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Sample records for svm based model

  1. SVM Intrusion Detection Model Based on Compressed Sampling

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    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.

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

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    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%.

  3. svmPRAT: SVM-based Protein Residue Annotation Toolkit

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    Kauffman Christopher

    2009-12-01

    Full Text Available Abstract Background Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. Results We present a general purpose protein residue annotation toolkit (svmPRAT to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that accurately captures signals and pattern for training effective predictive models. Conclusions In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residue-wise contact order estimation, DNA-binding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of state-of-the-art transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat

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

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    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.

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

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    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.

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

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    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.

  7. Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

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    Tanabe, Kazutoshi; Lučić, Bono; Amić, Dragan; Kurita, Takio; Kaihara, Mikio; Onodera, Natsuo; Suzuki, Takahiro

    2010-11-01

    The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures

  8. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

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    C. Fernandez-Lozano

    2013-01-01

    Full Text Available 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.

  9. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

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    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. PMID:24453933

  10. A PSO-SVM-based 24 Hours Power Load Forecasting Model

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    Yu Xiaoxu

    2015-01-01

    Full Text Available In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.

  11. Wind Power Prediction Based on LS-SVM Model with Error Correction

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    ZHANG, Y.

    2017-02-01

    Full Text Available As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM. Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.

  12. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

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    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  13. Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

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    Yi Yang

    2014-01-01

    Full Text Available Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO based on the generalized autoregressive conditional heteroskedasticity (GARCH model and support vector machine (SVM to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models.

  14. SVM Method used to Study Gender Differences Based on Microelement

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    Chun, Yang; Yuan, Liu; Jun, Du; Bin, Tang

    [objective] Intelligent Algorithm of SVM is used for studying gender differences based on microelement data, which provide reference For the application of Microelement in healthy people, such as providing technical support for the investigation of cases.[Method] Our Long-term test results on hair microelement of health people were consolidated. Support vector machine (SVM) is used to classified model of male and female based on microelement data. The radical basis function (RBF) is adopted as a kernel function of SVM, and the model adjusts C and σ to build the optimization classifier, [Result] Healthy population of men and women of manganese, cadmium and nickel are quite different, The classified model of Microelement based on SVM can classifies the male and female, the correct classification ratio set to be 81.71% and 66.47% by SVM based on 7 test date and 3 test data selection. [conclusion] The classified model of microelement data based on SVM can classifies male and female.

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

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    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.

  16. Accurate Multisteps Traffic Flow Prediction Based on SVM

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    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.

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

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    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.

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

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    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.

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

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    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.

  20. A novel stepwise support vector machine (SVM) method based on ...

    African Journals Online (AJOL)

    ajl yemi

    2011-11-23

    Nov 23, 2011 ... began to use computational approaches, particularly machine learning methods to identify pre-miRNAs (Xue et al., 2005; Huang et al., 2007; Jiang et al., 2007). Xue et al. (2005) presented a support vector machine (SVM)- based classifier called triplet-SVM, which classifies human pre-miRNAs from pseudo ...

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

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    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.

  2. Research on Classification of Chinese Text Data Based on SVM

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    Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

    2017-09-01

    Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

  3. SVM ensemble based transfer learning for large-scale membrane proteins discrimination.

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    Mei, Suyu

    2014-01-07

    Membrane proteins play important roles in molecular trans-membrane transport, ligand-receptor recognition, cell-cell interaction, enzyme catalysis, host immune defense response and infectious disease pathways. Up to present, discriminating membrane proteins remains a challenging problem from the viewpoints of biological experimental determination and computational modeling. This work presents SVM ensemble based transfer learning model for membrane proteins discrimination (SVM-TLM). To reduce the data constraints on computational modeling, this method investigates the effectiveness of transferring the homolog knowledge to the target membrane proteins under the framework of probability weighted ensemble learning. As compared to multiple kernel learning based transfer learning model, the method takes the advantages of sparseness based SVM optimization on large data, thus more computationally efficient for large protein data analysis. The experiments on large membrane protein benchmark dataset show that SVM-TLM achieves significantly better cross validation performance than the baseline model. © 2013 Elsevier Ltd. All rights reserved.

  4. Combination model of empirical mode decomposition and SVM for river flow forecasting

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    Ismail, Shuhaida; Shabri, Ani

    2017-04-01

    A reliable prediction of river flow is always important for sound planning and smooth operation of the water resource system. In this study, a combination models based on Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) model referred as EMD-SVM is proposed for estimating future value of monthly river flow data. The proposed EMD-SVM has three important stages. The first stage, the data were decomposed into several numbers of Intrinsic Mode Functions (IMF) and a residual using EMD technique. In the second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied SVM as forecasting tool to perform the river flow forecasting. To assess the effectiveness of EMD-SVM model, Selangor and Bernam Rivers were used as case studies. The experiment results stated that the proposed EMD-SVM have outperformed other model based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (r). This indicating that EMD-SVM is a useful tool to predict complex time series with non-stationary and nonlinearity issues as well as a promising new method for river flow forecasting.

  5. 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

  6. SA-SVM based automated diagnostic system for skin cancer

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    Masood, Ammara; Al-Jumaily, Adel

    2015-03-01

    Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %.

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

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    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.

  8. 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)

  9. SVM-Based Control System for a Robot Manipulator

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    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.

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

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    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.

  11. Oil spill detection from SAR image using SVM based classification

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    A. A. Matkan

    2013-09-01

    Full Text Available In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results.

  12. 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.

  13. 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.

  14. Melancholia EEG classification based on CSSD and SVM

    Science.gov (United States)

    Shi, Jian-Jun; Yuan, Qing-Wu; Zhou, La-Wu

    2011-10-01

    It takes an important role to get the disease information from melancholia electroencephalograph (EEG). Firstly, A common spatial subspace decomposition (CSSD) method was used to extract features from 16-channel EEG of melancholia and normal healthy persons. Then based on support vector machines (SVM), a classifier was designed to train and test its classification capability between Melancholia and healthy persons. The results indicated that the proposed method can reach a higher accuracy as 95% in EEG classification, while the accuracy of the method based on wavelet is only 88%.That is, the proposed method is feasible for the melancholia diagnosis and research.

  15. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-11-01

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

  17. Fault Diagnosis for Constant Deceleration Braking System of Mine Hoist based on Principal Component Analysis and SVM

    Directory of Open Access Journals (Sweden)

    Li Juan-Juan

    2017-01-01

    Full Text Available Based on AMESim simulation platform, the pressure-time curve of constant deceleration braking system is obtained in this paper firstly, by simulating three typical faults of brake, the spring stiffness decrease, the brake shoe friction coefficient decrease and brake leaking. Then pressure data on the curve for each time are seen as a variable and the curve is chosen as the fault sample, analysed by the method of Principal Component Analysis (PCA. Last, principal components or sum of variance contribution rates more than 95% are selected as sample eigenvalues and Support Vector Machine (SVM is used for fault diagnosis. Diagnosis results show that all testing faults can be identified accurately, which indicates SVM model has an extremely excellent ability to identify faults. To further verify the performance of SVM for fault identification, BP neural network is established to compare. The result shows that SVM model is more accurate than BP neural network in fault recognition.

  18. 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.

  19. 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.

  20. Spectral Reconstruction Based on Svm for Cross Calibration

    Science.gov (United States)

    Gao, H.; Ma, Y.; Liu, W.; He, H.

    2017-05-01

    Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR) hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF), SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE) which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

  1. SPECTRAL RECONSTRUCTION BASED ON SVM FOR CROSS CALIBRATION

    Directory of Open Access Journals (Sweden)

    H. Gao

    2017-05-01

    Full Text Available Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor’s passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF, SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

  2. SVM-based learning control of space robots in capturing operation.

    Science.gov (United States)

    Huang, Panfeng; Xu, Yangsheng

    2007-12-01

    In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.

  3. 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.

  4. Feature selection based on SVM significance maps for classification of dementia

    NARCIS (Netherlands)

    E.E. Bron (Esther); M. Smits (Marion); J.C. van Swieten (John); W.J. Niessen (Wiro); S. Klein (Stefan)

    2014-01-01

    textabstractSupport vector machine significance maps (SVM p-maps) previously showed clusters of significantly different voxels in dementiarelated brain regions. We propose a novel feature selection method for classification of dementia based on these p-maps. In our approach, the SVM p-maps are

  5. A Realistic Seizure Prediction Study Based on Multiclass SVM.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

    2017-05-01

    A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

  6. DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)

    DEFF Research Database (Denmark)

    Swierczynski, Dariusz; Kazmierkowski, Marian P.; Blaabjerg, Frede

    2002-01-01

    DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)......DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)...

  7. SVM-based automatic diagnosis method for keratoconus

    Science.gov (United States)

    Gao, Yuhong; Wu, Qiang; Li, Jing; Sun, Jiande; Wan, Wenbo

    2017-06-01

    Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

  8. A modular spectrum sensing system based on PSO-SVM.

    Science.gov (United States)

    Cai, Zhuoran; Zhao, Honglin; Yang, Zhutian; Mo, Yun

    2012-11-08

    In the cognitive radio system, spectrum sensing for detecting the presence of primary users in a licensed spectrum is a fundamental problem. Energy detection is the most popular spectrum sensing scheme used to differentiate the case where the primary user’s signal is present from the case where there is only noise. In fact, the nature of spectrum sensing can be taken as a binary classification problem, and energy detection is a linear classifier. If the signal-to-noise ratio (SNR) of the received signal is low, and the number of received signal samples for sensing is small, the binary classification problem is linearly inseparable. In this situation the performance of energy detection will decrease seriously. In this paper, a novel approach for obtaining a nonlinear threshold based on support vector machine with particle swarm optimization (PSO-SVM) to replace the linear threshold used in traditional energy detection is proposed. Simulations demonstrate that the performance of the proposed algorithm is much better than that of traditional energy detection.

  9. 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.

  10. 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

    Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

  11. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  12. 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

  13. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

    Directory of Open Access Journals (Sweden)

    Ting Zhou

    2017-10-01

    Full Text Available Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT preprocess and support vector machine (SVM was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN, regular SVM, and wavelet preprocessed artificial neural networks (WANN models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE, Pearson correlation coefficient (r, root mean square error (RMSE, and Nash-Sutcliffe efficiency (NSE were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

  14. [SVM-based qualitative analysis of Muscat Hamburg wine produced in Tianjin region].

    Science.gov (United States)

    Zhang, Jun; Wang, Fang; Wei, Ji-Ping; Li, Chang-Wen; Yang, Hua; Shao, Chun-Fu; Zhang, Fu-Qing; Yin, Ji-Tai; Xiao, Dong-Guang

    2011-01-01

    The purpose was to achieve the identification of Muscat Hamburg wines produced in Tianjin region through scanning and analyzing dry white wine samples of different grape varieties and regions by infrared spectroscopy technology. A support vector machine (SVM) based method was introduced to analyze infrared spectra of dry white wines. The pretreatment processes of the IR spectra were also elaborated, including baseline adjustment, noise Elimination, standard normalization and eliminating the main component of abnormal sample points. The authors selected great quantity of dry white wine samples of different grape regions including 511 Muscat Hamburg wine samples, 438 Italian Riesling wine samples, 307 Chardonnay wine samples, 29 Ugni Blanc wine samples, 44 Rkatsiteli wine samples, 31 longan wine samples and 79 ZeHong wine samples. According to different classification problems, 80% of IR spectra of the wine samples were used to establish discrimination models with SVM-based method, and the remaining 20% of IR spectra were used for the validation of the discrimination models. Experimental results showed that the proposed method is effective, since high classification accuracy, identification rate and rejecting rate were achieved: over 97% for the white wine samples of different grape varieties, meanwhile over 98% for the Muscat Hamburg wine samples produced in different regions. So the method developed in this paper played a good role in the qualitative classification and discrimination of Muscat Hamburg wines produced in Tianjin region. This novel method has a considerable potential and a rosy application future due to the expeditiousness, stability and easy-operation of FTIR method, as well as the veracity and credibility of SVM method.

  15. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

    Science.gov (United States)

    Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

    2016-08-19

    A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

  16. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

    Directory of Open Access Journals (Sweden)

    Xiaohui Lin

    2017-12-01

    Full Text Available Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

  17. Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding

    Science.gov (United States)

    Gao, Xiangdong; Liu, Guiqian

    2015-01-01

    During deep penetration laser welding, there exist plume (weak plasma) and spatters, which are the results of weld material ejection due to strong laser heating. The characteristics of plume and spatters are related to welding stability and quality. Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW. An ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images. Plume area, laser beam path through the plume, swing angle, distance between laser beam focus and plume image centroid, abscissa of plume centroid and spatter numbers are defined as eigenvalues, and the weld bead width was used as a characteristic parameter that reflected welding stability. Welding status was distinguished by SVM (support vector machine) after data normalization and characteristic analysis. Also, PCA (principal components analysis) feature extraction was used to reduce the dimensions of feature space, and PSO (particle swarm optimization) was used to optimize the parameters of SVM. Finally a classification model based on SVM was established to estimate the weld bead width and welding stability. Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width, thus providing an experimental example of monitoring high-power disk laser welding quality.

  18. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    Science.gov (United States)

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  19. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  20. Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

    Directory of Open Access Journals (Sweden)

    Lin-sheng Huo

    2016-01-01

    Full Text Available An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude (CCFA with the support vector machine (SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one (OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

  1. Hybrid SVM-HMM based recognition algorithm for pen-based tutoring system

    Science.gov (United States)

    Yuan, Zhenming; Pan, Hong

    2007-11-01

    Pen-based computing takes advantage of human skill with the pen, which is more than a substitute for the mouse. A hybrid SVM-HMM based recognition algorithm is presented for pen-based single stroke diagram. The algorithm includes five steps: sampling and pre-processing, segmentation, formal feature computing, SVM based feature classification, and HMM based symbol recognition. The formal feature of a stroke is composed of five static features and one dynamic feature. A group of one-to-one combinations of binary SVMs are used as feature classifiers to produce fixed length feature vectors, each of which is produced by the probability output with Sigmoid function and act as the posterior probability of observation of HMM. Finally HMMs are employed as final recognizer to recognize the unknown stroke. Based on this algorithm, a tutoring system is designed to identify the sketches of the flowchart diagrams. Experiment results show the hybrid algorithm has a good learning and recognition ability, which is benefited from combining the SVM's classification ability of static properties with the HMM's recognition ability of dynamic properties.

  2. 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.

  3. 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...... analyzed with loads connected to △. But it will be further researched in the future. Third, PLECS is briefly introduced. Fourth, the modeling of diode clamping three-level inverter is briefly presented with PLECS. Finally, a series of simulations are carried out. The simulation results tell us PLECS...... 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....

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

    Directory of Open Access Journals (Sweden)

    Sukomal Mandal

    2012-06-01

    Full Text Available The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN, Support Vector Machine (SVM and Adaptive Neuro Fuzzy Inference system (ANFIS models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correlation coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

  5. SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

    Science.gov (United States)

    Wang, Yao; Zhang, Zhongzhao; Ma, Lin; Chen, Jiamei

    2014-01-01

    Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

  6. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

  8. Energy Management in Wireless Sensor Networks Based on Naive Bayes, MLP, and SVM Classifications: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Abdulaziz Y. Barnawi

    2016-01-01

    Full Text Available Maximizing wireless sensor networks (WSNs lifetime is a primary objective in the design of these networks. Intelligent energy management models can assist designers to achieve this objective. These models aim to reduce the number of selected sensors to report environmental measurements and, hence, achieve higher energy efficiency while maintaining the desired level of accuracy in the reported measurement. In this paper, we present a comparative study of three intelligent models based on Naive Bayes, Multilayer Perceptrons (MLP, and Support Vector Machine (SVM classifiers. Simulation results show that Linear-SVM selects sensors that produce higher energy efficiency compared to those selected by MLP and Naive Bayes for the same WSNs Lifetime Extension Factor.

  9. An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

    Directory of Open Access Journals (Sweden)

    Samy Bakheet

    2017-01-01

    Full Text Available Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine model on an optimized set of HOG (Histogram of Oriented Gradient based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively, without sacrificing computational soundness.

  10. 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.

  11. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

    Directory of Open Access Journals (Sweden)

    Yongzheng Xu

    2016-08-01

    Full Text Available A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J and linear SVM classifier with HOG feature (HOG + SVM methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

  12. 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.

  13. Hyperspectral recognition of processing tomato early blight based on GA and SVM

    Science.gov (United States)

    Yin, Xiaojun; Zhao, SiFeng

    2013-03-01

    Processing tomato early blight seriously affect the yield and quality of its.Determine the leaves spectrum of different disease severity level of processing tomato early blight.We take the sensitive bands of processing tomato early blight as support vector machine input vector.Through the genetic algorithm(GA) to optimize the parameters of SVM, We could recognize different disease severity level of processing tomato early blight.The result show:the sensitive bands of different disease severity levels of processing tomato early blight is 628-643nm and 689-692nm.The sensitive bands are as the GA and SVM input vector.We get the best penalty parameters is 0.129 and kernel function parameters is 3.479.We make classification training and testing by polynomial nuclear,radial basis function nuclear,Sigmoid nuclear.The best classification model is the radial basis function nuclear of SVM. Training accuracy is 84.615%,Testing accuracy is 80.681%.It is combined GA and SVM to achieve multi-classification of processing tomato early blight.It is provided the technical support of prediction processing tomato early blight occurrence, development and diffusion rule in large areas.

  14. AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

    Science.gov (United States)

    Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

    2017-04-01

    Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

  15. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

    OpenAIRE

    Xiaogang Ning; Xiangguo Lin; Jixian Zhang

    2013-01-01

    Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is ...

  16. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Directory of Open Access Journals (Sweden)

    S. Ganapathy

    2012-01-01

    Full Text Available Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  17. Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

    Science.gov (United States)

    Ganapathy, S; Yogesh, P; Kannan, A

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  18. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Science.gov (United States)

    Ganapathy, S.; Yogesh, P.; Kannan, A.

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

  19. 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.

  20. Classification of surface defects on bridge cable based on PSO-SVM

    Science.gov (United States)

    Li, Xinke; Gao, Chao; Guo, Yongcai; Shao, Yanhua; He, Fuliang

    2014-07-01

    Distributed machine vision system was applied for the detection on the cable surface defect of the cable-stayed bridge, and access to surface defects including longitudinal cracking, transverse cracking, surface erosion and scarring pit holes and other scars. In order to achieve the automatic classification of surface defects, firstly, part of the texture features, gray features and shape features on the defect image were selected as the target classification feature quantities; then the particle swarm optimization (PSO) was introduced to optimize the punitive coefficient and kernel function parameter of the support vector machine (SVM) model; and finally the objective of defects was identified with the help of the PSOSVM classifier. Recognition experiments were performed on cable surface defects, presenting a recognition rate of 96.25 percent. The results showed that PSO-SVM has high recognition rate for classification of surface defects on bridge cable.

  1. 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.

  2. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  3. Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF.

    Science.gov (United States)

    Oktay, Ayse Betul; Akgul, Yusuf Sinan

    2013-09-01

    This paper presents a method for localizing and labeling the lumbar vertebrae and intervertebral discs in mid-sagittal MR image slices. The approach is based on a Markov-chain-like graphical model of the ordered discs and vertebrae in the lumbar spine. The graphical model is formulated by combining local image features and semiglobal geometrical information. The local image features are extracted from the image by employing pyramidal histogram of oriented gradients (PHOG) and a novel descriptor that we call image projection descriptor (IPD). These features are trained with support vector machines (SVM) and each pixel in the target image is locally assigned a score. These local scores are combined with the semiglobal geometrical information like the distance ratio and angle between the neighboring structures under the Markov random field (MRF) framework. An exact localization of discs and vertebrae is inferred from the MRF by finding a maximum a posteriori solution efficiently using dynamic programming. As a result of the novel features introduced, our system can scale-invariantly localize discs and vertebra at the same time even in the existence of missing structures. The proposed system is tested and validated on a clinical lumbar spine MR image dataset containing 80 subjects of which 64 have disc- and vertebra-related diseases and abnormalities. The experiments show that our system is successful even in abnormal cases and our results are comparable to the state of the art.

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

    Science.gov (United States)

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

    2017-01-28

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

  5. Power line identification of millimeter wave radar based on PCA-GS-SVM

    Science.gov (United States)

    Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

    2017-12-01

    Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

  6. Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

    Science.gov (United States)

    Wang, Huiya; Feng, Jun; Wang, Hongyu

    2017-07-20

    Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

  7. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    Science.gov (United States)

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Jiping Xiong

    2017-03-01

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

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

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2017-09-01

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

  10. Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

    Science.gov (United States)

    Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang

    2017-07-01

    Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.

  11. [Selection of Characteristic Wavelengths Using SPA and Qualitative Discrimination of Mildew Degree of Corn Kernels Based on SVM].

    Science.gov (United States)

    Yuan, Ying; Wang, Wei; Chu, Xuan; Xi, Ming-jie

    2016-01-01

    The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.

  12. Adaptive SVM for Data Stream Classification

    Directory of Open Access Journals (Sweden)

    Isah A. Lawal

    2017-07-01

    Full Text Available In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

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

    National Research Council Canada - National Science Library

    Hyo-Sik Ham; Hwan-Hee Kim; Myung-Sup Kim; Mi-Jung Choi

    2014-01-01

    .... 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.

  14. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Directory of Open Access Journals (Sweden)

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  15. Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

    Directory of Open Access Journals (Sweden)

    Jian Luo

    2016-01-01

    Full Text Available A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.

  16. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

    Directory of Open Access Journals (Sweden)

    Sumaiya Thaseen Ikram

    2016-06-01

    Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

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

    Directory of Open Access Journals (Sweden)

    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.

  18. Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM

    Directory of Open Access Journals (Sweden)

    Haodong Yuan

    2017-01-01

    Full Text Available A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC and adaptive PSO-optimized support vector machine (SVM is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

  19. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs.

    Science.gov (United States)

    Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

    2012-01-01

    In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

  20. APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

    Directory of Open Access Journals (Sweden)

    C. Bao

    2012-07-01

    Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

  1. SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Jagadeesh D.Pujari

    2016-06-01

    Full Text Available Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM and artificial neural network (ANN classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

  2. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

    Science.gov (United States)

    Li, Qiang; Gu, Yu; Jia, Jing

    2017-01-30

    Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  3. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-01-01

    Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

  4. Text independent writer identification based on Gabor filter and SVM classifier

    Science.gov (United States)

    Feng, Jun; Zhu, Yanhai

    2006-11-01

    Writer identification has become a hot topic in pattern recognition and machine learning research area. This paper studies on the technology of text independent writer identification based on texture analysis. At first in the preprocessing stage the uniform texture images are created from the input document. An approach for improved characters segmentation is presented based on analysis for the character elements and their topological relations. Then the 32-channel Gabor filter is utilized to extract 64 texture features of writing image by calculating the mean values and the standard deviations of filtering output images. Finally, multi-class support vector machines (SVM) classifier is adopted to fulfill the identification task. The experiment result shows that the scheme is effective and promising.

  5. A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam

    Science.gov (United States)

    Chen, Zhang; Peng, Zhenming; Peng, Lingbing; Liao, Dongyi; He, Xin

    2011-11-01

    With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree. The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the average detection time for each image less than 1 second.

  6. 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.

  7. Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

    Directory of Open Access Journals (Sweden)

    Yussouf Nahayo

    2016-04-01

    Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

  8. 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.

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

    Science.gov (United States)

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

    2015-12-01

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

  10. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

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

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  11. Linear regression-based efficient SVM learning for large-scale classification.

    Science.gov (United States)

    Wu, Jianxin; Yang, Hao

    2015-10-01

    For large-scale classification tasks, especially in the classification of images, additive kernels have shown a state-of-the-art accuracy. However, even with the recent development of fast algorithms, learning speed and the ability to handle large-scale tasks are still open problems. This paper proposes algorithms for large-scale support vector machines (SVM) classification and other tasks using additive kernels. First, a linear regression SVM framework for general nonlinear kernel is proposed using linear regression to approximate gradient computations in the learning process. Second, we propose a power mean SVM (PmSVM) algorithm for all additive kernels using nonsymmetric explanatory variable functions. This nonsymmetric kernel approximation has advantages over the existing methods: 1) it does not require closed-form Fourier transforms and 2) it does not require extra training for the approximation either. Compared on benchmark large-scale classification data sets with millions of examples or millions of dense feature dimensions, PmSVM has achieved the highest learning speed and highest accuracy among recent algorithms in most cases.

  12. An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

    Directory of Open Access Journals (Sweden)

    Yasmine Rezgui

    2017-01-01

    Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.

  13. 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.

  14. Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

    Science.gov (United States)

    Younghak Shin; Balasingham, Ilangko

    2017-07-01

    Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Hongzhuan Zhao

    2016-04-01

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

  18. Identifying 1 Method of Meat Containing Excessive Moisture Based on hyperspectral and SVM Multi-Information Fusion

    Directory of Open Access Journals (Sweden)

    Guo Peiyuan

    2016-01-01

    Full Text Available In this paper, a quick and accurate detection method which can identify whether the meat contain excessive moisture is mentioned. By using near-infrared spectroscopy measurement and SVM Multi-Information Fusion, the meat moisture content model has been established. In order to improve the accuracy of NIR measurement predicted model and to reduce the measurement sensitivity, utilizing image information and the PH value data as the parameters of the meat moisture content model. The study concluded that the theory and method can be further extended to the detection of other related meat agricultural products.

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

    Science.gov (United States)

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

    2015-03-01

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

  20. Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

    Science.gov (United States)

    Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

    2017-09-01

    Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed

  1. POLSAR LAND COVER CLASSIFICATION BASED ON HIDDEN POLARIMETRIC FEATURES IN ROTATION DOMAIN AND SVM CLASSIFIER

    Directory of Open Access Journals (Sweden)

    C.-S. Tao

    2017-09-01

    Full Text Available Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets’ scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy

  2. A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

    Directory of Open Access Journals (Sweden)

    Cao Junxiang

    2015-01-01

    Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

  3. A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

    Directory of Open Access Journals (Sweden)

    Chenchen Huang

    2014-01-01

    Full Text Available Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

  4. SVM-BALSA: Remote Homology Detection based on Bayesian Sequence Alignment

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Oehmen, Chris S.; Matzke, Melissa M.

    2005-11-10

    Using biopolymer sequence comparison methods to identify evolutionarily related proteins is one of the most common tasks in bioinformatics. Recently, support vector machines (SVMs) utilizing statistical learning theory have been employed in the problem of remote homology detection and shown to outperform iterative profile methods such as PSI-BLAST. In this study we demonstrate the utilization of a Bayesian alignment score, which accounts for the uncertainty of all possible alignments, in the SVM construction improves sensitivity compared to the traditional dynamic programming implementation.

  5. a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

    Science.gov (United States)

    Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

    2014-10-01

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

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

    Directory of Open Access Journals (Sweden)

    B. Yekkehkhany

    2014-10-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ruizhi Chen

    2012-05-01

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

  9. Research on Chinese web page SVM classifer based on information gain

    Directory of Open Access Journals (Sweden)

    PAN Zhengcai

    2013-06-01

    Full Text Available In order to improve the efficiency and accuracy of text classification,optimization and improvement are made for defects and deficiencies of the feature dimensionality reduction method and traditional information gain method in text classification of Chinese web pages.At first,part-of-speech filtering and synonyms merging processes are taken for the first feature dimension reduction of feature items.Then,an improved information gain method is proposed for feature weighting computation of feature items.Finally,the classification algorithm of Support Vector Machine (SVM is used for text classification of Chinese web pages.Both theoretical analysis and experimental results show that this method has better performance and classification results than traditional method.

  10. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.

    Science.gov (United States)

    Janjarasjitt, Suparerk

    2017-02-13

    In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event. Overall, the excellent performance on patient-dependent epileptic seizure classification is obtained with the average accuracy, sensitivity, and specificity of, respectively, 0.9687, 0.7299, and 0.9813. The vector composed of two wavelet-based features of scalp EEGs provide the best performance on patient-dependent epileptic seizure classification in most cases, i.e., 19 cases out of 24. The wavelet-based features corresponding to the 32-64, 8-16, and 4-8 Hz subbands of scalp EEGs are the mostly used features providing the best performance on patient-dependent classification. Furthermore, the performance on both patient-dependent and patient-independent epileptic seizure classifications are also validated using tenfold cross-validation. From the patient-independent epileptic seizure classification validated using tenfold cross-validation, it is shown that the best classification performance is achieved using the wavelet-based features corresponding to the 64-128 and 4-8 Hz subbands of scalp EEGs.

  11. QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components.

    Science.gov (United States)

    Shahlaei, Mohsen; Sabet, Razieh; Ziari, Maryam Bahman; Moeinifard, Behzad; Fassihi, Afshin; Karbakhsh, Reza

    2010-10-01

    Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R(2)=0.91 and R(CV)(2)=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

  12. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

    Directory of Open Access Journals (Sweden)

    Fei Ye

    Full Text Available This paper proposes a new support vector machine (SVM optimization scheme based on an improved chaotic fly optimization algorithm (FOA with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

  13. An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

    Science.gov (United States)

    Lou, Xin Yuan; Sun, Lin Fu

    2017-01-01

    This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

  14. An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

    Directory of Open Access Journals (Sweden)

    Safat B. Wali

    2015-01-01

    Full Text Available The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%, and processing time (0.43 s. The area under the receiver operating characteristic (ROC curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

  15. STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

    Directory of Open Access Journals (Sweden)

    Muhammad Athoillah

    2015-03-01

    Full Text Available Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM, K-Nearest Neighbor (K-NN, and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

  16. Using an Integrated Group Decision Method Based on SVM, TFN-RS-AHP, and TOPSIS-CD for Cloud Service Supplier Selection

    Directory of Open Access Journals (Sweden)

    Lian-hui Li

    2017-01-01

    Full Text Available To solve the cloud service supplier selection problem under the background of cloud computing emergence, an integrated group decision method is proposed. The cloud service supplier selection index framework is built from two perspectives of technology and technology management. Support vector machine- (SVM- based classification model is applied for the preliminary screening to reduce the number of candidate suppliers. A triangular fuzzy number-rough sets-analytic hierarchy process (TFN-RS-AHP method is designed to calculate supplier’s index value by expert’s wisdom and experience. The index weight is determined by criteria importance through intercriteria correlation (CRITIC. The suppliers are evaluated by the improved TOPSIS replacing Euclidean distance with connection distance (TOPSIS-CD. An electric power enterprise’s case is given to illustrate the correctness and feasibility of the proposed method.

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

    Directory of Open Access Journals (Sweden)

    Emily S W Wong

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

  18. A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Sunil Tyagi

    2017-04-01

    Full Text Available A classification technique using Support Vector Machine (SVM classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

  19. Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.

    Digital Repository Service at National Institute of Oceanography (India)

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

    In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA...

  20. Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

    Directory of Open Access Journals (Sweden)

    Fatma Ben Salem

    2014-01-01

    Full Text Available This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM and variable structure control (VSC. The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the performances of adaptive controllers of VSC-DTC-SVM monitored induction motor drive in a wide speed range and even in the presence of parameters uncertainties and mismatching disturbances. Better estimations of the stator resistance, the rotor resistance, and the mutual inductance yield improvements of induction motor performances using VSC-DTC-SVM, thereby facilitating torque ripple minimization. Simulation results verified the performances of the proposed approach.

  1. Performance Analysis of DTC-SVM Sliding Mode Controllers-Based Parameters Estimator of Electric Motor Speed Drive

    OpenAIRE

    Ben Salem, Fatma; Derbel, Nabil

    2014-01-01

    This paper is concerned with a framework which unifies direct torque control space vector modulation (DTC-SVM) and variable structure control (VSC). The result is a hybrid VSC-DTC-SVM controller design which eliminates several major limitations of the two individual controls and retains merits of both controllers. It has been shown that obtained control laws are very sensitive to variations of the stator resistance, the rotor resistance, and the mutual inductance. This paper discusses the per...

  2. A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

    Directory of Open Access Journals (Sweden)

    Shaojiang Dong

    2014-01-01

    Full Text Available A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA to extract the characteristic features and the Morlet kernel support vector machine (MSVM to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD to obtain the corresponding intrinsic mode function (IMF. The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

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

    Directory of Open Access Journals (Sweden)

    Debbie Johan Arredondo Arteaga

    2017-01-01

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

  4. 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.

  5. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

    Directory of Open Access Journals (Sweden)

    Xiaogang Ning

    2013-07-01

    Full Text Available Object-based point cloud analysis (OBPA is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

  6. 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.

  7. Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach.

    Science.gov (United States)

    Chen, Ye; Storrs, Judd; Tan, Lirong; Mazlack, Lawrence J; Lee, Jing-Huei; Lu, Long J

    2014-01-15

    Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases. Copyright © 2013 Elsevier B.V. All rights reserved.

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

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

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

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

    Science.gov (United States)

    Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra; Maulik, Ujjwal

    2010-11-12

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

  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. A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

    Directory of Open Access Journals (Sweden)

    Ujwalla Gawande

    2013-01-01

    Full Text Available Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

  12. An SVM-based distal lung image classification using texture descriptors.

    Science.gov (United States)

    Désir, Chesner; Petitjean, Caroline; Heutte, Laurent; Thiberville, Luc; Salaün, Mathieu

    2012-06-01

    A novel imaging technique can now provide microscopic images of the distal lung in vivo, for which quantitative analysis tools need to be developed. In this paper, we present an image classification system that is able to discriminate between normal and pathological images. Different feature spaces for discrimination are investigated and evaluated using a support vector machine. Best classification rates reach up to 90% and 95% on non-smoker and smoker groups, respectively. A feature selection process is also implemented, that allows us to gain some insight about these images. Whereas further tests on extended databases are needed, these first results indicate that efficient computer based automated classification of normal vs. pathological images of the distal lung is feasible. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. 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.

  14. Mesial temporal lobe epilepsy lateralization using SPHARM-based features of hippocampus and SVM

    Science.gov (United States)

    Esmaeilzadeh, Mohammad; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh

    2012-02-01

    This paper improves the Lateralization (identification of the epileptogenic hippocampus) accuracy in Mesial Temporal Lobe Epilepsy (mTLE). In patients with this kind of epilepsy, usually one of the brain's hippocampi is the focus of the epileptic seizures, and resection of the seizure focus is the ultimate treatment to control or reduce the seizures. Moreover, the epileptogenic hippocampus is prone to shrinkage and deformation; therefore, shape analysis of the hippocampus is advantageous in the preoperative assessment for the Lateralization. The method utilized for shape analysis is the Spherical Harmonics (SPHARM). In this method, the shape of interest is decomposed using a set of bases functions and the obtained coefficients of expansion are the features describing the shape. To perform shape comparison and analysis, some pre- and post-processing steps such as "alignment of different subjects' hippocampi" and the "reduction of feature-space dimension" are required. To this end, first order ellipsoid is used for alignment. For dimension reduction, we propose to keep only the SPHARM coefficients with maximum conformity to the hippocampus shape. Then, using these coefficients of normal and epileptic subjects along with 3D invariants, specific lateralization indices are proposed. Consequently, the 1536 SPHARM coefficients of each subject are summarized into 3 indices, where for each index the negative (positive) value shows that the left (right) hippocampus is deformed (diseased). Employing these indices, the best achieved lateralization accuracy for clustering and classification algorithms are 85% and 92%, respectively. This is a significant improvement compared to the conventional volumetric method.

  15. 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.

  16. Accurate Fluid Level Measurement in Dynamic Environment Using Ultrasonic Sensor and ν-SVM

    Directory of Open Access Journals (Sweden)

    Jenny TERZIC

    2009-10-01

    Full Text Available A fluid level measurement system based on a single Ultrasonic Sensor and Support Vector Machines (SVM based signal processing and classification system has been developed to determine the fluid level in automotive fuel tanks. The novel approach based on the ν-SVM classification method uses the Radial Basis Function (RBF to compensate for the measurement error induced by the sloshing effects in the tank caused by vehicle motion. A broad investigation on selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter, has also been presented. Field drive trials were performed under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted fluid level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of fluid level measurement.

  17. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE

    Directory of Open Access Journals (Sweden)

    Yuan Sui

    2015-01-01

    Full Text Available In lung cancer computer-aided detection/diagnosis (CAD systems, classification of regions of interest (ROI is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.

  18. A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

    Science.gov (United States)

    Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

    2017-01-01

    The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

  19. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Directory of Open Access Journals (Sweden)

    Nur Diyana Kamarudin

    2017-01-01

    Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  20. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

    Directory of Open Access Journals (Sweden)

    Zhijun Liao

    2016-01-01

    Full Text Available Gamma-aminobutyric acid type-A receptors (GABAARs belong to multisubunit membrane spanning ligand-gated ion channels (LGICs which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABAARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins’ physicochemical properties, amino acids composition and position, a GABAAR classifier was first constructed using a 188-dimensional (188D algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC and ProtrWeb web-based algorithms for human GABAAR proteins. Then, four classifiers including gradient boosting decision tree (GBDT, random forest (RF, a library for support vector machine (libSVM, and k-nearest neighbor (k-NN were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew’s correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABAAR classifier was successfully constructed using only the protein sequence information.

  1. Research on Bearing Fault Diagnosis Using APSO-SVM Method

    Directory of Open Access Journals (Sweden)

    Guangchun Yang

    2014-07-01

    Full Text Available According to the statistics, over 30 % of rotating equipment faults occurred in bearings. Therefore, the fault diagnosis of bearing has a great significance. To achieve effective bearing faults diagnosis, a diagnosis model based on support vector machine (SVM and accelerated particle swarm optimization (APSO for bearing fault diagnosis is proposed. Firstly, empirical mode decomposition (EMD is adopted to decompose the fault signal into sum of several intrinsic mode function (IMF. Then, the feature vectors for bearing fault diagnosis are obtained from the IMF energy. Finally, the fault mode is identified by SVM model which is optimized by APSO. The experiment results show that the proposed diagnosis method can identify the bearing fault type effectively.

  2. 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.

  3. The system evaluation for report writing skills of summary by HGA-SVM with Ontology: Medical case study in problem based learning

    Science.gov (United States)

    Yenaeng, Sasikanchana; Saelee, Somkid; Samai, Wirachai

    2018-01-01

    The system evaluation for report writing skills of summary by Hybrid Genetic Algorithm-Support Vector Machines (HGA-SVM) with Ontology of Medical Case Study in Problem Based Learning (PBL) is a system was developed as a guideline of scoring for the facilitators or medical teacher. The essay answers come from medical student of medical education courses in the nervous system motion and Behavior I and II subject, a third year medical student 20 groups of 9-10 people, the Faculty of Medicine in Prince of Songkla University (PSU). The audit committee have the opinion that the ratings of individual facilitators are inadequate, this system to solve such problems. In this paper proposes a development of the system evaluation for report writing skills of summary by HGA-SVM with Ontology of medical case study in PBL which the mean scores of machine learning score and humans (facilitators) score were not different at the significantly level .05 all 3 essay parts contain problem essay part, hypothesis essay part and learning objective essay part. The result show that, the average score all 3 essay parts that were not significantly different from the rate at the level of significance .05.

  4. Estimating grassland biomass using SVM band shaving of hyperspectral data

    NARCIS (Netherlands)

    Clevers, J.G.P.W.; Heijden, van der G.W.A.M.; Verzakov, S.; Schaepman, M.E.

    2007-01-01

    In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM

  5. Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

    Directory of Open Access Journals (Sweden)

    Ling-li Jiang

    2014-01-01

    Full Text Available Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

  6. Parallelization of multicategory support vector machines (PMC-SVM for classifying microarray data

    Directory of Open Access Journals (Sweden)

    Deng Youping

    2006-12-01

    Full Text Available Abstract Background Multicategory Support Vector Machines (MC-SVM are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance computing techniques. Results In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM have been developed based on the sequential minimum optimization-type decomposition method for support vector machines (SMO-SVM. It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster for multicategory classification of microarray data. PMC-SVM has been analyzed and evaluated using four microarray datasets with multiple diagnostic categories, such as different cancer types and normal tissue types. Conclusion The experiments show that the PMC-SVM can significantly improve the performance of classification of microarray data without loss of accuracy, compared with previous work.

  7. Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

    Science.gov (United States)

    Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

    Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field

  8. 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.

  9. H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.

    Science.gov (United States)

    Ebina, Teppei; Suzuki, Ryosuke; Tsuji, Ryotaro; Kuroda, Yutaka

    2014-08-01

    Domain linker prediction is attracting much interest as it can help identifying novel domains suitable for high throughput proteomics analysis. Here, we report H-DROP, an SVM-based Helical Domain linker pRediction using OPtimal features. H-DROP is, to the best of our knowledge, the first predictor for specifically and effectively identifying helical linkers. This was made possible first because a large training dataset became available from IS-Dom, and second because we selected a small number of optimal features from a huge number of potential ones. The training helical linker dataset, which included 261 helical linkers, was constructed by detecting helical residues at the boundary regions of two independent structural domains listed in our previously reported IS-Dom dataset. 45 optimal feature candidates were selected from 3,000 features by random forest, which were further reduced to 26 optimal features by stepwise selection. The prediction sensitivity and precision of H-DROP were 35.2 and 38.8%, respectively. These values were over 10.7% higher than those of control methods including our previously developed DROP, which is a coil linker predictor, and PPRODO, which is trained with un-differentiated domain boundary sequences. Overall, these results indicated that helical linkers can be predicted from sequence information alone by using a strictly curated training data set for helical linkers and carefully selected set of optimal features. H-DROP is available at http://domserv.lab.tuat.ac.jp.

  10. 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.

  11. Estimating grassland biomass using SVM band shaving of hyperspectral data

    OpenAIRE

    Clevers, J G P W; van Der Heijden, G.W.A.M.; Verzakov, S; Schaepman, M. E.

    2007-01-01

    In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM band shaving technique was compared with a partial least squares (PLS) and a stepwise forward selection analysis. Using their results, a range of vegetation indices was used as predictors for grasslan...

  12. 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.

  13. Customer and performance rating in QFD using SVM classification

    Science.gov (United States)

    Dzulkifli, Syarizul Amri; Salleh, Mohd Najib Mohd; Leman, A. M.

    2017-09-01

    In a classification problem, where each input is associated to one output. Training data is used to create a model which predicts values to the true function. SVM is a popular method for binary classification due to their theoretical foundation and good generalization performance. However, when trained with noisy data, the decision hyperplane might deviate from optimal position because of the sum of misclassification errors in the objective function. In this paper, we introduce fuzzy in weighted learning approach for improving the accuracy of Support Vector Machine (SVM) classification. The main aim of this work is to determine appropriate weighted for SVM to adjust the parameters of learning method from a given set of noisy input to output data. The performance and customer rating in Quality Function Deployment (QFD) is used as our case study to determine implementing fuzzy SVM is highly scalable for very large data sets and generating high classification accuracy.

  14. PENGEMBANGAN MODEL SUPPORT VECTOR MACHINES (SVM DENGAN MEMPERBANYAK DATASET UNTUK PREDIKSI BISNIS FOREX MENGGUNAKAN METODE KERNEL TRICK

    Directory of Open Access Journals (Sweden)

    adi sucipto

    2017-09-01

    Full Text Available There are many types of investments that can be used to generate income, such as in the form of land, houses, gold, precious metals etc., there are also in the form of financial assets such as stocks, mutual funds, bonds and money markets or capital markets. One of the investments that attract enough attention today is the capital market investment. The purpose of this study is to predict and improve the accuracy of foreign exchange rates on forex business by using the Support Vector Machine model as a model for predicting and using more data sets compared with previous research that is as many as 1558 dataset. This study uses currency exchange rate data obtained from PT. Best Profit Future Cab. Surabaya is already in the form of data consisting of open, high, low, close attributes by using the current data of Euro currency exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 As much as 1689 dataset, After conducting research using Support Vector Machine model with kernel trick method to predict Forex using current data of Euro exchange rate to USA Dollar with period every 1 minutes from May 12, 2016 at 09.51 until 13 May 2016 at 12:30 as much as 1689 The dataset yielded a considerable prediction accuracy of 97.86%, with this considerable accuracy indicating that the movement of the Euro currency exchange rate to the USA Dollar on May 12 to May 13, 2016 can be predicted precisely.

  15. Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

    Science.gov (United States)

    de Carvalho Filho, Antonio Oseas; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

    2017-08-01

    Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

  16. 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.

  17. 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.

  18. Robust Non-Linear Direct Torque and Flux Control of Adjustable Speed Sensorless PMSM Drive Based on SVM Using a PI Predictive Controller

    Directory of Open Access Journals (Sweden)

    F. Naceri

    2010-01-01

    Full Text Available This paper presents a new sensorless direct torque control method for voltage inverter – fed PMSM. The control methodis used a modified Direct Torque Control scheme with constant inverter switching frequency using Space Vector Modulation(DTC-SVM. The variation of stator and rotor resistance due to changes in temperature or frequency deteriorates theperformance of DTC-SVM controller by introducing errors in the estimated flux linkage and the electromagnetic torque.As a result, this approach will not be suitable for high power drives such as those used in tractions, as they require goodtorque control performance at considerably lower frequency. A novel stator resistance estimator is proposed. The estimationmethod is implemented using the Extended Kalman Filter. Finally extensive simulation results are presented to validate theproposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

  19. Data Driven Constraints for the SVM

    DEFF Research Database (Denmark)

    Darkner, Sune; Clemmensen, Line Katrine Harder

    2012-01-01

    . Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use...... classifier solution, compared to the SVM i.e. reduces variance and improves classification rates. We present a quantitative measure of the information level contained in the pairing and test the method on simulated as well as a high-dimensional paired data set of ear-canal surfaces....

  20. A Linear-RBF Multikernel SVM to Classify Big Text Corpora

    Directory of Open Access Journals (Sweden)

    R. Romero

    2015-01-01

    Full Text Available 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.

  1. A structural SVM approach for reference parsing.

    Science.gov (United States)

    Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

    2011-06-09

    Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

  2. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

    Science.gov (United States)

    She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

    2015-01-01

    Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  3. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification

    Directory of Open Access Journals (Sweden)

    Qingshan She

    2015-01-01

    Full Text Available Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

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

    DEFF Research Database (Denmark)

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

    2009-01-01

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

  5. 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.

  6. Research on Intersession Variability Compensation for MLLR-SVM Speaker Recognition

    Science.gov (United States)

    Zhong, Shan; Shan, Yuxiang; He, Liang; Liu, Jia

    One of the most important challenges in speaker recognition is intersession variability (ISV), primarily cross-channel effects. Recent NIST speaker recognition evaluations (SRE) include a multilingual scenario with training conversations involving multilingual speakers collected in a number of other languages, leading to further performance decline. One important reason for this is that more and more researchers are using phonetic clustering to introduce high level information to improve speaker recognition. But such language dependent methods do not work well in multilingual conditions. In this paper, we study both language and channel mismatch using a support vector machine (SVM) speaker recognition system. Maximum likelihood linear regression (MLLR) transforms adapting a universal background model (UBM) are adopted as features. We first introduce a novel language independent statistical binary-decision tree to reduce multi-language effects, and compare this data-driven approach with a traditional knowledge based one. We also construct a framework for channel compensation using feature-domain latent factor analysis (LFA) and MLLR supervector kernel-based nuisance attribute projection (NAP) in the model-domain. Results on the NIST SRE 2006 1conv4w-1conv4w/mic corpus show significant improvement. We also compare our compensated MLLR-SVM system with state-of-the-art cepstral Gaussian mixture and SVM systems, and combine them for a further improvement.

  7. 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.

  8. Research on big data risk assessment of major transformer defects and faults fusing power grid, equipment and environment based on SVM

    Science.gov (United States)

    Guo, Lijuan; Yan, Haijun; Gao, Wensheng; Chen, Yun; Hao, Yongqi

    2018-01-01

    With the development of power big data, considering the wider power system data, the appropriate large data analysis method can be used to mine the potential law and value of power big data. On the basis of considering all kinds of monitoring data and defects and fault records of main transformer, the paper integrates the power grid, equipment as well as environment data and uses SVM as the main algorithm to evaluate the risk of the main transformer. It gets and compares the evaluation results under different modes, and proves that the risk assessment algorithms and schemes have certain effectiveness. This paper provides a new idea for data fusion of smart grid, and provides a reference for further big data evaluation of power grid equipment.

  9. SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

    Directory of Open Access Journals (Sweden)

    Marina M-C Vidovic

    Full Text Available Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs achieve state-of-the-art performances in genomic discrimination tasks, but--due to its black-box character--motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs--regardless of their length and complexity--underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

  10. SVM2Motif--Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor.

    Science.gov (United States)

    Vidovic, Marina M-C; Görnitz, Nico; Müller, Klaus-Robert; Rätsch, Gunnar; Kloft, Marius

    2015-01-01

    Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs) achieve state-of-the-art performances in genomic discrimination tasks, but--due to its black-box character--motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs) allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs--regardless of their length and complexity--underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

  11. An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

    Directory of Open Access Journals (Sweden)

    Yan Wei

    2017-01-01

    Full Text Available In order to develop a new and effective prediction system, the full potential of support vector machine (SVM was explored by using an improved grey wolf optimization (GWO strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC curve, sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

  12. Using support vector machine models for crash injury severity analysis.

    Science.gov (United States)

    Li, Zhibin; Liu, Pan; Wang, Wei; Xu, Chengcheng

    2012-03-01

    The study presented in this paper investigated the possibility of using support vector machine (SVM) models for crash injury severity analysis. Based on crash data collected at 326 freeway diverge areas, a SVM model was developed for predicting the injury severity associated with individual crashes. An ordered probit (OP) model was also developed using the same dataset. The research team compared the performance of the SVM model and the OP model. It was found that the SVM model produced better prediction performance for crash injury severity than did the OP model. The percent of correct prediction for the SVM model was found to be 48.8%, which was higher than that produced by the OP model (44.0%). Even though the SVM model may suffer from the multi-class classification problem, it still provides better prediction results for small proportion injury severities than the OP model does. The research also investigated the potential of using the SVM model for evaluating the impacts of external factors on crash injury severities. The sensitivity analysis results show that the SVM model produced comparable results regarding the impacts of variables on crash injury severity as compared to the OP model. For several variables such as the length of the exit ramp and the shoulder width of the freeway mainline, the results of the SVM model are more reasonable than those of the OP model. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

    Directory of Open Access Journals (Sweden)

    Hong Men

    2018-01-01

    Full Text Available Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA and Partial Least Squares (PLS. Support Vector Machine (SVM, Random Forest (RF, and Extreme Learning Machine (ELM were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

  14. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

    Science.gov (United States)

    Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan

    2018-01-01

    Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level. PMID:29346328

  15. CEAI: CCM based Email Authorship Identification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah

    2013-01-01

    reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10......In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...

  16. Disorder recognition in clinical texts using multi-label structured SVM.

    Science.gov (United States)

    Lin, Wutao; Ji, Donghong; Lu, Yanan

    2017-01-31

    Information extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in clinical narratives. But recognition of some more complicated disorder mentions like overlapping ones is still an open issue. This paper proposes a multi-label structured Support Vector Machine (SVM) based method for disorder mention recognition. We present a multi-label scheme which could be used in complicated entity recognition tasks. We performed three sets of experiments to evaluate our model. Our best F1-Score on the 2013 Conference and Labs of the Evaluation Forum data set is 0.7343. There are six types of labels in our multi-label scheme, all of which are represented by 24-bit binary numbers. The binary digits of each label contain information about different disorder mentions. Our multi-label method can recognize not only disorder mentions in the form of contiguous or discontiguous words but also mentions whose spans overlap with each other. The experiments indicate that our multi-label structured SVM model outperforms the condition random field (CRF) model for this disorder mention recognition task. The experiments show that our multi-label scheme surpasses the baseline. Especially for overlapping disorder mentions, the F1-Score of our multi-label scheme is 0.1428 higher than the baseline BIOHD1234 scheme. This multi-label structured SVM based approach is demonstrated to work well with this disorder recognition task. The novel multi-label scheme we presented is superior to the baseline and it can be used in other models to solve various types of complicated entity recognition tasks as well.

  17. Classifying smoke in laparoscopic videos using SVM

    Directory of Open Access Journals (Sweden)

    Alshirbaji Tamer Abdulbaki

    2017-09-01

    Full Text Available Smoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames is around 84%, with the sensitivity (i.e. correctly detected smoke frames and the specificity (i.e. correctly detected non-smoke frames are 89% and 80%, respectively.

  18. 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.

  19. 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.

  20. Fault diagnosis of monoblock centrifugal pump using SVM

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    V. Muralidharan

    2014-09-01

    Full Text Available Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of SVM algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump.

  1. A novel transmission line protection using DOST and SVM

    Directory of Open Access Journals (Sweden)

    M. Jaya Bharata Reddy

    2016-06-01

    Full Text Available This paper proposes a smart fault detection, classification and location (SFDCL methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST. The methodology is based on synchronized current measurements from remote telemetry units (RTUs installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS and artificial neural network (ANN, for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.

  2. Application of ANFIS and SVM Systems in Order to Estimate Monthly Reference Crop Evapotranspiration in the Northwest of Iran

    Directory of Open Access Journals (Sweden)

    F. Ahmadi

    2016-10-01

    Full Text Available Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19. Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering. A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1 and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9. SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15 based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12. The SVM is originally a two-class Classifier that separates the classes

  3. Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine

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    Lutao Liu

    2013-01-01

    Full Text Available A new method of transformer fault diagnosis based on relevance vector machine (RVM is proposed. Bayesian estimation is applied to support vector machine (SVM in the novel algorithm, which made fault diagnosis system work more effectively. In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples. The process of transformer fault diagnosis for four working statuses is given in experiments and simulations. The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP neural networks and general SVM methods.

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

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    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.

  5. Detecting microcalcifications in mammograms by using SVM method for the diagnostics of breast cancer

    Science.gov (United States)

    Wan, Baikun; Wang, Ruiping; Qi, Hongzhi; Cao, Xuchen

    2005-01-01

    Support vector machine (SVM) is a new statistical learning method. Compared with the classical machine learning methods, SVM learning discipline is to minimize the structural risk instead of the empirical risk of the classical methods, and it gives better generative performance. Because SVM algorithm is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one. In this paper a SVM algorithm is applied to detect the micro-calcifications (MCCs) in mammograms for the diagnostics of breast cancer that has not been reported yet. It had been tested with 10 mammograms and the results show that the algorithm can achieve a higher true positive in comparison with artificial neural network (ANN) based on the empirical risk minimization, and is valuable for further study and application in the clinical engineering.

  6. Hardware realization of an SVM algorithm implemented in FPGAs

    Science.gov (United States)

    Wiśniewski, Remigiusz; Bazydło, Grzegorz; Szcześniak, Paweł

    2017-08-01

    The paper proposes a technique of hardware realization of a space vector modulation (SVM) of state function switching in matrix converter (MC), oriented on the implementation in a single field programmable gate array (FPGA). In MC the SVM method is based on the instantaneous space-vector representation of input currents and output voltages. The traditional computation algorithms usually involve digital signal processors (DSPs) which consumes the large number of power transistors (18 transistors and 18 independent PWM outputs) and "non-standard positions of control pulses" during the switching sequence. Recently, hardware implementations become popular since computed operations may be executed much faster and efficient due to nature of the digital devices (especially concurrency). In the paper, we propose a hardware algorithm of SVM computation. In opposite to the existing techniques, the presented solution applies COordinate Rotation DIgital Computer (CORDIC) method to solve the trigonometric operations. Furthermore, adequate arithmetic modules (that is, sub-devices) used for intermediate calculations, such as code converters or proper sectors selectors (for output voltages and input current) are presented in detail. The proposed technique has been implemented as a design described with the use of Verilog hardware description language. The preliminary results of logic implementation oriented on the Xilinx FPGA (particularly, low-cost device from Artix-7 family from Xilinx was used) are also presented.

  7. Towards understanding the influence of SVM hyperparameters

    CSIR Research Space (South Africa)

    Van Heerden, CJ

    2010-11-01

    Full Text Available -consuming and resource-intensive. On large datasets, 10-fold cross-validation grid searches can become intractable without supercomputers or high performance computing clusters. They present theoretical and empirical arguments as to how SVM hyperparameters scale with N...

  8. Object detection based on deformable part model

    Science.gov (United States)

    Wei, Lei; Xu, Zhiyong

    2016-09-01

    In complex scene, considering traditional object detection methods based on feature points have exposed many problems, such as undetected points, low detected ratio and cannot well process object occlusion and scaling situation, this paper proposes a detection method which based on a deformable part model. The method uses histogram of oriented gradient (HOG) feature as the object description, and the deformable part model includes a global template and several high-resolution templates. And the method uses the support vector machine (SVM) training the object model. In the learning process, after the HOG feature extracted, the method modifies the HOG feature, and then uses the principal component analysis (PCA) method reducing feature dimensions to avoid over-learning, and improve the detection rate in the detection process. The experiment results shows that the method proposed can better process object occlusion or scaled situation, and there's also an improvement in detection ratio.

  9. A satellite-based global landslide model

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    A. Farahmand

    2013-05-01

    Full Text Available Landslides are devastating phenomena that cause huge damage around the world. This paper presents a quasi-global landslide model derived using satellite precipitation data, land-use land cover maps, and 250 m topography information. This suggested landslide model is based on the Support Vector Machines (SVM, a machine learning algorithm. The National Aeronautics and Space Administration (NASA Goddard Space Flight Center (GSFC landslide inventory data is used as observations and reference data. In all, 70% of the data are used for model development and training, whereas 30% are used for validation and verification. The results of 100 random subsamples of available landslide observations revealed that the suggested landslide model can predict historical landslides reliably. The average error of 100 iterations of landslide prediction is estimated to be approximately 7%, while approximately 2% false landslide events are observed.

  10. Forecasting Dry Bulk Freight Index with Improved SVM

    Directory of Open Access Journals (Sweden)

    Qianqian Han

    2014-01-01

    Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

  11. Vector machine techniques for modeling of seismic liquefaction data

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    Pijush Samui

    2014-06-01

    Full Text Available This article employs three soft computing techniques, Support Vector Machine (SVM; Least Square Support Vector Machine (LSSVM and Relevance Vector Machine (RVM, for prediction of liquefaction susceptibility of soil. SVM and LSSVM are based on the structural risk minimization (SRM principle which seeks to minimize an upper bound of the generalization error consisting of the sum of the training error and a confidence interval. RVM is a sparse Bayesian kernel machine. SVM, LSSVM and RVM have been used as classification tools. The developed SVM, LSSVM and RVM give equations for prediction of liquefaction susceptibility of soil. A comparative study has been carried out between the developed SVM, LSSVM and RVM models. The results from this article indicate that the developed SVM gives the best performance for prediction of liquefaction susceptibility of soil.

  12. A Novel Lithium Ion Battery Autonomous Strategy Improvement Based on SVM-DTC for Urban Electric Vehicle under Several Speeds Tests

    OpenAIRE

    Nasri Abdelfatah; Gasbaoui Brahim

    2011-01-01

    One of the main challenges in the modern commercialized electric vehicle (EV) is the battery management system. In this paper a novel strategy of EV power management is presented based on direct torque space vector modulation. We used the battery state of charge (SOC) which is the percent of residual capacity by nominal capacity. The proper estimation of SOC of Lithium-ion battery provides an energy management system in EV. The proposed controller provides a good torque control and speed stab...

  13. 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.

  14. Efficient and Privacy-Preserving Online Medical Prediagnosis Framework Using Nonlinear SVM.

    Science.gov (United States)

    Zhu, Hui; Liu, Xiaoxia; Lu, Rongxing; Li, Hui

    2017-05-01

    With the advances of machine learning algorithms and the pervasiveness of network terminals, the 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 prediagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an e fficient and privacy-preserving online medical prediagnosis 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 multiparty 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.

  15. 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.

  16. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

  17. Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

    OpenAIRE

    Yang, Yi; Dong, Yao; Chen, Yanhua; Li, Caihong

    2014-01-01

    Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting metho...

  18. A novel robust adaptive control algorithm and application to DTC-SVM of AC drives

    Directory of Open Access Journals (Sweden)

    Belkacem Sebti

    2010-01-01

    Full Text Available In this paper a new robust adaptive control algorithm for AC machine is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy. The MCS algorithm is a significant development of MRAC and is similary based on the hyper stability theory of Popov. The hyperstability theory guarantees the global asymptotic stability of the error vector (i.e. the difference between the reference model and system states. Finally, a new approach has been successfully implemented to DTC-SVM. Discussion on theoretical aspects, such as, selection of a reference model, stability analysis, gain adaptive and steady state error are included. Results of simulations are also presented.

  19. 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.

  20. Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features

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    Lamei Zhang

    2010-01-01

    Full Text Available The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM and Support Vector Machine (SVM is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix (GCM, SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area (DK, Denmark. The preliminary result indicates that this method can classify most of the areas correctly.

  1. Penerapan Support Vector Machine (SVM untuk Pengkategorian Penelitian

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    Fithri Selva Jumeilah

    2017-07-01

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

  2. Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM.

    Science.gov (United States)

    Roushangar, Kiyoumars; Valizadeh, Reyhaneh; Ghasempour, Roghayeh

    2017-10-01

    Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F 1 (Froude number) and (h 2- h 1 )/h 1 (h 1 and h 2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F 1 and h 1 /B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

  3. 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

  4. 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.

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

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

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

  6. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-01-01

    Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

  7. Optimal parameters of the SVM for temperature prediction

    Directory of Open Access Journals (Sweden)

    X. Shi

    2015-05-01

    Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

  8. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Science.gov (United States)

    Zhu, Fei; Shen, Bairong

    2012-01-01

    Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1) of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  9. Combined SVM-CRFs for biological named entity recognition with maximal bidirectional squeezing.

    Directory of Open Access Journals (Sweden)

    Fei Zhu

    Full Text Available Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs and conditional random fields (CRFs, which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F(1 of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

  10. [Application of SVM and wavelet analysis in EEG classification].

    Science.gov (United States)

    Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei

    2011-04-01

    We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.

  11. Neural cell image segmentation method based on support vector machine

    Science.gov (United States)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  12. Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

    Science.gov (United States)

    Mukhopadhyay, Sabyasachi; Das, Nandan K.; Kurmi, Indrajit; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

    2017-10-01

    We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

  13. Identification of potential ACAT-2 selective inhibitors using pharmacophore, SVM and SVR from Chinese herbs.

    Science.gov (United States)

    Qiao, Lian-Sheng; Zhang, Xian-Bao; Jiang, Lu-di; Zhang, Yan-Ling; Li, Gong-Yu

    2016-11-01

    Acyl-coenzyme A cholesterol acyltransferase (ACAT) plays an important role in maintaining cellular and organismal cholesterol homeostasis. Two types of ACAT isozymes with different functions exist in mammals, named ACAT-1 and ACAT-2. Numerous studies showed that ACAT-2 selective inhibitors are effective for the treatment of hypercholesterolemia and atherosclerosis. However, as a typical endoplasmic reticulum protein, ACAT-2 protein has not been purified and revealed, so combinatorial ligand-based methods might be the optimal strategy for discovering the ACAT-2 selective inhibitors. In this study, selective pharmacophore models of ACAT-1 inhibitors and ACAT-2 inhibitors were built, respectively. The optimal pharmacophore model for each subtype was identified and utilized as queries for the Traditional Chinese Medicine Database screening. A total of 180 potential ACAT-2 selective inhibitors were obtained, which were identified using an ACAT-2 pharmacophore and not by our ACAT-1 model. Selective SVM model and bioactive SVR model were generated for further identification of the obtained ACAT-2 inhibitors. Ten compounds were finally obtained with predicted inhibitory activities toward ACAT-2. Hydrogen bond acceptor, 2D autocorrelations, GETAWAY descriptors, and BCUT descriptors were identified as key structural features for selectivity and activity of ACAT-2 inhibitors. This study provides a reasonable ligand-based approach to discover potential ACAT-2 selective inhibitors from Chinese herbs, which could help in further screening and development of ACAT-2 selective inhibitors.

  14. [A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model].

    Science.gov (United States)

    Zhang, Jiyang; Zhang, Daibing; Zhang, Wei; Xie, Hongwei

    2012-09-01

    The online reversed-phase liquid chromatography (RPLC) contributes a lot for the large scale mass spectrometry based protein identification in proteomics. Retention time (RT) as an important evidence can be used to distinguish the false positive/true positive peptide identifications. Because of the nonlinear concentration curve of organic phase in the whole range of run time and the interactions among peptides, the sequence based RT prediction of peptides has low accuracy and is difficult to generalize in practice, and thus is less effective in the validation of peptide identifications. A serial and parallel support vector machine (SP-SVM) method was proposed to characterize the nonlinear effect of organic phase concentration and the interactions among peptides. The SP-SVM contains a support vector regression (SVR) only for model training (named as p-SVR) and 4 SVM models (named as C-SVM, 1-SVR, s-SVR and n-SVR) for the RT prediction. After distinguishing the peptide chromatographic behavior by C-SVM, 1-SVR and s-SVR were used to predict the peptide RT specifically to improve the accuracy. Then the peptide RT was normalized by n-SVR to characterize the peptide interactions. The prediction accuracy was improved significantly by applying this method to the processing of the complex sample dataset. The coefficient of the determination between predictive and experimental RTs reaches 0. 95, the prediction error range was less than 20% of the total LC run time for more than 95% cases, and less than 10% of the total LC run time for more than 70% cases. The performance of this model reaches the best of known so far. More important, the SP-SVM method provides a framework to take into account the interactions among peptides in chromatographic separation, and its performance can be improved further by introducing new data processing and experiment strategy.

  15. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  16. Effects of Process Parameters on the Extraction of Quercetin and Rutin from the Stalks of Euonymus Alatus (Thumb. Sieb and Predictive Model Based on Least Squares Support Vector Machine Optimized by an Improved Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jiangqing Liao

    2016-11-01

    Full Text Available Ultrasonic-assisted extraction (UAE of quercetin and rutin from the stalks of Euonymus alatus (Thunb. Sieb in our laboratory, which aimed at evaluating and optimizing the process parameters, was investigated in this work. In addition, process parameters such as ethanol solution concentration, solvent volume/sample ratio, ultrasound power and extraction time, ultrasound frequency and extraction temperature were also first applied for evaluating the influence of extraction of quercetin and rutin. Optimum process parameters obtained were: ethanol solution 60%, extraction time 30 min, solvent volume/sample ratio 40 mL/g, ultrasound power 200 W, extraction temperature 30 °C and ultrasound frequency 80 kHz. Further a hybrid predictive model, which is based on least squares support vector machine (LS-SVM in combination with improved fruit fly optimization algorithm (IFOA, was first used to predict the UAE process. The established IFOA-LS-SVM model, in which six process parameters and extraction yields of quercetin and rutin were used as input variables and output variables, respectively, successfully predicted the extraction yields of quercetin and rutin with a low error. Moreover, by comparison with SVM, LS-SVM and multiple regression models, IFOA-LS-SVM model has higher accuracy and faster convergence. Results proved that the proposed model is capable of predicting extraction yields of quercetin and rutin in UAE process.

  17. Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors.

    Science.gov (United States)

    Wang, Ling; Wang, Maolin; Yan, Aixia; Dai, Bin

    2013-02-01

    Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9 % for SOM1, 92.6 % for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.

  18. Support Vector Machines Parameter Selection Based on Combined Taguchi Method and Staelin Method for E-mail Spam Filtering

    Directory of Open Access Journals (Sweden)

    Wei-Chih Hsu

    2012-04-01

    Full Text Available Support vector machines (SVM are a powerful tool for building good spam filtering models. However, the performance of the model depends on parameter selection. Parameter selection of SVM will affect classification performance seriously during training process. In this study, we use combined Taguchi method and Staelin method to optimize the SVM-based E-mail Spam Filtering model and promote spam filtering accuracy. We compare it with other parameters optimization methods, such as grid search. Six real-world mail data sets are selected to demonstrate the effectiveness and feasibility of the method. The results show that our proposed methods can find the effective model with high classification accuracy

  19. Penilaian Esai Jawaban Bahasa Indonesia Menggunakan Metode Svm - Lsa Dengan Fitur Generik

    OpenAIRE

    Adhitia, Rama; Purwarianti, Ayu

    2009-01-01

    Paper ini mengkaji sebuah solusi untuk permasalahan penilaian jawaban esai secara otomatis dengan menggabungkan support vector machine (SVM) sebagai teknik klasifikasi teks otomatis dengan LSA sebagai USAha untuk menangani sinonim dan polisemi antar index term. Berbeda dengan sistem penilaian esai yang biasa yakni fitur yang digunakan berupa index term, fitur yang digunakan proses penilaian jawaban esai adalah berupa fitur generic yang memungkinkan pengujian model penilaian esai untuk berbaga...

  20. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    Science.gov (United States)

    Liu, Tian-Ling; Su, Qi-Ya; Sun, Qun; Yang, Li-Ming

    2012-06-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling due to its advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds is proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA and PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results for three different spectral regions show that the performances of three methods, i. e. PCA+SVM, LLE+SVM, PLS+SVM, are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data.

  1. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

    DEFF Research Database (Denmark)

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

    2013-01-01

    This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... voltage signal injection method has been chosen for sensorless control design. PMSM is modelled at high frequencies, and a rotor speed and position estimation algorithm is proposed. The proposed estimator is designed and implemented using MATLAB/Simulink® and is tested under several operating conditions...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

  2. 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.

  3. A self-trained semisupervised SVM approach to the remote sensing land cover classification

    Science.gov (United States)

    Liu, Ying; Zhang, Bai; Wang, Li-min; Wang, Nan

    2013-09-01

    Support vector machines (SVM) are nowadays receiving increasing attention in remote sensing applications although this technique is very sensitive to the parameters setting and training set definition. Self-training is an effective semisupervised method, which can reduce the effort needed to prepare the training set by training the model with a small number of labeled examples and an additional set of unlabeled examples. In this study, a novel semisupervised SVM model that uses self-training approach is proposed to address the problem of remote sensing land cover classification. The key characteristics of this approach are that (1) the self-adaptive mutation particle swarm optimization algorithm is introduced to get the optimum parameters that improve the generalization performance of the SVM classifier, and (2) the Gustafson-Kessel fuzzy clustering algorithm is proposed for the selection of unlabeled points to reduce the impact of ineffective labels. The effectiveness of the proposed technique is evaluated firstly with samples from remote sensing data and then by identifying different land cover regions in the remote sensing imagery. Experimental results show that accuracy level is increased by applying this learning scheme, which results in the smallest generalization error compared with the other schemes.

  4. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

    Directory of Open Access Journals (Sweden)

    Jie Xu

    2013-01-01

    Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

  5. Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

    Directory of Open Access Journals (Sweden)

    Junyu Wei

    2016-01-01

    Full Text Available Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE and multiclass classification of support vector machine (SVM, we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP, the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP, arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

  6. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Directory of Open Access Journals (Sweden)

    QingJun Song

    Full Text Available Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB algorithm plus Support vector machine (SVM is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  7. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  8. CEAI: CCM-based email authorship identification model

    Directory of Open Access Journals (Sweden)

    Sarwat Nizamani

    2013-11-01

    Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].

  9. A comparison of non-symmetric entropy-based classification trees and support vector machine for cardiovascular risk stratification.

    Science.gov (United States)

    Singh, Anima; Guttag, John V

    2011-01-01

    Classification tree-based risk stratification models generate easily interpretable classification rules. This feature makes classification tree-based models appealing for use in a clinical setting, provided that they have comparable accuracy to other methods. In this paper, we present and evaluate the performance of a non-symmetric entropy-based classification tree algorithm. The algorithm is designed to accommodate class imbalance found in many medical datasets. We evaluate the performance of this algorithm, and compare it to that of SVM-based classifiers, when applied to 4219 non-ST elevation acute coronary syndrome patients. We generated SVM-based classifiers using three different strategies for handling class imbalance: cost-sensitive SVM learning, synthetic minority oversampling (SMOTE), and random majority undersampling. We used both linear and radial basis kernel-based SVMs. Our classification tree models outperformed SVM-based classifiers generated using each of the three techniques. On average, the classification tree models yielded a 14% improvement in G-score and a 21% improvement in F-score relative to the linear SVM classifiers with the best performance. Similarly, our classification tree models yielded a 12% improvement in G-score and a 21% improvement in the F-score over the best RBF kernel-based SVM classifiers.

  10. Arrhythmia classification using SVM with selected features | Kohli ...

    African Journals Online (AJOL)

    The various types of arrhythmias in the cardiac arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. ECG arrhythmia datasets are of generally ...

  11. Support vector machines for predictive modeling in heterogeneous catalysis: a comprehensive introduction and overfitting investigation based on two real applications.

    Science.gov (United States)

    Baumes, L A; Serra, J M; Serna, P; Corma, A

    2006-01-01

    This works provides an introduction to support vector machines (SVMs) for predictive modeling in heterogeneous catalysis, describing step by step the methodology with a highlighting of the points which make such technique an attractive approach. We first investigate linear SVMs, working in detail through a simple example based on experimental data derived from a study aiming at optimizing olefin epoxidation catalysts applying high-throughput experimentation. This case study has been chosen to underline SVM features in a visual manner because of the few catalytic variables investigated. It is shown how SVMs transform original data into another representation space of higher dimensionality. The concepts of Vapnik-Chervonenkis dimension and structural risk minimization are introduced. The SVM methodology is evaluated with a second catalytic application, that is, light paraffin isomerization. Finally, we discuss why SVMs is a strategic method, as compared to other machine learning techniques, such as neural networks or induction trees, and why emphasis is put on the problem of overfitting.

  12. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction

    Directory of Open Access Journals (Sweden)

    Yongjian Li

    2016-01-01

    Full Text Available Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM using vibration signals is proposed which is conducted in three main steps. Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information. Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions. Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type. To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters. Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.

  13. An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Deepak Bhatt

    2012-07-01

    Full Text Available Micro Electro Mechanical System (MEMS-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.

  14. An enhanced MEMS error modeling approach based on Nu-Support Vector Regression.

    Science.gov (United States)

    Bhatt, Deepak; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay

    2012-01-01

    Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10-35% for gyroscopes and 61-76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.

  15. Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

    Science.gov (United States)

    Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P

    2017-04-01

    Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Support vector machines for seizure detection in an animal model of chronic epilepsy

    Science.gov (United States)

    Nandan, Manu; Talathi, Sachin S.; Myers, Stephen; Ditto, William L.; Khargonekar, Pramod P.; Carney, Paul R.

    2010-06-01

    We compare the performance of three support vector machine (SVM) types: weighted SVM, one-class SVM and support vector data description (SVDD) for the application of seizure detection in an animal model of chronic epilepsy. Large EEG datasets (273 h and 91 h respectively, with a sampling rate of 1 kHz) from two groups of rats with chronic epilepsy were used in this study. For each of these EEG datasets, we extracted three energy-based seizure detection features: mean energy, mean curve length and wavelet energy. Using these features we performed twofold cross-validation to obtain the performance statistics: sensitivity (S), specificity (K) and detection latency (τ) as a function of control parameters for the given SVM. Optimal control parameters for each SVM type that produced the best seizure detection statistics were then identified using two independent strategies. Performance of each SVM type is ranked based on the overall seizure detection performance through an optimality index metric (O). We found that SVDD not only performed better than the other SVM types in terms of highest value of the mean optimality index metric (\\skew3\\bar{O} ) but also gave a more reliable performance across the two EEG datasets.

  17. In silico screening of estrogen-like chemicals based on different nonlinear classification models.

    Science.gov (United States)

    Liu, Huanxiang; Papa, Ester; Walker, John D; Gramatica, Paola

    2007-07-01

    Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that are adversely affecting human and wildlife health through a variety of mechanisms. There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, because of the large number of such chemicals in the environment. In this study, quantitative structure activity relationship (QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (CP-ANN), and k nearest neighbour (kNN)) based on molecular structural descriptors. The models were externally validated by 87 chemicals (prediction set) not included in the training set. All three methods can give satisfactory prediction results both for training and prediction sets, and the most accurate model was obtained by the LS-SVM approach through the comparison of performance. In addition, our model was also applied to about 58,000 discrete organic chemicals; about 76% were predicted not to bind to Estrogen Receptor. The obtained results indicate that the proposed QSAR models are robust, widely applicable and could provide a feasible and practical tool for the rapid screening of potential estrogens.

  18. 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.

  19. Model Comparison for Breast Cancer Prognosis Based on Clinical Data.

    Directory of Open Access Journals (Sweden)

    Sabri Boughorbel

    Full Text Available We compared the performance of several prediction techniques for breast cancer prognosis, based on AU-ROC performance (Area Under ROC for different prognosis periods. The analyzed dataset contained 1,981 patients and from an initial 25 variables, the 11 most common clinical predictors were retained. We compared eight models from a wide spectrum of predictive models, namely; Generalized Linear Model (GLM, GLM-Net, Partial Least Square (PLS, Support Vector Machines (SVM, Random Forests (RF, Neural Networks, k-Nearest Neighbors (k-NN and Boosted Trees. In order to compare these models, paired t-test was applied on the model performance differences obtained from data resampling. Random Forests, Boosted Trees, Partial Least Square and GLMNet have superior overall performance, however they are only slightly higher than the other models. The comparative analysis also allowed us to define a relative variable importance as the average of variable importance from the different models. Two sets of variables are identified from this analysis. The first includes number of positive lymph nodes, tumor size, cancer grade and estrogen receptor, all has an important influence on model predictability. The second set incudes variables related to histological parameters and treatment types. The short term vs long term contribution of the clinical variables are also analyzed from the comparative models. From the various cancer treatment plans, the combination of Chemo/Radio therapy leads to the largest impact on cancer prognosis.

  20. The efficacy of support vector machines (SVM) in robust ...

    Indian Academy of Sciences (India)

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

  1. Modeling and Control of Grid Side Converter in Wind Power Generation System Based on Synchronous VFDPC with PLL

    DEFF Research Database (Denmark)

    Guo, Yougui; Zeng, Ping; Li, Lijuan

    2011-01-01

    Virtual flux oriented direct power control (VFDPC) is combined space vector modulation (SVM) with PI of DC-link voltage, active power and reactive power to control the grid side converter in wind power generation system in this paper. VFDPC has reached good performances with PLL (phase lock loop......). First the mathematical models of grid side converter, LCL filter and phase lock loop are given. Then the control strategy of grid side converter-based wind power generation system is given in detail. Finally the simulation model is modeled consisting of power circuits, such as the grid side converter...... in wind power generation system....

  2. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

    Directory of Open Access Journals (Sweden)

    Xiang-ming Gao

    2017-01-01

    Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  3. Parallel SVM for the analysis of hyperspectral data

    Science.gov (United States)

    Cavallaro, Gabriele; Atli Benediktsson, Jón; Riedel, Morris

    2014-05-01

    .e., borders, edges, discontinuities, surfaces, shapes) by performing a detailed physical analysis of the structures. Mathematical morphology provides very useful tools which allow enriching the image analysis when dealing with very high resolution (VHR) images. One of the most promising of the recent developments in the field of pattern recognition are Support Vector Machines (SVMs). These are supervised learning methods which are widely used for classification and regression. In such a context, our work aims to explore some issues regarding the SVMs. In particular, SVMs require a significant computational and storage capacity due to the large number of training vectors used for the analysis of very high spatial and spectral resolution remote sensing data. Specifically, we will adopt a parallel SVM based on the iterative MapReduce in order to analyze large scale classification problems by improving the computation speed and preserving the classification accuracies.

  4. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion

    Directory of Open Access Journals (Sweden)

    Lijun Zhang

    2018-02-01

    Full Text Available Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.

  5. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

    Science.gov (United States)

    Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

    2018-01-01

    T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95

  6. Simulation and prediction of ion transport in the reclamation of sodic soils with gypsum based on the support vector machine.

    Science.gov (United States)

    Wang, Jinman; Bai, Zhongke; Yang, Peiling

    2014-01-01

    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 Ca(2+) 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-08-15

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

  9. Model-Based Testing

    NARCIS (Netherlands)

    Timmer, Mark; Brinksma, Hendrik; Stoelinga, Mariëlle Ida Antoinette; Broy, M.; Leuxner, C.; Hoare, C.A.R.

    This paper provides a comprehensive introduction to a framework for formal testing using labelled transition systems, based on an extension and reformulation of the ioco theory introduced by Tretmans. We introduce the underlying models needed to specify the requirements, and formalise the notion of

  10. Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction.

    Science.gov (United States)

    Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

    2014-01-01

    A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.

  11. A unified classification model based on robust optimization.

    Science.gov (United States)

    Takeda, Akiko; Mitsugi, Hiroyuki; Kanamori, Takafumi

    2013-03-01

    A wide variety of machine learning algorithms such as the support vector machine (SVM), minimax probability machine (MPM), and Fisher discriminant analysis (FDA) exist for binary classification. The purpose of this letter is to provide a unified classification model that includes these models through a robust optimization approach. This unified model has several benefits. One is that the extensions and improvements intended for SVMs become applicable to MPM and FDA, and vice versa. For example, we can obtain nonconvex variants of MPM and FDA by mimicking Perez-Cruz, Weston, Hermann, and Schölkopf's (2003) extension from convex ν-SVM to nonconvex Eν-SVM. Another benefit is to provide theoretical results concerning these learning methods at once by dealing with the unified model. We give a statistical interpretation of the unified classification model and prove that the model is a good approximation for the worst-case minimization of an expected loss with respect to the uncertain probability distribution. We also propose a nonconvex optimization algorithm that can be applied to nonconvex variants of existing learning methods and show promising numerical results.

  12. 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.

  13. Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM

    Directory of Open Access Journals (Sweden)

    Shih-Ting Yang

    2013-01-01

    Full Text Available In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM and particle swarm optimization (PSO for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM, were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

  14. Realization of SVM Algorithm for Indirect Matrix Converter and Its Application in Power Factor Control

    Directory of Open Access Journals (Sweden)

    Gang Li

    2015-01-01

    Full Text Available Compared with AC-DC-AC converter, matrix converter (MC has several advantages for its bidirectional power flow, controllable power factor, and the absence of large energy storage in dc-link. The topology of MC includes direct matrix converter (DMC and indirect matrix converter (IMC. IMC has received great attention worldwide because of its easy implementation and safe commutation. Space vector PWM (SVM algorithm for indirect matrix converter is realized on DSP and CPLD platform in this paper. The control of the rectifier and inverter in IMC can be decoupled because of the intermediate dc-link. The space vector modulation scheme for IMC is discussed and the PWM sequences for the rectifier and inverter are generated. And a two-step commutation of zero current switching (ZCS in the rectifier is achieved. Input power factor of IMC can be changed by adjusting the angle of the reference current vector. Experimental tests have been conducted on a RB-IGBT based indirect matrix converter prototype. The results verify the performance of the SVM algorithm and the ability of power factor correction.

  15. Skull base tumor model.

    Science.gov (United States)

    Gragnaniello, Cristian; Nader, Remi; van Doormaal, Tristan; Kamel, Mahmoud; Voormolen, Eduard H J; Lasio, Giovanni; Aboud, Emad; Regli, Luca; Tulleken, Cornelius A F; Al-Mefty, Ossama

    2010-11-01

    Resident duty-hours restrictions have now been instituted in many countries worldwide. Shortened training times and increased public scrutiny of surgical competency have led to a move away from the traditional apprenticeship model of training. The development of educational models for brain anatomy is a fascinating innovation allowing neurosurgeons to train without the need to practice on real patients and it may be a solution to achieve competency within a shortened training period. The authors describe the use of Stratathane resin ST-504 polymer (SRSP), which is inserted at different intracranial locations to closely mimic meningiomas and other pathological entities of the skull base, in a cadaveric model, for use in neurosurgical training. Silicone-injected and pressurized cadaveric heads were used for studying the SRSP model. The SRSP presents unique intrinsic metamorphic characteristics: liquid at first, it expands and foams when injected into the desired area of the brain, forming a solid tumorlike structure. The authors injected SRSP via different passages that did not influence routes used for the surgical approach for resection of the simulated lesion. For example, SRSP injection routes included endonasal transsphenoidal or transoral approaches if lesions were to be removed through standard skull base approach, or, alternatively, SRSP was injected via a cranial approach if the removal was planned to be via the transsphenoidal or transoral route. The model was set in place in 3 countries (US, Italy, and The Netherlands), and a pool of 13 physicians from 4 different institutions (all surgeons and surgeons in training) participated in evaluating it and provided feedback. All 13 evaluating physicians had overall positive impressions of the model. The overall score on 9 components evaluated--including comparison between the tumor model and real tumor cases, perioperative requirements, general impression, and applicability--was 88% (100% being the best possible

  16. A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

    Science.gov (United States)

    Callegari, Mattia; Marin, Carlo; Notarnicola, Claudia; Carturan, Luca; Covi, Federico; Galos, Stephan; Seppi, Roberto

    2016-10-01

    In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as

  17. A novel stepwise support vector machine (SVM) method based on ...

    African Journals Online (AJOL)

    MicroRNAs (miRNAs) are a class of non-coding RNAs that are produced from miRNA precursors (premiRNAs) with stem-loop structure. At present, development of computational approach for pre-miRNA identification continues to be a challenging task, in which feature selection is greatly important. Here, we first extracted ...

  18. Model Based Definition

    Science.gov (United States)

    Rowe, Sidney E.

    2010-01-01

    In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.

  19. DISEÑO Y EVALUACIÓN DE UN CLASIFICADOR DE TEXTURAS BASADO EN LS-SVM

    Directory of Open Access Journals (Sweden)

    Beitmantt Cárdenas Quintero

    2013-07-01

    Full Text Available Evaluar el desempeño y el costo computacional de diferentes arquitecturas y metodologías Least Square Support Vector Machine (LS-SVM ante la segmentación de imágenes por textura y a partir de dichos resultados postular un modelo de un clasificador de texturas LS-SVM.  Metodología: Ante un problema de clasificación binaria representado por la segmentación  de 32 imágenes, organizadas en 4 grupos y formadas por pares de texturas típicas (granito/corteza, ladrillo/tapicería, madera/mármol, tejido/pelaje, se mide y compara el desempeño y el costo computacional de dos tipos de núcleo (Radial / Polinomial, dos funciones de optimización (mínimo local / búsqueda exhaustiva y dos funciones de costo (validación cruzada aleatoria / Validación cruzada dejando al menos uno en una LS-SVM que toma como entrada los pixeles que conforman la vecindad cruz del pixel a evaluar (no se hace extracción de características. Resultados: LS-SVM como clasificador de texturas, presenta mejor desempeño y exige menor costo computacional cuando utiliza un kernel de base radial y una función de optimización basada en un algoritmo de búsqueda de mínimos locales acompañado de una función de costo que use validación cruzada aleatoria.

  20. Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

    Science.gov (United States)

    Kim, Hanguen; Lee, Sangwon; Lee, Dongsung; Choi, Soonmin; Ju, Jinsun; Myung, Hyun

    2015-05-26

    In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  1. Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

    Directory of Open Access Journals (Sweden)

    Hanguen Kim

    2015-05-01

    Full Text Available In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit, so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.

  2. Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

    Directory of Open Access Journals (Sweden)

    Lentka Łukasz

    2015-09-01

    Full Text Available This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

  3. 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.

  4. A Non-Destructive Method for Distinguishing Reindeer Antler (Rangifer tarandus from Red Deer Antler (Cervus elaphus Using X-Ray Micro-Tomography Coupled with SVM Classifiers.

    Directory of Open Access Journals (Sweden)

    Alexandre Lefebvre

    Full Text Available Over the last decade, biomedical 3D-imaging tools have gained widespread use in the analysis of prehistoric bone artefacts. While initial attempts to characterise the major categories used in osseous industry (i.e. bone, antler, and dentine/ivory have been successful, the taxonomic determination of prehistoric artefacts remains to be investigated. The distinction between reindeer and red deer antler can be challenging, particularly in cases of anthropic and/or taphonomic modifications. In addition to the range of destructive physicochemical identification methods available (mass spectrometry, isotopic ratio, and DNA analysis, X-ray micro-tomography (micro-CT provides convincing non-destructive 3D images and analyses. This paper presents the experimental protocol (sample scans, image processing, and statistical analysis we have developed in order to identify modern and archaeological antler collections (from Isturitz, France. This original method is based on bone microstructure analysis combined with advanced statistical support vector machine (SVM classifiers. A combination of six microarchitecture biomarkers (bone volume fraction, trabecular number, trabecular separation, trabecular thickness, trabecular bone pattern factor, and structure model index were screened using micro-CT in order to characterise internal alveolar structure. Overall, reindeer alveoli presented a tighter mesh than red deer alveoli, and statistical analysis allowed us to distinguish archaeological antler by species with an accuracy of 96%, regardless of anatomical location on the antler. In conclusion, micro-CT combined with SVM classifiers proves to be a promising additional non-destructive method for antler identification, suitable for archaeological artefacts whose degree of human modification and cultural heritage or scientific value has previously made it impossible (tools, ornaments, etc..

  5. Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies.

    Science.gov (United States)

    Tang, Li-Juan; Zhou, Yan-Ping; Jiang, Jian-Hui; Zou, Hong-Yan; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-01

    The support vector machine (SVM) has been receiving increasing interest in an area of QSAR study for its ability in function approximation and remarkable generalization performance. However, selection of support vectors and intensive optimization of kernel width of a nonlinear SVM are inclined to get trapped into local optima, leading to an increased risk of underfitting or overfitting. To overcome these problems, a new nonlinear SVM algorithm is proposed using adaptive kernel transform based on a radial basis function network (RBFN) as optimized by particle swarm optimization (PSO). The new algorithm incorporates a nonlinear transform of the original variables to feature space via a RBFN with one input and one hidden layer. Such a transform intrinsically yields a kernel transform of the original variables. A synergetic optimization of all parameters including kernel centers and kernel widths as well as SVM model coefficients using PSO enables the determination of a flexible kernel transform according to the performance of the total model. The implementation of PSO demonstrates a relatively high efficiency in convergence to a desired optimum. Applications of the proposed algorithm to QSAR studies of binding affinity of HIV-1 reverse transcriptase inhibitors and activity of 1-phenylbenzimidazoles reveal that the new algorithm provides superior performance to the backpropagation neural network and a conventional nonlinear SVM, indicating that this algorithm holds great promise in nonlinear SVM learning.

  6. An improved conjugate gradient scheme to the solution of least squares SVM.

    Science.gov (United States)

    Chu, Wei; Ong, Chong Jin; Keerthi, S Sathiya

    2005-03-01

    The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.

  7. Model-based distance sampling

    OpenAIRE

    Buckland, Stephen Terrence; Oedekoven, Cornelia Sabrina; Borchers, David Louis

    2015-01-01

    CSO was part-funded by EPSRC/NERC Grant EP/1000917/1. Conventional distance sampling adopts a mixed approach, using model-based methods for the detection process, and design-based methods to estimate animal abundance in the study region, given estimated probabilities of detection. In recent years, there has been increasing interest in fully model-based methods. Model-based methods are less robust for estimating animal abundance than conventional methods, but offer several advantages: they ...

  8. Multitask SVM learning for remote sensing data classification

    Science.gov (United States)

    Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

    2010-10-01

    Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

  9. Method for gesture based modeling

    DEFF Research Database (Denmark)

    2006-01-01

    A computer program based method is described for creating models using gestures. On an input device, such as an electronic whiteboard, a user draws a gesture which is recognized by a computer program and interpreted relative to a predetermined meta-model. Based on the interpretation, an algorithm...... is assigned to the gesture drawn by the user. The executed algorithm may, for example, consist in creating a new model element, modifying an existing model element, or deleting an existing model element....

  10. Research of Video Steganalysis Algorithm Based on H265 Protocol

    Directory of Open Access Journals (Sweden)

    Wu Kaicheng

    2015-01-01

    This paper researches LSB matching VSA based on H265 protocol with the research background of 26 original Video sequences, it firstly extracts classification features out from training samples as input of SVM, and trains in SVM to obtain high-quality category classification model, and then tests whether there is suspicious information in the video sample. The experimental results show that VSA algorithm based on LSB matching can be more practical to obtain all frame embedded secret information and carrier and video of local frame embedded. In addition, VSA adopts the method of frame by frame with a strong robustness in resisting attack in the corresponding time domain.

  11. The Prediction Model of Dam Uplift Pressure Based on Random Forest

    Science.gov (United States)

    Li, Xing; Su, Huaizhi; Hu, Jiang

    2017-09-01

    The prediction of the dam uplift pressure is of great significance in the dam safety monitoring. Based on the comprehensive consideration of various factors, 18 parameters are selected as the main factors affecting the prediction of uplift pressure, use the actual monitoring data of uplift pressure as the evaluation factors for the prediction model, based on the random forest algorithm and support vector machine to build the dam uplift pressure prediction model to predict the uplift pressure of the dam, and the predict performance of the two models were compared and analyzed. At the same time, based on the established random forest prediction model, the significance of each factor is analyzed, and the importance of each factor of the prediction model is calculated by the importance function. Results showed that: (1) RF prediction model can quickly and accurately predict the uplift pressure value according to the influence factors, the average prediction accuracy is above 96%, compared with the support vector machine (SVM) model, random forest model has better robustness, better prediction precision and faster convergence speed, and the random forest model is more robust to missing data and unbalanced data. (2) The effect of water level on uplift pressure is the largest, and the influence of rainfall on the uplift pressure is the smallest compared with other factors.

  12. Model-Based Reasoning

    Science.gov (United States)

    Ifenthaler, Dirk; Seel, Norbert M.

    2013-01-01

    In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper…

  13. Accurate Determination of Geographical Origin of Tea Based on Terahertz Spectroscopy

    Directory of Open Access Journals (Sweden)

    Mingliang Li

    2017-02-01

    Full Text Available This paper proposes a structured model for the identification of green tea, as well as tracing its geographical origins. Considering that the features of different types of green tea are similar under THz time-domain spectroscopy, we designed a program to perform principal component analysis (PCA of the spectroscopic data of various green tea samples and to determine the data sequences of principal components. We then established a training set for the principal components to train a support vector machine (SVM model via a genetic algorithm (GA. We used this model to optimize the parameters and develop a GA-based SVM model with an identification rate of 96.25% for the tested samples. Taken together, our results confirm that THz time-domain spectroscopy combined with GA-SVM can be effectively applied to rapidly identify types of green tea with different geographical origins.

  14. QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

    Science.gov (United States)

    Qin, Zijian; Wang, Maolin; Yan, Aixia

    2017-07-01

    In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Model-based Software Engineering

    DEFF Research Database (Denmark)

    Kindler, Ekkart

    2010-01-01

    The vision of model-based software engineering is to make models the main focus of software development and to automatically generate software from these models. Part of that idea works already today. But, there are still difficulties when it comes to behaviour. Actually, there is no lack in models...

  16. Principles of models based engineering

    Energy Technology Data Exchange (ETDEWEB)

    Dolin, R.M.; Hefele, J.

    1996-11-01

    This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

  17. Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation.

    Directory of Open Access Journals (Sweden)

    Justin E Brown

    Full Text Available Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI and support vector machine (SVM learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001. Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be

  18. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

    DEFF Research Database (Denmark)

    Basar, Mehmet Sertug

    2013-01-01

    This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

  19. SVM to detect the presence of visitors in a smart home environment.

    Science.gov (United States)

    Petersen, Johanna; Larimer, Nicole; Kaye, Jeffrey A; Pavel, Misha; Hayes, Tamara L

    2012-01-01

    With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.

  20. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM.

    Science.gov (United States)

    Wang, Qi; Luo, ZhiHao; Huang, JinCai; Feng, YangHe; Liu, Zhong

    2017-01-01

    Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.

  1. gis-based hydrological model based hydrological model upstream

    African Journals Online (AJOL)

    eobe

    Hydrological. Hydrological modeling tools have been increasingl modeling tools have been increasingl watershed watershed level. The application of these tools hav. The application of these tools hav sensing and G sensing and Geographical Information System (GIS) eographical Information System (GIS) based models ...

  2. Application of Support Vector Machine-Based Semiactive Control for Seismic Protection of Structures with Magnetorheological Dampers

    Directory of Open Access Journals (Sweden)

    Chunxiang Li

    2012-01-01

    Full Text Available Based on recent research by Li and Liu in 2011, this paper proposes the application of support vector machine- (SVM- based semiactive control methodology for seismic protection of structures with magnetorheological (MR dampers. An important and challenging task of designing the MR dampers is to develop an effective semiactive control strategy that can fully exploit the capabilities of MR dampers. However, amplification of the local acceleration response of structures exists in the widely used semiactive control strategies, namely “Switch” control strategies. Then the SVM-based semiactive control strategy has been employed to design MR dampers. Firstly, the LQR controller for the numerical model of a multistory structure formulated using the dynamic dense method is constructed by using the classic LQR control theory. Secondly, an SVM model which comprises the observers and controllers in the control system is designed and trained to emulate the performance of the LQR controller. Finally, an online autofeedback semiactive control strategy is developed by resorting to SVM and then used for designing MR dampers. Simulation results show that the MR dampers utilizing the SVM-based semiactive control algorithm, which eliminates the local acceleration amplification phenomenon, can remarkably reduce the displacement, velocity, and acceleration responses of the structure.

  3. 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

  4. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Directory of Open Access Journals (Sweden)

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  5. Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

    Directory of Open Access Journals (Sweden)

    Jayro Santiago-Paz

    2015-09-01

    Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

  6. Activity-based DEVS modeling

    DEFF Research Database (Denmark)

    Alshareef, Abdurrahman; Sarjoughian, Hessam S.; Zarrin, Bahram

    2018-01-01

    Use of model-driven approaches has been increasing to significantly benefit the process of building complex systems. Recently, an approach for specifying model behavior using UML activities has been devised to support the creation of DEVS models in a disciplined manner based on the model driven...... architecture and the UML concepts. In this paper, we further this work by grounding Activity-based DEVS modeling and developing a fully-fledged modeling engine to demonstrate applicability. We also detail the relevant aspects of the created metamodel in terms of modeling and simulation. A significant number...... of the artifacts of the UML 2.5 activities and actions, from the vantage point of DEVS behavioral modeling, is covered in details. Their semantics are discussed to the extent of time-accurate requirements for simulation. We characterize them in correspondence with the specification of the atomic model behavior. We...

  7. Graph Model Based Indoor Tracking

    DEFF Research Database (Denmark)

    Jensen, Christian Søndergaard; Lu, Hua; Yang, Bin

    2009-01-01

    The tracking of the locations of moving objects in large indoor spaces is important, as it enables a range of applications related to, e.g., security and indoor navigation and guidance. This paper presents a graph model based approach to indoor tracking that offers a uniform data management...... infrastructure for different symbolic positioning technologies, e.g., Bluetooth and RFID. More specifically, the paper proposes a model of indoor space that comprises a base graph and mappings that represent the topology of indoor space at different levels. The resulting model can be used for one or several...... indoor positioning technologies. Focusing on RFID-based positioning, an RFID specific reader deployment graph model is built from the base graph model. This model is then used in several algorithms for constructing and refining trajectories from raw RFID readings. Empirical studies with implementations...

  8. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

    Science.gov (United States)

    Quej, Victor H.; Almorox, Javier; Arnaldo, Javier A.; Saito, Laurel

    2017-03-01

    Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.

  9. A novel method of artery stenosis diagnosis using transfer function and support vector machine based on transmission line model: A numerical simulation and validation study.

    Science.gov (United States)

    Xiao, Hanguang; Avolio, Alberto; Huang, Decai

    2016-06-01

    Transfer function (TF) is an important parameter for the analysis and understanding of hemodynamics when arterial stenosis exists in human arterial tree. Aimed to validate the feasibility of using TF to diagnose arterial stenosis, the forward problem and inverse problem were simulated and discussed. A calculation method of TF between ascending aorta and any other artery was proposed based on a 55 segment transmission line model (TLM) of human artery tree. The effects of artery stenosis on TF were studied in two aspects: stenosis degree and position. The degree of arterial stenosis was specified to be 10-90% in three representative arteries: carotid, aorta and iliac artery, respectively. In order to validate the feasibility of diagnosis of artery stenosis using TF and support vector machine (SVM), a database of TF was established to simulate the real conditions of artery stenosis based on the TLM model. And a diagnosis model of artery stenosis was built by using SVM and the database. The simulating results showed the modulus and phase of TF were decreasing sharply from frequency 2 to 10Hz with the stenosis degree increasing and displayed their unique and nonlinear characteristics when frequency is higher than 10Hz. The diagnosis results showed the average accuracy was above 76% for the stenosis from 10% to 90% degree, and the diagnosis accuracies of moderate (50%) and serious (90%) stenosis were 87% and 99%, respectively. When the stenosis degree increased to 90%, the accuracy of stenosis localization reached up to 94% for most of arteries. The proposed method of combining TF and SVM is a theoretically feasible method for diagnosis of artery stenosis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. Event-Based Activity Modeling

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2004-01-01

    We present and discuss a modeling approach that supports event-based modeling of information and activity in information systems. Interacting human actors and IT-actors may carry out such activity. We use events to create meaningful relations between information structures and the related...... activities inside and outside an IT-system. We use event-activity diagrams to model activity. Such diagrams support the modeling of activity flow, object flow, shared events, triggering events, and interrupting events....

  11. Event-Based Conceptual Modeling

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    The paper demonstrates that a wide variety of event-based modeling approaches are based on special cases of the same general event concept, and that the general event concept can be used to unify the otherwise unrelated fields of information modeling and process modeling. A set of event......-based modeling approaches are analyzed and the results are used to formulate a general event concept that can be used for unifying the seemingly unrelated event concepts. Events are characterized as short-duration processes that have participants, consequences, and properties, and that may be modeled in terms...... of information structures. The general event concept can be used to guide systems analysis and design and to improve modeling approaches....

  12. Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.

    Science.gov (United States)

    Abdulkadir, Ahmed; Mortamet, Bénédicte; Vemuri, Prashanthi; Jack, Clifford R; Krueger, Gunnar; Klöppel, Stefan

    2011-10-01

    Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of

  13. Modeling Guru: Knowledge Base for NASA Modelers

    Science.gov (United States)

    Seablom, M. S.; Wojcik, G. S.; van Aartsen, B. H.

    2009-05-01

    Modeling Guru is an on-line knowledge-sharing resource for anyone involved with or interested in NASA's scientific models or High End Computing (HEC) systems. Developed and maintained by the NASA's Software Integration and Visualization Office (SIVO) and the NASA Center for Computational Sciences (NCCS), Modeling Guru's combined forums and knowledge base for research and collaboration is becoming a repository for the accumulated expertise of NASA's scientific modeling and HEC communities. All NASA modelers and associates are encouraged to participate and provide knowledge about the models and systems so that other users may benefit from their experience. Modeling Guru is divided into a hierarchy of communities, each with its own set forums and knowledge base documents. Current modeling communities include those for space science, land and atmospheric dynamics, atmospheric chemistry, and oceanography. In addition, there are communities focused on NCCS systems, HEC tools and libraries, and programming and scripting languages. Anyone may view most of the content on Modeling Guru (available at http://modelingguru.nasa.gov/), but you must log in to post messages and subscribe to community postings. The site offers a full range of "Web 2.0" features, including discussion forums, "wiki" document generation, document uploading, RSS feeds, search tools, blogs, email notification, and "breadcrumb" links. A discussion (a.k.a. forum "thread") is used to post comments, solicit feedback, or ask questions. If marked as a question, SIVO will monitor the thread, and normally respond within a day. Discussions can include embedded images, tables, and formatting through the use of the Rich Text Editor. Also, the user can add "Tags" to their thread to facilitate later searches. The "knowledge base" is comprised of documents that are used to capture and share expertise with others. The default "wiki" document lets users edit within the browser so others can easily collaborate on the

  14. A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Shengzhi; Ming, Bo; Huang, Qiang; Leng, Guoyong; Hou, Beibei

    2017-05-05

    It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecasting models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.

  15. Base Flow Model Validation Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The program focuses on turbulence modeling enhancements for predicting high-speed rocket base flows. A key component of the effort is the collection of high-fidelity...

  16. Event-Based Conceptual Modeling

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2009-01-01

    The purpose of the paper is to obtain insight into and provide practical advice for event-based conceptual modeling. We analyze a set of event concepts and use the results to formulate a conceptual event model that is used to identify guidelines for creation of dynamic process models and static...... information models. We characterize events as short-duration processes that have participants, consequences, and properties, and that may be modeled in terms of information structures. The conceptual event model is used to characterize a variety of event concepts and it is used to illustrate how events can...... be used to integrate dynamic modeling of processes and static modeling of information structures. The results are unique in the sense that no other general event concept has been used to unify a similar broad variety of seemingly incompatible event concepts. The general event concept can be used...

  17. 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.

  18. Computer Based Modelling and Simulation

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 6; Issue 3. Computer Based Modelling and Simulation - Modelling Deterministic Systems. N K Srinivasan. General Article Volume 6 Issue 3 March 2001 pp 46-54. Fulltext. Click here to view fulltext PDF. Permanent link:

  19. Máquinas de soporte vectorial (svm) para la detección de nódulos pulmonares en tomografía axial computarizada (tac)

    OpenAIRE

    Ledezma Garrido, Willmar

    2012-01-01

    El cáncer de pulmón es uno de los mas comunes en el mundo y los nódulos pulmonares son su principal indicador de alerta temprana para su diagnóstico. Se presenta un proyecto para la detección de nódulos pulmonares con el uso de máquinas de soporte vectorial (svm), usando el kernel función de base radial gausiana (RBFG), previo a la aplicación de la svm, se hace un trabajo de procesamiento de imágenes que incluye la extracción de la región de interés y extracción de las características que ide...

  20. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

    2010-12-15

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

  1. FUSION OF NON-THERMAL AND THERMAL SATELLITE IMAGES BY BOOSTED SVM CLASSIFIERS FOR CLOUD DETECTION

    Directory of Open Access Journals (Sweden)

    N. Ghasemian

    2017-09-01

    Full Text Available The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

  2. Fusion of Non-Thermal and Thermal Satellite Images by Boosted Svm Classifiers for Cloud Detection

    Science.gov (United States)

    Ghasemian, N.; Akhoondzadeh, M.

    2017-09-01

    The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C) the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

  3. Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach.

    Science.gov (United States)

    Al-Shargie, Fares; Tang, Tong Boon; Badruddin, Nasreen; Kiguchi, Masashi

    2017-10-18

    Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

  4. Nonlinear Inertia Classification Model and Application

    Directory of Open Access Journals (Sweden)

    Mei Wang

    2014-01-01

    Full Text Available Classification model of support vector machine (SVM overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM is proposed after the nonlinear inertia convergence (NICPSO is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

  5. 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

  6. Energy-efficient SVM learning control system for biped walking robots.

    Science.gov (United States)

    Wang, Liyang; Liu, Zhi; Chen, Chun Lung Philip; Zhang, Yun; Lee, Sukhan; Chen, Xin

    2013-05-01

    An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.

  7. Quadratic divergence regularized SVM for optic disc segmentation.

    Science.gov (United States)

    Cheng, Jun; Tao, Dacheng; Wong, Damon Wing Kee; Liu, Jiang

    2017-05-01

    Machine learning has been used in many retinal image processing applications such as optic disc segmentation. It assumes that the training and testing data sets have the same feature distribution. However, retinal images are often collected under different conditions and may have different feature distributions. Therefore, the models trained from one data set may not work well for another data set. However, it is often too expensive and time consuming to label the needed training data and rebuild the models for all different data sets. In this paper, we propose a novel quadratic divergence regularized support vector machine (QDSVM) to transfer the knowledge from domains with sufficient training data to domains with limited or even no training data. The proposed method simultaneously minimizes the distribution difference between the source domain and target domain while training the classifier. Experimental results show that the proposed transfer learning based method reduces the classification error in superpixel level from 14.2% without transfer learning to 2.4% with transfer learning. The proposed method is effective to transfer the label knowledge from source to target domain, which enables it to be used for optic disc segmentation in data sets with different feature distributions.

  8. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

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

    Directory of Open Access Journals (Sweden)

    Oman Somantri

    2017-01-01

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

  10. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

    Science.gov (United States)

    Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-30

    Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.

  11. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    Science.gov (United States)

    Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  12. HMM-based Trust Model

    DEFF Research Database (Denmark)

    ElSalamouny, Ehab; Nielsen, Mogens; Sassone, Vladimiro

    2010-01-01

    Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either assume fixed behaviour for the principals or incorporate the notion of ‘decay' as an ad hoc approach to cope with thei...... the major limitation of existing Beta trust model. We show the consistency of the HMM-based trust model and contrast it against the well known Beta trust model with the decay principle in terms of the estimation precision....

  13. Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

    Directory of Open Access Journals (Sweden)

    Yu Wang

    2015-01-01

    Full Text Available A new reliability-based design optimization (RBDO method based on support vector machines (SVM and the Most Probable Point (MPP is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

  14. Model-based equipment diagnosis

    Science.gov (United States)

    Collins, David J.; Strojwas, Andrzej J.; Mozumder, P. K.

    1994-09-01

    A versatile methodology is described in which equipment models have been incorporated into a single process diagnostic system for the PECVD of silicon nitride. The diagnosis system has been developed and tested with data collected using an Applied Materials Precision 5000 single wafer reactor. The parametric equipment diagnosis system provides the basis for optimal control of multiple process responses by the classification of potential sources of equipment faults without the assistance of in-situ sensor data. The basis for the diagnosis system is the use of tuned empirical equipment models which have been developed using a physically-based experimental design. Nine individual site-specific models were used to provide an effective method of modeling the spatially-dependent process variations across the wafer with better sensitivity than mean-based models. The diagnostic system has been tested using data that was produced by adjusting the actual equipment controls to artificially simulate a variety of possible subtle equipment drifts and shifts. Statistical algorithms have been implemented which detect equipment drift, shift and variance stability faults using the difference between the predicted process responses to the off-line measured process responses. Fault classification algorithms have been developed to classify the most likely causes for the process drifts and shifts using a pattern recognition system based upon flexible discriminant analysis.

  15. Ligand and structure-based classification models for Prediction of P-glycoprotein inhibitors

    DEFF Research Database (Denmark)

    Klepsch, Freya; Poongavanam, Vasanthanathan; Ecker, Gerhard Franz

    2014-01-01

    obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random forest and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using...... in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization....

  16. Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

    Science.gov (United States)

    Song, Hong; Chen, Lei; Gao, RuiQi; Bogdan, Iordachescu Ilie Mihaita; Yang, Jian; Wang, Shuliang; Dong, Wentian; Quan, Wenxiang; Dang, Weimin; Yu, Xin

    2017-12-20

    Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

  17. Using evolutionary computation to optimize an SVM used in detecting buried objects in FLIR imagery

    Science.gov (United States)

    Paino, Alex; Popescu, Mihail; Keller, James M.; Stone, Kevin

    2013-06-01

    In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a camera installed on a moving vehicle. The overall algorithm consists of a spot-finding procedure (to look for potential targets) followed by the extraction of several features from the neighborhood of each spot. The features include local binary pattern (LBP) and histogram of oriented gradients (HOG) as these are good at detecting texture classes. Finally, we project and sum each hit into UTM space along with its confidence value (obtained from the SVM), producing a confidence map for ROC analysis. In this work, we use an Evolutionary Computation Algorithm (ECA) to optimize various parameters involved in the system, such as the combination of features used, parameters on the Canny edge detector, the SVM kernel, and various HOG and LBP parameters. To validate our approach, we compare results obtained from an SVM using parameters obtained through our ECA technique with those previously selected by hand through several iterations of "guess and check".

  18. SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

    Science.gov (United States)

    Schmid, Maurizio; Riganti-Fulginei, Francesco; Bernabucci, Ivan; Laudani, Antonino; Bibbo, Daniele; Muscillo, Rossana; Salvini, Alessandro; Conforto, Silvia

    2013-01-01

    Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

  19. A feature-based approach to modeling protein-protein interaction hot spots.

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

  20. Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine.

    Science.gov (United States)

    Zheng, Hong; Zheng, Peng; Zhao, Liangcai; Jia, Jianmin; Tang, Shengli; Xu, Pengtao; Xie, Peng; Gao, Hongchang

    2017-01-01

    Major depressive (MD) disorder is a serious psychiatric disorder that can result in suicidal behavior if not treated. The MD diagnosis using a standardized instrument instead of a structured interview will be advantageous for treatment and management of the MD, but so far no such technique exists. We developed an integrated analytical method of NMR-based metabolomics and least squares-support vector machine (LS-SVM) for predictive diagnosis of the MD. The metabolite profiles in clinical plasma samples obtained from 72 depressive patients and 54 healthy subjects were analyzed by NMR spectroscopy. Then, LS-SVM models with different kernels were trained and tested using 80% and 20% of samples, respectively. We found that the best performance for the MD prediction was achieved by LS-SVM equipped with RBF kernel. Moreover, the predictive performance of the MD using multi-biomarkers was largely improved as compared with that using a single biomarker. In this study, the LS-SVM-RBF using glucose-lipid signaling can achieve the MD prediction with the AUC values of 0.94 (0.89-0.99) in the training set and 0.96 (0.92-1.00) in the test set. The LS-SVM-RBF using glucose-lipid signaling obtained from NMR spectroscopy can be used as an auxiliary diagnostic tool for the MD. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

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    Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

    2014-11-14

    Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

  2. Dynamic Energy Consumption and Emission Modelling of Container Terminal based on Multi Agents

    Directory of Open Access Journals (Sweden)

    Hou Jue

    2017-01-01

    Full Text Available Environmental protection and energy saving pressure press the increasing attention of container terminal operators. In order to comply with the more and more strict environmental regulation, reducing energy consumption and air pollution emissions, meanwhile, optimizing the operation efficiency, which, is an urgent problem to container terminal operator of China. This paper based on the characteristic of Container Terminal Operation System (CTOS, which includes several sections of container product processes, consist of berth allocation problem, truck dispatching problem, yard allocation problem and auxiliary process. Dynamic energy consumption and emissions characteristic of each equipment and process is modelled, this paper presents the architecture of CTOS based on the multi agent system with early-warning model, which is based on multi-class support vector machines (SVM. A simulation on container terminal is built on the JADE platform to support the decision-making of container terminal, which can reduce energy consumption and air pollution emissions, allows the container terminal operator to be more flexible in their decision to meet the Emission Control Area regulation and Green Port Plan of China.

  3. Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models.

    Science.gov (United States)

    Fernandez-Lozano, Carlos; Cuiñas, Rubén F; Seoane, José A; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R

    2015-11-07

    Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders the direct association of the signaling activity with the molecular structure. Therefore, the proposed solution involves the use of protein star graphs for the peptide sequence information encoding into specific topological indices calculated with S2SNet tool. The Quantitative Structure-Activity Relationship classification model obtained with Machine Learning techniques is able to predict new signaling peptides. The best classification model is the first signaling prediction model, which is based on eleven descriptors and it was obtained using the Support Vector Machines-Recursive Feature Elimination (SVM-RFE) technique with the Laplacian kernel (RFE-LAP) and an AUROC of 0.961. Testing a set of 3114 proteins of unknown function from the PDB database assessed the prediction performance of the model. Important signaling pathways are presented for three UniprotIDs (34 PDBs) with a signaling prediction greater than 98.0%. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Energy based hybrid turbulence modeling

    Science.gov (United States)

    Haering, Sigfried; Moser, Robert

    2015-11-01

    Traditional hybrid approaches exhibit deficiencies when used for fluctuating smooth-wall separation and reattachment necessitating ad-hoc delaying functions and model tuning making them no longer useful as a predictive tool. Additionally, complex geometries and flows often require high cell aspect-ratios and large grid gradients as a compromise between resolution and cost. Such transitions and inconsistencies in resolution detrimentally effect the fidelity of the simulation. We present the continued development of a new hybrid RANS/LES modeling approach specifically developed to address these challenges. In general, modeled turbulence is returned to resolved scales by reduced or negative model viscosity until a balance between theoretical and actual modeled turbulent kinetic energy is attained provided the available resolution. Anisotropy in the grid and resolved field are directly integrated into this balance. A viscosity-based correction is proposed to account for resolution inhomogeneities. Both the hybrid framework and resolution gradient corrections are energy conserving through an exchange of resolved and modeled turbulence.

  5. Model-based tomographic reconstruction

    Science.gov (United States)

    Chambers, David H; Lehman, Sean K; Goodman, Dennis M

    2012-06-26

    A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

  6. Model-Based Security Testing

    Directory of Open Access Journals (Sweden)

    Ina Schieferdecker

    2012-02-01

    Full Text Available Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.

  7. Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2013-01-01

    Full Text Available A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized -dimensional Principal Component Analysis (GND-PCA is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.

  8. Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

    Directory of Open Access Journals (Sweden)

    Jun Lin

    2018-01-01

    Full Text Available The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA and a generalized regression neural network (GRNN using an improved fruit fly optimization algorithm (FFOA to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM and gray model (GM methods.

  9. An Appraisal Model Based on a Synthetic Feature Selection Approach for Students’ Academic Achievement

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    Ching-Hsue Cheng

    2017-11-01

    Full Text Available Obtaining necessary information (and even extracting hidden messages from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years because it can be used to find historical patterns and employ machine learning to aid in decision-making. When we find unexpected rules or patterns from the data, they are likely to be of high value. This paper proposes a synthetic feature selection approach (SFSA, which is combined with a support vector machine (SVM to extract patterns and find the key features that influence students’ academic achievement. For verifying the proposed model, two databases, namely, “Student Profile” and “Tutorship Record”, were collected from an elementary school in Taiwan, and were concatenated into an integrated dataset based on students’ names as a research dataset. The results indicate the following: (1 the accuracy of the proposed feature selection approach is better than that of the Minimum-Redundancy-Maximum-Relevance (mRMR approach; (2 the proposed model is better than the listing methods when the six least influential features have been deleted; and (3 the proposed model can enhance the accuracy and facilitate the interpretation of the pattern from a hybrid-type dataset of students’ academic achievement.

  10. A chest-shape target automatic detection method based on Deformable Part Models

    Science.gov (United States)

    Zhang, Mo; Jin, Weiqi; Li, Li

    2016-10-01

    Automatic weapon platform is one of the important research directions at domestic and overseas, it needs to accomplish fast searching for the object to be shot under complex background. Therefore, fast detection for given target is the foundation of further task. Considering that chest-shape target is common target of shoot practice, this paper treats chestshape target as the target and studies target automatic detection method based on Deformable Part Models. The algorithm computes Histograms of Oriented Gradient(HOG) features of the target and trains a model using Latent variable Support Vector Machine(SVM); In this model, target image is divided into several parts then we can obtain foot filter and part filters; Finally, the algorithm detects the target at the HOG features pyramid with method of sliding window. The running time of extracting HOG pyramid with lookup table can be shorten by 36%. The result indicates that this algorithm can detect the chest-shape target in natural environments indoors or outdoors. The true positive rate of detection reaches 76% with many hard samples, and the false positive rate approaches 0. Running on a PC (Intel(R)Core(TM) i5-4200H CPU) with C++ language, the detection time of images with the resolution of 640 × 480 is 2.093s. According to TI company run library about image pyramid and convolution for DM642 and other hardware, our detection algorithm is expected to be implemented on hardware platform, and it has application prospect in actual system.

  11. Crowdsourcing Based 3d Modeling

    Science.gov (United States)

    Somogyi, A.; Barsi, A.; Molnar, B.; Lovas, T.

    2016-06-01

    Web-based photo albums that support organizing and viewing the users' images are widely used. These services provide a convenient solution for storing, editing and sharing images. In many cases, the users attach geotags to the images in order to enable using them e.g. in location based applications on social networks. Our paper discusses a procedure that collects open access images from a site frequently visited by tourists. Geotagged pictures showing the image of a sight or tourist attraction are selected and processed in photogrammetric processing software that produces the 3D model of the captured object. For the particular investigation we selected three attractions in Budapest. To assess the geometrical accuracy, we used laser scanner and DSLR as well as smart phone photography to derive reference values to enable verifying the spatial model obtained from the web-album images. The investigation shows how detailed and accurate models could be derived applying photogrammetric processing software, simply by using images of the community, without visiting the site.

  12. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  13. Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea.

    Science.gov (United States)

    Kwon, Yong-Su; Bae, Mi-Jung; Chung, Namil; Lee, Yeo-Rang; Hwang, Suntae; Kim, Sang-Ae; Choi, Young Jean; Park, Young-Seuk

    2015-10-20

    Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation) and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA) based on mosquito occurrence; and three prediction models, support vector machine (SVM), classification and regression tree (CART), and random forest (RF). We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.

  14. Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

    Science.gov (United States)

    Muduli, Pradyut; Das, Sarat

    2014-06-01

    This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

  15. A method of real-time fault diagnosis for power transformers based on vibration analysis

    Science.gov (United States)

    Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

    2015-11-01

    In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

  16. 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.

  17. Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

    Science.gov (United States)

    Sanal Kumar, K. P.; Bhavani, R., Dr.

    2017-08-01

    Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

  18. Extreme learning machine-based classification of ADHD using brain structural MRI data.

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    Xiaolong Peng

    Full Text Available BACKGROUND: Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1 Propose an ADHD classification model using the extreme learning machine (ELM algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2 Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM methods and analyze which brain segments are involved in ADHD. METHODS: High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc. were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. RESULTS: We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. CONCLUSION: Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

  19. Extreme learning machine-based classification of ADHD using brain structural MRI data.

    Science.gov (United States)

    Peng, Xiaolong; Lin, Pan; Zhang, Tongsheng; Wang, Jue

    2013-01-01

    Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD. High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

  20. Budget Online Learning Algorithm for Least Squares SVM.

    Science.gov (United States)

    Jian, Ling; Shen, Shuqian; Li, Jundong; Liang, Xijun; Li, Lei

    2017-09-01

    Batch-mode least squares support vector machine (LSSVM) is often associated with unbounded number of support vectors (SVs'), making it unsuitable for applications involving large-scale streaming data. Limited-scale LSSVM, which allows efficient updating, seems to be a good solution to tackle this issue. In this paper, to train the limited-scale LSSVM dynamically, we present a budget online LSSVM (BOLSSVM) algorithm. Methodologically, by setting a fixed budget for SVs', we are able to update the LSSVM model according to the updated SVs' set dynamically without retraining from scratch. In particular, when a new small chunk of SVs' substitute for the old ones, the proposed algorithm employs a low rank correction technology and the Sherman-Morrison-Woodbury formula to compute the inverse of saddle point matrix derived from the LSSVM's Karush-Kuhn-Tucker (KKT) system, which, in turn, updates the LSSVM model efficiently. In this way, the proposed BOLSSVM algorithm is especially useful for online prediction tasks. Another merit of the proposed BOLSSVM is that it can be used for k -fold cross validation. Specifically, compared with batch-mode learning methods, the computational complexity of the proposed BOLSSVM method is significantly reduced from O(n4) to O(n3) for leave-one-out cross validation with n training samples. The experimental results of classification and regression on benchmark data sets and real-world applications show the validity and effectiveness of the proposed BOLSSVM algorithm.

  1. Probability output modeling for support vector machines

    Science.gov (United States)

    Zhang, Xiang; Xiao, Xiaoling; Tian, Jinwen; Liu, Jian

    2007-11-01

    In this paper we propose an approach to model the posterior probability output of multi-class SVMs. The sigmoid function is used to estimate the posterior probability output in binary classification. This approach modeling the posterior probability output of multi-class SVMs is achieved by directly solving the equations that are based on the combination of the probability outputs of binary classifiers using the Bayes's rule. The differences and different weights among these two-class SVM classifiers, based on the posterior probability, are considered and given for the combination of the probability outputs among these two-class SVM classifiers in this method. The comparative experiment results show that our method achieves the better classification precision and the better probability distribution of the posterior probability than the pairwise couping method and the Hastie's optimization method.

  2. RBPPred: predicting RNA-binding proteins from sequence using SVM.

    Science.gov (United States)

    Zhang, Xiaoli; Liu, Shiyong

    2017-03-15

    Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computational prediction is much more efficient than experimental methods and may have guiding significance on the experiment design. In this study, we present the RBPPred (an RNA-binding protein predictor), a new method based on the support vector machine, to predict whether a protein binds RNAs, based on a comprehensive feature representation. By integrating the physicochemical properties with the evolutionary information of protein sequences, the new approach RBPPred performed much better than state-of-the-art methods. The results show that RBPPred correctly predicted 83% of 2780 RBPs and 96% out of 7093 non-RBPs with MCC of 0.808 using the 10-fold cross validation. Furthermore, we achieved a sensitivity of 84%, specificity of 97% and MCC of 0.788 on the testing set of human proteome. In addition we tested the capability of RBPPred to identify new RBPs, which further confirmed the practicability and predictability of the method. RBPPred program can be accessed at: http://rnabinding.com/RBPPred.html . liushiyong@gmail.com. Supplementary data are available at Bioinformatics online.

  3. Automated Classification and Removal of EEG Artifacts with SVM and Wavelet-ICA.

    Science.gov (United States)

    Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

    2017-07-04

    Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pre-trained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multi-channel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

  4. Detection of Cross Site Scripting Attack in Wireless Networks Using n-Gram and SVM

    Directory of Open Access Journals (Sweden)

    Jun-Ho Choi

    2012-01-01

    Full Text Available Large parts of attacks targeting the web are aiming at the weak point of web application. Even though SQL injection, which is the form of XSS (Cross Site Scripting attacks, is not a threat to the system to operate the web site, it is very critical to the places that deal with the important information because sensitive information can be obtained and falsified. In this paper, the method to detect themalicious SQL injection script code which is the typical XSS attack using n-Gram indexing and SVM (Support Vector Machine is proposed. In order to test the proposed method, the test was conducted after classifying each data set as normal code and malicious code, and the malicious script code was detected by applying index term generated by n-Gram and data set generated by code dictionary to SVM classifier. As a result, when the malicious script code detection was conducted using n-Gram index term and SVM, the superior performance could be identified in detecting malicious script and the more improved results than existing methods could be seen in the malicious script code detection recall.

  5. A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality

    Directory of Open Access Journals (Sweden)

    John Gialelis

    2015-11-01

    Full Text Available This paper compares two supervised learning algorithms for predicting the sleep stages based on the human brain activity. The first step of the presented work regards feature extraction from real human electroencephalography (EEG data together with its corresponding sleep stages that are utilized for training a support vector machine (SVM, and a fuzzy inference system (FIS algorithm. Then, the trained algorithms are used to predict the sleep stages of real human patients. Extended comparison results are demonstrated which indicate that both classifiers could be utilized as a basis for an unobtrusive sleep quality assessment.

  6. Research on BOM based composable modeling method

    NARCIS (Netherlands)

    Zhang, M.; He, Q.; Gong, J.

    2013-01-01

    Composable modeling method has been a research hotpot in the area of Modeling and Simulation for a long time. In order to increase the reuse and interoperability of BOM based model, this paper put forward a composable modeling method based on BOM, studied on the basic theory of composable modeling

  7. Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm

    Directory of Open Access Journals (Sweden)

    Tiannan Ma

    2016-02-01

    Full Text Available Icing on power transmission lines is a serious threat to the security and stability of the power grid, and it is necessary to establish a forecasting model to make accurate predictions of icing thickness. In order to improve the forecasting accuracy with regard to icing thickness, this paper proposes a combination model based on a wavelet support vector machine (w-SVM and a quantum fireworks algorithm (QFA for prediction. First, this paper uses the wavelet kernel function to replace the Gaussian wavelet kernel function and improve the nonlinear mapping ability of the SVM. Second, the regular fireworks algorithm is improved by combining it with a quantum optimization algorithm to strengthen optimization performance. Lastly, the parameters of w-SVM are optimized using the QFA model, and the QFA-w-SVM icing thickness forecasting model is established. Through verification using real-world examples, the results show that the proposed method has a higher forecasting accuracy and the model is effective and feasible.

  8. Cellular automata for simulating land use changes based on support vector machines

    Science.gov (United States)

    Yang, Qingsheng; Li, Xia; Shi, Xun

    2008-06-01

    Cellular automata (CA) have been increasingly used to simulate urban sprawl and land use dynamics. A major issue in CA is defining appropriate transition rules based on training data. Linear boundaries have been widely used to define the rules. However, urban land use dynamics and many other geographical phenomena are highly complex and require nonlinear boundaries for the rules. In this study, we tested the support vector machines (SVM) as a method for constructing nonlinear transition rules for CA. SVM is good at dealing with nonlinear complex relationships. Its basic idea is to project input vectors to a higher dimensional Hilbert feature space, in which an optimal classifying hyperplane can be constructed through structural risk minimization and margin maximization. The optimal hyperplane is unique and its optimality is global. The proposed SVM-CA model was implemented using Visual Basic, ArcObjects®, and OSU-SVM. A case study simulating the urban development in the Shenzhen City, China demonstrates that the proposed model can achieve high accuracy and overcome some limitations of existing CA models in simulating complex urban systems.

  9. Generalized classification modeling of activated sludge process based on microscopic image analysis.

    Science.gov (United States)

    Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Lo, Po Kim; Yap, Vooi Voon

    2018-01-01

    The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

  10. Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

    Directory of Open Access Journals (Sweden)

    Chien-Wei Fu

    Full Text Available As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D are computed and classified using the support vector machine (SVM for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

  11. Observation-Based Modeling for Model-Based Testing

    NARCIS (Netherlands)

    Kanstrén, T.; Piel, E.; Gross, H.G.

    2009-01-01

    One of the single most important reasons that modeling and modelbased testing are not yet common practice in industry is the perceived difficulty of making the models up to the level of detail and quality required for their automated processing. Models unleash their full potential only through

  12. Online Order Priority Evaluation Based on Hybrid Harmony Search Algorithm of Optimized Support Vector Machines

    OpenAIRE

    Yuanyuan Zhao; Qian Chen

    2014-01-01

    To make production plan, online order priority evaluation is the current priority weakness of online order evaluation model. This thesis proposes an online order priority evaluation model based on hybrid harmony search algorithm of optimized support vector machine (HHS-SVM). Firstly, an online order priority evaluation index system is build, and then support vector machine is adopted to build an online order priority evaluation model; secondly, harmony search algorithm is used to optimize the...

  13. Product modelling for model-based maintenance

    NARCIS (Netherlands)

    van Houten, Frederikus J.A.M.; Tomiyama, T.; Salomons, O.W.

    1998-01-01

    The paper describes the fundamental concepts of maintenance and the role that information technology can play in the support of maintenance activities. Function-Behaviour-State modelling is used to describe faults and deterioration of mechanisms in terms of user perception and measurable quantities.

  14. Guide to APA-Based Models

    Science.gov (United States)

    Robins, Robert E.; Delisi, Donald P.

    2008-01-01

    In Robins and Delisi (2008), a linear decay model, a new IGE model by Sarpkaya (2006), and a series of APA-Based models were scored using data from three airports. This report is a guide to the APA-based models.

  15. A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

    Directory of Open Access Journals (Sweden)

    Baofeng Shi

    2015-01-01

    Full Text Available We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

  16. Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods

    Science.gov (United States)

    Lee, Jung-Hyun; Sameen, Maher Ibrahim; Pradhan, Biswajeet; Park, Hyuck-Jin

    2018-02-01

    This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (AUC = 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited.

  17. The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Min Suk; Kavitha, Muthu Subash [Dept. of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul (Korea, Republic of); Asano, Akira [Graduate School of Engineering, Hiroshima University, Hiroshima (Japan); Taguchi, Akira [Dept. of Oral and Maxillofacial Radiology, Matsumoto Dental University, Nagano (Japan)

    2013-09-15

    To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

  18. Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

    Directory of Open Access Journals (Sweden)

    Jianfeng Zhang

    2017-01-01

    Full Text Available Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM. Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA, while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA, the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM is of great value, indicating the feasibility of digitalized tongue diagnosis.

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

    DEFF Research Database (Denmark)

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

    2008-01-01

    invest substantial effort in the assessment of cardiac toxicity of drugs. The development of in silico tools to filter out potential hERG channel inhibitors in earlystages of the drug discovery process is of considerable interest. Here, we describe binary classification models based on a large...... and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 mu m and achieved an overall accuracy up...... to 94% with a Matthews coefficient correlation (MCC) of 0.86 (F-measure of 0.90 for blockers and 0.95 for nonblockers). The model at a 40 urn threshold showed the best performance and was validated internally (MCC of 0.40 and F-measure of 0.57 for blockers and 0.81 for nonblockers, using a leave...

  20. gis-based hydrological model based hydrological model upstream

    African Journals Online (AJOL)

    eobe

    er river catchments in Nigeria graphical data [2]. A spatial hydrology which simulates the water flow and pecified region of the earth using GIS. In view of this, the use of modeling with GIS provides the platform to processes tailored towards hydrologic dely applied hydrological models for in recent time is the Soil and Water.

  1. 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.

  2. Trace-Based Code Generation for Model-Based Testing

    NARCIS (Netherlands)

    Kanstrén, T.; Piel, E.; Gross, H.G.

    2009-01-01

    Paper Submitted for review at the Eighth International Conference on Generative Programming and Component Engineering. Model-based testing can be a powerful means to generate test cases for the system under test. However, creating a useful model for model-based testing requires expertise in the

  3. A feature-based approach to modeling protein–protein interaction hot spots

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-01-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions. PMID:19273533

  4. Support-vector-machine based automatic performance modelling and optimisation for analogue and mixed-signal designs

    OpenAIRE

    Ren, Xianqiang

    2008-01-01

    The growing popularity of analogue and mixed-signal (AMS) ASIC and SoC designs for communication applications has led to an increasing requirement for high efficiency performance modelling and optimisation methodologies in AMS synthesis systems. Recently, the support vector machine (SVM) method has been introduced into this challenging field. This research has studied the application of SVMs to AMS performance modelling in terms of the computational cost and prediction accuracy. A novel...

  5. Model-Based Testing of Probabilistic Systems

    NARCIS (Netherlands)

    Gerhold, Marcus; Stoelinga, Mariëlle Ida Antoinette; Stevens, Perdita; Wasowski, Andzej

    This paper presents a model-based testing framework for probabilistic systems. We provide algorithms to generate, execute and evaluate test cases from a probabilistic requirements model. In doing so, we connect ioco-theory for model-based testing and statistical hypothesis testing: our ioco-style

  6. Model-based DSL frameworks

    NARCIS (Netherlands)

    Ivanov, Ivan; Bézivin, J.; Jouault, F.; Valduriez, P.

    2006-01-01

    More than five years ago, the OMG proposed the Model Driven Architecture (MDA™) approach to deal with the separation of platform dependent and independent aspects in information systems. Since then, the initial idea of MDA evolved and Model Driven Engineering (MDE) is being increasingly promoted to

  7. Model based design introduction: modeling game controllers to microprocessor architectures

    Science.gov (United States)

    Jungwirth, Patrick; Badawy, Abdel-Hameed

    2017-04-01

    We present an introduction to model based design. Model based design is a visual representation, generally a block diagram, to model and incrementally develop a complex system. Model based design is a commonly used design methodology for digital signal processing, control systems, and embedded systems. Model based design's philosophy is: to solve a problem - a step at a time. The approach can be compared to a series of steps to converge to a solution. A block diagram simulation tool allows a design to be simulated with real world measurement data. For example, if an analog control system is being upgraded to a digital control system, the analog sensor input signals can be recorded. The digital control algorithm can be simulated with the real world sensor data. The output from the simulated digital control system can then be compared to the old analog based control system. Model based design can compared to Agile software develop. The Agile software development goal is to develop working software in incremental steps. Progress is measured in completed and tested code units. Progress is measured in model based design by completed and tested blocks. We present a concept for a video game controller and then use model based design to iterate the design towards a working system. We will also describe a model based design effort to develop an OS Friendly Microprocessor Architecture based on the RISC-V.

  8. Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches

    Science.gov (United States)

    Khalilinezhad, Mahdieh; Minaei, Behrooz; Vernazza, Gianni; Dellepiane, Silvana

    2015-03-01

    Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naïve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

  9. Base Flow Model Validation Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The innovation is the systematic "building-block" validation of CFD/turbulence models employing a GUI driven CFD code (RPFM) and existing as well as new data sets to...

  10. A Novel and Practical Chromatographic "Fingerprint-ROC-SVM" Strategy Applied to Quality Analysis of Traditional Chinese Medicine Injections: Using KuDieZi Injection as a Case Study.

    Science.gov (United States)

    Yang, Bin; Wang, Yuan; Shan, Lanlan; Zou, Jingtao; Wu, Yuanyuan; Yang, Feifan; Zhang, Yani; Li, Yubo; Zhang, Yanjun

    2017-07-23

    Fingerprinting is widely and commonly used in the quality control of traditional Chinese medicine (TCM) injections. However, current studies informed that the fingerprint similarity evaluation was less sensitive and easily generated false positive results. For this reason, a novel and practical chromatographic "Fingerprint-ROC-SVM" strategy was established by using KuDieZi (KDZ) injection as a case study in the present article. Firstly, the chromatographic fingerprints of KDZ injection were obtained by UPLC and the common characteristic peaks were identified with UPLC/Q-TOF-MS under the same chromatographic conditions. Then, the receiver operating characteristic (ROC) curve was used to optimize common characteristic peaks by the AUCs value greater than 0.7. Finally, a support vector machine (SVM) model, with the accuracy of 97.06%, was established by the optimized characteristic peaks and applied to monitor the quality of KDZ injection. As a result, the established model could sensitively and accurately distinguish the qualified products (QPs) with the unqualified products (UPs), high-temperature processed samples (HTPs) and high-illumination processed samples (HIPs) of KDZ injection, and the prediction accuracy was 100.00%, 93.75% and 100.00%, respectively. Furthermore, through the comparison with other chemometrics methods, the superiority of the novel analytical strategy was more prominent. It indicated that the novel and practical chromatographic "Fingerprint-ROC-SVM" strategy could be further applied to facilitate the development of the quality analysis of TCM injections.

  11. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data. PMID:26005413

  12. PV panel model based on datasheet values

    DEFF Research Database (Denmark)

    Sera, Dezso; Teodorescu, Remus; Rodriguez, Pedro

    2007-01-01

    This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell....... Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested....

  13. Traceability in Model-Based Testing

    Directory of Open Access Journals (Sweden)

    Mathew George

    2012-11-01

    Full Text Available The growing complexities of software and the demand for shorter time to market are two important challenges that face today’s IT industry. These challenges demand the increase of both productivity and quality of software. Model-based testing is a promising technique for meeting these challenges. Traceability modeling is a key issue and challenge in model-based testing. Relationships between the different models will help to navigate from one model to another, and trace back to the respective requirements and the design model when the test fails. In this paper, we present an approach for bridging the gaps between the different models in model-based testing. We propose relation definition markup language (RDML for defining the relationships between models.

  14. Firm Based Trade Models and Turkish Economy

    Directory of Open Access Journals (Sweden)

    Nilüfer ARGIN

    2015-12-01

    Full Text Available Among all international trade models, only The Firm Based Trade Models explains firm’s action and behavior in the world trade. The Firm Based Trade Models focuses on the trade behavior of individual firms that actually make intra industry trade. Firm Based Trade Models can explain globalization process truly. These approaches include multinational cooperation, supply chain and outsourcing also. Our paper aims to explain and analyze Turkish export with Firm Based Trade Models’ context. We use UNCTAD data on exports by SITC Rev 3 categorization to explain total export and 255 products and calculate intensive-extensive margins of Turkish firms.

  15. Lévy-based growth models

    DEFF Research Database (Denmark)

    Jónsdóttir, Kristjana Ýr; Schmiegel, Jürgen; Jensen, Eva Bjørn Vedel

    2008-01-01

    In the present paper, we give a condensed review, for the nonspecialist reader, of a new modelling framework for spatio-temporal processes, based on Lévy theory. We show the potential of the approach in stochastic geometry and spatial statistics by studying Lévy-based growth modelling of planar...... objects. The growth models considered are spatio-temporal stochastic processes on the circle. As a by product, flexible new models for space–time covariance functions on the circle are provided. An application of the Lévy-based growth models to tumour growth is discussed....

  16. Modelling of acid-base equilibria.

    Science.gov (United States)

    Jabor, A; Kazda, A

    1995-01-01

    A quantitative evaluation of metabolic acid-base component is described. The model is based on Stewart's analysis of acid-base chemistry. The metabolic component of acid-base disturbances is divided into four partial metabolic disorders; they can result from abnormal concentrations of chloride, albumin and phosphate disturbances, or from appearance of abnormal unidentified strong anions. The efficiency of the model is sufficient, quantitative partial results are given in the same units as base excess. In complex acid-base disturbances, such as are seen in critically ill patients, a detailed analysis of the specific components of the metabolic acid-base status allows one to plan specific therapeutic interventions.

  17. Distributed Prognostics Based on Structural Model Decomposition

    Data.gov (United States)

    National Aeronautics and Space Administration — Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based...

  18. Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

    Science.gov (United States)

    Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

    2016-12-01

    Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

  19. Computer Based Modelling and Simulation

    Indian Academy of Sciences (India)

    Likewise, ships and buildings are built by naval and civil architects. While these are useful, they are, in most cases, static models. We are ..... The basic theory of transition from one state to another was developed by the Russian mathematician. Andrei Markov and hence the name Markov chains. Andrei Markov [1856-1922] ...

  20. Residual-based model diagnosis methods for mixture cure models.

    Science.gov (United States)

    Peng, Yingwei; Taylor, Jeremy M G

    2017-06-01

    Model diagnosis, an important issue in statistical modeling, has not yet been addressed adequately for cure models. We focus on mixture cure models in this work and propose some residual-based methods to examine the fit of the mixture cure model, particularly the fit of the latency part of the mixture cure model. The new methods extend the classical residual-based methods to the mixture cure model. Numerical work shows that the proposed methods are capable of detecting lack-of-fit of a mixture cure model, particularly in the latency part, such as outliers, improper covariate functional form, or nonproportionality in hazards if the proportional hazards assumption is employed in the latency part. The methods are illustrated with two real data sets that were previously analyzed with mixture cure models. © 2016, The International Biometric Society.

  1. On the integration of object-based models and field-based models in GIS

    OpenAIRE

    Kjenstad, Kjell

    2006-01-01

    This paper proposes a common base-model for the classical object-based and field-based conceptual models in GIS. The model, which is called the PGOModel or 'Parameterized Geographic Object Model', is given a formal definition by using the UML modelling language. Within the scope of the paper, it has been shown that the PGOModel encompasses the classical object-based and field-based models. Two extensive examples demonstrate the application of the PGO model. The PGOModel seems ontologically we...

  2. Model Validation in Ontology Based Transformations

    Directory of Open Access Journals (Sweden)

    Jesús M. Almendros-Jiménez

    2012-10-01

    Full Text Available Model Driven Engineering (MDE is an emerging approach of software engineering. MDE emphasizes the construction of models from which the implementation should be derived by applying model transformations. The Ontology Definition Meta-model (ODM has been proposed as a profile for UML models of the Web Ontology Language (OWL. In this context, transformations of UML models can be mapped into ODM/OWL transformations. On the other hand, model validation is a crucial task in model transformation. Meta-modeling permits to give a syntactic structure to source and target models. However, semantic requirements have to be imposed on source and target models. A given transformation will be sound when source and target models fulfill the syntactic and semantic requirements. In this paper, we present an approach for model validation in ODM based transformations. Adopting a logic programming based transformational approach we will show how it is possible to transform and validate models. Properties to be validated range from structural and semantic requirements of models (pre and post conditions to properties of the transformation (invariants. The approach has been applied to a well-known example of model transformation: the Entity-Relationship (ER to Relational Model (RM transformation.

  3. An acoustical model based monitoring network

    NARCIS (Netherlands)

    Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

    2010-01-01

    In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

  4. Model-based estimation for official statistics

    NARCIS (Netherlands)

    van den Brakel, J.; Bethlehem, J.

    2008-01-01

    Design-based and model-assisted estimation procedures are widely applied by most of the European national statistical institutes. There are, however, situations were model-based approaches can have additional value in the production of official statistics, e.g. to deal with small sample sizes,

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

    Science.gov (United States)

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

    2017-08-01

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

  6. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

    DEFF Research Database (Denmark)

    Rasmussen, P.M.; Madsen, Kristoffer H; Lund, T.E.

    There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus...... on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification methods. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We...... discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging...

  7. Agent-based modeling of sustainable behaviors

    CERN Document Server

    Sánchez-Maroño, Noelia; Fontenla-Romero, Oscar; Polhill, J; Craig, Tony; Bajo, Javier; Corchado, Juan

    2017-01-01

    Using the O.D.D. (Overview, Design concepts, Detail) protocol, this title explores the role of agent-based modeling in predicting the feasibility of various approaches to sustainability. The chapters incorporated in this volume consist of real case studies to illustrate the utility of agent-based modeling and complexity theory in discovering a path to more efficient and sustainable lifestyles. The topics covered within include: households' attitudes toward recycling, designing decision trees for representing sustainable behaviors, negotiation-based parking allocation, auction-based traffic signal control, and others. This selection of papers will be of interest to social scientists who wish to learn more about agent-based modeling as well as experts in the field of agent-based modeling.

  8. Model-based Abstraction of Data Provenance

    DEFF Research Database (Denmark)

    Probst, Christian W.; Hansen, René Rydhof

    2014-01-01

    to bigger models, and the analyses adapt accordingly. Our approach extends provenance both with the origin of data, the actors and processes involved in the handling of data, and policies applied while doing so. The model and corresponding analyses are based on a formal model of spatial and organisational......Identifying provenance of data provides insights to the origin of data and intermediate results, and has recently gained increased interest due to data-centric applications. In this work we extend a data-centric system view with actors handling the data and policies restricting actions....... This extension is based on provenance analysis performed on system models. System models have been introduced to model and analyse spatial and organisational aspects of organisations, to identify, e.g., potential insider threats. Both the models and analyses are naturally modular; models can be combined...

  9. Exploring an Interactive Value-Adding Data-Driven Model of Consumer Electronics Supply Chain Based on Least Squares Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiao-le Wan

    2016-01-01

    Full Text Available The differences in supply chains and their competitiveness depend on the differences in supply chain value creation systems. On the basis of the theory of value cocreation, this study investigates the interactive value creation of consumer electronics supply chains from the perspective of the interaction and added value created by the main value creation bodies in supply chains. Least squares support vector machine (LS-SVM is innovatively introduced into the study on consumer electronics supply chains. A data-driven model is also established, the parameters of the method and kernel functions are optimized and selected, and an LS-SVM algorithm of consumer electronics supply chains is proposed to deal with the limited number of samples. Then, an empirical analysis of the top 10 smartphone supply chains in the Chinese market is conducted, and the LS-SVM model and other forecasting methods are compared. Results suggest that the LS-SVM model achieves a good predictive accuracy. This study also analyzes the value-adding structure of supply chains from the perspective of interaction and enriches the theory of value creation among supply chains. This study is conducive to helping consumer electronics enterprises to conduct market analyses and determine value growth points accurately.

  10. Environmental noise forecasting based on support vector machine

    Science.gov (United States)

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

    2018-01-01

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

  11. Automated visualization of rule-based models

    Science.gov (United States)

    Tapia, Jose-Juan; Faeder, James R.

    2017-01-01

    Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models. PMID:29131816

  12. Automated visualization of rule-based models.

    Science.gov (United States)

    Sekar, John Arul Prakash; Tapia, Jose-Juan; Faeder, James R

    2017-11-01

    Frameworks such as BioNetGen, Kappa and Simmune use "reaction rules" to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models.

  13. Automated visualization of rule-based models.

    Directory of Open Access Journals (Sweden)

    John Arul Prakash Sekar

    2017-11-01

    Full Text Available Frameworks such as BioNetGen, Kappa and Simmune use "reaction rules" to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i a compact rule visualization that efficiently displays each rule, (ii the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models.

  14. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

    Science.gov (United States)

    Xu, Chong; Xu, Xiwei; Dai, Fuchu; Saraf, Arun K.

    2012-09-01

    The main purpose of this study is to compare the following six GIS-based models for susceptibility mapping of earthquake triggered landslides: bivariate statistics (BS), logistic regression (LR), artificial neural networks (ANN), and three types of support vector machine (SVM) models that use the three different kernel functions linear, polynomial, and radial basis. The models are applied in a tributary watershed of the Fu River, a tributary of the Jialing River, which is part of the area of China affected by the May 12, 2008 Wenchuan earthquake. For this purpose, eleven thematic data layers are used: landslide inventory, slope angle, aspect, elevation, curvature, distance from drainages, topographic wetness index (TWI), distance from main roads, distance from surface rupture, peak ground acceleration (PGA), and lithology. The data layers were specifically constructed for analysis in this study. In the subsequent stage of the study, susceptibility maps were produced using the six models and the same input for each one. The validations of the resulting susceptibility maps were performed and compared by means of two values of area under curve (AUC) that represent the respective success rates and prediction rates. The AUC values obtained from all six results showed that the LR model provides the highest success rate (AUC=80.34) and the highest prediction rate (AUC=80.27). The SVM (radial basis function) model generates the second-highest success rate (AUC=80.302) and the second-highest prediction rate (AUC=80.151), which are close to the value from the LR model. The results using the SVM (linear) model show the lowest AUC values. The AUC values from the SVM (linear) model are only 72.52 (success rates) and 72.533 (prediction rates). Furthermore, the results also show that the radial basis function is the most appropriate kernel function of the three kernel functions applied using the SVM model for susceptibility mapping of earthquake triggered landslides in the study

  15. Deeper understanding about the genetic structure of dengue virus using SVM

    Directory of Open Access Journals (Sweden)

    Choi Subin

    2016-01-01

    Full Text Available Dengue fever, mainly found in the tropical and subtropical regions, is carried by mosquitoes. With the help of greenhouse effect, places considered to be a Dengue safe-zone are becoming more and more dangerous. Dengue fever shows similar aspects to MERS, which caused heavy casualties in South Korea; Dengue virus does not have clear treatments nor vaccines like MERS. Development of Dengue vaccine is actively investigated lately. However, it is not easy to succeed; the fact that Dengue’s 4 serotypes have different properties and that repeated infections worsen the symptoms. This research aims to analyze the 4 serotypes (DENV1, DENV2, DENV3, DENV4 using SVM and ANN algorithms to investigate the constraints in the development of Dengue’s vaccines and treatments.

  16. Comparison of CIV, SIV and AIV using Decision Tree and SVM

    Directory of Open Access Journals (Sweden)

    Park Hyorin

    2016-01-01

    Full Text Available The H3N2, the canine influenza virus has numerous types of animal hosts that can live and reproduce on. They mostly settle on pigs and birds. However, some concerned voices are rising that there is high possibility that humans could be an additional victim for the canine flu. Consequently, our project group expect that the information about the H3N2’s DNA are valuable, since the information could attribute to development of vaccine and medicine. In the experiments of analysing the properties of CIV, Canine Influenza Virus with the comparison of SIV, Swine Influenza Virus and AIV, Avian Influenza Virus with the decision tree and SVM, Support Vector Machine. The result came out that CIV, SIV and AIV are alike but also different in some aspects.

  17. AN IMPLEMENTATION OF EIS-SVM CLASSIFIER USING RESEARCH ARTICLES FOR TEXT CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    B Ramesh

    2016-04-01

    Full Text Available Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find root word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.

  18. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  19. Culturicon model: A new model for cultural-based emoticon

    Science.gov (United States)

    Zukhi, Mohd Zhafri Bin Mohd; Hussain, Azham

    2017-10-01

    Emoticons are popular among distributed collective interaction user in expressing their emotion, gestures and actions. Emoticons have been proved to be able to avoid misunderstanding of the message, attention saving and improved the communications among different native speakers. However, beside the benefits that emoticons can provide, the study regarding emoticons in cultural perspective is still lacking. As emoticons are crucial in global communication, culture should be one of the extensively research aspect in distributed collective interaction. Therefore, this study attempt to explore and develop model for cultural-based emoticon. Three cultural models that have been used in Human-Computer Interaction were studied which are the Hall Culture Model, Trompenaars and Hampden Culture Model and Hofstede Culture Model. The dimensions from these three models will be used in developing the proposed cultural-based emoticon model.

  20. Model-Based Prognostics of Hybrid Systems

    Science.gov (United States)

    Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

    2015-01-01

    Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

  1. Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.

    Science.gov (United States)

    Brinkmann, Benjamin H; Patterson, Edward E; Vite, Charles; Vasoli, Vincent M; Crepeau, Daniel; Stead, Matt; Howbert, J Jeffry; Cherkassky, Vladimir; Wagenaar, Joost B; Litt, Brian; Worrell, Gregory A

    2015-01-01

    Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (pdogs analyzed.

  2. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

    Science.gov (United States)

    Dyrba, Martin; Grothe, Michel; Kirste, Thomas; Teipel, Stefan J

    2015-06-01

    Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. © 2015 Wiley Periodicals, Inc.

  3. Integration of Simulink Models with Component-based Software Models

    Directory of Open Access Journals (Sweden)

    MARIAN, N.

    2008-06-01

    Full Text Available Model based development aims to facilitate the development of embedded control systems by emphasizing the separation of the design level from the implementation level. Model based design involves the use of multiple models that represent different views of a system, having different semantics of abstract system descriptions. Usually, in mechatronics systems, design proceeds by iterating model construction, model analysis, and model transformation. Constructing a MATLAB/Simulink model, a plant and controller behavior is simulated using graphical blocks to represent mathematical and logical constructs and process flow, then software code is generated. A Simulink model is a representation of the design or implementation of a physical system that satisfies a set of requirements. A software component-based system aims to organize system architecture and behavior as a means of computation, communication and constraints, using computational blocks and aggregates for both discrete and continuous behavior, different interconnection and execution disciplines for event-based and time-based controllers, and so on, to encompass the demands to more functionality, at even lower prices, and with opposite constraints. COMDES (Component-based Design of Software for Distributed Embedded Systems is such a component-based system framework developed by the software engineering group of Mads Clausen Institute for Product Innovation (MCI, University of Southern Denmark. Once specified, the software model has to be analyzed. One way of doing that is to integrate in wrapper files the model back into Simulink S-functions, and use its extensive simulation features, thus allowing an early exploration of the possible design choices over multiple disciplines. The paper describes a safe translation of a restricted set of MATLAB/Simulink blocks to COMDES software components, both for continuous and discrete behavior, and the transformation of the software system into the S

  4. Agent Based Reasoning in Multilevel Flow Modeling

    DEFF Research Database (Denmark)

    Lind, Morten; Zhang, Xinxin

    2012-01-01

    to launch the MFM Workbench into an agent based environment, which can complement disadvantages of the original software. The agent-based MFM Workbench is centered on a concept called “Blackboard System” and use an event based mechanism to arrange the reasoning tasks. This design will support the new......Multilevel Flow Modeling (MFM) is a modeling method used for modeling complex industrial plant. Currently, MFM is supported with a standalone software tool called MFM Workbench, which is equipped with causal-relation analysis and other functionalities. The aim of this paper is to offer a new design...

  5. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...

  6. Multimode model based defect characterization in composites

    Science.gov (United States)

    Roberts, R.; Holland, S.; Gregory, E.

    2016-02-01

    A newly-initiated research program for model-based defect characterization in CFRP composites is summarized. The work utilizes computational models of the interaction of NDE probing energy fields (ultrasound and thermography), to determine 1) the measured signal dependence on material and defect properties (forward problem), and 2) an assessment of performance-critical defect properties from analysis of measured NDE signals (inverse problem). Work is reported on model implementation for inspection of CFRP laminates containing delamination and porosity. Forward predictions of measurement response are presented, as well as examples of model-based inversion of measured data for the estimation of defect parameters.

  7. Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

    Directory of Open Access Journals (Sweden)

    Jabar H. Yousif

    2017-07-01

    Full Text Available The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV panels based on a self-organizing feature map (SOFM model. The proposed model is compared with other models, including the multi-layer perceptron (MLP and support vector machine (SVM models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE, mean absolute error (MAE, correlation (R, and coefficient of determination (R2. The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

  8. Execution-Based Model Checking of Interrupt-Based Systems

    Science.gov (United States)

    Drusinsky, Doron; Havelund, Klaus

    2003-01-01

    Execution-based model checking (EMC) is a verification technique based on executing a multi-threaded/multiprocess program repeatedly in a systematic manner in order to explore the different interleavings of the program. This is in contrast to traditional model checking, where a model of a system is analyzed Several execution-based model-checking tools exist at this point, such as for example Verisoft and Java PathFinder. The most common formal specification languages used by EMC tools are un- timed, either just assertions, or linear-time temporal logic (LTL). An alternative verification technique is Runtime Execution Monitoring (REM), which is based on monitor- ing the execution of a program, checking that the execution trace conforms to a requirement specification. The Temporal Rover and DBRover are such tools. They provide a very rich specification language, being an extension of LTL with real-time constraints and time-series. We show how execution-based model checking, combined with runtime execution monitoring, can be used for the verification of a large class of safety critical systems commonly known as interrupt-based systems. The proposed approach is novel in that: (i) it supports model checking of a large class of applications not practically verifiable using conventional EMC tools, (ii) it supports verification of LTL assertions extended with real-time and time-series constraints, and (iii) it supports the verification of custom schedulers.

  9. Model-Based Motion Tracking of Infants

    DEFF Research Database (Denmark)

    Olsen, Mikkel Damgaard; Herskind, Anna; Nielsen, Jens Bo

    2014-01-01

    Even though motion tracking is a widely used technique to analyze and measure human movements, only a few studies focus on motion tracking of infants. In recent years, a number of studies have emerged focusing on analyzing the motion pattern of infants, using computer vision. Most of these studies...... are based on 2D images, but few are based on 3D information. In this paper, we present a model-based approach for tracking infants in 3D. The study extends a novel study on graph-based motion tracking of infants and we show that the extension improves the tracking results. A 3D model is constructed...

  10. Computer Based Modelling and Simulation-Modelling and ...

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 6; Issue 4. Computer Based Modelling and Simulation-Modelling and Simulation with Probability and Throwing Dice. N K Srinivasan. General Article Volume 6 Issue 4 April 2001 pp 69-77 ...

  11. A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization.

    Science.gov (United States)

    Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

    2016-01-01

    With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.

  12. PDF-based heterogeneous multiscale filtration model.

    Science.gov (United States)

    Gong, Jian; Rutland, Christopher J

    2015-04-21

    Motivated by modeling of gasoline particulate filters (GPFs), a probability density function (PDF) based heterogeneous multiscale filtration (HMF) model is developed to calculate filtration efficiency of clean particulate filters. A new methodology based on statistical theory and classic filtration theory is developed in the HMF model. Based on the analysis of experimental porosimetry data, a pore size probability density function is introduced to represent heterogeneity and multiscale characteristics of the porous wall. The filtration efficiency of a filter can be calculated as the sum of the contributions of individual collectors. The resulting HMF model overcomes the limitations of classic mean filtration models which rely on tuning of the mean collector size. Sensitivity analysis shows that the HMF model recovers the classical mean model when the pore size variance is very small. The HMF model is validated by fundamental filtration experimental data from different scales of filter samples. The model shows a good agreement with experimental data at various operating conditions. The effects of the microstructure of filters on filtration efficiency as well as the most penetrating particle size are correctly predicted by the model.

  13. The functionality-based application confinement model

    OpenAIRE

    Schreuders, ZC; Payne, C.; Mcgill, T.

    2013-01-01

    This paper presents the functionality-based application confinement (FBAC) access control model. FBAC is an application-oriented access control model, intended to restrict processes to the behaviour that is authorised by end users, administrators, and processes, in order to limit the damage that can be caused by malicious code, due to software vulnerabilities or malware. FBAC is unique in its ability to limit applications to finely grained access control rules based on high-level easy-to-unde...

  14. Unsteady aerodynamic modeling at high angles of attack using support vector machines

    Directory of Open Access Journals (Sweden)

    Wang Qing

    2015-06-01

    Full Text Available Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism. Support vector machines (SVMs based on statistical learning theory provide a novel tool for nonlinear system modeling. The work presented here examines the feasibility of applying SVMs to high angle-of-attack unsteady aerodynamic modeling field. Mainly, after a review of SVMs, several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail, such as selection of input variables, selection of output variables and determination of SVM parameters. The least squares SVM (LS-SVM models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration, and then used to predict the aerodynamic responses in other tests. The predictions are in good agreement with the test data, which indicates the satisfying learning and generalization performance of LS-SVMs.

  15. Springer handbook of model-based science

    CERN Document Server

    Bertolotti, Tommaso

    2017-01-01

    The handbook offers the first comprehensive reference guide to the interdisciplinary field of model-based reasoning. It highlights the role of models as mediators between theory and experimentation, and as educational devices, as well as their relevance in testing hypotheses and explanatory functions. The Springer Handbook merges philosophical, cognitive and epistemological perspectives on models with the more practical needs related to the application of this tool across various disciplines and practices. The result is a unique, reliable source of information that guides readers toward an understanding of different aspects of model-based science, such as the theoretical and cognitive nature of models, as well as their practical and logical aspects. The inferential role of models in hypothetical reasoning, abduction and creativity once they are constructed, adopted, and manipulated for different scientific and technological purposes is also discussed. Written by a group of internationally renowned experts in ...

  16. Model Based Control of Reefer Container Systems

    DEFF Research Database (Denmark)

    Sørensen, Kresten Kjær

    This thesis is concerned with the development of model based control for the Star Cool refrigerated container (reefer) with the objective of reducing energy consumption. This project has been carried out under the Danish Industrial PhD programme and has been financed by Lodam together with the Da......This thesis is concerned with the development of model based control for the Star Cool refrigerated container (reefer) with the objective of reducing energy consumption. This project has been carried out under the Danish Industrial PhD programme and has been financed by Lodam together...... with the Danish Ministry of Science, Technology and Innovation. The main contributions in this thesis are on the subjects of modeling, simulation and control of a reefer and experimental model validation. A modular nonlinear simulation model is developed using a control oriented approach, resulting in a model...

  17. Econophysics of agent-based models

    CERN Document Server

    Aoyama, Hideaki; Chakrabarti, Bikas; Chakraborti, Anirban; Ghosh, Asim

    2014-01-01

    The primary goal of this book is to present the research findings and conclusions of physicists, economists, mathematicians and financial engineers working in the field of "Econophysics" who have undertaken agent-based modelling, comparison with empirical studies and related investigations. Most standard economic models assume the existence of the representative agent, who is “perfectly rational” and applies the utility maximization principle when taking action. One reason for this is the desire to keep models mathematically tractable: no tools are available to economists for solving non-linear models of heterogeneous adaptive agents without explicit optimization. In contrast, multi-agent models, which originated from statistical physics considerations, allow us to go beyond the prototype theories of traditional economics involving the representative agent. This book is based on the Econophys-Kolkata VII Workshop, at which many such modelling efforts were presented. In the book, leading researchers in the...

  18. Probabilistic Model-Based Background Subtraction

    DEFF Research Database (Denmark)

    Krüger, Volker; Andersen, Jakob; Prehn, Thomas

    2005-01-01

    manner. Bayesian propagation over time is used for proper model selection and tracking during model-based background subtraction. Bayes propagation is attractive in our application as it allows to deal with uncertainties during tracking. We have tested our approach on suitable outdoor video data....

  19. Model-based testing for software safety

    NARCIS (Netherlands)

    Gurbuz, Havva Gulay; Tekinerdogan, Bedir

    2017-01-01

    Testing safety-critical systems is crucial since a failure or malfunction may result in death or serious injuries to people, equipment, or environment. An important challenge in testing is the derivation of test cases that can identify the potential faults. Model-based testing adopts models of a

  20. Agent-based modelling of cholera diffusion

    NARCIS (Netherlands)

    Augustijn-Beckers, Petronella; Doldersum, Tom; Useya, Juliana; Augustijn, Dionysius C.M.

    2016-01-01

    This paper introduces a spatially explicit agent-based simulation model for micro-scale cholera diffusion. The model simulates both an environmental reservoir of naturally occurring V.cholerae bacteria and hyperinfectious V. cholerae. Objective of the research is to test if runoff from open refuse

  1. Approximation Algorithms for Model-Based Diagnosis

    NARCIS (Netherlands)

    Feldman, A.B.

    2010-01-01

    Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation

  2. Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2017-11-01

    Full Text Available With the increase in energy demand, extreme climates have gained increasing attention. Ice disasters on transmission lines can cause gap discharge and icing flashover electrical failures, which can lead to mechanical failure of the tower, conductor, and insulators, causing significant harm to people’s daily life and work. To address this challenge, an intelligent combinational model is proposed based on improved empirical mode decomposition and support vector machine for short-term forecasting of ice cover thickness. Firstly, in light of the characteristics of ice cover thickness data, fast independent component analysis (FICA is implemented to smooth the abnormal situation on the curve trend of the original data for prediction. Secondly, ensemble empirical mode decomposition (EEMD decomposes data after denoising it into different components from high frequency to low frequency, and support vector machine (SVM is introduced to predict the sequence of different components. Then, some modifications are performed on the standard SVM algorithm to accelerate the convergence speed. Combined with the advantages of genetic algorithm and tabu search, the combination algorithm is introduced to optimize the parameters of support vector machine. To improve the prediction accuracy, the kernel function of the support vector machine is adaptively adopted according to the complexity of different sequences. Finally, prediction results for each component series are added to obtain the overall ice cover thickness. A 220 kV DC transmission line in the Hunan Region is taken as the case study to verify the practicability and effectiveness of the proposed method. Meanwhile, we select SVM optimized by genetic algorithm (GA-SVM and traditional SVM algorithm for comparison, and use the error function of mean absolute percentage error (MAPE, root mean square error (RMSE and mean absolute error (MAE to compare prediction accuracy. Finally, we find that these improvements

  3. Interactivity in video-based models

    OpenAIRE

    Wouters, Pieter; Tabbers, Huib; Paas, Fred

    2007-01-01

    textabstractIn this review we argue that interactivity can be effective in video-based models to engage learners in relevant cognitive processes. We do not treat modeling as an isolated instructional method but adopted the social cognitive model of sequential skill acquisition in which learners start with observation and finish with independent, self-regulated performance. Moreover, we concur with the notion that interactivity should emphasize the cognitive processes that learners engage in w...

  4. Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.

    Science.gov (United States)

    Wang, Yanlu; Li, Tie-Qiang

    2015-01-01

    Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

  5. Accept & Reject Statement-Based Uncertainty Models

    NARCIS (Netherlands)

    E. Quaeghebeur (Erik); G. de Cooman; F. Hermans (Felienne)

    2015-01-01

    textabstractWe develop a framework for modelling and reasoning with uncertainty based on accept and reject statements about gambles. It generalises the frameworks found in the literature based on statements of acceptability, desirability, or favourability and clarifies their relative position. Next

  6. Information modelling and knowledge bases XXV

    CERN Document Server

    Tokuda, T; Jaakkola, H; Yoshida, N

    2014-01-01

    Because of our ever increasing use of and reliance on technology and information systems, information modelling and knowledge bases continue to be important topics in those academic communities concerned with data handling and computer science. As the information itself becomes more complex, so do the levels of abstraction and the databases themselves. This book is part of the series Information Modelling and Knowledge Bases, which concentrates on a variety of themes in the important domains of conceptual modeling, design and specification of information systems, multimedia information modelin

  7. Internet resources for agent-based modelling.

    Science.gov (United States)

    Devillers, J; Devillers, H; Decourtye, A; Aupinel, P

    2010-04-01

    The use of agent-based models (ABMs) is steadily increasing in all the disciplines including environmental chemistry and toxicology. This growth is mainly driven by their ability to address problems that conventional modelling techniques cannot, such as the change of scale or the emergence of unanticipated phenomena resulting from interactions between their constitutive goal-directed agents. After a brief introduction on the basic principles of agent-based modelling and the presentation of selected case studies, the main software resources available on the Internet are presented. An attempt is made to estimate the complexity of these tools versus their potentialities and flexibility.

  8. Integration of Simulink Models with Component-based Software Models

    DEFF Research Database (Denmark)

    Marian, Nicolae

    2008-01-01

    , communication and constraints, using computational blocks and aggregates for both discrete and continuous behaviour, different interconnection and execution disciplines for event-based and time-based controllers, and so on, to encompass the demands to more functionality, at even lower prices, and with opposite...... of abstract system descriptions. Usually, in mechatronics systems, design proceeds by iterating model construction, model analysis, and model transformation. Constructing a MATLAB/Simulink model, a plant and controller behavior is simulated using graphical blocks to represent mathematical and logical...... to be analyzed. One way of doing that is to integrate in wrapper files the model back into Simulink S-functions, and use its extensive simulation features, thus allowing an early exploration of the possible design choices over multiple disciplines. The paper describes a safe translation of a restricted set...

  9. Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

    Directory of Open Access Journals (Sweden)

    Jiaming Liu

    2016-01-01

    Full Text Available Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA method combined with three statistical downscaling methods, which are support vector machine (SVM, BCC/RCG-Weather Generators (BCC/RCG-WG, and Statistics Downscaling Model (SDSM, is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB. The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

  10. Port Hinterland Modelling Based on Port Choice

    Directory of Open Access Journals (Sweden)

    Tomaž Kramberger

    2015-06-01

    Full Text Available This paper presents a new approach for hinterland modelling based on the results of port choice modelling. The paper follows the idea that the shippers’ port choice is a trade-off between various objective and subjective factors. The presented model tackles the problem by applying the AHP method in order to obtain ports’ preference rates based on subjective factors, and combine them with objective factors, which include port operation costs, sailing times, and land transport costs using MILP. The ports’ hinterlands are modelled by finding the optimal port of choice for different locations across Europe and merging the identical results. The model can be used in order to produce captive hinterland of ports and can also be exploited in order to analyse how changes in the traffic infrastructure influence the size of hinterlands.

  11. Multi-Domain Modeling Based on Modelica

    Directory of Open Access Journals (Sweden)

    Liu Jun

    2016-01-01

    Full Text Available With the application of simulation technology in large-scale and multi-field problems, multi-domain unified modeling become an effective way to solve these problems. This paper introduces several basic methods and advantages of the multidisciplinary model, and focuses on the simulation based on Modelica language. The Modelica/Mworks is a newly developed simulation software with features of an object-oriented and non-casual language for modeling of the large, multi-domain system, which makes the model easier to grasp, develop and maintain.It This article shows the single degree of freedom mechanical vibration system based on Modelica language special connection mechanism in Mworks. This method that multi-domain modeling has simple and feasible, high reusability. it closer to the physical system, and many other advantages.

  12. Modelling Carbon Nanotubes-Based Mediatorless Biosensor

    Directory of Open Access Journals (Sweden)

    Julija Razumiene

    2012-07-01

    Full Text Available This paper presents a mathematical model of carbon nanotubes-based mediatorless biosensor. The developed model is based on nonlinear non-stationary reaction-diffusion equations. The model involves four layers (compartments: a layer of enzyme solution entrapped on a terylene membrane, a layer of the single walled carbon nanotubes deposited on a perforated membrane, and an outer diffusion layer. The biosensor response and sensitivity are investigated by changing the model parameters with a special emphasis on the mediatorless transfer of the electrons in the layer of the enzyme-loaded carbon nanotubes. The numerical simulation at transient and steady state conditions was carried out using the finite difference technique. The mathematical model and the numerical solution were validated by experimental data. The obtained agreement between the simulation results and the experimental data was admissible at different concentrations of the substrate.

  13. NASA Software Cost Estimation Model: An Analogy Based Estimation Model

    Science.gov (United States)

    Hihn, Jairus; Juster, Leora; Menzies, Tim; Mathew, George; Johnson, James

    2015-01-01

    The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K-­ nearest neighbor prediction model performance on the same data set.

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

    Science.gov (United States)

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

    2010-11-01

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

  15. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

    Science.gov (United States)

    Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

    2015-12-01

    The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    National Research Council Canada - National Science Library

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

    2015-01-01

    .... normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used...

  17. Classification of Auditory Evoked Potentials based on the wavelet decomposition and SVM network

    Directory of Open Access Journals (Sweden)

    Michał Suchocki

    2015-12-01

    Full Text Available For electrophysiological hearing assessment and diagnosis of brain stem lesions, the most often used are auditory brainstem evoked potentials of short latency. They are characterized by successively arranged maxima as a function of time, called waves. Morphology of the course, in particular, the timing and amplitude of each wave, allow a neurologist to make diagnose, what is not an easy task. A neurologist should be experienced, concentrated, and should have very good perception. In order to support his diagnostic process, the authors have developed an algorithm implementing the automated classification of auditory evoked potentials to the group of pathological and physiological cases, the sensitivity and specificity determined for an independent test group (of 50 cases of respectively 84% and 88%.[b]Keywords[/b]: biomedical engineering, brainstem auditory evoked potentials, wavelet decomposition, support vector machine

  18. WITHDRAWN: Automatic epileptic seizure detection in EEGs based on MF-DFA and SVM.

    Science.gov (United States)

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

    2016-09-09

    This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Integration of Simulink Models with Component-based Software Models

    DEFF Research Database (Denmark)

    Marian, Nicolae; Top, Søren

    2008-01-01

    of abstract system descriptions. Usually, in mechatronics systems, design proceeds by iterating model construction, model analysis, and model transformation. Constructing a MATLAB/Simulink model, a plant and controller behavior is simulated using graphical blocks to represent mathematical and logical......, communication and constraints, using computational blocks and aggregates for both discrete and continuous behaviour, different interconnection and execution disciplines for event-based and time-based controllers, and so on, to encompass the demands to more functionality, at even lower prices, and with opposite...... of MATLAB/Simulink blocks to COMDES software components, both for continuous and discrete behaviour, and the transformation of the software system into the S-functions. The general aim of this work is the improvement of multi-disciplinary development of embedded systems with the focus on the relation...

  20. Requirements Engineering Model: Role Based Goal Oriented Model

    Directory of Open Access Journals (Sweden)

    Sandfreni

    2016-01-01

    Full Text Available Requirements engineering approach through intentional perspective is one of the arguments that appear in the field of requirement engineering. That approach can explain the characteristics of the behavior of an actor. The usage Goal Based Workflow and KAOS method in iStar modeling might help the system analyst to gain knowledge about the internal process inside each of actor sequentially, such that the whole sequential activity to achieve the goal are exposed clearly in those actor’s internal process. The adoption of the concept of the role of RACI diagram on Role Based Goal Oriented Model system analyst gain complete knowledge about requirements of actor who involve in a system. System analyst might also distinguish the dependency between each actor in each process. Those dependencies are exhibited in strategic dependency model. In addition, the internal activities of the actor are also shown in strategic rationale model.

  1. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  2. 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

    Asphaltene precipitation/deposition and its imposing difficulties are drastic issues in petroleum industry. Monitoring the asphaltene stability conditions in crude oil systems is still a challenge and has been subject of many studies. In this work, the Refractive Index (RI) of several oil samples...

  3. MEGen: A Physiologically Based Pharmacokinetic Model Generator

    Directory of Open Access Journals (Sweden)

    George D Loizou

    2011-11-01

    Full Text Available Physiologically based pharmacokinetic models are being used in an increasing number of different areas. These not only include the human safety assessment of pharmaceuticals, pesticides, biocides and environmental chemicals but also for food animal, wild mammal and avian risk assessment. The value of PBPK models is that they are tools for estimating tissue dosimetry by integrating in vitro and in vivo mechanistic, pharmacokinetic and toxicological information through their explicit mathematical description of important anatomical, physiological and biochemical determinants of chemical uptake, disposition and elimination. However, PBPK models are perceived as complex, data hungry, resource intensive and time consuming. In addition, model validation and verification are hindered by the relative complexity of the equations. To begin to address these issues a freely available web application for the rapid construction and documentation of bespoke PBPK models is under development. Here we present an overview of the current capabilities of MEGen, a model equation generator and parameter database and discuss future developments.

  4. A Nursing Practice Model Based on Christ: The Agape Model.

    Science.gov (United States)

    Eckerd, Nancy

    2017-06-07

    Nine out of 10 American adults believe Jesus was a real person, and almost two-thirds have made a commitment to Jesus Christ. Research further supports that spiritual beliefs and religious practices influence overall health and well-being. Christian nurses need a practice model that helps them serve as kingdom nurses. This article introduces the Agape Model, based on the agape love and characteristics of Christ, upon which Christian nurses may align their practice to provide Christ-centered care.

  5. Spatial interactions in agent-based modeling

    CERN Document Server

    Ausloos, Marcel; Merlone, Ugo

    2014-01-01

    Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution o...

  6. Fourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognition

    OpenAIRE

    Bohi, Amine; Prandi, Dario; Guis, Vincente; Bouchara, Frédéric; Gauthier, Jean-Paul

    2016-01-01

    International audience; In this paper we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group $SE(2,N)=\\mathbb Z_N\\rtimes \\mathbb{R}^2$. The proposed technique is based on generalized Fourier descriptors on the latter group, which are invariant to natural geometric transformations (rotations, translations). These descriptors are then used to feed an SVM classifier. We...

  7. Speed Sensorless Direct Torque Control of a PMSM Drive using Space Vector Modulation Based MRAS and Stator Resistance Estimator

    OpenAIRE

    A. Ameur; B. Mokhtari; N. Essounbouli; L. Mokrani

    2012-01-01

    This paper presents a speed sensorless direct torque control scheme using space vector modulation (DTC-SVM) for permanent magnet synchronous motor (PMSM) drive based a Model Reference Adaptive System (MRAS) algorithm and stator resistance estimator. The MRAS is utilized to estimate speed and stator resistance and compensate the effects of parameter variation on stator resistance, which makes flux and torque estimation more accurate and insensitive to parameter variation. ...

  8. A Multiagent Based Model for Tactical Planning

    Science.gov (United States)

    2002-10-01

    Pub. Co. 1985. [10] Castillo, J.M. Aproximación mediante procedimientos de Inteligencia Artificial al planeamiento táctico. Doctoral Thesis...been developed under the same conceptual model and using similar Artificial Intelligence Tools. We use four different stimulus/response agents in...The conceptual model is built on base of the Agents theory. To implement the different agents we have used Artificial Intelligence techniques such

  9. Quality Model Based on Cots Quality Attributes

    OpenAIRE

    Jawad Alkhateeb; Khaled Musa

    2013-01-01

    The quality of software is essential to corporations in making their commercial software. Good or poorquality to software plays an important role to some systems such as embedded systems, real-time systems,and control systems that play an important aspect in human life. Software products or commercial off theshelf software are usually programmed based on a software quality model. In the software engineeringfield, each quality model contains a set of attributes or characteristics that drives i...

  10. Modelling Web-Based Instructional Systems

    OpenAIRE

    Symeon Retalis; Paris Avgeriou

    2002-01-01

    The size and complexity of modern instructional systems, which are based on the World Wide Web, bring about great intricacy in their crafting, as there is not enough knowledge or experience in this field. This imposes the use of new instructional design models in order to achieve risk-mitigation, cost and time efficiency, high pedagogical quality of the end product, which will capitalise on the potential of the networked technologies. This paper presents a model for constructing such systems,...

  11. Behavioural queuing: an agent based modelling approach

    OpenAIRE

    Sankaranarayanan Karthik; Arturo Delgado-Alvarez Carlos; R Larsen Erik; van Ackere Ann

    2012-01-01

    Queueing research has a plethora of applications and has been an area of study spanning from telecommunications to economics. Traditionally studies on queueing has mainly concentrated on design performance and running of the service facility with customers arriving following a stochastic process. In this paper we take an agent based modeling approach to develop a behavioral model of a queueing system using Cellular Automata (CA). We study how adaptive expectation along with a simple informati...

  12. Model-based testing for embedded systems

    CERN Document Server

    Zander, Justyna; Mosterman, Pieter J

    2011-01-01

    What the experts have to say about Model-Based Testing for Embedded Systems: "This book is exactly what is needed at the exact right time in this fast-growing area. From its beginnings over 10 years ago of deriving tests from UML statecharts, model-based testing has matured into a topic with both breadth and depth. Testing embedded systems is a natural application of MBT, and this book hits the nail exactly on the head. Numerous topics are presented clearly, thoroughly, and concisely in this cutting-edge book. The authors are world-class leading experts in this area and teach us well-used

  13. Predictor-Based Model Reference Adaptive Control

    Science.gov (United States)

    Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.

    2009-01-01

    This paper is devoted to robust, Predictor-based Model Reference Adaptive Control (PMRAC) design. The proposed adaptive system is compared with the now-classical Model Reference Adaptive Control (MRAC) architecture. Simulation examples are presented. Numerical evidence indicates that the proposed PMRAC tracking architecture has better than MRAC transient characteristics. In this paper, we presented a state-predictor based direct adaptive tracking design methodology for multi-input dynamical systems, with partially known dynamics. Efficiency of the design was demonstrated using short period dynamics of an aircraft. Formal proof of the reported PMRAC benefits constitute future research and will be reported elsewhere.

  14. Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

    Directory of Open Access Journals (Sweden)

    Jianwei Liu

    2013-01-01

    Full Text Available Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

  15. Iterative reweighted noninteger norm regularizing SVM for gene expression data classification.

    Science.gov (United States)

    Liu, Jianwei; Li, Shuang Cheng; Luo, Xionglin

    2013-01-01

    Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm. Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

  16. Model based defect characterization in composites

    Science.gov (United States)

    Roberts, R.; Holland, S.

    2017-02-01

    Work is reported on model-based defect characterization in CFRP composites. The work utilizes computational models of the interaction of NDE probing energy fields (ultrasound and thermography), to determine 1) the measured signal dependence on material and defect properties (forward problem), and 2) an assessment of performance-critical defect properties from analysis of measured NDE signals (inverse problem). Work is reported on model implementation for inspection of CFRP laminates containing multi-ply impact-induced delamination, with application in this paper focusing on ultrasound. A companion paper in these proceedings summarizes corresponding activity in thermography. Inversion of ultrasound data is demonstrated showing the quantitative extraction of damage properties.

  17. A Cognitive Model Based on Neuromodulated Plasticity

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2016-01-01

    Full Text Available Associative learning, including classical conditioning and operant conditioning, is regarded as the most fundamental type of learning for animals and human beings. Many models have been proposed surrounding classical conditioning or operant conditioning. However, a unified and integrated model to explain the two types of conditioning is much less studied. Here, a model based on neuromodulated synaptic plasticity is presented. The model is bioinspired including multistored memory module and simulated VTA dopaminergic neurons to produce reward signal. The synaptic weights are modified according to the reward signal, which simulates the change of associative strengths in associative learning. The experiment results in real robots prove the suitability and validity of the proposed model.

  18. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  19. Modeling Personalized Email Prioritization: Classification-based and Regression-based Approaches

    Energy Technology Data Exchange (ETDEWEB)

    Yoo S.; Yang, Y.; Carbonell, J.

    2011-10-24

    Email overload, even after spam filtering, presents a serious productivity challenge for busy professionals and executives. One solution is automated prioritization of incoming emails to ensure the most important are read and processed quickly, while others are processed later as/if time permits in declining priority levels. This paper presents a study of machine learning approaches to email prioritization into discrete levels, comparing ordinal regression versus classier cascades. Given the ordinal nature of discrete email priority levels, SVM ordinal regression would be expected to perform well, but surprisingly a cascade of SVM classifiers significantly outperforms ordinal regression for email prioritization. In contrast, SVM regression performs well -- better than classifiers -- on selected UCI data sets. This unexpected performance inversion is analyzed and results are presented, providing core functionality for email prioritization systems.

  20. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...

  1. Incident Duration Modeling Using Flexible Parametric Hazard-Based Models

    Directory of Open Access Journals (Sweden)

    Ruimin Li

    2014-01-01

    Full Text Available Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.

  2. Model-Based Power Plant Master Control

    Energy Technology Data Exchange (ETDEWEB)

    Boman, Katarina; Thomas, Jean; Funkquist, Jonas

    2010-08-15

    The main goal of the project has been to evaluate the potential of a coordinated master control for a solid fuel power plant in terms of tracking capability, stability and robustness. The control strategy has been model-based predictive control (MPC) and the plant used in the case study has been the Vattenfall power plant Idbaecken in Nykoeping. A dynamic plant model based on nonlinear physical models was used to imitate the true plant in MATLAB/SIMULINK simulations. The basis for this model was already developed in previous Vattenfall internal projects, along with a simulation model of the existing control implementation with traditional PID controllers. The existing PID control is used as a reference performance, and it has been thoroughly studied and tuned in these previous Vattenfall internal projects. A turbine model was developed with characteristics based on the results of steady-state simulations of the plant using the software EBSILON. Using the derived model as a representative for the actual process, an MPC control strategy was developed using linearization and gain-scheduling. The control signal constraints (rate of change) and constraints on outputs were implemented to comply with plant constraints. After tuning the MPC control parameters, a number of simulation scenarios were performed to compare the MPC strategy with the existing PID control structure. The simulation scenarios also included cases highlighting the robustness properties of the MPC strategy. From the study, the main conclusions are: - The proposed Master MPC controller shows excellent set-point tracking performance even though the plant has strong interactions and non-linearity, and the controls and their rate of change are bounded. - The proposed Master MPC controller is robust, stable in the presence of disturbances and parameter variations. Even though the current study only considered a very small number of the possible disturbances and modelling errors, the considered cases are

  3. Comparison between SARS CoV and MERS CoV Using Apriori Algorithm, Decision Tree, SVM

    Directory of Open Access Journals (Sweden)

    Jang Seongpil

    2016-01-01

    Full Text Available MERS (Middle East Respiratory Syndrome is a worldwide disease these days. The number of infected people is 1038(08/03/2015 in Saudi Arabia and 186(08/03/2015 in South Korea. MERS is all over the world including Europe and the fatality rate is 38.8%, East Asia and the Middle East. The MERS is also known as a cousin of SARS (Severe Acute Respiratory Syndrome because both diseases show similar symptoms such as high fever and difficulty in breathing. This is why we compared MERS with SARS. We used data of the spike glycoprotein from NCBI. As a way of analyzing the protein, apriori algorithm, decision tree, SVM were used, and particularly SVM was iterated by normal, polynomial, and sigmoid. The result came out that the MERS and the SARS are alike but also different in some way.

  4. Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods.

    Science.gov (United States)

    Chang, Chia-Yun; Hsu, Ming-Tsung; Esposito, Emilio Xavier; Tseng, Yufeng J

    2013-04-22

    The traditional biological assay is very time-consuming, and thus the ability to quickly screen large numbers of compounds against a specific biological target is appealing. To speed up the biological evaluation of compounds, high-throughput screening is widely used in the fields of biomedical, biological information, and drug discovery. The research presented in this study focuses on the use of support vector machines, a machine learning method, various classes of molecular descriptors, and different sampling techniques to overcome overfitting to classify compounds for cytotoxicity with respect to the Jurkat cell line. The cell cytotoxicity data set is imbalanced (a few active compounds and very many inactive compounds), and the ability of the predictive modeling methods is adversely affected in these situations. Commonly imbalanced data sets are overfit with respect to the dominant classified end point; in this study the models routinely overfit toward inactive (noncytotoxic) compounds when the imbalance was substantial. Support vector machine (SVM) models were used to probe the proficiency of different classes of molecular descriptors and oversampling ratios. The SVM models were constructed from 4D-FPs, MOE (1D, 2D, and 21/2D), noNP+MOE, and CATS2D trial descriptors pools and compared to the predictive abilities of CATS2D-based random forest models. Compared to previous results in the literature, the SVM models built from oversampled data sets exhibited better predictive abilities for the training and external test sets.

  5. 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.

  6. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

    Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  7. Support vector machine based on adaptive acceleration particle swarm optimization.

    Science.gov (United States)

    Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

    2014-01-01

    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  8. Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine.

    Science.gov (United States)

    Choi, Soo Beom; Park, Jee Soo; Chung, Jai Won; Yoo, Tae Keun; Kim, Deok Won

    2014-01-01

    We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI). We obtained an accuracy of 100%, relative classifier information (RCI) of 1.0, and a kappa index of 1.0 by applying the models of support vector machines one-versus-one (SVM-OVO), SVM one-versus-rest (OVR), and directed acyclic graph SVM (DAGSVM), using the ratio of genes between categories to within-category sums of squares (BW) feature selection method. Each of these three models selected only four features to categorize the 12 groups, resulting in a time-saving and cost-effective strategy for diagnosing neuromuscular diseases. In addition, a gene symbol, SPP1 was selected as the top-ranked gene by the BW method. We confirmed relationships between the gene (SPP1) and Duchenne muscular dystrophy (DMD) from a previous study. With our models as clinically helpful tools, neuromuscular diseases could be classified quickly using a computer, thereby giving a time-saving, cost-effective, and accurate diagnosis.

  9. Modelling Web-Based Instructional Systems

    NARCIS (Netherlands)

    Retalis, Symeon; Avgeriou, Paris

    2002-01-01

    The size and complexity of modern instructional systems, which are based on the World Wide Web, bring about great intricacy in their crafting, as there is not enough knowledge or experience in this field. This imposes the use of new instructional design models in order to achieve risk-mitigation,

  10. Optimization-Based Models of Muscle Coordination

    OpenAIRE

    Prilutsky, Boris I.; Zatsiorsky, Vladimir M.

    2002-01-01

    Optimization-based models may provide reasonably accurate estimates of activation and force patterns of individual muscles in selected well-learned tasks with submaximal efforts. Such optimization criteria as minimum energy expenditure, minimum muscle fatigue, and minimum sense of effort seem most promising.

  11. Optimization-based models of muscle coordination.

    Science.gov (United States)

    Prilutsky, Boris I; Zatsiorsky, Vladimir M

    2002-01-01

    Optimization-based models may provide reasonably accurate estimates of activation and force patterns of individual muscles in selected well-learned tasks with submaximal efforts. Such optimization criteria as minimum energy expenditure, minimum muscle fatigue, and minimum sense of effort seem most promising.

  12. Model based development of engine control algorithms

    NARCIS (Netherlands)

    Dekker, H.J.; Sturm, W.L.

    1996-01-01

    Model based development of engine control systems has several advantages. The development time and costs are strongly reduced because much of the development and optimization work is carried out by simulating both engine and control system. After optimizing the control algorithm it can be executed

  13. 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

  14. Dynamic Ligand Based Pharmacophore Modeling and Virtual ...

    Indian Academy of Sciences (India)

    user

    : 1, 2, or 3 for low, medium, or high. •. PercentHuman- OralAbsorption Predicted human oral absorption on 0 to 100% scale. The prediction is based on a quantitative multiple linear regression model. This property usually correlates well with ...

  15. Agent Based Modelling for Social Simulation

    NARCIS (Netherlands)

    Smit, S.K.; Ubink, E.M.; Vecht, B. van der; Langley, D.J.

    2013-01-01

    This document is the result of an exploratory project looking into the status of, and opportunities for Agent Based Modelling (ABM) at TNO. The project focussed on ABM applications containing social interactions and human factors, which we termed ABM for social simulation (ABM4SS). During the course

  16. Indiana Distributive Education Competency Based Model.

    Science.gov (United States)

    Davis, Rod; And Others

    This Indiana distributive education competency-based curriculum model is designed to help teachers and local administrators plan and conduct a comprehensive marketing and distributive education program. It is divided into three levels--one level for each year of a three-year program. The competencies common to a variety of marketing and…

  17. Néron Models and Base Change

    DEFF Research Database (Denmark)

    Halle, Lars Halvard; Nicaise, Johannes

    on Néron component groups, Edixhoven’s filtration and the base change conductor of Chai and Yu, and we study these invariants using various techniques such as models of curves, sheaves on Grothendieck sites and non-archimedean uniformization. We then apply our results to the study of motivic zeta functions...

  18. Atomic Action Refinement in Model Based Testing

    NARCIS (Netherlands)

    van der Bijl, H.M.; Rensink, Arend; Tretmans, G.J.

    2007-01-01

    In model based testing (MBT) test cases are derived from a specification of the system that we want to test. In general the specification is more abstract than the implementation. This may result in 1) test cases that are not executable, because their actions are too abstract (the implementation

  19. Deriving Framework Usages Based on Behavioral Models

    Science.gov (United States)

    Zenmyo, Teruyoshi; Kobayashi, Takashi; Saeki, Motoshi

    One of the critical issue in framework-based software development is a huge introduction cost caused by technical gap between developers and users of frameworks. This paper proposes a technique for deriving framework usages to implement a given requirements specification. By using the derived usages, the users can use the frameworks without understanding the framework in detail. Requirements specifications which describe definite behavioral requirements cannot be related to frameworks in as-is since the frameworks do not have definite control structure so that the users can customize them to suit given requirements specifications. To cope with this issue, a new technique based on satisfiability problems (SAT) is employed to derive the control structures of the framework model. In the proposed technique, requirements specifications and frameworks are modeled based on Labeled Transition Systems (LTSs) with branch conditions represented by predicates. Truth assignments of the branch conditions in the framework models are not given initially for representing the customizable control structure. The derivation of truth assignments of the branch conditions is regarded as the SAT by assuming relations between termination states of the requirements specification model and ones of the framework model. This derivation technique is incorporated into a technique we have proposed previously for relating actions of requirements specifications to ones of frameworks. Furthermore, this paper discuss a case study of typical use cases in e-commerce systems.

  20. Introducing Waqf Based Takaful Model in India

    Directory of Open Access Journals (Sweden)

    Syed Ahmed Salman

    2014-03-01

    Full Text Available Objective – Waqf is a unique feature of the socioeconomic system of Islam in a multi- religious and developing country like India. India is a rich country with waqf assets. The history of waqf in India can be traced back to 800 years ago. Most of the researchers, suggest how waqf can be used a tool to mitigate the poverty of Muslims. India has the third highest Muslim population after Indonesia and Pakistan. However, the majority of Muslims belong to the low income group and they are in need of help. It is believed that waqf can be utilized for the betterment of Indian Muslim community. Among the available uses of waqf assets, the main objective of this paper is to introduce waqf based takaful model in India. In addition, how this proposed model can be adopted in India is highlighted.Methods – Library research is applied since this paper relies on secondary data by thoroughlyreviewing the most relevant literature.Result – India as a rich country with waqf assets should fully utilize the resources to help the Muslims through takaful.Conclusion – In this study, we have proposed waqf based takaful model with the combination of the concepts mudarabah and wakalah for India. We recommend this model based on the background of the  country and situations. Since we have not tested the viability of this model in India, future research should be continued on this testing.Keywords : Wakaf, Takaful, Kemiskinan dan India