基本信息
尚明生  男  博导  中国科学院重庆绿色智能技术研究院
电子邮件: msshang@cigit.ac.cn
通信地址: 重庆北碚区水土镇水土高新园方正大道266号
邮政编码: 400714

研究领域

大数据智能计算及应用

招生信息


招生专业
081203-计算机应用技术
081202-计算机软件与理论
招生方向
大数据挖掘
智能信息处理
推荐系统

教育背景

2003-09--2007-12   电子科技大学   博士
2000-09--2003-05   电子科技大学   硕士

工作经历

   
工作简历
2015-07~现在, 中科院重庆绿色智能技术研究院, 研究员
2014-08~2015-01,瑞士弗里堡大学, 访问学者
2011-08~2011-10,美国罗切斯特大学, 访问学者
2010-08~2015-07,电子科技大学, 教授,博士生导师
2007-12~2009-01,美国明尼苏达大学, 访问学者
2002-02~2010-07,电子科技大学, 讲师/副教授

专利与奖励

   
奖励信息
(1) 智慧金融集成生物识别关键技术及应用, 一等奖, 省级, 2018
(2) 智慧金融中的集成生物识别关键技术及应用, 一等奖, 其他, 2018
(3) 国务院政府特殊津贴, 国家级, 2018
专利成果
[1] 史晓雨, 尚明生. 一种融合用户偏好预测的深度强化学习推荐方法. CN202111519219.9, 2021-12-06.

[2] 张学睿, 尚明生, 张帆, 姚远, 郑志浩. 一种基于三阶级联架构的YOLOv3的远景目标检测方法. CN: CN113239813A, 2021-08-10.

[3] 林远长, 唐晨, 何国田, 尚明生, 刘东. 一种智能喷涂机器人系统及其喷涂方法. CN: CN112642619A, 2021-04-13.

[4] 林远长, 刘东, 代康, 何玉泽, 何国田, 尚明生. 一种磁流变弹性体变阻器. CN: CN112582120A, 2021-03-30.

[5] 刘东, 林远长, 刘宗辉, 何国田, 尚明生. 一种从额头温度估计体核温度的方法及其应用. CN: CN112487692A, 2021-03-12.

[6] 刘东, 林远长, 何国田, 刘宗辉, 尚明生. 一种红外热成像相机标定装置. CN: CN112465919A, 2021-03-09.

[7] 王国胤, 董建华, 尚明生, 严胡勇, 王浩林, 郑志浩, 史晓雨. 基于邻域粗糙集和PCA融合的数据分类预测方法. CN: CN107016416B, 2021-02-12.

[8] 袁野, 李超华, 罗辛, 尚明生, 吴迪. 一种视频数据线性偏差主特征提取装置和方法. CN: CN107808163B, 2020-12-29.

[9] 袁野, 许明, 罗辛, 尚明生. 一种Web服务吞吐量时变隐特征分析装置和方法. CN: CN112131080A, 2020-12-25.

[10] 袁野, 罗辛, 尚明生, 吴迪. 一种视频数据多维非负隐特征的提取装置和方法. CN: CN107704830B, 2020-12-08.

[11] 张能锋, 袁野, 罗辛, 尚明生. 一种基于多层随机隐特征模型的网页广告投放装置和方法. CN: CN112036963A, 2020-12-04.

[12] 陈琳, 郑小强, 尚明生, 朱帆. 基于生成对抗学习的行人属性识别方法. CN: CN112016490A, 2020-12-01.

[13] 史晓雨, 尚明生, 王思源. 一种对抗攻击敏感的文本分类方法. CN: CN111984762A, 2020-11-24.

[14] 张学睿, 尚明生, 张帆, 姚远, 郑志浩. 一种基于DIOU损失函数的训练网络的方法. CN: CN111931915A, 2020-11-13.

[15] 史晓雨, 尚明生, 吕元鑫, 冉龙玉. 一种混凝土生产配合比的智能设计方法. CN: CN110435009B, 2020-11-10.

[16] 陈琳, 尚明生, 朱帆. 基于对抗学习的化合物图像分子结构式提取方法. CN: CN111860507A, 2020-10-30.

[17] 周博天, 尚明生, 闪锟, 马健荣, 封雷. 一种水华期藻类群落结构高光谱识别方法. CN: CN111795941A, 2020-10-20.

[18] 陈琳, 宋小军, 尚明生, 朱帆. 一种自适应人群计数系统及自适应人群计数方法. CN: CN111639585A, 2020-09-08.

[19] 姚远, 郑志浩, 张学睿, 张帆, 尚明生. 一种小样本下复杂环境的目标识别方法. CN: CN111582345A, 2020-08-25.

[20] 郑志浩, 姚远, 张学睿, 张帆, 尚明生. 一种基于稀疏样本的视频压缩方法. CN: CN111565318A, 2020-08-21.

[21] 林远长, 汪凌峰, 何国田, 尚明生. 一种应用于手术机器人的器械和设备. CN: CN110664486A, 2020-01-10.

[22] 史晓雨, 尚明生, 罗梦珍, 白亚男. 一种面向不平衡文本数据的自分类方法. CN: CN110609898A, 2019-12-24.

[23] 尚明生, 史晓雨. 一种基于用户自主选择的个性化推荐方法和系统. CN: CN105512183B, 2019-10-11.

[24] 陈琳, 彭彬彬, 尚明生, 朱帆. 一种超高像素的组织病理图像分割方法. CN: CN110288613A, 2019-09-27.

[25] 史晓雨, 尚明生, 吕元鑫. 一种混凝土28d抗压强度预测方法. CN: CN110263431A, 2019-09-20.

[26] 朱帆, 尚明生, 陈琳. 基于相关系数的中风灌注成像病变区域检测系统及方法. CN: CN110236544A, 2019-09-17.

[27] 罗辛, 吴昊, 陈敏治, 尚明生, 刘志刚, 钟裕荣. 一种基于偏置张量分解的云服务响应时间预测方法和装置. CN: CN110113180A, 2019-08-09.

[28] 罗辛, 吴昊, 尚明生, 陈敏治, 钟裕荣, 王德贤. 一种时序网络动态隐特征抽取方法和装置. CN: CN110083631A, 2019-08-02.

[29] 封丽, 封雷, 李崇明, 尚明生, 周博天, 闪坤, 程艳茹, 张君, 刘鑫, 刘异齐, 张韵, 史晓雨. 一种基于用户优先度的遥感分发方法及系统. CN: CN107104956B, 2019-07-26.

[30] 史晓雨, 冀倩倩, 尚明生. 一种不完备专利自动标引方法. CN: CN109726299A, 2019-05-07.

[31] 史晓雨, 尚明生, 白亚男. 一种高效能数据中心云服务器资源自主管理方法和系统. CN: CN109491760A, 2019-03-19.

[32] 尚明生, 李锴, 张航. 一种微博转发量预测方法. CN: CN105550275B, 2019-02-26.

[33] 田文洪, 黄超杰, 王金, 尚明生. 一种解决Spark数据倾斜问题的负载均衡方法及装置. 中国: CN108572873A, 2018.09.25.

[34] 张帆, 张学睿, 王国胤, 尚明生. 一种海量动态数据管理方法. 中国: CN105426506B, 2018-10-02.

[35] 田文洪, 王金, 何博, 叶宇飞, 尚明生, 史晓雨. 一种基于深度强化学习的资源调度方法和系统. 中国: CN108595267A, 2018-09-28.

[36] 吴迪, 李超华, 尚明生, 罗辛, 袁野. 一种基于数据密度峰值的自标记半监督分类方法及装置. 中国: CN106778859A, 2017-05-31.

[37] 史晓雨, 尚明生, 田文洪, 罗辛. 一种能耗感知的云计算服务器资源在线管理方法和系统. 中国: CN106648890A, 2017-05-10.

[38] 王国胤, 徐计, 邓伟辉, 尚明生, 张学睿. 一种基于密度峰值的高效层次聚类方法. 中国: CN105631465A, 2016-06-01.

[39] 尚明生, 李健, 史晓雨. 一种在线系统中用户定制推荐系统的方法. 中国: CN105404678A, 2016-03-16.

[40] 王国胤, 田亚兰, 徐计, 尚明生, 张学睿. 一种非线性对流扩散方程的粒计算加速求解方法. 中国: CN105224504A, 2016-01-06.

出版信息

发表论文
[1] Liu, Mei, Peng, Bo, Shang, Mingsheng. Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network. COMPLEX & INTELLIGENT SYSTEMS. 2021, https://www.webofscience.com/wos/woscc/full-record/WOS:000635145200003.
[2] Luo, Xin, Liu, Zhigang, Li, Shuai, Shang, Mingsheng, Wang, Zidong. A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS[J]. 2021, 51(1): 610-620, http://dx.doi.org/10.1109/TSMC.2018.2875452.
[3] Luo, Xin, Liu, Zhigang, Shang, Mingsheng, Lou, Jungang, Zhou, MengChu. Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING[J]. 2021, 8(1): 463-476, https://www.webofscience.com/wos/woscc/full-record/WOS:000631202700037.
[4] Xin, Luo, Yuan, Ye, Zhou, MengChu, Liu, Zhigang, Shang, Mingsheng. Non-Negative Latent Factor Model Based on beta-Divergence for Recommender Systems. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS[J]. 2021, 51(8): 4612-4623, [5] Wu, Di, Luo, Xin, Shang, Mingsheng, He, Yi, Wang, Guoyin, Zhou, MengChu. A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS[J]. 2021, 51(7): 4285-4296, http://dx.doi.org/10.1109/TSMC.2019.2931393.
[6] Luo, Xin, Zhou, Mengchu, Li, Shuai, Wu, Di, Liu, Zhigang, Shang, Mingsheng. Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems. IEEE TRANSACTIONS ON BIG DATA[J]. 2021, 7(1): 227-240, http://dx.doi.org/10.1109/TBDATA.2019.2916868.
[7] Luo, Xin, Wang, Zidong, Shang, Mingsheng. An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS[J]. 2021, 51(6): 3522-3532, http://dx.doi.org/10.1109/TSMC.2019.2930525.
[8] Ming-Sheng Shang. A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices. IEEE Transactions on Big Data. 2020, [9] Ming-Sheng Shang. Temporal Web Service QoS Prediction via Kalman Filter-Iincorporated Dynamic Latent Factor Analysis. ECAI 2020. 2020, [10] Ming-Sheng Shang. Large-scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems. IEEE Transaction on Big Data. 2020, [11] Yuanxin Lv, Xiaoyu Shi, Longyu Ran, Mingsheng Shang. Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method. Advances in natural computation, fuzzy systems and knowledge discovery. Volume 2 /. 2020, 557-565, http://dx.doi.org/10.1007/978-3-030-32591-6_60.
[12] Longyu Ran, Xiaoyu Shi, Mingsheng Shang. SLAs-aware Online Task Scheduling based on Deep Reinforcement Learning Method in Cloud Environment. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems : Volume 3 of 4.null. 2020, 1518-1525, [13] Ming-Sheng Shang. A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems. WWW 2020. 2020, [14] Ming-Sheng Shang. Momentum-incorporated Symmetric Non-negative Latent Factor Models. IEEE Transactions on Big Data. 2020, [15] Ming-Sheng Shang. A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction. IEEE Transactions on Knowledge and Data Engineering. 2020, [16] Mengzhen Luo, Xiaoyu Shi, Qianqian Ji, Mingsheng Shang, Xianbo He, Weiguo Tao. A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label. Advances in natural computation, fuzzy systems and knowledge discovery. Volume 2 /. 2020, 566-573, http://dx.doi.org/10.1007/978-3-030-32591-6_61.
[17] Shan, Kun, Wang, Xiaoxiao, Yang, Hong, Zhou, Botian, Song, Lirong, Shang, Mingsheng. Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management. HARMFUL ALGAE[J]. 2020, 94: http://dx.doi.org/10.1016/j.hal.2020.101807.
[18] Zhou, Botian, Shang, Mingsheng, Feng, Li, Shan, Kun, Feng, Lei, Ma, Jianrong, Liu, Xiangnan, Wu, Ling. Long-term remote tracking the dynamics of surface water turbidity using a density peaks -based classification: A case study in the Three Gorges Reservoir, China. ECOLOGICAL INDICATORS[J]. 2020, 116: http://dx.doi.org/10.1016/j.ecolind.2020.106539.
[19] Luo, Xin, Zhou, MengChu, Li, Shuai, Hu, Lun, Shang, Mingsheng. Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications. IEEE TRANSACTIONS ON CYBERNETICS[J]. 2020, 50(5): 1844-1855, http://dx.doi.org/10.1109/TCYB.2019.2894283.
[20] Wu, Di, Luo, Xin, Shang, Mingsheng, He, Yi, Wang, Guoyin, Wu, Xindong, Yang, Q, Zhou, ZH, Gong, Z, Zhang, ML, Huang, SJ. A Data-Aware Latent Factor Model for Web Service QoS Prediction. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2019, PT Inull. 2019, 11439: 384-399, [21] Mingsheng Shang, Xin Luo, Zhigang Liu, Jia Chen, Ye Yuan, MengChu Zhou. Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications. 自动化学报:英文版. 2019, 131-141, http://lib.cqvip.com/Qikan/Article/Detail?id=90687266504849574849484949.
[22] Shang, Mingsheng, Luo, Xin, Liu, Zhigang, Chen, Jia, Yuan, Ye, Zhou, MengChu. Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications. IEEE-CAA JOURNAL OF AUTOMATICA SINICA[J]. 2019, 6(1): 131-141, http://lib.cqvip.com/Qikan/Article/Detail?id=90687266504849574849484949.
[23] Ming-Sheng Shang. A Posterior-neighborhood-regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction. IEEE Transactions on Services Computing. 2019, [24] Shan Kun, Shang Mingsheng, Zhou Botian, Li Lin, Wang Xiaoxiao, Yang Hong, Song Lirong. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. 2019, http://119.78.100.158/handle/2HF3EXSE/130843.
[25] Wang, Qingxian, Chen, Minzhi, Shang, Mingsheng, Luo, Xin. A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction. NEUROCOMPUTING[J]. 2019, 367: 299-307, http://dx.doi.org/10.1016/j.neucom.2019.08.026.
[26] Wu, Di, He, Yi, Luo, Xin, Shang, Mingsheng, Wu, Xindong, Baru, C, Huan, J, Khan, L, Hu, XH, Ak, R, Tian, Y, Barga, R, Zaniolo, C, Lee, K, Ye, YF. Online Feature Selection with Capricious Streaming Features: A General Framework. 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)null. 2019, 683-688, [27] Wang, Qingxian, Peng, Binbin, Shi, Xiaoyu, Shang, Tianqi, Shang, Mingsheng. DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction. IEEE ACCESS[J]. 2019, 7: 60186-60198, https://doaj.org/article/d522fc56faf64aca8146e94d4248af44.
[28] Jiang, Jiajia, Xia, Yunni, Shang, Mingsheng, IEEE. A Fast Autoencoder-based Recommender. 2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC)null. 2019, 1732-1737, [29] Shan, Kun, Shang, Mingsheng, Zhou, Botian, Li, Lin, Wang, Xiaoxiao, Yang, Hong, Song, Lirong. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. HARMFUL ALGAE[J]. 2019, 83(1): 14-24, http://dx.doi.org/10.1016/j.hal.2019.01.005.
[30] Zhou, Botian, Shang, Mingsheng, Zhang, Sheng, Feng, Li, Li, Xiangnan, Wu, Ling, Feng, Lei, Shan, Kun. Remote examination of the seasonal succession of phytoplankton assemblages from time-varying trends. JOURNAL OF ENVIRONMENTAL MANAGEMENT[J]. 2019, 246: 687-694, http://dx.doi.org/10.1016/j.jenvman.2019.06.035.
[31] Zhong, LinFeng, Shang, MingSheng, Chen, XiaoLong, Cai, ShiMing. Identifying the influential nodes via eigen-centrality from the differences and similarities of structure. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS[J]. 2018, 510: 77-82, http://dx.doi.org/10.1016/j.physa.2018.06.115.
[32] Yuan, Ye, Luo, Xin, Shang, MingSheng. Effects of preprocessing and training biases in latent factor models for recommender systems. NEUROCOMPUTING[J]. 2018, 275: 2019-2030, http://dx.doi.org/10.1016/j.neucom.2017.10.040.
[33] Shi Xiaoyu, Shang MingSheng, Tian Wenhong, Khushnood Abbas, Wang Shuai, Wu Tianshu. Autonomic performance management of cloud server based on adaptive control method. 15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018null. 2018, 1-6, http://119.78.100.138/handle/2HOD01W0/7952.
[34] Wu, Di, Luo, Xin, Wang, Guoyin, Shang, Mingsheng, Yuan, Ye, Yan, Huyong. A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS[J]. 2018, 14(3): 909-920, http://dx.doi.org/10.1109/TII.2017.2737827.
[35] Luo, Xin, Zhou, MengChu, Li, Shuai, Shang, MingSheng. An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS[J]. 2018, 14(5): 2011-2022, http://119.78.100.138/handle/2HOD01W0/8021.
[36] Zhang Ying, Shi Xiaoyu, Shang MingSheng, Mao Yelu. TCR: Temporal-CNN for Reviews Based Recommendation System. 2nd International Conference on Deep Learning Technologies, ICDLT 2018null. 2018, 71-75, http://119.78.100.138/handle/2HOD01W0/7948.
[37] Liu Zhigang, Luo Xin, Li Shuai, Shang Mingsheng, IEEE. Accelerated Non-negative Latent Factor Analysis on High-dimensional and Sparse Matrices via Generalized Momentum Method. 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)null. 2018, 3051-3056, [38] Zhou, Botian, Shang, Mingsheng, Wang, Guoyin, Zhang, Sheng, Feng, Li, Liu, Xiangnan, Wu, Ling, Shan, Kun. Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI). SCIENCE OF THE TOTAL ENVIRONMENT[J]. 2018, 628-629: 848-857, http://dx.doi.org/10.1016/j.scitotenv.2018.02.097.
[39] Wang, QingXian, Luo, Xin, Li, Yan, Shi, XiaoYu, Gu, Liang, Shang, MingSheng. Incremental Slope-one recommenders. NEUROCOMPUTING[J]. 2018, 272: 606-618, http://dx.doi.org/10.1016/j.neucom.2017.07.033.
[40] Xu Jianjun, Chen Lin, Peng Binbin, Shang Mingsheng. Fully Convolutional Neural Networks for Tissue Histopathology Image Classification and Segmentation. 25th IEEE International Conference on Image Processing, ICIP 2018null. 2018, 1403-1407, http://119.78.100.138/handle/2HOD01W0/7963.
[41] Tian, Wenhong, He, Majun, Guo, Wenxia, Huang, Wenqiang, Shi, Xiaoyu, Shang, Mingsheng, Toosi, Adel Nadjaran, Buyya, Rajkumar. On minimizing total energy consumption in the scheduling of virtual machine reservations. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS[J]. 2018, 113: 64-74, http://119.78.100.138/handle/2HOD01W0/8051.
[42] Peng, Binbin, Chen, Lin, Shang, Mingsheng, Xu, Jianjun, IEEE. FULLY CONVOLUTIONAL NEURAL NETWORKS FOR TISSUE HISTOPATHOLOGY IMAGE CLASSIFICATION AND SEGMENTATIONN. 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)null. 2018, 1403-1407, [43] Wu Di, Shang Mingsheng, Wang Guoyin, Li Li. A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution. 15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018null. 2018, 1-6, http://119.78.100.138/handle/2HOD01W0/7953.
[44] Chen Jia, Luo Xin, IEEE. Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications. 2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC)null. 2018, [45] Wu, Di, Shang, Mingsheng, Luo, Xin, Xu, Ji, Yan, Huyong, Deng, Weihui, Wang, Guoyin. Self-training semi-supervised classification based on density peaks of data. NEUROCOMPUTING[J]. 2018, 275: 180-191, http://dx.doi.org/10.1016/j.neucom.2017.05.072.
[46] Abbas, Khushnood, Shang, Mingsheng, Abbasi, Alireza, Luo, Xin, Xu, Jian Jun, Zhang, YuXia. Popularity and Novelty Dynamics in Evolving Networks. SCIENTIFIC REPORTS[J]. 2018, 8(1): https://doaj.org/article/86d5d46f478d40fdba436bd5559ac4c9.
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[52] Shi, Xiaoyu, Shang, MingSheng, Luo, Xin, Khushnood, Abbas, Li, Jian. Long-term effects of user preference-oriented recommendation method on the evolution of online system. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS[J]. 2017, 467: 490-498, http://dx.doi.org/10.1016/j.physa.2016.10.033.
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[59] Zhou, Botian, Shang, Mingsheng, Wang, Guoyin, Feng, Li, Shan, Kun, Liu, Xiangnan, Wu, Ling, Zhang, Xuerui. Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH[J]. 2017, 24(23): 19044-19056, https://www.webofscience.com/wos/woscc/full-record/WOS:000407723100027.
[60] Shi XiaoYu, Luo Xin, Shang MingSheng, Cai XinYi, Fortino G, Zhou MC, Lukszo Z, Vasilakos AV, Basile F, Palau C, Liotta A, Fanti MP, Guerrieri A, Vinci A. Empirical Analysis of Collaborative Filtering-based Recommenders in Temporally Evolving Systems. PROCEEDINGS OF THE 2017 IEEE 14TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC 2017)null. 2017, 406-412, [61] Wang, QingXian, Zhang, JunJie, Shi, XiaoYu, Shang, MingSheng. User Heterogeneity and Individualized Recommender. CHINESE PHYSICS LETTERS[J]. 2017, 34(6): http://lib.cqvip.com/Qikan/Article/Detail?id=672497629.
[62] 王庆先, 张君杰, 史晓雨, 尚明生. User Heterogeneity and Individualized Recommender. 中国物理快报:英文版. 2017, 34(6): 135-138, http://lib.cqvip.com/Qikan/Article/Detail?id=672497629.
[63] Yan, Huyong, Wu, Di, Huang, Yu, Wang, Guoyin, Shang, Mingsheng, Xu, Jianjun, Shi, Xiaoyu, Shan, Kun, Zhou, Botian, Zhao, Yufei. Water eutrophication assessment based on rough set and multidimensional cloud model. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS[J]. 2017, 164: 103-112, http://dx.doi.org/10.1016/j.chemolab.2017.02.005.
[64] Luo, Xin, Sun, Jianpei, Wang, Zidong, Li, Shuai, Shang, Mingsheng. Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS[J]. 2017, 13(6): 3098-3107, http://dx.doi.org/10.1109/TII.2017.2724769.
[65] Xue LiYuan, Zeng RongQiang, An Wei, Wang QingXian, Shang MingSheng, Chen G, Shen H, Chen M. Experiments on Neighborhood Combination Strategies for Bi-objective Unconstrained Binary Quadratic Programming Problem. PARALLEL ARCHITECTURE, ALGORITHM AND PROGRAMMING, PAAP 2017null. 2017, 729: 444-453, [66] 柳文艳, 张玉霞, 蔡世民, 何嘉林, 尚明生. 基于复杂行为响应的传染病爆发问题的研究. 复杂系统与复杂性科学[J]. 2017, 8-14, http://lib.cqvip.com/Qikan/Article/Detail?id=70908884504849554849484850.
[67] Wu, Di, Yan, Huyong, Shang, Mingsheng, Shan, Kun, Wang, Guoyin. Water eutrophication evaluation based on semi-supervised classification: A case study in Three Gorges Reservoir. ECOLOGICAL INDICATORS[J]. 2017, 81: 362-372, http://dx.doi.org/10.1016/j.ecolind.2017.06.004.
[68] Zhang Yinyan, Li Shuai, Luo Xin, Shang Mingsheng, IEEE. A dynamic neural controller for adaptive optimal control of permanent magnet DC motors. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)null. 2017, 839-844, [69] Zhao, YaoDong, Cai, ShiMin, Tang, Ming, Shang, MinSheng. Coarse cluster enhancing collaborative recommendation for social network systems. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS[J]. 2017, 483: 209-218, http://dx.doi.org/10.1016/j.physa.2017.04.131.
[70] 尚明生. 推荐系统:从个性化算法到算法的个性化. 西华师范大学学报:自然科学版[J]. 2016, 37(1): 61-66+3, http://lib.cqvip.com/Qikan/Article/Detail?id=668612200.
[71] Hu, Xiao, Zeng, An, Shang, MingSheng. Recommendation in evolving online networks. EUROPEAN PHYSICAL JOURNAL B[J]. 2016, 89(2): https://www.webofscience.com/wos/woscc/full-record/WOS:000375218200002.
[72] Yan Huyong, Huang Yu, Wang Guoyin, Zhang Xuerui, Shang Mingsheng, Feng Lei, Dong Jianhua, Shan Kun, Wu Di, Zhou Botian, Yuan Ye. Water eutrophication evaluation based on rough set and petri nets: A case study in Xiangxi-River, Three Gorges Reservoir. ECOLOGICAL INDICATORS[J]. 2016, 69: 463-472, http://dx.doi.org/10.1016/j.ecolind.2016.05.010.
[73] Luo Xin, Shang Mingsheng, Li Shuai, Bonchi F, DomingoFerrer J, BaezaYates R, Zhou ZH, Wu X. Efficient Extraction of Non-negative Latent Factors from High-dimensional and Sparse Matrices in Industrial Applications. 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)null. 2016, 311-319, [74] Zhang, YuXia, Liao, Hao, Medo, Matus, Shang, MingSheng, Yeung, Chi Ho. Study of market model describing the contrary behaviors of informed and uninformed agents: Being minority and being majority. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS[J]. 2016, 450: 486-496, http://dx.doi.org/10.1016/j.physa.2016.01.041.
[75] Yan, Hu Yong, Zhang, Xue Rui, Dong, Jian Hua, Shang, Ming Sheng, Shan, Kun, Wu, Di, Yuan, Ye, Wang, Xu, Meng, Hui, Huang, Yu, Wang, Guo Yin. Spatial and temporal relation rule acquisition of eutrophication in Da'ning River based on rough set theory. ECOLOGICAL INDICATORS[J]. 2016, 66: 180-189, http://dx.doi.org/10.1016/j.ecolind.2016.01.032.
[76] Luo, Xin, Zhou, Mengchu, Shang, Mingsheng, Li, Shuai, Xia, Yunni. A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices. IEEE ACCESS[J]. 2016, 4: 2649-2655, https://doaj.org/article/b392e80f231c46628245b9f637e903ac.
[77] Ren, ZhuoMing, Kong, Yixiu, Shang, MingShang, Zhang, YiCheng. A generalized model via random walks for information filtering. PHYSICS LETTERS A[J]. 2016, 380(34): 2608-2614, http://dx.doi.org/10.1016/j.physleta.2016.06.009.
[78] Zeng, Wei, Fang, Meiling, Shao, Junming, Shang, Mingsheng. Uncovering the essential links in online commercial networks. SCIENTIFIC REPORTS[J]. 2016, 6: https://www.webofscience.com/wos/woscc/full-record/WOS:000384171200002.
[79] Huo Chao, Shang MingSheng, Wang Yang, Zeng RongQiang. An effective genetic algorithm with uniform crossover for Bi-objective unconstrained binary quadratic programming problem. 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016null. 2016, 58-67, http://www.chinair.org.cn/handle/1471x/1660733.
[80] Huo Chao, Shang Mingsheng, Wang Yang, Zeng Rongqiang. Multi-parent Crossover Based Genetic Algorithm for Bi-Objective Unconstrained Binary Quadratic Programming Problem. 11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016null. 2016, 10-19, http://www.chinair.org.cn/handle/1471x/1660735.
[81] Zhong, LinFeng, Liu, JianGuo, Shang, MingSheng. Iterative resource allocation based on propagation feature of node for identifying the influential nodes. PHYSICS LETTERS A[J]. 2015, 379(38): 2272-2276, http://dx.doi.org/10.1016/j.physleta.2015.05.021.
[82] 尚明生, 邱晓刚. 社会网络及其上的传播动力学集成研究. 系统工程理论与实践[J]. 2015, 35(10): 2557-2563, [83] 段杰明, 尚明生, 蔡世民, 张玉霞. 基于自规避随机游走的节点排序算法 (EI收录). 《物理学报》[J]. 2015, 61-68, http://www.corc.org.cn/handle/1471x/2210122.
[84] Shang MingSheng, Qiu XiaoGang. An integration platform for modelling the dynamics of opinion and epidemic on social network. Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice[J]. 2015, 35(10): 2557-2563, http://www.chinair.org.cn/handle/1471x/1660724.
[85] Gao, Jian, Dong, YuWei, Shang, MingSheng, Cai, ShiMin, Zhou, Tao. Group-based ranking method for online rating systems with spamming attacks. EPL[J]. 2015, 110(2): https://www.webofscience.com/wos/woscc/full-record/WOS:000355984500032.
[86] Zeng, Wei, Zeng, An, Liu, Hao, Shang, MingSheng, Zhang, YiCheng. Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering. PLOS ONE[J]. 2014, 9(10): https://doaj.org/article/526ebd8abab244ed9b72164ddbe8895e.
[87] Guan, Yuan, Cai, Shimin, Shang, Mingsheng. Recommendation algorithm based on item quality and user rating preferences. FRONTIERS OF COMPUTER SCIENCE[J]. 2014, 8(2): 289-297, https://www.webofscience.com/wos/woscc/full-record/WOS:000334183200011.
[88] Guan, Yuan, Zhao, Dandan, Zeng, An, Shang, MingSheng. Preference of online users and personalized recommendations. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS[J]. 2013, 392(16): 3417-3423, http://dx.doi.org/10.1016/j.physa.2013.03.045.
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发表著作
(1) 网络舆情信息分析与处理技术, WLYX, 科学出版社, 2015-02, 第 1 作者
(2) 水生态环境感知信息分析系统研究及应用, 科学出版社, 2020-10, 第 1 作者

科研活动

   
科研项目
( 1 ) 动态演化在线系统中的信息推荐问题研究, 主持, 国家级, 2014-01--2017-12
( 2 ) 中科院****, 主持, 部委级, 2016-01--2018-12
( 3 ) 大数据结构与关系的度量与简约计算, 参与, 国家级, 2015-01--2018-12
( 4 ) 面向云计算数据中心的低能耗高性能自主计算研究, 主持, 省级, 2016-01--2018-12
( 5 ) 洛丁智慧照明系统开发, 主持, 院级, 2016-01--2025-09
( 6 ) 初等数学问题求解关键技术及系统, 参与, 国家级, 2015-01--2017-12
( 7 ) 大数据基础科研与开发平台建设, 主持, 省级, 2016-04--2016-12
( 8 ) 面向高维稀疏时变数据的宏观趋势预测研究, 主持, 国家级, 2017-01--2019-12
( 9 ) 悦来新城海绵城市监测与信息平台建设, 主持, 院级, 2017-03--2020-12
( 10 ) 慢病创新服务研发及慢病示范应用, 参与, 院级, 2017-09--2018-12
( 11 ) 基于大数据的服务交易关键技术研究与应用示范, 主持, 省级, 2018-01--2019-12
( 12 ) 大数据与智能计算重庆市重点实验室, 主持, 省级, 2016-12--2018-12
( 13 ) 智慧慢病服药(scp)管理工程示范基地建设, 参与, 部委级, 2018-01--2019-06
( 14 ) 医药专利大数据智能分析决策系统与应用示范, 参与, 省级, 2018-01--2019-12
( 15 ) 基于深度学习的长效推荐技术研究, 参与, 国家级, 2019-01--2021-12
( 16 ) 监控检测技术研究, 主持, 国家级, 2019-02--2021-01
( 17 ) 数字城市大数据联合实验室, 主持, 院级, 2019-10--2022-10
( 18 ) 面向海绵城市运维大数据的高维稀疏张量分析方法研究, 主持, 国家级, 2021-01--2024-12
( 19 ) 新药研发大数据平台, 参与, 国家级, 2020-07--2022-06
( 20 ) 智能终端软件, 主持, 国家级, 2020-06--2021-07
参与会议
(1)Research and applications in Bigdata   一带一路地方合作委员会首次大会暨“人工智能助推城市治理”   2018-12-10
(2)大数据智能的研究及应用   大数据智能化前沿科技学术报告会   2018-11-13
(3)大数据时代的信息获取   2018-07-07
(4)Symmetric Non-negative Latent Factor Models for Undirected Large Networks   2017-08-20

指导学生

已指导学生

彭彬彬  硕士研究生  081203-计算机应用技术  

现指导学生

袁野  博士研究生  081203-计算机应用技术  

徐晓宇  博士研究生  081203-计算机应用技术  

王韬  硕士研究生  081203-计算机应用技术  

冉龙宇  硕士研究生  085211-计算机技术