An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

Rapid classification of copper concentrate by portable laser-induced breakdown spectroscopy combined with transfer learning and deep convolutional neural network

This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer learning.Four pretrained CNN models were compared.The LIBS profiles were augmented into 2D matrices.Three transfer learning methods were tried.All the models got a high classification accuracy of>92%,with the highest at 96.2%for VGG16.These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting.The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.

Arabidopsis ENOR3 regulates RNAi-mediated antiviral defense

Viruses can infect host plants to cause severe diseases and substantial agricultural loss, while plants have evolved RNA interference （RNAi） strategy to defend against viral infection. Despite enormous efforts, only a few host proteins in RNAi pathway were shown to mediate antiviral defense, including RNA-dependent RNA polymerase I （RDRI）, RDR6, DICER-LIKE 2 （DCL2） and DCL4. In this study, we carried out a genetic screen for antiviral factors of RNAi pathway in Arabidopsis rdr6 background via inoculation with a 2b- deficient Cucumber Mosaic Virus （CMV-△2b）. We identified a mutant susceptible to CMV-△2h, referred to as enhancer o ojrdr6 （enor） 3-1 rdr6, and found that ENOR3 encodes a functionally unknown protein with high homology to the mammalian Non Imprinted in Prader-Willi/Angelman （NIPA） magnesium transporters. ENOR3 inhibits accumulation of CMV-△2b and acts additively with RDR1, RDR6, DCL2 and DCL4 in antivira/ defense. These results uncover that ENOR3 is a key component in antiviral RNAi Dathwav, and provide new insights into antiviral immunity.