. The classifier specificity factor which accounted for differentiating content specific from supplementary flows ranged between 98.37% and 99.57%. Furthermore, the computational performance and accuracy of the proposed methodology in comparison with similar machine learning techniques lead us to recommend its extension to other applications in achieving highly granular real-time traffic classification.

  4. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    Directory of Open Access Journals (Sweden)

    Rita Melo

    2016-07-01

    Full Text Available Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM, for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

  5. Effect of reinforcement on the cutting forces while machining metal matrix composites–An experimental approach

    Directory of Open Access Journals (Sweden)

    Ch. Shoba

    2015-12-01

    Full Text Available Hybrid metal matrix composites are of great interest for researchers in recent years, because of their attractive superior properties over traditional materials and single reinforced composites. The machinabilty of hybrid composites becomes vital for manufacturing industries. The need to study the influence of process parameters on the cutting forces in turning such hybrid composite under dry environment is essentially required. In the present study, the influence of machining parameters, e.g. cutting speed, feed and depth of cut on the cutting force components, namely feed force (Ff, cutting force (Fc, and radial force (Fd has been investigated. Investigations were performed on 0, 2, 4, 6 and 8 wt% Silicon carbide (SiC and rice husk ash (RHA reinforced composite specimens. A comparison was made between the reinforced and unreinforced composites. The results proved that all the cutting force components decrease with the increase in the weight percentage of the reinforcement: this was probably due to the dislocation densities generated from the thermal mismatch between the reinforcement and the matrix. Experimental evidence also showed that built-up edge (BUE is formed during machining of low percentage reinforced composites at high speed and high depth of cut. The formation of BUE was captured by SEM, therefore confirming the result. The decrease of cutting force components with lower cutting speed and higher feed and depth of cut was also highlighted. The related mechanisms are explained and presented.

  6. Machine-learning approaches for classifying haplogroup from Y chromosome STR data.

    Directory of Open Access Journals (Sweden)

    Joseph Schlecht

    2008-06-01

    Full Text Available Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs. Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.

  7. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Iftikhar Ali

    2015-12-01

    Full Text Available The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV, specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture from remote sensing data using machine learning methods.

  8. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    Science.gov (United States)

    Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D. G.; Cordeiro, Maria Natália D. S.; Gümüş, Zeynep H.; Costa, Joaquim; Bonvin, Alexandre M. J. J.; Moreira, Irina S.

    2016-01-01

    Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. PMID:27472327

  9. Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems.

    Science.gov (United States)

    Kew, William; Mitchell, John B O

    2015-09-01

    The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. This investigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the 'wisdom of crowds' principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data preprocessing methodology was found to be crucial to performance of each method too. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Neuropsychological test selection for cognitive impairment classification: A machine learning approach.

    Science.gov (United States)

    Weakley, Alyssa; Williams, Jennifer A; Schmitter-Edgecombe, Maureen; Cook, Diane J

    2015-01-01

    Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI), or dementia using a suite of classification techniques. Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis; Clinical Dementia Rating, CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals with CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. A total of 27 demographic, psychological, and neuropsychological variables were available for variable selection. No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0-99.1%), geometric mean (60.9-98.1%), sensitivity (44.2-100%), and specificity (52.7-100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2-9 variables were required for classification and varied between datasets in a clinically meaningful way. The current study results reveal that machine learning techniques can accurately classify cognitive impairment and reduce the number of measures required for diagnosis.

  11. A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Xiaohui Li

    2010-01-01

    Full Text Available A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.

  12. Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach.

    Science.gov (United States)

    Mohan, Dhanya Menoth; Kumar, Parmod; Mahmood, Faisal; Wong, Kian Foong; Agrawal, Abhishek; Elgendi, Mohamed; Shukla, Rohit; Ang, Natania; Ching, April; Dauwels, Justin; Chan, Alice H D

    2016-01-01

    The purpose of the study is to examine the effect of subliminal priming in terms of the perception of images influenced by words with positive, negative, and neutral emotional content, through electroencephalograms (EEGs). Participants were instructed to rate how much they like the stimuli images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words that exhibit positive, negative, and neutral connotations with respect to the images. Simultaneously, the EEGs were recorded. Statistical tests such as repeated measures ANOVAs and two-tailed paired-samples t-tests were performed to measure significant differences in the likability ratings among the three prime affect types; the results showed a strong shift in the likeness judgment for the images in the positively primed condition compared to the other two. The acquired EEGs were examined to assess the difference in brain activity associated with the three different conditions. The consistent results obtained confirmed the overall priming effect on participants' explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs), and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively for average-trial binary classifier and average-trial multi-class further emphasize that the ERPs encode information about the different kinds of primes.

  13. Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach.

    Directory of Open Access Journals (Sweden)

    Dhanya Menoth Mohan

    Full Text Available The purpose of the study is to examine the effect of subliminal priming in terms of the perception of images influenced by words with positive, negative, and neutral emotional content, through electroencephalograms (EEGs. Participants were instructed to rate how much they like the stimuli images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words that exhibit positive, negative, and neutral connotations with respect to the images. Simultaneously, the EEGs were recorded. Statistical tests such as repeated measures ANOVAs and two-tailed paired-samples t-tests were performed to measure significant differences in the likability ratings among the three prime affect types; the results showed a strong shift in the likeness judgment for the images in the positively primed condition compared to the other two. The acquired EEGs were examined to assess the difference in brain activity associated with the three different conditions. The consistent results obtained confirmed the overall priming effect on participants' explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs, and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively for average-trial binary classifier and average-trial multi-class further emphasize that the ERPs encode information about the different kinds of primes.

  14. CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

    Energy Technology Data Exchange (ETDEWEB)

    Ruff, Kiersten M. [Computational and Systems Biology Program and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130-4899 (United States); Harmon, Tyler S. [Department of Physics and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130-4899 (United States); Pappu, Rohit V., E-mail: pappu@wustl.edu [Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis, CB 1097, St. Louis, Missouri 63130-4899 (United States)

    2015-12-28

    We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences.

  15. Hemodynamic modelling of BOLD fMRI - A machine learning approach

    DEFF Research Database (Denmark)

    Jacobsen, Danjal Jakup

    2007-01-01

    This Ph.D. thesis concerns the application of machine learning methods to hemodynamic models for BOLD fMRI data. Several such models have been proposed by different researchers, and they have in common a basis in physiological knowledge of the hemodynamic processes involved in the generation...... of the BOLD signal. The BOLD signal is modelled as a non-linear function of underlying, hidden (non-measurable) hemodynamic state variables. The focus of this thesis work has been to develop methods for learning the parameters of such models, both in their traditional formulation, and in a state space...... formulation. In the latter, noise enters at the level of the hidden states, as well as in the BOLD measurements themselves. A framework has been developed to allow approximate posterior distributions of model parameters to be learned from real fMRI data. This is accomplished with Markov chain Monte Carlo...

  16. Performance Optimization of Electrical Discharge Machining (Die Sinker for Al-6061 via Taguchi Approach

    Directory of Open Access Journals (Sweden)

    Muhammad Qaiser Saleem

    2015-04-01

    Full Text Available This paper parametrically optimizes the EDM (Electrical Discharge Machining process in die sinking mode for material removal rate, surface roughness and edge quality of aluminum alloy Al-6061. The effect of eight parameters namely discharge current, pulse on-time, pulse off-time, auxiliary current, working time, jump time distance, servo speed and work piece hardness are investigated. Taguchi's orthogonal array L18 is employed herein for experimentation. ANOVA (Analysis of Variance with F-ratio criterion at 95% confidence level is used for identification of significant parameters whereas SNR (Signal to Noise Ratio is used for determination of optimum levels. Optimization obtained for Al-6061 with parametric combination investigated herein is validated by the confirmation run.

  17. Application of Machine Learning Approaches in Intrusion Detection System: A Survey

    Directory of Open Access Journals (Sweden)

    Nutan Farah Haq

    2015-03-01

    Full Text Available Network security is one of the major concerns of the modern era. With the rapid development and massive usage of internet over the past decade, the vulnerabilities of network security have become an important issue. Intrusion detection system is used to identify unauthorized access and unusual attacks over the secured networks. Over the past years, many studies have been conducted on the intrusion detection system. However, in order to understand the current status of implementation of machine learning techniques for solving the intrusion detection problems this survey paper enlisted the 49 related studies in the time frame between 2009 and 2014 focusing on the architecture of the single, hybrid and ensemble classifier design. This survey paper also includes a statistical comparison of classifier algorithms, datasets being used and some other experimental setups as well as consideration of feature selection step.

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

    Science.gov (United States)

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

    2014-07-01

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

  19. Offering a New Approach for Approximate Pattern Matching in Example-Based Machine Translation

    Directory of Open Access Journals (Sweden)

    Reza Akbari

    2015-01-01

    Full Text Available In this article, a new model is proposed in order to measure the degree of similarity between two sentences in machine translation based on example. The proposed model has applied genetic algorithm beside a new fitness function which is based on semantic load matching between the two sentences. Here, verbs are considered as the heart of a sentence because they are the main part of a sentence and carry the major part of the semantic load in the sentence; therefore more attention is paid to the verbs in the fitness function. It is noteworthy that the proposed model is largely dependent on the verbal part and the extracted synonyms from WordNet as well as the arrangement of words. The results are promising by precision and recall, indicating that the proposed method improves the quality of the retrieved matched sentences.

  20. Ice breakup forecast in the reach of the Yellow River: the support vector machines approach

    Directory of Open Access Journals (Sweden)

    H. Zhou

    2009-04-01

    Full Text Available Accurate lead-time forecast of ice breakup is one of the key aspects for ice flood prevention and reducing losses. In this paper, a new data-driven model based on the Statistical Learning Theory was employed for ice breakup prediction. The model, known as Support Vector Machine (SVM, follows the principle that aims at minimizing the structural risk rather than the empirical risk. In order to estimate the appropriate parameters of the SVM, Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM-UA algorithm is performed through exponential transformation. A case study was conducted in the reach of the Yellow River. Results from the proposed model showed a promising performance compared with that from artificial neural network, so the model can be considered as an alternative and practical tool for ice breakup forecast.

  1. Detection of broken rotor bars in induction machines: An approach using wavelet packets in MCSA

    Science.gov (United States)

    Escobar-Moreira, León; Antonino-Daviu, José; Riera-Guasp, Martin

    2012-12-01

    Bar breaking diagnosis in electrical induction cage motors is a topic of interest due to their extensive use in industry. In contrast to the typical method of using Fourier analysis of the steady-state stator current, Discrete Wavelet Transform (DWT) methods have been found to better analyze the time changing nature of the current spectrum of these machines at start-up when broken bars exist [1]. This paper advances the analysis to Wavelet Packets (WP) in order to study the high order harmonic components of the spectrum which constitute a useful source of information in situations where tracing the low-frequency fault harmonics (sideband components) may not reach a definite diagnostic (i.e. presence of low-frequency load torque oscillations, effect of inter-bar currents, etc...).

  2. A machine learning approach to the potential-field method for implicit modeling of geological structures

    Science.gov (United States)

    Gonçalves, Ítalo Gomes; Kumaira, Sissa; Guadagnin, Felipe

    2017-06-01

    Implicit modeling has experienced a rise in popularity over the last decade due to its advantages in terms of speed and reproducibility in comparison with manual digitization of geological structures. The potential-field method consists in interpolating a scalar function that indicates to which side of a geological boundary a given point belongs to, based on cokriging of point data and structural orientations. This work proposes a vector potential-field solution from a machine learning perspective, recasting the problem as multi-class classification, which alleviates some of the original method's assumptions. The potentials related to each geological class are interpreted in a compositional data framework. Variogram modeling is avoided through the use of maximum likelihood to train the model, and an uncertainty measure is introduced. The methodology was applied to the modeling of a sample dataset provided with the software Move™. The calculations were implemented in the R language and 3D visualizations were prepared with the rgl package.

  3. Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.

    Science.gov (United States)

    Rodriguez, Laritza M; Fushman, Dina Demner

    2015-01-01

    With regular expressions and manual review, 18,342 FDA-approved drug product labels were processed to determine if the five standard pregnancy drug risk categories were mentioned in the label. After excluding 81 drugs with multiple-risk categories, 83% of the labels had a risk category within the text and 17% labels did not. We trained a Sequential Minimal Optimization algorithm on the labels containing pregnancy risk information segmented into standard document sections. For the evaluation of the classifier on the testing set, we used the Micromedex drug risk categories. The precautions section had the best performance for assigning drug risk categories, achieving Accuracy 0.79, Precision 0.66, Recall 0.64 and F1 measure 0.65. Missing pregnancy risk categories could be suggested using machine learning algorithms trained on the existing publicly available pregnancy risk information.

  4. Mechatronics Approach for a Controlled Actuation of the Presser Foot Mechanism on an Industrial Sewing Machine

    Directory of Open Access Journals (Sweden)

    L. F. Silva

    2000-01-01

    Full Text Available This paper describes the study of the research program being carried out on the feeding system of an industrial overlock sewing machine. The results obtained from the presser foot bar displacement and compression force, together with the graphic kinematic analysis, which includes the velocity and acceleration taken from the displacement-time curves of the presser bar, led to further understanding of the feeding system dynamics. This study is providing the basis for the development of a redesigned and optimized fabric feeding system. The new actuation system, based on a proportional force solenoid integrated in the presser foot bar, will be also discussed as an important contribution to achieving a desired dynamic behaviour at high sewing speeds.

  5. Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey

    Directory of Open Access Journals (Sweden)

    Ankit Chaudhary

    2011-02-01

    Full Text Available Hand gestures recognition (HGR is one of the main areas of research for the engineers,scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today manyresearchers in the academia and industry are working on different application to make interactions moreeasy, natural and convenient without wearing any extra device. HGR can be applied from games controlto vision enabled robot control, from virtual reality to smart home systems. In this paper we arediscussing work done in the area of hand gesture recognition where focus is on the intelligentapproaches including soft computing based methods like artificial neural network, fuzzy logic, geneticalgorithms etc. The methods in the preprocessing of image for segmentation and hand imageconstruction also taken into study. Most researchers used fingertips for hand detection in appearancebased modeling. Finally the comparison of results given by different researchers is also presented.

  6. Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine-Learning Approach

    Science.gov (United States)

    Zahedinejad, Ehsan; Ghosh, Joydip; Sanders, Barry C.

    2016-11-01

    Three-qubit quantum gates are key ingredients for quantum error correction and quantum-information processing. We generate quantum-control procedures to design three types of three-qubit gates, namely Toffoli, controlled-not-not, and Fredkin gates. The design procedures are applicable to a system comprising three nearest-neighbor-coupled superconducting artificial atoms. For each three-qubit gate, the numerical simulation of the proposed scheme achieves 99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant quantum computing. We test our procedure in the presence of decoherence-induced noise and show its robustness against random external noise generated by the control electronics. The three-qubit gates are designed via the machine-learning algorithm called subspace-selective self-adaptive differential evolution.

  7. ON THE APPLICATION OF PARTIAL BARRIERS FOR SPINNING MACHINE NOISE CONTROL: A THEORETICAL AND EXPERIMENTAL APPROACH

    Directory of Open Access Journals (Sweden)

    M. R. Monazzam, A. Nezafat

    2007-04-01

    Full Text Available Noise is one of the most serious challenges in modern community. In some specific industries, according to the nature of process, this challenge is more threatening. This paper describes a means of noise control for spinning machine based on experimental measurements. Also advantages and disadvantages of the control procedure are added. Different factors which may affect the performance of the barrier in this situation are also mentioned. To provide a good estimation of the control measure, a theoretical formula is also described and it is compared with the field data. Good agreement between the results of filed measurements and theoretical presented model was achieved. No obvious noise reduction was seen by partial indoor barriers in low absorbent enclosed spaces, since the reflection from multiple hard surfaces is the main dominated factor in the tested environment. At the end, the situation of the environment and standards, which are necessary in attaining the ideal results, are explained.

  8. Modular Approach to Designing Computer Cultural Systems: Culture as a Thermodynamic Machine

    Directory of Open Access Journals (Sweden)

    Leland Gilsen

    2015-01-01

    Full Text Available Culture is a complex non-linear system. In order to design computer simulations of cultural systems, it is necessary to break the system down into sub-systems. Human culture is modular. It consists of sets of people that belong to economic units. Access to, and control over matter, energy and information is postulated as the key to development of cultural simulations. Because resources in the real world are patchy, access to and control over resources is expressed in two related arenas: economics (direct control and politics (non-direct control. The best way to create models for cultural ecology/economics lies in an energy-information-economic paradigm based on general systems theory and an understanding of the "thermodynamics" of ecology, or culture as a thermodynamic machine.

  9. Direct Torque Control of Sensorless Induction Machine Drives: A Two-Stage Kalman Filter Approach

    Directory of Open Access Journals (Sweden)

    Jinliang Zhang

    2015-01-01

    Full Text Available Extended Kalman filter (EKF has been widely applied for sensorless direct torque control (DTC in induction machines (IMs. One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. Simulation and experimental results verify the performance of the proposed TEKF estimator for DTC of IMs.

  10. Machine Learning Approaches to Classification of Seafloor Features from High Resolution Sonar Data

    Science.gov (United States)

    Smith, D. G.; Ed, L.; Sofge, D.; Elmore, P. A.; Petry, F.

    2014-12-01

    Navigation charts provide topographic maps of the seafloor created from swaths of sonar data. Converting sonar data to a topographic map is a manual, labor-intensive process that can be greatly assisted by contextual information obtained from automated classification of geomorphological structures. Finding structures such as seamounts can be challenging, as there are no established rules that can be used for decision-making. Often times, it is a determination that is made by human expertise. A variety of feature metrics may be useful for this task and we use a large number of metrics relevant to the task of finding seamounts. We demonstrate this ability in locating seamounts by two related machine learning techniques. As well as achieving good accuracy in classification, the human-understandable set of metrics that are most important for the results are discussed.

  11. In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids

    Energy Technology Data Exchange (ETDEWEB)

    Kangas, Lars J.; Metz, Thomas O.; Isaac, Georgis; Schrom, Brian T.; Ginovska-Pangovska, Bojana; Wang, Luning; Tan, Li; Lewis, Robert R.; Miller, John H.

    2012-05-15

    Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissocia-tion tandem mass spectrometry. A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity.

  12. Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

    Science.gov (United States)

    Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

    2016-01-01

    Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

  13. ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

    Science.gov (United States)

    Wang, Shuangquan; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

    2016-08-01

    Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

  14. A new support vector machine based multiuser detection scheme

    Institute of Scientific and Technical Information of China (English)

    WANG Yong-jian; ZHAO Hong-lin

    2008-01-01

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

  15. A Cross-Entropy-Based Admission Control Optimization Approach for Heterogeneous Virtual Machine Placement in Public Clouds

    Directory of Open Access Journals (Sweden)

    Li Pan

    2016-03-01

    Full Text Available Virtualization technologies make it possible for cloud providers to consolidate multiple IaaS provisions into a single server in the form of virtual machines (VMs. Additionally, in order to fulfill the divergent service requirements from multiple users, a cloud provider needs to offer several types of VM instances, which are associated with varying configurations and performance, as well as different prices. In such a heterogeneous virtual machine placement process, one significant problem faced by a cloud provider is how to optimally accept and place multiple VM service requests into its cloud data centers to achieve revenue maximization. To address this issue, in this paper, we first formulate such a revenue maximization problem during VM admission control as a multiple-dimensional knapsack problem, which is known to be NP-hard to solve. Then, we propose to use a cross-entropy-based optimization approach to address this revenue maximization problem, by obtaining a near-optimal eligible set for the provider to accept into its data centers, from the waiting VM service requests in the system. Finally, through extensive experiments and measurements in a simulated environment with the settings of VM instance classes derived from real-world cloud systems, we show that our proposed cross-entropy-based admission control optimization algorithm is efficient and effective in maximizing cloud providers’ revenue in a public cloud computing environment.

  16. Landscape epidemiology and machine learning: A geospatial approach to modeling West Nile virus risk in the United States

    Science.gov (United States)

    Young, Sean Gregory

    The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a geospatial approach to the study of WNV risk, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables are used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the small numbers problem is also tested and ultimately discarded. Finally a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.

  17. Development of a state machine sequencer for the Keck Interferometer: evolution, development, and lessons learned using a CASE tool approach

    Science.gov (United States)

    Reder, Leonard J.; Booth, Andrew; Hsieh, Jonathan; Summers, Kellee R.

    2004-09-01

    This paper presents a discussion of the evolution of a sequencer from a simple Experimental Physics and Industrial Control System (EPICS) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a Computer Aided Software Engineering (CASE) tool approach. The main purpose of the Interferometer Sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations to be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Harel finite state machine software program designed to orchestrate several lower-level hardware and software hard real-time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORBA, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.

  18. Development of a State Machine Sequencer for the Keck Interferometer: Evolution, Development and Lessons Learned using a CASE Tool Approach

    Science.gov (United States)

    Rede, Leonard J.; Booth, Andrew; Hsieh, Jonathon; Summer, Kellee

    2004-01-01

    This paper presents a discussion of the evolution of a sequencer from a simple EPICS (Experimental Physics and Industrial Control System) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a CASE (Computer Aided Software Engineering) tool approach. The main purpose of the sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Hare1 finite state machine, software program designed to orchestrate several lower-level hardware and software hard real time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORB A, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.

  19. A General and Intuitive Approach to Understand and Compare the Torque Production Capability of AC Machines

    DEFF Research Database (Denmark)

    Wang, Dong; Lu, Kaiyuan; Rasmussen, Peter Omand

    2014-01-01

    -frame, through complicated mathematical manipulations. This is a more mathematical approach rather than explaining the physics behind torque production, which even brings a lot of difficulties to specialist. This paper introduces a general and intuitive approach to obtain the dq-frame torque equation of various...

  20. Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning approach

    Science.gov (United States)

    2014-01-01

    Background Genome wide association studies are now widely used in the livestock sector to estimate the association among single nucleotide polymorphisms (SNPs) distributed across the whole genome and one or more trait. As computational power increases, the use of machine learning techniques to analyze large genome wide datasets becomes possible. Methods The objective of this study was to identify SNPs associated with the three traits simulated in the 16th MAS-QTL workshop dataset using the Random Forest (RF) approach. The approach was applied to single and multiple trait estimated breeding values, and on yield deviations and to compare them with the results of the GRAMMAR-CG method. Results The two QTL mapping methods used, GRAMMAR-CG and RF, were successful in identifying the main QTLs for trait 1 on chromosomes 1 and 4, for trait 2 on chromosomes 1, 4 and 5 and for trait 3 on chromosomes 1, 2 and 3. Conclusions The results of the RF approach were confirmed by the GRAMMAR-CG method and validated by the effective QTL position, even if their approach to unravel cryptic genetic structure is different. Furthermore, both methods showed complementary findings. However, when the variance explained by the QTL is low, they both failed to detect significant associations. PMID:25519518

  1. PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

    Science.gov (United States)

    Deneke, Carlus; Rentzsch, Robert; Renard, Bernhard Y.

    2017-01-01

    The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results. PMID:28051068

  2. PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

    Science.gov (United States)

    Deneke, Carlus; Rentzsch, Robert; Renard, Bernhard Y.

    2017-01-01

    The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results.

  3. Machine medical ethics

    CERN Document Server

    Pontier, Matthijs

    2015-01-01

    The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

  4. Third-order point contact approach for five-axis sculptured surface machining using non-ball-end tools(II): Tool positioning strategy

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Based on the mathematical model describing the third-order approximation of the cutter envelope surface according to one given cutter location(CL),a tool positioning strategy is proposed for efficiently machining free-form surfaces with non-ball-end cutters.The optimal CL is obtained by adjusting the inclination and tilt angles of the cutter until its envelope surface and the design surface have the third-order contact at the cutter contact(CC)point,which results in a wide machining strip.The strategy can handle the constraints of machine joint angle limits,global collision avoidance and tool path smoothness in a nature way,and can be applied to general rotary cutters and complex surfaces.Numerical examples demonstrate that the third-order point contact approach can improve the machining strip width greatly as compared with the recently reported second-order one.

  5. Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-01-01

    Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

  6. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

    Science.gov (United States)

    Li, Weizhi; Mo, Weirong; Zhang, Xu; Squiers, John J.; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2015-12-01

    Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm's burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm's accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

  7. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.

    Science.gov (United States)

    Li, Weizhi; Mo, Weirong; Zhang, Xu; Squiers, John J; Lu, Yang; Sellke, Eric W; Fan, Wensheng; DiMaio, J Michael; Thatcher, Jeffrey E

    2015-12-01

    Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z -test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

  8. Machine Learning approaches on Diagnostic Term Encoding with the ICD for Clinical Documentation.

    Science.gov (United States)

    Atutxa, Aitziber; Perez, Alicia; Casillas, Arantza

    2017-08-24

    This work focuses on data mining applied to the clinical documentation domain. Diagnostic Terms (DTs) are used as keywords to retrieve valuable information from Electronic Health Records (EHRs). Indeed, they are encoded manually by experts following the International Classification of Diseases (ICD). The goal of this work is to explore the aid of text mining on DT encoding. From the machine learning (ML) perspective, this is a high-dimensional classification task, as it comprises thousands of codes. This work delves into a robust representation of the instances to improve ML results. The proposed system is able to find the right ICD-code among more than 1,500 possible ICD-codes with 92% precision for the main disease (primary class) and 88% for the main disease together with the non-essential modifiers (fully-specified class). The methodology employed is simple and portable. According to the experts from public hospitals, the system is very useful in particular for documentation and pharmaco-surveillance services. In fact, they reported an accuracy of 91.2% on a small randomly extracted test. Hence, together with this paper, we made the software publicly available in order to help the clinical and research community.

  9. A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers

    Science.gov (United States)

    Vignola, Emanuele; Steinmann, Stephan N.; Vandegehuchte, Bart D.; Curulla, Daniel; Stamatakis, Michail; Sautet, Philippe

    2017-08-01

    The accurate description of the energy of adsorbate layers is crucial for the understanding of chemistry at interfaces. For heterogeneous catalysis, not only the interaction of the adsorbate with the surface but also the adsorbate-adsorbate lateral interactions significantly affect the activation energies of reactions. Modeling the interactions of the adsorbates with the catalyst surface and with each other can be efficiently achieved in the cluster expansion Hamiltonian formalism, which has recently been implemented in a graph-theoretical kinetic Monte Carlo (kMC) scheme to describe multi-dentate species. Automating the development of the cluster expansion Hamiltonians for catalytic systems is challenging and requires the mapping of adsorbate configurations for extended adsorbates onto a graphical lattice. The current work adopts machine learning methods to reach this goal. Clusters are automatically detected based on formalized, but intuitive chemical concepts. The corresponding energy coefficients for the cluster expansion are calculated by an inversion scheme. The potential of this method is demonstrated for the example of ethylene adsorption on Pd(111), for which we propose several expansions, depending on the graphical lattice. It turns out that for this system, the best description is obtained as a combination of single molecule patterns and a few coupling terms accounting for lateral interactions.

  10. Hidden Markov models and other machine learning approaches in computational molecular biology

    Energy Technology Data Exchange (ETDEWEB)

    Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.

  11. Soft brain-machine interfaces for assistive robotics: A novel control approach.

    Science.gov (United States)

    Schiatti, Lucia; Tessadori, Jacopo; Barresi, Giacinto; Mattos, Leonardo S; Ajoudani, Arash

    2017-07-01

    Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.

  12. IDENTIFYING NETWORK LOAD BALANCING REQUIREMENTS ON HISTORICAL TRAFFIC FLOW USING MACHINE LEARNING APPROACH FOR FLEXIBLE MULTIPATH NETWORKS

    Directory of Open Access Journals (Sweden)

    K BALAMURUGAN

    2010-08-01

    Full Text Available Multipath routing uses several paths to distribute traffic from a source to destination. This not only improves network performance but also achieves load balancing and fault tolerance. Though multipath routing is not deployed widely in the internet, current systems chooses the paths with the same lowest cost to the destination and the same administrative distance.In this paper we propose a flexible multipath network wherein multipath routing algorithm is invoked when the quality of service is affected. Our proposal provides a method to gradually shift from a single path existing network to a reliable multipath network in the future.We have used machine learning approach to identify the instance when network load balancing is required with good results.

  13. An information theory based approach for quantitative evaluation of man-machine interface complexity

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Hyun Gook

    1999-02-15

    In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

  14. Separating depressive comorbidity from panic disorder: A combined functional magnetic resonance imaging and machine learning approach.

    Science.gov (United States)

    Lueken, Ulrike; Straube, Benjamin; Yang, Yunbo; Hahn, Tim; Beesdo-Baum, Katja; Wittchen, Hans-Ulrich; Konrad, Carsten; Ströhle, Andreas; Wittmann, André; Gerlach, Alexander L; Pfleiderer, Bettina; Arolt, Volker; Kircher, Tilo

    2015-09-15

    Depression is frequent in panic disorder (PD); yet, little is known about its influence on the neural substrates of PD. Difficulties in fear inhibition during safety signal processing have been reported as a pathophysiological feature of PD that is attenuated by depression. We investigated the impact of comorbid depression in PD with agoraphobia (AG) on the neural correlates of fear conditioning and the potential of machine learning to predict comorbidity status on the individual patient level based on neural characteristics. Fifty-nine PD/AG patients including 26 (44%) with a comorbid depressive disorder (PD/AG+DEP) underwent functional magnetic resonance imaging (fMRI). Comorbidity status was predicted using a random undersampling tree ensemble in a leave-one-out cross-validation framework. PD/AG-DEP patients showed altered neural activation during safety signal processing, while +DEP patients exhibited generally decreased dorsolateral prefrontal and insular activation. Comorbidity status was correctly predicted in 79% of patients (sensitivity: 73%; specificity: 85%) based on brain activation during fear conditioning (corrected for potential confounders: accuracy: 73%; sensitivity: 77%; specificity: 70%). No primary depressed patients were available; only medication-free patients were included. Major depression and dysthymia were collapsed (power considerations). Neurofunctional activation during safety signal processing differed between patients with or without comorbid depression, a finding which may explain heterogeneous results across previous studies. These findings demonstrate the relevance of comorbidity when investigating neurofunctional substrates of anxiety disorders. Predicting individual comorbidity status may translate neurofunctional data into clinically relevant information which might aid in planning individualized treatment. The study was registered with the ISRCTN80046034. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse

    Science.gov (United States)

    Garrard, Peter; Rentoumi, Vassiliki; Gesierich, Benno; Miller, Bruce; Gorno-Tempini, Maria Luisa

    2014-01-01

    Advances in automatic text classification have been necessitated by the rapid increase in the availability of digital documents. Machine learning (ML) algorithms can ‘learn’ from data: for instance a ML system can be trained on a set of features derived from written texts belonging to known categories, and learn to distinguish between them. Such a trained system can then be used to classify unseen texts. In this paper, we explore the potential of the technique to classify transcribed speech samples along clinical dimensions, using vocabulary data alone. We report the accuracy with which two related ML algorithms [naive Bayes Gaussian (NBG) and naive Bayes multinomial (NBM)] categorized picture descriptions produced by: 32 semantic dementia (SD) patients versus 10 healthy, age-matched controls; and SD patients with left- (n = 21) versus right-predominant (n = 11) patterns of temporal lobe atrophy. We used information gain (IG) to identify the vocabulary features that were most informative to each of these two distinctions. In the SD versus control classification task, both algorithms achieved accuracies of greater than 90%. In the right- versus left-temporal lobe predominant classification, NBM achieved a high level of accuracy (88%), but this was achieved by both NBM and NBG when the features used in the training set were restricted to those with high values of IG. The most informative features for the patient versus control task were low frequency content words, generic terms and components of metanarrative statements. For the right versus left task the number of informative lexical features was too small to support any specific inferences. An enriched feature set, including values derived from Quantitative Production Analysis (QPA) may shed further light on this little understood distinction. PMID:23876449

  16. "What is relevant in a text document?": An interpretable machine learning approach

    Science.gov (United States)

    Arras, Leila; Horn, Franziska; Montavon, Grégoire; Müller, Klaus-Robert

    2017-01-01

    Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text’s category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications. PMID:28800619

  17. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

    Science.gov (United States)

    Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

    2016-03-01

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  18. Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse.

    Science.gov (United States)

    Garrard, Peter; Rentoumi, Vassiliki; Gesierich, Benno; Miller, Bruce; Gorno-Tempini, Maria Luisa

    2014-06-01

    Advances in automatic text classification have been necessitated by the rapid increase in the availability of digital documents. Machine learning (ML) algorithms can 'learn' from data: for instance a ML system can be trained on a set of features derived from written texts belonging to known categories, and learn to distinguish between them. Such a trained system can then be used to classify unseen texts. In this paper, we explore the potential of the technique to classify transcribed speech samples along clinical dimensions, using vocabulary data alone. We report the accuracy with which two related ML algorithms [naive Bayes Gaussian (NBG) and naive Bayes multinomial (NBM)] categorized picture descriptions produced by: 32 semantic dementia (SD) patients versus 10 healthy, age-matched controls; and SD patients with left- (n = 21) versus right-predominant (n = 11) patterns of temporal lobe atrophy. We used information gain (IG) to identify the vocabulary features that were most informative to each of these two distinctions. In the SD versus control classification task, both algorithms achieved accuracies of greater than 90%. In the right- versus left-temporal lobe predominant classification, NBM achieved a high level of accuracy (88%), but this was achieved by both NBM and NBG when the features used in the training set were restricted to those with high values of IG. The most informative features for the patient versus control task were low frequency content words, generic terms and components of metanarrative statements. For the right versus left task the number of informative lexical features was too small to support any specific inferences. An enriched feature set, including values derived from Quantitative Production Analysis (QPA) may shed further light on this little understood distinction. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Cross-trial prediction of treatment outcome in depression: a machine learning approach.

    Science.gov (United States)

    Chekroud, Adam Mourad; Zotti, Ryan Joseph; Shehzad, Zarrar; Gueorguieva, Ralitza; Johnson, Marcia K; Trivedi, Madhukar H; Cannon, Tyrone D; Krystal, John Harrison; Corlett, Philip Robert

    2016-03-01

    Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram. We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863). We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms. Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant. Yale University. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. A UNIFIED APPROACH FOR DETECTION AND PREVENTION OF DDOS ATTACKS USING ENHANCED SUPPORT VECTOR MACHINES AND FILTERING MECHANISMS

    Directory of Open Access Journals (Sweden)

    T. Subbulakshmi

    2014-10-01

    Full Text Available Distributed Denial of Service (DDoS attacks were considered to be a tremendous threat to the current information security infrastructure. During DDoS attack, multiple malicious hosts that are recruited by the attackers launch a coordinated attack against one host or a network victim, which cause denial of service to legitimate users. The existing techniques suffer from more number of false alarms and more human intervention for attack detection. The objective of this paper is to monitor the network online which automatically initiates detection mechanism if there is any suspicious activity and also defense the hosts from being arrived at the network. Both spoofed and non spoofed IP’s are detected in this approach. Non spoofed IP’s are detected using Enhanced Support Vector Machines (ESVM and spoofed IP’s are detected using Hop Count Filtering (HCF mechanism. The detected IP’s are maintained separately to initiate the defense process. The attack strength is calculated using Lanchester Law which initiates the defense mechanism. Based on the calculated attack strength any of the defense schemes such as Rate based limiting or History based IP filtering is automatically initiated to drop the packets from the suspected IP. The integrated online monitoring approach for detection and defense of DDoS attacks is deployed in an experimental testbed. The online approach is found to be obvious in the field of integrated DDoS detection and defense.

  1. Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

    Science.gov (United States)

    Khan, Faisal; Enzmann, Frieder; Kersten, Michael

    2016-03-01

    Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

  2. Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

    Directory of Open Access Journals (Sweden)

    Divya Tomar

    2015-01-01

    Full Text Available There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM as a classification approach, sequential forward selection (SFS as a search strategy, and correlation feature selection (CFS to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.

  3. When Machines Design Machines!

    DEFF Research Database (Denmark)

    2011-01-01

    Until recently we were the sole designers, alone in the driving seat making all the decisions. But, we have created a world of complexity way beyond human ability to understand, control, and govern. Machines now do more trades than humans on stock markets, they control our power, water, gas...... and food supplies, manage our elevators, microclimates, automobiles and transport systems, and manufacture almost everything. It should come as no surprise that machines are now designing machines. The chips that power our computers and mobile phones, the robots and commercial processing plants on which we...... depend, all are now largely designed by machines. So what of us - will be totally usurped, or are we looking at a new symbiosis with human and artificial intelligences combined to realise the best outcomes possible. In most respects we have no choice! Human abilities alone cannot solve any of the major...

  4. Application of Support Vector Machine-Based Semiactive Control for Seismic Protection of Structures with Magnetorheological Dampers

    Directory of Open Access Journals (Sweden)

    Chunxiang Li

    2012-01-01

    Full Text Available Based on recent research by Li and Liu in 2011, this paper proposes the application of support vector machine- (SVM- based semiactive control methodology for seismic protection of structures with magnetorheological (MR dampers. An important and challenging task of designing the MR dampers is to develop an effective semiactive control strategy that can fully exploit the capabilities of MR dampers. However, amplification of the local acceleration response of structures exists in the widely used semiactive control strategies, namely “Switch” control strategies. Then the SVM-based semiactive control strategy has been employed to design MR dampers. Firstly, the LQR controller for the numerical model of a multistory structure formulated using the dynamic dense method is constructed by using the classic LQR control theory. Secondly, an SVM model which comprises the observers and controllers in the control system is designed and trained to emulate the performance of the LQR controller. Finally, an online autofeedback semiactive control strategy is developed by resorting to SVM and then used for designing MR dampers. Simulation results show that the MR dampers utilizing the SVM-based semiactive control algorithm, which eliminates the local acceleration amplification phenomenon, can remarkably reduce the displacement, velocity, and acceleration responses of the structure.

  5. An Approach for Evaluating Reliability of Man-Machine System in Evolving Environment

    Institute of Scientific and Technical Information of China (English)

    Wang Wuhong; Zhang Dianye; Cao Qi

    1996-01-01

    In this paper,the technique for human error rate prediction (THERP) is first presented to discuss rationale and principal advantages and disadvantages.Then, based on operator behaviour paradigm which can describe human characteristics,an approach is formulated in a mathematical way as a means of evaluating the effect of operator erroneous actions on reliability of manmachine systems in dynamical evolving environment.

  6. Explanation-Based Knowledge Acquisition of Schemas in Practical Electronics: A Machine Learning Approach

    Science.gov (United States)

    1990-09-12

    and forms the kernel of the various envisionment algorithms developed by researchers in qualitative reasoning (e.g. Kuipes, 1984). In Figure 4.11 the...makes this problem more acute since the sort of envisionment approach outlined in Chapter 4 creates additional search spaces due to the distinction

  7. A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces.

    Science.gov (United States)

    Oweiss, Karim G

    2006-07-01

    This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

  8. A Classical Fuzzy Approach for Software Effort Estimation on Machine Learning Technique

    CERN Document Server

    Malathi, S

    2011-01-01

    Software Cost Estimation with resounding reliability,productivity and development effort is a challenging and onerous task. This has incited the software community to give much needed thrust and delve into extensive research in software effort estimation for evolving sophisticated methods. Estimation by analogy is one of the expedient techniques in software effort estimation field. However, the methodology utilized for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and precise manner. A new approach has been developed in this paper to estimate software effort for projects represented by categorical or numerical data using reasoning by analogy and fuzzy approach. The existing historical data sets, analyzed with fuzzy logic, produce accurate results in comparison to the data set analyzed with the earlier methodologies.

  9. A Classical Fuzzy Approach for Software Effort Estimation on Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    S.Malathi

    2011-11-01

    Full Text Available Software Cost Estimation with resounding reliability, productivity and development effort is a challenging and onerous task. This has incited the software community to give much needed thrust and delve into extensive research in software effort estimation for evolving sophisticated methods. Estimation by analogy is one of the expedient techniques in software effort estimation field. However, the methodology utilized for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and precise manner. A new approach has been developed in this paper to estimate software effort for projects represented by categorical or numerical data using reasoning by analogy and fuzzy approach. The existing historical datasets, analyzed with fuzzy logic, produce accurate results in comparison to the dataset analyzed with the earlier methodologies.

  10. Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

    Directory of Open Access Journals (Sweden)

    Hyangsun Han

    2015-07-01

    Full Text Available As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI, a payload of the Communication, Ocean, and Meteorological Satellite (COMS, was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2, based on three machine learning approaches—decision trees (DT, random forest (RF, and support vector machines (SVM. CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15–75 min before the lightning occurrence were used as input variables for CI detection. RF produced slightly higher performance (probability of detection (POD of 75.5% and false alarm rate (FAR of 46.2% than DT (POD of 70.7% and FAR of 46.6% for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30–45 min in advance using COMS MI data.

  11. Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.

    Science.gov (United States)

    Fuller, John A; Berlinicke, Cynthia A; Inglese, James; Zack, Donald J

    2016-01-01

    High content analysis (HCA) has become a leading methodology in phenotypic drug discovery efforts. Typical HCA workflows include imaging cells using an automated microscope and analyzing the data using algorithms designed to quantify one or more specific phenotypes of interest. Due to the richness of high content data, unappreciated phenotypic changes may be discovered in existing image sets using interactive machine-learning based software systems. Primary postnatal day four retinal cells from the photoreceptor (PR) labeled QRX-EGFP reporter mice were isolated, seeded, treated with a set of 234 profiled kinase inhibitors and then cultured for 1 week. The cells were imaged with an Acumen plate-based laser cytometer to determine the number and intensity of GFP-expressing, i.e. PR, cells. Wells displaying intensities and counts above threshold values of interest were re-imaged at a higher resolution with an INCell2000 automated microscope. The images were analyzed with an open source HCA analysis tool, PhenoRipper (Rajaram et al., Nat Methods 9:635-637, 2012), to identify the high GFP-inducing treatments that additionally resulted in diverse phenotypes compared to the vehicle control samples. The pyrimidinopyrimidone kinase inhibitor CHEMBL-1766490, a pan kinase inhibitor whose major known targets are p38α and the Src family member lck, was identified as an inducer of photoreceptor neuritogenesis by using the open-source HCA program PhenoRipper. This finding was corroborated using a cell-based method of image analysis that measures quantitative differences in the mean neurite length in GFP expressing cells. Interacting with data using machine learning algorithms may complement traditional HCA approaches by leading to the discovery of small molecule-induced cellular phenotypes in addition to those upon which the investigator is initially focusing.

  12. Miscanthus spatial location as seen by farmers: A machine learning approach to model real criteria

    OpenAIRE

    Martin, Laura; Wohlfahrt, Julie

    2016-01-01

    Highlights • Farmers' criteria to locate real miscanthus fields were investigated. • We modelled agronomic, morphological and contextual field characteristics. • Boosted regression tree method used to upscale from supply area to the regional level. • Small and complex-shaped farmer's blocks resulted to be relevant to locate miscanthus. • Our approach provided miscanthus location probabilities from farm to landscape levels.   Abstract. Miscanthus is an emerging crop...

  13. A New Approach in Downscaling Microwave Soil Moisture Product using Machine Learning

    Science.gov (United States)

    Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid

    2016-04-01

    Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.

  14. Dynamic Viral Glycoprotein Machines: Approaches for Probing Transient States That Drive Membrane Fusion

    Directory of Open Access Journals (Sweden)

    Natalie K. Garcia

    2016-01-01

    Full Text Available The fusion glycoproteins that decorate the surface of enveloped viruses undergo dramatic conformational changes in the course of engaging with target cells through receptor interactions and during cell entry. These refolding events ultimately drive the fusion of viral and cellular membranes leading to delivery of the genetic cargo. While well-established methods for structure determination such as X-ray crystallography have provided detailed structures of fusion proteins in the pre- and post-fusion fusion states, to understand mechanistically how these fusion glycoproteins perform their structural calisthenics and drive membrane fusion requires new analytical approaches that enable dynamic intermediate states to be probed. Methods including structural mass spectrometry, small-angle X-ray scattering, and electron microscopy have begun to provide new insight into pathways of conformational change and fusion protein function. In combination, the approaches provide a significantly richer portrait of viral fusion glycoprotein structural variation and fusion activation as well as inhibition by neutralizing agents. Here recent studies that highlight the utility of these complementary approaches will be reviewed with a focus on the well-characterized influenza virus hemagglutinin fusion glycoprotein system.

  15. A Machine Learning Approach to Estimate Riverbank Geotechnical Parameters from Sediment Particle Size Data

    Science.gov (United States)

    Iwashita, Fabio; Brooks, Andrew; Spencer, John; Borombovits, Daniel; Curwen, Graeme; Olley, Jon

    2015-04-01

    Assessing bank stability using geotechnical models traditionally involves the laborious collection of data on the bank and floodplain stratigraphy, as well as in-situ geotechnical data for each sedimentary unit within a river bank. The application of geotechnical bank stability models are limited to those sites where extensive field data has been collected, where their ability to provide predictions of bank erosion at the reach scale are limited without a very extensive and expensive field data collection program. Some challenges in the construction and application of riverbank erosion and hydraulic numerical models are their one-dimensionality, steady-state requirements, lack of calibration data, and nonuniqueness. Also, numerical models commonly can be too rigid with respect to detecting unexpected features like the onset of trends, non-linear relations, or patterns restricted to sub-samples of a data set. These shortcomings create the need for an alternate modelling approach capable of using available data. The application of the Self-Organizing Maps (SOM) approach is well-suited to the analysis of noisy, sparse, nonlinear, multidimensional, and scale-dependent data. It is a type of unsupervised artificial neural network with hybrid competitive-cooperative learning. In this work we present a method that uses a database of geotechnical data collected at over 100 sites throughout Queensland State, Australia, to develop a modelling approach that enables geotechnical parameters (soil effective cohesion, friction angle, soil erodibility and critical stress) to be derived from sediment particle size data (PSD). The model framework and predicted values were evaluated using two methods, splitting the dataset into training and validation set, and through a Bootstrap approach. The basis of Bootstrap cross-validation is a leave-one-out strategy. This requires leaving one data value out of the training set while creating a new SOM to estimate that missing value based on the

  16. Prediction and Optimization Approaches for Modeling and Selection of Optimum Machining Parameters in CNC down Milling Operation

    Directory of Open Access Journals (Sweden)

    Asaad A. Abdullah

    2014-04-01

    Full Text Available In this study, we suggested intelligent approach to predict and optimize the cutting parameters when down milling of 45# steel material with cutting tool PTHK- (Ø10*20C*10D*75L -4F-1.0R under dry condition. The experiments were performed statistically according to four factors with three levels in Taguchi experimental design method. Adaptive Neuro-fuzzy inference system is utilized to establish the relationship between the inputs and output parameter exploiting the Taguchi orthogonal array L27. The Particle Swarm Optimized-Adaptive Neuro-Fuzzy Inference System (PSOANFIS is suggested to select the best cutting parameters providing the lower surface through from the experimental data using ANFIS models to predict objective functions. The PSOANFIS optimization approach that improves the surface quality from 0.212 to 0.202, as well as the cutting time is also reduced from 7.5 to 4.78 sec according to machining parameters before and after optimization process. From these results, it can be readily achieved that the advanced study is trusted and suitable for solving other problems encountered in metal cutting operations and the same surface roughness.

  17. A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

    Science.gov (United States)

    Pfeiffenberger, Erik; Chaleil, Raphael A G; Moal, Iain H; Bates, Paul A

    2017-03-01

    Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near-native from incorrect clusters. The results show that our approach is able to identify clusters containing near-native protein-protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528-543. © 2016 Wiley Periodicals, Inc.

  18. Extending the Evolutionary Robotics approach to flying machines: an application to MAV teams.

    Science.gov (United States)

    Ruini, Fabio; Cangelosi, Angelo

    2009-01-01

    The work presented in this article focuses on the use of embodied neural networks--developed through Evolutionary Robotics and Multi-Agent Systems methodologies--as autonomous distributed controllers for Micro-unmanned Aerial Vehicle (MAV) teams. The main aim of the research is to extend the range of domains that could be successfully tackled by the Evolutionary Robotics approach. The flying robots realm is an area that has not been yet thoroughly investigated by this discipline. This is due to the lack of an affordable and reliable robotic platform to use for carrying out experiments, and to the difficulty and the high computational load involved in experiments based upon a realistic software simulator for aircraft. We believe that the most recent improvements to the state of the art now permit the investigation of this domain. For demonstrating this point, two different evolutionary computer simulation models are presented in this article. The first model, which uses a simplified 2D test environment, has resulted in controllers evolved with the following capabilities: (1) navigation through unknown environments, (2) obstacle-avoidance, (3) tracking of a movable target, and (4) execution of cooperative and coordinated behaviors based on implicit communication strategies. In order to improve the robustness of these results and their potential use in real MAV teams, a more sophisticated 3D model is presented herein. The results obtained so far using the two models demonstrate the feasibility of the chosen approach for further research on the design of autonomous controllers for MAVs.

  19. Some Solution Approaches to Reduce the Imbalance of Workload in Parallel Machines while Planning in Flexible Manufacturing System

    Directory of Open Access Journals (Sweden)

    B.V.Raghavendra,

    2010-05-01

    Full Text Available The loading problem in a Flexible Manufacturing System (FMS is viewed as selecting a subset of jobs from a job pool and allocating the jobs among the machines. Balancing the workload on the parallel machines will reduce the bottleneck and improve the utilization of the machine tools. In this paper an effort is made for developing thestrategies in the pre-release / planning stage which will reduce the imbalance between the parallel machines. Two different strategies are developed and the traditional sequencing shortest and longest rocessing time rule is applied to determine the relative performance index. An illustrative example is accompanies with the shortest processing time.

  20. Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics

    Directory of Open Access Journals (Sweden)

    Ahmed TOUMI

    2009-12-01

    Full Text Available Self-Organizing Maps (SOM is an excellent method of analyzingmultidimensional data. The SOM based classification is attractive, due to itsunsupervised learning and topology preserving properties. In this paper, theperformance of the self-organizing methods is investigated in induction motorrotor fault detection and severity evaluation. The SOM is based on motor currentsignature analysis (MCSA. The agglomerative hierarchical algorithms using theWard’s method is applied to automatically dividing the map into interestinginterpretable groups of map units that correspond to clusters in the input data. Theresults obtained with this approach make it possible to detect a rotor bar fault justdirectly from the visualization results. The system is also able to estimate theextent of rotor faults.

  1. Towards a Virtual Machine Approach to Resilient and Safe Mobile Robots

    DEFF Research Database (Denmark)

    Adam, Marian Sorin; Kuhrmann, Marco; Schultz, Ulrik Pagh

    Mobile robots are advanced systems that often need to operate in unstructured environments, which raises the complexity of the software. Many components are important for the overall reliability and safety of the robot, but reducing the risk of errors by making the software resilient is both......, enabling a wide range of strategies for graceful degradation in the presence of partial failures. We use standard virtualization techniques as a flexible means to segregate the safety-critical parts of the system, combined with an automatically generated runtime monitoring component in charge of fast...... switching between different implementations of the non-critical parts of the system. We report on the overall architecture and implementation, and demonstrate our approach using software for a commercial mobile robot....

  2. Ionic channel current burst analysis by a machine learning based approach.

    Science.gov (United States)

    Rauch, Giuseppe; Bertolini, Simona; Sacile, Roberto; Giacomini, Mauro; Ruggiero, Carmelina

    2011-09-01

    A new method to analyze single ionic channel current conduction is presented. It is based on an automatic classification by K-means algorithm and on the concept of information entropy. This method is used to study the conductance of multistate ion current jumps induced by tetanus toxin in planar lipid bilayers. A comparison is presented with the widely used Gaussian best fit approach, whose main drawback is the fact that it is based on the manual choice of the base line and of meaningful fragments of current signal. On the contrary, the proposed method is able to automatically process a great amount of information and to remove spurious transitions and multichannels. The number of levels and their amplitudes do not have to be known a priori. In this way the presented method is able to produce a reliable evaluation of the conductance levels and their characteristic parameters in a short time.

  3. Towards a Virtual Machine Approach to Resilient and Safe Mobile Robots

    DEFF Research Database (Denmark)

    Adam, Marian Sorin; Kuhrmann, Marco; Schultz, Ulrik Pagh

    2016-01-01

    Mobile robots are advanced systems that often need to operate in unstructured environments, which raises the complexity of the software. Many components are important for the overall reliability and safety of the robot, but reducing the risk of errors by making the software resilient is both...... complicated and expensive. Nevertheless, a commercially successful robot often has to remain safe while maintaining as much as possible from the required functionality, even in the presence of partial failures. In this paper, we propose a flexible way to improve the reliability of existing robot software...... switching between different implementations of the non-critical parts of the system. We report on the overall architecture and implementation, and demonstrate our approach using software for a commercial mobile robot....

  4. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Directory of Open Access Journals (Sweden)

    Nur Diyana Kamarudin

    2017-01-01

    Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  5. A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Science.gov (United States)

    Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

    2017-01-01

    In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  6. A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

    Science.gov (United States)

    Mei, Suyu; Zhu, Hao

    2015-01-26

    Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

  7. Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals.

    Science.gov (United States)

    Kalatzis, I; Piliouras, N; Ventouras, E; Papageorgiou, C C; Rabavilas, A D; Cavouras, D

    2004-07-01

    A computer-based classification system has been designed capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised 25 patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEG activity was recorded and digitized from 15 scalp electrodes (leads). Seventeen features related to the shape of the waveform were generated and were employed in the design of an optimum support vector machine (SVM) classifier at each lead. The outcomes of those SVM classifiers were selected by a majority-vote engine (MVE), which assigned each subject to either the normal or depressive classes. MVE classification accuracy was 94% when using all leads and 92% or 82% when using only the right or left scalp leads, respectively. These findings support the hypothesis that depression is associated with dysfunction of right hemisphere mechanisms mediating the processing of information that assigns a specific response to a specific stimulus, as those mechanisms are reflected by the P600 component of ERPs. Our method may aid the further understanding of the neurophysiology underlying depression, due to its potentiality to integrate theories of depression and psychophysiology.

  8. Towards harmonized seismic analysis across Europe using supervised machine learning approaches

    Science.gov (United States)

    Zaccarelli, Riccardo; Bindi, Dino; Cotton, Fabrice; Strollo, Angelo

    2017-04-01

    . These classes might be a simply binary case (e.g., "good for analysis" vs "bad") or more complex one (e.g., "good for analysis" vs "low SNR", "multi-event", "bad coda envelope"). It is important to stress the fact that our approach can be generalized to any use case providing, as in any supervised approach, an adequate training set of labelled data, a feature-set, a statistical classifier, and finally model validation and evaluation. Examples of use cases considered to develop the system prototype are the characterization of the ground motion in low seismic areas; harmonized spectral analysis across Europe for source and attenuation studies; magnitude calibration; coda analysis for attenuation studies.

  9. Machine learning approach to detect intruders in database based on hexplet data structure

    Directory of Open Access Journals (Sweden)

    Saad M. Darwish

    2016-09-01

    Full Text Available Most of valuable information resources for any organization are stored in the database; it is a serious subject to protect this information against intruders. However, conventional security mechanisms are not designed to detect anomalous actions of database users. An intrusion detection system (IDS, delivers an extra layer of security that cannot be guaranteed by built-in security tools, is the ideal solution to defend databases from intruders. This paper suggests an anomaly detection approach that summarizes the raw transactional SQL queries into a compact data structure called hexplet, which can model normal database access behavior (abstract the user's profile and recognize impostors specifically tailored for role-based access control (RBAC database system. This hexplet lets us to preserve the correlation among SQL statements in the same transaction by exploiting the information in the transaction-log entry with the aim to improve detection accuracy specially those inside the organization and behave strange behavior. The model utilizes naive Bayes classifier (NBC as the simplest supervised learning technique for creating profiles and evaluating the legitimacy of a transaction. Experimental results show the performance of the proposed model in the term of detection rate.

  10. Assessing the potential information content of multicomponent visual signals: a machine learning approach

    Science.gov (United States)

    Allen, William L.; Higham, James P.

    2015-01-01

    Careful investigation of the form of animal signals can offer novel insights into their function. Here, we deconstruct the face patterns of a tribe of primates, the guenons (Cercopithecini), and examine the information that is potentially available in the perceptual dimensions of their multicomponent displays. Using standardized colour-calibrated images of guenon faces, we measure variation in appearance both within and between species. Overall face pattern was quantified using the computer vision ‘eigenface’ technique, and eyebrow and nose-spot focal traits were described using computational image segmentation and shape analysis. Discriminant function analyses established whether these perceptual dimensions could be used to reliably classify species identity, individual identity, age and sex, and, if so, identify the dimensions that carry this information. Across the 12 species studied, we found that both overall face pattern and focal trait differences could be used to categorize species and individuals reliably, whereas correct classification of age category and sex was not possible. This pattern makes sense, as guenons often form mixed-species groups in which familiar conspecifics develop complex differentiated social relationships but where the presence of heterospecifics creates hybridization risk. Our approach should be broadly applicable to the investigation of visual signal function across the animal kingdom. PMID:25652832

  11. A laboratory scale approach to polymer solar cells using one coating/printing machine, flexible substrates, no ITO, no vacuum and no spincoating

    DEFF Research Database (Denmark)

    Carlé, Jon Eggert; Andersen, Thomas Rieks; Helgesen, Martin

    2013-01-01

    Printing of the silver back electrode under ambient conditions using simple laboratory equipment has been the missing link to fully replace evaporated metal electrodes. Here we demonstrate how a recently developed roll coater is further developed into a single machine that enables processing of all...... layers of the polymer solar cell without moving the substrate from one machine to another. The novel approach to polymer solar cells is readily scalable using one compact laboratory scale coating/printing machine that is directly compatible with industrial and pilot scale roll-to-roll processing. The use...... of the techniques was successfully demonstrated in one continuous roll process on flexible polyethyleneterphthalate (PET) substrates and polymer solar cells were prepared by solution processing of five layers using only slot-die coating and flexographic printing. The devices obtained did not employ indium...

  12. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  13. A Machine-learning Approach to Measuring the Escape of Ionizing Radiation from Galaxies in the Reionization Epoch

    Science.gov (United States)

    Jensen, Hannes; Zackrisson, Erik; Pelckmans, Kristiaan; Binggeli, Christian; Ausmees, Kristiina; Lundholm, Ulrika

    2016-08-01

    Recent observations of galaxies at z≳ 7, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization. However, scenarios of galaxy-driven reionization hinge on the assumption that the average escape fraction of ionizing photons is significantly higher for galaxies in the reionization epoch than in the local universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will enable spectroscopic observations of large samples of reionization-epoch galaxies. While the leakage of ionizing photons will not be directly measurable from these spectra, the leakage is predicted to have an indirect effect on the spectral slope and the strength of nebular emission lines in the rest-frame ultraviolet and optical. Here, we apply a machine learning technique known as lasso regression on mock JWST/NIRSpec observations of simulated z = 7 galaxies in order to obtain a model that can predict the escape fraction from JWST/NIRSpec data. Barring systematic biases in the simulated spectra, our method is able to retrieve the escape fraction with a mean absolute error of {{Δ }}{f}{esc}≈ 0.12 for spectra with signal-to-noise ratio ≈ 5 at a rest-frame wavelength of 1500 Å for our fiducial simulation. This prediction accuracy represents a significant improvement over previous similar approaches.

  14. A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces

    Science.gov (United States)

    Long, Jinyi; Yu, Zhuliang

    2010-01-01

    Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small. PMID:21886673

  15. Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

    Science.gov (United States)

    Cakar, Tarik; Koker, Rasit

    2015-01-01

    A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

  16. Simultaneous selection and scheduling with sequence-dependent setup times, lateness penalties, and machine availability constraint: Heuristic approaches

    Directory of Open Access Journals (Sweden)

    Mohammad Hossein Zarei

    2016-01-01

    Full Text Available Job selection and scheduling are among the most important decisions for production planning in today’s manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of candidate jobs and schedule them in such a way that the total net profit is maximized. The cost components considered here include jobs' processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS and simulated annealing (SA, were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time.

  17. Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach

    Directory of Open Access Journals (Sweden)

    Tarik Cakar

    2015-01-01

    Full Text Available A particle swarm optimization algorithm (PSO has been used to solve the single machine total weighted tardiness problem (SMTWT with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA and simulated annealing (SA have been used. In the subhybrid system (GA and SA, GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

  18. Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

    Science.gov (United States)

    Kachach, Redouane; Cañas, José María

    2016-05-01

    Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

  19. Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2014-01-01

    Full Text Available Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC and engineering process control (EPC has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM and artificial neural network (ANN approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.

  20. Dynamism in a Semiconductor Industrial Machine Allocation Problem using a Hybrid of the Bio-inspired and Musical-Harmony Approach

    Science.gov (United States)

    Kalsom Yusof, Umi; Nor Akmal Khalid, Mohd

    2015-05-01

    Semiconductor industries need to constantly adjust to the rapid pace of change in the market. Most manufactured products usually have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of the machine allocation plan known for its complexity. Various studies have been performed to balance productivity and flexibility in the flexible manufacturing system (FMS). Many approaches have been developed by the researchers to determine the suitable balance between exploration (global improvement) and exploitation (local improvement). However, not much work has been focused on the domain of machine allocation problem that considers the effects of machine breakdowns. This paper develops a model to minimize the effect of machine breakdowns, thus increasing the productivity. The objectives are to minimize system unbalance and makespan as well as increase throughput while satisfying the technological constraints such as machine time availability. To examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on real industrial datasets were performed with applications of intelligence techniques, that is, a hybrid of genetic algorithm and harmony search. The result aims to obtain a feasible solution to the domain problem.

  1. Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

    Science.gov (United States)

    Abu, Arpah; Leow, Lee Kien; Ramli, Rosli; Omar, Hasmahzaiti

    2016-12-22

    Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs. At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community. This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

  2. Parameters Optimization Research of SVM Based on Improvement FOA%基于改进FOA的SVM参数优化研究

    Institute of Scientific and Technical Information of China (English)

    张前图; 曾真真; 毛凯; 冯明峰; 宋振宇

    2016-01-01

    为了提高支持向量机(SVM)分类性能,同时针对果蝇优化算法(FOA)寻优精度不高和易陷入局部最优的特点,提出了一种改进的FOA算法(LFOA),并将其应用于SVM的参数寻优中。该方法在运算个过程中根据果蝇种群的进化程度,动态的将种群分为较差子群和较优子群;较差子群在最优个体的指导下以基本FOA算法进行全局搜索,较优子群则围绕最优个体做Levy飞行,进行精细化局部搜索;两个子群的信息通过全局最优个体的更新和种群个体的重组进行交换。通过对UCI数据库中几个经典数据集的分类测试结果表明,基于LFOA优化SVM参数能够提高SVM的分类性能,效果优于其他几种方法。%In order to overcome the problems of support vector machine (SVM) parameters selection and the demerits of fruit fly optimization algorithm,such as low convergence precision and easily relapsing into local optimum,an improvement FOA (LFOA) is presented.Firstly,the fruit fly group is dynamically divided into advanced subgroup and drawback subgroup according to its own evolutionary level.Secondly,a global search with FOA is made for drawback subgroup with the guidance of the best individual and a finely local search is made for advanced subgroup that do Levy flight around the best individual.Finally,two subgroups exchange information by updating the overall optimum and recombining the subgroups.The classify experiment results of several data set in UCI data base show that SVM parameters optimization based on LFOA can improvement the classify performance of SVM and is better than some other method.

  3. 基于多微商核函数的SVM话者确认%Multiple Derivative Kernel for SVM Based Speaker Verification

    Institute of Scientific and Technical Information of China (English)

    许敏强; 戴蓓蒨; 刘青松; 许东星

    2011-01-01

    A multiple derivative kernel (MDK) based method is proposed, combining Gaussian mixture model (GMM) and support vector machine (SVM), and it is applied to text-independent speaker verification. In order to combine GMM and SVM, MDK computes multiple derivatives from speaker feature distribution, which is modeled by GMM. Then, the multiple derivatives are taken as the input of SVM. The framework of the multiple derivative kernel based SVM method (MDK-SVM) for speaker verification is as follows. Firstly, features are abstracted from utterances and are compensated using factor analysis method in the feature domain. Secondly, these features are used for training GMM distribution. Thirdly, multiple derivative kernel is computed from the GMM distribution, and used as the input of the SVMs for speaker modeling. Finally, the performance of MDK-SVM is evaluated on the NIST SRE 01 2min-lmin dataset. The proposed MDK-SVM system gives reduction in equal error rate (EER) and minimum detection cost function (MinDCF) compared with factor analysis Gaussian mixture model (FAGMM) system, Fisher kernel SVM system and Kullback-Leibler divergence based SVM system.%给出了一种基于多微商核函数(MDK)的结合高斯混合模型(GMM)和支持向量机(SVM)的方法,并应用于SVM文本无关话者确认.从GMM话者语音特征概率分布出发,用多阶微商描述GMM概率分布,将GMM和SVM结合的问题转化为用多阶微商建立SVM话者模型的问题.首先对说话人语音进行基于因子分析的参数域失配补偿,用GMM描述失配补偿后的话者语音特征的概率分布;然后对GMM求多阶微商;最后构建多微商核函数,建立多SVM话者模型.在NIST' 01 2min-1min话者确认数据库上的实验表明,基于多微商棱函数的SVM话者确认系统性能优于基于失配补偿的GMM系统,也比基于失配补偿的Fisher核函数SVM话者系统和基于失配补偿的Kullback-Leibler(KL)距离SVM话者系统有较大的提高.

  4. 基于SVM的AMI环境下用电异常检测研究%SVM Based Energy Consumption Abnormality Detection in AMI System

    Institute of Scientific and Technical Information of China (English)

    简富俊; 曹敏; 王磊; 孙中伟; 张建伟; 王洪亮

    2014-01-01

    Electrical power system is facing serious security problems due to Advanced Metering Infrastructure(AMI) system which introduces a lot of new technologies in traditional electrical power system. As a result of smart grid, the contradiction of openness and security is increased which will give rise to the increase of electricity fraud. How to detect electricity fraud has become a new issue of grid informatization. On the basis of the AMI’s architecture, the paper adopts One-class SVM technique to detect the abnormal behavior of electricity users which works at a non-supervision Machine learning mode and can get a high accuracy of detection in small sample or unbalanced classification environment. In order to reduce the false alarm rate of the system, the system uses filtering method to handle the test results of SVM classification processing. System can improve the efficiency of electrical inspection and reduce the Non-Technical Losses(NTL) of power system. The paper also gives an implementation of the system which verifies execution efficiency and detection efficiency of the algorithm by real example.%高级测量体系的建设在传统电力系统中引入了许多新技术,对电力系统安全提出了新的考验。网络的开放性和安全性之间的矛盾加大,使得非法电力用户窃电的手段增多,如何有效检测窃电成为电网信息化的一个新问题。根据高级测量体系系统架构的特点,使用One-class SVM无监督机器学习架构对电力用户负荷异常进行检测,可以在小样本、样本分类不均衡环境下提高检测的准确性。使用对检测结果过滤的方法对检测结果进行分类处理,降低系统的虚警率。系统能提高用电稽查效率,降低电力系统的非技术性损失。最后对系统进行架构搭建实现,使用真实算例验证了算法的执行效率和检测效率。

  5. Rotating electrical machines

    CERN Document Server

    Le Doeuff, René

    2013-01-01

    In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

  6. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2013-01-01

    Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

  7. Threat Assessment of Targets Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

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

    2006-01-01

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

  8. Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification.

    Science.gov (United States)

    Gaonkar, Bilwaj; Davatzikos, Christos

    2013-09-01

    Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a thousandfold speedup of the permutation testing procedure, thereby rendering it feasible to perform such tests on standard computers. The speedup achieved makes SVM based group difference analysis competitive with standard univariate group difference analysis methods.

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

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Hong Zhong; Wang, Hai Kun; Li, Yan Feng; Zhang, Longlong; Liu, Zhiliang [University of Electronic Science and Technology of China, Chengdu (China)

    2015-01-15

    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.

  10. Reducing Deadline Miss Rate for Grid Workloads running in Virtual Machines: a deadline-aware and adaptive approach

    CERN Document Server

    Khalid, Omer; Anthony, Richard; Petridis, Miltos

    2011-01-01

    This thesis explores three major areas of research; integration of virutalization into sci- entific grid infrastructures, evaluation of the virtualization overhead on HPC grid job’s performance, and optimization of job execution times to increase their throughput by reducing job deadline miss rate. Integration of the virtualization into the grid to deploy on-demand virtual machines for jobs in a way that is transparent to the end users and have minimum impact on the existing system poses a significant challenge. This involves the creation of virtual machines, decompression of the operating system image, adapting the virtual environ- ment to satisfy software requirements of the job, constant update of the job state once it’s running with out modifying batch system or existing grid middleware, and finally bringing the host machine back to a consistent state. To facilitate this research, an existing and in production pilot job framework has been modified to deploy virtual machines on demand on the grid using...

  11. Research of Electric Discharge Machining Approach Based on Intellectual Control%基于智能控制的电火花加工方法的研究

    Institute of Scientific and Technical Information of China (English)

    杨洋; 燕冬; 朱贤峰; 黄金诚; 张杨; 何天

    2013-01-01

    The electric discharge machine (EDM) machining system is a multi-parameter non-linear stochastic system,and presendy,most of the EDM machines are based on the certain parameters machining.This approach of machining was less effective,the main reason was that the EDM parameters could not be improved at following the process and accommodated the different processing environment in the whole process.In order to solve problems above,a machining method based on intelligent control was proposed.The fuzzy controller designed with mathematical tool of Matlab was used to control the discharge gap and adjust the discharge pulse size in order to achieve the purpose of optimizing the discharge status under very bad discharge environment.A series of experiments are made to identify the feasibility of the control method.%电火花成型加工系统是一种多参数非线性的随机系统,而目前,大多数电火花成型加工机床都是采用定参数加工的方式,机床加工效率低,主要原因在于电加工参数不能随着加工进程的推进而改善,以适应整个加工过程中不同的加工环境.为解决上述问题,提出了一种基于智能控制的加工方法;借助数学工具Matlab设计的模糊控制器对放电间隙的控制和在放电环境恶化下对脉间大小的控制以达到优化放电状态的目的.最后通过实验验证了控制方法的可行性.

  12. What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

    Science.gov (United States)

    Binder, Harald

    2014-07-01

    This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.

  13. Computational Models of Financial Price Prediction: A Survey of Neural Networks, Kernel Machines and Evolutionary Computation Approaches

    Directory of Open Access Journals (Sweden)

    Javier Sandoval

    2011-12-01

    Full Text Available A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted.  The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets.  The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.

  14. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

    Science.gov (United States)

    Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

    2017-06-19

    A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

  15. Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

    Directory of Open Access Journals (Sweden)

    Zhi-Xin Yang

    2016-05-01

    Full Text Available Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL, the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

  16. A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine.

    Science.gov (United States)

    Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

    2013-10-01

    In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth.

  17. Complex Approach to Conceptual Design of Machine Mechanically Extracting Oil from Jatropha curcas L. Seeds for Biomass-Based Fuel Production.

    Science.gov (United States)

    Mašín, Ivan; Petrů, Michal

    One of important sources of biomass-based fuel is Jatropha curcas L. Great attention is paid to the biofuel produced from the oil extracted from the Jatropha curcas L. seeds. A mechanised extraction is the most efficient and feasible method for oil extraction for small-scale farmers but there is a need to extract oil in more efficient manner which would increase the labour productivity, decrease production costs, and increase benefits of small-scale farmers. On the other hand innovators should be aware that further machines development is possible only when applying the systematic approach and design methodology in all stages of engineering design. Systematic approach in this case means that designers and development engineers rigorously apply scientific knowledge, integrate different constraints and user priorities, carefully plan product and activities, and systematically solve technical problems. This paper therefore deals with the complex approach to design specification determining that can bring new innovative concepts to design of mechanical machines for oil extraction. The presented case study as the main part of the paper is focused on new concept of screw of machine mechanically extracting oil from Jatropha curcas L. seeds.

  18. Complex Approach to Conceptual Design of Machine Mechanically Extracting Oil from Jatropha curcas L. Seeds for Biomass-Based Fuel Production

    Science.gov (United States)

    Mašín, Ivan

    2016-01-01

    One of important sources of biomass-based fuel is Jatropha curcas L. Great attention is paid to the biofuel produced from the oil extracted from the Jatropha curcas L. seeds. A mechanised extraction is the most efficient and feasible method for oil extraction for small-scale farmers but there is a need to extract oil in more efficient manner which would increase the labour productivity, decrease production costs, and increase benefits of small-scale farmers. On the other hand innovators should be aware that further machines development is possible only when applying the systematic approach and design methodology in all stages of engineering design. Systematic approach in this case means that designers and development engineers rigorously apply scientific knowledge, integrate different constraints and user priorities, carefully plan product and activities, and systematically solve technical problems. This paper therefore deals with the complex approach to design specification determining that can bring new innovative concepts to design of mechanical machines for oil extraction. The presented case study as the main part of the paper is focused on new concept of screw of machine mechanically extracting oil from Jatropha curcas L. seeds. PMID:27668259

  19. Machinability evaluation of machinable ceramics with fuzzy theory

    Institute of Scientific and Technical Information of China (English)

    YU Ai-bing; ZHONG Li-jun; TAN Ye-fa

    2005-01-01

    The property parameters and machining output parameters were selected for machinability evaluation of machinable ceramics. Based on fuzzy evaluation theory, two-stage fuzzy evaluation approach was applied to consider these parameters. Two-stage fuzzy comprehensive evaluation model was proposed to evaluate machinability of machinable ceramic materials. Ce-ZrO2/CePO4 composites were fabricated and machined for evaluation of machinable ceramics. Material removal rates and specific normal grinding forces were measured. The parameters concerned with machinability were selected as alternative set. Five grades were chosen for the machinability evaluation of machnable ceramics. Machinability grades of machinable ceramics were determined through fuzzy operation. Ductile marks are observed on Ce-ZrO2/CePO4 machined surface. Five prepared Ce-ZrO2/CePO4 composites are classified as three machinability grades according to the fuzzy comprehensive evaluation results. The machinability grades of Ce-ZrO2/CePO4 composites are concerned with CePO4 content.

  20. Decomposition of forging dies for machining planning

    CERN Document Server

    Tapie, Laurent; Anselmetti, Bernard

    2009-01-01

    This paper will provide a method to decompose forging dies for machining planning in the case of high speed machining finishing operations. This method lies on a machining feature approach model presented in the following paper. The two main decomposition phases, called Basic Machining Features Extraction and Process Planning Generation, are presented. These two decomposition phases integrates machining resources models and expert machining knowledge to provide an outstanding process planning.