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Chen K, Hu J, He J. A framework for automatically extracting over-voltage features based on sparse autoencoder. IEEE Trans Smart Grid, 2016, 9: 594–604
Ren H, Chai Y, Qu J F, et al. A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: a case study on cryogenic propellant loading system. Neurocomputing, 2018, 275: 2111–2125
Zhang Q, Yang L T, Chen Z. Deep Computation Model for Unsupervised Feature Learning on Big Data. IEEE Trans Serv Comput, 2016, 9: 161–171
Ren H, Chai Y, Qu J F, et al. Deep learning for fault diagnosis: the state of the art and challenge. Control Decis, 2017, 32: 1345–1358
Xie Z W, Zeng Z, Zhou G Y, et al. Topic enhanced deep structured semantic models for knowledge base question answering. Sci China Inf Sci, 2017, 60: 110103
Qu W, Wang D L, Feng S, et al. A novel cross-modal hashing algorithm based on multimodal deep learning. Sci China Inf Sci, 2017, 60: 092104
Xu Z B, Sun J. Model-driven deep-learning. Natl Sci Rev, 2018, 5: 22–24
Guo L H, Guo C G, Li L, et al. Two-stage local constrained sparse coding for fine-grained visual categorization. Sci China Inf Sci, 2018, 61: 018104
Jiang P, Hu Z, Liu J, et al. Fault diagnosis based on chemical sensor data with an active deep neural network. Sensors, 2016, 16: 1695
This work was supported by National Natural Science Foundation of China (Grant Nos. 61633005, 61673076, 61773080), Natural Science Foundation of Chongqing, China (Grant No. cstc2016jcyjA0504), Fundamental Research Funds for the Central Universities (Grant Nos. 106112016CDJXZ238826, 2018CDYJSY0055), and Natural Science Research Project of the Higher Education Institutions of Jiangsu Province (Grant No. 18KJB510006).
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Ren, H., Li, N., Chai, Y. et al. The input pattern problem on deep learning applied to signal analysis and processing to achieve fault diagnosis. Sci. China Inf. Sci. 62, 229202 (2019). https://doi.org/10.1007/s11432-018-9564-6