自动化学院
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常雷雷
上传时间:2022-09-15 浏览次数:297

一、导师照片

 

二、基本信息

常雷雷  Leilei Chang副研究员

所属学院: 自动化学院(人工智能学院)

导师类别 硕士生导师

科研方向: 控制科学与工程(置信规则库、证据推理,及在数据驱动下的复杂系统建模、风险与安全性评估、控制、预测以及在多个实际问题中的应用等)

硕士招生学院:自动化学院(人工智能学院)

联系方式leileichang@hotmail.com(欢迎有兴趣的同学主动邮件联系)

 

三、教育和学术经历 

教育经历:

2011/1 - 2014/6,国防科学技术大学,管理科学与工程专业,博士,导师:李孟军教授;

2008/9 - 2010/12,国防科学技术大学,技术经济及管理专业,硕士,导师:李孟军教授;

2004/9 - 2008/7,中南大学,交通运输专业,学士。

科研与学术工作经历:

2014/7  2021.2,火箭军工程大学作战保障学院,讲师

2021/6 至今,  杭州电子科技大学,副研究员

 

三、学术论文

(一)代表性论文(仅列出一作和通讯)

[1]          Chang L L, Zhang L M*, Xu X B, Multi-dimensional Cloud-based Belief Rule Base Approach for Complex System Modeling, Expert Systems With Applications, 202, accepted, DOI: XXXX. (IF: 6.954, JCR Q1)

[2]          Chang L L, Fu C*, Wu Z J, Liu W Y, Yang S L, A data-driven method using BRB with data reliability and expert knowledge for complex systems modeling, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2021, DOI: 10.1109/TSMC.2021.3095524. (IF: 13.451, JCR Q1)

[3]          Chang L L, Zhang L M*, Explainable Data-driven Optimization for Complex Systems with Non-Preferential Multiple Outputs using Belief Rule Base, Applied Soft Computing, 2021, 110, 107581. (IF: 6.725, JCR Q1)

[4]          Chang L L, Zhang L M*, Fu C, Chen Y W, Transparent digital twin for output control using the belief rule base, IEEE Transactions on Cybernetics, 2021, DOI: 10.1109/TCYB.2021.3063285. (IF: 11.448, JCR Q1)

[5]          Chang L L, Zhang L M, Xu X J, Correlation-oriented Complex System Structural Risk Asses**ent using Copula and Belief Rule Base, Information Sciences, 2021, 564: 220-236. (IF: 6.795, JCR Q1)

[6]          Chang L L, Xu X J, Liu Z G, Qian B, Xu X B, Chen Y W, BRB Prediction with Customized Attributes Weights and Tradeoff Analysis for Concurrent Fault Diagnosis under Uncertainty, IEEE Systems Journal, 2021, 15(1) 1179-1190. (IF: 3.931, JCR Q1)

[7]          Chang L L, Fu C*, Wu Z J, Liu W Y, Yang S L, Data-driven ****ysis of radiologists’ behavior for diagnosing thyroid nodules, IEEE Journal of Biomedical and Health Informatics, 2020, 24 (11) 3111-3123. (IF: 5.722, JCR Q1)

[8]          Chang L L, Fu C*, Zhu W, Liu W Y, Belief Rule Mining Using the Evidential Reasoning Rule for Medical Diagnosis, International Journal of Approximate Reasoning, 2020, 130, 273-291. (IF: 2.678, JCR Q1)

[9]          Chang L L, Dong W, Yang J B, Sun X Y, Xu X B, Xu X J, Zhang L M*, Hybrid Belief Rule Base for Regional Railway Safety Asses**ent with Data and Knowledge under Uncertainty, Information Sciences, 2020, 518, 376-395. (IF: 6.795, JCR Q1)

[10]       Chang L L, Zhou Z J, Liao H, Chen Y W, Tan X, Herrera F, Generic disjunctive belief rule base modeling, inferencing, and optimization, IEEE Transactions on Fuzzy Systems, 2019, 476: 1866-1880. (IF: 12.029, JCR Q1)

[11]       Chang L L, Chen Y W, Hao Z Y, Zhou Z J, Xu X B, Xu X J, Tan, X, Indirect Disjunctive Belief Rule Base Modeling using limited Conjunctive Rules: Two Possible Means, International Journal of Approximate Reasoning, 2019, 108: 1-20. (IF: 2.678, JCR Q1)

[12]       Chang L L, Jiang J, Sun J B, Chen Y W, Zhou Z J, Xu X B, Tan X*, Disjunctive belief rule base spreading for threat level asses**ent with heterogeneous, insufficient, and missing information, Information Sciences, 2019, 476: 106-131. (IF: 6.795, JCR Q1)

[13]       Chang L L*, Zhou Z J, Chen Y W, Liao T J, Hu Y, Yang L H, Belief rule base structure and parameter joint optimization under disjunctive assumption for nonlinear complex system modeling, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2018, 48 (9): 1542-1554. (IF: 13.451, JCR Q1)

[14]       Chang L L, Zhou Z J, Chen Y W, Xu X B, Sun J B, Liao T J, Tan X*, Akaike Information Criterion-based Conjunctive Belief Rule Base Learning for Complex System Modeling, Knowledge-Based Systems, 2018,161 47-64. (IF: 8.083, JCR Q1)

[15]       Chang L L*, Zhou Z J, You Y, Yang L H, Zhou Z G, Belief rule based expert system for classification problems with new rule activation and weight calculation procedures, Information Sciences, 2016, 335: 75-91. (IF: 6.795, JCR Q1)

[16]       Chang L L*, Sun J B, Jiang J, Li M J, Parameter learning for the belief rule base system in the residual life probability prediction of metalized film capacitor. Knowledge-Based Systems, 2015, 73: 69-80. (IF: 8.083, JCR Q1)

[17]       Chang L L*, Zhou Y, Jiang J, Li M J, Zhang X H, Structure learning for belief rule base expert system: a comparative study, Knowledge-Based Systems, 2013, 39: 159-172. (IF: 8.083, JCR Q1)

[18]       Chang L L*, Li M J, Jiang J, A variable weight approach for evidential reasoning, Journal of Central South University, 2013, 20: 2202-2211. (IF: 1.716, JCR Q2)

[19]       Chang L L, Li M J, Cheng B, Zeng P, Integration-centric approach to system readiness asses**ent based on evidential reasoning. Journal of Systems Engineering and Electronics, 2013, 23(6): 881-890. (IF: 1.186, JCR Q3)

[20]       Jiang J#, Chang L L#, Zhang L M, Xu X J, Retraceable and online multi-objective active optimal control using belief rule base, Knowledge-Based Systems, 2021, 233: 107553. (IF: 8.083, JCR Q1)

[21]       Zhu W, Chang L L*, Sun J B, Wu G H, Xu X B, Xu X J, Parallel Multi-Population Optimization for Belief Rule Base Learning, Information Sciences, 2021, 556, 436-458. (IF: 6.795, JCR Q1)

[22]       Zhou Y, Chang L L*, Qian B, A belief rule based model for information fusion with insufficient multi-sensor data and domain knowledge using evolutionary algorithms with operator recommendations, Soft Computing, 2019, 23 (13), 5129-5142. (IF: 3.643, JCR Q2)

[23]       Tan X, Chang L L*, Chen Y W, Hao Z Y, Wu G H, Cooperative and distributed multi-objective optimization for belief rule base, IEEE Systems Journal, 2022, 16(1), 777-788. (IF: 3.931, JCR Q1)

[24]       Chao Fu, Qianqian Zhan, Chang L L*, Wenyou Liu, Shanlin Yang, Multi-criteria appraisal recommendation, Journal of the Operational Research Society, 2021, DOI: 10.1080/01605682.2021.2023674. (IF: 2.860, JCR Q3)

[25]       常雷雷,徐晓滨,徐晓健,基于主导从属框架的变结构置信规则库多目标优化方法,系统工程理论与实践2022, 42(2): 514-526.

[26]       常雷雷,李孟军*,鲁延京,程贲,张晓航,基于主成分分析的置信规则库结构学习方法,系统工程理论与实践2014, 34(5): 1297-1304.

[27]       常雷雷,李孟军*,项成安,证据推理方法中不完备信息影响因素分析,国防科技大学学报2013, 35(1): 175-179.

[28]       孙建彬,常雷雷*,谭跃进,姜江,周志杰,基于双层模型的置信规则库参数与结构联合优化方法,系统工程理论与实践201838(4): 983-993.

[29]       雷杰,徐晓滨,徐晓健,常雷雷*基于置信规则库的并发故障诊断方法系统工程与电子技术202042(2): 497-504.

[30]       王小燕,孙建彬,赵青松,常雷雷*,不完备信息条件下基于置信规则库的能力满足度评估方法,系统工程与电子技术201941(11)2507-2513.

[31]       徐晓滨,朱伟,徐晓健,侯平智,常雷雷*,基于平行多种群与冗余基因策略的置信规则库优化方法,自动化学报2019,已录用

[32]       韩润繁,陈桂明,常雷雷*,凌晓东,基于置信规则库的海基系统性能退化机理分析与预测,控制与决策2019, 34 (3): 470-478.

(二)专著

[1]          常雷雷,孙建彬,徐晓滨,徐晓健,侯平智,并集置信规则库建模、优化及应用,科学出版社,2021.10.

[2]          常雷雷,李孟军,汪刘应,姜江,周志杰,装备技术体系设计与评估,科学出版社,2018.5.

[3]          周志杰,陈玉旺,胡昌华,张邦成,常雷雷,证据推理、置信规则库与复杂系统建模,科学出版社,2017.2.

[4]          姜江,陈英武,常雷雷, 证据网络推理学习理论及应用, 科学出版社, 2013.10.

(三)译著

[1]          常雷雷,汪刘应,周宇,何其芳,大数据与智能计算,国防工业出版社, 2017.5.

[2]          陈桂明,徐建国,李博,常雷雷,复杂系统中大数据分析与实践,国防工业出版社,2018.8.

四、学术论文

[1]          电子信息系统复杂电磁环境效应国家重点实验室开放基金,2022K0302B,电子信息系统可解释可追溯效能评估与提升技术,2021.10-2023.0910万元,在研,主持;

[2]          国家自然科学基金青年基金,71601180,基于置信规则库最优决策结构的装备体系保障性评估方法,2017/1-2019/1218万元,已结题,主持;

[3]          军委装备发展部国防科技重点实验室基金,9140C89020416,武器装备体系成熟度预测与发展规划方案技术风险评估研究,2017/07-2019/0740万元,主持,已结题;

[4]          横向课题(中电十四所),太空态势感知装备体系贡献率评估方法研究,2017/05-2017/1215万元,主持,已结题;

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