Qu Jianfeng, Associate Professor of Chongqing University, China

He received his Ph.D in control theory and control engineering from Chongqing University in 2009. He is currently associate professor and doctoral supervisor of School of automation, Chongqing University.

He mainly studies the weak signal detection, intelligent control and fault diagnosis, signal processing and intelligent control, Intelligent fault diagnosis. His interests include intelligent detection and control systems, autonomous device.

He has made original contributions to intelligent detection and fault diagnosis. He has received more than 8 scientific programs and his works (over 30 papers) are published in famous journals.

He has more than 10 medium-sized intelligent detection and control systems and autonomous device development experience, he also owns 20 invention patents, some of which have been transferred through technology. He also won 8 science and technology awards.

Contact Information

Telephone: +86-23-65112173

Fax: +86-23-65112173

E-mail:qujianfeng@cqu.edu.cn

Address: Room 1901, Main Building, Campus A, Chongqing University

No.174, Shapingba Centre Street, Shapingba District, Chongqing City 400044, P.R.China

Education

Ph.D., (Major in Control Theory and Control Engineering), Chongqing University, Chongqing, P.R.China, 2009.

B.S., (Major in Automation), Chongqing University, Chongqing, P.R.China, 2000.

Academic Experience

Associate Professor, 1/2016-present, Chongqing University, Chongqing, P.R.China.

Lecturer, 3/2010-12/2015, Chongqing University, Chongqing, P. R. China.

Research Interests

weak signal detection and processing

Intelligent fault diagnosis

Autonomous device

Publications

Part I- Referred Journal Papers (selected)

[1] Zhong T, J Qu,  Fang X , et al. The Intermittent Fault Diagnosis of Analog Circuits Based on EEMD-DBN[J]. Neurocomputing, 2021.

[2] Wang Z , Qu J , Fang X , et al. Prediction of early stabilization time of electrolytic capacitor based on ARIMA-Bi_LSTM hybrid model[J]. Neurocomputing, 2020, 403.

[3] Ren H , Qu J F , Chai Y , et al. Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8):1345-1358.

[4] Ren H , Chai Y , Qu J , et al. A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system[J]. NEUROCOMPUTING, 2017:S0925231217317149.

[5] Hao R , Qu J , Yi C , et al. Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals[J]. APPLIED SCIENCES-BASEL, 2018, 8(9):1528.

Part II- Conference Papers

Over 10 Conference papers including Chinese Conference on Control and Decision (CCDC) , Chinese Control Conference (CCC), etc.

Patents

Hold over 20 patents

Research Grants

Received over 10 research grants/awards