常雷雷

欢迎同行交流。

对于研究生同学：

1，欢迎有一定计算机（必须）、数学（最好）和英语（不强求）基础的同学联系。

2，不要求任何专业基础。

3，全力支持同学申请国家奖学金、支持在完成科研任务得的前提下开展实习。

4，目前主要开展理论研究工作，非硬件。

5，想混过研究生阶段的同学不要联系。

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，管理科学与工程专业，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。

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

数据驱动下的复杂系统建模、评估与优化、多属性决策、信息融合与处理、智能医疗诊断与决策

[1]       横向课题，基于神经网络的XXXX评估方法研究，2022.10-2024.10，主持；

[2]       横向课题，XXXXXXX评测模块开发，2024.6-2024.12，主持.

[1]       国家自然科学基金面上项目，72471067，面向非周期复杂系统的风险评估与主动防护方法及应用，2025/1-2028/12，40万元，在研，主持；

[2]       浙江省属高校基本科研业务费项目：GK239909299001-010，基于透明置信推理的智能采掘安全性评估与主动安全控制，2023.01-2024.12，20万元，在研，主持；

[3]       浙江省基础公益研究计划项目：LTGG23F030003，基于风险溯源的危化企业安全评估与提升技术与系统研发，2023.01-2025.12，10万元，在研，主持；

[4]       173技术基础项目，2023-JCJQ-JJ-0746，不完备数据条件下导弹XX预测技术，2024.01-2024.12，80万元，在研，合作单位主持（15万元）；

[5]       国家卫生健康委员会科研基金，WKJ-ZJ-2435，胃癌筛查与精准诊断新技术研发与应用示范，2024.1-2026.12，15万元，在研，合作单位主持；

[6]       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，2021.10-2023.09，10万元，已结题，主持；

[7]       国家重点研发计划项目，2022YFE0210700，中国-奥地利人工智能与先进制造“一带一路”联合实验室建设与智能物联网关键技术联合研究，498万元，参与，在研。

[8]       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，2021.10-2023.09，10万元，主持，已结题；

[9]       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，2017/1-2019/12，18万元，主持，已结题；

[10]       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，2017/07-2019/07，40万元，主持，已结题；

[11]       国家自然科学基金应急管理项目，61751304，不确定小样本环境下优化决策规则的提取与深度学习，2018/01-2021/12，260万元，参与，已结题；

[12]       国家自然科学基金面上项目，61773388，幂集辨识框架下置信规则库建模方法及在导弹武器故障预测中的应用，2018/01-2021/12，65万元，参与，已结题；

[13]       国家自然科学基金青年基金，71201168，证据网络推理、学习方法及应用研究，2013/01-2015/12，19万元，参与，已结题；

[1]       Chang L L, Yu C H（研究生）, Zhang L M*, Xu X B, Dustdar S, Safety assessment of tunnel construction based on counterintuitivity detection using multi-profile multi-model ensemble learning, Expert Systems With Applications, 2024, 240: 122459. 

[2]       Chang L L, Zhang L M*, Modified boosting and bagging for building tilt rate prediction in tunnel construction, Automation in Construction, 2023, 155: 105059. 

[3]       Chang L L, Zhang L M*, Xu X B, Causality-based multi-model ensemble learning for safety assessment in metro tunnel construction, Reliability Engineering & System Safety, 2023, 234: 109168.

[4]       Chang L L, Liu H（研究生）, Zhang L M*, Xu X B, Adaptive learning for single-output complex systems via data augmentation and data type identification, Applied Soft Computing, 2023, 132: 109895. 

[5]       Chang L L, Fu C*, Wu Z J, Liu W Y, A data-driven method using BRB with data reliability and expert knowledge for complex systems modeling, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2022, 52(11): 6729-6743.

[6]       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, 2022, 52(10): 10364-10378.

[7]       Chang L L, Zhang L M*, Chang W J, Xu X B, Safety-oriented credibility-based fuzzy incremental learning for dependent outputs prediction, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2023, 53(1): 380-393.

[8]       Chang L L, Song X T（研究生）, Zhang L M*, Uncertainty-oriented reliability and risk-based output control for complex systems with compatibility considerations, Information Sciences, 2022, 606: 512-530.

[9]       Chang L L, Zhang L M*, Xu X B, Randomness-oriented multi-dimensional cloud-based belief rule base approach for complex system modeling, Expert Systems With Applications, 2022, 203: 117283. 

[10]    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. 

[11]    Chang L L, Zhang L M, Xu X J, Correlation-oriented complex system structural risk assessment using copula and belief rule base, Information Sciences, 2021, 564: 220-236.

[12]    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. 

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

[14]    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.

[15]    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 assessment with data and knowledge under uncertainty, Information Sciences, 2020, 518: 376-395. 

[16]    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. 

[17]    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.

[18]    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 assessment with heterogeneous, insufficient, and missing information, Information Sciences, 2019, 476: 106-131.

[19]    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. 

[20]    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. 

[21]    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. 

[22]    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. 

[23]    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. 

[24]    Chang L L*, Li M J, Jiang J, A variable weight approach for evidential reasoning,Journal of Central South University, 2013, 20: 2202-2211.

[25]    Chang L L, Li M J, Cheng B, Zeng P, Integration-centric approach to system readiness assessment based on evidential reasoning, Journal of Systems Engineering and Electronics, 2013, 23(6): 881-890. 

[26]    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.

[27]    Zhu W（研究生）, Chang L L*, Sun J B, Wu G H, Xu X B, Xu X J, Parallel multipopulation optimization for belief rule base learning, Information Sciences, 2021, 556: 436-458. 

[28]    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. 

[29]    Tan X, Chang L L*, Chen Y W, Hao Z Y, Wu G H, Cooperative and distributed multiobjective optimization for heterogeneous belief rule base, IEEE Systems Journal, 2022, 16(1): 777-788. 

[30]    Fu C, Zhan Q S, Chang L L*, Liu W Y, Yang S L, Multi-criteria appraisal recommendation, Journal of the Operational Research Society, 2023, 74(1): 81-92. 

[31]    Li X, Jiang J, Sun J B, Yu H Y, Chang L L*, Accountable capability improvement based on interpretable capability evaluation using belief rule base. Journal of Systems Engineering and Electronics, 2023, accepted.

[32]    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.

[33]    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.

[34]    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.

[35]    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，2018，38(4): 983-993.

[36]    宋鑫涛，常雷雷*，戴家栋，徐晓滨，徐晓健，面向大型工程多安全指标的解析可追溯安全控制方法，控制与决策，2024，39(1): 95-102.

[37]    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，2020，42(2): 497-504.

[38]    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，2019，41(11)2507-2513.

[39]    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，2022，48(8)，2007-2017.

[40]    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.

[41]    Zhou Z J*, Chang L L, Hu C H, Han X X, Zhou Z G, A new BRB-ER-based model for assessing the lives of products using both failure data and expert knowledge, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2015, 99: 1-15. 

[42]    Fu C, Hou B B, Xue M, Chang L L, Liu W Y, Extended belief rule-based system with accurate rule weights and efficient rule activation for diagnosis of thyroid nodules, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2023, 53(1): 251-263. 

[43]    Xu X B, Guo H H Zhang Z H, Yu S E, Chang L L, Steyskal F, Brunauer G, A cloud model-based interval-valued evidence fusion method and its application in fault diagnosis, Information Sciences, 2024, 658: 119995.

[44]    Cao Y, Tang S W, Yao R Q, Chang L L, Yin X J, Interpretable hierarchical belief rule base expert system for complex system modeling,Measurement, 2024, 236: 114033. 

[45]    Xu X B, Zhang D Q, Bai Y, Chang L L, Li J N, Evidence reasoning rule-based classifier with uncertainty quantification, Information Sciences, 2020, 516: 192-204. 

[46]    Weng X, Xu X B, Chang L L, Hou P, Wang G, Dustdar S, Evidence fusion-based alarm system design considering coarse and fine changes of process variable, Journal of Process Control, 2022, 113: 68-79. 

[47]    Xu X J, Zhao Z Z, Xu X B, Yang J B, Chang L L, Xu X P, Wang G D, Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models, Knowledge-Based Systems, 2020, 190:105324

[48]    Zhou Z J, Hu G Y, Hu C H, Wen C L, Chang L L, A survey of belief rule base expert system, IEEE Transactions on Systems, Man, Cybernetics: Systems, 2019, 51(8): 4944-4958. 

[49]    Sun J B, Huang J, Chang L L, Jiang J, Tan Y J, BRBcast: A new approach to belief rule-based system parameter learning via extended causal strength logic, Information Sciences, 2018, 44: 51-71.

[50]    Yang L H, Wang Y M, Chang L L, Fu Y G, A disjunctive belief rule-based expert system for bridge risk assessment with dynamic parameter optimization model, Computer & Industrial Engineering, 2017, 113: 459-474. 

[51]    Feng Z C, Zhou Z J*, Hu C H, Chang L L, Hu G Y, Zhao F J, A new belief rule base model with attribute reliability, IEEE Transactions on Fuzzy Systems, 2019, 27 (5): 903-916. 

[52]    Wang Y M*, Yang L H, Fu Y G, Chang L L, Chin K S, Dynamic rule adjustment approach for optimizing belief rule-base expert system, Knowledge-based Systems, 2016, 96: 40-60. 

[53]    Zhao F J, Zhou Z J*, Hu C H, Chang L L, Zhou Z G, A new evidential reasoning-based method for online safety assessment of complex systems, IEEE Transaction on Systems, Man, Cybernetics: Systems, 2018, 48(6): 954-966. 

[54]    Li G L, Zhou, Z J, Hu C H, Chang L L, Zhang H T, Yu C Q, An optimal safety assessment model for complex systems considering correlation and redundancy, International Journal of Approximate Reasoning, 2019, 104: 38-56. 

[55]    Li G L, Zhou Z J*, Hu C H, Chang L L, Zhou Z G, A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base,Safety Sciences, 2017, 93: 108-120.

[56]    Li Z C, Qian B, Hu R, Chang L L, Yang J B, An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups, Knowledge-Based Systems, 2019, 173: 83-112. 

[57]    Chang W J, Fu C, Chang L L, Yang S L, Triangular bounded consistency of interval-valued fuzzy preference relations, IEEE Transactions on Fuzzy Systems, 2022, 30(12): 5511-5525. 

一、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.

二、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.

[1]       基于并集信度规则库推理的船用柴油机异常磨损诊断方法，ZL201910211395.2，2019年3月，常雷雷，雷杰，徐晓滨，徐晓健，黄大荣，韩德强，侯平智.

[2]       基于集成学习的复杂工程施工安全评估方法，202310419709.4，2023.07.21，常雷雷，李翔，侯平智，徐晓滨，陶志刚，王嘉，章振杰，冯静.

[3]       一种基于集合运算的兼容多模型输出融合方法，202311521769.3，2023.11.15，常雷雷，余晨浩，徐晓滨，曹友，侯平智，冯静，章振杰，马枫.

[4]       基于特征匹配度和异类子模型融合的安全性评估方法，202311493590.1 ，2023.11.9，常雷雷，张云硕，施凡，徐晓滨，曹友，侯平智，张泽辉，徐晓健，马枫.

[5]       一种基于Bagging改进的矿井内数据预处理方法，202210494493.3，2022.8.5，常雷雷，宋鑫涛，徐晓滨，陶志刚，黄曼，马成荣，李轶，翁旭.

[6]       一种基于AdaBoost算法的复杂采掘工程高韧性安全评估方法，202310882745.4，2023.7.18，施凡，常雷雷，徐晓滨.

[7]       基于随机性修正信度规则系统的螺旋桨卷气效应识别方法，ZL202010186171.3，2020年7月，徐晓滨，雷杰，常雷雷，高海波，马枫.

[8]       基于混合启发式规则系统的铁路运输风险概率计算方法，ZL201811516247.3，2019年4月，徐晓滨，雷杰，常雷雷, 侯平智，胡燕祝，黄大荣.

[9]       基于可调输入LSTM模型的道岔转辙机故障预测方法，202210768550.2，2022.6.30，徐晓滨，董峻，常雷雷，郭豪豪，翁旭，张泽辉，冯静，侯平智.

[10]    基于证据推理规则的缺血性脑卒中程度辨识建模方法，202210394039.0，2022.4.14，刘珂舟，付广玉，徐晓滨，常雷雷，蔡正厅，魏劭农，印梦婕.

[1]       滑坡地质灾害多源协同感知与智能融合预警关键技术与应用，中国商业联合会科技进步特等奖，2023.12，7/12（徐晓滨、陶志刚、黄  曼、侯平智、马成荣、黄  鑫、常雷雷、何满潮、章振杰、张海江、黄永亮、王  嘉）.

[2]       精密机电系统故障诊断与预报技术的研究与应用，中国电子学会科技进步二等奖，2015.12，6/12（张邦成，周志杰，王占礼，高智，尹晓静，常雷雷，韩晓霞，崔高健，张袅娜，李慧，李静，徐兵，庞在祥，马辉，谷东伟）.