性别：男
职称：工学博士
毕业院校：2020年于东南大学自动化学院获得控制科学与工程博士学位。同年加入上海海事大学信息工程学院。
联系方式：信工楼405，上海海事大学，上海市浦东新区海港大道1550号
邮箱：yldong@shmtu.edu.cn
教学情况：《人工智能导论》（2020-2021学年2学期）
研究方向：不确定性推理，信息融合，机器学习，目标识别(行为识别)
科研项目：
[1] 参与国家自然科学基金(面上)项目“面向大数据的DSmT近似推理及其目标识别应用研究”, 项目编号(**);
[2] 参与开放课题“面向复杂工业过程大数据的多源信息融合实时监测与故障诊断研究”(基金号: PAL-N201704);
[3] 参与企事业委托项目 “XXX目标识别”;
[4] 参与企事业委托项目 “XXX 识别软件库”.
主要发表论文：
[1] Yilin Dong, Xinde Li, Jean Dezert, Md Noor-A-Radim, Mohamad Omar Khyam and Shuzhi Sam Ge, Dezert-Smarandache Theory-based fusion for human activity recognition in body sensor networks, IEEE Transactions on Industrial Informatics, 2020, 16(11): 7138-7149;
[2] Yilin Dong, Xinde Li, Jean Dezert and Shuzhi Sam Ge, A novel multi-criteria discounting combination approach for multi-sensor fusion[J], IEEE Sensors Journal, 2019, 19(20):9411-9421;
[3] Yilin Dong, Xinde Li and Yihai Liu, A fast combination method in DSmT and its application to recommender system[J], PLoS ONE, 2018, 13(1): 3214-3239;
[4] Xinde Li, Yilin Dong, Chaomin Luo, A method of aircraft target recognition based on LLE and HMM[J], International Journal of Robotics and Automation, 32(2), 2017;
[5] Yilin Dong, Xinde Li and Jean Dezert, Combination of sources of evidence with distinct frames of discernment[C], 2018 21st International Conference on Information Fusion (FUSION 2018), 2018: 2513-2520;
[6] Yilin Dong, Xinde Li and Jean Dezert, Rough set classifier based on DSmT, 21st International Conference on Information Fusion (FUSION 2018), 2018: 2497-2504;
[7] Yilin Dong, Xinde Li and Jean Dezert, A hierarchical flexible coarsening method to combine BBAs in probabilities[C], 20th International Conference on Information Fusion(FUSION 2017), 2017:95-105;
[8] Yilin Dong, Xinde Li and Jean Dezert, A new probabilistic transformation based on evolutionary algorithm for decision making[C], 20th International Conference on Information Fusion(FUSION 2017), 2017:1434-11441;
[9] Yilin Dong, Xinde Li, DSmT-Based Fusion System for Fault Detection and Identification in Industrial Process, 1st International Conference on Industrial Artificial Intelligence (IAI 2019), 2019: 793-800.
