朱庆生

人物经历

朱庆生，1978年进入重庆大学计算机专业学习，先后获得学士、硕士、博士学位；大学毕业后一直在重庆大学计算机学院（系）工作。期间，1993至1994年在英国牛津大学和伦敦大学做访问学者，从师于时任英国计算机学会理事长Roger Johnson教授（现任欧洲信息学会主席）；1998年在香港浸会大学计算机系做客座研究员；2001至2002年在美国伊利诺大学芝加哥分校做访问教授，合作导师Carl Chang教授时任IEEE计算机学会主席（他2014年获CCF海外杰出贡献奖）。

主要成就

朱庆生同志主要从事多媒体信息技术、软件方法和重用技术、电子商务等领域的研究。先后承担了国家自然科学基金、国家863、教育部、航天部和重庆市科委下达的20余项部委科研任务。主编教材有《多媒体电脑实用技术》、《信息系统开发方法及原理》、《全国计算机等级考试辅导教材》等，先后在《电子学报》、《计算机辅助设计与图形学学报》等核心刊物和IEEE重要国际会议上发表论文50余篇，其中被SCIE/EI等检索系统收录11篇。曾担任中国西部地区计算机技术学术交流会和全国计算机教学研讨会等全国性会议的程序委员会主席；曾担任The 11 th Joint International Computer Conference ― JICC2005国际会议程序委员会主席（Co-Chair）和 The 5 th Int. Conf. for Young Computer Scientists —ICYCS99和The 3rd Int. Conf. on Wavelet Analysis and Its Applications ― ICWAA2003等国际会议的程序委员会委员。已招收和培养硕士研究生36人，博士生4人。

主持完成国家自然科学基金3项、国家科技支撑计划2项、国家863计划2项、教育部及重庆市省部级科研项目10余项；先后获得原广电部科技进步一等奖、中国国防科技进步二等奖、四川省和重庆市科技进步奖共7项；获权国家发明专利10余项、国家软件著作权6项；合作发表学术论文被SCI收录80余篇。作为大会主席或程序委员会主席承办了IEEE国际会议ITAIC2014、ICISME2012等4个会议，曾担任若干国际会议的程序委员。

主持国家精品课程和国家精品资源共享课程《Java程序设计》教育部-IBM精品课程和重庆市精品课程《Java并行编程实践》，主编国家“十一五”规划教材《Java程序设计》等教材，主持重庆市重大和重点教改项目各1项。已培养毕业博士生32人、毕业硕士生87人。

论文

[1] Cheng Dongdong; Zhu Qingsheng. A Novel Cluster Validity Index Based on Local Cores.

IEEE TRANS ON NEURAL NETWORKS AND LEARNING SYSTEMS

. 2019, 30(4): 985-999

[2] Lu Jianyun; Zhu Qingsheng. A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

. 2018, 72: 213-227

[3] Wu Quanwang; Zhou MengChu; Zhu Qingsheng; Xia Yunni. VCG Auction-Based Dynamic Pricing for Multigranularity Service Composition.

IEEE TRANS ON AUTOMATION SCIENCE AND ENGINEERING

. 2018, 15(2): 796-805

[4] Yang Lijun; Zhu Qingsheng. Natural neighborhood graph-based instance reduction algorithm without parameters,

APPLIED SOFT COMPUTING.

2018, 70: 279-287

[5] Luo Xin; Zhou MengChu; Li Shuai;Xia YunNi;Zhu, QingSheng. Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data.

IEEE TRANS ON CYBERNETICS

. 2018,48(4): 1216-1228

[6] Wu Quanwang; Zhou MengChu; Zhu Qingsheng; Xia, Yunni. Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds.

IEEE TRANS ON PARALLEL AND DISTRIBUTED SYSTEMS

.2018, 28(12): 3401-3412

[7] Lu Jianyun; Zhu Qingsheng. An Effective Algorithm Based on Density Clustering Framework.

IEEE ACCESS.

2017, 5: 4991-5000

[8] Huang Jinlong; Zhu Qingsheng. A novel outlier cluster detection algorithm without top-n parameter.

KNOWLEDGE-BASED SYSTEMS

. 2017, 121: 32-40

[9] Huang Jinlong; Zhu Qingsheng. QCC: a novel clustering algorithm based on Quasi-Cluster Centers.

MACHINE LEARNING.

2017, 106(3): 337-357

[10] Yang Lijun; Zhu Qingsheng. Adaptive edited natural neighbor algorithm.

NEUROCOMPUTING

. 2017, 230: 427-433

[11] Cheng Dongdong; Zhu Qingsheng. Natural neighbor-based clustering algorithm with local representatives.

KNOWLEDGE-BASED SYSTEMS.

2017, 123: 238-253

[12] Wu Quanwang; Ishikawa Fuyuki; Zhu Qingsheng. QoS-Aware Multigranularity Service Composition: Modeling and Optimization.

IEEE TRANS ON SYSTEMS MAN CYBERNETICS -SYSTEMS

2016, 46(11): 1565-1577

[13] Zhu Qingsheng; Feng Ji; Huang Jinlong. Weighted natural neighborhood graph: an adaptive structure for clustering and outlier detection with no neighborhood parameter.

CLUSTER COMPUTING

. 2016, 19(3): 1385-1397

[14] Yang Lijun; Zhu Qingsheng. Natural Neighbor Reduction Algorithm for Instance-based Learning.

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE

. 2016, 10(4): 59-73

[15] Luo Xin; Zhou MengChu; Li Shuai; You Zhuhong; Xia Yunni; Zhu Qingsheng. A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method.

IEEE TRANS ON NEURAL NETWORKS AND LEARNING SYSTEMS

.?2016, 27(3): 579-592

[16] Zhu Qingsheng; Feng Ji; Huang Jinlong. Natural neighbor: A self-adaptive neighborhood method without parameter K.

PATTERN RECOGNITION LETTERS

, 2016, 80: 30-36

[17] Luo Xin; Zhou MengChu; Xia Yunni; Zhu Qingsheng. Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models.

IEEE TRANS ON NEURAL NETWORKS AND LEARNING SYSTEMS

.2016, 27(3): 524-537

[18] Huang Jinlong; Zhu Qingsheng. A Non-parameter Outlier Detection Algorithm based on Natural Neighbor.

KNOWLEDGE-BASED SYSTEMS.

2016, v 92: 71-77

[19] Luo Xin; Zhou MengChu; Leung Hareton; Xia Yunni; Zhu Qingsheng. An Incremental and Static Combined Scheme for Matrix-Factorization-Based Collaborative Filtering.

IEEE TRANS ON AUTOMATION SCIENCE AND ENGINEERING

.2016, 13(1): 333-343

[20] Wu Quanwang; Zhu Qingsheng; Li Peng. A Neural Network based Reputation Bootstrapping Approach for Service Selection.

Enterprise Information Systems.

2015, 9(7): 768-784

[21] Luo Xin; Zhou Mengchu; Li Shuai; Xia Yunni; You Zhuhong; Zhu Qingsheng. An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems.

IEEE TRANS ON INDUSTRIAL INFORMATICS.

2015, 11(4): 946-956

[22] Li Songyang; Gao Jixun; Zhu Qingsheng. A Dynamic Root Simulation Model in Response to Soil Moisture Heterogeneity.

MATHEMATICS AND COMPUTERS IN SIMULATION.

2015, v113: 40-50

[23] Xia YunNi; Zhou MengChu; Luo Xin; Zhu Qingsheng. Stochastic Modeling and Performance Analysis of Migration-Enabled and Error-Prone Clouds.

IEEE TRANS ON INDUSTRIAL INFORMATICS.

2015, 11(2): 495-504

[24] Xia Yunni; Zhou Mengchu; Luo Xin; Pang Shanchen; Zhu Qingsheng. A Stochastic Approach to Analysis of Energy-aware DVS-enabled Cloud Datacenters.

IEEE Trans on Systems, Man, and Cybernetics: Systems

.2015, 45(1): 73-83

[25] Xia Yunni; Zhou Mengchu; Luo Xin; Zhu Qingsheng. Stochastic Modeling and Quality Evaluation of Infrastructure-as-a-Service Clouds.

IEEE Trans on Automation Science and Engineering

.2015, 12(1): 162-170

[26] Wan Jiaqiang; Zhu Qingsheng. Outlier Detection based on Transitive Closure.

Intelligent Data Analysis.

2015, 19(1): 145-160

[27] Wu Quanwang; Zhu Qingsheng. A Correlation-driven Optimal Service Selection Approach for Virtual Enterprise Establishment.

Journal of Intelligent Manufacturing.

2014, 25(6): 1441-1453

[28] Wu Quanwang; Zhu Qingsheng. Broker-based SLA-aware composite service provisioning.

Journal of Systems and Software

. 2014. v96: 194-201

[29] Wu Quanwang; Zhu Qingsheng. A Robust Decentralized Reputation Management System for Service Selection.

International Journal for Light and Electron Optics.

2014, 125(11): 2692-2701

[30] Zhu Qingsheng; Zhang Sulan; Zeng Lingqiu. Multi-Resolution Foliage Faithful to the Visual and Botanic Concepts.

JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE

. 2014, 11(4): 1007-1017

[31] Luo Xin; Zhou Mengchu; Xia Yunni; Zhu Qingsheng. An Efficient Non-Negative Matrix-Factorization -Based Approach to Collaborative Filtering for Recommender Systems.

IEEE TRANS ON INDUSTRIAL INFORMATICS.

2014, 10(2): 1273-1284

[32] Xia Yunni; Luo Xin; Li Jia; Zhu Qingsheng. A Petri-Net-Based Approach to Reliability Determination of Ontology-Based Service Compositions.

IEEE TRANS ON SYSTEMS MAN CYBERNETICS- SYSTEMS

. 2013, 43(5): 1240-1247

[33] Luo Xin; Xia Yunni; Zhu Qingsheng.Boosting the K-Nearest-Neighborhood based incremental collaborative filtering.

KNOWLEDGE-BASED SYSTEMS

. 2013, v53: 90-99

[34] Huang Xin; Lee Jia; Yang Ruilong; Zhu Qingsheng. Simple and Flexible Self-Reproducing Structures in Asynchronous Cellular Automata and Their Dynamics.

INTERNATIONAL JOURNAL OF MODERN PHYSICS C

. 2013, 24(3)

[35] Zhu Qingsheng; Li Songyang; Liu Ji. Using L-Systems for Simulating Virtual Plant Plasticity Related to Water Status.

JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE

. 2013, 10(3): 561-568

[36] Wu Quanwang; Zhu Qingsheng.Transactional and QoS-aware dynamic service composition based on ant colony optimization.

FUTURE GENERATION COMPUTER SYSTEMS

. 2013, 29(5): 1112-1119

[37] Luo Xin; Xia Yunni; Zhu Qingsheng. Applying the learning rate adaptation to the matrix factorization based collaborative filtering.

KNOWLEDGE-BASED SYSTEMS.

2013, v37: 154-164

[38] Lee Jia; Imai Katsunobu; Zhu Qingsheng. Fluctuation-driven computing on number-conserving cellular automata.

INFORMATION SCIENCES

. 2012, v187: 266-276

[39] Lee Jia; Zhu Qingsheng. A Direct Proof of Turing Universality of Delay-Insensitive Circuits.

INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING

. 2012, 8(2): 107-118

[40] Xia Yunni; Liu Yi; Liu Ji; Zhu Qingsheng. Modeling and Performance Evaluation of BPEL Processes: A Stochastic-Petri-Net-Based Approach.

IEEE TRANS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS

. 2012, 42(2): 503-510[2]

专利

[1] 朱庆生;曾令秋;葛亮;刘骥;屈洪春.基于Web的虚拟植物可视化系统及虚拟植物构建方法，2011.12.28，ZL200910104108.4

[2] 朱庆生，葛亮，刘骥，曾令秋，李松阳.虚拟植物生长的展示方法，2012.01.11，ZL201010204666.0

[3] 夏云霓,陈鹏,杨瑞龙,朱庆生. DAML-S组合服务可靠性分析方法，2014.05.07，zl201110351649.4已市场化交易

[4] 朱庆生，张程，刘慧君，周明强，曾令秋.一种固定区域内幼童位置监控和搜索系统，2014.11.19，ZL201210066736.X

[5] 夏云霓，陈鹏，罗辛，刘杰，朱庆生.一种web服务连接成功率的预测方法，2015.01.07, ZL201210245592.4已市场化交易

[6] 夏云霓，陈鹏，罗辛，吴磊，朱庆生.一种基于时间序列的Web服务响应时间预测方法，2015.01.07, ZL201210168757.2

[7] 韩庆文，曾令秋，贺雪莹，朱庆生.基于道路异常区域评估的路径选择方法及系统，2016.03.02，ZL201310722747.3

[8] 罗辛，葛亮，夏云霓，朱庆生.一种用户-商品点击率自适应预测装置和预测方法，2016.03.23, ZL201310162681.7

[9] 朱庆生，姚成亮，栗铭瑶.一种基于KNN的密度峰值聚类方法，2018.12,28，CN201811617176.6

[10] 朱庆生，栗铭瑶，姚成亮.一种基于密度的层次聚类方法，2019.3.27，CN201910237841.7

教材

朱庆生，古平.国家级规划教材《Java程序设计》第二版.清华大学出版社. 2017[2]

项目

[1] 国家自然科学基金面上项目，61272194，自然最近邻居的特性分析与应用研究，主持，2013/01-2016/12

[2] 国家科技支撑计划重点项目，2011BAH25B00，统筹城乡现代服务关键技术研究与支撑平台构建，主持，2011/010-2014/9

[3] 国家自然科学基金面上项目，61073058，结构离群特征分析与释义空间建模研究，主持2011/01-2013/12

[4] 国家自然科学基金面上项目，60773082，生理引擎驱动的虚拟植物多分辨率展示关键技术研究，主持，2008/01-2010/12

[5] 国家科技支撑计划课题，2007BAH08B04，重庆“便民e站”服务平台，主持，2008/01-2010/12

[6] 重庆市人工智能技术创新重大主题专项重点研发项目，CSTC2017rgun-zdyfx0040，基于城市感知大数据的公共安全事件智能监测关键技术与示范应用，主持，2018/01-2020/12[2]

社会任职

朱庆生同志是中国计算机学会和中国电子学会高级会员，当前任中国计算机学会常务理事兼教育委员会副主任和多媒体专委会常委，全国高等学校计算机教育研究会学术委员会主任，全国核心刊物《计算机科学》和《重庆大学学报（英文版）》杂志编委，重庆计算机学会理事长，重庆市信息化领导小组咨询专家，重庆市自然科学类职称评审委员会成员。全国高校计算机教育研究会副理事长、中国计算机学会理事、重庆市人工智能学会副理事长、ACM重庆分会副主席、软件理论与技术重庆市重点实验室主任、重庆市信息化领导小组咨询专家。曾先后担任中国计算机学会常务理事和教育专委会副主任、电子部全国工科电子类教学指导委员会委员、重庆市电子商务协会副理事长。

获奖情况奖项全称获奖作品一等奖《视频动态分析仪》二等奖《遥测数据压缩系统》三等奖《轧钢生产信息动态管理系统》