姓    名：杨柯

性    别：女

出生年月：1987.08

职称/职务：讲师

学位/学历：博士

联系方式：k_yang@whut.edu.cn

教育经历：

2019.11-2020.11，加州大学洛杉矶分校，B.John Garrick风险研究所，国家公派联合培养博士

2017.09-2021.6，武汉理工大学，交通运输工程系，博士

2015.09-2017.6，华中科技大学，工程管理系，硕士

2006.09-2010.6，武汉理工大学，材料成型及控制工程，学士

工作简历：

2021.12-今，武汉理工大学，交通与物流工程学院，讲师

    研究方向：

    1.状态监控与智能故障预警预测；

    2.基于大数据的设备维修智能化；

    3.以可靠性为导向的设备健康管理；

    4.人工智能算法的研究与应用

    主要教学科研成果：

[1]Yang Ke, Liu Yiliu, Yao Yunan, Fan Shidong, Ali Mosleh. Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience [J]. Journal of Manufacturing Systems, 2021,61:746-756.

[2]Yang Ke, Yang Taiwei, Yao Yunan, Fan Shidong. A transfer learning-based convolutional neural network and its novel application in ship spare-parts classification [J]. Ocean & Coastal Management,2021,215:105971.

[3]Yang Ke, Wang Yongjian, Yao Yunan, Fan Shidong. Remaining useful life prediction via long-short time memory neural network with novel partial least squares and genetic algorithm [J]. Quality and Reliability Engineering International, 2021,37(3):1080-1098.

[4]Yang Ke, Yuan Junlang, Xiong Ting, Wang Bin, Fan Shidong. A novel principal component analysis integrating long short-term memory network and its application in productivity prediction of cutter suction dredgers [J]. Applied Sciences, 2021, 11(17), 8159.

[5]Yang Ke, Wang Yongjian, Fan Shidong, Ali Mosleh. Multi-criteria spare parts classification using the deep convolutional neural network method [J]. Applied Sciences, 2021, 11(17), 7088.

[6] Wang Yongjian, Yang Ke, Li Hongguang. Industrial time-series modeling via adapted receptive field temporal convolution networks integrating regularly updated multi-region operations based on PCA [J]. Chemical Engineering Science, 2020, 228:115956.

[7]杨柯，范世东.基于长短期记忆网络时序数据趋势预测及应用[J].推进技术，2021,42（3）：675-682.

[8]Yang Ke, Yao Yunan, Fan Shidong. Research on Maintenance Support System Based on Complex Network [C]. In proceedings of the 7th International Conference on Traffic and Logistic Engineering (ICTLE 2019). Aug. 21-23, 2019. Paris, France.

主要科研项目：

1. 高技术船舶科研项目智能船舶1.0研发专项---设备运行与维护智能系统项目（工信部联装函[2016]544），参与；国家工信部项目，2017.01-2019.12。

2. 绞吸式挖泥船数字化切削系统时变模型及可视化研究（编号：5207124），主要参与；国家自然科学基金面上项目，2021.01-2024.12。