Name： Hui Liu
Professional Title：Associate Professor
Research Interests： Intelligent sprayer and controller, Power quality analysis, Machine learning, Robtics vision
Agricultural robot, Biomedical signal process
E-mail：amity@ujs.edu.cn
Research Projects
[1]Hui Liu, etc. “Development of unmanned precision variable-rate sprayer and research on its pesticide reduction application”，funded by Jiangsu Provincial Key Research and Development Program (Grant No. BE2018372).
[2]Hui Liu, etc. “AgriRobot: autonomous agriculture robot system for precision spraying”，funded by Jiangsu International Science and Technology Cooperation Project (Grant No. BZ2017067).
[3]Hui Liu, etc. “Study of a laser sensor-guided variable-rate sprayer to match various canopy characteristics under field environment for orchard spray applications” funded by National Natural Science Foundation of China (Grant No. 51505195).
[4]Hui Liu, etc. “Study of automatic target detection and intelligent spray system” funded by National Science Foundation for Post-doctoral Scientists of China (Grant No. 2014M550272).
[5]Hui Liu, etc. “Study of laser sensor-guided variable-rate sprayers using multi-information integrated decision methods” funded by Natural Science Foundation of Jiangsu Province, China (Grant No. BK20130501 ).
Selected Publications
[1]Hui Liu, Fida Hussain, Yue Shen. Power quality disturbances classification using compressive sensing and maximum likelihood. IETE Technical Review. 2018, 35(4):359-368.
[2]Hui Liu, Fida Hussain, Yue Shen, Sheeraz Arif, Aamir Nazir and Muhammad Abubakar, Complex power quality disturbances classification via curvelet transform and deep learning. Electric Power Systems Research, 2018,163,Part A, 1-9.
[3]Liu, H., Zhu, H. Evaluation of a laser scanning sensor in detection of complex-shaped targets for variable-rate sprayer development [J]. Transactions of the ASABE, 2016, 57(5):1181-1192.
[4]Liu, H., Zhu, H., Shen, Y., Ozkan, H.E. Development of digital flow control system for multi-channel variable-rate sprayers. Transactions of the ASABE. 2014, 57(1): 273-281.
[5]Liu H, Hussain F, Yue S, etc. Classification of multiple power quality events via compressed deep learning. Int Trans Electr Energ Syst. 2019. https://doi.org/10.1002/2050-7038.12010.
