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Sample records for machine svm models

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

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-12-01

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

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

    Science.gov (United States)

    Malvoni, M; De Giorgi, M G; Congedo, P M

    2016-12-01

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

  3. Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

    Science.gov (United States)

    Zhang, Xia; Amin, Elizabeth Ambrose

    2016-01-01

    Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets.

  4. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

    Science.gov (United States)

    Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

    2014-08-01

    Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. GenSVM: a generalized multiclass support vector machine

    NARCIS (Netherlands)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  7. 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.

  8. 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.

  9. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

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

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    Mohammad Mahmudul Alam Mia

    2015-02-01

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

  11. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

    OpenAIRE

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user...

  12. A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology

    Institute of Scientific and Technical Information of China (English)

    Qun Wang; Zhuyun Liu; Zhongren Peng

    2015-01-01

    The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short⁃term travel time forecasting on urban arterials, a prediction model ( PSO⁃SVM) combining support vector machine ( SVM) and particle swarm optimization ( PSO) is developed. Travel time data collected with Bluetooth devices are used to calibrate the proposed model. Field experiments show that the PSO⁃SVM model ’ s error indicators are lower than the single SVM model and the BP neural network (BPNN)model. Particularly, the mean⁃absolute percentage error (MAPE) of PSO⁃SVM is only 9�453 4 %which is less than that of the single SVM model ( 12�230 2 %) and the BPNN model ( 15�314 7 %) . The results indicate that the proposed PSO⁃SVM model is feasible and more effective than other models for short⁃term travel time prediction on urban arterials.

  13. Forecasting Models for Hydropower Unit Stability Using LS-SVM

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    Liangliang Qiao

    2015-01-01

    Full Text Available This paper discusses a least square support vector machine (LS-SVM approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in Y-direction of lower generator bearing (LGB and pressure in draft tube (DT. A heuristic method such as a neural network using Backpropagation (NNBP is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.

  14. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

    Science.gov (United States)

    Khan, Saranjam; Ullah, Rahat; Khan, Asifullah; Wahab, Noorul; Bilal, Muhammad; Ahmed, Mushtaq

    2016-06-01

    The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

  15. Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.

    Science.gov (United States)

    Fernandez-Lozano, C; Canto, C; Gestal, M; Andrade-Garda, J M; Rabuñal, J R; Dorado, J; Pazos, A

    2013-01-01

    Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

  16. 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

  17. Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis

    Science.gov (United States)

    Rostami, Alireza; Masoudi, Mohammad; Ghaderi-Ardakani, Alireza; Arabloo, Milad; Amani, Mahmood

    2016-06-01

    Among the most significant physical characteristics of porous media, the effective thermal conductivity (ETC) is used for estimating the thermal enhanced oil recovery process efficiency, hydrocarbon reservoir thermal design, and numerical simulation. This paper reports the implementation of an innovative least square support vector machine (LS-SVM) algorithm for the development of enhanced model capable of predicting the ETCs of dry sandstones. By means of several statistical parameters, the validity of the presented model was evaluated. The prediction of the developed model for determining the ETCs of dry sandstones was in excellent agreement with the reported data with a coefficient of determination value ({R}2) of 0.983 and an average absolute relative deviation of 0.35 %. Results from present research show that the proposed LS-SVM model is robust, reliable, and efficient in calculating the ETCs of sandstones.

  18. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines.

    Science.gov (United States)

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net.

  19. lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

    Science.gov (United States)

    Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

    2015-01-01

    Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

  20. SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

    Institute of Scientific and Technical Information of China (English)

    钟伟民; 何国龙; 皮道映; 孙优贤

    2005-01-01

    A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

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

    Science.gov (United States)

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

    2011-10-01

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

  2. SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hua; WANG Yun-jia; LI Yong-feng

    2009-01-01

    A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed. Based on least squares support vector machine (LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction. Many of the geological and mining factors involved are related in a nonlinear way. The new model is based on statistical theory (SLT) and empirical risk minimization (ERM) principles. Typical data collected from observation stations were used for the learning and training samples. The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network (BPNN) model. The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model. The LS-SVM model was faster in computation and had better generalized performance. It provides a highly effective method for calculating the predicting parameters of the probability-integral method.

  3. Forecasting Financial Distress of Chinese High-tech Manufacturing Companies Based on a Hybrid Model of GA-SVM

    Institute of Scientific and Technical Information of China (English)

    SONG Xin-ping; DING Yong-sheng; GE Yan; LONG Quan

    2008-01-01

    Owing to the radical changing of Chinese economy, it is essential to build an effective financial distress prediction model. In this paper, we present a genetic algorithm (GA) approach for optimizing parameters of support vector machine (SVM). We validate the proposed model on datasets of Chinese high-tech manufacturing industry. Experimental results reveal that the proposed GA-SVM model can compare to and even outperform other exiting classifiers. Compared to grid-search algorithm, the proposed GA-based takes less time to optimize SVM parameter without degrading the prediction accuracy of SVM.

  4. Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

    Institute of Scientific and Technical Information of China (English)

    Wang Hongjun; Xu Xiaoli; Rosen B G

    2014-01-01

    Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine (PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

  5. Image Reconstruction Using Multi Layer Perceptron MLP And Support Vector Machine SVM Classifier And Study Of Classification Accuracy

    Directory of Open Access Journals (Sweden)

    Shovasis Kumar Biswas

    2015-02-01

    Full Text Available Abstract Support Vector Machine SVM and back-propagation neural network BPNN has been applied successfully in many areas for example rule extraction classification and evaluation. In this paper we studied the back-propagation algorithm for training the multilayer artificial neural network and a support vector machine for data classification and image reconstruction aspects. A model focused on SVM with Gaussian RBF kernel is utilized here for data classification. Back propagation neural network is viewed as one of the most straightforward and is most general methods used for supervised training of multilayered neural network. We compared a support vector machine SVM with a back-propagation neural network BPNN for the task of data classification and image reconstruction. We made a comparison between the performances of the multi-class classification of these two learning methods. Comparing with these two methods we can conclude that the classification accuracy of the support vector machine is better and algorithm is much faster than the MLP with back propagation algorithm.

  6. Multi-view L2-SVM and its multi-view core vector machine.

    Science.gov (United States)

    Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

    2016-03-01

    In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets.

  7. 基于支持向量机逼近的内模控制系统及应用%SVM Approximate-based Internal Model Control Strategy

    Institute of Scientific and Technical Information of China (English)

    王耀南; 袁小芳

    2008-01-01

    A support vector machine (SVM) approximate-based internal model control (IMC) strategy is presented for the steam valving control of synchronous generators. The proposed SVM IMC strategy includes two main parts: SVM approximate inverse controller and uncertainty compensation in the internal model structure. The SVM inverse controller is derived directly using an input-output approximation approach via Taylor expansion, and it is implemented through nonlinear system identification without further online training. Frthermore, a robustness filter is used for uncertainty compensation in the internal model structure.Simulations show the effectiveness of the SVM IMC strategy for the steam valving control.

  8. Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

    Science.gov (United States)

    Zhang, Qigui; Deng, Kai

    2016-12-01

    As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

  9. A Hybrid Prediction Method of Thermal Extension Error for Boring Machine Based on PCA and LS-SVM

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    Cheng Qiang

    2017-01-01

    Full Text Available Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA and least squares support vector machine (LS-SVM model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.

  10. 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.

  11. Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM Method

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    Nian Zhang

    2014-08-01

    Full Text Available The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF kernel framework was built on the data set to optimize the tuning parameters and to obtain the moderated output. The training process of LS-SVM was designed to select both kernel parameters and regularization constants. The USGS real-time water data were used as time series input. 50% of the data were used for training, and 50% were used for testing. The experimental results showed that the LS-SVM algorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field.

  12. SVM Model for Identification of human GPCRs

    CERN Document Server

    Shrivastava, Sonal; Malik, M M

    2010-01-01

    G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors in eukaryotes and they possess seven transmembrane a-helical domains. GPCRs are usually classified into several functionally distinct families that play a key role in cellular signalling and regulation of basic physiological processes. We can develop statistical models based on these common features that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. In this study, SVM based classification model has been developed for the identification of human gpcr sequences. Sequences of Level 1 subfamilies of Class A rhodopsin is considered as case study. In the present study, an attempt has been made to classify GPCRs on the basis of species. The present study classifies human gpcr sequences with rest of the species available in GPCRDB. Classification is based on specific information derived from the n-terminal and extracellular loops of the sequ...

  13. Prediction of nuclear proteins using SVM and HMM models

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    Raghava Gajendra PS

    2009-01-01

    Full Text Available Abstract Background The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy. Results All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins http://www.imtech.res.in/raghava/nppred/. Conclusion This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear

  14. SVM and SVM Ensembles in Breast Cancer Prediction

    Science.gov (United States)

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. PMID:28060807

  15. SVM and SVM Ensembles in Breast Cancer Prediction.

    Science.gov (United States)

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

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

    OpenAIRE

    Bresson, Xavier; Zhang, Ruiliang

    2012-01-01

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

  17. Improvments of Payload-based Intrusion Detection Models by Using Noise Against Fuzzy SVM

    Directory of Open Access Journals (Sweden)

    Guiling Zhang

    2011-02-01

    Full Text Available Intrusion detection plays a very important role in network security system. It is proved to analyze the payload of network protocol and to model a payload-based anomaly detector (PAYL can successfully detect outliers of network servers.  This paper extends these works by applying a new noise-reduced fuzzy support vector machine (fSVM to improve the detection rate at lower false positive rate. The new noisy against fuzzy SVM is applied to analyzing 1-gram, 2-grams and 2v-grams distribution classification of network payloads, which constructs three different intrusion detection models, respectively. These new intrusion detection models employ reconstruction error based fuzzy membership function to reduce the noisy of the data and to solve the sharp boundary problem, respectively. Experimental results based on DARPA data set demonstrated that the proposed schemes can achieve higher detection rate at very low false positive rate than the original and general SVM methods.

  18. Segmentasi Citra menggunakan Support Vector Machine (SVM dan Ellipsoid Region Search Strategy (ERSS Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

    Directory of Open Access Journals (Sweden)

    Lukman Hakim

    2016-02-01

    . Firstly, the pixel-level color feature and texture feature of the image, which is used as input to SVM model (classifier, are extracted via the local homogeneity and Gray Level Co-Occurrence Matrix (GLCM. Then, determine class of classifier using Arimoto based ERSS thresholding. Finally, the color image is segmented with the trained SVM model (classifier. This image segmentation result less satisfied segmented image with 69 % accuracy. Feature reduction is needed to get an effective image segmentation. Key word: image segmentation, support vector machine, ERSS Arimoto Entropy, feature extraction.

  19. Laos Organization Name Using Cascaded Model Based on SVM and CRF

    Directory of Open Access Journals (Sweden)

    Duan Shaopeng

    2017-01-01

    Full Text Available According to the characteristics of Laos organization name, this paper proposes a two layer model based on conditional random field (CRF and support vector machine (SVM for Laos organization name recognition. A layer of model uses CRF to recognition simple organization name, and the result is used to support the decision of the second level. Based on the driving method, the second layer uses SVM and CRF to recognition the complicated organization name. Finally, the results of the two levels are combined, And by a subsequent treatment to correct results of low confidence recognition. The results show that this approach based on SVM and CRF is efficient in recognizing organization name through open test for real linguistics, and the recalling rate achieve 80. 83%and the precision rate achieves 82. 75%.

  20. Model selection for SVM using mutative scale chaos optimization algorithm%变尺度混沌优化支持向量机模型选择

    Institute of Scientific and Technical Information of China (English)

    刘清坤; 阙沛文; 费春国; 宋寿鹏

    2006-01-01

    This paper proposes a new search strategy using mutative scale chaos optimization algorithm (MSCO) for model selection of support vector machine (SVM). It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of SVM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.

  1. Performance of the Angstrom-Prescott Model (A-P) and SVM and ANN techniques to estimate daily global solar irradiation in Botucatu/SP/Brazil

    Science.gov (United States)

    da Silva, Maurício Bruno Prado; Francisco Escobedo, João; Juliana Rossi, Taiza; dos Santos, Cícero Manoel; da Silva, Sílvia Helena Modenese Gorla

    2017-07-01

    This study describes the comparative study of different methods for estimating daily global solar irradiation (H): Angstrom-Prescott (A-P) model and two Machine Learning techniques (ML) - Support Vector Machine (SVM) and Artificial Neural Network (ANN). The H database was measured from 1996 to 2011 in Botucatu/SP/Brazil. Different combinations of input variables were adopted. MBE, RMSE, d Willmott, r and r2 statistical indicators obtained in the validation of A-P and SVM and ANN models showed that: SVM technique has better performance in estimating H than A-P and ANN models. A-P model has better performance in estimating H than ANN.

  2. Pressure Model of Control Valve Based on LS-SVM with the Fruit Fly Algorithm

    Directory of Open Access Journals (Sweden)

    Huang Aiqin

    2014-07-01

    Full Text Available Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM, which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10−3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.

  3. SVM and ANFIS Models for precipitaton Modeling (Case Study: GonbadKavouse

    Directory of Open Access Journals (Sweden)

    N. Zabet Pishkhani

    2016-10-01

    Full Text Available Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM. In this paper the ability of artificial intelligence methods including support vector machine (SVM and adaptive neuro fuzzy inference system (ANFIS were analyzed in monthly precipitation prediction. Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model

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

    Institute of Scientific and Technical Information of China (English)

    Zhejing BA; Youxian SUN

    2008-01-01

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

  5. 支持向量机研究进展%Advances of Support Vector Machines(SVM)

    Institute of Scientific and Technical Information of China (English)

    顾亚祥; 丁世飞

    2011-01-01

    Support vector machines(SVM) are widespread attended for its excellent ability to learn, that are based on statistical learning theory. But in dealing with large-scale quadratic programming(QP) problem, traditional SVM will take too long time of training time, and has low efficiency and so on. This paper made a summarize of the new progress in the SVM training of algorithm,and made analysis and comparison on main algorithm,pointed out the advantages and disadvantages of them,focused on new progress in the current study — Fuzzy Support Vector Machine and Granular Support Vector Machine. Then the two mainly applications — classification and regression of SVM were discussed. Finally, the article gave the future research directions on SVM prediction.%基于统计学习理论的支持向量机(Support vector machines,SVM)以其优秀的学习能力受到广泛的关注.但传统支持向量机在处理大规模二次规划问题时会出现训练时间长、效率低下等问题.对SVM训练算法的最新研究成果进行了综述,对主要算法进行了比较深入的分析和比较,指出了各自的优点及其存在的问题,并且着重介绍了目前研究的新进展--模糊SVM和粒度SVM.接着论述了SVM主要的两方面应用--分类和回归.最后给出了今后SVM研究方向的预见.

  6. Support vector machine applied in QSAR modelling

    Institute of Scientific and Technical Information of China (English)

    MEI Hu; ZHOU Yuan; LIANG Guizhao; LI Zhiliang

    2005-01-01

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

  7. SVM classification model in depression recognition based on mutation PSO parameter optimization

    Directory of Open Access Journals (Sweden)

    Zhang Ming

    2017-01-01

    Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

  8. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

    Directory of Open Access Journals (Sweden)

    Yu Yang

    2008-05-01

    Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

  9. MULTI SUPPORT VECTOR MACHINES DECISION MODEL AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    阎威武; 陈治纲; 邵惠鹤

    2002-01-01

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

  10. SVM Intrusion Detection Model Based on Compressed Sampling

    Directory of Open Access Journals (Sweden)

    Shanxiong Chen

    2016-01-01

    Full Text Available Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling. We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.

  11. 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.

  12. 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.

  13. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  14. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-06-28

    A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is

  15. A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

    Institute of Scientific and Technical Information of China (English)

    CAO Shu-gang; LIU Yan-bao; WANG Yan-ping

    2008-01-01

    To improve the precision and reliability in predicting methane hazard in working face of coal mine, we have proposed a forecasting and forewarning model for methane hazard based on the least square support vector (LS-SVM) multi-classifier and regression machine. For the forecasting model, the methane concentration can be considered as a nonlinear time series and the time series analysis method is adopted to predict the change in methane concentration using LS-SVM regression. For the forewarning model, which is based on the forecasting results, by the multi-classification method of LS-SVM, the methane hazard was identified to four grades: normal, attention, warning and danger. According to the forewarning results, corresponding measures are taken. The model was used to forecast and forewarn the K9 working face. The results obtained by LS-SVM regression show that the forecast- ing have a high precision and forewarning results based on a LS-SVM multi-classifier are credible. Therefore, it is an effective model building method for continuous prediction of methane concentration and hazard forewarning in working face.

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

    Directory of Open Access Journals (Sweden)

    Nurul Arneida Husin

    2012-04-01

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

  17. The Application of Support Vector Machine (svm) Using Cielab Color Model, Color Intensity and Color Constancy as Features for Ortho Image Classification of Benthic Habitats in Hinatuan, Surigao del Sur, Philippines

    Science.gov (United States)

    Cubillas, J. E.; Japitana, M.

    2016-06-01

    This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  18. THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    J. E. Cubillas

    2016-06-01

    Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  19. 基于 SVM-ARIMA的大坝变形预测模型%SVM-ARIMA Forecasting Model for Dam Deformation

    Institute of Scientific and Technical Information of China (English)

    沈寿亮; 刘天祥; 宋锦焘; 姜彦作; 梁睿斌

    2014-01-01

    大坝变形的实测值序列是一个非线性、非平稳的时间序列,支持向量机引入核函数后能有效解决非线性问题,因此可用支持向量机对大坝变形进行预测。为了提高预测精度,进一步对残差序列进行分析,通过ARIMA模型对残差序列进行预测,建立了SVM-ARIMA组合模型。将大坝变形时间序列分为趋势项和误差项,分别用SVM和ARIMA模型进行预测,综合两项结果得到模型的预测值。结合实测资料对模型进行检验,结果表明组合模型精度较高。%The monitoring data of the dam deformation is a nonlinear and non-stationary time series. After the introduction of kernel function,sup-port vector machine can solve the nonlinear problem effectively,so support vector machine was used to predict the dam deformation. In order to im-prove the prediction accuracy,ARIMA model was used to further analysis the residual series,therefore,SVM-ARIMA combination model was es-tablished to predict the dam deformation. The time series of dam deformation was divided into two parts:the trend term and the error term,they were predicted by SVM and ARIMA models respectively,the sum of the two parts was the final predicted value. At last,there was a comparison be-tween the measured data and the predicted one combined with a project. The result indicates that the accuracy of the SVM-ARIMA model is high, completely meeting the project needs.

  20. Application of SVM classifier in thermographic image classification for early detection of breast cancer

    Science.gov (United States)

    Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

    2016-09-01

    This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

  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. [Based on the LS-SVM modeling method determination of soil available N and available K by using near-infrared spectroscopy].

    Science.gov (United States)

    Liu, Xue-Mei; Liu, Jian-She

    2012-11-01

    Visible infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement accuracy of soil properties,namely, available nitrogen(N) and available potassium(K). Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC) and Savitzky-Golay smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares (PLS) and least squares-support vector machine (LS-SVM) models analysis were implemented for calibration models. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including PCA(PCs), latent variables (LVs), and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The performance of the model was evaluated by the correlation coefficient (r2) and RMSEP. The optimal EWs-LS-SVM models were achieved, and the correlation coefficient (r2) and RMSEP were 0.82 and 17.2 for N and 0.72 and 15.0 for K, respectively. The results indicated that visible and short wave-near infrared spectroscopy (Vis/SW-NIRS)(325-1 075 nm) combined with LS-SVM could be utilized as a precision method for the determination of soil properties.

  4. Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery.

    Science.gov (United States)

    Sun, Huiyong; Pan, Peichen; Tian, Sheng; Xu, Lei; Kong, Xiaotian; Li, Youyong; Dan Li; Hou, Tingjun

    2016-04-22

    The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF). As for the investigated targets, the optimized MIEC-SVM models performed much better than the models based on the default SVM parameters and Autodock for the tested datasets. Then, the proposed strategy was utilized to screen the Specs database for discovering potential inhibitors of the ALK kinase. The experimental results showed that the optimized MIEC-SVM model, which identified 7 actives with IC50 < 10 μM from 50 purchased compounds (namely hit rate of 14%, and 4 in nM level) and performed much better than Autodock (3 actives with IC50 < 10 μM from 50 purchased compounds, namely hit rate of 6%, and 2 in nM level), suggesting that the proposed strategy is a powerful tool in structure-based virtual screening.

  5. A new type SVM-projected SVM

    Institute of Scientific and Technical Information of China (English)

    ZHU; Yongsheng; ZHANG; Youyun

    2004-01-01

    Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.

  6. 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.

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

    Institute of Scientific and Technical Information of China (English)

    张浩然; 韩正之; 冯瑞; 于志强

    2003-01-01

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

  8. SVM异常数据识别的比例风险预测模型%Proportional Hazards Model Prediction Model Study Based on SVM Abnormal Date Recognition

    Institute of Scientific and Technical Information of China (English)

    孙磊; 冯添乐; 张星辉

    2012-01-01

    剩余寿命预测在设备维修管理中扮演着重要的角色,准确的剩余寿命预测对制定维修策略起着至关重要的作用,从而可以有效避免设备故障的发生.提出一种基于支持向量机(SVM)异常数据识别的比例风险模型(PHM)用于剩余寿命的预测,该模式利用支持向量机和比例风险模型分别实现异常状态数据的识别和剩余寿命的预测.案例研究表明,SVM -PHM模型较PHM模型具有更好的预测精度.%The Remaining Useful Life (RUL) forecasting of the unit plays a significant role in maintenance management. The accurate RUL prediction based on the current and previous health condition of the unit is essential to make a timely maintenance decision for failure avoidance. In this paper, it presents proportional hazards model assembled with Support Vector Machine (SVM) to forecast RUL. In this method, it amploys SVM and PHM to identify abnormal data and RUL forecasting The case shows, the precision of predicting by SVM-PHM has a better performance than the original PHM.

  9. Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN

    Directory of Open Access Journals (Sweden)

    Chi Xie

    2015-01-01

    Full Text Available There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA model, the support vector machine (SVM model, and the back-propagation neural network (BPNN model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD and the RMB exchange rate against Euro (RMB/EUR are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.

  10. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

    Science.gov (United States)

    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-07-25

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer.

  11. Modeling of SVM Diode Clamping Three-Level Inverter Connected to Grid

    DEFF Research Database (Denmark)

    Guo, Yougui; Zeng, Ping; Zhu, Jieqiong

    2011-01-01

    PLECS is used to model the diode clamping three-level inverter connected to grid and good results are obtained. First the output voltage SVM is described for diode clamping three-level inverter with loads connected to Y. Then the output voltage SVM of diode clamping three-level inverter is simply...... is very powerful tool to real power circuits and it is very easy to simulate them. They have also verified that SVM control strategy is feasible to control the diode clamping three-level inverter....

  12. Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

    Science.gov (United States)

    Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

    2014-11-01

    As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of

  13. SVM CLASSIFICATION:ITS CONTENTS AND CHALLENGES

    Institute of Scientific and Technical Information of China (English)

    YueShihong; LiPing; HaoPeiyi

    2003-01-01

    SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. In particular,they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically. There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation. In this paper,survey ofthe key contents on this subject, focusing on the most well-known models based on kernel substitution, namely SVM, as well as the activated fields at present and the development tendency,is presented.

  14. SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control%基于2次核SVM的单步非线性模型预测控制

    Institute of Scientific and Technical Information of China (English)

    钟伟民; 何国龙; 皮道映; 孙优贤

    2005-01-01

    A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

  15. 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.

  16. Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

    Science.gov (United States)

    Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

    2014-04-01

    Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates.

  17. B-SPLINE-BASED SVM MODEL AND ITS APPLICATIONS TO OIL WATER-FLOODED STATUS IDENTIFICATION

    Institute of Scientific and Technical Information of China (English)

    Shang Fuhua; Zhao Tiejun; Yi Xiongying

    2007-01-01

    A method of B-spline transform for signal feature extraction is developed. With the B-spline,the log-signal space is mapped into the vector space. An efficient algorithm based on Support Vector Machine (SVM) to automatically identify the water-flooded status of oil-saturated stratum is described.The experiments show that this algorithm can improve the performances for the identification and the generalization in the case of a limited set of samples.

  18. 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.

  19. Predictive modeling of human operator cognitive state via sparse and robust support vector machines.

    Science.gov (United States)

    Zhang, Jian-Hua; Qin, Pan-Pan; Raisch, Jörg; Wang, Ru-Bin

    2013-10-01

    The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human-machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.

  20. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

    Science.gov (United States)

    Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

    2015-01-01

    Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  1. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2015-01-01

    Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

  2. The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan

    Indian Academy of Sciences (India)

    Ramakrushna Reddy; Rajesh R Nair

    2013-10-01

    This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.

  3. SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu; Shao Huihe; Wang Xiaofan

    2004-01-01

    Soft sensor is widely used in industrial process control.It plays an important role to improve the quality of product and assure safety in production.The core of soft sensor is to construct soft sensing model.A new soft sensing modeling method based on support vector machine (SVM) is proposed.SVM is a new machine learning method based on statistical learning theory and is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima.The proposed methods are applied to the estimation of frozen point of light diesel oil in distillation column.The estimated outputs of soft sensing model based on SVM match the real values of frozen point and follow varying trend of frozen point very well.Experiment results show that SVM provides a new effective method for soft sensing modeling and has promising application in industrial process applications.

  4. Universum Learning for Multiclass SVM

    OpenAIRE

    Dhar, Sauptik; Ramakrishnan, Naveen; Cherkassky, Vladimir; Shah, Mohak

    2016-01-01

    We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose a span bound for MU-SVM that can be used for model selection thereby avoiding resampling. Empirical results demonstrate the effectiveness of MU-SVM and the proposed bound.

  5. Diagnostic accuracy of Parkinson disease by support vector machine (SVM) analysis of 123I-FP-CIT brain SPECT data: implications of putaminal findings and age.

    Science.gov (United States)

    Palumbo, Barbara; Fravolini, Mario Luca; Buresta, Tommaso; Pompili, Filippo; Forini, Nevio; Nigro, Pasquale; Calabresi, Paolo; Tambasco, Nicola

    2014-12-01

    Brain single-photon-emission-computerized tomography (SPECT) with I-ioflupane (I-FP-CIT) is useful to diagnose Parkinson disease (PD). To investigate the diagnostic performance of I-FP-CIT brain SPECT with semiquantitative analysis by Basal Ganglia V2 software (BasGan), we evaluated semiquantitative data of patients with suspect of PD by a support vector machine classifier (SVM), a powerful supervised classification algorithm.I-FP-CIT SPECT with BasGan analysis was performed in 90 patients with suspect of PD showing mild symptoms (bradykinesia-rigidity and mild tremor). PD was confirmed in 56 patients, 34 resulted non-PD (essential tremor and drug-induced Parkinsonism). A clinical follow-up of at least 6 months confirmed diagnosis. To investigate BasGan diagnostic performance we trained SVM classification models featuring different descriptors using both a "leave-one-out" and a "five-fold" method. In the first study we used as class descriptors the semiquantitative radiopharmaceutical uptake values in the left (L) and right (R) putamen (P) and in the L and R caudate nucleus (C) for a total of 4 descriptors (CL, CR, PL, PR). In the second study each patient was described only by CL and CR, while in the third by PL and PR descriptors. Age was added as a further descriptor to evaluate its influence in the classification performance.I-FP-CIT SPECT with BasGan analysis reached a classification performance higher than 73.9% in all the models. Considering the "Leave-one-out" method, PL and PR were better predictors (accuracy of 91% for all patients) than CL and CR descriptors; using PL, PR, CL, and CR diagnostic accuracy was similar to that of PL and PR descriptors in the different groups. Adding age as a further descriptor accuracy improved in all the models. The best results were obtained by using all the 5 descriptors both in PD and non-PD subjects (CR and CL + PR and PL + age = 96.4% and 94.1%, respectively). Similar results were observed for the "five

  6. Predication of Crane Condition Parameters Based on SVM and AR

    Science.gov (United States)

    Xiuzhong, Xu; Xiong, Hu; Congxiao, Zhou

    2011-07-01

    Through statistic analysis of vibration signals of motor on the container crane hoisting mechanism in a port, the feature vectors with vibration are obtained. Through data preprocessing and training data, Training models of condition parameters based on support vector machine (SVM) are established. The testing data of condition monitoring parameters can be predicted by the training models. During training the models, the penalty parameter and kernel function of model are optimized by cross validation. In order to analysis the accurate of SVM model, autoregressive model is used to predict the trend of vibration. The research showed the predicted results of model using SVM are better than the results by autoregressive (AR) modeling.

  7. Screening for Prediabetes Using Machine Learning Models

    Directory of Open Access Journals (Sweden)

    Soo Beom Choi

    2014-01-01

    Full Text Available The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES were used, excluding subjects with diabetes. The KNHANES 2010 data (n=4685 were used for training and internal validation, while data from KNHANES 2011 (n=4566 were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN and support vector machine (SVM and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729 and the screening score model (0.712, respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

  8. SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

    Science.gov (United States)

    Li, Ying Hong; Xu, Jing Yu; Tao, Lin; Li, Xiao Feng; Li, Shuang; Zeng, Xian; Chen, Shang Ying; Zhang, Peng; Qin, Chu; Zhang, Cheng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

    2016-01-01

    Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

  9. Semisupervised learning using Bayesian interpretation: application to LS-SVM.

    Science.gov (United States)

    Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

    2011-04-01

    Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

  10. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  11. Fatigue Life Prediction of Ductile Iron Based on DE-SVM Algorithm

    Science.gov (United States)

    Yiqun, Ma; Xiaoping, Wang; lun, An

    the model, predicting fatigue life of ductile iron, based on SVM (Support Vector Machine, SVM) has been established. For it is easy to fall into local optimum during parameter optimization of SVM, DE (Differential Evolution algorithm, DE) algorithm was adopted to optimize to improve prediction precision. Fatigue life of ductile iron is predicted combining with concrete examples, and simulation experiment to optimize SVM is conducted adopting GA (Genetic Algorithm), ACO (Ant Colony Optimization) and POS (Partial Swarm Optimization). Results reveal that DE-SVM algorithm is of a better prediction performance.

  12. Steady Modeling for an Ammonia Synthesis Reactor Based on a Novel CDEAS-LS-SVM Model

    Directory of Open Access Journals (Sweden)

    Zhuoqian Liu

    2014-01-01

    Full Text Available A steady-state mathematical model is built in order to represent plant behavior under stationary operating conditions. A novel modeling using LS-SVR based on Cultural Differential Evolution with Ant Search is proposed. LS-SVM is adopted to establish the model of the net value of ammonia. The modeling method has fast convergence speed and good global adaptability for identification of the ammonia synthesis process. The LS-SVR model was established using the above-mentioned method. Simulation results verify the validity of the method.

  13. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

    Science.gov (United States)

    Lu, Huiling; Zhang, Junjie; Shi, Hongbin

    2016-01-01

    In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.

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

    Institute of Scientific and Technical Information of China (English)

    SUN Hua-li; XIE Jian-ying

    2007-01-01

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

  15. 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.

  16. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

    Directory of Open Access Journals (Sweden)

    Shaojiang Dong

    2014-01-01

    Full Text Available Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

  17. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

    Science.gov (United States)

    Subasi, Abdulhamit

    2013-06-01

    Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.

  18. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  19. Hybrid SVM/HMM Method for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    刘江华; 陈佳品; 程君实

    2004-01-01

    A face recognition system based on Support Vector Machine (SVM) and Hidden Markov Model (HMM) has been proposed. The powerful discriminative ability of SVM is combined with the temporal modeling ability of HMM. The output of SVM is moderated to be probability output, which replaces the Mixture of Gauss (MOG) in HMM. Wavelet transformation is used to extract observation vector, which reduces the data dimension and improves the robustness.The hybrid system is compared with pure HMM face recognition method based on ORL face database and Yale face database. Experiments results show that the hybrid method has better performance.

  20. 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.

  1. A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2014-06-27

    Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

  2. Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model

    Directory of Open Access Journals (Sweden)

    Han Jiang

    2016-01-01

    Full Text Available Recently, a number of short-term speed prediction approaches have been developed, in which most algorithms are based on machine learning and statistical theory. This paper examined the multistep ahead prediction performance of eight different models using the 2-minute travel speed data collected from three Remote Traffic Microwave Sensors located on a southbound segment of 4th ring road in Beijing City. Specifically, we consider five machine learning methods: Back Propagation Neural Network (BPNN, nonlinear autoregressive model with exogenous inputs neural network (NARXNN, support vector machine with radial basis function as kernel function (SVM-RBF, Support Vector Machine with Linear Function (SVM-LIN, and Multilinear Regression (MLR as candidate. Three statistical models are also selected: Autoregressive Integrated Moving Average (ARIMA, Vector Autoregression (VAR, and Space-Time (ST model. From the prediction results, we find the following meaningful results: (1 the prediction accuracy of speed deteriorates as the prediction time steps increase for all models; (2 the BPNN, NARXNN, and SVM-RBF can clearly outperform two traditional statistical models: ARIMA and VAR; (3 the prediction performance of ANN is superior to that of SVM and MLR; (4 as time step increases, the ST model can consistently provide the lowest MAE comparing with ARIMA and VAR.

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

    Science.gov (United States)

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

    2017-08-01

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

  4. Relative Attribute SVM+ Learning for Age Estimation.

    Science.gov (United States)

    Wang, Shengzheng; Tao, Dacheng; Yang, Jie

    2016-03-01

    When estimating age, human experts can provide privileged information that encodes the facial attributes of aging, such as smoothness, face shape, face acne, wrinkles, and bags under-eyes. In automatic age estimation, privileged information is unavailable to test images. To overcome this problem, we hypothesize that asymmetric information can be explored and exploited to improve the generalizability of the trained model. Using the learning using privileged information (LUPI) framework, we tested this hypothesis by carefully defining relative attributes for support vector machine (SVM+) to improve the performance of age estimation. We term this specific setting as relative attribute SVM+ (raSVM+), in which the privileged information enables separation of outliers from inliers at the training stage and effectively manipulates slack variables and age determination errors during model training, and thus guides the trained predictor toward a generalizable solution. Experimentally, the superiority of raSVM+ was confirmed by comparing it with state-of-the-art algorithms on the face and gesture recognition research network (FG-NET) and craniofacial longitudinal morphological face aging databases. raSVM+ is a promising development that improves age estimation, with the mean absolute error reaching 4.07 on FG-NET.

  5. Robust DTC-SVM Method for Matrix Converter Drives with Model Reference Adaptive Control Scheme

    DEFF Research Database (Denmark)

    Lee, Kyo Beum; Huh, Sunghoi; Sim, Kyung-Hun

    2007-01-01

    strategy using space vector modulations and a deadbeat algorithm in the stator flux reference frame. The lumped disturbances such as parameter variation and load disturbance of the system are estimated by a neuro-sliding mode approach based on model reference adaptive control (MRAC). An adaptive observer......This paper presents a new robust DTC-SVM control system for high performance induction motor drives fed by a matrix converter with variable structure - model reference adaptive control scheme (VS-MRAC). It is possible to combine the advantages of matrix converters with the advantages of the DTC...

  6. A novel excitation controller using support vector machines and approximate models

    Institute of Scientific and Technical Information of China (English)

    Xiaofang YUAN; Yaonan WANG; Shutao LI

    2008-01-01

    This paper proposes a novel excitation controller using suppon vector machines(SVM)and approximate models.The nonlinear control law is derived directly based on an input-output approximation method via Taylor expansion,which not only avoids complex control development and intensive computation,but also avoids online learning or adjust.ment.Only a general SVM modelling technique is involved in both model identification and controller implementation.The robustness of the stability is rigorously established using the Lyapunov method.Several simulations demonstrate the effectiveness of the proposed excitation controller.

  7. PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    Sari Ayu Wulandari

    2016-06-01

    Full Text Available Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w, dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means, variasi Varians, skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri  PCA (Principle Com­­ponent Analysis. Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent, PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks.

  8. A linear-RBF multikernel SVM to classify big text corpora.

    Science.gov (United States)

    Romero, R; Iglesias, E L; Borrajo, L

    2015-01-01

    Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

  9. Highly accurate SVM model with automatic feature selection for word sense disambiguation

    Institute of Scientific and Technical Information of China (English)

    王浩; 陈贵林; 吴连献

    2004-01-01

    A novel algorithm for word sense disambiguation(WSD) that is based on SVM model improved with automatic feature selection is introduced. This learning method employs rich contextual features to predict the proper senses for specific words. Experimental results show that this algorithm can achieve an execellent performance on the set of data released during the SENSEEVAL-2 competition. We present the results obtained and discuss the transplantation of this algorithm to other languages such as Chinese. Experimental results on Chinese corpus show that our algorithm achieves an accuracy of 70.0 % even with small training data.

  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. Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines

    Energy Technology Data Exchange (ETDEWEB)

    Li, Chun-Hua; Zhu, Xin-Jian; Cao, Guang-Yi; Sui, Sheng; Hu, Ming-Ruo [Fuel Cell Research Institute, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240 (China)

    2008-01-03

    This paper reports a Hammerstein modeling study of a proton exchange membrane fuel cell (PEMFC) stack using least squares support vector machines (LS-SVM). PEMFC is a complex nonlinear, multi-input and multi-output (MIMO) system that is hard to model by traditional methodologies. Due to the generalization performance of LS-SVM being independent of the dimensionality of the input data and the particularly simple structure of the Hammerstein model, a MIMO SVM-ARX (linear autoregression model with exogenous input) Hammerstein model is used to represent the PEMFC stack in this paper. The linear model parameters and the static nonlinearity can be obtained simultaneously by solving a set of linear equations followed by the singular value decomposition (SVD). The simulation tests demonstrate the obtained SVM-ARX Hammerstein model can efficiently approximate the dynamic behavior of a PEMFC stack. Furthermore, based on the proposed SVM-ARX Hammerstein model, valid control strategy studies such as predictive control, robust control can be developed. (author)

  13. 果蝇优化SVM模型对有机化合物熔点的预测%The Prediction of Organic Compound Melting Point by SVM Model Optimized by Drosophila Algorithm

    Institute of Scientific and Technical Information of China (English)

    张维涛

    2015-01-01

    In order to solve the support vector machine ( SVM) model of blindness problem in parameter selection and further improve the model’s learning performance and generalization ability, the drosophila optimization algorithm ( FOA) was introduced into the field. A SVM method based on optimization of drosophila melanogaster was presented. The method used drosophila optimization algorithm to select the SVM penalty factor and kernel function parameter global optimization, so as to establish the SVM classification model, and based on this, the model was applied to practical problems. The model was applied to the melting point of organic compounds in prediction problems. The experimental results showed that efficiency was high based on the SVM model of drosophila optimization, the practical application effect was good.%为了有效解决支持向量机( SVM)模型在参数选择上的盲目性问题,进而提高该模型的学习性能和泛化能力,将果蝇优化算法( FOA)引入该领域,提出了一种基于果蝇优化的SVM方法。该方法首先运用果蝇优化优化算法选择全局最优的SVM惩罚因子和核函数参数,从而建立SVM分类模型,进而基于该模型对实际问题进行应用。将该模型应用于对有机化合物的熔点预测问题中,实验结果表明,基于果蝇优化的SVM模型效率高,实际应用效果好。

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

    Directory of Open Access Journals (Sweden)

    Bhusana Premanode

    2013-01-01

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

  15. 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.

  16. COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

    Directory of Open Access Journals (Sweden)

    M. J. Baheti

    2012-01-01

    Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

  17. Ecological Footprint Model Using the Support Vector Machine Technique

    Science.gov (United States)

    Ma, Haibo; Chang, Wenjuan; Cui, Guangbai

    2012-01-01

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

  18. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  19. SVM regression model based on f ruit fly optimization%基于果蝇优化的支持向量机回归模型

    Institute of Scientific and Technical Information of China (English)

    赵伟

    2015-01-01

    给出一种基于果蝇优化的支持向量机回归模型。将支持向量机惩罚因子和核函数参数初始化为果蝇群体,根据果蝇优化算法原理,依据适应度最优原则进行迭代觅食,搜索最优参数,建立模型。将该模型用于分析有机化合物熔点预测问题,结果显示,该模型预测均方误差为3.02%,相关系数达到89.39%。%A support vector machine (SVM ) regression model based on fruit fly optimization algorithm is proposed .Initialize the SVM penalty factor and kernel function parameter as a fruit fly group .By the rule of fruit fly optimization ,execute iterative optimal foraging according to the fitness principle until the optimal parameters are sought out ,thus ,the model can be set up .Use this model to analyze the melting point prediction of organic compounds ,it turns out that ,the prediction error is 3 .02% , and the correlation coefficient is 89 .39% .

  20. Efficient HIK SVM learning for image classification.

    Science.gov (United States)

    Wu, Jianxin

    2012-10-01

    Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

  1. Modelling soil water retention using support vector machines with genetic algorithm optimisation.

    Science.gov (United States)

    Lamorski, Krzysztof; Sławiński, Cezary; Moreno, Felix; Barna, Gyöngyi; Skierucha, Wojciech; Arrue, José L

    2014-01-01

    This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: -0.98, -3.10, -9.81, -31.02, -491.66, and -1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models' parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67-0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

  2. Image Segmentation Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  3. 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.

  4. Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation

    Directory of Open Access Journals (Sweden)

    Krzysztof Lamorski

    2014-01-01

    Full Text Available This work presents point pedotransfer function (PTF models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: –0.98, –3.10, –9.81, –31.02, –491.66, and –1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models’ parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67–0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

  5. Identification of eggs from different production systems based on hyperspectra and CS-SVM.

    Science.gov (United States)

    Sun, J; Cong, S; Mao, H; Zhou, X; Wu, X; Zhang, X

    2017-01-19

    1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables (SNV) combined with Savitzky-Golay (SG) were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) to establish a SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

  6. Credit risk evaluation using adaptive Lq penalty SVM with Gauss kernel

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensio...

  7. 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…

  8. SVM with discriminative dynamic time alignment

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In the past several years, support vector machines (SVM) have achieved a huge success in many field, especially in pattern recognition. But the standard SVM cannot deal with length-variable vectors, which is one severe obstacle for its applications to some important areas, such as speech recognition and part-of-speech tagging. The paper proposed a novel SVM with discriminative dynamic time alignment (DDTA-SVM) to solve this problem. When training DDTA-SVM classifier, according to the category information of the training Samples, different time alignment strategies were adopted to manipulate them in the kernel functions, which contributed to great improvement for training speed and generalization capability of the classifier. Since the alignment operator was embedded in kernel functions, the training algorithms of standard SVM were still compatible in DDTA-SVM. In order to increase the reliability of the classification, a new classification algorithm was suggested. The preliminary experimental results on Chinese confusable syllables speech classification task show that DDTA-SVM obtains faster convergence speed and better classification performance than dynamic time alignment kernel SVM (DTAK-SVM).Moreover, DDTA-SVM also gives higher classification precision compared to the conventional HMM. This proves that the proposed method is effective, especially for confusable lengthvariable pattern classification tasks.

  9. Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Man Zhu

    2017-03-01

    Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

  10. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

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

    Science.gov (United States)

    Liu, Xiaoyong; Fu, Hui

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xiaoyong Liu

    2014-01-01

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

  13. Study on phase retardation characteristic of LCVR using dispersion analysis and SVM

    Institute of Scientific and Technical Information of China (English)

    HU; Dongmei; LIU; Quan; NIU; Guocheng; ZHU; Yifeng; YU; Lintao

    2015-01-01

    To calibrate the phase retardance of a Liquid crystal variable retarder(LCVR),its birefringence dispersion characteristic was analyzed,and the Support vector machines(SVM) algorithm was adopted to establish the prediction model.The obtained SVM decision function was used as a part of LCVR phase retardance,which was generated by the driving voltage.The experimental verification was carried out with a 568 nm laser.The results show that the deviation of the experimental value and the theoretical value is about 0.0061λ.SVM method could be used as an effective method for LCVR phase retardance characteristic calibration.

  14. Segmentation of Magnetic Resonance Imaging MRI using LS-SVM and Wavelet Multiresolution Analysis

    Directory of Open Access Journals (Sweden)

    Luis A. Muñoz-Bedoya

    2013-11-01

    Full Text Available Currently, support vector machines (SVM have become a powerful tool to solve nonlinear classification problems. For the optimization of the tool, has developed a reformulation known as LS-SVM (Support Vector Machine least squares, which works with a model based on function minimization and Lagrange polynomials. Therefore, this paper presents a method for segmentation of magnetic resonance images specifically to study the morphology of the lungs and reach the quantification of relevant features in these images using SVM and LS-SVM. In addition to sorting technique in this work using techniques such as wavelet analysis to eliminate irrelevant information (compression and Splines algorithms to interpolate the information found and quantify the characteristics, which in this work were based on the recognition area, shape and abnormal structures present in the lung of these images.

  15. Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

    Science.gov (United States)

    Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

    2014-10-01

    Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

  16. 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...

  17. 铁路扣件图像检测中的RBF-SVM模型优化%Optimization of RBF-SVM model in railway fastener detection system

    Institute of Scientific and Technical Information of China (English)

    刘甲甲; 王凯; 袁建英; 江晓亮; 李柏林

    2014-01-01

    在开发的铁路扣件检测系统中,RBF-SVM被作为扣件图像分类识别的分类器。核参数的选择是RBF-SVM模型优化研究中的重要问题,将量子粒子群算法应用于参数的优化选择,在(c,γ)参数可调范围内产生初始种群,将种群中的个体作为RBF-SVM的参数进行学习;经过多次迭代获得最佳参数对(c,γ),并将该参数对作为RBF-SVM的核参数训练支持向量机。实验表明,QPSO的性能优于传统的 PSO算法,该方法在解决支持向量机优化方面表现出了高效的收敛性和稳定性,并且在该方法的基础上形成的铁路扣件检测算法是切实可行的。%In the railway fastener detection system, RBF-SVM is used as image classifier for railway fasteners. The selection of kernel parameters is an important problem in RBF-SVM research. A parameter selection method based on quantum genet-ic algorithm(QPSO)is presented. Initial population is produced in the adjustable range of parameters c and γ, and individuals in it are used as the parameters of RBF-SVM to calculation; then by multi-iterations, the parameters (c,γ) are obtained which are corresponding to fitness of population, and used as kernel parameters of Radial Basis kernel Function of Support Vector Machine(RBF-SVM)to training model. The experimental results indicate that the QPSO algorithm outperforms PSO algorithm. It has a high convergence and stability, and the detection algorithm of rail fastener based on it is practicable.

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

    Institute of Scientific and Technical Information of China (English)

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

    2004-01-01

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

  19. 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.

  20. Least Squares Support Vector Machine Based Real-Time Fault Diagnosis Model for Gas Path Parameters of Aero Engines

    Institute of Scientific and Technical Information of China (English)

    WANG Xu-hui; HUANG Sheng-guo; WANG Ye; LIU Yong-jian; SHU Ping

    2009-01-01

    Least squares support vector machine (LS-SVM) is applied in gas path fault diagnosis for aero engines.Firstly,the deviation data of engine cruise are analyzed.Then,model selection is conducted using pattern search method.Finally,by decoding aircraft communication addressing and reporting system (ACARS) report,a real-time cruise data set is acquired,and the diagnosis model is adopted to process data.In contrast to the radial basis function (RBF) neutral network,LS-SVM is more suitable for real-time diagnosis of gas turbine engine.

  1. 基于加权聚类质心的 SVM 不平衡分类方法%Support vector machine imbalanced data classification based on weighted clustering centroid

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

    Classification of imbalanced data has become a research hot topic in machine learning .Traditional classi-fication algorithms assume that different classes have balanced distribution or equal misclassification cost , thus, making it hard to get ideal result of classifications .A support vector machine (SVM) classification method based on weighted clustering centroid was proposed in this paper .First, unsupervised clustering was applied to the positive and negative samples respectively to extract the clustering centroid of each clustering , which was represented the most in compactness of the clustering sample .Next, all clustering centroids formed a new set of balance training .In order to minimize the information loss during clustering , each clustering centroid was associated with a weight factor that was defined proportional to the number of samples of the class .Finally, all clustering centroids and weight fac-tors participated in the training of the improved SVM model .Experimental results show that the proposed method can make the sample selected from model train sets more typical and improve the classification performance better than other sampling techniques for dealing with imbalanced data .%  不平衡数据分类是机器学习研究的热点问题,传统分类算法假定不同类别具有平衡分布或误分代价相同,难以得到理想的分类结果。提出一种基于加权聚类质心的SVM分类方法,在正负类样本上分别进行聚类,对每个聚类,用聚类质心和权重因子代表聚类内样本分布和数量,相等类别数量的质心和权重因子参与SVM模型训练。实验结果表明,该方法使模型的训练样本具有较高的代表性,分类性能与其他采样方法相比得到了提升。

  2. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    Science.gov (United States)

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  3. Efficient iris recognition via ICA feature and SVM classifier

    Institute of Scientific and Technical Information of China (English)

    Wang Yong; Xu Luping

    2007-01-01

    To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate, an iris recognition approach for combining SVM with ICA feature extraction model is presented. SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem. And ICA is a feature extraction technique which can be considered a generalization of principal component analysis. In this paper, ICA is used to generate a set of subsequences of feature vectors for iris feature extraction. Then each subsequence is classified using support vector machine sequence kernels. Experiments are made on CASIA iris database, the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.

  4. F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

    OpenAIRE

    Wu, Xiaohe; Zuo, Wangmeng; ZHU, YUANYUAN; Lin, Liang

    2015-01-01

    The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of the transformation matrix to be diagonal, or are non-convex and computationally expensive. In this ...

  5. 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.

  6. 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/.

  7. [Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

    Science.gov (United States)

    Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

    2013-03-01

    Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

  8. A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity

    Directory of Open Access Journals (Sweden)

    Thanh-Dao Tran

    2012-04-01

    Full Text Available Benzo[c]phenanthridine (BCP derivatives were identified as topoisomerase I (TOP-I targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors identified from a large set via a random forest algorithm. Overall accuracy of up to 87% and a Matthews coefficient correlation (MCC of 0.71 were obtained after this SVM classifier was validated internally by a test set of 15 compounds. For two external test sets, 89% and 80% BCP compounds, respectively, were correctly predicted. The results indicated that our SVM model could be used as the filter for designing new BCP compounds with higher TOP-I inhibitory activity.

  9. 基于 SVM 的便携式睡眠监测系统设计%A design of sleep monitoring system based on support vector machines

    Institute of Scientific and Technical Information of China (English)

    林秀晶; 钱松荣

    2015-01-01

    Objective Sleep monitoring is an important part of the analysis of sleep quality , yet the sleep monitoring system available now is complex and cumbersome .A portable sleep monitoring system based on support vector machines ( SVM) is proposed in this paper with great convenience and efficiency .Methods The system’ s hardware consists of the server and the user equipment .The user equipment with high portability is used for data acquisition and data transmission . The server is used for data analysis and resource maintenance.SVM is adopted as the automatic sleep analysis algorithm in the server .Based on extracted features, sleep stages are got with directed acyclic graph as the multi-classification method.Results The research results based on patient EEG analysis show that the system can reach a high accuracy rate and take short analysis time average analysis time of 1.45 seconds.Conclusions The compact user equipment is highly portable , and it can feedback the correct result to the users in real time , thus confirming that the design has a promising future in sleep monitoring .%目的:睡眠监测是睡眠质量分析中重要的环节,但目前的睡眠监测系统复杂而且难以携带。本文提出基于支持向量机的便携式睡眠监测系统,以方便地实时监控睡眠。方法该系统硬件部分由服务器和用户端设备构成,其中用户端设备负责数据采集和数据传输,服务器端负责数据分析及相关的资源管理。睡眠分析软件采用支持向量机( support vector machines , SVM)作为分析算法,在提取特征值的基础上,以有向无环图作为多分类策略分析得到睡眠的时相。结果对于患者的睡眠脑电实验表明分析正确率高,所需的分析时间短。结论该系统用户端设备体积小,方便携带,分析正确率高,实时性好,在睡眠监测领域具有良好的应用前景。

  10. Discrimination of Rice Varieties using LS-SVM Classification Algorithms and Hyperspectral Data

    Directory of Open Access Journals (Sweden)

    Jin Xiaming

    2015-03-01

    Full Text Available Fast discrimination of rice varieties plays a key role in the rice processing industry and benefits the management of rice in the supermarket. In order to discriminate rice varieties in a fast and nondestructive way, hyperspectral technology and several classification algorithms were used in this study. The hyperspectral data of 250 rice samples of 5 varieties were obtained using FieldSpec®3 spectrometer. Multiplication Scatter Correction (MSC was used to preprocess the raw spectra. Principal Component Analysis (PCA was used to reduce the dimension of raw spectra. To investigate the influence of different linear and non-linear classification algorithms on the discrimination results, K-Nearest Neighbors (KNN, Support Vector Machine (SVM and Least Square Support Vector Machine (LS-SVM were used to develop the discrimination models respectively. Then the performances of these three multivariate classification methods were compared according to the discrimination accuracy. The number of Principal Components (PCs and K parameter of KNN, kernel function of SVM or LS-SVM, were optimized by cross-validation in corresponding models. One hundred and twenty five rice samples (25 of each variety were chosen as calibration set and the remaining 125 rice samples were prediction set. The experiment results showed that, the optimal PCs was 8 and the cross-validation accuracy of KNN (K = 2, SVM, LS-SVM were 94.4, 96.8 and 100%, respectively, while the prediction accuracy of KNN (K = 2, SVM, LS-SVM were 89.6, 93.6 and 100%, respectively. The results indicated that LS-SVM performed the best in the discrimination of rice varieties.

  11. Machine learning in sedimentation modelling.

    Science.gov (United States)

    Bhattacharya, B; Solomatine, D P

    2006-03-01

    The paper presents machine learning (ML) models that predict sedimentation in the harbour basin of the Port of Rotterdam. The important factors affecting the sedimentation process such as waves, wind, tides, surge, river discharge, etc. are studied, the corresponding time series data is analysed, missing values are estimated and the most important variables behind the process are chosen as the inputs. Two ML methods are used: MLP ANN and M5 model tree. The latter is a collection of piece-wise linear regression models, each being an expert for a particular region of the input space. The models are trained on the data collected during 1992-1998 and tested by the data of 1999-2000. The predictive accuracy of the models is found to be adequate for the potential use in the operational decision making.

  12. 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.

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

    CERN Document Server

    Zhou, Tianyi; Wu, Xindong

    2010-01-01

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

  14. Rolling forecasting model of PM2.5 concentration based on support vector machine and particle swarm optimization

    Science.gov (United States)

    Zhang, Chang-Jiang; Dai, Li-Jie; Ma, Lei-Ming

    2016-10-01

    The data of current PM2.5 model forecasting greatly deviate from the measured concentration. In order to solve this problem, Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) are combined to build a rolling forecasting model. The important parameters (C and γ) of SVM are optimized by PSO. The data (from February to July in 2015), consisting of measured PM2.5 concentration, PM2.5 model forecasting concentration and five main model forecasting meteorological factors, are provided by Shanghai Meteorological Bureau in Pudong New Area. The rolling model is used to forecast hourly PM2.5 concentration in 12 hours in advance and the nighttime average concentration (mean value from 9 pm to next day 8 am) during the upcoming day. The training data and the optimal parameters of SVM model are different in every forecasting, that is to say, different models (dynamic models) are built in every forecasting. SVM model is compared with Radical Basis Function Neural Network (RBFNN), Multi-variable Linear Regression (MLR) and WRF-CHEM. Experimental results show that the proposed model improves the forecasting accuracy of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day. SVM model performs better than MLR, RBFNN and WRF-CHEM. SVM model greatly improves the forecasting accuracy of PM2.5 concentration one hour in advance, according with the result concluded from previous research. The rolling forecasting model can be applied to the field of PM2.5 concentration forecasting, and can offer help to meteorological administration in PM2.5 concentration monitoring and forecasting.

  15. Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram

    CERN Document Server

    Nishizuka, N; Kubo, Y; Den, M; Watari, S; Ishii, M

    2016-01-01

    We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 h. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetogram, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions from the full-disk magnetogram, from which 60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine learning algorithms: the support vector machine (SVM), k-nearest neighbors (k-NN), and ...

  16. SVM CLASSIFICATION :ITS CONTENTS AND CHALLENGES%SVM法分类:它的内容和挑战

    Institute of Scientific and Technical Information of China (English)

    岳士弘; 李平; 郝沛毅

    2003-01-01

    SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. In particular,they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically. There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation. In this paper,survey of the key contents on this subject, focusing on the most well-known models based on kernel substitution, namely SVM, as well as the activated fields at present and the development tendency ,is presented.

  17. Using scores of amino acid topological descriptors for quantitative sequence-mobility modeling of peptides based on support vector machine

    Institute of Scientific and Technical Information of China (English)

    LIANG Guizhao; YANG Shanbin; ZHOU Yuan; ZHOU Peng; LI Zhiliang

    2006-01-01

    Scores of amino acid topological descriptors (SATD) derived from principle components analysis of a matrix of 1262 structural variables related to 23 amino acids were employed to express the structure of 125 peptides in different length.Quantitative sequence-mobility modelings (QSMMs)were constructed using partial least square (PLS)and support vector machine (SVM), respectively. As new amino acid scales, SATD including plentiful information related to biological activity were easily manipulated. Better results were obtained compared to those obtained with PLS, which indicated that SVM presented robust stability and excellent predictive ability for electrophoretic mobilities. These results show that there is a wide prospect for the applications of SATD and SVM regression in QSMMs.

  18. Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

    Science.gov (United States)

    Forman, Barton A.; Reichle, Rolf Helmut

    2014-01-01

    A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

  19. A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

    2014-06-01

    To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).

  20. Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

    Directory of Open Access Journals (Sweden)

    Young Min Kim

    2016-06-01

    Full Text Available The current Building Energy Performance Simulation (BEPS tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN, Support Vector Machine (SVM, and Gaussian Process (GP for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS, and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

  1. Application of SVM in Analyzing the Headstream of Gushing Water in Coal Mine

    Institute of Scientific and Technical Information of China (English)

    YAN Zhi-gang; ZHANG Hai-rong; DU Pei-jun

    2006-01-01

    To recognize the presence of the headstream of gushing water in coal mines, the SVM (Support Vector Machine) was proposed to analyze the gushing water based on hydrogeochemical methods. First, the SVM model for headstream analysis was trained on the water sample of available headstreams, and then we used this to predict the unknown samples, which were validated in practice by comparing the predicted results with the actual results. The experimental results show that the SVM is a feasible method to differentiate between two headstreams and the H-SVMs (Hierachical SVMs) is a preferable way to deal with the problem of multi-headstreams. Compared with other methods, the SVM is based on a strict mathematical theory with a simple structure and good generalization properties. As well, the support vector W in the decision function can describe the weights of the recognition factors of water samples, which is very important for the analysis of headstreams of gushing water in coal mines.

  2. LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR LS-SVM: a new chemometric tool for multivariate regression. Comparison of LS-SVM and pls regression for determination of common adulterants in powdered milk by nir spectroscopy

    Directory of Open Access Journals (Sweden)

    Marco F. Ferrão

    2007-08-01

    Full Text Available Least-squares support vector machines (LS-SVM were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

  3. 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.

  4. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  5. SVM for Solving Forward Problems of EIT.

    Science.gov (United States)

    Wu, Youxi; Li, Ying; Guo, Lei; Yan, Weili; Shen, Xueqin; Fu, Kun

    2005-01-01

    Support Vector Machine (SVM) can be seen as a new machine learning way which is based on the idea of VC dimensions and the principle of structural risk minimization rather than empirical risk minimization. SVM can be used for classification and regression. Support Vector Regression (SVR) is a very important branch of Support Vector Machine. Partial Differential Equations (PDEs) have been successfully treated by using SVR in previous works. The forward problems of EIT are the basis of EIT inverse problems. The forward problem's essence is to solve PDEs. The method has been successfully tested on the forward problems of EIT and has yielded accurate results.

  6. Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

    Science.gov (United States)

    Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

    2017-07-01

    In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

  7. A Comparison of Different Machine Transliteration Models

    CERN Document Server

    Choi, K; Oh, J; 10.1613/jair.1999

    2011-01-01

    Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models -- grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model -- have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this com...

  8. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

  9. 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.

  10. Sales Growth Rate Forecasting Using Improved PSO and SVM

    Directory of Open Access Journals (Sweden)

    Xibin Wang

    2014-01-01

    Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

  11. SVM multiuser detection based on heuristic kernel

    Institute of Scientific and Technical Information of China (English)

    Yang Tao; Hu Bo

    2007-01-01

    A support vector machine (SVM) based multiuser detection (MUD) scheme in code-division multiple-access (CDMA) system is proposed. In this scheme, the equivalent support vector (SV) is obtained through a kernel sparsity approximation algorithm, which avoids the conventional costly quadratic programming (QP) procedure in SVM. Besides, the coefficient of the SV is attained through the solution to a generalized eigenproblem. Simulation results show that the proposed scheme has almost the same bit error rate (BER) as the standard SVM and is better than minimum mean square error (MMSE) scheme. Meanwhile, it has a low computation complexity.

  12. Rough set models of Physarum machines

    Science.gov (United States)

    Pancerz, Krzysztof; Schumann, Andrew

    2015-04-01

    In this paper, we consider transition system models of behaviour of Physarum machines in terms of rough set theory. A Physarum machine, a biological computing device implemented in the plasmodium of Physarum polycephalum (true slime mould), is a natural transition system. In the behaviour of Physarum machines, one can notice some ambiguity in Physarum motions that influences exact anticipation of states of machines in time. To model this ambiguity, we propose to use rough set models created over transition systems. Rough sets are an appropriate tool to deal with rough (ambiguous, imprecise) concepts in the universe of discourse.

  13. A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

    Directory of Open Access Journals (Sweden)

    Fei Li

    2015-11-01

    Full Text Available Quantitative structure-activity relationships (QSARs were determined using partial least square (PLS and support vector machine (SVM. The predicted values by the final QSAR models were in good agreement with the corresponding experimental values. Chemical estrogenic activities are related to atomic properties (atomic Sanderson electronegativities, van der Waals volumes and polarizabilities. Comparison of the results obtained from two models, the SVM method exhibited better overall performances. Besides, three PLS models were constructed for some specific families based on their chemical structures. These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.

  14. Investigating driver injury severity patterns in rollover crashes using support vector machine models.

    Science.gov (United States)

    Chen, Cong; Zhang, Guohui; Qian, Zhen; Tarefder, Rafiqul A; Tian, Zong

    2016-05-01

    Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.

  15. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing and Artificial Intelligence Models (ANN, SVM: The Case of Greek Electricity Market

    Directory of Open Access Journals (Sweden)

    George P. Papaioannou

    2016-08-01

    Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

  16. Determining the Relationship between U.S. County-Level Adult Obesity Rate and Multiple Risk Factors by PLS Regression and SVM Modeling Approaches

    Directory of Open Access Journals (Sweden)

    Chau-Kuang Chen

    2015-02-01

    Full Text Available Data from the Center for Disease Control (CDC has shown that the obesity rate doubled among adults within the past two decades. This upsurge was the result of changes in human behavior and environment. Partial least squares (PLS regression and support vector machine (SVM models were conducted to determine the relationship between U.S. county-level adult obesity rate and multiple risk factors. The outcome variable was the adult obesity rate. The 23 risk factors were categorized into four domains of the social ecological model including biological/behavioral factor, socioeconomic status, food environment, and physical environment. Of the 23 risk factors related to adult obesity, the top eight significant risk factors with high normalized importance were identified including physical inactivity, natural amenity, percent of households receiving SNAP benefits, and percent of all restaurants being fast food. The study results were consistent with those in the literature. The study showed that adult obesity rate was influenced by biological/behavioral factor, socioeconomic status, food environment, and physical environment embedded in the social ecological theory. By analyzing multiple risk factors of obesity in the communities, may lead to the proposal of more comprehensive and integrated policies and intervention programs to solve the population-based problem.

  17. 基于云支持向量机模型的短期风电功率预测%Short-term wind power forecasting based on cloud SVM model

    Institute of Scientific and Technical Information of China (English)

    凌武能; 杭乃善; 李如琦

    2013-01-01

    A CSVM(Cloud Support Vector Machine) model combining the cloud model and the SVM(Support Vector Machine) is proposed for the short-term wind power forecasting,which applies the cloud transformation to extract the qualitative attribute of wind speed data and uses SVM to build the relationship between wind speed and wind power.The forecasts for the next 24 hours' wind power show that,the forecasts at a particular point of the presented model is a set of discrete values with stabilized bias.The backward cloud algorithm is applied to calculate the expectation of the forecast set as the deterministic prediction,which is more accurate than that forecasted by SVM model or ARIMA(Auto-Regressive Integrated Moving Average) model.The presented model is effective for short-term wind power forecasting.%将云模型和支持向量机(SVM)相结合,提出一种适合短期风电功率预测的云支持向量机模型.该模型采用云变换方法提取风速序列的定性特征,并通过SVM建立风速特征与风电功率间的关系.对未来24h的风电功率预测结果显示,该模型在某个点上的预测值是一个有稳定倾向的离散值集合.采用逆向云算法求取集合的期望值作为确定性预测结果,并与SVM和自回归求和移动平均(ARIMA)模型的预测结果相比较,结果表明云支持向量机具有更高的预测精度,预测效果显著,因此,该模型可有效应用于短期风电功率预测.

  18. 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.

  19. In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.

    Science.gov (United States)

    Zhang, Hui; Chen, Qing-Yi; Xiang, Ming-Li; Ma, Chang-Ying; Huang, Qi; Yang, Sheng-Yong

    2009-02-01

    Drug-induced mitochondrial toxicity has become one of the key reasons for which some drugs fail to enter market or are withdrawn from market. Thus early identification of new chemical entities that injure mitochondrial function grows to be very necessary to produce safer drugs and directly reduce attrition rate in later stages of drug development. In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient method (CG) for parameter optimization (GA-CG-SVM), has been employed to develop prediction model of mitochondrial toxicity. We firstly collected 288 compounds, including 171 MT+ and 117 MT-, from different literature resources. Then these compounds were randomly separated into a training set (253 compounds) and a test set (35 compounds). The overall prediction accuracy for the training set by means of 5-fold cross-validation is 84.59%. Further, the SVM model was evaluated by using the independent test set. The overall prediction accuracy for the test set is 77.14%. These clearly indicate that the mitochondrial toxicity is predictable. Meanwhile impacts of the feature selection and SVM parameter optimization on the quality of SVM model were also examined and discussed. The results implicate the potential of the proposed GA-CG-SVM in facilitating the prediction of mitochondrial toxicity.

  20. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Science.gov (United States)

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  1. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    Full Text Available In optical printed Chinese character recognition (OPCCR, many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  2. Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

    Science.gov (United States)

    Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

    2010-12-01

    We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

  3. SVM Model with Unequal Sample Number Between Classes%样本数目不对称时的SVM模型

    Institute of Scientific and Technical Information of China (English)

    肖健华; 吴今培

    2003-01-01

    The paper briefly introduces the principle of SVM in sorting first. Then using normal distribution as an example,the paper points out that the SVM can not get excellent sorting ability when the sample numbers of different class are different greatly. A method has been designed to compute the penalty parameter to error samples. On the basis of different penalty parameter an advanced SVM algorithm has been developed. Two simulated examples show the new algorithm is feasible.

  4. Gazing-detection of human eyes based on SVM

    Institute of Scientific and Technical Information of China (English)

    LI Su-mei; ZHANG Yan-xin; CHANG Sheng-jiang; SHEN Jin-yuan

    2005-01-01

    A method for gazing-detection of human eyes using Support Vector Machine (SVM) based on statistic learning theory (SLT) is proposed.According to the criteria of structural risk minimization of SVM,the errors between sample-data and model-data are minimized and the upper bound of predicting error of the model is also reduced.As a result,the generalization ability of the model is much improved.The simulation results show that,when limited training samples are used,the correct recognition rate of the tested samples can be as high as 100%,which is much better than some previous results obtained by other methods.The higher processing speed enables the system to distinguish gazing or not-gazing in real-time.

  5. A Single Disease Cost Variance Analysis Model Based on CUSUM and SVM%基于CUSUM控制图和SVM的单病种成本差异分析

    Institute of Scientific and Technical Information of China (English)

    刘子先; 邹刘霞; 徐靖

    2012-01-01

    CUSUM控制图可用来监测均值的偏移.在单病种成本值发生重大差异前,短期内会发生一定失控趋势,CUSUM控制图却不能及时发出失控信号.针对成本差异分析中存在的问题,提出了基于支持向量机(SVM)的智能化单病种成本差异分析模型来替代CUSUM控制图.首先利用支持向量机(SVM)自身良好的泛化能力经过训练后获得复杂成本值之间的内在规律,然后对测试样本进行有效的预测分类,及时给出失控的样本点.将模型应用到单纯性阑尾炎病人样本中,与CUSUM控制图方法作比较,结果显示了该模型比CUSUM控制图更加及时发现有失控趋势或失控的样本点.%CUSUM control chart can be used to detect the shifts in the mean. Before the significant cost variance of single disease,the cost will have certain out-control trend in the short term,but the CUSUM control chart couldn't timely forecast. In view of the problems existing in the cost difference analysis,this paper put forward an intelligent method based on support vector machine (SVM) of single disease cost variance analysis model as an alternative approach to CUSUM control chart. Support vector machine (SVM) used its good generalization ability after training to obtain the internal rules from complex cost data, then the test sample points for effective prediction classification and timely forecast points of out-control. The model was applied to the simple appendicitis surgery, by comparing the method with the CUSUM control chart model,the results suggest this model is more sensitive than CUSUM control chart.

  6. Infrared face recognition based on binary particle swarm optimization and SVM-wrapper model

    Science.gov (United States)

    Xie, Zhihua; Liu, Guodong

    2015-10-01

    Infrared facial imaging, being light- independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Robust feature selection and representation is a key issue for infrared face recognition research. This paper proposes a novel infrared face recognition method based on local binary pattern (LBP). LBP can improve the robust of infrared face recognition under different environment situations. How to make full use of the discriminant ability in LBP patterns is an important problem. A search algorithm combination binary particle swarm with SVM is used to find out the best discriminative subset in LBP features. Experimental results show that the proposed method outperforms traditional LBP based infrared face recognition methods. It can significantly improve the recognition performance of infrared face recognition.

  7. Density-based penalty parameter optimization on C-SVM.

    Science.gov (United States)

    Liu, Yun; Lian, Jie; Bartolacci, Michael R; Zeng, Qing-An

    2014-01-01

    The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

  8. Quantum-inspired evolutionary tuning of SVM parameters

    Institute of Scientific and Technical Information of China (English)

    Zhiyong Luo; Ping Wang; Yinguo Li; Wenfeng Zhang; Wei Tang; Min Xiang

    2008-01-01

    The most commonly used parameters selection method for support vector machines (SVM) is cross-validation, which needs a longtime complicated calculation. In this paper, a novel regularization parameter and a kernel parameter tuning approach of SVM are presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and the improved LS-SVM with wavelet kernel can provide better precision.

  9. [Study on application of SVM in prediction of coronary heart disease].

    Science.gov (United States)

    Zhu, Yue; Wu, Jianghua; Fang, Ying

    2013-12-01

    Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

  10. Deep Support Vector Machines for Regression Problems

    NARCIS (Netherlands)

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

    2013-01-01

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

  11. Deep Support Vector Machines for Regression Problems

    NARCIS (Netherlands)

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

    2013-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  13. Robust support vector machine-trained fuzzy system.

    Science.gov (United States)

    Forghani, Yahya; Yazdi, Hadi Sadoghi

    2014-02-01

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

  14. Research of semi-definite programming SVM model%半定规划支持向量机模型的研究

    Institute of Scientific and Technical Information of China (English)

    张敏; 覃华; 苏一丹

    2011-01-01

    The classification accuracy and generalization ability of SVM model is largely dependent on the selection of model parameters.The traditional parameter selection lacks of theoretical support, spends too much time, and the classification precision is not always perfect. Against those problems, a semi-definite programming SVM model is proposed which is able to distinguish the validaty of a given set of kernel parameters and get a better kernel matrix by combining simple ones using combination coefficients, to improve the accuracy of SVM. Experimental results on UCI datasets show that using the new method to identify effective kernel parameters is feasible and the semi-definite programming SVM model is superior to the standard SVM. Further more, the generalization ability of heterogeneous kernel semi-definite programming SVM model significantly outperform homogeneous kernel semi-definite programming SVM model.%支持向量机(support vector machincs,SVM)的分类精度和泛化能力会受到核函数及其工作参数的影响,传统的核函数参数选择方法缺乏理论支持,花费的时间较多,效果也不一定理想.针对此问题,提出一种基于半定规划的SVM模型,利用半定规划来判别一组给定的核函数工作参数是否有效,并能用有效的核函数工作参数组合计算出更优的核矩阵,提高SVM模型的分类精度.在UCI数据集上的实验结果表明,用此方法判别核函数工作参数是可行的,所组合出的半定规划SVM模型的泛化能力优于传统的SVM模型,并且异构核半定规划SVM模型的泛化能力优于同构核半定规划SVM模型.

  15. Improved Approach Based on SVM for License Plate Character Recognition

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-hua; WANG Xiao-guang

    2005-01-01

    An improved approach based on support vector machine (SVM) called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extracted. A minimal set called the margin vector set, which contains all support vectors, is extracted. These margin vectors compose new training data and construct the classifier by using the general SVM optimized. The experimental results show that the improved SVM method does well at correct rate and training speed.

  16. Microcanonical Annealing and Threshold Accepting for Parameter Determination and Feature Selection of Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Seyyid Ahmed Medjahed

    2016-12-01

    Full Text Available Support vector machine (SVM is a popular classification technique with many diverse applications. Parameter determination and feature selection significantly influences the classification accuracy rate and the SVM model quality. This paper proposes two novel approaches based on: Microcanonical Annealing (MA-SVM and Threshold Accepting (TA-SVM to determine the optimal value parameter and the relevant features subset, without reducing SVM classification accuracy. In order to evaluate the performance of MA-SVM and TA-SVM, several public datasets are employed to compute the classification accuracy rate. The proposed approaches were tested in the context of medical diagnosis. Also, we tested the approaches on DNA microarray datasets used for cancer diagnosis. The results obtained by the MA-SVM and TA-SVM algorithms are shown to be superior and have given a good performance in the DNA microarray data sets which are characterized by the large number of features. Therefore, the MA-SVM and TA-SVM approaches are well suited for parameter determination and feature selection in SVM.

  17. SVM for density estimation and application to medical image segmentation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhao; ZHANG Su; ZHANG Chen-xi; CHEN Ya-zhu

    2006-01-01

    A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process.Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

  18. Intrusion Awareness Based on Data Fusion and SVM Classification

    Directory of Open Access Journals (Sweden)

    Ramnaresh Sharma

    2012-06-01

    Full Text Available Network intrusion awareness is important factor for risk analysis of network security. In the current decade various method and framework are available for intrusion detection and security awareness. Some method based on knowledge discovery process and some framework based on neural network. These entire model take rule based decision for the generation of security alerts. In this paper we proposed a novel method for intrusion awareness using data fusion and SVM classification. Data fusion work on the biases of features gathering of event. Support vector machine is super classifier of data. Here we used SVM for the detection of closed item of ruled based technique. Our proposed method simulate on KDD1999 DARPA data set and get better empirical evaluation result in comparison of rule based technique and neural network model.

  19. Intrusion Awareness Based on Data Fusion and SVM Classification

    Directory of Open Access Journals (Sweden)

    Ramnaresh Sharma

    2012-06-01

    Full Text Available Network intrusion awareness is important factor forrisk analysis of network security. In the currentdecade various method and framework are availablefor intrusion detection and security awareness.Some method based on knowledge discovery processand some framework based on neural network.These entire model take rule based decision for thegeneration of security alerts. In this paper weproposed a novel method for intrusion awarenessusing data fusion and SVM classification. Datafusion work on the biases of features gathering ofevent. Support vector machine is super classifier ofdata. Here we used SVM for the detection of closeditem of ruled based technique. Our proposedmethod simulate on KDD1999 DARPA data set andget better empirical evaluation result in comparisonof rule based technique and neural network model.

  20. 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.

  1. [Measurement of soil organic matter and available K based on SPA-LS-SVM].

    Science.gov (United States)

    Zhang, Hai-Liang; Liu, Xue-Mei; He, Yong

    2014-05-01

    Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LSSVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0. 860 2 and 2. 98 for OM and 0. 730 5 and 15. 78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325 approximately 1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.

  2. A Novel Method for Evaluating the Cardiotoxicity of Traditional Chinese Medicine Compatibility by Using Support Vector Machine Model Combined with Metabonomics

    Directory of Open Access Journals (Sweden)

    Yubo Li

    2016-01-01

    Full Text Available Traditional biochemical and histopathological tests have been used to evaluate the safety of traditional Chinese medicine (TCM compatibility for a long time. But these methods lack high sensitivity and specificity. In the previous study, we have found ten biomarkers related to cardiotoxicity and established a support vector machine (SVM prediction model. Results showed a good sensitivity and specificity. Therefore, in this study, we used SVM model combined with metabonomics UPLC/Q-TOF-MS technology to build a rapid and sensitivity and specificity method to predict the cardiotoxicity of TCM compatibility. This study firstly applied SVM model to the prediction of cardiotoxicity in TCM compatibility containing Aconiti Lateralis Radix Praeparata and further identified whether the cardiotoxicity increased after Aconiti Lateralis Radix Praeparata combined with other TCM. This study provides a new idea for studying the evaluation of the cardiotoxicity caused by compatibility of TCM.

  3. Classification of 5-HT(1A) receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM.

    Science.gov (United States)

    Cheng, Zhengjun; Zhang, Yuntao; Zhou, Changhong; Zhang, Wenjun; Gao, Shibo

    2009-07-29

    In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.

  4. Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

    Directory of Open Access Journals (Sweden)

    Wenjun Zhang

    2009-07-01

    Full Text Available In the present work, the support vector machine (SVM and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO and the stepwise multiple linear regression (Stepwise-MLR methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.

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

    Directory of Open Access Journals (Sweden)

    Cheng-Long Ma

    2014-01-01

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

  6. GPU-based Parallel SVM Algorithm%GPU的并行支持向量机算法

    Institute of Scientific and Technical Information of China (English)

    DO Thanh-Nghi; NGUYEN Van-Hoa; POULET Francois

    2009-01-01

    提出了一种新的并行增量式支持向量机算法来解决图形处理单元(GPU)中大规模数据集的分类问题.SVM以及核相关方法可以用来创建精确分类模型,但学习过程需要大量内存和很长时间.扩展了Suykens和Vandewalle提出的最少次方SVM(LS-SVM)方法来建立增量和并行算法.新算法使用图形处理器以低代价获得高系统性能.实现表明,在UCI和Delve数据集上,基于GPU并行增量算法较CPU实现方法快130倍.而且比现行算法,如LibSVM、SVM-perf和CB-SVM等快的多(超过2500倍).%A new parallel and incremental support vector machine (SVM) algorithm for the classification of very large datasets on graphics processing units (GPUs) is presented. SVM and kernel related methods have shown to build accurate models but the learning task usually needs a quadratic program so that this task for large datasets re-quires large memory capacity and long time. A recent least squares SVM (LS-SVM) proposed by Suykens and Van-dewalle for building incremental and parallel algorithm is extended. The new algorithm uses graphics processors to gain high performance at low cost. Numerical test results on UCI and Delve dataset repositories show that this para-llel incremental algorithm using GPUs is about 130 times faster than its CPU implementation and often significantly faster (over 2 500 times) than state-of-the-art algorithms like LibSVM, SVM-perf and CB-SVM.

  7. Vibration fault diagnosis for steam turbine by using support vector machine based on fruit fly optimization algorithm%基于 FOA -SVM 的汽轮机振动故障诊断

    Institute of Scientific and Technical Information of China (English)

    石志标; 苗莹

    2014-01-01

    为解决支持向量机算法(Support Vector Machine,SVM)的核函数参数及惩罚因子参数选取的盲目性,利用果蝇优化算法(Fruit Fly Optimization Algorithm,FOA)对 SVM中参数进行优化。提出基于 FOA 的 SVM故障诊断算法,并对汽轮机故障实验数据进行模式识别。该算法能对 SVM相关参数自动寻优,且能达到较理想的全局最优解。通过与常用的粒子群算法(Particle Swarm Optimization,PSO)与遗传算法(Genetic Algorithm,GA)优化后支持向量机进行对比。结果表明,FOA -SVM算法稳定、识别速度快、识别率高。%In order to solve the problem that the selection of the kernel function parameters and penalty factor parameters in the support vector machine(SVM)algorithm is blindfold,the fruit fly optimization algorithm (FOA)was applied to optimize the parameters in SVM.A fault diagnosis algorithm of SVM based on FOA was put forward,and then the pattern recognition of experimental turbine failure data was performed.The algorithm can optimize the SVMparameters automatically,and achieve ideal global optimal solution.Comparing with the SVMwhich is optimized by the commonly used methods of the particle swarm optimization(PSO)and the Genetic Algorithm (GA),the results demonstrate that FOA-SVMhas the fastest recognition speed and the highest recognition rate.

  8. A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

    Science.gov (United States)

    Wu, Jiang; Ji, Yanju; Zhao, Ling; Ji, Mengying; Ye, Zhuang; Li, Suyi

    2016-01-01

    Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

  9. A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

    Directory of Open Access Journals (Sweden)

    Jiang Wu

    2016-01-01

    Full Text Available Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA and support vector machine (SVM was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

  10. Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

    Science.gov (United States)

    Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

    2017-07-01

    Soil temperature (T s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T s. For this aim, the monthly mean data of the T s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T min, T max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R s, R n, R a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u 2); and water vapor pressure (Vp) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R 2) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T max, T min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R 2 = 0.995) and for RH, V p, P, and u 2 inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R 2 = 0.996), respectively.

  11. Time series prediction of mining subsidence based on a SVM

    Institute of Scientific and Technical Information of China (English)

    Li Peixian; Tan Zhixiang; Yah Lili; Deng Kazhong

    2011-01-01

    In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines (SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used asindicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%.the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.

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

    OpenAIRE

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

    2007-01-01

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

  13. Using SVM to construct a Chinese dependency parser

    Institute of Scientific and Technical Information of China (English)

    XU Yun; ZHANG Feng

    2006-01-01

    In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated.Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.

  14. Quality-Oriented Classification of Aircraft Material Based on SVM

    Directory of Open Access Journals (Sweden)

    Hongxia Cai

    2014-01-01

    Full Text Available The existing material classification is proposed to improve the inventory management. However, different materials have the different quality-related attributes, especially in the aircraft industry. In order to reduce the cost without sacrificing the quality, we propose a quality-oriented material classification system considering the material quality character, Quality cost, and Quality influence. Analytic Hierarchy Process helps to make feature selection and classification decision. We use the improved Kraljic Portfolio Matrix to establish the three-dimensional classification model. The aircraft materials can be divided into eight types, including general type, key type, risk type, and leveraged type. Aiming to improve the classification accuracy of various materials, the algorithm of Support Vector Machine is introduced. Finally, we compare the SVM and BP neural network in the application. The results prove that the SVM algorithm is more efficient and accurate and the quality-oriented material classification is valuable.

  15. Diagnosis of asphaltene stability in crude oil through “two parameters” SVM model

    DEFF Research Database (Denmark)

    Chamkalani, Ali; Mohammadi, Amir H.; Eslamimanesh, Ali

    2012-01-01

    is determined using the existing SARA fractions experimental data for this purpose. The powerful Least-Square modification of Support Vector Machine (LSSVM) strategy is applied to develop a computer program, by which the asphaltene stability region can be determined for various crudes. The developed two...

  16. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Denglong [Fuli School of Food Equipment Engineering and Science, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China); Zhang, Zaoxiao, E-mail: zhangzx@mail.xjtu.edu.cn [State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China); School of Chemical Engineering and Technology, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China)

    2016-07-05

    Highlights: • The intelligent network models were built to predict contaminant gas concentrations. • The improved network models coupled with Gaussian dispersion model were presented. • New model has high efficiency and accuracy for concentration prediction. • New model were applied to indentify the leakage source with satisfied results. - Abstract: Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.

  17. A chaotic agricultural machines production growth model

    OpenAIRE

    Jablanović, Vesna D.

    2011-01-01

    Chaos theory, as a set of ideas, explains the structure in aperiodic, unpredictable dynamic systems. The basic aim of this paper is to provide a relatively simple agricultural machines production growth model that is capable of generating stable equilibrium, cycles, or chaos. A key hypothesis of this work is based on the idea that the coefficient π = 1 + α plays a crucial role in explaining local stability of the agricultural machines production, where α is an autonomous growth rate of the ag...

  18. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; SubbaRao; Harish, N.; Lokesha

    , 2010). Fig. 2 shows the architecture of neural network. The Feed Forward Network (FFN) commonly used for supervised learning which consists of three layers, namely I-number of nodes in input layer, M-number of nodes in hidden layer and o- number... transfer function (tansig) and linear transfer function (purelin) in the output layer, and to train the network Levenberg-Marquardt (LM) Algorithm (trainlm) is used (Haykin, 1999). Fig. 2 Architecture of neural network. SUPPORT VECTOR MACHINE...

  19. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

    Directory of Open Access Journals (Sweden)

    Gabere MN

    2016-06-01

    Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1

  20. Modelling and simulation of multitechnological machine systems

    Energy Technology Data Exchange (ETDEWEB)

    Holopainen, T. (ed.) [VTT Manufacturing Technology, Espoo (Finland)

    2001-07-01

    The Smart Machines and Systems 2010 (SMART) technology programme 1997-2000 aimed at supporting the machine and electromechanical industries in incorporating the modern technology into their products and processes. The public research projects in this programme were planned to accumulate the latest research results and transfer them for the benefit of industrial product development. The major research topic in the SMART programme was called Modelling and Simulation of Multitechnological Mechatronic Systems. The behaviour of modern machine systems and subsystems addresses many different types of physical phenomena and their mutual interactions: mechanical behaviour of structures, electromagnetic effects, hydraulics, vibrations and acoustics etc. together with associated control systems and software. The actual research was carried out in three separate projects called Modelling and Simulation of Mechtronic Machine Systems for Product Development and Condition Monitoring Purposes (MASI), Virtual Testing of Hydraulically Driven Machines (HYSI), and Control of Low Frequency Vibration of a Mobile Machine (AKSUS). This publication contains the papers presented at the final seminar of these three research projects, held on November 30th at Otaniemi Espoo. (orig.)

  1. Automatic Language Identification with Discriminative Language Characterization Based on SVM

    Science.gov (United States)

    Suo, Hongbin; Li, Ming; Lu, Ping; Yan, Yonghong

    Robust automatic language identification (LID) is the task of identifying the language from a short utterance spoken by an unknown speaker. The mainstream approaches include parallel phone recognition language modeling (PPRLM), support vector machine (SVM) and the general Gaussian mixture models (GMMs). These systems map the cepstral features of spoken utterances into high level scores by classifiers. In this paper, in order to increase the dimension of the score vector and alleviate the inter-speaker variability within the same language, multiple data groups based on supervised speaker clustering are employed to generate the discriminative language characterization score vectors (DLCSV). The back-end SVM classifiers are used to model the probability distribution of each target language in the DLCSV space. Finally, the output scores of back-end classifiers are calibrated by a pair-wise posterior probability estimation (PPPE) algorithm. The proposed language identification frameworks are evaluated on 2003 NIST Language Recognition Evaluation (LRE) databases and the experiments show that the system described in this paper produces comparable results to the existing systems. Especially, the SVM framework achieves an equal error rate (EER) of 4.0% in the 30-second task and outperforms the state-of-art systems by more than 30% relative error reduction. Besides, the performances of proposed PPRLM and GMMs algorithms achieve an EER of 5.1% and 5.0% respectively.

  2. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

    2014-01-01

    Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

  3. SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

    2014-01-01

    Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

  4. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

    Science.gov (United States)

    Yao, Yiqing; Xu, Xiaosu

    2017-01-01

    In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. PMID:28245549

  5. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages.

    Science.gov (United States)

    Yao, Yiqing; Xu, Xiaosu

    2017-02-24

    In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

  6. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

    Directory of Open Access Journals (Sweden)

    Yiqing Yao

    2017-02-01

    Full Text Available In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS outages, a novel robust least squares support vector machine (LS-SVM-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS. The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

  7. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere.

    Science.gov (United States)

    Ma, Denglong; Zhang, Zaoxiao

    2016-07-05

    Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.

  8. Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

    Directory of Open Access Journals (Sweden)

    Babangida Nuraddeen Muhammad

    2016-01-01

    Full Text Available Pore-water pressure (PWP is influenced by climatic changes, especially rainfall. These changes may affect the stability of, particularly unsaturated slopes. Thus monitoring the changes in PWP resulting from climatic factors has become an important part of effective slope management. However, this monitoring requires field instrumentation program, which is resource and labour expensive. Recently, soft computing modelling has become an alternative. Low degree polynomial kernel support vector machine (SVM was evaluated in modelling the PWP changes. The developed model used pore-water pressure and rainfall data collected from an instrumented slope. Wrapper technique was used to select input features and k-fold cross validation was used to calibrate the model parameters. The developed model showed great promise in modelling the pore-water pressure changes. High correlation, with coefficient of determination of 0.9694 between the predicted and observed changes was obtained. The one degree polynomial SVM model yielded competitive result, and can be used to provide lead time records of PWP which can aid in better slope management.

  9. Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Ali Asghar Besalatpour

    2016-02-01

    stability. Conclusion: The pixel-scale soil aggregate stability predicted that using the developed SVM and MLR models demonstrates the usefulness of incorporating topographic and vegetation information along with the soil properties as predictors. However, the SVM model achieved more accuracy in predicting soil aggregate stability compared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some soil physical properties such as geometric mean diameter of soil aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmed by the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectiveness with respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate soil scientists to work on it further in the future.

  10. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  11. Hybrid NN/SVM Computational System for Optimizing Designs

    Science.gov (United States)

    Rai, Man Mohan

    2009-01-01

    A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily

  12. A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

    Directory of Open Access Journals (Sweden)

    Prasertsak Charoen

    2017-04-01

    Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

  13. FORECASTING NIKKEI 225 INDEX WITH SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    HUANG Wei; Yoshiteru Nakamori; WANG Shouyang; YU Lean

    2003-01-01

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

  14. Novel temperature modeling and compensation method for bias of ring laser gyroscope based on least-squares support vector machine

    Institute of Scientific and Technical Information of China (English)

    Xudong Yu; Yu Wang; Guo Wei; Pengfei Zhang; Xingwu Long

    2011-01-01

    Bias of ring-laser-gyroscope (RLG) changes with temperature in a nonlinear way. This is an important restraining factor for improving the accuracy of RLG. Considering the limitations of least-squares regression and neural network, we propose a new method of temperature compensation of RLG bias-building function regression model using least-squares support vector machine (LS-SVM). Static and dynamic temperature experiments of RLG bias are carried out to validate the effectiveness of the proposed method. Moreover,the traditional least-squares regression method is compared with the LS-SVM-based method. The results show the maximum error of RLG bias drops by almost two orders of magnitude after static temperature compensation, while bias stability of RLG improves by one order of magnitude after dynamic temperature compensation. Thus, the proposed method reduces the influence of temperature variation on the bias of the RLG effectively and improves the accuracy of the gyro scope considerably.%@@ Bias of ring-laser-gyroscope (RLG) changes with temperature in a nonlinear way.This is an important restraining factor for improving the accuracy of RLG.Considering the limitations of least-squares regression and neural network, we propose a new method of temperature compensation of RLG bias-building function regression model using least-squares support vector machine (LS-SVM).Static and dynamic temperature experiments of RLG bias are carried out to validate the effectiveness of the proposed method.Moreover,the traditional least-squares regression method is compared with the LS-SVM-based method.

  15. Modeling of synchronous machines with magnetic saturation

    Energy Technology Data Exchange (ETDEWEB)

    Rehaoulia, H. [Universite de Tunis-Ecole Superieure des Sciences et Techniques de Tunis (Unite de Recherche CSSS), 5 Avenue Taha Hussein Tunis 10008 (Tunisia); Henao, H.; Capolino, G.A. [Universite de Picardie Jules Vernes-Centre de Robotique, d' Electrotechnique et d' Automatique (UPRES-EA3299), 33 Rue Saint Leu, 80039 Amiens Cedex 1 (France)

    2007-04-15

    This paper deals with a method to derive multiple models of saturated round rotor synchronous machines, based on different selections of state-space variables. By considering the machine currents and fluxes as space vectors, possible d-q models are discussed and adequately numbered. As a result several novel models are found and presented. It is shown that the total number of d-q models for a synchronous machine, with basic dampers, is 64 and therefore much higher than known. Found models are classified into three families: current, flux and mixed models. These latter, the mixed ones, constitute the major part (52) and hence offer a large choice. Regarding magnetic saturation, the paper also presents a method to account for whatever the choice of state-space variables. The approach consists of just elaborating the saturation model with winding currents as main variables and deriving all the other models from it, by ordinary mathematical manipulations. The paper emphasizes the ability of the proposed approach to develop any existing model without exception. An application to prove the validity of the method and the equivalence between all developed models is reported. (author)

  16. Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA

    Institute of Scientific and Technical Information of China (English)

    Li Sun; Deyu Wang

    2012-01-01

    In this paper a hybrid process of modeling and optimization,which integrates a support vector machine (SVM) and genetic algorithm (GA),was introduced to reduce the high time cost in structural optimization of ships.SVM,which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization,can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems,such as FEM analysis.The GA,as a powerful optimization technique,possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods,which makes it suitable for optimizing models built by SVM.Based on the SVM-GA strategy,optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR),which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity.The time cost of this optimization with SVM-GA has been sharply reduced,many more loops have been processed within a small amount of time and the design has been improved remarkably.

  17. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of poten

  18. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  19. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    Science.gov (United States)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  20. Kernel Projection Algorithm for Large-Scale SVM Problems

    Institute of Scientific and Technical Information of China (English)

    王家琦; 陶卿; 王珏

    2002-01-01

    Support Vector Machine (SVM) has become a very effective method in sta-tistical machine learning and it has proved that training SVM is to solve Nearest Point pairProblem (NPP) between two disjoint closed convex sets. Later Keerthi pointed out that it isdifficult to apply classical excellent geometric algorithms directly to SVM and so designed anew geometric algorithm for SVM. In this article, a new algorithm for geometrically solvingSVM, Kernel Projection Algorithm, is presented based on the theorem on fixed-points of pro-jection mapping. This new algorithm makes it easy to apply classical geometric algorithmsto solving SVM and is more understandable than Keerthi's. Experiments show that the newalgorithm can also handle large-scale SVM problems. Geometric algorithms for SVM, such asKeerthi's algorithm, require that two closed convex sets be disjoint and otherwise the algo-rithms are meaningless. In this article, this requirement will be guaranteed in theory by usingthe theoretic result on universal kernel functions.

  1. DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

    Science.gov (United States)

    Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

    2016-12-08

    In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

  2. Less is More: Data Processing with SVM for Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

    XIAO Hai-jun; HONG Fan; WANG Ling

    2009-01-01

    To improve the detection rate and lower down the false positive rate in intrusion detection system,dimensionality reduction is widely used in the intrusion detection system.For this purpose,a data processing (DP) with support vector machine (SVM) was built.Different from traditionally identifying the redundant data before purging the audit data by expert knowledge or utilizing different kinds of subsets of the available 41-connection attributes to build a classifier,the proposed strategy first removes the attributes whose correlation with another attribute exceeds a threshold,and then classifies two sequence samples as one class while removing either of the two samples whose similarity exceeds a threshold.The results of performance experiments showed that the strategy of DP and SVM is superior to the other existing data reduction strategies (e.g.,audit reduction,rule extraction,and feature selection),and that the detection model based on DP and SVM outperforms those based on data mining,soft computing,and hierarchical principal component analysis neural networks.

  3. Static Voltage Stability Analysis by Using SVM and Neural Network

    Directory of Open Access Journals (Sweden)

    Mehdi Hajian

    2013-01-01

    Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

  4. 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.

  5. Estimating coal reserves using a support vector machine

    Institute of Scientific and Technical Information of China (English)

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

    2008-01-01

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

  6. Ranking Support Vector Machine with Kernel Approximation.

    Science.gov (United States)

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

    2017-01-01

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

  7. Mandarin Digits Speech Recognition Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    XIE Xiang; KUANG Jing-ming

    2005-01-01

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

  8. 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.

  9. Gaussian小波SVM及其混沌时间序列预测%Gaussian Wavelet SVM and Its Applications to Chaotic Time Series Forecasting

    Institute of Scientific and Technical Information of China (English)

    郑永康; 陈维荣; 戴朝华; 王维博

    2009-01-01

    To improve the accuracy of chaotic time series forecasting,Gaussiun wavelet support vector machine (SVM) forecasting model is proposed,which combines the wavelet technology with SVM kernel function method,and based on that the wavelet is beneficial to extracting imperceptible features of signal.It is proved that the even order derivative Ganssian wavelet function is an admissible translation-invariant kernel of SVM,and corresponding Ganssian wavelet SVM is constructed.The chaotic time series is reconstructed in phase space,and the vector in phase space reconstruction is used as the input of SVM.The experiments of forecasting Chen's chaotic time series and load chaotic time series are conducted using the proposed SVM,the conventional radial basis SVM and the Morlet wavelet SVM respectively.The comparison results show that Gaussian wavelet SVM has better performance than the other two SVMs.%为了提高混沌时间序列的预测精度,针对小波有利于信号细微特征提取的优点,结合小波技术和SVM的核函数方法,提出基于Gaussian小波SVM的混沌时间序列预测模型.证明了偶数阶Ganssian小波函数满足SVM平移不变核条件,并构建相应的Gaussian小波SVM.时混沌时间序列进行相空间重构,将重构相空间中的向量作为SVM的输入参量.用Ganssian小波SVM与常用的径向基SVM及Morlet小渡SVM进行对比实验,通过对Chen's混沌时间序列和负荷混沌时间序列的预测,结果表明,Ganssian小波SVM的效果比其他两种SVM更好.

  10. Seizure prediction using polynomial SVM classification.

    Science.gov (United States)

    Zisheng Zhang; Parhi, Keshab K

    2015-08-01

    This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients with low hardware complexity and low power consumption. In the proposed approach, we first compute the spectrogram of the input fragmented EEG signals from a few electrodes. Each fragmented data clip is ten minutes in duration. Band powers, relative spectral powers and ratios of spectral powers are extracted as features. The features are then subjected to electrode selection and feature selection using classification and regression tree. The baseline experiment uses all features from selected electrodes and these features are then subjected to a radial basis function kernel support vector machine (RBF-SVM) classifier. The proposed method further selects a small number features from the selected electrodes and train a polynomial support vector machine (SVM) classifier with degree of 2 on these features. Prediction performances are compared between the baseline experiment and the proposed method. The algorithm is tested using intra-cranial EEG (iEEG) from the American Epilepsy Society Seizure Prediction Challenge database. The baseline experiment using a large number of features and RBF-SVM achieves a 100% sensitivity and an average AUC of 0.9985, while the proposed algorithm using only a small number of features and polynomial SVM with degree of 2 can achieve a sensitivity of 100.0%, an average area under curve (AUC) of 0.9795. For both experiments, only 10% of the available training data are used for training.

  11. 智能天线中的SVM应用研究%Research on Application of SVM in Smart Antenna

    Institute of Scientific and Technical Information of China (English)

    周围; 李志林

    2011-01-01

    支持向量机(SVM)是一种新的机器学习方法.介绍了支持向量机的基本原理,通过使用SVM对接收信号的数据处理,得到波束形成器的最佳权向量解,达到存在干扰的情况下波束形成的理想效果.利用SVM求解AR模型中的系数,对波达方向的估计具有良好的稳健性.%Support Vector Machine (SVM) is a new method of machine learning. In this paper, the basic principle of SVM is described. By processing the received signal data to use the SVM, the best beamformer weight vector solution can be got and the ideal results for beamforming in the case of interference. Using SVM to solve the coefficients of AR model, Estimates of the DOA has good robustness.

  12. PMSM System Controlled by SVM-DTC

    Directory of Open Access Journals (Sweden)

    Zhang Weiwei

    2016-01-01

    Full Text Available Direct Torque Control (DTC, proposed after Vector Control (VC, has been widely used in speed regulation system due to its good dynamic performance. In order to solving the shortage of poor torque stationary properties of traditional DTC, this paper introduces direct torque control using space vector modulations (SVM-DTC in Permanent Magnet Synchronous Motor (PMSM system, which reduces torque ripple by using SVM and maintains fast dynamic response. Simulation model of SVM-DTC under MATLAB condition has been set up and compared with system controlled by traditional DTC. The results testify that the PMSM system based on SVM-DTC has high rate dynamic response, high stationary precision, and good robustness when load has a disturbance.

  13. The dynamic financial distress prediction method of EBW-VSTW-SVM

    Science.gov (United States)

    Sun, Jie; Li, Hui; Chang, Pei-Chann; He, Kai-Yu

    2016-07-01

    Financial distress prediction (FDP) takes important role in corporate financial risk management. Most of former researches in this field tried to construct effective static FDP (SFDP) models that are difficult to be embedded into enterprise information systems, because they are based on horizontal data-sets collected outside the modelling enterprise by defining the financial distress as the absolute conditions such as bankruptcy or insolvency. This paper attempts to propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise's vertical sliding time window (VSTW). The dynamic FDP (DFDP) method is named EBW-VSTW-SVM, which keeps updating the FDP model dynamically with time goes on and only needs the historic financial data of the modelling enterprise itself and thus is easier to be embedded into enterprise information systems. The DFDP method of EBW-VSTW-SVM consists of four steps, namely evaluation of vertical relative financial distress (VRFD) based on EBW, construction of training data-set for DFDP modelling according to VSTW, training of DFDP model based on SVM and DFDP for the future time point. We carry out case studies for two listed pharmaceutical companies and experimental analysis for some other companies to simulate the sliding of enterprise vertical time window. The results indicated that the proposed approach was feasible and efficient to help managers improve corporate financial management.

  14. 基于支持向量机的食醋总酸近红外光谱建模%Near infrared modeling of total acid content in vinegars based on LS-SVM

    Institute of Scientific and Technical Information of China (English)

    邹小波; 陈正伟; 石吉勇; 王开亮; 蒋培; 黄晓纬

    2011-01-01

    Using 95 vinegar samples with different types and from different areas as raw materials, spectrum analysis for total acid in vinegar was carried out with application of least squares support vector machine(LS-SVM) based on statistics. The pretreatment spectra was conducted principal component analysis(PCA) to get principal component signals as the input variables to establish the near-infrared spectral model of total acid in vinegar, and comparison with partial least squares(PLS) model and backward interval partial least squares(biPLS) model was also studied. The results showed that correlation coefficient (rc) of calibration set and cross-validation root mean square error were 0.9614 and 0.2192, respectively, and correlation coefficient (rp) of prediction set and cross-validation root mean square error were 0.919 and 0.3226, respectively. Correlations of near infrared spectrum and contents of acid in was non linear. The model application of LS-SVM had accuracy than that of PLS and biPLS.%为了得到稳定可靠的食醋总酸光谱模型,以不同产地、不同种类的95个食醋样品为研究对象,应用基于统计学原理的最小二乘支持向量机(LS-SVM)对食醋总酸含量进行光谱分析.对预处理后的光谱进行主成分分析(PCA),以主成分信号作为输入变量建立食醋总酸含量的近红外光谱模型,并与偏最小二乘法(PLS)和向后区间偏最小二乘法(biPLS)模型进行比较.结果表明,LS-SVM模型中的校正集中的相关系数(rc)和交互验证均方根误差(RMSECV)分别达到0.9614和0.2192,预测集相关系数(rp)和预测均方根误差(RMSEP)分别达到和0.919和0.3226,均优于PLS和biPLS模型.研究表明,近红外光谱与食醋总酸含量呈非线性关系,采用LS-SVM建立的模型预测性能更好,精度更高.

  15. An IPSO-SVM algorithm for security state prediction of mine production logistics system

    Science.gov (United States)

    Zhang, Yanliang; Lei, Junhui; Ma, Qiuli; Chen, Xin; Bi, Runfang

    2017-06-01

    A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.

  16. 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.

  17. 基于多核支持向量机的非线性模型预测控制%Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

    Institute of Scientific and Technical Information of China (English)

    包哲静; 皮道映; 孙优贤

    2007-01-01

    Multi-kernel-based support vector machine (SVM) model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.

  18. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.

    Science.gov (United States)

    Gaonkar, Bilwaj; T Shinohara, Russell; Davatzikos, Christos

    2015-08-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification.

  19. 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...

  20. MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  1. Magnetic field modelling of machine and multiple machine systems using dynamic reluctance mesh modelling

    OpenAIRE

    Yao, Li

    2006-01-01

    This thesis concerns the modified and improved, time-stepping, dynamic reluctance mesh (DRM) modelling technique for machines and its application to multiple machine systems with their control algorithms. Improvements are suggested which enable the stable solution of the resulting complex non-linear equations. The concept of finite element (FE) derived, overlap-curves has been introduced to facilitate the evaluation of the air-gap reluctances linking the teeth on the rotor to those on the sta...

  2. Prediction in Marketing Using the Support Vector Machine

    OpenAIRE

    Dapeng Cui; David Curry

    2005-01-01

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

  3. Loop-length-dependent SVM prediction of domain linkers for high-throughput structural proteomics.

    Science.gov (United States)

    Ebina, Teppei; Toh, Hiroyuki; Kuroda, Yutaka

    2009-01-01

    The prediction of structural domains in novel protein sequences is becoming of practical importance. One important area of application is the development of computer-aided techniques for identifying, at a low cost, novel protein domain targets for large-scale functional and structural proteomics. Here, we report a loop-length-dependent support vector machine (SVM) prediction of domain linkers, which are loops separating two structural domains. (DLP-SVM is freely available at: http://www.tuat.ac.jp/ approximately domserv/cgi-bin/DLP-SVM.cgi.) We constructed three loop-length-dependent SVM predictors of domain linkers (SVM-All, SVM-Long and SVM-Short), and also built SVM-Joint, which combines the results of SVM-Short and SVM-Long into a single consolidated prediction. The performances of SVM-Joint were, in most aspects, the highest, with a sensitivity of 59.7% and a specificity of 43.6%, which indicated that the specificity and the sensitivity were improved by over 2 and 3% respectively, when loop-length-dependent characteristics were taken into account. Furthermore, the sensitivity and specificity of SVM-Joint were, respectively, 37.6 and 17.4% higher than those of a random guess, and also superior to those of previously reported domain linker predictors. These results indicate that SVMs can be used to predict domain linkers, and that loop-length-dependent characteristics are useful for improving SVM prediction performances.

  4. Development and evaluation of cost-sensitive universum-SVM.

    Science.gov (United States)

    Dhar, Sauptik; Cherkassky, Vladimir

    2015-04-01

    Many machine learning applications involve analysis of high-dimensional data, where the number of input features is larger than/comparable to the number of data samples. Standard classification methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. One such new learning methodology is called learning through contradiction or Universum-support vector machine (U-SVM). Recent studies have shown U-SVM to be quite effective for sparse high-dimensional data sets. However, all these earlier studies have used balanced data sets with equal misclassification costs. This paper extends the U-SVM formulation to problems with different misclassification costs, and presents practical conditions for the effectiveness of this cost-sensitive U-SVM. Several empirical comparisons are presented to validate the proposed approach.

  5. Study on Support Vector Machine model for determination of quasi-geoid%似大地水准面的支持向量机模型研究

    Institute of Scientific and Technical Information of China (English)

    郝伟涛; 郭向前; 米川

    2012-01-01

    Support Vector Machine ( SVM ) is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. Aiming at the determination of large area complex quasi-geoid, only depending on the CPS leveling data, this paper first introduced the principle of SVM briefly, then chose the parameters and built the quasi-geoid model. Through taking an example and comparing with the Neural Network model, the correctness and effectiveness of the SVM model were demonstrated finally.%支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种新的具有较好泛化性能的回归方法.本文简要介绍了SVM原理,针对大面积复杂似大地水准面的确定问题,仅依据测区的GPS水准实测数据,利用SVM方法整体建模.通过工程实例并与神经网络模型进行对比,证实了SVM似大地水准面模型的可靠性.

  6. Signal peptide discrimination and cleavage site identification using SVM and NN.

    Science.gov (United States)

    Kazemian, H B; Yusuf, S A; White, K

    2014-02-01

    About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model.

  7. Online LS-SVM for function estimation and classification

    Institute of Scientific and Technical Information of China (English)

    Jianghua Liu; Jia-pin Chen; Shan Jiang; Junshi Cheng

    2003-01-01

    An online algorithm for training LS-SVM (Least Square Support Vector Machines) was proposed for the application of function estimation and classification. Online LS-SVM means that LS-SVM can be trained in an incremental way, and can be pruned to get sparse approximation in a decremental way. When a SV (Support Vector) is added or removed, the online algorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Online algorithm is especially useful to realistic function estimation problem such as system identification. The experiments with benchmark function estimation problem and classification problem show the validity of this online algorithm.

  8. Supply Chain Dynamic Performance Measurement Based on BSC and SVM

    Directory of Open Access Journals (Sweden)

    Yan Hong

    2013-01-01

    Full Text Available Now individual contest among enterprises has been turning into collective contest among supply chains. Supply chain management (SCM has been a major component of competitive strategy to enhance organizational productivity and profitability. In recent years, organizational performance measurement and metrics have received much attention from researchers and practitioners. The foundation of proper supply chain performance assessment system is the basis of its effective operation and management. Most of the traditional supply chain performance evaluation is a static evaluation, while the actual supply chain is a dynamic system, therefore need to adapt with ways to carry out the evaluation. In order to meet the needs of the dynamic alliance's overall performance evaluation, this paper extended the traditional four Balanced Scorecard dimension into five. On this basis, established the five Balanced Scorecard dimension of supply chain, and also established a three-layered of quantitative index system according to this model. Measured then each performance indexs value by using the theory of Fuzzy Analytic Hierarchy Process, meanwhile reduced the number of input of the Support Vector Machine (SVM by using classification method, finally, got performance evaluations result by using the weighted Least Squares Support Vector Machine (LS-SVM, which provides the basis for rational analysis and decision-making of the supply chain.

  9. 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

  10. A comparative QSPR study on aqueous solubility of polycyclic aromatic hydrocarbons by GA-SVM, GA-RBFNN and GA-PLS

    Institute of Scientific and Technical Information of China (English)

    Jun QI; Jia WEI; Changhong SUN; Tao PAN

    2011-01-01

    A novel method to develop quantitative structure-property relationship(QSPR)models of organic contaminants was proposed based on genetic algorithm (GA)and support vector machine(SVM).GA was used to perform the variable selection and SVM was used to construct QSPR models.In this study,GA-SVM was applied to develop the QSPR model for aqueous solubility (Sw,mol·L-1)of polycyclic aromatic hydrocarbons (PAHs).The R2(0.98)of the model developed by GASVM indicated a good predictive precision for 1g Sw values of PAHs.According to leave-one-out(LOO)cross validation,the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network(GA-RBFNN)and genetic algorithm-partial leastsquares(GA-PLS)regression.The comparisons showed that the cross validation correlation coefficient(Q2LOO =0.92)and root mean square error of LOO cross validation (RMSELoo =0.49)of GA-SVM were the highest and lowest,respectively,which illustrated that GA-SVM was more suitable to develop QSPR model for the lg Sw values of PAHs than GA-RBFNN and GA-PLS.

  11. Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR.

    Science.gov (United States)

    Wu, Hsin-Yun; Gong, Cihun-Siyong Alex; Lin, Shih-Pin; Chang, Kuang-Yi; Tsou, Mei-Yung; Ting, Chien-Kun

    2016-06-01

    Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.

  12. 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.

  13. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

    Science.gov (United States)

    Goetz, J. N.; Brenning, A.; Petschko, H.; Leopold, P.

    2015-08-01

    Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings. Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan

  14. Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM

    Directory of Open Access Journals (Sweden)

    Xunyuan Yin

    2013-01-01

    Full Text Available The strapdown inertial navigation systems (SINS have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs, and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the -SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.

  15. Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles

    Directory of Open Access Journals (Sweden)

    Liliya Demidova

    2016-05-01

    Full Text Available The problem with development of the support vector machine (SVM classifiers using modified particle swarm optimization (PSO algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

  16. Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

    Science.gov (United States)

    Leong, Max K.; Syu, Ren-Guei; Ding, Yi-Lung; Weng, Ching-Feng

    2017-01-01

    The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928–0.988,  = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

  17. 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.

  18. 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.

  19. 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

  20. Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM).

    Science.gov (United States)

    Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

    2017-04-15

    In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

  1. Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

    Science.gov (United States)

    Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

    2017-04-01

    In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

  2. Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-Learning Model

    Directory of Open Access Journals (Sweden)

    Bo Du

    2017-01-01

    Full Text Available A hybrid machine-learning model, integrating an artificial neural network (ANN and a support vector machine (SVM model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE. The proposed hybrid model can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans.

  3. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    Science.gov (United States)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support

  4. Application of PC-ANN and PC-LS-SVM in QSAR of CCR1 antagonist compounds: a comparative study.

    Science.gov (United States)

    Shahlaei, Mohsen; Fassihi, Afshin; Saghaie, Lotfollah

    2010-04-01

    Principal component regression (PCR), principal component-artificial neural network (PC-ANN), and principal component-least squares-support vector machine (PC-LS-SVM) as regression methods were investigated for building quantitative structure-activity relationships for the prediction of inhibitory activity of some CCR1 antagonists. Nonlinear methods (PC-ANN and PC-LS-SVM) were better than the PCR method considerably in the goodness of fit and predictivity parameters and other criteria for evaluation of the proposed model. These results reflect a nonlinear relationship between the principal components obtained from molecular descriptors and the inhibitory activity of this set of molecules. The maximum variance in activity of the molecules, in PCR method was 45.5%, whereas nonlinear methods, PC-ANN and PC-LS-SVM, could explain more than 93.7% and 95.6% variance in activity data respectively.

  5. Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models for siRNA Potency.

    Science.gov (United States)

    Ebalunode, Jerry O; Zheng, Weifan

    2010-12-17

    Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models. However, sequence-based features have limited ability to characterize siRNAs, especially chemically modified ones. In this study, we proposed a cheminformatics approach to describe siRNAs. Principal component scores (z1, z2, z3, z4) have been derived for each of the 5 nucleotides (A, U, G, C, T) from the descriptor matrix computed by the MOE program. Descriptors of a given siRNA sequence are simply the concatenation of the z values of its composing nucleotides. Thus, for each of the 2431 siRNA sequences in the Huesken dataset, 76 descriptors were generated for the 19-NT representation, and 84 descriptors were generated for the 21-NT representation of siRNAs. Support Vector Machine regression (SVMR) was employed to develop predictive models. In all cases, the models achieved Pearson correlation coefficient r and R about 0.84 and 0.65 for the training sets and test sets, respectively. A minimum of 25 % of the whole dataset was needed to obtain predictive models that could accurately predict 75 % of the remaining siRNAs. Thus, for the first time, a cheminformatics approach has been developed to successfully model the structure-potency relationship in siRNA-based gene silencing data, which has laid a solid foundation for quantitative modeling of chemically modified siRNAs.

  6. Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Kezhen Yan

    2013-01-01

    Full Text Available Compressive strength and splitting tensile strength are both important parameters that are utilized for characterization concrete mechanical properties. This paper aims to show a possible applicability of support vector machine (SVM to predict the splitting tensile strength of concrete from compressive strength of concrete, a SVM model was built, trained, and tested using the available experimental data gathered from the literature. All of the results predicted by the SVM model are compared with results obtained from experimental data, and we found that the predicted splitting tensile strength of concrete is in good agreement with the experimental data. The splitting tensile strength results predicted by SVM are also compared to those obtained by using empirical results of the building codes and various models. These comparisons show that SVM has strong potential as a feasible tool for predicting splitting tensile strength from compressive strength.

  7. 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.

  8. 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.

  9. A survey of supervised machine learning models for mobile-phone based pathogen identification and classification

    Science.gov (United States)

    Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan

    2017-03-01

    Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

  10. A Knowledge base model for complex forging die machining

    CERN Document Server

    Mawussi, Kwamiwi; 10.1016/j.cie.2011.02.016

    2011-01-01

    Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometric features. Technological data and topological relations are aggregated to a geometric feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate mac...

  11. A language for easy and efficient modeling of Turing machines

    Institute of Scientific and Technical Information of China (English)

    Pinaki Chakraborty

    2007-01-01

    A Turing Machine Description Language (TMDL) is developed for easy and efficient modeling of Turing machines.TMDL supports formal symbolic representation of Turing machines. The grammar for the language is also provided. Then a fast singlepass compiler is developed for TMDL. The scope of code optimization in the compiler is examined. An interpreter is used to simulate the exact behavior of the compiled Turing machines. A dynamically allocated and resizable array is used to simulate the infinite tape of a Turing machine. The procedure for simulating composite Turing machines is also explained. In this paper, two sample Turing machines have been designed in TMDL and their simulations are discussed. The TMDL can be extended to model the different variations of the standard Turing machine.

  12. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

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

  13. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  14. A Knowledge base model for complex forging die machining

    OpenAIRE

    Mawussi, Kwamiwi; Tapie, Laurent

    2011-01-01

    International audience; Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometric features. Technological data and ...

  15. Classification of 5-HT1A receptor agonists and antagonists using GA-SVM method

    Institute of Scientific and Technical Information of China (English)

    Xue-lian ZHU; Hai-yan CAI; Zhi-jian XU; Yong WANG; He-yao WANG; Ao ZHANG; Wei-liang ZHU

    2011-01-01

    Aim:To construct a reliable computational model for the classification of agonists and antagonists of 5-HT1A receptor.Methods:Support vector machine (SVM),a well-known machine learning method,was employed to build a prediction model,and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters,C and r of the SVM model.The overall dataset used in this study comprised 284 ligands of the 5-HT1A receptor with diverse structures reported in the literatures.Results:A SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT1A receptor.The predictive accuracy for training and test sets was 0.942 and 0.865,respectively.For compounds with probability estimate higher than 0.7,the predictive accuracy of the model for training and test sets was 0.954 and 0.927,respectively.To further validate our model,the receiver operating characteristic (ROC) curve was plotted,and the Area-Under-the-ROC-Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set.Conclusion:A reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT1A receptor.To our knowledge,this is the first effort for the classification of 5-HT1A receptor agonists and antagonists based on a diverse dataset.This method may be used to classify the ligands of other members of the GPCR family.

  16. A Study of BCI Signal Pattern Recognition by Using Quasi-Newton-SVM Method

    Institute of Scientific and Technical Information of China (English)

    YANG Chang-chun; MA Zheng-hua; SUN Yu-qiang; ZOU Ling

    2006-01-01

    The recognition of electroencephalogram (EEG) signals is the key of brain computer interface (BCI).Aimed at the problem that the recognition rate of EEG by using support vector machine (SVM) is low in BCI,based on the assumption that a well-defined physiological signal which also has a smooth form"hides" inside the noisy EEG signal,a Quasi-Newton-SVM recognition method based on Quasi-Newton method and SVM algorithm was presented.Firstly,the EEG signals were preprocessed by Quasi-Newton method and got the signals which were fit for SVM.Secondly,the preprocessed signals were classified by SVM method.The present simulation results indicated the Quasi-Newton-SVM approach improved the recognition rate compared with using SVM method; we also discussed the relationship between the artificial smooth signals and the classification errors.

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

    Directory of Open Access Journals (Sweden)

    Jian Shi

    2016-11-01

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

  18. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.

    Science.gov (United States)

    Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin

    2016-01-15

    Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods.

  19. TRANSLATOR OF FINITE STATE MACHINE MODEL PARAMETERS FROM MATLAB ENVIRONMENT INTO HUMAN-MACHINE INTERFACE APPLICATION

    OpenAIRE

    2012-01-01

    Technology and means for automatic translation of FSM model parameters from Matlab application to human-machine interface application is proposed. The example of technology application to the electric apparatus model is described.

  20. Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier

    Directory of Open Access Journals (Sweden)

    Jianming Zhang

    2015-01-01

    Full Text Available The position of the hinge point of mitral annulus (MA is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM classifier. The innovations are as follows: (1 designing a local context feature for MA in cardiac ultrasound image; (2 applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3 designing a weighted density field of candidates which represents the blocks of candidates; and (4 estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.

  1. Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier.

    Science.gov (United States)

    Zhang, Jianming; Liu, Yangchun; Xu, Wei

    2015-01-01

    The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM) classifier. The innovations are as follows: (1) designing a local context feature for MA in cardiac ultrasound image; (2) applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3) designing a weighted density field of candidates which represents the blocks of candidates; and (4) estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.

  2. Application of SVM in analyzing the headstream of gushing water in coal mine

    Energy Technology Data Exchange (ETDEWEB)

    Yan, Z.; Zhang, H.; Du, P. [China University of Mining and Technology, Xuzhou (China). School of Environmental and Spatial Informatics

    2006-12-15

    To recognize the presence of the headstream of gushing water in coal mines, the SVM (Support Vector Machine) was proposed to analyze the gushing water based on hydrogeochemical methods. First, the SVM model for headstream analysis was trained on the water sample of available headstreams, and then we used this to predict the unknown samples, which were validated in practice by comparing the predicted results with the actual results. The experimental results show that the SVM is a feasible method to differentiate between two headstreams and the H-SVMs (Hierachical SVMs) is a preferable way to deal with the problem of multi-headstreams. Compared with other methods, the SVM is based on a strict mathematical theory with a simple structure and good generalization properties. As well, the support vector W in the decision function can describe the weights of the recognition factors of water samples, which is very important for the analysis of headstreams of gushing water in coal mines. 11 refs., 1 fig., 7 tabs.

  3. Thermal models of electric machines with dynamic workloads

    Directory of Open Access Journals (Sweden)

    Christian Pohlandt

    2015-07-01

    Full Text Available Electric powertrains are increasingly used in off-highway machines because of easy controllability and excellent overall efficiency. The main goals are increasing the energy efficiency of the machine and the optimization of the work process. The thermal behaviour of electric machines with dynamic workloads applied to is a key design factor for electric powertrains in off-highway machines. This article introduces a methodology to model the thermal behaviour of electric machines. Using a noncausal modelling approach, an electric powertrain is analysed for dynamic workloads. Cause-effect relationships and reasons for increasing temperature are considered as well as various cooling techniques. The validation of the overall simulation model of the powertrain with measured field data workloads provides convincing results to evaluate numerous applications of electric machines in off-highway machines.

  4. Compression method based on training dataset of SVM

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The method to compress the training dataset of Support Vector Machine (SVM) based on the character of the Support Vector Machine is proposed.First,the distance between the unit in two training datasets,and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset.The time spent in training SVM with the training dataset compressed by the method is shortened obviously.The result of the experiment shows that the algorithm is effective.

  5. CCH-based geometric algorithms for SVM and applications

    Institute of Scientific and Technical Information of China (English)

    Xin-jun PENG; Yi-fei WANG

    2009-01-01

    The support vector machine (SVM) is a novel machine learning tool in data mining. In this paper, the geometric approach based on the compressed convex hull (CCH) with a mathematical framework is introduced to solve SVM classification problems. Compared with the reduced convex hull (RCH), CCH preserves the shape of geometric solids for data sets; meanwhile, it is easy to give the necessary and sufficient condition for determining its extreme points. As practical applications of CCH, spare and probabilistic speed-up geometric algorithms are developed. Results of numerical experiments show that the proposed algorithms can reduce kernel calculations and display nice performances.

  6. An Optimal SVM with Feature Selection Using Multiobjective PSO

    Directory of Open Access Journals (Sweden)

    Iman Behravan

    2016-01-01

    Full Text Available Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, C, and the kernel factor, σ. Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM. The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks.

  7. 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.

  8. Recognition of car model based on support vector machine%基于支持向量机的轿车车型识别

    Institute of Scientific and Technical Information of China (English)

    冯超; 贺俊吉; 史立

    2011-01-01

    为了从轿车图像中快速、准确地识别出轿车车型,采用支持向量机(Support Vector Machine,SVM)方法作为分类器,以轿车的长、宽、高和轴距等4个特征参数作为输入特征向量,并根据这些特征向量对不同车型进行分类和识别.实验结果表明,对11个品牌15种车型的识别准确率达100%.本研究表明,在正确选取轿车的特征参数基础上,采用SVM方法识别轿车车型可以达到很好的效果,SVM方法在智能交通管理系统等领域具有较高的应用价值.%To recognize models from the car pictures quickly and accurately, the Support Vector Machine ( SVM) method is used as classifier, and four feature parameters of cars, including length, width, height and wheelbase, are chosen as the input of characteristic vectors, and variant car models are classified and identified according to the characteristic vectors. The experimental results show that accuracy rate of recognition reaches 100% for as many as 15 various models of 11 brands. The experiment indicates that car model recognition based on the right selection of feature parameters is of good performance by using the SVM method. This SVM method has a high application value in the intelligent traffic management systems and other relative fields.

  9. Nonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bloreactor

    Institute of Scientific and Technical Information of China (English)

    冯絮影; 于涛; 王建林

    2012-01-01

    In this study, Saccharomyces cerevisiae (baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine, which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process, and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control, experimental results demonstrate the robustness, effectiveness and advantages of the new controller.

  10. Predicting enhancer activity and variant impact using gkm-SVM.

    Science.gov (United States)

    Beer, Michael A

    2017-01-25

    We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone.

  11. An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets.

    Science.gov (United States)

    Shi, Yingzhong; Chung, Fu-Lai; Wang, Shitong

    2015-09-01

    Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.

  12. Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs.

    Science.gov (United States)

    Zhu, Biyun; Chen, Hui; Chen, Budong; Xu, Yan; Zhang, Kuan

    2014-02-01

    This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung fields on DRs of 85 healthy controls and 40 patients with stage I and stage II pneumoconiosis. DTs with algorithm C5.0 and SVMs with four different kernels were trained by samples with two combinations of the texture features to classify a DR as of a healthy subject or of a patient with pneumoconiosis. All of the models were developed with fivefold cross-validation, and the final performances of each model were compared by the area under receiver operating characteristic (ROC) curve. For both SVM (with a radial basis function kernel) and DT (with algorithm C5.0), areas under ROC curves (AUCs) were 0.94 ± 0.02 and 0.86 ± 0.04 (P = 0.02) when using the full feature set and 0.95 ± 0.02 and 0.88 ± 0.04 (P = 0.05) when using the selected feature set, respectively. When built on the selected texture features, the SVM with a polynomial kernel showed a higher diagnostic performance with an AUC value of 0.97 ± 0.02 than SVMs with a linear kernel, a radial basis function kernel and a sigmoid kernel with AUC values of 0.96 ± 0.02 (P = 0.37), 0.95 ± 0.02 (P = 0.24), and 0.90 ± 0.03 (P = 0.01), respectively. The SVM model with a polynomial kernel built on the selected feature set showed the highest diagnostic performance among all tested models when using either all the wavelet texture features or the selected ones. The model has a good potential in diagnosing pneumoconiosis based on digital chest radiographs.

  13. Yarn Properties Prediction Based on Machine Learning Method

    Institute of Scientific and Technical Information of China (English)

    YANG Jian-guo; L(U) Zhi-jun; LI Bei-zhi

    2007-01-01

    Although many works have been done to constructprediction models on yarn processing quality, the relationbetween spinning variables and yam properties has not beenestablished conclusively so far. Support vector machines(SVMs), based on statistical learning theory, are gainingapplications in the areas of machine learning and patternrecognition because of the high accuracy and goodgeneralization capability. This study briefly introduces theSVM regression algorithms, and presents the SVM basedsystem architecture for predicting yam properties. Model.selection which amounts to search in hyper-parameter spaceis performed for study of suitable parameters with grid-research method. Experimental results have been comparedwith those of artificial neural network(ANN) models. Theinvestigation indicates that in the small data sets and real-life production, SVM models are capable of remaining thestability of predictive accuracy, and more suitable for noisyand dynamic spinning process.

  14. 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.

  15. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

    Directory of Open Access Journals (Sweden)

    Li Zhen

    2008-05-01

    Full Text Available Abstract Background Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML methods. Results The classification performance of Artificial Neural Networks (ANN, K-Nearest Neighbors (KNN, Linear Discriminant Analysis (LDA, Naïve Bayes (NB, Recursive Partitioning and Regression Trees (RPART, and Support Vector Machines (SVM in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated in vitro assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5 were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA. Conclusion We have developed a novel simulation model to evaluate machine learning methods for the

  16. EMOTIONAL SPEECH RECOGNITION BASED ON SVM WITH GMM SUPERVECTOR

    Institute of Scientific and Technical Information of China (English)

    Chen Yanxiang; Xie Jian

    2012-01-01

    Emotion recognition from speech is an important field of research in human computer interaction.In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition.Because of the importance of variance in reflecting the distribution of speech,the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector.Comparative experiments from five aspects are conducted to study their corresponding effect to system performance.The experiment results,which indicate that the influence of number of mixtures is strong as well as influence of duration is weak,provide basis for the train set selection of Universal Background Model (UBM).

  17. 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.

  18. A Machine-Learning-Driven Sky Model.

    Science.gov (United States)

    Satylmys, Pynar; Bashford-Rogers, Thomas; Chalmers, Alan; Debattista, Kurt

    2017-01-01

    Sky illumination is responsible for much of the lighting in a virtual environment. A machine-learning-based approach can compactly represent sky illumination from both existing analytic sky models and from captured environment maps. The proposed approach can approximate the captured lighting at a significantly reduced memory cost and enable smooth transitions of sky lighting to be created from a small set of environment maps captured at discrete times of day. The author's results demonstrate accuracy close to the ground truth for both analytical and capture-based methods. The approach has a low runtime overhead, so it can be used as a generic approach for both offline and real-time applications.

  19. 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.

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

    Science.gov (United States)

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

    2016-04-01

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

  1. Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

    Directory of Open Access Journals (Sweden)

    Kyle A McQuisten

    Full Text Available BACKGROUND: Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models. PRINCIPAL FINDINGS: Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation. CONCLUSIONS: The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features

  2. Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine.

    Directory of Open Access Journals (Sweden)

    Jun Meng

    Full Text Available Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA. Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA-target interactions.Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.

  3. Person-specific named entity recognition using SVM with rich feature sets

    Institute of Scientific and Technical Information of China (English)

    Hui; NIE

    2012-01-01

    Purpose:The purpose of the study is to explore the potential use of nature language process(NLP)and machine learning(ML)techniques and intents to find a feasible strategy and effective approach to fulfill the NER task for Web oriented person-specific information extraction.Design/methodology/approach:An SVM-based multi-classification approach combined with a set of rich NLP features derived from state-of-the-art NLP techniques has been proposed to fulfill the NER task.A group of experiments has been designed to investigate the influence of various NLP-based features to the performance of the system,especially the semantic features.Optimal parameter settings regarding with SVM models,including kernel functions,margin parameter of SVM model and the context window size,have been explored through experiments as well.Findings:The SVM-based multi-classification approach has been proved to be effective for the NER task.This work shows that NLP-based features are of great importance in datadriven NE recognition,particularly the semantic features.The study indicates that higher order kernel function may not be desirable for the specific classification problem in practical application.The simple linear-kernel SVM model performed better in this case.Moreover,the modified SVM models with uneven margin parameter are more common and flexible,which have been proved to solve the imbalanced data problem better.Research limitations/implications:The SVM-based approach for NER problem is only proved to be effective on limited experiment data.Further research need to be conducted on the large batch of real Web data.In addition,the performance of the NER system need be tested when incorporated into a complete IE framework.Originality/value:The specially designed experiments make it feasible to fully explore the characters of the data and obtain the optimal parameter settings for the NER task,leading to a preferable rate in recall,precision and F1measures.The overall system performance

  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 comparison of numerical and machine-learning modeling of soil water content with limited input data

    Science.gov (United States)

    Karandish, Fatemeh; Šimůnek, Jiří

    2016-12-01

    Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a

  6. Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

    Science.gov (United States)

    Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

    2011-06-01

    As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed.

  7. 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.

  8. Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier

    Directory of Open Access Journals (Sweden)

    Kevin O'Neill

    2010-01-01

    Full Text Available The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP method to locate targets, the normalized surface magnetic source (NSMS model to characterize them, and a support vector machine (SVM to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

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

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2009-01-01

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

  11. Incremental support vector machines for fast reliable image recognition

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-10-15

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

  12. Application of SVM and ELM Methods to Predict Location and Magnitude Leakage of Pipelines on Water Distribution Network

    Directory of Open Access Journals (Sweden)

    A.Ejah Umraeni Salam

    2015-06-01

    Full Text Available In this research, the system of leakage of pipelines detection will be done by a computerized technique by using analysis of pressure monitoring as a determinant of presence of pipeline leaks in the water distribution network. The pressure data obtained from EPANET software, namely a modeling in a hydraulic system. This study uses two methods, artificial intelligence, namely Support Vector Machine (SVM and Extreme Learning Machine (ELM which the results can be compared in order to predict the magnitude and location of leakage. Overall, both of these methods can be used to predict the magnitude and location of leakage. The accuracy of predictions for the magnitude and location of leakage of these methods is based on the value of NRMSE. In this case the results obtained by using the method of ELM are more accurate compared than the method of SVM of the entire pipeline systems.

  13. PSO-SVM Identification Model for Driving Sustained Attention Level Based on EEG%基于EEG的驾驶持续性注意水平PSO-SVM识别模型

    Institute of Scientific and Technical Information of China (English)

    郭孜政; 吴志敏; 潘雨帆; 余刚; 张骏

    2016-01-01

    In order to recognize driving sustained attention effectively, an identification method for sustained attention level was proposed based on the signal of electroencephalograph ( EEG ) . Firstly, taking the driver' s reaction time to random events as indexes, a dividing method for sustained attention levels was proposed. Secondly, using average spectrum amplitude from the bands of (θ(4~8 Hz) ,α(8~13 Hz) ,β(13~30 Hz) ) of EEG and its' ration value (α+β)/β,α/β, (θ+α)/(α+β) ,θ/βand (α+β)/θ as characteristic indexes, combining the particle swarm optimization ( PSO ) with support vector machine (SVM),an identification model for identifying sustained attention level was proposed. Finally, based on the data from driving simulating, the identification model was tested. The result shows that the average accuracy rate of model is 93. 02℅ and the method is applicable to identification of driving sustained attention level.%为了对驾驶持续性注意水平予以有效识别,基于脑电( EEG)信号特征指标构建了一种持续性注意水平识别方法. 以驾驶行为绩效为客观测评指标,提出了一种驾驶持续性注意水平等级划分方法. 在此基础上,选取驾驶员EEG波段 (θ(4~8 Hz)、α(8~13 Hz)、β(13~30 Hz))的频谱幅值及其组合指标(α+β)/β、α/β、(θ+α)/(α+β)、θ/β、(α+β)/θ作为特征指标,将粒子群优化( PSO)算法与支持向量机( SVM)相结合,构建了驾驶持续性注意水平识别算法. 最后,基于驾驶模拟器实验数据对该模型予以试算. 结果表明模型识别平均正确率可达93. 02℅.该方法可用于对驾驶员持续性注意水平的识别.

  14. Short-term Load Forecasting Based on ARIMA-LS-SVM Model%基于ARIMA和LS-SVM组合模型的短期负荷预测

    Institute of Scientific and Technical Information of China (English)

    刘国徽; 刘小满; 余雪芳; 王勇

    2010-01-01

    经实例预测分析发现,利用累积式自回归动平均法(autoregressive integrated moving average,ARIMA)进行电力短期负荷预测时所得误差序列有较明显的周期规律性,针对此现象及其原因,为提高预测精度,提出采用最小二乘支持向量机(least squares support vector machine,LS-SVM)对ARIMA预测误差进行修正的ARIMA-LS-SVM组合模型;利用该改进模型对哈尔滨电网负荷进行实例预测,结果表明:该方法能够提高短期负荷的预测精度,并且具有较强的推广性和应用能力.

  15. Study on flaw identification of ultrasonic signal for large shafts based on optimal support vector machine

    Institute of Scientific and Technical Information of China (English)

    Zhao Xiufen; Yin Guofu; Tian Guiyun; Yin Ying

    2008-01-01

    Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.

  16. 基于修正SVM-KNN组合算法的汉语专有名词自动抽取%Automatic Extraction on Chinese Proper Names Based on a Modified SVM-KNN Classifier

    Institute of Scientific and Technical Information of China (English)

    李丽双; 党延忠; 李丹

    2011-01-01

    Extracting Chinese proper names is a key step in the fields of text mining, information retrieval and machine translation.This paper presents a method of extracting proper names from Chinese texts based on the fusion of support vector machine (SVM) and modified K nearest neighbors (KNN).Different classifiers are used for classifying the different test samples in spatial distributions.In the class phase, the algorithm computes the distance from the test sample to the hyperplane of SVM.If the distance is greater than the given threshold, the test sample would be classified on SVM;otherwise, the KNN algorithm will be used.In the practical training corpora, the negative class is represented by a large number of examples while the positive one is represented by only a few.To fit the unbalanced data, a normalized KNN classifier is proposed to modify classic KNN.The experimental results show that this model is more efficient than sole SVM and classic SVM-KNN in extracting Chinese proper names.The modified SVM-KNN model can be generalized to other fields of machine learning with unbalanced class distribution.%专有名词的自动抽取是文本挖掘、信息检索和机器翻译等领域的关键技术.本文研究了组合SVM和KNN两种分类器进行汉语专有名词自动抽取的方法.对样本在空间的不同分布使用不同的分类方法,当测试样本与SVM最优超平面的距离大于给定的阈值时使用SVM分类,否则使用KNN;在实际训练语料中,常常是负类样本数远多于正类样本数,而传统KNN方法对不平衡训练集存在敏感性,所以提出了用归一化的思想对传统的KNN方法进行修正.实验表明,用SVM与修正的KNN组合算法进行汉语专有名词抽取比单一的SVM方法以及原始的SVM-KNN方法更具优越性,而且这种方法可以推广到其他非平衡分布样本的分类问题.

  17. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder

    Science.gov (United States)

    Yu, J S; Xue, A Y; Redei, E E; Bagheri, N

    2016-01-01

    Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnosed or misdiagnosed. An objective blood-based diagnostic test using transcript levels of a panel of markers would provide an invaluable tool for MDD as the infrastructure—including equipment, trained personnel, billing, and governmental approval—for similar tests is well established in clinics worldwide. Here we present a supervised classification model utilizing support vector machines (SVMs) for the analysis of transcriptomic data readily obtained from a peripheral blood specimen. The model was trained on data from subjects with MDD (n=32) and age- and gender-matched controls (n=32). This SVM model provides a cross-validated sensitivity and specificity of 90.6% for the diagnosis of MDD using a panel of 10 transcripts. We applied a logistic equation on the SVM model and quantified a likelihood of depression score. This score gives the probability of a MDD diagnosis and allows the tuning of specificity and sensitivity for individual patients to bring personalized medicine closer in psychiatry. PMID:27779627

  18. 基于SVM短时交通流量预测%Short-term Traffic Flow Prediction Based on SVM

    Institute of Scientific and Technical Information of China (English)

    蒋晓峰; 许伦辉; 朱悦

    2012-01-01

    Traffic flow prediction is a very important area in intelligent transportation systems. Traditional prediction methods have a very wide range of applications in the traffic prediction. But traditional prediction methods does not work very well in short-term traffic flow prediction because of the complexity of the influencing factors. With the development of machine learning and data mining,traffic flow prediction with a combination of machine learning and data mining has become more and more important as a research area. In this paper,SVM (Support Vector Machine) is used to build a short-term traffic flow prediction model,and Genetic Algorithm (GA) is used to optimize the SVM penalty factor C and kernel parameter a as well. The results of different kernel functions of SVM are compared,including polynomial kernel and RBF kernel. RBF SVM plays better than polynomial SVM with less training time and higher accuracy and SVM is very suitable for short-term traffic flow prediction.%交通流量预测是智能交通系统中非常重要的研究领域,传统的预测方法在交通流量预测中有着非常广泛的应用.但是,在短时交通流量预测中,由于其影响因素错综复杂,传统的预测方法对于短时交通流量不能很好地进行预测.随着机器学习和数据挖掘各种理论的不断提出及完善,机器学习和数据挖掘与交通流量预测的结合是智能交通系统未来发展的一个重要方向.本文利用SVM (support vector machine)构建了短时交通流量预测模型,并利用遗传算法(genetic algorithm)对SVM的惩罚参数C和核参数σ进行优化,同时比较SVM中不同核函数,包括多项式核函数(polynomial kernel)和径向基核函数(RBF kernel)的预测效果.径向基SVM (RBF SVM)训练时间要比多项式SVM (polynomial SVM)短,预测准确率和精度也要比多项式SVM要好.从仿真结果上看,SVM非常适合应用于短时交通流量预测,能够取得很好的预测效果与精度.

  19. Prediction of effluent concentration in a wastewater treatment plant using machine learning models.

    Science.gov (United States)

    Guo, Hong; Jeong, Kwanho; Lim, Jiyeon; Jo, Jeongwon; Kim, Young Mo; Park, Jong-pyo; Kim, Joon Ha; Cho, Kyung Hwa

    2015-06-01

    Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R2), Nash-Sutcliff efficiency (NSE), relative efficiency criteria (drel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process. Copyright © 2015. Published by Elsevier B.V.

  20. Modeling of Exterior Rotor Permanent Magnet Machines with Concentrated Windings

    NARCIS (Netherlands)

    Vu Xuan, H.

    2012-01-01

    In this thesis modeling, analysis, design and measurement of exterior rotor permanent magnet (PM) machines with concentrated windings are dealt with. Special attention is paid to slotting effect. The PM machine is integrated in flywheel and used for small-scale ship application. Analytical model and

  1. "Active Flux" DTFC-SVM Sensorless Control of IPMSM

    DEFF Research Database (Denmark)

    Boldea, Ion; Codruta Paicu, Mihaela; Gheorghe-Daniel, Andreescu,

    2009-01-01

    . The concept of "active flux" (or "torque producing flux") turns all the rotor salient-pole ac machines into fully nonsalient-pole ones. A new function for Lq inductance depending on torque is introduced to model the magnetic saturation. Notable simplification in the rotor position and speed estimation...... is obtained, because the active flux position is identical with the rotor position. Extensive experimental results are presented to verify the principles and to demonstrate the effectiveness of the proposed sensorless control system. With the active flux observer, the IPMSM drive system operates from very low......This paper proposes an implementation of a motionsensorless control system in wide speed range based on "active flux" observer, and direct torque and flux control with space vector modulation (DTFC-SVM) for the interior permanent magnet synchronous motor (IPMSM), without signal injection...

  2. Prediction of Aerosol Optical Depth in West Asia: Machine Learning Methods versus Numerical Models

    Science.gov (United States)

    Omid Nabavi, Seyed; Haimberger, Leopold; Abbasi, Reyhaneh; Samimi, Cyrus

    2017-04-01

    Dust-prone areas of West Asia are releasing increasingly large amounts of dust particles during warm months. Because of the lack of ground-based observations in the region, this phenomenon is mainly monitored through remotely sensed aerosol products. The recent development of mesoscale Numerical Models (NMs) has offered an unprecedented opportunity to predict dust emission, and, subsequently Aerosol Optical Depth (AOD), at finer spatial and temporal resolutions. Nevertheless, the significant uncertainties in input data and simulations of dust activation and transport limit the performance of numerical models in dust prediction. The presented study aims to evaluate if machine-learning algorithms (MLAs), which require much less computational expense, can yield the same or even better performance than NMs. Deep blue (DB) AOD, which is observed by satellites but also predicted by MLAs and NMs, is used for validation. We concentrate our evaluations on the over dry Iraq plains, known as the main origin of recently intensified dust storms in West Asia. Here we examine the performance of four MLAs including Linear regression Model (LM), Support Vector Machine (SVM), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS). The Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) and the Dust REgional Atmosphere Model (DREAM) are included as NMs. The MACC aerosol re-analysis of European Centre for Medium-range Weather Forecast (ECMWF) is also included, although it has assimilated satellite-based AOD data. Using the Recursive Feature Elimination (RFE) method, nine environmental features including soil moisture and temperature, NDVI, dust source function, albedo, dust uplift potential, vertical velocity, precipitation and 9-month SPEI drought index are selected for dust (AOD) modeling by MLAs. During the feature selection process, we noticed that NDVI and SPEI are of the highest importance in MLAs predictions. The data set was divided

  3. 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.

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

    Institute of Scientific and Technical Information of China (English)

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

    2006-01-01

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

  5. A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs

    Directory of Open Access Journals (Sweden)

    Jieqiong Su

    2015-04-01

    Full Text Available With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA and the support vector machine (SVM was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir.

  6. [Research on living tree volume forecast based on PSO embedding SVM].

    Science.gov (United States)

    Jiao, You-Quan; Feng, Zhong-Ke; Zhao, Li-Xi; Xu, Wei-Heng; Cao, Zhong

    2014-01-01

    In order to establish volume model,living trees have to be fallen and be divided into many sections, which is a kind of destructive experiment. So hundreds of thousands of trees have been fallen down each year in China. To solve this problem, a new method called living tree volume accurate measurement without falling tree was proposed in the present paper. In the method, new measuring methods and calculation ways are used by using photoelectric theodolite and auxiliary artificial measurement. The diameter at breast height and diameter at ground was measured manually, and diameters at other heights were obtained by photoelectric theodolite. Tree volume and height of each tree was calculated by a special software that was programmed by the authors. Zhonglin aspens No. 107 were selected as experiment object, and 400 data records were obtained. Based on these data, a nonlinear intelligent living tree volume prediction model with Particle Swarm Optimization algorithm based on support vector machines (PSO-SVM) was established. Three hundred data records including tree height and diameter at breast height were randomly selected form a total of 400 data records as input data, tree volume as output data, using PSO-SVM tool box of Matlab7.11, thus a tree volume model was obtained. One hundred data records were used to test the volume model. The results show that the complex correlation coefficient (R2) between predicted and measured values is 0. 91, which is 2% higher than the value calculated by classic Spurr binary volume model, and the mean absolute error rates were reduced by 0.44%. Compared with Spurr binary volume model, PSO-SVM model has self-learning and self-adaption ability,moreover, with the characteristics of high prediction accuracy, fast learning speed,and a small sample size requirement, PSO-SVM model with well prospect is worth popularization and application.

  7. Magnetic equivalent circuit model for unipolar hybrid excitation synchronous machine

    Directory of Open Access Journals (Sweden)

    Kupiec Emil

    2015-03-01

    Full Text Available Lately, there has been increased interest in hybrid excitation electrical machines. Hybrid excitation is a construction that combines permanent magnet excitation with wound field excitation. Within the general classification, these machines can be classified as modified synchronous machines or inductor machines. These machines may be applied as motors and generators. The complexity of electromagnetic phenomena which occur as a result of coupling of magnetic fluxes of separate excitation systems with perpendicular magnetic axis is a motivation to formulate various mathematical models of these machines. The presented paper discusses the construction of a unipolar hybrid excitation synchronous machine. The magnetic equivalent circuit model including nonlinear magnetization curves is presented. Based on this model, it is possible to determine the multi-parameter relationships between the induced voltage and magnetomotive force in the excitation winding. Particular attention has been paid to the analysis of the impact of additional stator and rotor yokes on above relationship. Induced voltage determines the remaining operating parameters of the machine, both in the motor and generator mode of operation. The analysis of chosen correlations results in an identification of the effective control range of electromotive force of the machine.

  8. MODEL STUDY OF THE DOUBLE FED MACHINE WITH CURRENT CONTROL

    Directory of Open Access Journals (Sweden)

    A. S. Lyapin

    2016-07-01

    Full Text Available The paper deals with modeling results of the double fed induction machine with current control in the rotor circuit. We show the most promising applications of electric drives on the basis of the double fed induction machine and their advantages. We present and consider functional scheme of the electric drive on the basis of the double fed induction machine with current control. Equations are obtained for creation of such machine mathematical model. Expressions for vector projections of rotor current are given. According to the obtained results, the change of the vector projections of rotor current ensures operation of the double fed induction machine with the specified values of active and reactive stator power throughout the variation range of sliding motion. We consider static characteristics of double fed machine with current control. Energy processes proceeding in the machine are analyzed. We confirm the operationpossibility of double fed induction machine with current controlin the rotor circuit with given values of active and reactive stator power. The presented results can be used for creation of mathematical models and static characteristics of double fed machines with current control of various capacities.

  9. 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.

  10. 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.

  11. Machine Cognition Models: EPAM and GPS

    CERN Document Server

    Elouafiq, Ali

    2012-01-01

    Through history, the human being tried to relay its daily tasks to other creatures, which was the main reason behind the rise of civilizations. It started with deploying animals to automate tasks in the field of agriculture(bulls), transportation (e.g. horses and donkeys), and even communication (pigeons). Millenniums after, come the Golden age with "Al-jazari" and other Muslim inventors, which were the pioneers of automation, this has given birth to industrial revolution in Europe, centuries after. At the end of the nineteenth century, a new era was to begin, the computational era, the most advanced technological and scientific development that is driving the mankind and the reason behind all the evolutions of science; such as medicine, communication, education, and physics. At this edge of technology engineers and scientists are trying to model a machine that behaves the same as they do, which pushed us to think about designing and implementing "Things that-Thinks", then artificial intelligence was. In this...

  12. Modelling Effectiveness of Machine Gun Fire

    OpenAIRE

    Dutta, D.; S. Sabhanval

    2002-01-01

    Machine gun is an effective infantry weapon which can cause heavy damage to enemy targets, if sited in a tactically favourable position. It can be engaged effectively against both static and moving targets. The paper deals with the determination of target vulnerability under effective machine gun fire considering relevant tactical parameters, eg, target aiming point, trajectory of fire, sweep angle, target frontage, posture, direction of attack, etc.

  13. Modelling Effectiveness of Machine Gun Fire

    Directory of Open Access Journals (Sweden)

    D. Dutta

    2002-04-01

    Full Text Available Machine gun is an effective infantry weapon which can cause heavy damage to enemy targets, if sited in a tactically favourable position. It can be engaged effectively against both static and moving targets. The paper deals with the determination of target vulnerability under effective machine gun fire considering relevant tactical parameters, eg, target aiming point, trajectory of fire, sweep angle, target frontage, posture, direction of attack, etc.

  14. The generalization ability of online SVM classification based on Markov sampling.

    Science.gov (United States)

    Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

    2015-03-01

    In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

  15. 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

  16. Model of Pulsed Electrical Discharge Machining (EDM using RL Circuit

    Directory of Open Access Journals (Sweden)

    Ade Erawan Bin Minhat

    2014-10-01

    Full Text Available This article presents a model of pulsed Electrical Discharge Machining (EDM using RL circuit. There are several mathematical models have been successfully developed based on the initial, ignition and discharge phase of current and voltage gap. According to these models, the circuit schematic of transistor pulse power generator has been designed using electrical model in Matlab Simulink software to identify the profile of voltage and current during machining process. Then, the simulation results are compared with the experimental results.

  17. Generative Modeling for Machine Learning on the D-Wave

    Energy Technology Data Exchange (ETDEWEB)

    Thulasidasan, Sunil [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Information Sciences Group

    2016-11-15

    These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.

  18. A NOVEL SVM FOR GROUND PENETRATING SYNTHETIC APERTURE RADAR LANDMINE DETECTION

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

  19. [Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

    Science.gov (United States)

    Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

    2013-12-01

    The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

  20. On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine%基于支持向量机的发酵过程生物量在线估计

    Institute of Scientific and Technical Information of China (English)

    王建林; 于涛; 金翠云

    2006-01-01

    Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine (SVM) for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

  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. 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.

  3. 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.

  4. Generating Turing Machines by Use of Other Computation Models

    Directory of Open Access Journals (Sweden)

    Leszek Dubiel

    2003-01-01

    Full Text Available For each problem that can be solved there exists algorithm, which can be described with a program of Turing machine. Because this is very simple model programs tend to be very complicated and hard to analyse by human. The best practice to solve given type of problems is to define a new model of computation that allows for quick and easy programming, and then to emulate its operation with Turing machine. This article shows how to define most suitable model for computation on natural numbers and defines Turing machine that emulates its operation.

  5. Testing and Modeling of Machine Properties in Resistance Welding

    DEFF Research Database (Denmark)

    Wu, Pei

    electrode force, and the time of stabilizing does not depend on the level of the force. An additional spring mounted in the welding head improves the machine touching behavior due to a soft electrode application, but this results in longer time of oscillation of the electrode force, especially when......The objective of this work has been to test and model the machine properties including the mechanical properties and the electrical properties in resistance welding. The results are used to simulate the welding process more accurately. The state of the art in testing and modeling machine properties...... in resistance welding has been described based on a comprehensive literature study. The present thesis has been subdivided into two parts: Part I: Mechanical properties of resistance welding machines. Part II: Electrical properties of resistance welding machines. In part I, the electrode force in the squeeze...

  6. 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.

  7. MOBILE GEO-LOCATION ALGORITHM BASED ON LS-SVM

    Institute of Scientific and Technical Information of China (English)

    Sun Guolin; Guo Wei

    2005-01-01

    Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.

  8. Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

    Directory of Open Access Journals (Sweden)

    Mingjian Sun

    2015-01-01

    Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

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

    Directory of Open Access Journals (Sweden)

    Chen Cao

    2007-10-01

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

  10. [Rapid determination of COD in aquaculture water based on LS-SVM with ultraviolet/visible spectroscopy].

    Science.gov (United States)

    Liu, Xue-Mei; Zhang, Hai-Liang

    2014-10-01

    Ultraviolet/visible (UV/Vis) spectroscopy was studied for the rapid determination of chemical oxygen demand (COD), which was an indicator to measure the concentration of organic matter in aquaculture water. In order to reduce the influence of the absolute noises of the spectra, the extracted 135 absorbance spectra were preprocessed by Savitzky-Golay smoothing (SG), EMD, and wavelet transform (WT) methods. The preprocessed spectra were then used to select latent variables (LVs) by partial least squares (PLS) methods. Partial least squares (PLS) was used to build models with the full spectra, and back- propagation neural network (BPNN) and least square support vector machine (LS-SVM) were applied to build models with the selected LVs. The overall results showed that BPNN and LS-SVM models performed better than PLS models, and the LS-SVM models with LVs based on WT preprocessed spectra obtained the best results with the determination coefficient (r2) and RMSE being 0. 83 and 14. 78 mg · L(-1) for calibration set, and 0.82 and 14.82 mg · L(-1) for the prediction set respectively. The method showed the best performance in LS-SVM model. The results indicated that it was feasible to use UV/Vis with LVs which were obtained by PLS method, combined with LS-SVM calibration could be applied to the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

  11. 基于Mexican Wv-SVM的震灾人员存活量模型%The Survival Amount Model Based on Mexican Wavelet Nuclear-SVM in Earthquake Disaster

    Institute of Scientific and Technical Information of China (English)

    黄星; 袁明; 王绍玉

    2016-01-01

    有效预测震灾人员的存活情况是紧急配置应急资源和提高救援效率的首要工作.为提高震灾人员存活预测的精度,本文首先依据区域灾害系统理论和现有研究成果提出震灾人员存活预测指标.其次,针对震灾人员存活量指标数据的小样本、高维度、非线性特征,考虑将支持向量机(Support Vector Machine,SVM)模型引入震灾人员存活量预测中,为有效降低SVM在高维空间中非线性分类的误差,采用Mexican母小波核函数替换满足Mercer内积条件的核函数,以改变常规核函数缩小偏差的局限性,提出用于预测震灾人员存活量的Mexican小波SVM(Mexican Wavelet-SVM,Mexican W(u)-SVM)模型.数值算例表明:相比于标准SVM、BP神经网络,Mexican W(u)-SVM模型具有预测精度好、训练速度快和运行稳定性好的特征,证明了模型的可靠和有效.

  12. MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

    Directory of Open Access Journals (Sweden)

    R. J. Candare

    2016-06-01

    Full Text Available This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

  13. Mapping of High Value Crops Through AN Object-Based Svm Model Using LIDAR Data and Orthophoto in Agusan del Norte Philippines

    Science.gov (United States)

    Candare, Rudolph Joshua; Japitana, Michelle; Cubillas, James Earl; Ramirez, Cherry Bryan

    2016-06-01

    This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

  14. A SVM framework for fault detection of the braking system in a high speed train

    Science.gov (United States)

    Liu, Jie; Li, Yan-Fu; Zio, Enrico

    2017-03-01

    In April 2015, the number of operating High Speed Trains (HSTs) in the world has reached 3603. An efficient, effective and very reliable braking system is evidently very critical for trains running at a speed around 300 km/h. Failure of a highly reliable braking system is a rare event and, consequently, informative recorded data on fault conditions are scarce. This renders the fault detection problem a classification problem with highly unbalanced data. In this paper, a Support Vector Machine (SVM) framework, including feature selection, feature vector selection, model construction and decision boundary optimization, is proposed for tackling this problem. Feature vector selection can largely reduce the data size and, thus, the computational burden. The constructed model is a modified version of the least square SVM, in which a higher cost is assigned to the error of classification of faulty conditions than the error of classification of normal conditions. The proposed framework is successfully validated on a number of public unbalanced datasets. Then, it is applied for the fault detection of braking systems in HST: in comparison with several SVM approaches for unbalanced datasets, the proposed framework gives better results.

  15. A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

    Science.gov (United States)

    Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

    2017-08-01

    The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

  16. Lamb Wave Damage Quantification Using GA-Based LS-SVM

    Directory of Open Access Journals (Sweden)

    Fuqiang Sun

    2017-06-01

    Full Text Available Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM and a genetic algorithm (GA. Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

  17. IMPROVED LS-SVM USING ACO TO ESTIMATE FLASHOVER VOLTAGE OF POLLUTED INSULATORS

    Directory of Open Access Journals (Sweden)

    SID AHMED BESSEDIK

    2017-01-01

    Full Text Available The reliability of insulators under polluted environment is one of the guiding factors in the insulation coordination of high voltage transmission lines. In order to improve understanding of the flashover phenomenon in polluted insulators, several experimental studies and mathematical approaches have been made‎ in‎ last‎ year’s.‎ In‎ this‎ paper,‎ the‎ critical flashover voltage behavior of polluted insulators has been calculated and a hybrid model between machine Learning (ML and optimization technique has been proposed. For this purpose, firstly the ant colony optimization (ACO technique is utilized to optimize the hyper-parameters needed in least squares support vector machines (LS-SVM. Then, a LS-SVM-ACO model is designed to establish a nonlinear model between the characteristics of the insulator and the critical flashover voltage. The data used to train the model and test its performance is derived from experimental measurements and a mathematical model. The results obtained from the proposed model are in good accord with other mathematical and experimental results of previous researchers.

  18. Learning using privileged information: SVM+ and weighted SVM.

    Science.gov (United States)

    Lapin, Maksim; Hein, Matthias; Schiele, Bernt

    2014-05-01

    Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time-a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available.

  19. Probabilistic models and machine learning in structural bioinformatics

    DEFF Research Database (Denmark)

    Hamelryck, Thomas

    2009-01-01

    . Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis...

  20. Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,separately.Our results are compared with the previously best known β-turn prediction methods and are shown to give comparative performance.Most significantly,the SVM model provides some information related to β-turn residues in proteins.The results demonstrate that the present combination approach may be used in the prediction of protein structures.

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

    Science.gov (United States)

    Jändel, Magnus

    2014-01-01

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

  2. A fast SVM training algorithm based on the set segmentation and k-means clustering

    Institute of Scientific and Technical Information of China (English)

    YANG Xiaowei; LIN Daying; HAO Zhifeng; LIANG Yanchun; LIU Guirong; HAN Xu

    2003-01-01

    At present, studies on training algorithms for support vector machines (SVM) are important issues in the field of machine learning. It is a challenging task to improve the efficiency of the algorithm without reducing the generalization performance of SVM. To face this challenge, a new SVM training algorithm based on the set segmentation and k-means clustering is presented in this paper. The new idea is to divide all the original training data into many subsets, followed by clustering each subset using k-means clustering and finally train SVM using the new data set obtained from clustering centroids. Considering that the decomposition algorithm such as SVMlight is one of the major methods for solving support vector machines, the SVMlight is used in our experiments. Simulations on different types of problems show that the proposed method can solve efficiently not only large linear classification problems but also large nonlinear ones.

  3. 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.

  4. Debris Flow Hazard Assessment Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    YUAN Lifeng; ZHANG Youshui

    2006-01-01

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

  5. 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.

  6. Osteoporosis Recognition Based on Similarity Metric with SVM

    Directory of Open Access Journals (Sweden)

    Ke Zhou

    2016-06-01

    Full Text Available The purpose: Applying different techniques of classification to osteoporotic bone tissue texture analysis, exploring the recognition rate of the different classification methods. Methods: Using gray-level co-occurrence matrix (GLCM and running a length matrix texture analysis to extract bone tissue slice image characteristic parameters, and to classify respectively 4x and 10x microscope images of the two groups: the sham (SHAM and the ovariectomized (OVX group image. Results: The metric support vector machine (SVM classification algorithm, based on SVM learning or recognition rate, was higher than the stand-alone measure, and the classification results were stable. Conclusion: Measurement of the SVM classification algorithm for osteoporotic bone slices texture analysis revealed a high recognition rate.

  7. 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.

  8. A Financial Risk Premonition Model Based on the Hybrid Orthogonal Genetic Algorithm for Global Optimization and Support Vector Machine%混合HOGA-SVM财务风险预警模型实证研究

    Institute of Scientific and Technical Information of China (English)

    丁德臣

    2011-01-01

    目前涉及遗传算法与支持向量机相结合的预测模型中,遗传算法基本上采用的是标准算法.但是在对全局函数的优化中,一般的遗传算法容易陷入局部最优,从而降低遗传算法收敛速度和搜索精度,进而影响财务风险预警模型的精度与速度.基于此,提出了基于混合全局优化正交遗传算法(HOGA)和支持向量机(SVM)的财务风险预警模型(HOGA-SVM),通过使用混合全局优化正交遗传算法连同支持向量机来改进支持向量机进行财务风险预警的效果.结果显示,提出的模型不仅提高了财务风险预警的准确率和速度,而且模型的两类分类错误率(尤其是第一类分类错误率)相对其他模型也有了明显下降.未来的工作可以把模型的应用扩大到多分类的财务风险预警问题中.%Financial risk premonition can exert a significant influence on a company's survival and growth. Many financial risk premonition models generally fall into two categories: the traditional statistics model and the Al model. A hybrid model that incorporates hybrid genetic algorithms and support vector machines ( SVM ) is becoming an important financial risk promotion model.The simple genetic algorithm is frequently used in the hybrid financial risk premonition model. However, convergence rate has a weak performance in the optimization of multi-objective functions. A financial risk premonition model based on the hybrid orthogonal genetic algorithm for global optimization and support vector machine (HOGA-SVM) is proposed to improve the effect of financial risk premonition.HOGA is used to optimize both subset features and SVM parameters simultaneously. The optimization process includes five steps" ( 1 ) encode subset features and SVM parameters. The chromosomes of SVM parameters are encoded as a 16-bit string that consists of 8 bits standing for C and the other 8 bits standing for. The chromosomes of subset features were further encoded as

  9. 基于改进萤火虫算法的 SVM 核参数选取%SVM KERNEL PARAMETER SELECTION BASED ON IMPROVED GSO

    Institute of Scientific and Technical Information of China (English)

    杨海; 丁毅; 沈海斌

    2015-01-01

    支持向量机(SVM)是一种性能优异的机器学习算法,其核函数参数的选取对于建模精度以及泛化能力有着重要的影响。提出一种基于改进萤火虫算法的 SVM核函数参数选取方法,通过改进萤火虫位置更新公式并在移动过程中引入亮度特征从而确定最佳的 SVM核函数参数。实验表明,该算法选取的 SVM核函数参数在保证分类器收敛性能的同时,提高了分类精度,取得了良好的优化效果。%Support vector machine (SVM)is a machine learning algorithm with superior performance,the selection of its kernel function parameters greatly affects the modelling accuracy and generalisation ability.This paper proposes an SVMkernel function parameter selection method,it is based on the improved glowworm swarm optimisation (GSO).By improving the glowworm position update formula and introducing brightness feature to the process of moving,the method determines the optimal parameters of SVM kernel function.Experiment shows that the SVM kernel function parameters selected by the method improves classification accuracy while guaranteeing the convergence performance of the classifier,and thus achieves good optimisation effect.

  10. Research into a Feature Selection Method for Hyperspectral Imagery Using PSO and SVM

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images.We propose, a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM).Global optimal search performance of PSO is improved by using a chaotic optimization search technique.Granularity based grid search strategy is used to optimize the SVM model parameters.Parameter optimization and classification of the SVM are addressed using the training date corresponding to the feature subset.A false classification rate is adopted as a fitness function.Tests of feature selection and classification are carried out on a hyperspectral data set.Classification performances are also compared among different feature extraction methods commonly used today.Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands.A feasible approach is provided for feature selection and classification of hyperspectral image data.

  11. SVM Model for Identifying Building Damage Induced by Underground Mining%地采诱发建筑物损伤识别的SVM分析模型

    Institute of Scientific and Technical Information of China (English)

    冯东梅; 关秋燕; 邵良杉

    2015-01-01

    针对地采诱发建筑物损害预测中指标与建筑物损害的关系不确定性问题,综合应用相关分析法、鱼骨图理论及SVM原理构建地采诱发建筑物损害的分析模型。采用相关分析法及原因型鱼骨图模型分析指标与建筑物损害的关联度,计算各因素权重,用建筑物损害观测数据对指标加权的SVM模型进行训练和测试,测试结果良好。研究结果表明:鱼骨图模型可获得指标与建筑物损害的关系,量化输入指标的重要性;建筑物本身条件中与空区位置、建筑物状况的重要性明显高于其他指标,加大建筑物本身建设,可较好地改善建筑物抗损害能力;基于鱼骨图的SVM分析模型可以更好地考虑各指标对建筑物损害的综合影响,回估误判率较低。%In view of the uncertain relationships among indexes and buildings damage in mining-induced damage predic-ting,and combining with the characteristics of correlation analysis,fishbone diagram,and SVM,the analysis model for mining-induced buildings damage was established. Correlation analysis theory and fishbone diagram were used to analyze the correla-tions,and calculate the indexes′weights. The SVM model were trained and tested by a series of data from observations of min-ing-induced and damage degree of buildings,and the test results was good. The results show that the fishbone diagram can ob-tain the relationships between indexes and buildings′damage,and the importance of the quantified index;The building′s own conditions including its position in Gob and building conditions are more important than others obviously,and increasing the construction of building foundation can well improve the destruction resistance of buildings;SVM model based on fishbone dia-gram can better consider the effect of each index on damage degree induced by mining-induced,and get a lower ratio of mis-discrimination.

  12. 基于ARIMA-SVM组合模型的光功率趋势预测新方法%A New Method of Optical Power Trend Forecasting Using ARIMA-SVM Combination Model

    Institute of Scientific and Technical Information of China (English)

    王林; 郭健; 刘静; 李思洋

    2015-01-01

    针对电力通信系统光纤线路未来状态趋势预测问题,提出一种基于自回归移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)-支持向量机(Support Vector Machine,SVM)组合模型的光功率趋势预测法.根据光功率数据的非线性和时变性特点,首先利用小波变换将其分解并重构,然后设计一种基于混合核函数的SVM预测模型,对重构后的数据分别构建ARIMA模型与基于混合核函数的SVM模型并预测,最后组合2种模型的预测结果实现光功率趋势预测.分别采用ARIMA模型、径向基函数(Radical Basis Function,RBF)模型、SVM模型、ARIMA-RBF模型与该模型进行实验并对比,结果表明,基于混合核函数的ARIMA-SVM组合模型对光功率趋势预测最为准确,其预测精度及计算速度都有了显著提高,为未来光功率趋势发展提供了一种有效可行的预测新方法.

  13. Monitoring Vibration of A Model of Rotating Machine

    Directory of Open Access Journals (Sweden)

    Arko Djajadi

    2012-03-01

    Full Text Available Mechanical movement or motion of a rotating machine normally causes additional vibration. A vibration sensing device must be added to constantly monitor vibration level of the system having a rotating machine, since the vibration frequency and amplitude cannot be measured quantitatively by only sight or touch. If the vibration signals from the machine have a lot of noise, there are possibilities that the rotating machine has defects that can lead to failure. In this experimental research project, a vibration structure is constructed in a scaled model to simulate vibration and to monitor system performance in term of vibration level in case of rotation with balanced and unbalanced condition. In this scaled model, the output signal of the vibration sensor is processed in a microcontroller and then transferred to a computer via a serial communication medium, and plotted on the screen with data plotter software developed using C language. The signal waveform of the vibration is displayed to allow further analysis of the vibration. Vibration level monitor can be set in the microcontroller to allow shutdown of the rotating machine in case of excessive vibration to protect the rotating machine from further damage. Experiment results show the agreement with theory that unbalance condition on a rotating machine can lead to larger vibration amplitude compared to balance condition. Adding and reducing the mass for balancing can be performed to obtain lower vibration level. 

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

    OpenAIRE

    Kim, Sunghwan

    2016-01-01

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

  15. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  16. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

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

    Institute of Scientific and Technical Information of China (English)

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

    2004-01-01

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

  18. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

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

    Directory of Open Access Journals (Sweden)

    V. Malathi

    2007-01-01

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

  20. X: A Comprehensive Analytic Model for Parallel Machines

    Energy Technology Data Exchange (ETDEWEB)

    Li, Ang; Song, Shuaiwen; Brugel, Eric; Kumar, Akash; Chavarría-Miranda, Daniel; Corporaal, Henk

    2016-05-23

    To continuously comply with Moore’s Law, modern parallel machines become increasingly complex. Effectively tuning application performance for these machines therefore becomes a daunting task. Moreover, identifying performance bottlenecks at application and architecture level, as well as evaluating various optimization strategies, are becoming extremely difficult when the entanglement of numerous correlated factors is being presented. To tackle these challenges, we present a visual analytical model named “X”. It is intuitive and sufficiently flexible to track all the typical features of a parallel machine.

  1. Testing and Modeling of Mechanical Characteristics of Resistance Welding Machines

    DEFF Research Database (Denmark)

    Wu, Pei; Zhang, Wenqi; Bay, Niels;

    2003-01-01

    The dynamic mechanical response of resistance welding machine is very important to the weld quality in resistance welding especially in projection welding when collapse or deformation of work piece occurs. It is mainly governed by the mechanical parameters of machine. In this paper, a mathematical...... for both upper and lower electrode systems. This has laid a foundation for modeling the welding process and selecting the welding parameters considering the machine factors. The method is straightforward and easy to be applied in industry since the whole procedure is based on tests with no requirements...

  2. An Access Control Model of Virtual Machine Security

    Directory of Open Access Journals (Sweden)

    QIN Zhong-yuan

    2013-07-01

    Full Text Available Virtualization technology becomes a hot IT technolo gy with the popu-larity of Cloud Computing. However, new security issues arise with it. Specifically, the resources sharing and data communication in virtual machines are most con cerned. In this paper an access control model is proposed which combines the Chinese Wall a nd BLP model. BLP multi-level security model is introduced with corresponding improvement based on PCW (Prioritized Chinese Wall security model. This model can be used to safely co ntrol the resources and event behaviors in virtual machines. Experimental results show its eff ectiveness and safety.

  3. Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting

    Institute of Scientific and Technical Information of China (English)

    Rong Li; Hua-Ning Wang; Han He; Yan Mei; Zhan-Le Du

    2007-01-01

    A method combining the support vector machine(SVM)the K-Nearest Neighbors (KNN),labelled the SVM-KNN method,is used to construct a solar flare forecasting model.Based on a proven relationship between SVM and KNN.the SVM-KNN method improves the SVM algorithm of classification by taking advantage of the KNN algorithm according to the distribution of test samples in a feature space.In our flare forecast study.sunspots and 10cm radio flux data observe during Solar Cycle 23 are taken as predictors,and whether an M class flare will occur for each active region within two days will be predicted.The SVMKNN method is compared with the SVM and Neural networks-based method.The test results indicate that the rate of correct predictions from the SVM-KNN method is higher than that from the other two methods.This method shows promise as a practicable future forecasting model.

  4. A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar

    Directory of Open Access Journals (Sweden)

    Nicoletta Roberto

    2017-07-01

    Full Text Available An algorithm based on a support vector machine (SVM is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.

  5. 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。

  6. A SVM-GARCH Model for Stock Price Forecasting Based on Neighborhood Mutual Information%基于近邻互信息的SVM-GARCH股票价格预测模型研究

    Institute of Scientific and Technical Information of China (English)

    张贵生; 张信东

    2016-01-01

    为了克服传统线性模型分析处理收益率数据非线性因素的不足,本文提出一种新的基于近邻互信息特征选择的SVM-GARCH预测模型.该模型利用SVM处理高维非线性数据的优势,不仅包含了股指序列自身的历史数据信息,而且通过近邻互信息的方式融合了与目标股指数据关系密切的周边证券市场的相关变化信息.仿真实验结果表明,该模型在时序数据除噪、趋势判别以及预测的精确度等方面均优于传统的ARMA-GARCH模型.

  7. [Integration of soft and hard classifications using linear spectral mixture model and support vector machines].

    Science.gov (United States)

    Hu, Tan-Gao; Pan, Yao-Zhong; Zhang, Jin-Shui; Li, Ling-Ling; Le, Li

    2011-02-01

    This paper presents a new soft and hard classification. By analyzing the target objects in the image distribution, and calculating the adaptive threshold automatically, the image is divided into three regions: pure regions, non-target objects regions and mixed regions. For pure regions and non-target objects regions, hard classification method (support vector machine) is used to quickly extract classified results; For mixed regions, soft classification method (selective endmember for linear spectral mixture model) is used to extract the abundance of target objects. Finally, it generates an integrated soft and hard classification map. In order to evaluate the accuracy of this new method, it is compared with SVM and LSMM using ALOS image. The RMSE value of new method is 0.203, and total accuracy is 95.48%. Both overall accuracies and RMSE show that integration of hard and soft classification has a higher accuracy than single hard or soft classification. Experimental results prove that the new method can effectively solve the problem of mixed pixels, and can obviously improve image classification accuracy.

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

    Directory of Open Access Journals (Sweden)

    Ji-Long Liu

    2015-03-01

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

  9. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  10. SELF-BLM: Prediction of drug-target interactions via self-training SVM

    Science.gov (United States)

    Keum, Jongsoo; Nam, Hojung

    2017-01-01

    Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM. PMID:28192537

  11. Modeling powder encapsulation in dosator-based machines: I. Theory.

    Science.gov (United States)

    Khawam, Ammar

    2011-12-15

    Automatic encapsulation machines have two dosing principles: dosing disc and dosator. Dosator-based machines compress the powder to plugs that are transferred into capsules. The encapsulation process in dosator-based capsule machines was modeled in this work. A model was proposed to predict the weight and length of produced plugs. According to the model, the plug weight is a function of piston dimensions, powder-bed height, bulk powder density and precompression densification inside dosator while plug length is a function of piston height, set piston displacement, spring stiffness and powder compressibility. Powder densification within the dosator can be achieved by precompression, compression or both. Precompression densification depends on the powder to piston height ratio while compression densification depends on piston displacement against powder. This article provides the theoretical basis of the encapsulation model, including applications and limitations. The model will be applied to experimental data separately.

  12. Virtual Sensor for Calibration of Thermal Models of Machine Tools

    Directory of Open Access Journals (Sweden)

    Alexander Dementjev

    2014-01-01

    strictly depends on the accuracy of these machines, but they are prone to deformation caused by their own heat. The deformation needs to be compensated in order to assure accurate production. So an adequate model of the high-dimensional thermal deformation process must be created and parameters of this model must be evaluated. Unfortunately, such parameters are often unknown and cannot be calculated a priori. Parameter identification during real experiments is not an option for these models because of its high engineering and machine time effort. The installation of additional sensors to measure these parameters directly is uneconomical. Instead, an effective calibration of thermal models can be reached by combining real and virtual measurements on a machine tool during its real operation, without additional sensors installation. In this paper, a new approach for thermal model calibration is presented. The expected results are very promising and can be recommended as an effective solution for this class of problems.

  13. 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.

  14. [Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

    Science.gov (United States)

    Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

    2016-03-01

    Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

  15. Committee of machine learning predictors of hydrological models uncertainty

    Science.gov (United States)

    Kayastha, Nagendra; Solomatine, Dimitri

    2014-05-01

    In prediction of uncertainty based on machine learning methods, the results of various sampling schemes namely, Monte Carlo sampling (MCS), generalized likelihood uncertainty estimation (GLUE), Markov chain Monte Carlo (MCMC), shuffled complex evolution metropolis algorithm (SCEMUA), differential evolution adaptive metropolis (DREAM), particle swarm optimization (PSO) and adaptive cluster covering (ACCO)[1] used to build a predictive models. These models predict the uncertainty (quantiles of pdf) of a deterministic output from hydrological model [2]. Inputs to these models are the specially identified representative variables (past events precipitation and flows). The trained machine learning models are then employed to predict the model output uncertainty which is specific for the new input data. For each sampling scheme three machine learning methods namely, artificial neural networks, model tree, locally weighted regression are applied to predict output uncertainties. The problem here is that different sampling algorithms result in different data sets used to train different machine learning models which leads to several models (21 predictive uncertainty models). There is no clear evidence which model is the best since there is no basis for comparison. A solution could be to form a committee of all models and to sue a dynamic averaging scheme to generate the final output [3]. This approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model HBV in the Nzoia catchment in Kenya. [1] N. Kayastha, D. L. Shrestha and D. P. Solomatine. Experiments with several methods of parameter uncertainty estimation in hydrological modeling. Proc. 9th Intern. Conf. on Hydroinformatics, Tianjin, China, September 2010. [2] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press

  16. Twin support vector machines models, extensions and applications

    CERN Document Server

    Jayadeva; Chandra, Suresh

    2017-01-01

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

  17. Reconfiguration-based implementation of SVM classifier on FPGA for Classifying Microarray data.

    Science.gov (United States)

    Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin

    2013-01-01

    Classifying Microarray data, which are of high dimensional nature, requires high computational power. Support Vector Machines-based classifier (SVM) is among the most common and successful classifiers used in the analysis of Microarray data but also requires high computational power due to its complex mathematical architecture. Implementing SVM on hardware exploits the parallelism available within the algorithm kernels to accelerate the classification of Microarray data. In this work, a flexible, dynamically and partially reconfigurable implementation of the SVM classifier on Field Programmable Gate Array (FPGA) is presented. The SVM architecture achieved up to 85× speed-up over equivalent general purpose processor (GPP) showing the capability of FPGAs in enhancing the performance of SVM-based analysis of Microarray data as well as future bioinformatics applications.

  18. A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

    Directory of Open Access Journals (Sweden)

    Xiuzhen Guo

    2015-06-01

    Full Text Available In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC has been proposed to analyze signals of an electronic nose (E-nose used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO algorithm is implemented in conjunction with support vector machine (SVM for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

  19. A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance.

    Science.gov (United States)

    Guo, Xiuzhen; Peng, Chao; Zhang, Songlin; Yan, Jia; Duan, Shukai; Wang, Lidan; Jia, Pengfei; Tian, Fengchun

    2015-06-29

    In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

  20. A study on prediction of market tendency on the shanghai stock index based on GA-SVM method%GA-SVM对上证综指走势的预测研究

    Institute of Scientific and Technical Information of China (English)

    张伟; 李泓仪; 兰书梅; 张洁

    2012-01-01

    将支持向量机和遗传算法结合,建立了一种智能数据挖掘技术(GA-SVM),并用于对上证综指市场走势进行了探索.在这个混合的数据挖掘方法中,GA用于RBF参数的设定以及特征集的选择,从而智能的找到SVM的最佳参数,减少SVM特征值的复杂度,提高了SVM算法速度.SVM用于判断未来股票市场的走势,并与统计模型、时间序列模型方法、神经网络进行了对比.实验证明,GA-SVM优于其他几种方法,这种方法对于股票上涨或下跌的预测研究是有效的.%Support vector machine is an effective data mining technology for limited sample data,genetic algorithm is an excellent tool for global optimization. In this study,a hybrid data mining model which combine support vector machine with genetic algorithm (GA-SVM) is proposed to the prediction of market tendency on the shanghai stock index. In this hybrid data mining approach,GA is used to select the RBF parameters and the features, so that to find the best parameters of SVM. That can reduce model complexity of SVM and improve the speed of SVM;SVM is used to judge the future movement direction of the stock market based on the use of historical data. To validate GA-SVM method, we compared its performance with that of other methods (such as statistical method,time series method and neural network method). The experimental results show that GA-SVM is superior to other methods,implying that the GA-SVM approach is a promising alternative to stock market tendency prediction.

  1. Model for performance prediction in multi-axis machining

    CERN Document Server

    Lavernhe, Sylvain; Lartigue, Claire; 10.1007/s00170-007-1001-4

    2009-01-01

    This paper deals with a predictive model of kinematical performance in 5-axis milling within the context of High Speed Machining. Indeed, 5-axis high speed milling makes it possible to improve quality and productivity thanks to the degrees of freedom brought by the tool axis orientation. The tool axis orientation can be set efficiently in terms of productivity by considering kinematical constraints resulting from the set machine-tool/NC unit. Capacities of each axis as well as some NC unit functions can be expressed as limiting constraints. The proposed model relies on each axis displacement in the joint space of the machine-tool and predicts the most limiting axis for each trajectory segment. Thus, the calculation of the tool feedrate can be performed highlighting zones for which the programmed feedrate is not reached. This constitutes an indicator for trajectory optimization. The efficiency of the model is illustrated through examples. Finally, the model could be used for optimizing process planning.

  2. Assessing Implicit Knowledge in BIM Models with Machine Learning

    DEFF Research Database (Denmark)

    Krijnen, Thomas; Tamke, Martin

    2015-01-01

    architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models.......The promise, which comes along with Building Information Models, is that they are information rich, machine readable and represent the insights of multiple building disciplines within single or linked models. However, this knowledge has to be stated explicitly in order to be understood. Trained...

  3. Parameter optimization model in electrical discharge machining process

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.

  4. Multi products single machine EPQ model with immediate rework process

    Directory of Open Access Journals (Sweden)

    Jahangir Biabani

    2012-01-01

    Full Text Available This paper develops an economic production quantity (EPQ inventory model with rework process for a single stage production system with one machine. The existence of a unique machine results in limited production capacity. The aim of this research is to determine both the optimal cycle length and the optimal production quantity for each product to minimize the expected total cost (holding, production, setup, rework costs. The convexity of the inventory model is derived. Also the objective function is proved to be convex. The proposed inventory model is validated with illustrating numerical examples and the optimal period length and the total system cost are analyzed.

  5. Assessing Implicit Knowledge in BIM Models with Machine Learning

    DEFF Research Database (Denmark)

    Krijnen, Thomas; Tamke, Martin

    2015-01-01

    architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models.......The promise, which comes along with Building Information Models, is that they are information rich, machine readable and represent the insights of multiple building disciplines within single or linked models. However, this knowledge has to be stated explicitly in order to be understood. Trained...

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

    Institute of Scientific and Technical Information of China (English)

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

    2009-01-01

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

  7. Boosting Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Elkin Eduardo García Díaz

    2006-11-01

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

  8. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  9. Runoff Forecasting Based on Fruit Fly Optimization Algorithm and SVM Algorithm%基于果蝇优化算法的支持向量机径流预测

    Institute of Scientific and Technical Information of China (English)

    吴琼; 陈志军

    2015-01-01

    为了提高径流预测的精度和可靠性,将支持向量机应用到单因子月径流预测建模中。针对支持向量机模型参数的选择费时费力且效果差的问题,利用全局寻优的果蝇算法优化选择支持向量机的惩罚参数和核参数,提出了基于果蝇算法优化支持向量机参数的 FOA - SVM预测模型,并利用新疆某站的月径流历史数据进行了仿真测试。结果表明:与GA - SVM模型和 PSO - SVM模型相比,FOA - SVM模型能够提高径流预测的效率与精度。%In order to improve the accuracy and reliability of the runoff prediction,support vector machine was applied to a single factor to predict monthly runoff modeling. Support vector machine model parameter selection is time-consuming and the effect is poor,using fruit fly optimization algorithm to optimizate and select SVM punishment parameters and nuclear parameters,a SVM prediction model based on fruit fly optimization algorithm(FOA-SVM)was proposed. Taking historical data of a station in Xinjiang to simulate,the results show that compared with the GA-SVM model and the PSO-SVM model,FOA-SVM model can improve the efficiency and accuracy of runoff prediction.

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

    Science.gov (United States)

    Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

    2012-01-01

    A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

  11. 基于 RU-SMOTE-SVM 的金融市场极端风险预警研究%Research on Extreme Risk Warning for Financial Market Based on RU-SMOTE-SVM

    Institute of Scientific and Technical Information of China (English)

    林宇; 黄迅; 徐凯

    2013-01-01

      本文以上证综指和深证成指为研究对象,将随机欠采样(RU)、合成少数类过采样(SMOTE)与传统支持向量机(SVM)相结合,提出了一种改进的 SVM 模型---RU-SMOTE-SVM 模型来预测我国金融市场极端风险,并与传统 SVM、SMOTE-SVM、RU-SMOTE-NN 和 RU-SMOTE-DT 进行比较。实证结果表明,RU-SMOTE-SVM 既优于传统 SVM 模型,又比 SMOTE-SVM 具有更高的预测精度,同时还展示出比 RU-SMOTE-NN 和 RU-SMOTE-DT 更为优越的预测性能。%Taking the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index as the objects of research, this paper combines Random Under-Sampling(RU), Synthetic Minority Over-Sampling Technique (SMOTE) with Support Vector Machine ( SVM) to establish an improvement SVM---RU-SMOTE-SVM, which is applied to predict the extreme risk in Chinese financial market and compared with conventional SVM, SMOTE-SVM, RU-SMOTE-NN and RU-SMOTE-DT. The result of investigation illustrates that RU-SMOTE-SVM not only outperforms conventional SVM, but also has a higher predictive accuracy than SMOTE-SVM, simultaneously, has a more excellent predictive performance than RU-SMOTE-NN and RU-SMOTE-DT.

  12. Simulink Implementation of Indirect Vector Control of Induction Machine Model

    Directory of Open Access Journals (Sweden)

    V. Dhanunjayanaidu

    2014-04-01

    Full Text Available In this paper, a modular Simulink implementation of an induction machine model is described in a step-by-step approach. With the modular system, each block solves one of the model equations; therefore, unlike in black box models, all of the machine parameters are accessible for control and verification purposes.After the implementation, examples are given with the model used in different drive applications, such as open-loop constant V/Hz control and indirect vector control. To implement the induction machine model, the dynamic equivalent circuit of induction motor is taken and the model equations in flux linkage form are derived.Then the model is implemented in Simulink by transforming three phase voltages to d-q frame and the d-q currents back to three phase, also it includes unit vector calculation and induction machine d-q model.The inputs here are three phase voltages, load torque, speed of stator and the outputs are flux linkages and currents, electrical torque and speed of rotor.

  13. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  14. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

    Science.gov (United States)

    Xu, Chong; Dai, Fuchu; Xu, Xiwei; Lee, Yuan Hsi

    2012-04-01

    Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with

  15. 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)

  16. Incremental Learning with SVM for Multimodal Classification of Prostatic Adenocarcinoma

    OpenAIRE

    José Fernando García Molina; Lei Zheng; Metin Sertdemir; Dietmar J Dinter; Stefan Schönberg; Matthias Rädle

    2014-01-01

    Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correcti...

  17. Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [{sup 123}I]FP-CIT SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Huertas-Fernandez, I.; Benitez-Rivero, S.; Jesus, S.; Caceres-Redondo, M.T.; Martin-Rodriguez, J.F.; Carrillo, F. [Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla, Unidad de Trastornos del Movimiento, Servicio de Neurologia y Neurofisiologia Clinica, Seville (Spain); Garcia-Gomez, F.J.; Marin-Oyaga, V.A.; Lojo, J.A. [Hospital Universitario Virgen del Rocio, Servicio de Medicina Nuclear, UDIM, Seville (Spain); Garcia-Solis, D. [Hospital Universitario Virgen del Rocio, Servicio de Medicina Nuclear, UDIM, Seville (Spain); Centro de Investigacion Biomedica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Seville (Spain); Mir, P. [Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla, Unidad de Trastornos del Movimiento, Servicio de Neurologia y Neurofisiologia Clinica, Seville (Spain); Centro de Investigacion Biomedica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Seville (Spain)

    2015-01-15

    The study's objective was to develop diagnostic predictive models using data from two commonly used [{sup 123}I]FP-CIT SPECT assessment methods: region-of-interest (ROI) analysis and whole-brain voxel-based analysis. We included retrospectively 80 patients with vascular parkinsonism (VP) and 164 patients with Parkinson's disease (PD) who underwent [{sup 123}I]FP-CIT SPECT. Nuclear-medicine specialists evaluated the scans and calculated bilateral caudate and putamen [{sup 123}I]FP-CIT uptake and asymmetry indices using BRASS software. Statistical parametric mapping (SPM) was used to compare the radioligand uptake between the two diseases at the voxel level. Quantitative data from these two methods, together with potential confounding factors for dopamine transporter availability (sex, age, disease duration and severity), were used to build predictive models following a tenfold cross-validation scheme. The performance of logistic regression (LR), linear discriminant analysis and support vector machine (SVM) algorithms for ROI data, and their penalized versions for SPM data (penalized LR, penalized discriminant analysis and SVM), were assessed. Significant differences were found in the ROI analysis after covariate correction between VP and PD patients in [{sup 123}I]FP-CIT uptake in the more affected side of the putamen and the ipsilateral caudate. Age, disease duration and severity were also found to be informative in feeding the statistical model. SPM localized significant reductions in [{sup 123}I]FP-CIT uptake in PD with respect to VP in two specular clusters comprising areas corresponding to the left and right striatum. The diagnostic predictive accuracy of the LR model using ROI data was 90.3 % and of the SVM model using SPM data was 90.4 %. The predictive models built with ROI data and SPM data from [{sup 123}I]FP-CIT SPECT provide great discrimination accuracy between VP and PD. External validation of these methods is necessary to confirm their

  18. Linguistically motivated statistical machine translation models and algorithms

    CERN Document Server

    Xiong, Deyi

    2015-01-01

    This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-06-15

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-06-15

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

  1. 基于VQ-MAP和SVM融合的说话人识别系统%Speaker recognition system based on VQ-MAP and SVM

    Institute of Scientific and Technical Information of China (English)

    展领; 景新幸

    2011-01-01

    The traditional Support Vector Machine(SVM) in speaker recognition has high computational complexity. To solve this problem,this paper proposes a kind of speaker recognition system based on VQ-MAP and SVM which formulates Maximum A Posteriori Vector Quantization(VQ-MAP) procedure that adapts the mean vectors only. The result is the adapted speaker model and the parameter vectors of this model are used as the support vectors of SVM for speaker recognition.According to the results of simulation using Matlab,speaker recognition system based on VQ-MAP and SVM has significantly reduced computational complexity and the training time of SVM is short and it also has high recognition rate.%针对传统支持向量机(SVM)在说话人识别中运算量过大的问题,提出了VQ-MAP和SVM融合的说话人识别系统.它应用仅自适应均值向量的最大后验概率矢量量化过程(VQ-MAP),来得到自适应的说话人模型,用此模型中的参数向量作为支持向量应用于SVM来进行说话人识别.用Matlab进行仿真实验,结果表明,基于VQ-MAP和SVM融合的说话人识别系统大大降低了运算量,SVM训练时间短,且具有较高的识别率.

  2. Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2015-01-01

    Full Text Available A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.

  3. Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA.

    Science.gov (United States)

    Jiang, Huiyan; Zhao, Di; Zheng, Ruiping; Ma, Xiaoqi

    2015-01-01

    A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.

  4. Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

    Science.gov (United States)

    Wang, Xianfang; Du, Haoze; Tan, Jinglu

    2016-12-01

    Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

  5. Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

    Science.gov (United States)

    Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

    2015-01-01

    Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

  6. COMBINING FEATURE SCALING ESTIMATION WITH SVM CLASSIFIER DESIGN USING GA APPROACH

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients of various features to the classification task, measured by real-valued scaling, are estimated efficiently by using GA. And GA exploits heavy-bias operator to promote sparsity in the scaling of features. There are many potential benefits of this method:Feature selection is performed by eliminating irrelevant features whose scaling is zero, an SVM classifier that has enhanced generalization ability can be learned simultaneously. Experimental comparisons using original SVM and GA-SVM demonstrate both economical feature selection and excellent classification accuracy on junk e-mail recognition problem and Internet ad recognition problem. The experimental results show that comparing with original SVM classifier, the number of support vector decreases significantly and better classification results are achieved based on GA-SVM. It also demonstrates that GA can provide a simple, general, and powerful framework for tuning parameters in optimal problem, which directly improves the recognition performance and recognition rate of SVM.

  7. Efficient and Privacy-Preserving Online Medical Pre-Diagnosis Framework Using Nonlinear SVM.

    Science.gov (United States)

    Zhu, Hui; Liu, Xiaoxia; Lu, Rongxing; Li, Hui

    2016-03-29

    With the advances of machine learning algorithms and the pervasiveness of network terminals, online medical prediagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical pre-diagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an efficient and privacy-preserving online medical pre-diagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM). With eDiag, the sensitive personal health information can be processed without privacy disclosure during online prediagnosis service. Specifically, based on an improved expression for the nonlinear SVM, an efficient and privacy-preserving classification scheme is introduced with lightweight multi-party random masking and polynomial aggregation techniques. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that eDiag can ensure that users' health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing eDiag on smartphone and computer demonstrate eDiag's effectiveness in term of real online environment.

  8. WEB-BASED VIRTUAL CNC MACHINE MODELING AND OPERATION

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A CNC simulation system based on internet for operation training of manufacturing facility and manufacturing process simulation is proposed. Firstly, the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software. Then, a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language(VRML) by attaining communication among VRML, JavaApplet, JavaScript and Html so as to realize the virtual operation for CNC machine tool. Moreover, the algorithm of material removed simulation based on VRML Z-map is presented. The advantages of this algorithm include less memory requirement and much higher computation. Lastly, the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.

  9. Quantification of the chemical composition of lunar soil in terms of its reflectance spectra by PCA and SVM

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiaoyu; LI Chunlai; LU Chang

    2009-01-01

    In the second phase of the Chang'E Program an unmanned lunar rover will be launched onto the Moon. When ground scientists get a full understanding of the chemical composition of lunar soil around the rover, they can make more detailed survey plans for the rover and various payloads onboard so as to satisfy their scientific objectives. There is an obvious relationship between the reflectance of lunar soil and its chemical characteristics. Both principal component analysis (PCA) and support vector machine (SVM) models were applied to establishing the relationship between the reflectance spectra and chemical compositions of lunar highland and mare soil samples sent back by Apollo missions 11, 12, 14, 15, 16 and 17 and measured by Lunar Soil Characterization Consortium (LSCC). PCA was used to reduce and select the features of the reflectance spectra of lunar soil samples. Then, these features were put into SVM to estimate the abundances of various chemical components in lunar soil. We also compared the results of our measurement with those obtained by the SVM model [partial least squares (PLS)] and the principal component regression (PCR) model reported in literature. Our studies showed that with the exception of TiO2, the results of prediction of the abundances of chemical compounds in lunar soil by our model are much more reliable than those reported in literature. The reflectance spectra of lunar soil are closely related to the materials from which it was derived.

  10. 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.

  11. 基于梯形模型和支撑向量机的非结构化道路检测%Unstructured-lane detection based on trapezoidal model and SVM

    Institute of Scientific and Technical Information of China (English)

    张玉颖; 顾晓东; 汪源源

    2010-01-01

    在H.Jeong的梯形模型的基础上,提出了基于梯形模型和支撑向量机-SVM(Support Vector Machine)的道路检测算法.算法先对视频中提取的图像帧进行预处理,然后采用Kalman滤波及EM算法进行处理,接着用SVM得到道路检测结果,并进行滤波处理得到最终的检测结果.由于算法采用了比BP(Back Propagation)网络具有更好的分类识别效果的SVM,所以比采用BP网络的H.Jeong等人提出的模型具有更好的检测效果.该算法在预处理部分采用脉冲耦合神经网络即(PCNN-Pulse Coupled Neural Network)消除道路上的阴影,减少了光照变化对最终检测结果的不利影响.实验表明,与H.Jeong的梯形及BP算法相比,道路的检测效果更好.

  12. Optimal Model-Based Fault Estimation and Correction for Particle Accelerators and Industrial Plants Using Combined Support Vector Machines and First Principles Models

    Energy Technology Data Exchange (ETDEWEB)

    Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX

    2010-08-25

    Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for building parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the

  13. Optimal Model-Based Fault Estimation and Correction for Particle Accelerators and Industrial Plants Using Combined Support Vector Machines and First Principles Models

    Energy Technology Data Exchange (ETDEWEB)

    Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX

    2010-08-25

    Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for building parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the

  14. 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.

  15. [Genetic algorithm based multi-objective least square support vector machine for simultaneous determination of multiple components by near infrared spectroscopy].

    Science.gov (United States)

    Xu, Bing; Wang, Xing; Dhaene, Tom; Shi, Xin-Yuan; Couckuyt, Ivo; Bai, Yan; Qiao, Yan-Jiang

    2014-03-01

    The near infrared (NIR) spectrum contains a global signature of composition, and enables to predict different proper ties of the material. In the present paper, a genetic algorithm and an adaptive modeling technique were applied to build a multiobjective least square support vector machine (MLS-SVM), which was intended to simultaneously determine the concentrations of multiple components by NIR spectroscopy. Both the benchmark corn dataset and self-made Forsythia suspense dataset were used to test the proposed approach. Results show that a genetic algorithm combined with adaptive modeling allows to efficiently search the LS-SVM hyperparameter space. For the corn data, the performance of multi-objective LS-SVM was significantly better than models built with PLS1 and PLS2 algorithms. As for the Forsythia suspense data, the performance of multi-objective LS-SVM was equivalent to PLS1 and PLS2 models. In both datasets, the over-fitting phenomena were observed on RBFNN models. The single objective LS-SVM and MLS-SVM didn't show much difference, but the one-time modeling convenience al lows the potential application of MLS-SVM to multicomponent NIR analysis.

  16. 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.

  17. Model checking abstract state machines with answer set programming

    OpenAIRE

    2006-01-01

    Answer Set Programming (ASP) is a logic programming paradigm that has been shown as a useful tool in various application areas due to its expressive modelling language. These application areas include Bourided Model Checking (BMC). BMC is a verification technique that is recognized for its strong ability of finding errors in computer systems. To apply BMC, a system needs to be modelled in a formal specification language, such as the widely used formalism of Abstract State Machines (ASMs). In ...

  18. Research on Sina Microblogging Marketing Spam Review Detection Based on Support Vector Machine%基于 SVM 的新浪微博营销类水帖识别研究

    Institute of Scientific and Technical Information of China (English)

    叶施仁; 孙宁

    2015-01-01

    Using tremendous robot accounts to follow product twitters,and review the posts about mar-keting contents is a typical spam issue in Sina microblogging.This method could change the existing public opinions about the involved products and form fake hot topics.Based on similar behaviors from a set of ex-isting spam accounts,we attempt to identify these fake posts.Our method will use SVM to classify them according to text,time,clients and multiplicity among them.The test sets consists of several marketing twitters about automotive products using Sina Weibo APIs.The test results show that our method can find those well disguised reviews by spammers.%研究一种发现水帖的分类算法。该方法利用 SimHash 方法将发帖重复当成类似网页去重的问题处理,发帖内容的重复度和其他特征,如发帖的密集型、帐号名称的相似性,所使用的客户端等特征将用于水帖与正常发帖的分类。该文利用新浪微博 API 下载多个汽车营销账号下的交互数据作为实验数据,并使用 SVM 作为分类器。实验结果表明,该方法能够较好地发现那些伪装性非常好的水军所发布的水帖。

  19. Modelling, Construction, and Testing of a Simple HTS Machine Demonstrator

    DEFF Research Database (Denmark)

    Jensen, Bogi Bech; Abrahamsen, Asger Bech

    2011-01-01

    This paper describes the construction, modeling and experimental testing of a high temperature superconducting (HTS) machine prototype employing second generation (2G) coated conductors in the field winding. The prototype is constructed in a simple way, with the purpose of having an inexpensive w...

  20. [Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

    Science.gov (United States)

    Huang, Tao; Li, Xiao-yu; Xu, Meng-ling; Jin, Rui; Ku, Jing; Xu, Sen-miao; Wu, Zhen-zhong

    2015-01-01

    The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM

  1. 基于统计模型及SVM的低速率语音编码QIM隐写检测%Detection of QIM Steganography in Low Bit-Rate Speech Codec Based on Statistical Models and SVM

    Institute of Scientific and Technical Information of China (English)

    李松斌; 黄永峰; 卢记仓

    2013-01-01

    QIM(Quantization Index Modulation,量化索引调制)隐写在标量或矢量量化时嵌入机密信息,可在语音压缩编码过程中进行高隐蔽性的信息隐藏,文中试图对该种隐写进行检测.文中发现该种隐写将导致压缩语音流中的音素分布特性发生改变,提出了音素向量空间模型和音素状态转移模型对音素分布特性进行了量化表示.基于所得量化特征并结合SVM(Support Vector Machine,支持向量机)构建了隐写检测器.针对典型的低速率语音编码标准G.729以及G.723.1的实验表明,文中方法性能远优于现有检测方法,实现了对QIM隐写的快速准确检测.%Quantization Index Modulation (QIM) steganography,which embeds the secret information during the Vector Quantization,can hide information in low bit-rate speech codec with high imperceptibility.This paper tries to detect this type of steganography.For this purpose,starting from the speech generation and compress coding theory,this paper firstly analyzes the possible significant feature degradation through the QIM steganography in compressed audio stream deeply.And it finds that the QIM steganography will disturb the phoneme sequence in the stream,and inevitably make the imbalance and correlation characteristics of phoneme distribution in the sequence change.According to this discovery,this paper adopts the phoneme distribution characteristics as the key for the detection of the QIM steganography.In order to get the quantitative features of phoneme distribution characteristics,this paper designs the Phoneme Vector Space Model and the Phoneme State Transition Model to quantify the imbalance and correlation characteristics respectively.By combining the quantitative vector features with supervised learning classifier,this paper builds a high performance detector towards the QIM steganography in low bit-rate speech codec.The experiments show that,for the two typical low bit-rate speech codec:G.729 and G.723.1,the

  2. Comparison on neural networks and support vector machines in suppliers' selection

    Institute of Scientific and Technical Information of China (English)

    Hu Guosheng; Zhang Guohong

    2008-01-01

    Suppliers' selection in supply chain management (SCM) has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization,and uncontrolled convergence. A relatively new machine learning technique, support vector machine (SVM), which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers' samples are very insufficient. SVMs are adaptive to deal with small samples' training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.

  3. A general thermal model of machine tool spindle

    Directory of Open Access Journals (Sweden)

    Yanfang Dong

    2017-01-01

    Full Text Available As the core component of machine tool, the thermal characteristics of the spindle have a significant influence on machine tool running status. Lack of an accurate model of the spindle system, particularly the model of load–deformation coefficient between the bearing rolling elements and rings, severely limits the thermal error analytic precision of the spindle. In this article, bearing internal loads, especially the function relationships between the principal curvature difference F(ρ and auxiliary parameter nδ, semi-major axis a, and semi-minor axis b, have been determined; furthermore, high-precision heat generation combining the heat sinks in the spindle system is calculated; finally, an accurate thermal model of the spindle was established. Moreover, a conventional spindle with embedded fiber Bragg grating temperature sensors has been developed. By comparing the experiment results with simulation, it indicates that the model has good accuracy, which verifies the reliability of the modeling process.

  4. Analytical model for Stirling cycle machine design

    Energy Technology Data Exchange (ETDEWEB)

    Formosa, F. [Laboratoire SYMME, Universite de Savoie, BP 80439, 74944 Annecy le Vieux Cedex (France); Despesse, G. [Laboratoire Capteurs Actionneurs et Recuperation d' Energie, CEA-LETI-MINATEC, Grenoble (France)

    2010-10-15

    In order to study further the promising free piston Stirling engine architecture, there is a need of an analytical thermodynamic model which could be used in a dynamical analysis for preliminary design. To aim at more realistic values, the models have to take into account the heat losses and irreversibilities on the engine. An analytical model which encompasses the critical flaws of the regenerator and furthermore the heat exchangers effectivenesses has been developed. This model has been validated using the whole range of the experimental data available from the General Motor GPU-3 Stirling engine prototype. The effects of the technological and operating parameters on Stirling engine performance have been investigated. In addition to the regenerator influence, the effect of the cooler effectiveness is underlined. (author)

  5. Analytical model for Stirling cycle machine design

    CERN Document Server

    Formosa, Fabien; 10.1016/j.enconman.2010.02.010

    2013-01-01

    In order to study further the promising free piston Stirling engine architecture, there is a need of an analytical thermodynamic model which could be used in a dynamical analysis for preliminary design. To aim at more realistic values, the models have to take into account the heat losses and irreversibilities on the engine. An analytical model which encompasses the critical flaws of the regenerator and furthermore the heat exchangers effectivenesses has been developed. This model has been validated using the whole range of the experimental data available from the General Motor GPU-3 Stirling engine prototype. The effects of the technological and operating parameters on Stirling engine performance have been investigated. In addition to the regenerator influence, the effect of the cooler effectiveness is underlined.

  6. Thermal-mechanical modeling of laser ablation hybrid machining

    Science.gov (United States)

    Matin, Mohammad Kaiser

    2001-08-01

    Hard, brittle and wear-resistant materials like ceramics pose a problem when being machined using conventional machining processes. Machining ceramics even with a diamond cutting tool is very difficult and costly. Near net-shape processes, like laser evaporation, produce micro-cracks that require extra finishing. Thus it is anticipated that ceramic machining will have to continue to be explored with new-sprung techniques before ceramic materials become commonplace. This numerical investigation results from the numerical simulations of the thermal and mechanical modeling of simultaneous material removal from hard-to-machine materials using both laser ablation and conventional tool cutting utilizing the finite element method. The model is formulated using a two dimensional, planar, computational domain. The process simulation acronymed, LAHM (Laser Ablation Hybrid Machining), uses laser energy for two purposes. The first purpose is to remove the material by ablation. The second purpose is to heat the unremoved material that lies below the ablated material in order to ``soften'' it. The softened material is then simultaneously removed by conventional machining processes. The complete solution determines the temperature distribution and stress contours within the material and tracks the moving boundary that occurs due to material ablation. The temperature distribution is used to determine the distance below the phase change surface where sufficient ``softening'' has occurred, so that a cutting tool may be used to remove additional material. The model incorporated for tracking the ablative surface does not assume an isothermal melt phase (e.g. Stefan problem) for laser ablation. Both surface absorption and volume absorption of laser energy as function of depth have been considered in the models. LAHM, from the thermal and mechanical point of view is a complex machining process involving large deformations at high strain rates, thermal effects of the laser, removal of

  7. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2013-12-01

    Full Text Available The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM, combined with its sparsified version (sparse online LS-OC-SVM. LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

  8. The Hybrid Dynamic Prototype Construction and Parameter Optimization with Genetic Algorithm for Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chun-Liang Lu

    2015-10-01

    Full Text Available The optimized hybrid artificial intelligence model is a potential tool to deal with construction engineering and management problems. Support vector machine (SVM has achieved excellent performance in a wide variety of applications. Nevertheless, how to effectively reduce the training complexity for SVM is still a serious challenge. In this paper, a novel order-independent approach for instance selection, called the dynamic condensed nearest neighbor (DCNN rule, is proposed to adaptively construct prototypes in the training dataset and to reduce the redundant or noisy instances in a classification process for the SVM. Furthermore, a hybrid model based on the genetic algorithm (GA is proposed to simultaneously optimize the prototype construction and the SVM kernel parameters setting to enhance the classification accuracy. Several UCI benchmark datasets are considered to compare the proposed hybrid GA-DCNN-SVM approach with the previously published GA-based method. The experimental results illustrate that the proposed hybrid model outperforms the existing method and effectively improves the classification performance for the SVM.

  9. Applying Machine Trust Models to Forensic Investigations

    Science.gov (United States)

    Wojcik, Marika; Venter, Hein; Eloff, Jan; Olivier, Martin

    Digital forensics involves the identification, preservation, analysis and presentation of electronic evidence for use in legal proceedings. In the presence of contradictory evidence, forensic investigators need a means to determine which evidence can be trusted. This is particularly true in a trust model environment where computerised agents may make trust-based decisions that influence interactions within the system. This paper focuses on the analysis of evidence in trust-based environments and the determination of the degree to which evidence can be trusted. The trust model proposed in this work may be implemented in a tool for conducting trust-based forensic investigations. The model takes into account the trust environment and parameters that influence interactions in a computer network being investigated. Also, it allows for crimes to be reenacted to create more substantial evidentiary proof.

  10. Image Classification Using PSO-SVM and an RGB-D Sensor

    Directory of Open Access Journals (Sweden)

    Carlos López-Franco

    2014-01-01

    Full Text Available Image classification is a process that depends on the descriptor used to represent an object. To create such descriptors we use object models with rich information of the distribution of points. The object model stage is improved with an optimization process by spreading the point that conforms the mesh. In this paper, particle swarm optimization (PSO is used to improve the model generation, while for the classification problem a support vector machine (SVM is used. In order to measure the performance of the proposed method a group of objects from a public RGB-D object data set has been used. Experimental results show that our approach improves the distribution on the feature space of the model, which allows to reduce the number of support vectors obtained in the training process.

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

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, Mehmet Oezguer; Kruecker, Dirk; Melzer-Pellmann, Isabell [DESY, Hamburg (Germany)

    2016-07-01

    In this talk, the use of Support Vector Machines (SVM) is promoted for new-physics searches in high-energy physics. We developed an interface, called SVM HEP Interface (SVM-HINT), for a popular SVM library, LibSVM, and introduced a statistical-significance based hyper-parameter optimization algorithm for the new-physics searches. As example case study, a search for Supersymmetry at the Large Hadron Collider is given to demonstrate the capabilities of SVM using SVM-HINT.

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

    Science.gov (United States)

    An, Ji-Yong; You, Zhu-Hong; Meng, Fan-Rong; Xu, Shu-Juan; Wang, Yin

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-05-11

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

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

    Science.gov (United States)

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

    2016-12-01

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

  15. 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.

  16. Evaluation Model of Green Innovation Capability of Logistics Enterprise Based on SVM%基于支持向量机的物流企业绿色创新能力评价

    Institute of Scientific and Technical Information of China (English)

    李菽林

    2013-01-01

    物流不仅有物质循环利用、能源转化,而且有价值的转移和价值的实现,物流涉及了经济与生态环境两大系统.在资源约束、环境问题日趋严重的情况下,物流企业的绿色创新能力评价已经成为学术界的焦点之一.论文在分析绿色创新能力理念的基础上,引入支持向量机分析评价模型,构建物流企业绿色创新能力评价体系,并以湖南物流企业为样本,进行了实证评价分析.实证结果表明,相比与BP神经网络等其他评价方法,支持向量机评价结果更准确、直观,更适合物流企业绿色创新能力考评.%Logistics is an important part of the social reproduction, not only the physical logistics process recycling, energy conversion and transfer of value and value realization, the logistics involved in the two systems of economic and ecological environment. Resource constraints, environmental problems have become more severe cases, the evaluation of the green innovation capability of logistics business has become the focus of the academic community. Based on the concept of green innovation capability and the model of support vector machine, this paper builds the logistics enterprises evaluation index system of green innovation capability, and makes an empirical analysis on Hunan logistics enterprise. The results show that the evaluation results of SVM are more accurate, intuitive, more suitable for evaluation of green innovation capability of logistics enterprises, compared with the evaluation method of BP neural network.

  17. Generalized Quadratic Linearization of Machine Models

    OpenAIRE

    Parvathy Ayalur Krishnamoorthy; Kamaraj Vijayarajan; Devanathan Rajagopalan

    2011-01-01

    In the exact linearization of involutive nonlinear system models, the issue of singularity needs to be addressed in practical applications. The approximate linearization technique due to Krener, based on Taylor series expansion, apart from being applicable to noninvolutive systems, allows the singularity issue to be circumvented. But approximate linearization, while removing terms up to certain order, also introduces terms of higher order than those removed into the system. To overcome th...

  18. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2016-01-01

    Full Text Available Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD, which reduces the effect of noise on the wind speed data; (II artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM model are optimized by the cuckoo search (CS algorithm; (III parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

  19. Simulation Modeling and Analysis of Operator-Machine Ratio

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on a simulation model of a semiconductor manufacturer, operator-machine ratio (OMR) analysis is made using work study and time study. Through sensitivity analysis, it is found that labor utilization decreases with the increase of lot size.Meanwhile, it is able to identify that the OMR for this company should be improved from 1∶3 to 1∶5. An application result shows that the proposed model can effectively improve the OMR by 33%.

  20. Machine Learning and Cosmological Simulations I: Semi-Analytical Models

    OpenAIRE

    Kamdar, Harshil M.; Turk, Matthew J.; Brunner, Robert J.

    2015-01-01

    We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and (2) quantitatively analyzing the extent of the influence of dark matter halo properties on galaxies in the backdrop of semi-analytical models (SAMs). We use the influential Millennium Simulation and the corresponding Munich SAM to train and test various so...

  1. Non-linear calibration models for near infrared spectroscopy

    DEFF Research Database (Denmark)

    Ni, Wangdong; Nørgaard, Lars; Mørup, Morten

    2014-01-01

    Different calibration techniques are available for spectroscopic applications that show nonlinear behavior. This comprehensive comparative study presents a comparison of different nonlinear calibration techniques: kernel PLS (KPLS), support vector machines (SVM), least-squares SVM (LS-SVM), relev...

  2. Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

    Science.gov (United States)

    Zhu, Xiangrong; Shan, Yang; Li, Gaoyang; Huang, Anmin; Zhang, Zhuoyong

    2009-10-01

    A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis-NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x- y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard-Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.

  3. An improved HMM/SVM dynamic hand gesture recognition algorithm

    Science.gov (United States)

    Zhang, Yi; Yao, Yuanyuan; Luo, Yuan

    2015-10-01

    In order to improve the recognition rate and stability of dynamic hand gesture recognition, for the low accuracy rate of the classical HMM algorithm in train the B parameter, this paper proposed an improved HMM/SVM dynamic gesture recognition algorithm. In the calculation of the B parameter of HMM model, this paper introduced the SVM algorithm which has the strong ability of classification. Through the sigmoid function converted the state output of the SVM into the probability and treat this probability as the observation state transition probability of the HMM model. After this, it optimized the B parameter of HMM model and improved the recognition rate of the system. At the same time, it also enhanced the accuracy and the real-time performance of the human-computer interaction. Experiments show that this algorithm has a strong robustness under the complex background environment and the varying illumination environment. The average recognition rate increased from 86.4% to 97.55%.

  4. Knowledge in formation: The machine-modeled frame of mind

    Energy Technology Data Exchange (ETDEWEB)

    Shore, B.

    1996-12-31

    Artificial Intelligence researchers have used the digital computer as a model for the human mind in two different ways. Most obviously, the computer has been used as a tool on which simulations of thinking-as-programs are developed and tested. Less obvious, but of great significance, is the use of the computer as a conceptual model for the human mind. This essay traces the sources of this machine-modeled conception of cognition in a great variety of social institutions and everyday experienced treating them as {open_quotes}cultural models{close_quotes} which have contributed to the naturalness of The mine-as-machine paradigm for many Americans. The roots of these models antedate the actual development of modern computers, and take the form of a {open_quotes}modularity schema{close_quotes} that has shaped the cultural and cognitive landscape of modernity. The essay concludes with a consideration of some of the cognitive consequences of this extension of machine logic into modern life, and proposes an important distinction between information processing models of thought and meaning-making in how human cognition is conceptualized.

  5. Land Cover Classification from Full-Waveform LIDAR Data Based on Support Vector Machines

    Science.gov (United States)

    Zhou, M.; Li, C. R.; Ma, L.; Guan, H. C.

    2016-06-01

    In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

  6. LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    M. Zhou

    2016-06-01

    Full Text Available In this study, a land cover classification method based on multi-class Support Vector Machines (SVM is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs method and it showed that SVM method could achieve better classification results.

  7. Simulation and Prediction of Ion Transport in the Reclamation of Sodic Soils with Gypsum Based on the Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jinman Wang

    2014-01-01

    Full Text Available The effect of gypsum on the physical and chemical characteristics of sodic soils is nonlinear and controlled by multiple factors. The support vector machine (SVM is able to solve practical problems such as small samples, nonlinearity, high dimensions, and local minima points. This paper reports the use of the SVM regression method to predict changes in the chemical properties of sodic soils under different gypsum application rates in a soil column experiment and to evaluate the effect of gypsum reclamation on sodic soils. The research results show that (1 the SVM soil solute transport model using the Matlab toolbox represents the change in Ca2+ and Na+ in the soil solution and leachate well, with a high prediction accuracy. (2 Using the SVM model to predict the spatial and temporal variations in the soil solute content is feasible and does not require a specific mathematical model. The SVM model can take full advantage of the distribution characteristics of the training sample. (3 The workload of the soil solute transport prediction model based on the SVM is greatly reduced by not having to determine the hydrodynamic dispersion coefficient and retardation coefficient, and the model is thus highly practical.

  8. Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

    Science.gov (United States)

    Ahmadi, Hamed; Rodehutscord, Markus

    2017-01-01

    In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [R(2) = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM (R(2) = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR (R(2) = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel(®) calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

  9. The rise of machine consciousness: studying consciousness with computational models.

    Science.gov (United States)

    Reggia, James A

    2013-08-01

    Efforts to create computational models of consciousness have accelerated over the last two decades, creating a field that has become known as artificial consciousness. There have been two main motivations for this controversial work: to develop a better scientific understanding of the nature of human/animal consciousness and to produce machines that genuinely exhibit conscious awareness. This review begins by briefly explaining some of the concepts and terminology used by investigators working on machine consciousness, and summarizes key neurobiological correlates of human consciousness that are particularly relevant to past computational studies. Models of consciousness developed over the last twenty years are then surveyed. These models are largely found to fall into five categories based on the fundamental issue that their developers have selected as being most central to consciousness: a global workspace, information integration, an internal self-model, higher-level representations, or attention mechanisms. For each of these five categories, an overview of past work is given, a representative example is presented in some detail to illustrate the approach, and comments are provided on the contributions and limitations of the methodology. Three conclusions are offered about the state of the field based on this review: (1) computational modeling has become an effective and accepted methodology for the scientific study of consciousness, (2) existing computational models have successfully captured a number of neurobiological, cognitive, and behavioral correlates of conscious information processing as machine simulations, and (3) no existing approach to artificial consciousness has presented a compelling demonstration of phenomenal machine consciousness, or even clear evidence that artificial phenomenal consciousness will eventually be possible. The paper concludes by discussing the importance of continuing work in this area, considering the ethical issues it raises

  10. Age Estimation Based on CLM, Tree Mixture With Adaptive Neuron Fuzzy, Fuzzy Svm

    Directory of Open Access Journals (Sweden)

    Mohammad Saber Iraji

    2014-02-01

    Full Text Available As you know, age diagnosis based on the image is one of the most attractive topics in computer .In this paper, we present a intelligent model to estimate the age of face image. We use shape and texture feature extraction from FG-NET landmark image data set using AAM(Active Appearance Model, CLM (Constrained Local Model, tree Mixture algorithms. Finally, the obtained features were given as the training data to the ANFIS (adaptive neuro fuzzy influence system, FSVM (Fuzzy Support Vector Machine. Our experimental results show that In our proposed system, fuzzy svm has less errors and system worked more accurate and appropriative than prior methods. Our system is able to identify age of face image from different directions as is.

  11. Building Better Ecological Machines: Complexity Theory and Alternative Economic Models

    Directory of Open Access Journals (Sweden)

    Jess Bier

    2016-12-01

    Full Text Available Computer models of the economy are regularly used to predict economic phenomena and set financial policy. However, the conventional macroeconomic models are currently being reimagined after they failed to foresee the current economic crisis, the outlines of which began to be understood only in 2007-2008. In this article we analyze the most prominent of this reimagining: Agent-Based models (ABMs. ABMs are an influential alternative to standard economic models, and they are one focus of complexity theory, a discipline that is a more open successor to the conventional chaos and fractal modeling of the 1990s. The modelers who create ABMs claim that their models depict markets as ecologies, and that they are more responsive than conventional models that depict markets as machines. We challenge this presentation, arguing instead that recent modeling efforts amount to the creation of models as ecological machines. Our paper aims to contribute to an understanding of the organizing metaphors of macroeconomic models, which we argue is relevant conceptually and politically, e.g., when models are used for regulatory purposes.

  12. 粒子群优化的KFCM及SVM诊断模型在断路器故障诊断中的应用%Application of Particle Swarm Fused KFCM and Classification Model of SVM for Fault Diagnosis of Circuit Breaker

    Institute of Scientific and Technical Information of China (English)

    梅飞; 梅军; 郑建勇; 张思宇; 朱克东

    2013-01-01

    为了利用相对较少的故障数据样本对断路器主要故障类型进行较为准确的在线判断,提出了一种基于融合粒子群的模糊核聚类(particle swarm fused kernel fuzzy C-means, P-KFCM)与支持向量机(support vector machine,SVM)的故障诊断方法。通过对断路器分合闸电流信号的分析,找出与断路器主要故障类型相对应的特征量;据此对采样信号进行处理,建立故障特征样本空间;利用 P-KFCM 算法对故障训练样本进行预分类,并以此为基础建立多SVM故障预测模型。P-KFCM算法将粒子群(particle swarm optimization,PSO)的全局搜索能力融入KFCM中,有效的解决了局部最优问题,在一定程度上提升了诊断结果的可靠性。实验结果表明,该方法在诊断断路器主要机械故障方面能够取得较好的效果。%To make accurate judgments of circuit breakers’ main fault types in on-line system using relatively small amounts of fault data, a fault diagnostic method was proposed in this paper. This method combined particle swarm fused kernel fuzzy C-means (P-KFCM) and support vector machine (SVM). Through analysis of the opening and closing current signals, characteristic values corresponding to main fault types could be found, based on which we could process sample signals and establish feature space of fault samples. P-KFCM was utilized to pre-classify fault training samples, on the basis of which multi-SVM fault prediction model could be established. At the same time, it integrated global search ability of particle swarm optimization (PSO) into KFCM to solve local optimum, which would effectively improve reliability of diagnostic results. Experiment results have proved that the proposed method achieves perfect results in diagnosing circuit breakers’ main mechanical faults.

  13. A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

    Science.gov (United States)

    Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling

    2017-02-01

    Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R(2)) of the whole training set (training and prediction) was 0.924, and the R(2) of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q(2) value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.

  14. Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration

    Directory of Open Access Journals (Sweden)

    Minal Patel

    2016-01-01

    Full Text Available Service can be delivered anywhere and anytime in cloud computing using virtualization. The main issue to handle virtualized resources is to balance ongoing workloads. The migration of virtual machines has two major techniques: (i reducing dirty pages using CPU scheduling and (ii compressing memory pages. The available techniques for live migration are not able to predict dirty pages in advance. In the proposed framework, time series based prediction techniques are developed using historical analysis of past data. The time series is generated with transferring of memory pages iteratively. Here, two different regression based models of time series are proposed. The first model is developed using statistical probability based regression model and it is based on ARIMA (autoregressive integrated moving average model. The second one is developed using statistical learning based regression model and it uses SVR (support vector regression model. These models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. The ARIMA model is able to predict dirty pages with 91.74% accuracy and the SVR model is able to predict dirty pages with 94.61% accuracy that is higher than ARIMA.

  15. [NIR spectroscopy based on least square support vector machines for quality prediction of tomato juice].

    Science.gov (United States)

    Huang, Kang; Wang, Hui-jun; Xu, Hui-rong; Wang, Jian-ping; Ying, Yi-bin

    2009-04-01

    The application of least square support vector machines (LS-SVM) regression method based on statistics study theory to the analysis with near infrared (NIR) spectra of tomato juice was introduced in the present paper. In this method, LS-SVM was used for establishing model of spectral analysis, and was applied to predict the sugar contents (SC) and available acid (VA) in tomato juice samples. NIR transmission spectra of tomato juice were measured in the spectral range of 800-2,500 nm using InGaAs detector. The radial basis function (RBF) was adopted as a kernel function of LS-SVM. Sixty seven tomato juice samples were used as calibration set, and thirty three samples were used as validation set. The results of the method for sugar contents (SC) and available acid (VA) prediction were: a high correlation coefficient of 0.9903 and 0.9675, and a low root mean square error of prediction (RMSEP) of 0.0056 degree Brix and 0.0245, respectively. And compared to PLS and PCR methods, the performance of the LSSVM method was better. The results indicated that it was possible to built statistic models to quantify some common components in tomato juice using near-infrared (NIR) spectroscopy and least square support vector machines (LS-SVM) regression method as a nonlinear multivariate calibration procedure, and LS-SVM could be a rapid and accurate method for juice components determination based on NIR spectra.

  16. Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values.

    Science.gov (United States)

    Huang, Xiaolin; Shi, Lei; Suykens, Johan A K

    2016-04-08

    Applying the pinball loss in a support vector machine (SVM) classifier results in pin-SVM. The pinball loss is characterized by a parameter τ. Its value is related to the quantile level and different τ values are suitable for different problems. In this paper, we establish an algorithm to find the entire solution path for pin-SVM with different τ values. This algorithm is based on the fact that the optimal solution to pin-SVM is continuous and piecewise linear with respect to τ. We also show that the nonnegativity constraint on τ is not necessary, i.e., τ can be extended to negative values. First, in some applications, a negative τ leads to better accuracy. Second, τ = -1 corresponds to a simple solution that links SVM and the classical kernel rule. The solution for τ = -1 can be obtained directly and then be used as a starting point of the solution path. The proposed method efficiently traverses τ values through the solution path, and then achieves good performance by a suitable τ. In particular, $τ = 0$ corresponds to C-SVM, meaning that the traversal algorithm can output a result at least as good as C-SVM with respect to validation error.

  17. [LLE-SVM classification of apple mealiness based on hyperspectral scattering image].

    Science.gov (United States)

    Zhao, Gui-lin; Zhu, Qi-bing; Huang, Min

    2010-10-01

    Apple mealiness degree is an important factor for its internal quality. hyperspectral scattering, as a promising technique, was investigated for noninvasive measurement of apple mealiness. In the present paper, a locally linear embedding (LLE) coupled with support vector machine (SVM) was proposed to achieve classification because of large number of image data. LLE is a nonlinear lowering dimension method, which reveals the structure of the global nonlinearity by the local linear joint. This method can effectively calculate high-dimensional input data embedded in a low-dimensional space manifold. The dimension reduction of hyperspectral data was classified by SVM. Comparing the LLE-SVM classification method with the traditional SVM classification, the results indicated that the training accuracy obtained with the LLE-SVM was higher than that just with SVM; and the testing accuracy of the classifier changed a little before and after dimensionality reduction, and the range of fluctuation was less than 5%. It is expected that LLE-SVM method would provide an effective classification method for apple mealiness nondestructive detection using hyperspectral scattering image technique.

  18. Traffic Prediction Based on SVM Training Sample Divided by Time

    Directory of Open Access Journals (Sweden)

    Lingli Li

    2013-07-01

    Full Text Available In recent years, the volume of traffic is rapidly increasing. When vehicles running through the tunnel are more intensive or move slowly, the tunnel environment occurs deteriorated sharply, which affects the normal operation of the vehicle in the tunnel. This paper uses the result of previous mining association rules to select feature items and to establish four training samples divided by time. Then the training samples are utilized to create the SVM classification model. Finally the trained SVM model is used to prediction the tunnel traffic situation. Through traffic situation prediction, effective decisions can be made before traffic jams, and ensure that the tunnel traffic is normal.  

  19. An iris recognition method based on multi-orientation features and Non-symmetrical SVM

    Institute of Scientific and Technical Information of China (English)

    GU Hong-ying; ZHUANG Yue-ting; PAN Yun-he

    2005-01-01

    A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each sub-image and used to train Support Vector Machine (SVM) as iris classifiers. SVM has drawn great interest recently as one of the best classifiers in machine learning, although there is a problem in the use of traditional SVM for iris recognition. It cannot treat False Accept and False Reject differently with different security requirements. Therefore, a new kind of SVM called Non-symmetrical SVM is presented to classify the iris features. Experimental data shows that Non-symmetrical SVM can satisfy various security requirements in iris recognition applications. Feature sequence combined with spatial and frequency domain represents the variation details of the iris patterns properly. The results in this study demonstrate the potential of our new approach, and show that it performs more satisfactorily when compared to former algorithms.

  20. The Application of Correlation-SVM on the Dam Deformation Forecast%Correlation -SVM 模型在大坝变形预测中的应用

    Institute of Scientific and Technical Information of China (English)

    朱涛; 刘慕溪

    2015-01-01

    引起大坝变形的影响因素很多,即在利用支持向量机( SVM)模型进行大坝变形分析和预报的过程中,需要将所有的影响因子都输入到SVM模型中,这样会造成输入因子的不侧重性,基于此,本文对大坝变形的