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Terrain classification of the legged robot is one of the most important objects which can determine robot's performance because surface of the fields is often extremely diverse. In the flat surface case, robot can move fast and smoothly. However, it cannot move fast in the rough terrain. Unless robot knows which terrain, robot will be falling down and slippery. Therefore, robot must know their terrain...
In this paper, a novel approach combining kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. KPCA is used as a preprocessor of LSSVM, which maps the original input...
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