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COMPRESSIVE COMMINUTION MECHANISM OF PARTICLE BEDS
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作者 Yaojianqian Guo Nianqin +1 位作者 Huang Peng peng Ouyang Zhentang 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 1992年第4期1-7,共7页
Granular material mechanics,finite element analysis and crushing theory are applied to study the compressive comminution mechanism of particle beds in this paper.This is a new method by which we have established an eq... Granular material mechanics,finite element analysis and crushing theory are applied to study the compressive comminution mechanism of particle beds in this paper.This is a new method by which we have established an equivalent model of granular material,determined the values and distributions of contact forces and discovered a crushing law.The model has been tested on the newly designed equipment and proved to be correct.Some new characteristics and laws of compressive comminution of particle beds have been found. 展开更多
关键词 compressive comminution mechanism granular material equivalent model test machine of compressive comminution of particle beds
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Fault diagnosis for on-board equipment of train control system based on CNN and PSO-SVM hybrid model 被引量:1
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作者 LU Renjie LIN Haixiang +3 位作者 XU Li LU Ran ZHAO Zhengxiang BAI Wansheng 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第4期430-438,共9页
Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken a... Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment. 展开更多
关键词 on-board equipment fault diagnosis convolutional neural network(CNN) unbalanced text data particle swarm optimization-support vector machines(PSO-SVM)
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