秦娜

副教授

副教授 博士生导师 硕士生导师

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所在单位:电气工程学院

办公地点:西南交通大学犀浦校区电气工程学院

学位:工学博士学位

职称:副教授

主要任职:电子信息系副主任

毕业院校:西南交通大学

博士生导师

硕士生导师

论文成果

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[1]       Huang Deqing, Yang Wanqiu, Huang Tengfei, Qin Na*,Chen Yong. Iterative Learning Operation Control of High-Speed Trains with Adhesion Dynamics, IEEE Transactions on Control Systems Technology, Accecpted, DOI: 10.1109/TCST.2021.3049958 2020  SCI二区  

[2]       Huang Deqing, Li Shupan, Qin Na*,Zhang Yuanjie. Fault Diagnosis of High-speed Train Bogie Based on the Improved-CEEMDAN and 1-D CNN Algorithms. IEEE Transactions on Instrumentation and Measurement, Accecpted, 2020  DOI10.1109/TIM.2020.3047922 SCI 二区

[3]       Habib. U. K. Jadoon, Deqing. Huang, Na Qin*, and Z. Gong. Sensor Fault diagnosis and Unknown Disturbances Estimation of High Switching Frequency Single-phase PWM Rectifier. International Journal of Control, Automation and Systems, Accecpted, 2020  SCI 三区

[4]       Tian Xiangjin, Huang Deqing*, Qin Na, Gong Zifeng, Wang Qingyuan. Guaranteed Cost Optimal Control of High-Speed Train with Time-delay in Cruise Phase. International Journal of Control, Automation and Systems, Accecpted, 2020   SCI 四区

[5]       Dai Xi, Mao Yuxin, Huang Tianpeng, Qin Na. Automatic obstacle avoidance of quadrotor UAV via CNN-based Learning, Neurocomputing. 2020, 402: 346-348  SCI二区

[6]       Deqing Huang , Yuanzhe Fu , Na Qin*, Shibin Gao, Fault Diagnos is of High - Speed Train Bogie Based on LSTM NeuralNetwork, Science China -Information Sciences , 2021, 64 (1)119203:1–119203:3   DOI:10.1007/s11432-018-9543-8   A++  SCI 二区

[7]       Qin, Na; Liang, Kaiwei; Huang, Deqing; Ma, Lei; Kemp, Andrew H, Multiple Convolutional Recurrent Neural Networks for Fault Identification and Performance Degradation Evaluation of High-speed Train Bogie. IEEE Transactions on Neural Networks and Learning Systems. 2020, 31(12): 5363-5376  D.O.I10.1109/TNNLS.2020.2966744  SCI一区

[8]       Lingling Chen, Na Qin*, Xi Dai, and Deqing Huang, Fault Diagnosis of High-Speed Train Bogie Based on Capsule Network, IEEE Transactions on Instrumentation and Measurement.  2020, 69(9): 6203-6211  DOI10.1109/TIM.2020.2968161  IF:3.067.  A++  SCI 二区

[9]       Shupan. Li, Na Qin, Darong Huang, Deqing Huang* and L. Ke, Damage Localization of Stacker’s Track Based on EEMD-EMD and DBSCAN Cluster Algorithms, IEEE Transactions on Instrumentation and Measurement, 2020,69(5):1981-1992.  DOI: 10.1109/TIM.2019.2919375. IF:3.067.  A++  SCI 二区

[10]    Xia Jingkang, Huang Deqing, Li Yanan, Qin Na. Iterative learning of human partner's desired trajectory for proactive human-robot collaboration, International Journal of intelligent Robotics and applications, 2020, 4(2):229-242. 

[11]    Z. Wang, D. Huang, T. Huang, N. Qin. Active disturbance rejection control for a quadrotor UAV, IEEE 9th Data Driven Control and Learning Systems Conference, 2020:1-5.  EI会议

[12]    T. Huang, D. Huang, Z, Wang, N. Qin. A. Shah. Robust control for a quadrotor UAV based on linear quadratic regulator, 39th Chinese Control Conference, 2020:6893-6898.  EI会议

[13]    X. Zhao, N. Qin, J. Huang and Y. Miao. The Application of Adaptive A-star Algorithm in Layout of Spatial Pipeline, IEEE 9th Data Driven Control and Learning Systems Conference , 2020: 272-277.  EI会议

[14]    Z. Li, Z. Liu, Y. Hu, Y. Zhang, Z. Liu and N. Qin, Grayscale-information-based Segmentation Registration for Fault Diagnosis of Train Components, IEEE 9th Data Driven Control and Learning Systems Conference, 2020: 748-753.  EI会议

[15]    W. Sun, N. Qin, D. Huang, Z Liu, S Ni. QN-S3VM Method for Evaluation of Liver Functional Reserve, The China Automation Congress 2020, Accept.  EI会议

[16]    14. W. Lv, N. Qin, X. Zhao, P. Yuan, J. Huang. Pipe Routing of Reactor Based on Genetic Algorithm Combined with Adaptive A* Algorithm, The China Automation Congress 2020, Accept. EI会议

[17]    Liyuan Su, Lei Ma, Na Qin*, Deqing Huang and A. H. Kemp, Fault Diagnosis of High-Speed Train Bogie by Residual-Squeeze Net ,  IEEE Transactions on Industrial Informatics, 2019, 15(7):3856-3863.  DOI: 10.1109/TII.2019.2907373.  IF: 7.377 A++  SCI 一区

[18]    Yong Chen, Deqing Huang, Tianpeng Huang and Na Qin*, Tracking Control via Iterative Learning for High-Speed Trains With Distributed Input Constraints, IEEE Access, 2019, 6(7): 84591-84601.   DOI: 10.1109/ACCESS.2019.2924435. IF: 4.098 A+ SCI 三区

[19]    Chunmei Yang, Mengping Huang, Shupan Li, jianqiang Chen, Yao Yang, Na Qin, Deqing Huang* and Jian Shu*, Radiomics  model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma, Cancer Letters,2019,11(470): 1-7.   IF:6.508   SCI 一区

[20]    Changhe Huang, Na Qin, Deqing Huang*, Kaiwei LiangConvolutional Neural Network for Fault Diagnosis of High-Speed Train BogieThe 38th Chinese Control Conference, Guangzhou, China, 2019-07.  B+  EI会议 

[21]    Yao Yang, Na Qin, Deqing Huang*, Awais Shah, Label number Recognition Based on Convolutional Neural Networks in industrial products, 5th IFAC Symposium on Telematics Applications, Chengdu, China, 2019  会议

[22]    Yuanjie Zhang, Na Qin, Deqing Huang*, Kaiwei Liang, Fault Diagnosis of High-speed Train Bogie Based on Deep Neural Network, 5th IFAC Symposium on Telematics Applications, Chengdu, China, 2019  会议

[23]    Ndabarushimana Egone, Lei Ma*,QIN Na. Faults detection and identification based on robust residual generation for a single-phase PWM rectifier. 3rd IEEE Conference on Energy Internet and Energy system Integration, Changsha, China, Nov8, 2019.    EI会议

[24]    Yong Yang, Lei Ma, Deqing Huang, Na Qin*, Output Feedback Repetitive Learning Control of an Electrohydraulic Actuator of a Lower Limb Rehabilitation Exoskeleton, Computing in Science & Engineering, 2018,1(21):6-19. DOI: 10.1109/MCSE.2018.110150902  IF: 1.879 A SCI 四区

[25]    Tao Yu, Lei Ma, Na Qin*, Adaptive Cooperative Tracking Control of Multi-Agent Systems With Unknown Actuators Hysteresis, IEEE Access, 2018,6(6): 33015-33028.    DOI: 10.1109/ACCESS.2018.2845387  IF:4.098 A+   SCI 三区

[26]    Kaiwei Liang , Na Qin, Deqing Huang *, Yuanzhe Fu , Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie, Compexity, 2018(5):1-13.     DOI: 10.1155/2018/4501952,2018.  IF:2.591 A   SCI 二区

[27]    Yuanzhe Fu, Deqing Huang, Na Qin*, Kaiwei Liang, Yang Yang. High-Speed Railway Bogie Fault Diagnosis Using LSTM Neural Network. The 37th Chinese Control Conference, Wuhan, China, July 2018-07  B+  EI会议

[28]    Kaiwei Liang, Na Qin, Deqing Huang*,Lei Ma. 1D Convolutional Neural Networks For Fault Diagnosis of High-speed Train Bogie, The 23thInternational Conference on Digital Signal Processing, Shanghai, China, Nov.2018 EI会议

[29]    Liyuan Su, Lei Ma, Na Qin*, etc. Fault Diagnosis of High-speed Train Bogie Based on Spectrogram and Multi-channel Voting. DDCLS2018, Enshi, China, May 2018. EI会议

[30]    Yang Yang, Na Qin, Deqing Huang*, Yuanzhe Fu. Fault Diagnosis of High-speed Railway Bogies Based on LSTM2018 International Conference on Information, Cybernetics, and Computational Social SystemsHangzhou, China, August 2018  会议

[31]    Awais Shah, Deqing Huang, Yixing Chen, Xin Kang and Na Qin*. Robust Sliding Mode Control of Air Handling Unit for Energy Efficiency Enhancement. Energies. 2017,11(10):1815.  DOI10.3390/en10111815  IF:2.707 A+  SCI 二区

[32]    冯玎,林圣,张奥,孙小军,秦娜*. 基于连续时间马尔可夫退化过程的牵引供电设备可靠性预测方法研究. 电机工程学报,2017-04 , 37 (7) :1937-1945  A+  EI期刊

[33]    Na Qin*, Yongkui Sun, Pengju Gu, Lei Ma. Bogie Fault Identification Based on EEMD Information Entropy and Manifold Learning. The 20th World Congress of the International Federation of Automatic Control, Toules, France, 2017, 50(1):315-318 A  EI会议

[34]    Daochao Tang, Na Qin*, Weidong Jin. Fault Diagnosis of Single Yaw Damper Utilizing Hierarchical Multi-class Classifier. Journal of Computers , 2017,02(28) 5:94-104  EI期刊

[35]    Qin Na, Yongkui Sun, Lv Qian Yong, Weidong Jin. Deterioration Rate Estimation of High Speed Train Bogie Unit Based on Copula Function. The 35th Chinese Control Conference, Chengdu, China, 2016-07. B+   EI会议

[36]    Sun Yongkui, Qin Na*, Yu Zhibin, Jin Weidong. An approach to recognize air spring failure of high-speed trains using Lipschitz exponents. The 35th Chinese Control Conference, Chengdu, China, 2016-07. B+   EI会议

[37]    秦娜*,蒋鹏,孙永奎,金炜东.基于EEMD排列熵的高速列车转向架故障特征分析[J].振动.测试与诊断,2015-10,35(05):885-891+991.. A EI

[38]  秦娜*, 金炜东, 黄进等. 高速列车转向架故障信号的聚合经验模态分解和模糊熵特征分析[J]. 控制理论与应用, 2014-09,31(09):1245-1251. EI

[39]  秦娜*,王开云,金炜东,黄 进,孙永奎. 高速列车转向架故障的经验模态熵特征分析. 交通运输工程学报,2014-02,14(1):57-64. EI

[40]  秦娜*,金炜东,黄进,李智敏,刘景波. 基于EEMD样本熵的高速列车转向架故障特征提取. 西南交通大学学报,2014-02,49(1):1-6 EI

[41]  秦娜*,金炜东,黄 进,苟先太,蒋 鹏. 高速列车转向架故障信号的小波熵特征分析. 计算机应用研究,2013-12,30(12):3657-3663  

[42]  秦娜*,金炜东,黄 进,李智敏,刘景波. 基于EEMD的高速列车转向架故障诊断. 计算机工程,2013-12,39(12).pp:1-4 .. EI

[43]    Na QinWeidong JinJin HuangPeng JiangZhimin Li. High-speed Train Bogie Fault Signal Analysis Based on Wavelet Entropy Feature. 3rd International Conference on Advanced Engineering Materials and Technology, 2013, Zhangjiajie,China   EI会议