Chai Yi

TL;DR: In this article, an analog circuit fault diagnosis method based on an improved RBF neural network is presented, which includes the following steps that excitation is exerted on a circuit to be detected, and response signals are processed through improved wavelet packet transformation to extract fault characteristic signals; the extracted candidate characteristic signals are normalized to obtain fault characteristic vectors; the fault characteristic vector serving as samples are input into the neural network and classified to obtain a result of fault diagnosis.

TL;DR: In this paper, a new electroencephalogram analysis method was proposed for definite diagnosis making of clinical experience, the human brain state stage can be judged, and difficulties of electroencephalogram accurate quantitative analysis are avoided; meanwhile, reverse analysis on quantitative analysis results can be carried out, verification with the clinical medicine experience is finally achieved, and more scientific conclusions are obtained.

TL;DR: In this paper, a wireless sensor network node fault polling method based on a mobile robot, belonging to the field of fault diagnosis, is presented, which consists of the following specific steps: (1) distributing and networking sensor nodes; (2) determining distances among the nodes and coordinates of the nodes by virtue of RSSI (Received Signal Strength Indicator) distancing; (3) determining an optimal path that the robot traverses each node, and collecting corresponding data; (4) comparing the corresponding data with node data, judging whether a fault occurs and judging the type

TL;DR: Experimental results showed that the algorithm using SVM-KPCA with Gaussian-kernel had better recognition performance than the other three methods.