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共 134 条记录，以下是121-130 订阅

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Condition monitoring using pattern recognition techniques on data from acoustic emissions

Condition monitoring using pattern recognition techniques on...

引用

5th international conference on machine learning and Applications

作者： Yella, Siril Gupta, Naren Dougherty, Mark Dalarna Univ Dept Comp Engn Borlange Sweden Napier Univ Sch Engn Edinburgh EH14 1DJ Midlothian Scotland

ISBN: (纸本)9780769527352

Condition monitoring applications deploying the usage of impact acoustic techniques are mostly done intuitively by skilled personnel. In this article, a pattern recognition approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. the focus of this work is to use the approach as a part of a major research project in the rail inspection area, within the domain of Intelligent Transport Systems. Data from impact acoustic tests made on wooden beams have been used. the relation between condition of the wooden beams and respective sounds they make when struck, has been analyzed experimentally. Features were extracted from the acoustic emissions of wooden beams and were used for pattern classification. Features such as magnitude of the signal, natural logarithm of the magnitude and Mel-frequency cepstral coefficients, yielded good results. the extracted feature vectors were used as input to various pattern classifiers for further pattern recognition task. the effect of using classifiers like Support vector machines and Multi-layer perceptron has been tested and compared. Results obtained experimentally, demonstrate that Support vector machines provide good detection rates for the classification of impact acoustic signals in the NDT domain.

关键词： pattern recognition feature extraction ' support vector machines multi-layer Perceptron impact acoustics NDT

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An RBF network approach to flatness pattern recognition based on SVM learning

An RBF network approach to flatness pattern recognition base...

引用

5th international conference on machine learning and Cybernetics

作者： He, Hai-Tao Li, Nan Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

ISBN: (纸本)1424400600

In the traditional method of flatness pattern recognition known as neural network with a changing topological configuration, slow convergence and local minimum were observed. Moreover, the process of experimenting the initial parameters and structure of the neural network according to the experience before has been proved time-consuming and complex. In this paper, a new approach was proposed based on the structural equivalence of radial basis function (111117) network and Support Vector machines (SVM). the SMO algorithm was employed to obtain more optimal structure and initial parameters of RBF network, and then the BP algorithm was used to adjust RBF network slightly. the new approach with the advantages of SVM, such as fast learning and whole optimization, was efficient and intelligent.

关键词： flatness pattern recognition SVM RBF network SMO algorithm

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Design and implementation of modern elevator group control system

Design and implementation of modern elevator group control s...

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5th international conference on machine learning and Cybernetics

作者： Chen, Zhi-Ming Luo, Fei Xu, Yu-Ge Cao, Jian-Zhong South China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)1424400600

the design and implementation of a modern elevator group control system (EGCS) is introduced in this paper. the basic considerations of designing an EGCS are discussed, including related system parameters, evaluation criterions and traffic patterns. Least squares support vector machine algorithm is employed for traffic prediction. Using multi-support vector machine, the traffic pattern recognition is accomplished, then based on that recognition, the control strategies are generated. In the hall call assignment, the re-scheduling ability is achieved by the elevator suitability re-evaluation mechanism. A comparison with an older model demonstrates the effectiveness of the proposed EGCS.

关键词： elevator group control system traffic prediction traffic pattern recognition support vector machine hall call assignment

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A new recognition method for the handwritten manchu character unit

A new recognition method for the handwritten manchu characte...

引用

5th international conference on machine learning and Cybernetics

作者： Zhang, Guang-Yuan Li, Jing-Jiao Wang, Ai-Xia Shenyang Univ Sch Informat Engn Shenyang 110044 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)1424400600

the Manchu character recognition method based on Manchu character unit is an efficient method. In this method, the recognition accuracy rate of Manchu character unit has great influence on the final recognition result. As new approach to solve this problem, a hybrid wavelet neural network scheme has developed as a recognition method replaces the original mini-distance method. Both the learning samples set and testing samples set are used, experimental results demonstrate the method based on the wavelet neural network is more efficient than the original mini-distance method.

关键词： wavelet neural network Manchu pattern recognition character recognition

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Fuzzy neural hybrid system for cutting tool condition monitoring

Fuzzy neural hybrid system for cutting tool condition monito...

引用

5th international conference on machine learning and Cybernetics

作者： Fu, Pan Hope, A. D. Southwest JiaoTong Univ Fac Mech Engn Chengdu 610031 Peoples R China Southampton Inst Fac Syst Engn Southampton SO14 OYN Hants England

ISBN: (纸本)1424400600

In manufacturing processes it is very important that the condition of the cutting tool, particularly the indications when it should be changed, can be monitored. Cutting tool condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. the multi-sensor signals reflect the tool condition comprehensively. A unique fuzzy neural hybrid pattern recognition algorithm has been developed. the weighted approaching degree can measure the difference of signal features accurately and the neurofuzzy network combines the transparent representation of fuzzy system with the learning ability of neural networks. the algorithm has strong modeling and noise suppression ability. these leads to successful tool wear classification under a range of machining conditions.

关键词： sensor fusion feature extraction pattern recognition condition monitoring hybrid system

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Information classified recognition method based on fuzzy control

Information classified recognition method based on fuzzy con...

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5th international conference on machine learning and Cybernetics

作者： Ding, Shi-Fei Shi, Zhong-Zhi Zhu, Xi-Jun Shandong Agr Univ Coll Informat Sci & Engn Tai An 271018 Peoples R China Acad Sinica Comp Technol Inst Key Lab Intelligent Informat Proc Beijing 100080 Peoples R China Qingdao Technol Univ Sch Sci Qingdao 266033 Peoples R China

ISBN: (纸本)1424400600

In this paper, an information pattern recognition method based on fuzzy control is set up. On one hand, the modeling method of fuzzy information classified recognition pattern has been established. On the other hand, the data from Qufu City of Shandong Province during 14 years from 1990 to 2003 is processed and analyzed. the average temperature (degrees C) and rainfall (mm) in April each year are considered as the input of the system, a number of Aphis gossypii Glover (AGG) occurred for the Cotton in high period are considered as the output, Fuzzy information classified recognition pattern is set up in order to recognize the occurrence degree of the AGG the results of the returning recognition from 1990 to 2003 and the recognition for 2004 are satisfactory.

关键词： fuzzy control pattern recognition classified recognition pattern the Aphis gossypii Alover (AGG)

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TARGET recognition USING BAYESIAN DATA FUSION MEthOD

TARGET RECOGNITION USING BAYESIAN DATA FUSION METHOD

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5th international conference on machine learning and Cybernetics

作者： Sun, Yu-Qiu Tian, Jin-Wen Liu, Jian Yangtze Univ Sch Informat & Math Jinzhou 434023 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Educ Commiss Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Dept Elect Informat & Energy State Educ Commiss Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)1424400600

Multisensor information plays an important pole in the target recognition and other application fields. Fusion performance is tightly depended on the fusion level selectes and the approach used. Feature level fusion is a potential and difficult fusion level. Bayesian fusion method is an important theory in feature level. A new method is presented to fuse infrared images and recognize object in the paper. Firstly, Bayesian principles, fusion mode and recognition decision function are described. then, aiming at the features of mid-wave infrared image and long-wave infrared image, we use Bayesian probability to fuse them. Last, recognize target and background obtained with training and test pattern vectors. the experiment results show stability and feasibility of the fusion recognition using Bayesian decision theory in infrared image.

关键词： Bayesian decision data fusion target recognition infrared images

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A version of Isomap with explicit mapping

A version of Isomap with explicit mapping

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5th international conference on machine learning and Cybernetics

作者： Li, Chun-Guang Guo, Jun Chen, Guang Nie, Xiang-Fei Yang, Zhen Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Syst Lab Sch Informat Engn Beijing 100876 Peoples R China

ISBN: (纸本)1424400600

Recently several manifold learning algorithms have been presented for nonlinear dimensionality reduction. Isomap is one of them. However, Isomap suffers from a deficiency that it does not give an explicit mapping function, which is from high dimensional space to low dimensional target space. In this paper, a version of Isomap with explicit mapping, called E-Isomap, is proposed. In E-Isomap, the geodesic distance matrix is fed into a cost function and then Iterative Majorization is adopted to solve an optimization problem for obtaining both the low dimensional configuration and the nonlinear mapping. Owing to the existence of explicit mapping, this version of Isomap can be more easily used in pattern recognition than the original ones. the experiments on two benchmark data sets are given to demonstrate the performance of the presented method.

关键词： nonlinear dimensionality reduction manifold learning Isomap geodesic distance E-Isomap

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the research on an adaptive k-nearest neighbors classifier

The research on an adaptive k-nearest neighbors classifier

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5th international conference on machine learning and Cybernetics

作者： Yu, Xiao-Gao Yu, Xiao-Peng Hubei Univ Econ Wuhan 430205 Peoples R China Wuhan Univ Wuhan 430072 Peoples R China

ISBN: (纸本)1424400600

k-Nearest neighbor (KNNC) classifier is the most popular non-parametric classifier. But it requires much classification time to search k nearest neighbors of an unlabelled object point, which badly affects its efficiency and performance. In this paper, an adaptive k-nearest neighbors classifier (AKNNC) is proposed. the algorithm can find k nearest neighbors of the unlabelled point in a small hypersphere in order to improve the efficiencies and classify the point. the hypersphere's size can be automatically determined. It requires a quite moderate preprocessing effort, and the cost to classify an unlabelled point is O(ad)+O(k)(1 <= a << n). Our experiment shows the algorithm performance is superior to other known algorithms.

关键词： nearest neighbor pattern recognition hypersphere classification

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AN IMPLEMENTATION FRAMEWORK FOR KERNEL MEthODS WIth HIGH-DIMENSIONAL patternS

<bold>AN IMPLEMENTATION FRAMEWORK FOR KERNEL METHODS WITH HI...

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5th international conference on machine learning and Cybernetics

作者： Xu, Yong Sun, Bin Zhang, Chong-Yang Jin, Zhong Liu, Chuan-Cai Yang, Jing-Yu Harbin Inst Technol Shenzhen Grad Sch Biocomp Res Ctr Shenzhen 518055 Peoples R China Nanjing Univ Sci & Technol Dept Comp Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)1424400600

As nonlinear feature extraction methods, kernel methods have been widely applied in pattern recognition. However, for high dimensional data such as face images, a kernel method will correspond to a high computational cost. In this paper, a novel idea and framework are presented to implement the kernel methods on high-dimensional data. A remarkable character of the framework is that there are two feature extraction processes. the first feature extraction process is performed to transform high dimensional samples into low dimensional data. And, the second feature extraction process is implemented based on the obtained low dimensional data. With the novel framework, the kernel methods will become much efficient. Moreover, all kernel methods can work with the framework. the experiments on face images show the validity of this framework. Further more, with this framework, kernel methods can achieve higher classification accuracies in comparison with the naive kernel methods.

关键词： pattern recognition kernel methods nonlinear method of feature extraction face recognition

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