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Printed Arabic Character Recognition Using HMM

Journal of Computer Science & Technology 2004年第4期19卷 538-543页

作者： AbbasH.Hassin Xiang-LongTang Jia-FengLiu WeiZhao ComputerScienceDepartment HarbinInstituteofTechnologyHarbin150001P.R.China

The Arabic Language has a very rich vocabulary. More than 200 million peoplespeak this language as their native speaking, and over 1 billion people use it in severalreligion-related activities. In this paper a new technique is presented for recognizing printedArabic characters. After a word is segmented, each character/word is entirely transformed into afeature vector. The features of printed Arabic characters include strokes and bays in variousdirections, endpoints, intersection points, loops, dots and zigzags. The word skeleton is decomposedinto a number of links in orthographic order, and then it is transferred into a sequence of symbolsusing vector quantization. Single hidden Markov model has been used for recognizing the printedArabic characters. Experimental results show that the high recognition rate depends on the number ofstates in each sample.

关键词： pattern recognition off-line Arabic character recognition featureextraction hidden markov models

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CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES

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Chinese Journal of Mechanical Engineering 2002年第3期15卷 243-247页

作者： Lai Wuxing Zhang Guicai Shi Tielin Yang ShuziSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology,Wuhan 430074, China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074

Gears alternately mesh and detach in driving process, and then workingconditions of gears are alternately changing, so they are easy to be spalled and worn. But becauseof the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; theirfault features are difficult to extract. This study aims to propose an approach of gear faultsclassification, using the cumulants and support vector machines. The cumulants can eliminate theadditive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vectormachines as classifier, which is employed structural risk minimisation principle, is superior tothat of conventional neural networks, which is employed traditional empirical risk minimisationprinciple. Support vector machines as the classifier, and the third and fourth order cumulants asinput, gears faults are successfully recognized. The experimental results show that the method offault classification combining cumulants with support vector machines is very effective.

关键词： Support vector machine Gear Fault diagnosis Cumulant featureextraction

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KERNEL NEIGHBORHOOD PRESERVING EMBEDDING FOR CLASSIFICATION

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Journal of Electronics(China) 2009年第3期26卷 374-379页

作者： Tao Xiaoyan Ji Hongbing Men Jian School of Electronic Engineering Xidian University Xi＇an 710071 China The Telecommunication Engineering Institute Air Force Engineering University Xi＇an 710077 China

The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction ***, it is confined to linear transforms in the data *** this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature ***, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is ***, the KNPE algorithm is resolved through a transformed optimization problem and QR *** experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.

关键词： Kernel Neighborhood Preserving Embedding (KNPE) Neighborhood structure featureextraction QR decomposition

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