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1. Failure Detection and Validation of Multifunctional Self-validating Sensor Using WRVM Predictor | |||
SHEN Zhengguang,SONG Kai,WANG Qi | |||
Electrics, Communication and Autocontrol Technology 19 January 2012 | |||
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Abstract:A novel multifunctional self-validating sensor functional model is proposed for evaluation, improvement, and forecast of measurement value reliability. This paper presents a detailed introduction of main content--self-validating function, especially the quantitative health evaluation and remaining using life forecasting. As a fundamental self-validating function, a novel strategy based on relevance vector machine (RVM) coupled with wavelet analysis is proposed for failure detection, isolation, and recovery (FDIR) of multifunctional self-validating sensor. It centers on the working principle and on-line updating algorithm of WRVM predictor. The proposed WRVM predictor can also distinguish the fault free signal with sudden change from the failure signal and it is updated online for pursuit of signal change. A multifunctional self-validating sensor experimental system is designed to evaluate the performance of novel strategy. Results demonstrate that the proposed methodology provides a good solution to the FDIR of multifunctional self-validating sensor. | |||
TO cite this article:SHEN Zhengguang,SONG Kai,WANG Qi. Failure Detection and Validation of Multifunctional Self-validating Sensor Using WRVM Predictor[OL].[19 January 2012] http://en.paper.edu.cn/en_releasepaper/content/4463224 |
2. Audio Content Classification by Using Spectral Features | |||
Qian Xueming ,Liu Guizhong,Wang Huan ,Li Zhi ,Nan Nan,Wang Zhe ,Sun Li | |||
Electrics, Communication and Autocontrol Technology 10 February 2009 | |||
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Abstract:Audio information play important role in speaker identification and semantic based video content analysis, indexing and retrieval. Sometimes, audio clues are dominant in determining the story unit types. In this paper, a new temporal spectral feature including the proposed spectral histogram is integrated for audio content classification. By analysis the temporal spectral distribution, we adaptively determine the effective feature vectors for audio content discrimination. Finally, several one class support vector machine (SVM) are used to classify each audio clip into following five types: silence, pure music, pure speech, speech with noise background (Speech+Noise), and speech with music background (Speech+Music). Experimental results show the effectiveness of the proposed methods. | |||
TO cite this article:Qian Xueming ,Liu Guizhong,Wang Huan , et al. Audio Content Classification by Using Spectral Features[OL].[10 February 2009] http://en.paper.edu.cn/en_releasepaper/content/28696 |
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