In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD ...In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD is applied to the feature extraction of vehicle vibration signals. First, the multi-autocorrelation method is adopted in each input signal,so the noise is reduced effectively. Then, EMD is used to deal with these signals,and the intrinsic mode functions （IMFs） are obtained. Finally, for obtaining the feature information of these signals, the Hilbert transformation and the spectrum analysis are performed in some IMFs. Theoretical analysis and ex- periment verify the effectiveness of the method, which are valuable reference for the same engineering problems.展开更多
The increasing need of video based applications issues the importance of parsing and organizing the content in videos. However, the accurate understanding and manag- ing video contents at the semantic level is still i...The increasing need of video based applications issues the importance of parsing and organizing the content in videos. However, the accurate understanding and manag- ing video contents at the semantic level is still insufficient. The semantic gap between low level features and high level semantics cannot be bridged by manual or semi-automatic methods. In this paper, a semantic based model named video structural description （VSD） for representing and organizing the content in videos is proposed. Video structural descrip- tion aims at parsing video content into the text information, which uses spatiotemporal segmentation, feature selection, object recognition, and semantic web technology. The pro- posed model uses the predefined ontologies including con- cepts and their semantic relations to represent the contents in videos. The defined ontologies can be used to retrieve and organize videos unambiguously. In addition, besides the de- fined ontologies, the semantic relations between the videos are mined. The video resources are linked and organized by their related semantic relations.展开更多
基金Supported by the Scientific Research Foundation for the Imported Talents(YKJ201014)~~
文摘In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD is applied to the feature extraction of vehicle vibration signals. First, the multi-autocorrelation method is adopted in each input signal,so the noise is reduced effectively. Then, EMD is used to deal with these signals,and the intrinsic mode functions （IMFs） are obtained. Finally, for obtaining the feature information of these signals, the Hilbert transformation and the spectrum analysis are performed in some IMFs. Theoretical analysis and ex- periment verify the effectiveness of the method, which are valuable reference for the same engineering problems.
文摘The increasing need of video based applications issues the importance of parsing and organizing the content in videos. However, the accurate understanding and manag- ing video contents at the semantic level is still insufficient. The semantic gap between low level features and high level semantics cannot be bridged by manual or semi-automatic methods. In this paper, a semantic based model named video structural description （VSD） for representing and organizing the content in videos is proposed. Video structural descrip- tion aims at parsing video content into the text information, which uses spatiotemporal segmentation, feature selection, object recognition, and semantic web technology. The pro- posed model uses the predefined ontologies including con- cepts and their semantic relations to represent the contents in videos. The defined ontologies can be used to retrieve and organize videos unambiguously. In addition, besides the de- fined ontologies, the semantic relations between the videos are mined. The video resources are linked and organized by their related semantic relations.
