Aiming at the problem that Distributed Optical Fiber Acoustic Sensing (DAS) system will misjudge external intrusion signals, an intrusion signal discrimination method based on MFCC-Energy entropy feature and FTO-SVM is proposed. Firstly, the former 13-dimensional Mel-Frequency Cepstral Coefficients (MFCC) is extracted from the collected sound signal, and Principal Component Analysis (PCA) is used to reduce the dimension of MFCC; Secondly, the energy entropy of the collected sound signal is calculated, which fused with the reduced MFCC as the feature parameter of the collected sound signal; Finally, the extracted feature parameters are discriminated by using Support Vector Machine (SVM) with the hyperparameters optimized by the Fibonacci Tree Optimization (FTO) algorithm. The results show that the proposed method can effectively improve the system discrimination accuracy for intrusion signals, and is of great significance to perimeter security and fault diagnosis and other related fields.
With the rapid development of optoelectronic technology, it is more and more difficult for the students to grasp the related knowledge, and to have innovative thinking and innovative ability. The reason is that the students can’t understand that knowledge easily; In addition, the students find it is hard to find innovative projects to enhance themselves. This paper summarizes a teaching approach to impart innovative knowledge. The following is: help students to establish the following thinking, "according to the difficulties encountered in photovoltaic technology, identify and find the key problem, → converted into the standard TRIZ problem →find their own solutions. The results show that this approach plays an important role in cultivating students' creative thinking.
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