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2023
[74] Qin C., et al, An adaptive operating parameters decision-making method for shield machine considering geological environment. Tunnelling and Underground Space Technology, 2023, Uuder Revision. (SCI, IF: 6.407)
[73] Shi G.,Qin C.*, Zhang Z., Tao J., Liu C., Adaptive time-frequency-supported chirp component decomposition. Mechanical Systems and Signal Processing, 2023, Uuder Revision. (SCI, IF: 8.934)
[72] Shi G.,Qin C.*, Tao J., Zhang Z., Liu C., Towards precise complex AM-FM signals decomposition under strong noise conditions: TCMD. IEEE Transactions on Instrumentation & Measurement, 2023, Uuder Revision. (SCI, IF:5.332)
[71] Liu Y., Qin C.*, et al, A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data. Science China Technological Sciences, 2023, Uuder Revision. (SCI, IF: 3.903)
[70] 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, Uuder Revision. (SCI, IF: 5.019)
[69] 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)
[68] 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, Uuder Revision. (SCI, IF:7.136)
[67] LI Y., WU T., XIAO Y., Gong L., Huang Y. Qin C., LIU C., Path planning in continuous adjacent farmlands and robust path tracking control of a rice seeding robot in paddy Field. Computers and Electronics in Agriculture, 2023, https://doi.org/10.1016/j.compag.2023.107900. (SCI, IF: 6.757)
2022
[66] 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)
[65] 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)
[64] Shi G., Qin C.*, Zhang Z., Tao J., Liu C., SVNCD. IEEE Transactions on Industrial Informatics, 2022, Uuder Revision. (SCI, IF: 11.648)
[63] 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: 7.483)
[62] 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, https://doi.org/10.1049/cit2.12170. (SCI, IF: 7.984)
[61] 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: 3.903)
[60] 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: 5.131, 入选ESI高被引论文)
[59] 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)
[58] 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: 8.139)
[57] 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: 6.407)
[56] 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: 10.517)
[55] Jin Y., Qin C.*, et al, A novel deep wavelet convolutional neural network for actual ECG signal denoising. Biomedical Signal Processing and Control, 2022, Uuder Revision. (SCI, IF: 5.076)
[54] 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: 3.903)
[53] 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)
[52] 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:5.332)
[51] 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: 5.076, 入选ESI高被引论文)
[50] 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: 3.903)
[49] 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)
[48] 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: 5.102)
[47] 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: 5.131)
[46] 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
[45] 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)
[44] Qin C., Zeng H., Tao J., Xiao D., Yu H., Sun Y., Liu C., A chatter recognition approach for robotic drilling operations based on SCT. Mechanical Systems and Signal Processing, 2021, Revision submitted. (SCI, IF: 8.934)
[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: 5.131, 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: 8.139)
[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: 8.139)
[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:5.332)
[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: 10.517)
[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:5.332)
[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: 5.131)
[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: 5.131, 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: 5.131)
[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: 5.076)
[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:7.136)
[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:5.332)
[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:7.136)
[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.
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