大数据挖掘与智能计算团队是安徽工业大学计算机科学与技术学院科研团队之一，团队成员9人，其中教授1人，副教授3人，硕导5人，讲师5人，7人具有博士学位。主要研究大数据挖掘与分析、机器学习、计算机视觉、多媒体内容分析、自然语言处理等方向。近年来承担多项国家级、省部级和企业科研课题，获安徽省科技进步奖1项，中科院优秀博士论文奖1项，其它奖项若干。
团队成员
科研项目
1.国家自然科学基金青年项目，基于类属特征学习的鲁棒高效多标记学习方法研究
2.国家自然科学基金青年项目，连续状态空间模型未知下的在线强化学习方法
3.国家自然科学基金青年项目，基于单目摄像头的空中手写汉字识别关键技术研究
4.国家自然科学基金青年项目，等离子体对共振磁扰动响应的多模耦合效应研究
5.安徽省高校协同创新项目，工业互联网边缘智能计算平台及应用系统
6.安徽省高校协同创新项目，多模态内窥镜成像数据的多元属性获取与知识推理
7.自动目标识别重点实验室开放课题，精细化目标识别模型对抗学习技术
8.农科院，基于深度学习的害虫细粒度图像识别模型研发
9.CCF-蚂蚁科研基金，基于半监督学习的多源数据融合智能运维算法研究
10.安徽省自然科学基金面上项目，机载下视场景识别稳健性增强
11.自动目标识别重点实验室基金项目，精细化目标识别模型对抗学习技术
12.安徽高校自然科学研究重点项目，张量表征的在线机器学习理论与方法
13.国防基础科研计划项目，面向小样本弱小目标检测与识别的建模及学习推演
14.中国博士后科学基金特别资助项目/军队系统，面向××××××
15.安徽省科技厅自然科学基金，基于深度非负矩阵分解的多模态特征抽取算法研究
16.安徽省科技厅自然科学基金，基于四场约化磁流体模型的共振磁扰动相位效应调节等离子体响应的理论研究
17.安徽省高校自然科学基金，水利领域大数据知识图谱构建关键技术研究
18.安徽省高校自然科学基金，共振磁扰动线圈相位调节等离子体响应的理论模拟研究
19.安徽省高校自然科学基金，基于深度非负矩阵分解的高光谱图像解混算法研究
20.安徽省高校优秀青年人才支持计划项目，基于动态模式库的数据匮乏地区中小河流洪水预报方法研究
21.中国科学院近地空间重点实验室开放基金，空间和实验室等离子体中气球模/交换模驱动非线性演化触发三维磁场重联的理论模拟研究
学术论文
[1]Jun Huang, Guorong Li, Qingming Huang and Xindong Wu, Learning label Specific Features and Dependent Class Labels for Multi-Label Classification, IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12):3309-3323.(CCF A)
[2]Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi, Discovering Latent Class Labels for Multi-Label Learning, in International Joint Conference on Artificial Intelligence (IJCAI), pp. 3058-3064, Yokohama, Japan, 2020.(CCF A)
[3]Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, and Kenji Yamanishi, Multi-Label Learning with Missing and Completely Unobserved Labels, Data Mining and Knowledge Discovery, 35:1061-1086, 2021.(CCF B)
[4]Jun Huang, Feng Qin, Xiao Zheng, Zekai Cheng, Zhixiang Yuan, Weigang Zhang, and Qingming Huang, Improving Multi-Label Classification with Missing Labels by Learning Label-Specific Features. Information Sciences, 2019, 492:124-146.(CCF B)
[5]Jun Huang, Guorong Li, Qingming Huang and Xindong Wu, Joint Feature Selection and Classification for Multi-Label Learning, IEEE Transactions on Cybernetics, 2018, 48(3)：876-889. (CCF B)
[6]LeiFeng, JunHuang, et al. Regularized Matrix Factorization for Multilabel Learning With Missing Labels, IEEE Transactions on Cybernetics, 52(5):3710-3721, 2022. (CCF B)
[7]Jun Huang, Qian Xu,XiwenQu, et al. Improving Multi-Label Learning by Correlation Embedding, Applied Sciences, 11(24):1-16, 2021. (SCI 3区)
[8]QianqianCheng,JunHuang,HuiyiZhang, et al. Improving Multi-label learning by modeling local label and feature correlations, Intelligent Data Analysis, 2022，accept. (SCI 4区)
[9]LiangZhang,Kun Qian, Jun Huang, et al. Molecular dynamics simulation and machine learning of mechanical response in non-equiatomic FeCrNiCoMn high-entropy alloy, Journal of Materials Research and Technology, 13:2043-2054, 2021. (SCI 2区)
[10]Zipei Yan, Linchuan Xu, Atsushi Suzuki, Jing Wang, Jiannong Cao, and Jun Huang,RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation, IEEE BigData, 2022.
[11]Jun Huang, Yu Yan, Xiao Zheng, Xiwen Qu, and Xudong Hong,Discovering Unknown Labels for Multi-Label Image Classification, ICDMW(IncrLearn'22), 2022.
[12]Xue W, Zhang H, Wei X, et al. Optimizing Exploration-Exploitation Trade-off in Continuous Action Spaces via Q-ensemble. Pacific Rim International Conference on Artificial Intelligence, 2022, 148-160.
[13]Xue W, Zhong P, Zhang W, et al. Sample-based online learning for bi-regular hinge loss. International Journal of Machine Learning and Cybernetics, 2021, 12(6): 1753-1768.
[14]Xue W, Qi J, Shao G, et al. Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 4150-4166.
[15]Hang T, Feng J, Yan L, et al. Joint Extraction of Entities and Relations using Multi-label Tagging and Relational Alignment [J]. Neural Computing and Applications, 2022:1-16. (SCI二区)
[16]Hang T, Feng J, Wu Y, et al. Joint Extraction of Entities and Overlapping Relations using Source--target Entity Labeling [J]. Expert Systems with Applications, 2021,177:114853. (SCI一区，顶刊)
[17]Hang T, Feng J, Li X, et al. Water Sound Recognition Based on Support Vector Machine[C]. International Conference on Ubiquitous Information Management and Communication. Springer, Cham, 2019:986-995. (EI会议)
[18]Wenlong Huang. Unified theory of tearing mode growth from linear to Rutherford regime in the presence of local equilibrium current gradients, 64 055023 (2022), Plasma Physics and Controlled Fusion（SCI二区）.
[19]Wenlong Huang and Ping Zhu. Quasi-linear theory of forced magnetic reconnection for the transition from the linear to the Rutherford regime. Nuclear Fusion 61 036047 (2021)（SCI一区TOP）.
[20]Wenlong Huang and Ping Zhu, and Hui Chen, Analytical model for quasi-linear flow response to resonant magnetic perturbation in resistive-inertial and viscous-resistive regimes. Physics of Plasmas 27, 102514 (2020)（SCI）.
[21]Wenlong Huang and Ping Zhu. Analytical model of plasma response to external magnetic perturbation in absence of no-slip condition. Physics of Plasmas 27, 022514 (2020)（SCI）.
[22]Wenlong Huang, Haijun Ren and Xueqiao Xu. Electromagnetic Effect on Geodesic Acoustic Mode with Adiabatic Electrons, 26, 022506 (2019), Physics of Plasmas（SCI）.
[23]FangyuanMa, Ping Zhu, Xingting Yan, Wenlong Huang. A resistive MHD model and simulation on plasma flow evolution in the presence of resonant magnetic perturbation in a tokamak; 29, 072501 (2022), Physics of Plasmas（SCI）.
[24]Xue Li; Xiaobo Shen; Zhenqiu Shu; Qiaolin Ye; Chunxia Zhao; Graph regularized multilayer concept factorization for data representation, Neurocomputing, 2017, 238: 139-151.
[25]Xue Li; Jun Zhou; Lei Tong; Structured Discriminative Non-Negative Matrix Factorization for Hyperspectral Unmixing, IEEE International Conference on Image Processing, Phoenix, Arizona, USA, 2016.
[26]Rong Qian; Wan Xia; Wei Dong; Jingbo Zhu; Juanjuan Kong; Zekai Cheng(通信作者) ; Focus on larvae: A study of fine-grained image classification for agricultural pests, AGRICULTURAL MECHANIZATION IN ASIA AFRICA AND LATIN AMERICA, 2022.1, 53(1): 5103-5117. (SCI期刊论文)
[27]Cheng Zekai; Huang Rongqing; Qian Rong; Dong Wei; Zhu Jingbo; Liu Meifang; A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks, APPLIED SCIENCES-BASEL,2022.8,12(15):7378. (SCI期刊论文，SCI收录)
[28]Cheng Zekai; Liu Meifang; Qian Rong; Dong Wei; Zhu Jingbo; Huang Rongqing; Development of a Lightweight Crop Disease Image Identification Model Based on Attentional Feature Fusion; SENSORS,2022.8,22(15):5550 (SCI期刊论文，SCI、EI收录)
[29]Cheng Zekai ,Jinbo Guo;Team Analysis Based on Digital Twin Within RoboCup 2D Simulation. 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE2021)（会议论文,EI收录）
[30]ChengZekai,Xiawan.Fine-Grained Image Classification on Agricultural Pest Larvae. ASTFE 2021,IOP Conf. Series: Earth and Environmental Science 792 (2021) 012037.（会议论文）
[31]陈冰,张亨,程泽凯,董鹏,林超.Robocup2D仿真对抗中进攻行为的挖掘与验证[J].系统仿真学报,2018,30(12):4718-4726.
[32]陈冰,许非凡,徐涵延,程泽凯,刘诚.Robocup2D项目中Agent2D底层动作链机制的分析优化[J].系统仿真学报,2017,29(11):2782-2787.
[33]李雪;赵春霞;王琼;舒振球;基于稀疏约束的流形正则化概念分解算法,计算机辅助设计与图形学学报，2016，28（3）：381-394.
[34]李雪;赵春霞;舒振球;郭剑辉;基于超图正则化受限的概念分解算法,电子与信息学报, 2015. 37(3): 509-515.
[35]杭婷婷,冯钧,陆佳民.知识图谱构建技术:分类、调查和未来方向[J].计算机科学,2021,48(02):175-189. (CCF B)
[36]杭婷婷,郭学俊.软件工程应用型课程建设与实践[J].计算机教育,2016,259(07):134-136. (CCF C)
[37]冯钧,朱跃龙,杭婷婷,巫义锐,陆佳民,王文鹏,知识图谱研究与领域实践,人民邮电出版社,2022.(专著)
[38]董晓龙，黄俊，秦锋，洪旭东，基于多级联合的图池化学习，北京航天航空大学学报，2022. (中文核心，EI)
[39]王孝文,李乔,薛伟,钟平.联合γ-范数和TV-稀疏约束的红外弱小目标检测.航空兵器, 2022, 29(2): 30-38. (核心, CSCD)
国家发明专利
1.国家发明专利：一种中小河流实时洪水预报智能模型预报方法，201910653280.9，授权日期：2022.09.16
2.国家发明专利：基于水利知识-事理耦合网络的决策支持系统架构与方法，202010129002.6，授权日期：2022.08.26
3.国家发明专利：一种时空多元水文时间序列相似性度量方法，201810203059.9，授权日期：2022.03.08
4.国家发明专利：一种基于多特征融合技术的中小河流洪水预报方法及其预报系统，201810071703.1，授权日期：2021.11.23
研究生招生
本团队每年计划招生15名左右研究生，欢迎对人工智能相关方向（可以提前了解我们团队相关科研项目和科研论文）感兴趣的优秀本科生报考我们团队，建议在获得录取资格后和相关导师联系。本团队为研究生提供：
·经费保障:实验用设备，材料，服务器，参加学术活动等支出
·研究和学习数据挖掘、机器学习、计算机视觉、深度学习等方面的国际前沿学术问题和先进技术，资助参加高水平国际会议、学科竞赛等
·定期开展学术研讨等活动
·关注人文关怀，丰富多样团队活动，师生、同学之间关系融洽，互帮互助，共同进步
·宽松的成长环境，在锻炼中成长，以学生成长为目标的个性化指导
