2012.09-2018.12：上海交通大学，机械电子工程专业，博士（硕博连读，导师：刘成良教授）
2008.09-2012.06：西南交通大学，机械设计制造及其自动化专业，学士

2023.01 - 至今： 上海交通大学，机械与动力工程学院，机电控制与物流装备研究所，副教授，硕士/博士生导师
2021.03 - 2022.12： 上海交通大学，机械与动力工程学院，机电控制与物流装备研究所，助理教授，硕士/博士生导师
2019.01 - 2021.02： 上海交通大学，机械与动力工程学院，博士后（合作导师：刘成良教授）

2022.09 - 2023.09： 中华人民共和国科学技术部，农村科技司，专班科员

1. 信号处理、深度学习及大数据分析
2. 装备故障诊断、健康评估与智能运维
3. 机电液系统动力学建模/分析与控制
4. 机器人学与智能控制技术

欢迎对科研有热情的硕/博士生和博士后联系交流！
******每年有1个博士和2个硕士招生名额********

科研氛围欢乐、融洽，相互学习、帮助并追求卓越，每年有优秀研究成果发表于领域内国际权威期刊，多位同学获得国家奖学金等荣誉，欢迎优秀硕/博士研究生和博士后加盟！

2023.04-2026.03：上海市青年科技启明星人才计划"盾构机无人化管片拼装视觉伺服与力位控制技术研究(23QC1400400)"，项目负责人
2024.01-2027.12：国家自然科学基金面上项目"刀盘主轴承健康协同优化的隧道掘进机地质自顺应掘进研究(52375255)"，项目负责人
2021.01-2023.12：国家自然科学基金青年项目"柔性约束多变孔位机器人制孔系统耦合振动机理及智能抑振研究(52005326)"，项目负责人
2019.12-2022.11：国家重点研究计划子课题“地下工程装备全生命周期性能预测与优化技术(2019YFB1705203)”，子课题负责人
2020.11-2023.10：国家重点研究计划子课题“农机故障预警与备件精准预测技术研究(2020YFB1709604)”，子课题负责人
2023.11-2026.12：国家农业科技重大项目子课题“XXX机器人人机协同作业技术研发与应用(NK2023150202)”，子课题负责人
2022.04-2024.03：上海市自然科学基金面上项目"大型复杂孔位结构件机器人钻削系统耦合颤振机理及时滞调控研究(22ZR1432600)"，项目负责人
2021.01-2022.12：国家重点实验室自主课题“基于运行状态大数据的掘进机环境-载荷-性能预测及掘进参数优化研究(MSVZD202103)”，项目负责人
2019.01-2021.02：上海市“超级博士后”激励计划“健康评估、故障诊断、预测维护技术”，项目负责人
2019.11-2021.02：中国博士后科学基金面上项目“防空战车弱刚度机器人钻削系统耦合颤振机理与智能抑制(2019M661496)”，项目负责人
2021.03-2024.12：上海交通大学新进青年教师科研启动基金，项目负责人
2021.12-2022.12：企业合作项目“大型动力平台智能控制单元技术开发”，项目负责人

2021.01-2024.12：上海市人工智能重大专项“机器智能(2021SHZDZX0102)”，主要负责人
2020.01-2024.12：国家自然科学基金重点项目“超高速电梯动力学设计及响应控制（51935007）”，参与
2019.06-2023.05：国家重点研发计划项目“全断面隧道掘进装备运行服务平台及智能终端研制（2018YFB1702503）”，参与
2021.01-2024.12：国家自然科学基金面上项目“类矩形盾构环臂式电液伺服管片拼装机振动机理及其预测控制方法（52075320）”，参与
2014.01-2017.12：国家自然科学基金面上项目“单向变转速比例泵控非对称液压缸高精度位置控制方法研究(51375297)”，参与
2013.01-2017.12：国家973课题“液压推进系统的高刚度设计及振动能量耗吸技术（2013CB035403）”，参与
2017.12-2019.12：上海市科学技术委员会科研计划项目“基于CPS 的军用特种车智能制孔机器人系统及示范应用（17511109200）”， 参与
2015.01-2017.12：企业重大合作项目“矩形盾构液压拼装机器人智能化关键技术研究（2015-SK-01-02）”，参与
2014.12-2017.12：企业重大合作项目“机器人制孔钻削颤振分析及切削加工稳定性方法（SAMC14-JS-15-046）”，参与

在iScience, 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84篇（一作/通讯60篇，中科院1/2区64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上被引用近2600次, h-index=32。

2024
[85] 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, Under Revision. (SCI, IF: 4.600)
[84] 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, Under Revision. (SCI, IF:5.999)


2023
[83] Qin C.*, Huang G., et al, An adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting method for multiple shield machine tunneling parameters. Automation in Construction, 2023, Under Revision. (SCI, IF: 10.300)
[82] 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)
[81] 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)
[80] 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)
[79] 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)
[78] 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)
[77] 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)
[76] 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)
[75] 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)
[74] 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)
[73] 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)
[72] 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)
[71] 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)
[70] 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)
[69] 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)
[68] 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)
[67] 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)
[66] 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
[65] 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)
[64] 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)
[63] 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)
[62] 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)
[61] 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: 7.984, 入选ESI高被引论文)
[60] 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)
[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] Qin C., Shi G., et al, RCLSTMNet: A Residual-Convolutional-LSTM Neural Network for Forecasting Cutterhead Torque in Shield Machine, International Journal of Control, Automation and Systems, 2022, https://doi.org/10.1007/s12555-022-0104-x. (SCI, IF: 2.964)
[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.

教学工作：
2022春，课程名称：测试原理、传感与系统，授课对象：研究生，学时数：48
2024春，课程名称：测试原理、传感与系统，授课对象：研究生，学时数：48
2024春，课程名称：系统模型、分析与控制(A类)，授课对象：本科生，学时数：64

本科班主任：
工科试验班F2102107班（分流前）
工科试验班F2102012班（分流后）

指导（或协助指导）学生
2018-曾宏伟，硕士毕业，荣誉：研究生优秀奖学金、SMC奖学金、三好学生，毕业去向：华为技术有限公司武汉研究所
2019-刘明阳，硕士毕业，荣誉：研究生优秀奖学金，毕业去向：华为云计算有限公司
2019-石岗，硕士毕业，荣誉：研究生国家奖学金，毕业去向：上海交通大学机械与动力工程学院读博
2019-金衍瑞，博士毕业，荣誉：机动学院“学术之星"，研究生国家奖学金，毕业去向：上海交通大学机械与动力工程学院博士后
2021-黄国强，硕士毕业，荣誉：研究生国家奖学金，郭谢碧蓉奖学金, 上海交通大学优秀毕业生，毕业去向：上海交通大学机械与动力工程学院读博
2021-武睿宏，硕士毕业，荣誉：联合汽车电子奖学金，毕业去向：蚂蚁胜信（上海）信息技术有限公司
2022-石岗，博士在读
2022-李鸥，硕士在读
2022-许森涵，硕博连读
2022-王皓迪，硕博连读
2023-钟韬，硕士在读
2023-冯辰宇，硕士在读
2023-刘正阳，硕士在读
2024-黄国强，博士再读



本科毕设：
2021-2022：换刀机器人遥操作系统设计与实现，文泓珺，吴佳坤，刘浩

本科全员导师：
19级，张奕鹏（新加波国立大学），易子琛，任宇，谢舒心
20级，朱玮晔，方升健
21级，孙修杰、张一衡、周勇旬

申请和公开发明专利/软著43项(27项已获授权)：

[01] 盾构刀盘扭矩领域自适应迁移预测方法和系统. 中国，ZL202110775453.1
[02] 盾构刀盘扭矩多步预测方法和系统. 中国，ZL202110534801.6
[03] 自动定位夹紧装置及钢拱架的装配方法. 中国，ZL201811392006.2
[04] 钢拱架拼装中自动拧螺栓装置及方法. 中国，ZL201811341733.6
[05] 机械臂末端重复定位精度测量装置与方法. 中国，ZL201810322694.9
[06] 一种挖掘机液压缸泄漏检测方法和装置. 中国，ZL201711153639.3
[07] 一种基于粒子群优化算法PSO的TBM减振控制方法. 中国，ZL201711153618.1
[08] 油缸行程测量装置及其测量方法. 中国，ZL201711153636.X
[09] 强夯机夯深测量装置及测量方法. 中国，ZL2013101659280
[10] 一种油门控制装置. 中国，ZL201711204840.X
[11] 一种基于现场参数的硬岩TBM刀盘滚刀磨损实时评估方法. 中国，ZL202011023864.7
[12] 半监督的盾构隧道掌子面地质类型预估方法及系统. 中国，ZL202110552727.0
[13] 一种基于基因表达式编程的硬岩TBM滚刀磨损实时评估方法. 中国，ZL202011023865.1
[14] 一种柱塞泵空化程度检测方法、装置及终端. 中国，ZL202010109714.1
[15] 基于并联式神经网络的盾构机刀盘扭矩预测方法及系统. 中国，ZL202011225143.4
[16] 一种盾构机刀盘扭矩实时预测方法及系统. 中国，ZL202011220548.9
[17] 基于整体离散策略的铣削稳定性分析方法. 中国，ZL201810148967.2
[18] 工业机器人末端重复定位精度测量装置与方法. 中国，ZL201810321280.4
[19] 机械臂末端空间重复定位精度测量装置与方法. 中国，ZL201810322701.5
[20] 基于SVR与PSO的盾构利用率预测与操作参数优化方法及系统. 中国，ZL202110775455.0
[21] 工业机器人钻孔颤振监测软件(颤振监测软件). 中国，2018R11L1128007
[22] 工业机器人智能健康管理平台. 中国，2021SR0101081
[23] 拖拉机故障诊断与智能运维系统软件. 中国，2021SR0367168
[24] 盾构装备健康监控与智能运维平台. 中国，2021SR2088477
[25] 地下工程装备状态监测与地质感知系统. 中国，2022SR0191354
[26] 换刀机器人遥操作平台V1.0. 中国，2022SR0979797
[27] 柴油机失火抗噪和跨噪声域诊断方法及系统. 中国，ZL202210589677.8

[28] 一种TBM利用率预测的A-CNN方法. 中国，CN202110976876.X
[29] 机器人钻削颤振识别方法和系统. 中国，CN202111221868.0
[30] 一种基于深度卷积残差收缩网络的刀具磨损预测方法和系统. 中国，CN202111216259.6
[31] 一种基于混合深度神经网络的谐波减速器功率实时预测方法. 中国，CN202110672801.2
[32] 一种基于深度学习的机器人避障轨迹规划方法及系统. 中国，CN109213147.A
[33] 隧道掘进机操作参数地层自适应决策方法及系统. 中国，CN202210134013.2
[34] 全断面隧道掘进机刀盘扭矩长时间预测方法及系统. 中国，CN202210129526.4
[35] 基于深度残差网络的盾构机刀盘结泥饼诊断方法和系统. 中国，CN202210396985.9
[36] 基于历史运行数据的盾构机刀盘结泥饼诊断方法和系统. 中国，CN202210395715.6
[37] 一种基于PICA-VMD和Hilbert边际谱的轴向柱塞泵空化等级识别方法. 中国，CN110991544A
[38] 盾构机施工掌子面地质类型识别方法及系统. 中国，CN202210594109.7
[39] 超高速电梯滚动导靴磨损性能测试装置及测试方法. 中国，CN202210951167.0
[40] 一种基于盾构机运行参数的掘进掌子面岩土类型识别方法. 中国，CN202110407464.4
[41] 基于压力脉动相似性的柱塞泵异常检测方法和系统. 中国，CN202211128078.2
[42] 基于群体数据和多尺度自编码器的设备健康评估方法. 中国，CN202310100319.0
[43] 稀疏辅助的变分非线性信号分解方法与系统. 中国，CN202310355400.3
[44] 自适应的时频支持的调频信号分解方法与系统. 中国，CN202310869527.7

Reviewers：
担任 Advanced Science、Information Fusion、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Industrial Informatics、IEEE Transactions on Industrial Electronics 、Mechanical Systems and Signal Processing、Applied Energy等近50个国际期刊审稿人

[01] 2021.08 至今 IEEE Transactions on Neural Networks and Learning Systems 审稿人
[02] 2022.04 至今 IEEE Transactions on Industrial Informatics审稿人
[03] 2020.03 至今 IEEE Transactions on Industrial Electronics 审稿人
[04] 2021.08 至今 Mechanical Systems and Signal Processing 审稿人
[05] 2021.09 至今 IEEE Journal of Biomedical and Health Informatics 审稿人
[06] 2019.11 至今 Journal of Intelligent Manufacturing 审稿人
[07] 2016.09 至今 Nonlinear Dynamics 审稿人
[08] 2021.05 至今 Computers and Electronics in Agriculture 审稿人
[09] 2021.12 至今 ISA Transactions审稿人
[10] 2020.04 至今 Measurement 审稿人
[11] 2021.07 至今 Expert Systems With Applications 审稿人
[12] 2021.08 至今 Applied Acoustics 审稿人
[13] 2022.01 至今 Friction审稿人
[14] 2020.04 至今 CMES-Computer Modeling in Engineering & Sciences 审稿人
[15] 2020.04 至今 Complexity 审稿人
[16] 2019.11 至今 Artificial Intelligence in Agriculture 审稿人
[17] 2019.11 至今 IEEE Access 审稿人
[18] 2019.10 至今 Shock and Vibration 审稿人
[19] 2018.10 至今 International Journal of Advanced Manufacturing Technology 审稿人
[20] 2021.03 至今 Scientific Reports 审稿人
[21] 2021.06 至今 Geomechanics and Engineering 审稿人
[22] 2021.09 至今 Intelligent Systems with Applications 审稿人
[23] 2021.10 至今 Energies审稿人
[24] 2021.11 至今 Sensors审稿人
[25] 2022.01 至今 Knowledge Engineering Review审稿人
[26] 2022.01 至今 Electronics审稿人
[27] 2022.05 至今 Applied Energy审稿人
[28] 2022.06 至今 Artificial Intelligence Review审稿人
[29] 2022.08 至今 Mechanics Based Design of Structures and Machines审稿人
[30] 2022.09 至今 Journal of Experimental & Theoretical Artificial Intelligence审稿人
[31] 2022.09 至今 Advances in Mechanical Engineering审稿人
[32] 2022.09 至今 Chinese Journal of Aeronautics审稿人
[33] 2022.09 至今 IEEE Transactions on Instrumentation & Measurement审稿人
[34] 2022.09 至今 Journal of Vibration and Control审稿人
[35] 2022.10 至今《航空学报》审稿人
[36] 2022.11 至今 Advanced Engineering Informatics审稿人
[37] 2023.01 至今IEEE Sensors Journal审稿人
[38] 2023.03 至今Robotics and Computer-Integrated Manufacturing审稿人
[39] 2023.04 至今Heliyon审稿人
[40] 2023.05 至今IEEE Internet of Things Journal审稿人
[41] 2023.05 至今Knowledge-Based Systems审稿人
[42] 2023.07 至今Automation in Construction审稿人
[43] 2023.09 至今Tunnelling and Underground Space Technology审稿人
[44] 2023.09 至今Computer Methods and Programs in Biomedicine审稿人
[45] 2023.09 至今Biomedical Signal Processing and Control审稿人
[46] 2023.09 至今European Heart Journal–Digital Health审稿人
[47] 2024.03 至今Information Fusion (IF:18.6)审稿人
[48] 2024.03 至今Advanced Science (IF:15.1 )审稿人

Editorial board：
[01] 2022.02至今, International Journal of Hydromechatronics, Youth Editorial board member 青年编委
[02] 2022.04至今, Journal of Dynamics, Monitoring and Diagnostics, Youth Editorial board member 青年编委

Guest Editors：
[01] 2021.08-2022.08, 主题专刊 "Machine Learning in Vibration and Acoustics", Leading guest editor 客座主编, https://www.mdpi.com/journal/applsci/special_issues/Machine_Learning_Vibration_Acoustics
投稿截止日期：2022.21.31。

[02] 2021.08-2022.08, 主题专刊 "Machine Learning in Vibration and Acoustics 2.0", Leading guest editor 客座主编, https://www.mdpi.com/journal/applsci/special_issues/545M7JII39
投稿截止日期：31 July 2023。涉及振动和声学的信号处理和机器学习以及PHM均符合主题，欢迎积极赐稿!

[03] 2022.02至今, 主题专刊 "Machine Learning in Data Processing of Wireless Sensor Networks", Guest editor, https://journals.sagepub.com/page/dsn/collections/special-issues/machine-learning-in-data-processing-of-wireless-sensor-networks

[1] 2023年，上海市青年科技启明星，上海市科委
[2] 2023年，上海交通大学机械与动力工程学院年度考核优秀
[3] 2022年，上海交通大学优秀班主任
[4] 2022年，上海交通大学机械与动力工程学院优秀班主任
[5] 2022年，上海交通大学机械与动力工程学院年度考核优秀
[6] 2022年，2023年，连续2年入选全球前2%顶尖科学家榜单
[7] 2021年，农业农村部科技进步一等奖，中国农学会
[8] 2020年，中国机械工业科技进步特等奖，中国机械工业联合会
[9] 2020年，吴文俊人工智能科技进步二等奖，中国人工智能学会
[10] 2019年，上海市“超级博士后”激励计划，上海人力资源和社会保障局