A High-similarity shellfish recognition method based on convolutional neural network

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition.This 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 information.For 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 loss.The 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.

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The 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 capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,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 function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We 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 perspectives.Through 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 diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.

Rapid discovery and identification of 68 compounds in the active fraction from Xiao-Xu-Ming decoction(XXMD) by HPLC-HRMS and MTSF technique

Xiao-Xu-Ming decoction（XXMD） was a traditional Chinese prescription and first recorded in ＂Bei Ji Qian Jin Yao Fang＂.It has been widely used to treat theoplegia and the sequel of theoplegia in China.In the present work,high-performance liquid chromatography coupled with high resolution mass spectrometry（HPLC-HRMS） combined with the mass spectral tree similarity filter technique（MTSF）was used to rapidly discover and identify the compounds of the active fraction of XXMD.A total of 3362 compounds were automatically detected by HPLC-HRMS,and final 68 compounds were identified in the active fraction of XXMD.including 14 templated compounds（reference compounds）,50 related compounds fished by MTSF technique,and 4 unrelated compounds identified by manual method.This study successfully applied MTSF technology for the first time to discover and identify the components of Chinese prescription.The results demonstrated that MTSF technique should be useful to the discovery and identification of compounds in Chinese prescription.This study also proved that MTSF can be applied to the targeted phytochemical separation.