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Gyroscope Fault Diagnosis Using Fuzzy SVM to unbalanced samples

Transactions of Nanjing University of Aeronautics and Astronautics 2015年第1期32卷 16-21页

作者：罗秋凤张锐李勇杨忠清 UAV Research Institute Nanjing University of Aeronautics and Astronautics Department of Automation Nanjing University of Aeronautics and Astronautics

A novel fuzzy support vector machine based on unbalanced samples(FSVM-US)is proposed to solve the high false positive rate problem since the gyroscope output is susceptible to unmanned aerial vehicle(UAV)airborne electromagnetic environment and the gyroscope abnormal signal sample is rather ***,the standard deviation of samples projection to normal vector for SVM classifier hyper plane is *** imbalance feature expression reflecting the hyper plane shift for the number imbalance between samples and the dispersion imbalance within samples is *** the same time,the denoising factor is designed as the exponential decay function based on the Euclidean distance between each sample and the class ***,the imbalance feature expression and denoising factor are configured into the membership *** sample has its own weight denoted the importance to the ***,the classification simulation experiments on the gyroscope fault diagnosis system are conducted and FSVM-US is compared with the standard SVM,FSVM,and the four typical class imbalance learning(CIL)*** results show that FSVM-US classifier accuracy is 12% higher than that of the standard ***,FSVM-US is superior to the four CIL methods in total ***,the FSVMUS noise tolerance is also 17% higher than that of the standard SVM.

关键词： fault diagnosis gyroscope fuzzy support vector machine(FSVM) unbalanced samples membership function

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农业信息处理(英文) 2023年第2期10卷 149-163页

作者： Yang Zhang Jun Yue Aihuan Song Shixiang Jia Zhenbo Li School of Information and Electrical Engineering Ludong UniversityYantai 264025PR China Marine Biology Institute of Shandong Province Qingdao 266104PR China School of Information and Electrical Engineering China Agricultural UniversityBeijing 100193PR China

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish *** study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network(CNN).We first establish the shellfish image(SI)dataset with 68 species and 93574 images,and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid *** the shellfish recognition with unbalanced samples,a hybrid loss function,including regularization term and focus loss term,is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total *** experimental results show that the accuracy of shell-fish recognition of the proposed method is 93.95%,13.68%higher than the benchmark network(VGG16),and the accuracy of shellfish recognition is improved by 0.46%,17.41%,17.36%,4.46%,1.67%,and 1.03%respectively compared with AlexNet,GoogLeNet,ResNet50,SN_Net,MutualNet,and ResNeSt,which are used to verify the efficiency of the proposed method.

关键词： Shellfish recognition High similarity unbalanced samples Convolutional Neural Network Filter pruning and repairing Hybrid loss function

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