Selected Publications
在IEEE Transactions on Industrial Informatics, Mechanical Systems and Signal Processing, Automation in Construction, Tunnelling and Underground Space Technology, Geoscience Frontiers, Knowledge-Based Systems, Journal of Intelligent Manufacturing, Science China (中国科学)等国内外期刊上发表SCI论文91篇（中科院1/2区71篇，一作/通讯66篇，12篇入选ESI高被引论文，2篇入选ESI热点论文，*为通讯作者）, 在Google Scholar上被引用超3100次, h-index=36。
2024
[92] Qin C., Huang G., Yu H., et al, Adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting for multiple shield machine tunneling parameters. Automation in Construction, 2024, 165:105563. https://doi.org/10.1016/j.autcon.2024.105563. (SCI, IF: 10.300)
[91] Qin C., et al, RCLSTMNet: A Residual-Convolutional-LSTM Neural Network for Forecasting Cutterhead Torque in Shield Machine, International Journal of Control, Automation and Systems, 2024, 22(2):705-721. https://doi.org/10.1007/s12555-022-0104-x. (SCI, IF: 2.964)
[90] Shi G., Qin C.*, Zhang Z., Tao J., Liu C., Sparsity-assisted variationalnonlinear component decomposition. IEEE Transactions on Industrial Informatics, 2024, https://doi.org/10.1109/TII.2023.3321095. (SCI, IF: 12.300)
[89] Shi G.,,Qin C.*, et al, A novel decomposition and hybrid transfer learning-based method for multi-step cutterhead torque prediction of shield machine.Mechanical Systems and Signal Processing, 2024, 214:111362. https://doi.org/10.1016/j.ymssp.2024.111362. (SCI, IF: 8.934)
[88] Shi G.,,Qin C.*, et al, Towards complex multi-component pulse signal with strong noise: Deconvolution and time-frequency assisted mode decomposition.Mechanical Systems and Signal Processing, 2024, https://doi.org/10.1016/j.ymssp.2024.111274. (SCI, IF: 8.934)
[87] Huang G.,Qin C.*, et al, A novel multi-scale hybrid connected neural network for anti-noise rock fragmentation classification of tunnel boring machine . Tunnelling and Underground Space Technology, 2024, Under Revision. (SCI, IF: 6.900)
[86] Wang H.,Qin C.*, et al, A decoupled adversarial architecture-based hybrid modeling method for shield machine tunneling speed and cutterhead torque prediction. Tunnelling and Underground Space Technology, 2024, Under Revision. (SCI, IF: 6.900)
[85] Wang H.,Qin C.*, et al, Geological type recognition for shield machine using a semi-supervised variational auto-encoder-based adversarial method. Tunnelling and Underground Space Technology, 2024, Under Revision. (SCI, IF: 6.900)
[84] Wang H.,Qin C.*, et al, A real-time multi-head mixed attention mechanism-based prediction method for tunnel boring machine disc cutter wear. Science China Technological Sciences, 2024, https://doi.org/10.1007/s11431-024-2794-6. (SCI, IF: 4.600)
[83] Zhong T.,Qin C.*, et al, A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis. Science China Technological Sciences, 2024, 67:2594–2618. https://doi.org/10.1007/s11431-024-2734-x. (SCI, IF: 4.600)
[82] Li O.,Qin C.*, et al, A novel Multi-channel CNN-LSTM and Transformer-based Network for Diesel Engine Misfire Diagnosis under different noise conditions. Science China Technological Sciences, 2024, https://doi.org/10.1007/s11431-023-2698-2. (SCI, IF: 4.600)
[81] Xu S.,Qin C.*, et al, A novel Welch-CNN diesel engine misfire diagnosis framework combining multilevel residual denoising and multi-scale feature extraction-fusion. IEEE Transactions on Instrumentation & Measurement, 2024, Under Revisio. (SCI, IF:5.999)
[80] Liu Y., Liu J.,Qin C.*, et al, Optimized lightweight neural networks for accurate arrhythmia detection in clinical 12-lead ECG data. IEEE Transactions on Instrumentation & Measurement, 2024, https://doi.org/10.1109/TIM.2024.3449956. (SCI, IF:5.999)
[79] Wang S.,Tao J.*,Jiang Q.,Chen W., Qin C., et al, A Digital Twin Framework for Anomaly Detection in Industrial Robot systems Based on Multiple Physics-Informed Hybrid Probabilistic Convolutional Autoencoder. Journal of Manufacturing Systems, 2024, https://doi.org/10.1016/j.jmsy.2024.10.016. (SCI, IF:12.2)
2023
[78] Qin C., et al, An adaptive operating parameters decision-making method for shield machine considering geological environment. Tunnelling and Underground Space Technology, 2023, 141:105372. https://doi.org/10.1016/j.tust.2023.105372. (SCI, IF: 6.900)
[77] Qin C., Jin Y., Zhang Z., Yu H., Tao J., Sun H., Liu C., Anti-noise diesel engine misfire diagnosis using a multi-scale CNN-LSTM neural network with denoising module. CAAI Transactions on Intelligence Technology, 2023, 8:963–986. https://doi.org/10.1049/cit2.12170. (SCI, IF: 8.4, 入选ESI高被引论文)
[76] Qin C.*, Wu R., Huang G., Tao J., Liu C., A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging. Science China Technological Sciences, 2023, 66(2):512-527. https://doi.org/10.1007/s11431-022-2218-9. (SCI, IF: 4.60, 入选ESI高被引论文)
[75] Qin C., Huang G., Yu H., Wu R., Tao J., Liu C., Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction. Geoscience Frontiers, 2023, 14: 101519. https://doi.org/10.1016/j.gsf.2022.101519. (SCI, IF: 8.90, 入选ESI高被引论文)
[74] Qin C., Sun Y., et al, A chatter recognition approach for robotic drilling system based on synchroextracting chirplet transform. IEEE Sensors Journal, 2023, 23(22): 27670-27683. https://doi.org/10.1109/JSEN.2023.3322408. (SCI, IF: 4.300)
[73] Shi G.,Qin C.*, Zhang Z., Tao J., Liu C., Towards precise complex AM-FM signals decomposition under strong noise conditions: TCMD. Mechanical Systems and Signal Processing, 2023, 200:110602. https://doi.org/10.1016/j.ymssp.2023.110602. (SCI, IF: 8.934)
[72] Shi G.,Qin C.*, Tao J., Zhang Z., Liu C., Adaptive time-frequency-supported chirp component decomposition. IEEE Transactions on Instrumentation & Measurement, 2023, 72: 6505713. https://doi.org/10.1109/TIM.2023.3323958. (SCI, IF:5.999)
[71] Xu S.,Qin C.*, et al, A domain-adversarial wide-kernel convolutional neural network for noisy domain adaptive diesel engine misfire diagnosis. IEEE Transactions on Instrumentation & Measurement, 2024, 73: 3343796. (SCI, IF:5.999)
[70] Liu Y., Qin C.*, et al, A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data. Science China Technological Sciences, 2023, https://doi.org/10.1007/s11431-023-2460-2. (SCI, IF: 4.600)
[69] Liu Y., Liu J.,Qin C.*, et al, A deep learning-based acute coronary syndrome-related disease classification method: a cohort study for network interpretability and transfer learning. Applied Intelligence, 2023, https://doi.org/10.1007/s10489-023-04889-7. (SCI, IF: 5.299)
[68] Sun Y., Qin C.*,et al, Spectral Interference Rejection Algorithm for Beat Frequency Estimation in Machining Chatter. IEEE Transactions on Instrumentation & Measurement, 2023, 72:1-10. https://doi.org/10.1109/TIM.2023.3329216. (SCI, IF:5.999)
[67] Zhao M., Qin C.*,et al, An acceleration feedback-based active control method for high-speed elevator horizontal vibration. Journal of Vibration Engineering & Technologies, 2023, https://doi.org/10.1007/s42417-023-00955-z. (SCI)
[66] Tang R., Qin C.*,et al, An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator. Applied Sciences, 2023, https://doi.org/10.3390/app13127314. (SCI)
[65] Xia P, Huang Y. Qin C.,et al, Towards prognostic generalization: A domain conditional invariance and specificity disentanglement network for remaining useful life prediction. Journal of Intelligent Manufacturing, 2023, accepted. (SCI, IF:8.300)
[64] Xia P, Huang Y. Qin C.,et al, Adaptive Feature Utilization With Separate Gating Mechanism and Global Temporal Convolutional Network for Remaining Useful Life Prediction. IEEE Sensors Journal, 2023, https://doi.org/10.1109/JSEN.2023.3299432. (SCI, IF:4.299)
[63] Li W., Liu X., Wang D., Lu W., Yuan B,, Qin C.,et al, MITDCNN: A multi-modal input Transformer-based deep convolutional neural network for misfire signal detection in high-noise diesel engines. Expert Systems With Applications, 2023, https://doi.org/10.1016/j.eswa.2023.121797. (SCI, IF:8.412)
[62] Qin Y., Zhou J., Xiao D., Qin C., Qian Q., High-precision Cutterhead Torque Prediction for Tunnel Boring Machines using an Attention-based Embedded LSTM Neural Network. Measurement, 2023, https://doi.org/10.1016/j.measurement.2023.113888. (SCI, IF: 5.999)
[61] Sun H., Tao* J. , Qin C., Dong C., Xu S., Zhuang Q., Liu C., Multi-objective trajectory planning for segment assembly robots using a B-spline interpolation- and infeasible-updating non-dominated sorting-based method. Applied Soft Computing, 2023, https://doi.org/10.1016/j.asoc.2023.111216. (SCI, IF: 8.700)
2022
[60] Qin C., Shi G., Tao J., Yu H., Jin Y., Xiao D., Zhang Z., Liu C., An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine. Mechanical Systems and Signal Processing, 2022, 175:109148. (SCI, IF: 8.934, 入选ESI热点论文和高被引论文)
[59] Qin C., Xiao D.,Tao J., Yu H., Jin Y., Sun Y., Liu C., Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement, 2022, 194:111090. https://doi.org/10.1016/j.measurement.2022.111090. (SCI, IF: 5.999, 入选ESI高被引论文)
[58] Liu Y.#, Qin C.#*,et al, Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance. iScience, 2022, 25(11):105434. https://doi.org/10.1016/j.isci.2022.105434. (Cell子刊, SCI, IF: 6.107)
[57] Liu Y., Qin C.*,et al, An efficient neural network-based method for patient-specific information involved arrhythmia detection. Knowledge-Based System, 2022, 250:109021. https://doi.org/10.1016/j.knosys.2022.109021. (SCI, IF: 8.8)
[56] Yu H., Qin C.*, Tao J., Liu C., Liu Q., A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction. Tunnelling and Underground Space Technology, 2023, 133:104949. https://doi.org/10.1016/j.tust.2022.104949. (SCI, IF: 6.900, 入选ESI高被引论文)
[55] Yu H., Sun H., Tao J.*, Qin C.*, et al, A multi-stage data augmentation and ABi-ResNet-based method for EPB utilization factor prediction. Automation in Construction, 2023, 147: 104734. https://doi.org/10.1016/j.autcon.2022.104734. (SCI, IF: 10.300, 入选ESI高被引论文)
[54] Jin Y., Qin C.*, et al, A novel deep wavelet convolutional neural network for actual ECG signal denoising. Biomedical Signal Processing and Control, 2024, 87: 105480. (SCI, IF: 5.100)
[53] Jin Y., Qin C.*,Zhang Z., Tao J., Liu C., A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions. Science China Technological Sciences, 2022, 65:2551–2563. https://doi.org/10.1007/s11431-022-2109-4. (SCI, IF: 4.60)
[52] Wu R., Qin C.*, et al, Precise cutterhead clogging detection for shield tunnelling machine using a novel deep residual network, International Journal of Control, Automation and Systems, 2023, accepted. (SCI, IF: 2.964)
[51] Fu X., Tao J.*, Qin C.*, et al, A roller state-based fault diagnosis method for TBM main bearing using two-stream CNN with multi-channel detrending inputs. IEEE Transactions on Instrumentation & Measurement, 2022, https://doi.org/10.1109/TIM.2022.3212115. (SCI, IF:5.999)
[50] Jin Y., Li Z., Qin C.*, et al, A novel interpretable method based on attentional deep neural network for actual ECG quality assessment. Biomedical Signal Processing and Control, 2023, https://doi.org/10.1016/j.bspc.2022.104064. (SCI, IF: 5.100, 入选ESI高被引论文)
[49] Jin Y., Li Z., Liu Y., Liu J., Qin C.*, Zhao L., Liu C*, Multi-class 12-lead ECG automatic diagnosis based on a novel subdomain adaptive deep network. Science China Technological Sciences, 2022, https://doi.org/10.1007/s11431-022-2080-6. (SCI, IF: 4.60)
[48] Sun H., Tao J., Qin C.,et al, Optimal energy consumption and response capability assessment for hydraulic servo systems containing counterbalance valves". ASME Journal of Mechanical Design, 2023, 145(5): 053501. (SCI, IF: 3.441)
[47] Tao J., Yu H., Qin C., Sun H., Liu C., A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead". Neural Computing and Applications, 2022, https://doi.org/10.1007/s00521-022-07597-4. (SCI, IF: 6.000)
[46] Liu C., Ma X. Shi X., Han Y., Qin C., Hu S., NTScatNet: An Interpretable Convolutional Neural Network for Domain Generalization Diagnosis Tasks across Different Transmission Paths. Measurement, 2022, 204:112041. https://doi.org/10.1016/j.measurement.2022.112041. (SCI, IF: 5.999)
[45] Liu J., Jin Y., Liu Y, Li Z., Qin C., Chen X, Zhao L; Liu C., A novel P-QRS-T wave localization method in ECG Signals based on hybrid neural networks. Computers in Biology and Medicine, 2022, 150: 106110. https://doi.org/10.1016/j.compbiomed.2022.106110. (SCI, IF: 6.698)
2021
[44] Qin C., Shi G., Tao J., Yu H., Jin Y., Lei J., Liu C., Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network. Mechanical Systems and Signal Processing, 2021, 151: 107386. (SCI, IF: 8.934, 入选ESI高被引论文)
[43] Qin C., Jin Y., Tao J., Xiao D.,Yu H., Liu C., Shi G.,Lei J., Liu C., DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection. Measurement, 2021, 180: 109548. (SCI, IF: 5.999, 入选ESI热点论文和高被引论文)
[42] Jin Y., Qin C.*, Tao J., Liu C., An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mechanical Systems and Signal Processing, 2022, 165: 108312. https://doi.org/10.1016/j.ymssp.2021.108312. (SCI, IF: 8.934)
[41] Yu H., Tao J.*, Qin C.*, Liu M., Xiao D., Sun H., Liu C., A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing, 2022, 165:108353. https://doi.org/10.1016/j.ymssp.2021.108353. (SCI, IF: 8.934)
[40] Shi G., Qin C.*, Tao J., Liu C., A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque. Knowledge-Based System, 2021, 228:107213. (SCI, IF: 8.80)
[39] Xiao D., Qin C., Ge J., Xia P., Huang Y.*, Liu C., Self-attention-based adaptive remaining useful life prediction for IGBT with Monte Carlo dropout. Knowledge-Based System, 2022, 239:107902. https://doi.org/10.1016/j.knosys.2021.107902. (SCI, IF: 8.80)
[38] Jin Y., Qin C.*, Liu J., Li Z., Shi H., Lin K., Liu Y., Liu C.*, A novel incremental and interactive method for actual heartbeat classification with limited additional labeled samples. IEEE Transactions on Instrumentation & Measurement, 2021, 70: 2507212. (SCI, IF:5.999)
[37] Yu H., Tao J.*, Huang S., Qin C.*, Xiao D., Liu C., A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead. Automation in Construction, 2021, 124:103603. (SCI, IF: 10.300)
[36] Jin Y., Liu J., Liu Y., Qin C.*, Li Z., Xiao D., Zhao L., Liu C.*, A novel interpretable method based on dual-Level attentional deep neural network for actual Multi-label Arrhythmia detection. IEEE Transactions on Instrumentation & Measurement, 2022, 71:2500311. https://doi.org/10.1109/TIM.2021.3135330. (SCI, IF:5.999)
[35] Tao J., Qin C.*, Xiong Z., Gao X., Liu C., Optimization and control of cable tensions for hyper-redundant snake arm robots. International Journal of Control, Automation and Systems, 2021, 19: 3764–3775. (SCI, IF: 3.314)
[34] Xiao D., Qin C.*, Yu H., Huang Y.*, Liu C., Zhang J., Unsupervised Machine Fault Diagnosis for Noisy Domain Adaptation using marginal Denoising Autoencoder. Measurement, 2021, 176:109186. (SCI, IF: 5.999)
[33] Jin Y., Qin C.*, Huang Y.*, Liu C., Actual Bearing Compound Fault Diagnosis based on Active Learning and Decoupling Attentional Residual Network. Measurement, 2021, 173: 108500. (SCI, IF: 5.999, 入选ESI高被引论文)
[32] Yu H., Tao J.*, Qin C.*, Xiao D., Sun H., Liu C.，Rock mass type prediction for tunnel boring machine using a novel semi-supervised method. Measurement, 2021, 179: 10954. (SCI, IF: 5.999)
[31] Liu Y., Jin Y., Liu J., Qin C.*, Lin K., Shi H., Tao J., Zhao L., Liu C.*, Precise and efficient heartbeat classification using a novel lightweight-modified method. Biomedical Signal Processing and Control, 2021, 68:102771. (SCI, IF: 5.100)
[30] Xiao D., Qin C.*, Yu H., Huang Y.*，Liu C., Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization. Journal of Intelligent Manufacturing, 2021, 32(2): 377–391. (SCI, IF:8.300)
[29] Liu C., Qin C., Shi X., Wang Z., Zhang G., Han Y., TScatNet: An interpretable cross-domain intelligent diagnosis model with anti-noise and few-shot learning capability. IEEE Transactions on Instrumentation & Measurement, 2021, 70:9279302. (SCI, IF:5.999)
[28] Sun H., Tao J., Qin C., Yu H., Liu C., Dynamics modeling and bifurcation analysis for valve-controlled hydraulic cylinder system containing counterbalance valves. Journal of Vibration Engineering & Technologies, 2021, https://doi.org/10.1007/s42417-021-00342-6. (SCI)
[27] Li, B., Qin, C.*, Tao, J., Liu, C.,Failure Warning of Harmonic Reducer Based on Power Prediction. Journal of Physics: Conference Series, 2022, 2246(1), 012016
Before 2021
[26] Qin C., Tao J., Shi H., Xiao D., Li B., Liu C., A novel Chebyshev-wavelet-based approach for accurate and fast prediction of milling stability. Precision Engineering, 2020, 62:244–255. (SCI, IF:3.315, 入选ESI高被引论文)
[25] Qin C., Tao J., Xiao D., Shi H., Ling X., Liu C., Accurate and efficient stability prediction for milling operations using a Legendre-Chebyshev-based method. International Journal of Advanced Manufacturing Technology, 2020, 107(1–2): 247–258. (SCI, IF:3.563)
[24] Qin C., Tao J., Xiao D., Shi H., Li B., Liu C.. A Legendre wavelet–based stability prediction method for high-speed milling processes. International Journal of Advanced Manufacturing Technology, 2020, 108(7-8): 2397-2408. (SCI, IF:3.563)
[23] Qin C., Tao J., Liu C., A novel stability prediction method for milling operations using the holistic-interpolation scheme. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(13):4463–4475. (SCI)
[22] Qin C., Tao J., Liu C., A predictor-corrector-based holistic-discretization method for accurate and efficient milling stability analysis. International Journal of Advanced Manufacturing Technology, 2018, 96(5–8):2043–2054. (SCI, IF:3.563)
[21] Qin C., Tao J., Liu C., Stability analysis for milling operations using an Adams-Simpson-based method. International Journal of Advanced Manufacturing Technology, 2017, 92 (1–4):969–979. (SCI, IF:3.563)
[20] Qin C., Tao J., Liu C., An Adams-Moulton-based method for stability prediction of milling processes. International Journal of Advanced Manufacturing Technology, 2017, 89 (9–12):3049–3058. (SCI, IF:3.563)
[19] Tao J., Qin C.*, Xiao D., Shi H., Ling X., Li B., Liu C., Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. Journal of Intelligent Manufacturing, 2020, 31: 1243–1255. (SCI, IF:8.300)
[18] Jin Y., Qin C.*, Liu J., Lin K., Shi H., Huang Y., Liu C.*, A novel Domain Adaptive Residual Network for automatic Atrial Fibrillation Detection. Knowledge-Based System, 2020, 203:106122. (SCI, IF: 8.139)
[17] Tao J., Qin C.*, Xiao D., Shi H., Liu C., A pre-generated matrix-based method for real-time robotic drilling chatter monitoring. Chinese Journal of Aeronautics, 2019, 32(12): 2755–2764. (SCI, IF: 4.061)
[16] Wang H., Shi H., Lin K., Qin C.*, Zhao L., Huang Y., Liu C.*, A high-precision arrhythmia classification method based on dual fully connected neural network. Biomedical Signal Processing and Control, 2020, 58:101874. (SCI, IF: 5.076)
[15] Tao J., Qin C.*, Liu C., A synchroextracting-based method for early chatter identification of robotic drilling process. International Journal of Advanced Manufacturing Technology, 2019, 100(1–4):273–285. (SCI, IF:3.563)
[14] Tao J., Zeng H., Qin C.*, Liu C., Chatter detection in robotic drilling operations combining multi-synchrosqueezing transform and energy entropy. International Journal of Advanced Manufacturing Technology, 2019, 105(7–8): 2879–2890. (SCI, IF:3.563)
[13] Tao J., Qin C.*, Li W., Liu C., Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-accuracy time–frequency information of vibration signals. Sensors, 2019, 19:3280. (SCI, IF: 3.576)
[12] Jin Y., Qin C., Huang Y., Zhao W., Liu C., Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks. Knowledge-Based System, 2020, 193:105460. (SCI, IF: 8.139)
[11] Shi H., Qin C., Xiao D., Zhao L., Liu C., Automated heartbeat classification based on deep neural network with multiple input layers. Knowledge-Based System, 2020, 188:10503. (SCI, IF: 8.139)
[10] Shi H., Wang H., Qin C., Zhao L., Liu C.. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning. Computer Methods and Programs in Biomedicine, 2020, 187:105219. (SCI, IF: 7.027)
[09] Xiao, D., Tao, Z., Qin C., ...Huang, Y., Liu, C.,Fast Machine Fault Diagnosis Using Marginalized Denoising Autoencoders Based on Acoustic Signal. 2020 Prognostics and Health Management Conference, PHM-Besancon 2020, 2020, pp. 229–234, 9115517.
[08] Xiao D., Huang Y., Zhao L., Qin C., Shi H., Liu C., Domain adaptive motor fault diagnosis using deep transfer learning. IEEE Access, 2019, 7:80937-80949. (SCI, IF: 3.367)
[07] Xiao D., Huang Y., Qin C., Liu Z., Li Y., Liu C., Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(14):5131-5143. (SCI)
[06] Ling X., Tao J., Li B., Qin C., Liu C.. A Multi-physics modeling-based vibration prediction method for switched reluctance motors. Applied Sciences, 2019, 9(21):4544. (SCI)
[05] Xiao, D., Huang, Y., Qin C., ...Liu, C., Shan, Z., Health Assessment for Crane Pumps based on Vehicle Tests using Deep Autoencoder and Metric Learning. 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019, 2019, 8819387.
[04] Xiao D., Huang Y., Qin C., Shi H., Li Y., Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN. Shock and Vibration, 2019, 2019:8325218. (SCI)
[03] Tao J., Qin C., Liu C.. Milling Stability Prediction with Multiple Delays via the Extended Adams-Moulton-Based Method. Mathematical Problems in Engineering, 2017, 2017:7898369. (SCI)
[02] Shi H., Wang H., Huang Y., Zhao L., Qin C., Liu C., A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Computer Methods and Programs in Biomedicine, 2019, 171:1-1. (SCI, IF: 7.027)
[01] Qin C.*, Tao J., Wang M., Liu C., A novel approach for the acquisition of vibration signals of the end effector in robotic drilling. 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, 2016, 7748106:522–526.
