In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
This paper proposes a method which uses the extended edge analysis to supplement the inaccurate edge information for better vehicle detection during vehicle detection. The extended edge analysis method detects two ver...This paper proposes a method which uses the extended edge analysis to supplement the inaccurate edge information for better vehicle detection during vehicle detection. The extended edge analysis method detects two vertical edge items, which are the borderlines of both sides of the vehicle, by extending the horizontal edges inaccurately due to the illumination or noise existing on the image. The proposed method extracts the horizontal edges with the method of merging edges by using the horizontal edge information inside the Region of Interest （ROI）, which is set up on the pre-processing step. The bottona line is determined by detecting the shadow regions of the vehicle from the extracted hoodzontal edge one. The general width of the vehicle detecting and the extended edge analyzing methods are carried out side by side on the bottom line of the vehicle to determine width of the vehicle. Finally, the finmal vehicle is detected through the verification step. On the road image with conaplicate background, the vehicle detecting method based on the extended edge analysis is more efficient than the existing vehicle detecting method which uses the edge information. The excellence of the proposed vehicle detecting method is confirmed by carrying out the vehicle detecting experiment on the complicate road image.展开更多
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
基金supported bythe MKE(The Ministry of Knowledge Economy,Korea),the ITRC(Information Technology ResearchCenter)support program(NIPA-2010-(C1090-1021-0010)),the Brain Korea 21 Project in 2010
文摘This paper proposes a method which uses the extended edge analysis to supplement the inaccurate edge information for better vehicle detection during vehicle detection. The extended edge analysis method detects two vertical edge items, which are the borderlines of both sides of the vehicle, by extending the horizontal edges inaccurately due to the illumination or noise existing on the image. The proposed method extracts the horizontal edges with the method of merging edges by using the horizontal edge information inside the Region of Interest （ROI）, which is set up on the pre-processing step. The bottona line is determined by detecting the shadow regions of the vehicle from the extracted hoodzontal edge one. The general width of the vehicle detecting and the extended edge analyzing methods are carried out side by side on the bottom line of the vehicle to determine width of the vehicle. Finally, the finmal vehicle is detected through the verification step. On the road image with conaplicate background, the vehicle detecting method based on the extended edge analysis is more efficient than the existing vehicle detecting method which uses the edge information. The excellence of the proposed vehicle detecting method is confirmed by carrying out the vehicle detecting experiment on the complicate road image.
