[1] X Q Li. Research on key technology of fault prognostic and health management for complex equipment. Beijing Institute of Technology, 2014. (in Chinese)
[2] V Venkatasubramanian, R Rengaswamy, S N Kavuri, et al. A review of process fault detection and diagnosis:Part Ⅲ:Process history based methods. Computers & Chemical Engineering, 2003, 27(3):327-346.
[3] E Alpaydin. Introduction to machine learning. Cambridge MA:The MIT Press, 2004.
[4] H D M D Azevedo, A M Araújo, N Bouchonneau. A review of wind turbine bearing condition monitoring:state of the art and challenges. Renewable & Sustainable Energy Reviews, 2016, 56(4):368-379.
[5] H J Zhu, X Q Wang, T Rui, et al. Shift invariant sparse coding for blind source separation of single channel mechanical signal. Journal of Vibration Engineering, 2015, 28(4):625-632. (in Chinese)
[6] M Van, H J Kang, K S Shin. Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition. Science Measurement & Technology Iet, 2014, 8(6):571-578.
[7] A Y Goharrizi, N Sepehri. A wavelet-based approach to internal seal damage diagnosis in hydraulic actuators. IEEE Transactions on Industrial Electronics, 2010, 57(5):1755-1763.
[8] A Y Goharrizi, N Sepehri. Internal leakage detection in hydraulic actuators using empirical mode decomposition and Hilbert spectrum. IEEE Transactions on Instrumentation & Measurement, 2012, 61(2):368-378.
[9] B Boashash, P Black. An efficient real-time implementation of the Wigner-Ville distribution. IEEE Trans. on Acoust. Speech Signal Processing, 1987, 35(11):1611-1618.
[10] G S Hu. Modern signal processing course. Beijing:Tsinghua University Press, 2004. (in Chinese)
[11] M J Zhang, J Tang, X H He. EEMD method and its application in mechanical fault diagnosis. Beijing:National Defense Industry Press, 2015. (in Chinese)
[12] Y Amirat, V Choqueuse, M Benbouzid. EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component. Mechanical Systems & Signal Processing, 2013, 41(1-2):667-678.
[13] J D Zheng, J S Cheng, Y Yang. Improved EEMD algorithm and its application research. Vibration and Impact, 2013, 32(21):21-26. (in Chinese)
[14] K Chai, M J Zhang, J Huang, et al. Fault diagnosis of hydraulic system based on time-frequency characteristics and PCA-KELM. Journal of PLA University of Science and Technology, 2015(4):394-400. (in Chinese)
[15] J Huang, J Tang, M J Zhang, et al. An improved EMD based on cubic spline interpolation of extremum centers. Journal of Vibroengineering, 2015, 17(5):2393-2409.
[16] C Wang, Z L Wang, J Ma, et al. Fault diagnosis for hydraulic pump based on EEMD-KPCA and LVQ. Vibroengineering Procedia, 2014, 4(11):188-193.
[17] H Chen, M J Zhang, J Huang, et al. Fault diagnosis based on improved EEMD method and GA-SVM for leakage of hydraulic system. Chinese Hydraulics & Pneumatics, 2014, (9):32-38. (in Chinese)
[18] L M Li, Z S Wang. Feature selection of sudden failure based on affinity propagation clustering. Advanced Materials Research, 2012, 586(11):241-246.
[19] Z F Li, Y Chai, H F Li. Fault feature extraction method of rolling bearing based on singular value decomposition and morphological filtering. Application Research of Computers, 2012, 29(4):1314-1317. (in Chinese)
[20] H Yu, F Khan, V Garaniya. A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes. Aiche Journal, 2016, 62(5):1494-1513.
[21] D R Huang, C S Chen, G X Sun, et al. Linear discriminant analysis and back propagation neural network cooperative diagnosis method for multiple faults of complex equipment bearings. Acta Armamentarii, 2017, 38(8):1649-1657.
[22] Q Z Wang, X X Wang. Unified grey relational analysis on transformer DGA fault diagnosis. Open Mechanical Engineering Journal, 2014, 8(1):129-131.
[23] H M Liu, D W Liu, L Chen, et al. Fault diagnosis of hydraulic servo system using the unscented kalman filter. Asian Journal of Control, 2015, 16(6):1713-1725.
[24] H Gao, L Liang, X Chen, et al. Feature extraction and recognition for rolling element bearing fault utilizing short-time fourier transform and non-negative matrix factorization. Chinese Journal of Mechanical Engineering, 2015, 28(1):96-105.
[25] A Czajkowski, K Patan. Robust fault detection by means of echo state neural network. Advances in Intelligent Systems and Computing, 2016, 386(8):341-352.
[26] H Malik, S Mishra. Application of probabilistic neural network in fault diagnosis of wind turbine using FAST, TurbSim and Simulink. Procedia Computer Science, 2015, 58:186-193.
[27] Y Chen, Z M Zhen, H H Yu, et al. Application of fault tree analysis and fuzzy neural networks to fault diagnosis in the Internet of Things (IoT) for aquaculture. Sensors, 2017, 17(1):153.
[28] T Z Wang, J Qi, H Xu, et al. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter. ISA Transactions, 2016, 60(1):156-163.
[29] M J Zhang, J Tang, X M Zhang, et al. Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples. Chinese Journal of Mechanical Engineering, 2016, 29(2):396-405.
[30] Y H Xue, L Zhang, B J Wang, et al. Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis. Applied Intelligence, 2018, 48(10):3306-3331.
[31] Y C Xiao, N Kang, Y Hong, et al. Misalignment fault diagnosis of DFWT Based on IEMD energy entropy and PSO-SVM. Entropy, 2017, 19(1):https://doi.org/10.3390/e19010006.
[32] X Y Zhang, Y T Liang, J Z Zhou, et al. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement, 2015, 69(6):164-179.
[33] J Shang, M Y Chen, H Q Ji, et al. Dominant trend based logistic regression for fault diagnosis in nonstationary processes. Control Engineering Practice, 2017, 66(9):156-168. (in Chinese)
[34] P G Sreenath, G P Kumare, P Sundar, et al. Automobile gearbox fault diagnosis using Naive Bayes and decision tree algorithm. Applied Mechanics & Materials, 2015, 813-814:943-948.
[35] C Annachhatre, T H Austin, M Stamp. Hidden Markov models for malware classification. Journal of Computer Virology & Hacking Techniques, 2015, 11(2):59-73.
[36] L Enrique Sucar, C Bielza, E F Morales, et al. Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognition Letters, 2014, 41(1):14-22.
[37] W L Lu. Daquan of troubleshooting and repair for practical hydraulic machinery. Changsha:Hunan Science and Technology Publishing House, 1995.
[38] G G Ji, N Li, D M Xu. Fault analysis and removal of hydraulic drive system. Plant Maintenance Engineering, 1991, 3:36-38.
[39] L An, N Sepehri. Hydraulic actuator circuit fault detection using extended Kalman filter. American Control Conference, IEEE, 2003, 5(1):4261-4266.
[40] L An, N Sepehri. Leakage fault identification in a hydraulic positioning system using extended Kalman filter. American Control Conference, Proceedings of the IEEE, 2004, (4):3088-3093.
[41] H L Zhu, L Q Gao. Fault diagnosis of hydraulic system based on flow signal. Journal of Engineering Science, 2001, 23(1):66-70.
[42] Q L Du, K H Zhang. Condition monitoring and fault diagnosis of hydraulic pump based on inherent vibration signals. Journal of Agricultural Engineering, 2007, 23(4):120-123. (in Chinese)
[43] H X Chen, Patrick S K Chua, G H Lim. Vibration analysis with lifting scheme and generalized cross validation in fault diagnosis of water hydraulic system. Journal of Sound and Vibration, 2007, 301:458-480.
[44] W L Jiang, S Q Zhang, Y Q Wang. Wavelet transform method for fault diagnosis of hydraulic pump. Journal of Mechanical Engineering, 2001, 37(6):34-37. (in Chinese)
[45] W Z Du, Z F Zhou, X X Huang. The application of wavelet analysis to hydraulic cylinder leakage detection. Machine Tools and Hydraulic Pressure, 2003(6):318-319. (in Chinese)
[46] K H Abbott, P C Schutte. Faultfinder:A diagnostic expert system with graceful degradation for onboard aircraft applications. Mitteilung-Deutsche Forschungs-and Versuchsanstalt fuer Luft-and Raumfahrt, 1988:353-370.
[47] W J Crowther, K A Edge, C R Burrows, et al. Fault diagnosis of a hydraulic actuator circuit using neural networks-An output vector space classification approach. Proceedings of the Institution of Mechanical Engineers. Part I:Journal of Systems & Control Engineering, 1997, 212(1):57-68.
[48] S Amin, C Byington, M Watson. Fuzzy inference and fusion for health state diagnosis of hydraulic pumps and motors. Fuzzy Information Processing Society, Nafips Meeting of the North American. IEEE, 2005.
[49] H X Chen, Chua P S K, Lim G H. Feature extraction, optimization and classification by second generation wavelet and support vector machine for fault diagnosis of water hydraulic power system. International Journal of Fluid Power, 2006, 7(2):39-52.
[50] R A Saeed, A N Galybin, V Popov. 3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS. Mechanical Systems and Signal Processing, 2013, 34(1-2):259-276.
[51] W L Jiang, Y Q Wang, X D Kong, et al. New progress of fault detection and diagnosis technology for hydraulic system. China Mechanical Engineering, 1998, 9(9):58-60. (in Chinese)
[52] H W Mou. Fault diagnosis and expert system research of coking coal machinery hydraulic system. Hangzhou:Zhejiang University, 2008. (in Chinese)
[53] C Q Lu. Fault diagnosis of hydraulic pumps based on HHT and fuzzy C mean clustering. Qinhuangdao:Yanshan University, 2012. (in Chinese)
[54] H B Tang. Research on key technology of fault diagnosis for pumping hydraulic system of concrete pump truck. Guangzhou:Zhongnan University, 2012. (in Chinese)
[55] N Saravanan, K I Ramachandran. Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification. Expert Systems with Applications, 2009, 36(5):9564-9573.
[56] J B Ali, N Fnaiech, L Saidi, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 2015, 89(3):16-27.
[57] D Yao, J W Yang, X Li, et al. A hybrid approach for fault diagnosis of railway rolling bearings using STWD-EMD-GA-LSSVM. Mathematical Problems in Engineering, 2016, 2016(10):1-7.
[58] G E Hinton, R R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786):504-507.
[59] Y Lecun, Y Bengio, G Hinton. Deep learning. Nature, 2015, 521(7553):436.
[60] Y Bengio. Learning deep architectures for AI. Foundations & Trends in Machine Learning, 2009, 2:1-127.
[61] S Bengio, F Pereira, Y Singer, et al. Group sparse coding. International Conference on Neural Information Processing Systems, 2009, 22:82-89.
[62] P Vincent, H Larochelle, I Lajoie, et al. Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11(12):3371-3408.
[63] D H Ackley, G E Hinton, T J Sejnowski. A learning algorithm for boltzmann machines. Cognitive Science, 1985, 9(1):147-169.
[64] J Schmidhuber. Deep learning in neural networks:An overview. Neural Networks, 2015, 61:85-117.
[65] H R Li, S S Gu. A fast parallel algorithm for a recurrent neural network. Acta Automatica Sinica, 2004, 30(4):516-522.
[66] C Ledig, L Theis, F Huszar, et al. Photo-realistic single image super-resolution using a generative adversarial Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017:https://doi.org/10.1109/cvpr.2017.19.
[67] X Feng, Y D Zhang, J Glass. Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition. IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, May 04-09, 2014:1759-1763.
[68] J Maria, J Amaro, G Falcao. Stacked autoencoders using low-power accelerated architectures for object recognition in autonomous systems. Neural Processing Letters, 2016, 43(2):445-458.
[69] J Zabalza, J C Ren, J B Zheng, et al. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing, 2016, 214(C):1062.
[70] G E Dahl, D Yu, L Deng, et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio Speech & Language Processing, 2012, 20(1):30-42.
[71] Z Q Zhao, L C Jiao, J Q Zhao, et al. Discriminant deep belief network for high-resolution SAR image classification. Pattern Recognition, 2017, 61(1):686-701.
[72] P Zhong, Z Q Gong, S T Li, et al. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(6):3516-3530.
[73] T N Sainath, A R Mohamed, B Kingsbury, et al. Deep convolutional neural networks for LVCSR. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, May 26-31, 2013:8614-8618.
[74] A Krizhevsky, I Sutskever, G E Hinton. ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, December 03-06, 2012:1097-1105.
[75] Y Sun, X G Wang, X O Tang. Deep learning face representation by joint identification-verification. Advances in Neural Information Processing Systems, 2014, 27(6):1988-1996.
[76] A Karpathy, G Toderici, S Shetty, et al. Large-scale video classification with convolutional neural networks. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, June 23-28, 2014:1725-1732.
[77] G Arevian, C Panchev. Optimising the hystereses of a two context layer RNN for text classification. International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17, 2007:2936-2941.
[78] S Alsenan, M Ykhlef. Statistical machine translation context modelling with recurrent neural network and LDA. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016, 533:75-84.
[79] G Lev, G Sadeh, B Klein, et al. RNN fisher vectors for action recognition and image annotation. Computer Science, 2015, 9910(9):833-850.
[80] T Tsujimoto, Y Takahashi, S Takeuchi, et al. RNN with Russell's circumplex model for emotion estimation and emotional gesture generation. Evolutionary Computation, Vancouver, BC, Canada, November 21, 2016:1427-1431.
[81] S E Kahou, V Michalski, K Konda, et al. Recurrent neural networks for emotion recognition in video. International Conference on Multimodal Interaction, Seattle, Washington, USA, November 09-13, 2015:467-474.
[82] K F Wang, Y Lu, Y T Wang, et al. Parallel imaging:a new theoretical framework for image generation. Pattern Recognition and Artificial Intelligence, 2017, 30(7):577-587.
[83] Y J Liu, C H Dou, Q L Zhao. Hand-drawn image retrieval based on condition generation against network. Journal of Computer Aided Design and Graphics, 2017, 29(12):2336-2342.
[84] L F Mo, H L Jiang, S P Li. Video prediction based on deep learning:A review. Journal of intelligent systems, 2018, (1):85-96.
[85] F Jia, Y G Lei, J Lin, et al. Deep neural networks:a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems & Signal Processing, 2016, 72(5):303-315.
[86] H D Shao, H K Jiang, H W Zhao, et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 2017, 95(10):187-204.
[87] Z Y Chen, W H Li. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 2017, 99(3):1-10.
[88] L K Wang, X Y Zhao, J N Pei, et al. Transformer fault diagnosis using continuous sparse autoencoder. SpringerPlus, 2016, 5(1):448.
[89] L H Wang, Y Y Xie, Y H Zhang, et al. A fault diagnosis method for asynchronous motor using deep learning. Journal of Xi'an Jiaotong University, 2017, 51(10):128-134. (in Chinese)
[90] G He, Y L Cao, T F Ming, et al. Cavitation state recognition of centrifugal pump based on features of modified octave bands. Journal of Harbin Engineering University, 2017, 38(8):1263-1267, 1302. (in Chinese)
[91] D T Hoang, H J Kang. A bearing fault diagnosis method based on autoencoder and particle swarm optimization-Support Vector Machine. Intelligent Computing Theories and Application. Springer, Cham, 2018.
[92] S Q Tao, T Zhang, J Yang, et al. Bearing fault diagnosis method based on stacked autoencoder and softmax regression. 34th Chinese Control Conference (CCC), IEEE, Hangzhou, China, 2015.
[93] G F Liu, H Q Bao, B K Han. A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Mathematical Problems in Engineering, 2018, 2018:1-10.
[94] L Xu, M Y Cao, B Y Song, et al. Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network. Neurocomputing, 2018, 311(10):1-10.
[95] X N Zhang, X Zhou, C H Tang. A deep convolutional auto-encoding neural network and its application in bearing fault diagnosis. Journal of Xi'an Jiaotong University, 2018. (in Chinese)
[96] F T Wang, X F Liu, B S Guo, et al. Application of kernel auto-encoder based on firefly optimization in intershaft bearing fault diagnosis. Journal of Mechanical Engineering, 2019, 55(7):58-64. (in Chinese)
[97] B She, F Q Tian, W G Liang. Fault diagnosis based on a deep convolution variational autoencoder network. Chinese Journal of Scientific Instrument, 2018, 39(10):27-35. (in Chinese)
[98] P Tamilselvan, P Wang. Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 2013, 115(7):124-135.
[99] V T Tran, F Althobiani, A Ball. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Systems with Applications, 2014, 41(9):4113-4122.
[100] H D Shao, H K Jiang, X Zhang, et al. Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, 2015, 26(11):115002.
[101] J P Xie, Y Yang, T R Li, et al. Learning features from high speed train vibration signals with deep belief networks. International Joint Conference on Neural Networks, Beijing, China, July 06-11, 2014:2205-2210.
[102] X B Wang, J li, M H Yao, et al. Solar cells surface defect detection based on deep learning. Pattern Recognition and Artificial Intelligence, 2014, 27(6):517-523. (in Chinese)
[103] Y F Li, X Q Wang, M J Zhang, et al. An approach to fault diagnosis of rolling bearing using SVD and multiple DBN classifiers. Journal of Shanghai Jiaotong University, 2015, 49(5):681-686, 694. (in Chinese)
[104] X Q Wang, Y F Li, T Rui, et al. Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network. Journal of Vibroengineering, 2015, 17(3):1295-1308.
[105] M Gan, C Wang, C A Zhu. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems & Signal Processing, 2016, 72-73(2):92-104.
[106] Z Chen, C Li, R V Sánchez. Multi-layer neural network with deep belief network for gearbox fault diagnosis. Journal of Vibroengineering, 2015, 17(5):2379-2392.
[107] L Wen, X Y Li, L Gao, et al. A new convolutional neural network based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 2018, 65(11):5990-5998.
[108] D T Hoang, H J Kang. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognitive Systems Research, 2019, 53(1):42-50.
[109] S Guo, T Yang, G Wei. A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors, 2018, 18(5):1429.
[110] L H Wang, X P Zhao, J X Wu, et al. Motor fault diagnosis based on short-time Fourier transform and convolutional neural network. Chinese Journal of Mechanical Engineering, 2017, 30(6):1357-1368.
[111] J H Yuan, T Han, J Tang, et al. Intelligent fault diagnosis method for rolling bearings based on wavelet time-frequency diagram and CNN. Machine Design and Reasearch, 2017, (2):93-97. (in Chinese)
[112] J H Yuan, J Tang. Intelligent fault diagnosis method for rolling bearings based on MWT and CNN. Mechanical Transmission, 2016, (12):139-143. (in Chinese)
[113] T Ince, S Kiranyaz, L Eren, et al. Real-time motor fault detection by 1D convolutional neural networks. IEEE Transactions on Industrial Electronics, 2016, 63(11):7067-7075.
[114] D D Peng, Z L Liu, H Wang, et al. A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains. IEEE Access, 2018, 99(12):10278-10293.
[115] D Ciresan, U Meier, J Schmidhuber. Multi-column deep neural networks for image classification. Computer Vision and Pattern Recognition, IEEE, 2012.
[116] C Y Gan, K Danai. Fault diagnosis of the IFAC Benchmark Problem with a model-based recurrent neural network. IEEE International Conference on Control Applications, IEEE, 1999.
[117] L Li, L Y Ma, K Khorasani. A dynamic recurrent neural network fault diagnosis and isolation architecture for satellite's actuator/thruster failures. International Symposium on Neural Networks, Springer, Berlin, Heidelberg, 2005.
[118] H Liu, J Z Zhou, Y Zheng, et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Transactions, 2018, 77(6):167-178.
[119] D Željko, M Randić, G Krčelić. A multivariate approach to predicting quantity of failures in broadband networks based on a recurrent neural network. Journal of Network System and Management, 2016, 24(1):189-221.
[120] C Xu, G Wang, X G Liu, et al. Health status assessment and failure prediction for hard drives with recurrent neural network. IEEE Trans. on Computers, 2016, 65(11):3502-3508.
[121] Z Zhao, F L Wang, M X Jia, et al. Intermittent chaos and cepstrum analysis based early fault detection on shuttle valve of hydraulic tube tester. IEEE Transactions on Industrial Electronics, 2009, 56(7):2764-2770.
[122] J Du, S P Wang, H Y Zhang. Layered clustering multi-fault diagnosis for hydraulic piston pump. Mechanical Systems & Signal Processing, 2013, 36(2):487-504.
[123] S N Borade, R R Deshmukh, S Ramu. Face recognition using fusion of PCA and LDA:borda count approach. Control and Automation, Athens, Greece, June 21-24, 2016:1164-1167.
[124] Zheng-Ping Hu. Sparse representation algorithm for image recognition based on the combination of structured sparse and atom sparse. Journal of Signal Processing, 2013, 29(7):888-895.
[125] Z Cui, H Chang, S Shan, et al. Joint sparse representation for video-based face recognition. Neurocomputing, 2014, 135(8):306-312.
[126] L Badino, C Canevari, L Fadiga, et al. Deep-level acoustic-to-articulatory mapping for DBN-HMM based phone recognition. Spoken Language Technology Workshop, Miami, FL, USA, December 02-05, 2013:370-375.
[127] O Koller, S Zargaran, H Ney, et al. Deep sign:hybrid CNN-HMM for continuous sign language recognition. British Conference on Machine Vision, York, British, September 19-22, 2016.
[128] M D Zeiler, R Fergus. Visualizing and understanding convolutional networks. European Conference on Computer Vision, 2014:818-833.