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Multiclassification algorithm and its realization based on least square support vector machine.algorithm

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Journal of Systems Engineering and Electronics 2005年第4期16卷 901-907页

作者： Fan Youping Chen Yunping Sun Wansheng Li Yu Inst. of Bulk Grid Secttrity Faculty of Electrical Engineering Wuhan Univ. Wuhan 430072 P. R. China Automation Coll. Chongqing Univ. Chongqing 400044 P. R. China

As a new type of learning machine.developed on the basis of statistics learning theory, support vector machine.（SVM） plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifter. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM（LS-SVM） and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-dassiftcation is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and twospirals to measure the performance of the classifier.

关键词： control theory & control engineering artificial intelligence machine.learning support vector machine.

A Transmission Line Fault Classification Approach by support vector machine.

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Journal of Energy and Power Engineering 2011年第3期5卷 268-274页

作者： A.M. Ibrahim A.Y. Abdelaziz S.F. Mekhamer M. Ramadan Electrical Power and machine. Department Faculty of Engineering Ain Shams University Cairo Egypt

This paper presents an approach for shunt faults detection and classification in transmission line using support vector machine.（SVM）. The paper compares between using three line post-fault current samples for one-half cycle and one-fourth cycle from the inception of the fault as inputs for SVM. Two SVMs are used, first SVMabc is used for faulty phase detection and second SVMg is used for ground detection. SVMs with polynomial kernel with different degrees are used to obtain the best classification score. The classification test results show that the proposed method is accurate and reliable.

关键词： Transmission line protection fault detection fault classification support vector machine.

Atmospheric Environmental Quality Prediction Based on support vector machine

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Journal of Chemistry and Chemical Engineering 2010年第2期4卷 1-6页

作者： Peng Zhong Department of Electronic Systems and Electrical Systems Ecole Polytechnique University of Nantes Nantes 44600 France Department of Engineering Thermophysics South China University of Technology Guangzhou 510641 China

The economic development bring serious environmental problems to China, the quality of atmospheric environment has an important bearing on the forecast. Be aimed at the complexity and non-linear of the quality of atmospheric environment, chaos theory has been put forward which takes full advantage of using date information. Based on the reconstruction of time-series phase space of the quality of atmospheric environment with the use of least squares support vector machine.theory, the atmospheric environment prediction model was built up, and 25 years of historical data viewed as the raw data of the quality of the environment in Benxi City, giving a practical example, it shows that the results forecasted by the least squares support vector machine.and the actual results match the better the forecast accuracy also meet the engineering application.

关键词： Environmental quality time series phase space support vector machine.

Monitoring Grinding Wheel Redress-life Using support vector machine.

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International Journal of Automation and computing 2006年第1期3卷 56-62页

作者： Thitikorn Limchimchol School of Mechanical Materials and Manufacturing Engineering University of Nottingham Nottingham NG7 2RD UK

Condition monitoring is a very important aspect in automated manufacturing processes. Any malfunction of a machining process will deteriorate production quality and efficiency. This paper presents an application of support vector machine. in grinding process monitoring. The paper starts with an overview of grinding behaviour. Grinding force is analysed through a Short Time Fourier Transform （STFT） to identify features for condition monitoring. The support vector machine.（SVM） methodology is introduced as a powerful tool for the classification of different wheel wear situations. After training with available signal data, the SVM is able to identify the state of a grinding process. The requirement and strategy for using SVM for grinding process monitoring is discussed, while the result of the example illustrates how effective SVMs can be in determining wheel redress-life.

关键词： Monitoring grinding support vector machine.

Saudi License Plate Recognition Algorithm Based on support vector machine

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Journal of Electronic Science and Technology 2013年第4期11卷 424-428页

作者： Khaled Suwais Rana Al-Otaibi Ali Alshahrani the Faculty of Computer Studies Arab Open University the Computer and Electronics Research Institute King Abdulaziz City for Science and Technology

-License plate recognition （LPR） is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine.（SVM） algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

关键词： Image processing license platesrecognition systems support vector machine.

ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION

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Journal of Systems Science & Complexity 2010年第5期23卷 917-930页

作者： Limin LI Waiki CHING Yatming CHAN Hiroshi MAMITSUKA Department of Mathematics Xi'an Jiaotong University Xi'an 710049 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Pokfularn Road Hong Kong China Bioinformatics Center Institute for Chemical Research Kyoto University Gokasho Uji Kyoto 611-0011 Japan

Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions （PPI）. The author first construct kernels based on PPI networks, then apply support vector machine.（SVM） techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source.

关键词： Diffusion kernel kernel method Laplacian kernel local linear embedding (LLE) kernel protein function prediction support vector machine.

DESIGNING A HYBRID INTELLIGENT MINING SYSTEM FOR CREDIT RISK EVALUATION

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Journal of Systems Science & Complexity 2008年第4期21卷 527-539页

作者： Lean YU Shouyang WANG Fenghua WEN Kin Keung LAI Shaoyi HE Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China. School of Economy and Management Changsha University of Science ~ Technology Changsha 410076 China. Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong. Department of Information Systems and Operations Management College of Business Administration Cali fornia State University San Marcos San Marcos CA 92096-0001 USA.

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machine. is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machine. are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machine.. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine.classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.

关键词： Credit risk evaluation hybrid intelligent system rough sets support vector machine.

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

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Computers, Materials & Continua 2018年第5期55卷 201-211页

作者： Junjia Chen Wei Lu Yuileong Yeung Yingjie Xue Xianjin Liu Cong Lin Yue Zhang School of Data and Computer Science Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China Center for Faculty Development and Educational Technology Guangdong University of Finance and EconomicsGuangzhou 510320China Department of Computer Science College of Engineering and Computer ScienceEastern Lake AvenueOrlandoFLUSA.

In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information *** most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding *** this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial ***,the distortion maps can be calculated for cover and stego images and the difference between them is *** this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence *** proposed scheme first generates the corresponding distortion maps for cover and stego *** the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego ***,support vector machine.based on the gaussian kernel,is used to classify the *** with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.

关键词： Binary image steganalysis informational security embedding distortion distortion level map co-occurrence matrix support vector machine.

Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG

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Computer Modeling in Engineering & Sciences 2020年第2期122卷 721-741页

作者： Jingmin Guo Xiu Cheng Duanpo Wu Fujian Provincial Maternity and Children’s Hospital Affiliated Hospital of Fujian Medical UniversityFuzhouChina. School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina. Hangzhou Neuro Science and Technology Co. Ltd.HangzhouChina.

The grading of hypoxic-ischemic encephalopathy(HIE)contributes to the clinical decision making for neonates with *** this paper,an automated grading method based on electroencephalogram(EEG)data is proposed to describe the severity of HIE infants,namely mild asphyxia,moderate asphyxia and severe *** automated grading method is based on a multi-class support vector machine.SVM)classifier,and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch data with 4 s *** note,the correlation coefficient and asymmetry extracted in this paper have obvious discriminating capability in HIE infants *** experimental results show that the proposed method can achieve the classification accuracy of 78.3%,75.8%and 87.0%of the mild asphyxia group,moderate asphyxia group and severe asphyxia group,***,the overall accuracy and kappa used to evaluate the performance of the proposed method can reach 79.5%and 0.69,respectively.

关键词： Hypoxic-ischemic encephalopathy electroencephalograph neonate support vector machine.