Search

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Capital Institute of Physical Education is the property of Shoudu Tiyu Xueyuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Variable predictive model based class discriminate (VPMCD) is a new pattern recognition approach, which takes full advantage of the inhere relation between the feature value. The conception of LMD energy moment was presented in this paper. Aimed at the inhere relation between the feature value of roller bearing, combing the LMD energy moment and VPMCD, a novel intelligent fault diagnosis method was proposed. Firstly, the complicated non-stationary original vibration signal was de- composed into a set of product function components. Secondly, correlation analysis method was used to remove pseudo-components and the energy moment of real PF components with signal feature was extracted as eigenvector to express the fault information adequately. Lastly, VPMCD was served as the approach of pattern recognition to identify roller bearing fault type. The simulation results demonstrate the energy moment of PF components can reflect essential feature of non-stationary signal. The analysis results from practical roller bearings fault vibration signal show that the proposed method can be applied to small sample multiple classification intelligent fault diagnosis of roller bearing effectively. [ABSTRACT FROM AUTHOR]

As for the incremental learning of the unsupervised manifold learning algorithms the paper provides an incremental local tangent space alignment (LTSA) algorithm based on selecting landmark points for the clustering of incremental points. Firstly, the least angle regression (LARS) algorithm was used to select landmark points from original training samples, and then using those selected points and new samples the global coordinate matrix for all samples could be constructed. The lower-dimensional embedding coordinate of the incremental samples was estimated according to the global coordinate matrix and lower-dimensional embedding coordinate of the given training points. Then the lower-dimensional embedding coordinate of all samples was iteratively updated based on the eigenvalues of the global coordinate matrix. Experiments with the proposed method were carried out in four different cases of the vibration signal samples of rolling bearing. Results demonstrate the incremental LTSA algorithm based on selecting landmark points can preserve the class separability measure of the samples in different cases for rolling bearing. [ABSTRACT FROM AUTHOR]

This paper presents a revised type of Asymmetric AdaBoost algorithm--M-Asy AdaBoost. It can ensure the success of training process by sample weight distribution. The weight of classifier is optimized by adopting error rates of positive sample. The capability of recognizing positive sample is enhanced. The detection probability is improved and increased monotonously by restricting the classifier adding in the ensemble. The proposed algorithm can attain high detection rate with low false alarm ratio. In the end, it is proved by computer simulation that the M-Asy AdaBoost is effective. [ABSTRACT FROM AUTHOR]