The service life of the transformer is determined by its solid insulation performance. However, it is a rather difficult job to quantitatively evaluate the aging conditions of cellulose insulation materials of the transformer by traditional methods. The existing researches show that the cellulose aging kinetics model of cellulose can establish the functional relationship between the degree of polymerization (DP) and moisture content, aging temperature and aging duration. Therefore, based on the simultaneous considering Arrhenius equation and Ekenstam equation, the purpose of this work is to report an approach that can quantitatively evaluate the aging condition of cellulose insulation of transformer. Furthermore, the present finding of this paper can provide a novel idea for evaluating the aging state of transformer solid insulation.
Abstract. Wide-area video surveillance system can track object in a wide range. However, environments of different cameras are generally quite different, so object handoff and data integration among cameras are research difficulties. To solve these problems, a kind of non-overlapping multi-camera tracking system based on TLD framework is proposed. TLD framework maintains a unified sample classifier which uses affine transformation to generate new samples and updates classifier parameters through online learning which fuses data among cameras. To achieve object handoff, detection module scans video frame of a certain range of the camera. Then object result is obtained by comparing the similarity. Tracking module of origin framework uses optical flow based on feature points. In order to enhance tracking robustness of framework in complex environment, MeanShift and particle filter tracking algorithm based on color feature are used to replace of it and experiments are carried out. Experimental results show that system can achieve continuous tracking in non-overlapping multi-camera and the TLD framework based on MeanShift tracking algorithm has better robust and accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.