研究生学术英语论文演讲示范

1、Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks李超171110362022-4-7 IEEE Transactions on Neural Networks & Learning SystemsPublished in 2017Tim de BruinKim VerbertRobert BabuskaFault Diagnosis, Track Circuit, Recurrent Neural Network. structurestructure A brief introduction o

2、n railway track circuits The motivation of this paper This papers work A brief introduction on railway track circuitsA brief introduction on railway track circuitsFunction: detect the absence of a train in a section of railway track. No trainA train inTrain outSignal curve when a train pass a track

3、circuit The motivation of this paperThe motivation of this paper(1) Insulated joint defect (2) Conductive object across the insulated joints The motivation of this paperThe motivation of this paper (3)mechanical rail defectThe motivation of this paperThe motivation of this paper (4)electrical distur

4、banceThe motivation of this paperThe motivation of this paper (5)ballast degradation The motivation of this paperThe motivation of this paper (1)insulated joint defect (2)conductive object (3)mechanical rail defect (4)electrical disturbance (5)ballast degradationCause Failure in Railway Track Circui

5、t, which will lead to disastrous safety problemsThe motivation of this paperThe motivation of this paper (1)insulated joint defect (2)conductive object (3)mechanical rail defect (4)electrical disturbance (5)ballast degradation Depend on manual measurement Cost much time on fault diagnosis in an emer

6、gencyThe motivation of this paperThe motivation of this paper Therefore, in order to avoid artificial participation and shorten the fault detection time, the author of this paper hopes to use Neural Network to automatically and quickly identify these faults.The motivation of this paperThe motivation

7、 of this paper This papers workThis papers workNeural Network Memory unitInput & Output unitsControl unit This papers workThis papers workNeural Network This memory neural unit allows the trained neural networks to have better memory effects and faster training speeds Data input1st neural layer2nd neural layerOutput ResultResult1500 sequencess(Simulation and actual data) results of RNN and CNNRNNsAccura