邵小锐
发布时间： 2025-04-24 | 查看数：11
个人基本情况介绍：
邵小锐，1994年出生， 2022年于韩国釜庆大学获得工学博士学位，2022年3月至2025年2月在韩国釜庆大学担任研究教授、副研究工程师。2025年3月加入贵州大学计算机学院, 校聘副教授，硕士生导师，特岗教授。担任TCYB,RESS,ESWA,EAAI,AIR等20余个SCI期刊的审稿人。
招生方向：深度学习、人工智能、时序特征挖掘、故障识别、调度优化、数值大模型等。
招生要求：对科学研究具有浓厚兴趣、有较强的团队合作意识、勤奋诚实、态度端正
研究领域与方向：
长期致力于AI驱动的智能制造，主要研究方向包括：故障识别、时间序列预测、小样本学习、强化学习、量子计算等。
科研课题（2024年以来主要承担的科研项目）：
[1] 贵州大学自然科学类专项（特岗）科研基金项目，2025.03-2029.03，40万，主持。
学术论著(2020年以来发表的论文、专著)：
[1] Xiaorui Shao, Chang-Soo Kim, “TFFS: A trainable federal fusion strategy for multistep time series forecasting”, Information Sciences, Volume 679, 2024, 121126, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2024.121126. [中科院一区Top]
[2] Xiaorui Shao and Chang-Soo Kim， “Adaptive Multi-scale Attention Convolution Neural Network for Cross-Domain Fault Diagnosis”. Expert system With Applications, Volume 236, 2024, 121216.（中科院一区Top）
[3] Xiaorui Shao, Ahyoung Lee and Chang-Soo Kim， “DL-MSCNN: A General and Lightweight Framework for Fault Diagnosis with Limited Training Samples”. Journal of intelligent manufacturing, https://doi.org/10.1007/s10845-023-02217-x. （中科院一区Top）
[4] Xiaorui Shao, De Li, Ilkyeun Ra, and Chang-Soo Kim, “DSMT-1DCNN: Densely Supervised Multitask 1DCNN for Fault Diagnosis”, Knowledge-Based Systems,https://doi.org/10.1016/j.knosys.2024.111609. (中科院一区Top).
[5] Xiaorui Shao and C. S. kim. GAILS: An Effective Multi-Object Job Shop Scheduler Based on Gene Algorithm and Iterative Local Search”. Scientific Reports, (中科院二区）
[6] Xiaorui Shao, Chang Soo Kim, and Dae Geun Kim “Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory”. Computers, Materials and Continue 65(1): 545-561. (中科院二区)
[7] Xiaorui Shao and Chang-Soo Kim，“ Accurate Multi-Site Daily-Ahead Multi-Step PM2.5 Concentrations Forecasting Using Space-Shared CNN-LSTM.”,. Computers, Materials and Continue, vol. 70, no.3, pp. 5143–5160, 2022. (中科院二区)
[8] Xiaorui Shao and Chang-Soo Kim，“Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM with Time Location Considering Customer Behavior”. IEEE ACCESS.
[9] Xiaorui Shao, Chen Pu, Yuxin Zhang, and Chang-Soo Kim，“Doman Fusion CNN-LSTM for Short-Term Power Consumption Forecasting”, IEEE ACCESS, vol. 8, pp. 125263-125273, 2020.
[10] Xiaorui Shao, Chnag Soo Kim, and Palash Sontakke. “Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN-LSTM”, Energies 2020, 13, 1881. https://doi.org/10.3390/en13081881
[11]Xiaorui Shao, Lijiang Wang, Chang Soo Kim, Ilkyeun Ra.“Fault Diagnosis of Bearing based on convolution neural network using multi-domain features”. KSII Transactions on Internet and Information Systems.
[12] Xiaorui Shao and Chang-Soo Kim. “Self-Supervised Long-Short Term Memory Network for Solving Complex Job Shop Scheduling Problem,” KSII Transactions on Internet and Information Systems, vol. 15, no. 5, pp. 1610-1629, 2021. DOI: 10.3837/tiis.2021.05.002.
[13] Xiaorui Shao and Chang-Soo Kim, “Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing”. Sensors 2022, 22, 4156. https://doi.org/10.3390/s22114156.
[14] Xiaorui Shao and C. S. Kim, "An Adaptive Job Shop Scheduler Using Multilevel Convolutional Neural Network and Iterative Local Search,", IEEE Access, vol. 10, pp. 88079-88092, 2022, doi: 10.1109/ACCESS.2022.3188765.
[15] Xiaorui Shao and C. S. kim. “A Novel and Effective University Course Scheduler Using Adaptive Parallel Tabu Search and Simulated Annealing”, KSII Transactions on Internet and Information Systems. vol. 18, no. 4, pp. 843-859, 2024. DOI: 10.3837/tiis.2024.04.002.
发明专利及获奖情况（2019年以来）：
ICONI2019、2020 Outstanding paper rewards.
学术兼职及荣誉称号：
无
教学格言：
认真对待每一个学生，每一堂课。
电子邮件：