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Information Processing in Agriculture 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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Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

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Computers, Materials & Continua 2023年第9期76卷 3623-3651页

作者： Yang Yang Yuhan Long Yijing Lin Zhipeng Gao Lanlan Rui Peng Yu State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Engineering Nanyang Technological University639798Singapore Science and Technology on Communication Networks Laboratory The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050051China

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant *** existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and *** paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge ***,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss *** utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high ***,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side *** propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro *** comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault *** addition,experiments show that our approach has a smooth training process and good balance.

关键词： Fault diagnosis knowledge distillation edge-side lightweight model high similarity

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