博士/教授，安徽财经大学硕士生导师、安徽工业大学博士生导师，IEEE高级会员，安徽省学术与技术带头人，安徽省领军人才教学名师，宝钢基金会优秀教师。中国管理科学与工程学会理事、安徽省生物信息学会（筹）副理事长、安徽省机器人学会常务理事，安徽省电子信息自动化及通信类专业合作委员会主任委员。1998年和2004年于合肥工业大学分别获学士和硕士学位，2006年于中国科学技术大学获博士学位，2007年香港城市大学高级研究助理，2008-2012年美国路易斯维尔大学、范德堡大学博士后。
担任“智能控制与信息处理”安徽省科研平台创新团队负责人。主持国家自然科学基金项目4项、省部级项目6项、产学研项目10余项。发表学术论文200余篇，其中SCI收录论文120余篇，出版学术专著1部，论文被引5000余次。授权国家发明专利23项、实用新型专利3项，转化发明专利3项（金额105万）。第一完成人获得安徽省自然科学奖1项，担任3个期刊编委。研究兴趣包括：智能信息处理、生物医学信息挖掘、产品表面质量检测、数据信息系统研发等。
担任“信息处理系列课程”省级教学团队负责人。主持省级教学改革研究重大项目2项、参与1项，主持省级教学改革研究重点项目2项，其它省级质量工程项目7项。发表教学研究论文12篇，出版教材2部。获安徽省研究生教学成果奖特等奖1项、二等奖2项，安徽省教学成果奖一等奖2项。主要承担人工智能导论、工程数值计算方法、信号与系统等本科生课程，和模式识别、数据挖掘等研究生课程的教学。
一、科研项目
1.2021年，国家自然科学基金面上项目：“基于多源异质网络的药物-靶标相互作用预测拓研究”（No.62172004），60万。
2.2020年，产学研合作项目：“基于电机故障监测分析模型的诊断与评估技术研究”。
3.2020年，产学研合作项目：“基于深度学习模型的轴承故障分析研究”。
4.2019年，安徽省教育厅高校科学研究重大项目：“基于质谱分析的大规模代谢分子谱峰提取与识别研究”(KJ2019ZD05)。
5.2019年，重大产学研项目：“精密零件智能视觉检测系统研发”，宁波金创享智能。
6.2019年，重大产学研项目：“汽车零部件装备数据管理系统研发”，宁波均创智能。
7.2018年，安徽大学信息保障技术协同创新中心开放课题，“高速质谱库搜索算法”（ADXXBZ201705）。
8.2017年，安徽工业大学青年拔尖人才项目，“视频图像计数技术及开发”。
9.2017年，教育部冶金减排与资源综合利用重点实验室开放课题，“氮氧化物减排控制研究”。
10.2016年，安徽省科研平台创新团队项目：“智能控制与信息处理”。
11.2015年，国家自然科学基金面上项目：“基于网络拓扑结构的蛋白质相互作用数据质量控制与预测方法研究”（No.61472282）。
12.2015年，安徽省自然科学基金面上项目：“基于流形学习的蛋白质相互作用数据去噪与网络重建”（No.1508085MF129)。
13.2015年，安徽省高校优秀青年人才重点项目：“疾病相关的蛋白质相互作用网络功能分析研究”。
14.2013年，国家自然科学基金面上项目：“蛋白质结合面残基预测中的特征差异表达和协同作用研究”（No.61272269）。
15.2013年，重大产学研项目：“焦炉自动加热与自动火落判断系统”，山西焦炭集团。
16.2009年，国家自然科学基金青年项目：“基于结构域组成变换的蛋白质相互作用预测方法研究”（No.60803107）。
二、科研论文
(一) 国际期刊论文：（*通讯作者,#并列第一作者）
生物医学信息处理
1.Peng Chen,Huimin Shen,You Zhi Zhang, Bing Wang*, Pengying Gu,”SGNet: Sequence-based Convolution and Ligand Graph Network for Protein Binding Affinity Prediction”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: 10.1109/TCBB.2023.3262821
2.Wenjing Qiu*,Jiasheng Yang, Bing Wang, Min Yang,Geng Tian, Peizhen Wang, Jialiang Yang, “Evaluating microsatellite instability of colorectal cancer based on multimodal deep learning integrating histopathological and molecular data”, Frontiers in Oncology,2022, 12:925079, DOI: 10.3389/fonc.2022.925079
3.Junlin Xu, Lingyu Cui, Jujuan Zhuang, YajieMeng, Pingping Bing, Bingsheng He, Geng Tian, Choi Kwok Pui, Taoyang Wu, Bing Wang, Jialiang Yang, “Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data”,Computers in Biology and Medicine, 2022, 146:105697
4.Min Yang, Huandong Yang, Lei Ji, Xuan Hu,Geng Tian,Jialiang Yang,Bing Wang*, “A multi-omics machine learning framework in predicting the survival of colorectal cancer patients”, Computers in Biology and Medicine, 2022,
5.Sijie Yao, ChunHou Zheng, Bing Wang*, Peng Chen*, “A two‑step ensemble learning for predicting protein hot spot residues from whole protein sequence”, Amino Acids, 2022, 54:765–776https://doi.org/10.1007/s00726-022-03129-5
6.Hailiang Sun, Ailan Wang*, Bing Wang, Geng Tian, Jialiang Yang, Ming Liao,”Systematic tracing of susceptible animals to SARS-CoV-2 by a bioinformatics framework”, Frontiers in Microbiology, 2022, DOI: 10.3389/fmicb.2022.781770.
7.Zenghui Wang, Jun Zhang*, Xiaochu Zhang, Peng Chen*, Bing Wang*, “Transformer Model for Functional Near-Infrared Spectroscopy Classification”, IEEE Journal of Biomedical and Health Informatics, 2022,26(6):2559 - 2569,DOI: 10.1109/JBHI.2022.3140531
8.Minjie Li, Ziheng Wu, Wenyan Wang, Kun Lu, Jun Zhang, Yuming Zhou, Zhaoquan Chen, Dan Li, Shicheng Zheng, Peng Chen*, Bing Wang*, “Protein-Protein Interaction Sites Prediction Based on an Under-Sampling Strategy and Random Forest Algorithm”, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022, 19:3646-3654, DOI 10.1109/TCBB.2021.3123269
9.Bing Wang,Changqing Mei, Yuanyuan Wan, Yuming Zhou, Mu-Tian Cheng,Chun-Hou Zheng , Lei Wang Jun Zhang, Peng Chen*, Yan Xiong*, “Imbalance Data Processing Strategy for Protein Interaction Sites Prediction”, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021, 18(3):985-994, DOI:10.1109/TCBB.2019.2953908
10.Cheng Wang, Jun Zhang, Peng Chen*, Bing Wang*, Predicting drug–target interactions Based on the Ensemble Models of Multiple Feature Pairs,International journal of molecular sciences, 2021, 22(12):6598.
11.ShanShan Hu, Peng Chen*, DeNan Xia,Bing Wang and Jinyan Li, “A Convolutional Neural Networks to Discriminate Drug-target Interactions”.IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021, 18(4):1315-1324,DOI:10.1109/TCBB.2019.2940187.
12.Wenyan Wang,Panpan Lu, Yuming Zhou, Mu-tian Cheng, Yan Wang, Chun-hou Zheng,Yan Xiong, Peng Chen, Zhiwei Ji,Bing Wang*, “Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis”, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021, 18(3):1026-1034, DOI: 10.1109/TCBB.2020.2983894.
13.Chengliang Zhang, Youzhi Zhang, Bing Wang*, Peng Chen, “Prediction of Drug-target Binding Affinity by An Ensemble Lear ning System with Network Fusion Information”, Current Bioinformatics, 2021, 16 (10), 1223-1235.
14.Qiao Zhou, Jian Liu, Ling Xin, Yanyan Fang, Lei Wan, Dan Huang, JinchenGuo, Jianting Wen, Bing Wang*,“Exploratory Compatibility Regularity of Traditional Chinese Medicine on Osteoarthritis Treatment: A Data Mining and Random Walk-Based Identification”, Evidence-Based Complementary and Alternative Medicine 2021, https://doi.org/10.1155/2021/2361512
15.Zhiwei Ji*,Shu Tao,Bing Wang,“Editorial: Artificial Intelligence (AI) optimized Systems Modeling for the Deeper Understanding of Human Cancers”, Front.Bioeng.Biotechnol.,:10 Aug 2021, DOI: 10.3389/fbioe.2021.756314.
16.Waplnbo Zhu, Xianzuo Zhang, Shiyuan Fang*, Bing Wang* and Chen Zhu*, “Deep learning improves osteonecrosis prediction of femoral head after internal fixation using hybrid patient and radiograph variables”, Frontiers in Medicine,2020, doi: 10.3389/fmed.2020.573522（中科院小类二区）
17.Cheng Wang, Wenyan Wang, Kun Lu, Jun Zhang, Peng Chen*, Bing Wang*, “Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition”, International journal of molecular sciences, 2020, 21(16):5694.（中科院大类二区）
18.Huimin Shen, Youzhi Zhang, Chunhou Zheng, Bing Wang*, Peng Chen, “A Cascade graph convolutional network for predicting protein–ligand binding affinity”, International journal of molecular sciences, 2021, 22(8), 4023; https://doi.org/10.3390/ijms22084023
19.Jianting Wen, Jian Liu*, Bing Wang*, Hui Jiang, Lei Wan, Ling Xin, Yue Sun, Yanqiu Sun, Ying Zhang, Xinlei Du, Xin Wang, Jie Wang, “Identification of Key RNAs Associated with Self-perception of Patient in Rheumatoid Arthritis”, Frontiers in Medicine,2020, 7:567. doi: 10.3389/fmed.2020.00567（中科院小类二区）
20.Yanyan Fang, Jian Liu*, Ling Xin, Yue Sun, Lei Wan, Dan Huang, Jianting Wen, Ying Zhang, and Bing Wang*, “Identifying Compound Effect of Drugs on Rheumatoid Arthritis Treatment Based on the Association Rule and a Random Walking-BasedModel”, BioMed Research International, 2020,2020:4031015,doi:10.1155/2020/4031015
21.Xianzuo Zhang, Kun Chen, Xiaoxuan Chen, Nikolaos Kourkoumelis, Guoyuan Li, Bing Wang* and Chen Zhu*, “Integrative Analysis of Genomics and Transcriptome Data to Identify Regulation Networks in Female Osteoporosis”, Frontiers in Genetics,2020, doi:10.3389/fgene.2020.600097
22.ShanShan Hu, Peng Chen, PengyingGu, Bing Wang*,“A Deep Learning-based Chemical System for QSAR Prediction”, IEEE Journal of Biomedical and Health Informatics, 2020,24(10):3020-3028,DOI: 10.1109/JBHI.2020.2977009.（中科院大类二区）
23.Aijun Deng#, Huan Zhang#, Wenyan Wang, Jun Zhang, Dingdong Fan, Peng Chen*,Bing Wang*,“Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm”, International journal of molecular sciences, 2020, 21(7), 2274.（中科院大类二区）
24.Peng Chen, Tong Shen, Youzhi Zhang, Bing Wang*, “A Sequence-Segment Neighbor Encoding Schema for Protein Hotspot Residue Prediction”, Current Bioinformatics, 2020, 15(5),.
25.ShanShan Hu, Peng Chen*, Bing Wang and Jinyan Li, “Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks”. BMC bioinformatics2019, 20 (25), 1-12
26.Jiechen Li, Xueyong Li, Xiang Feng, Bing Wang, Bihai Zhao, Lei Wang, “A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations”, BMC bioinformatics2019, 20 (1), 1-13
27.Yangyang Wang, Qingxin Xiao, Peng Chen, Bing Wang*, “In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method”, International journal of molecular sciences 2019, 20 (17), 4106（中科院大类二区）
28.Ye Wang†, Changqing Mei†, Yuming Zhou, Yan Wang, Chunhou Zheng, Xiao Zhen, Yan Xiong, Peng Chen*,Jun Zhang and Bing Wang*, “Semi-supervised prediction of proteininteraction sites from unlabeled sampleInformation”,BMC Bioinformatics 2019, 20(S25):699
29.Quanya Liu,Bing Wang*, Peng Chen*, Zhang Jun. “Hot Spot prediction in protein-protein interactions by an ensemble learning”. BMC Systems Biology 2018, 12(9):132.
30.Muchun Zhu, Xiaoping Song, Peng Chen*, Wenyan Wang and Bing Wang*, “dbHDPLS: Database of Human Disease Protein-Ligand Structure”. Computational Biology and Chemistry 2019, 78:353-358.
31.Quanya Liu, Peng Chen*, Jun Zhang,Bing Wang* and Jinyan Li, “dbMPIKT: A kinetic and thermodynamic database of mutant protein interaction”. BMC Bioinformatics 2018, 19:455.
32.Bing Wang,Kun Lu, Xiao Zheng, Benyue Su, Yuming Zhou, Peng Chen*, Jun Zhang*, “Early Stage Identification of Alzheimer’s Disease Using a Two-stage Ensemble Classifier”, Current Bioinformatics, 2018, 13(5), 529-535.
33.Zhiwei Ji#, Bing Wang#, Ke Yan, Ligang Dong, GuanminMeng, Lei Shi, “A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy”, BMC systems biology,2017,11 (7), 127
34.Shan-Shan Hu, Peng Chen, Bing Wang, Jinyan Li, “Protein binding hot spots prediction from sequence only by a new ensemble learning method”, Amino acids, 2017, 49 (10), 1773-1785
35.Jinjian Jiang, Nian Wang, Peng Chen, Chunhou Zheng, Bing Wang*, “Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System”, International journal of molecular sciences, 2017, 18 (7), 1543（中科院大类二区）
36.JunZhang,MuchunZhu, PengChen,BingWang*,“DrugRPE:RandomProjectionEnsembleApproach to Drug-Protein Interaction Prediction”, Neurocomputing, 2017,228:256-262（中科院大类二区）
37.Jinjian Jiang, Nian Wang, Peng Chen, Jun Zhang, and Bing Wang*, “DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction”,BioMed Research International, 2017, Article ID 6340316
38.Ke Yan, Bing Wang, Holun Cheng, Zhiwei Ji, Jing Huang, and Zhigang Gao, “Molecular Skin Surface-Based Transformation Visualization between Biological Macromolecules”, Journal of Healthcare Engineering , 2017, Article ID 4818604
39.PengChen,JunZhang,XinGao,JinyanLi,andJun-fengXia,BingWang*,“ASequence-BasedDynamicEnsembleLearningSystemforProteinLigand-BindingSitePrediction”,IEEE/ACMTransactionsonComputationalBiologyandBioinformatics, 2016, 13(5):901-912 （中科院小类二区）
40.Rezaul Islam Khan, Yushu Wang, Shajia Afrin, Bing Wang, Yumin Liu, Xiaoqing Zhang, Lei Chen, Weiwen Zhang, Lin Gao, Gang Ma, “Transcriptional regulator PrqR plays a negative role in glucose metabolism and oxidative stress acclimation in Synechocystis sp. PCC 6803”, Scientific Reports, 2016, 6:32507.
41.Zhiwei Ji, GuanminMeng, Deshuang Huang, Xiaoqiang Yue, Bing Wang*, “NMFBFS: a NMF-based feature selection method in identifying pivotal clinical symptoms of Hepatocellular carcinoma”, Computational and Mathematical Methods in Medicine, 2015,2015:12
42.Zhiwei Ji, Bing Wang*, “Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm”, BioMed Research International, 2014,2014:12
43.Bing Wang*, De-shuang Huang,Changjun Jiang, A new strategy for protein interface identification using manifold learning method. IEEE Transactions on NanoBioscience,2014, 13(2):118-2
44.Zhu-Hong You, Ying-Ke Lei, Lin Zhu, Junfeng Xia, Bing Wang*. “Prediction of Protein-Protein Interactions from Amino Acid Sequences with Ensemble Extreme Learning Machines and Principal Component Analysis”. BMC Bioinformatics, 2013, 14(Suppl 8):S10
45.Lin Zhu, Zhu-Hong You,Bing Wang, De-Shuang Huang, “t-LSE:A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks”, Plos One, 2013,8(4): e58368
46.Bing Wang*, Wenlong Sun, Jun Zhang, and Peng Chen. “Current Status of Machine Learning-Based Methods for Identifying Protein-Protein Interaction Sites”,Current Bioinformatics, 2013, 8(2):177-182
47.Colins O. Eno, Guoping Zhao, AvinashanarayanVenkatanarayan, Bing Wang, Elsa R. Flores, Chi Li, “Noxa couples lysosomal membrane permeabilization and apoptosis during oxidative stress” Free Radical Biology & Medicine, 2013,65:26-37
48.Junfeng Xia,QingguoWang, PeilinJia, Bing Wang, WilliamPao, ZhongmingZhao, “NGS Catalog: A Database of Next Generation Sequencing Studies in Humans”. Human Mutation, 2012,33: E2341-2355
49.Bing Wang, Peng Chen, Jun Zhang, Guangxin Zhao and Xiang Zhang, “Inferring protein-protein interactions using a hybrid genetic algorithm/support vector machine method” , Protein & Peptide Letters, Vol.17, 2010,17:1079-1084
50.Bing Wang, Peng Chen, Peizhen Wang, Guangxin Zhao and Xiang Zhang, “Radial basis function neural network ensemble for predicting protein-protein interaction sites in heterocomplexes” Protein & Peptide Letters, 2010,17(9): 1111-1116
51.Peng Chen, Chunmei Liu, Legand Burge, Jinyan Li, Mahmood Mohammad, William Southerland, Clay Gloster and Bing Wang ,”DomSVR: domain boundary prediction with support vector regression from sequence information alone”, Amino Acids, 2010, 39(3):713-726
52.Peng Chen, Bing Wang, Hau San Wong, D.S.Huang,“Prediction of Protein B-factors Using Multi-class Bounded SVM”. Proteins & Peptide Letters,pp.2007,14(2):185-190
53.Bing Wang, Peng Chen, D.S.Huang, Jing-Jing Li, Tat-Ming Lok, Michael R. Lyu, “Predicting protein interaction sites from residue spatial sequence profile and evolution rate, FEBS Letters, 2006,580(2):380-384
54.Bing Wang, Hau San Wong, D.S.Huang “Inferring Protein-Protein Interaction Sites From Residue Evolutionary Conservation Information”, Protein & Peptide Letters, 2006,13(10):999-1005
55.Jing-Jing Li, D.S.Huang, Bing Wang, Peng Chen, “Identifying Protein-Protein Interfacial Residues in Heterocomplexes Using Residue Conservation Scores,” International Journal of Biological Macromolecules, 2006,38(3-5):241-247
56.Hong-Qiang Wang, D.S.Huang, Bing Wang, Optimization of radial basis function classifiers using simulated annealing algorithm for cancer classification, IEE Electronics Letters, 2005,41(11):630-632
工业图像
57.Wenyan Wang, ChunfengMi, Ziheng Wu, Kun Lu, Hongming Long, Baigen Pan, Dan Li, Jun Zhang, Peng Chen, Bing Wang*, “A Real-Time Steel Surface Defect Detection Approach with High Accuracy”, IEEE Transactions on Instrumentation & Measurement, 2022, 71:5005610, DOI:10.1109/TIM.2021.3127648.
58.Wenyan Wang, Kun Lu, Zhiheng Wu, Dan Li, Hongming Long, Jun Zhang, Peng Chen, and Bing Wang*, Surface Defects Classification of Hot Rolled Strip Based on Few-shot Learning,ISIJ International, 2022, Vol. 62 (2022), No. 6, pp. 1222–1226，doi:10.2355/isijinternational.ISIJINT-2020-051.
59.Wenyan Wang, Kun Lu, Zhiheng Wu, Hongming Long, Jun Zhang, Peng Chen, and Bing Wang*, Prediction Method of Core Dead Stock Column Temperature Based on PCA and Ridge Regression”, ISIJ International, 2021, 61 (11):2785–2791, doi:10.2355/isijinternational.ISIJINT-2020-497.
60.Wenyan Wang, Kun Lu, Zhiheng Wu, Hongming Long, Jun Zhang, Peng Chen, and Bing Wang*, “Surface Defects Classification of Hot Rolled Strip Based onImproved Convolutional Neural Network”, ISIJ International, 2021, 61 (5):1579–1583, doi:10.2355/isijinternational.ISIJINT-2020-451.
机器学习方法
61.Ziheng Wu, Bing Wang, Cong Li, A new robust fuzzy clustering framework considering different data weights in different clusters, Expert Systems With Applications, 2022, 206:117728
62.Dan Li, Qiannan Xu, Wenian Yu, Bing Wang, “SRP-AKAZE: An improved accelerated KAZE algorithm based on the sparse random projection”, IET Computer Vision, 2020, DOI: 10.1049/iet-cvi.2019.0622
63.Ziheng Wu*,Bing Wang, “DwfwFcm: An effective fuzzy c-means clusteringframework considering the different data weights and feature weights”, Journal of Intelligent & Fuzzy Systems, 2019, 37: 4339-4347.
64.Lin-Wei Ge, Jun hang, Yi Xia, Peng Chen,Bing Wang*, Chun-HouZheng,“Deep spatial attention hashing network for image retrieval”, Journal of Visual Communication and Image Representation, 63 (2019) 102577.
65.Fang Zhou, Tin-Yu Wu, Jun Liu, Bing Wang, and Mohammad S. Obaidat.“A 3D neural network for moving microorganism extraction”,Neural Computing& Applications, 2018.30(1), pp: 57-67.
66.Sen Xia, Peng Chen, Jun Zhang, Xiao-Ping Li, Bing Wang*, “Utilization of Rotation-Invariant Uniform LBP Histogram Distribution and Statistics of Connected Regions in Automatic Image Annotation Based on Multilabel Learning”, Neurocomputing, 2017,228: 11-18（中科院大类二区）
67.Zhiwei Ji, Zhu-Hong You, Shuping Deng, Bing Wang*. “Predicting dynamic deformation of retaining structure by LSSVR-based time series method”, Neurocomputing, 2014,137(5 ):165–172
68.Jian-XunMi, De-shuang Huang, Bing Wang, Xingjie Zhu. “The Nearest-Farthest Subspace Classification for Face Recognition”. Neurocomputing, 2013, 113(3) :241-250
（二）国内期刊论文：
69.陈兆权，王伟业，陈思乐，王超，徐笑娟，王兵，周郁明，卢新培，大气压脉冲微波Ar/N2等离子体射流的时空特性研究,中国科学:物理学 力学 天文学. 2024,54(03):235211
70.陈鹏;马子涵;章军;夏懿;王兵;梁栋，融合注意力机制的小麦赤霉病语义分割网络，中国农机化学报， 2023,44(04),145-152
71.周郁明; 穆世路; 杨华; 王兵; 陈兆权，Si/SiC混合开关最优门极延时及其在逆变器中的应用，电子学报，20230303，
72.程微;朱志峰;姚勇;王兵;周芳;唐得志，一种用于3D四轮定位的RANSAC改进算法（英文），Journal of Measurement Science and Instrumentation，20220906，Vol.13(4):407-417
73.陈兆权, 杨洁, 陈思乐, 徐笑娟, 罗进, 王兵, 周郁明, 卢新培，大气压脉冲调制微波发卡氩等离子体射流的电离行为研究，中国科学: 物理学力学天文学，2022，第52卷，第9期：29511
74.米春风，卢琨，汪文艳，王兵，基于机器视觉的带钢表面缺陷检测研究进展，安徽工业大学学报(自然科学版)，2022年第2期 180-188
75.张汉,张德祥,陈鹏,章军,王兵.并行注意力机制在图像语义分割中的应用[J].计算机工程与应用,2022,58(09):151-160.
76.马小陆，梅宏，谭毅波，龚瑞，王兵，蝴蝶优化算法的移动机器人全局路径规划研究，机械科学与技术，2022
77.黄健 郑春厚 章军 王兵 陈鹏，基于小样本度量迁移学习的表面缺陷检测， 《模式识别与人工智能》 2021年第5期407-414.
78.史杨潇,章军,陈鹏,王兵.基于轻量级网络的钢铁表面缺陷分类[J].计算机应用,2021,41(06):1836-1841.
79.马小陆; 梅宏; 龚瑞; 王兵; 吴紫恒，基于改进ACS算法的移动机器人路径规划研究”, 湖南大学学报(自然科学版)， 2021,48(12).
80.马小陆; 巩朝光; 王兵; 郑睿, 基于前瞻窗口的单线激光雷达特征提取方法研究，湖南文理学院学报(自然科学版). 2021,33(03)
81.马小陆; 袁书生; 王兵; 吴紫恒，均匀分布Logistic混沌序列的RRT*路径规划算法研究，机械科学与技术，2021-05-16（网络首发）
82.冯旭刚;鲍立昌;章家岩;王兵;魏新园;王胜;魏舜昊;徐帅;陈雨薇，热风炉拱顶温度模糊自适应滑模控制策略，西安交通大学学报. 2021,55(06)
83.马小陆，袁书生，王兵，吴紫恒，一种模拟激光雷达的自主移动机器人虚拟墙方法研究，《导航与定位学报》， 2020年第5期.
84.马小陆，王明明，王兵，YOLOv3在安全帽佩戴检测中的应用研究，《河北工程大学学报》（自然科学版），2020,37(04).
85.马小陆; 梅宏; 王兵; 吴紫恒，基于JPS策略的改进RRT*移动机器人全局路径规划算法，中国惯性技术学报. 2020,28(06)
86.马小陆，方洋，王兵，吴紫恒，一种改进的YOLO v3红外图像行人检测方法，《湖北理工学院学报》，2020,36(06).
87.周郁明*，蒋保国，刘航志，陈兆权，王兵. 包含SiC/SiO2 界面电荷的SiC MOSFET的SPICE 模型，中国电机工程学报，2019（19）：5604-5612+5888.
88.周郁明*，蒋保国，杨婷婷，王兵，陈兆权. SiC/SiO2 界面态电荷对SiC MOSFET 短路特性影响的研究，电子科技大学学报，2019，48（6）：947-953.
89.周郁明*, 武钰, 陈兆权, 王兵. 基于常通型碳化硅 JFET 的自供电固态断路器. 电力电子技术，2020，2：63-65
90.周郁明*，蒋保国，陈兆权，王兵. 场效应晶体管短路失效的数值模型. 西安电子科技大学学报，2019，46(4)：175-184.
91.周郁明, 刘航志, 杨婷婷, 王兵*, “碳化硅MOSFET电路模型及其应用”[J]. 《西安电子科技大学学报》, 2018,45(3):97-101
92.章家岩, 孟庆喜, 冯旭刚, 沈浩, 王兵，“迭代学习控制策略在连铸结晶器振动系统中的应用”[J]. 振动与冲击, 2018,37(17),93-100+106
93.周郁明, 刘航志, 杨婷婷, 王兵*,“碳化硅MOSFET的Matlab/Simulink建模及其温度特性评估”[J]. 南京航空航天大学学报, 2017, 49(6): 851-857
94.马小陆,王涛,王兵, “负电容压电阻尼振动系统控制器参数研究”[J]，传感器与微系统，2011，30(9)：44- 4 6.
95.王兵,陈科, “液压系统噪声产生原因分析及对策”[J]，合肥工业大学学报, 25(s1)：.978-981, 2005.
三、著作:
1.Bing Wang, Peng Chen, Jun Zhang, “Computational Intelligence in Protein-Ligand Interaction Analysis”, Elsevier, 2024, Paperback ISBN: 9780128243862, eBook ISBN: 9780128244357
2.Zhiwei Ji, Shu Tao and Bing Wang.“Artificial Intelligence (AI) Optimized Systems Modeling for the Deeper Understanding of Human Cancers”, Frontiers Media SA, 2021 ; ISBN, 2889717178, 9782889717170
3.Bing Wang and Xiang Zhang “Evolutionary computation applications in current bioinformatics”, New Achievements In Evolutionary Computation, book edited by: Peter Korosec, ISBN: 978-953-307-053-7, Publisher: Intech, Publishing Date: February 2010（章节）.
四、入选人才计划与获奖
1.2023年，入选安徽省高端人才引育行动项目-领军人才教学名师。
2.2023年，宝钢教育基金会优秀教师。
3.2023年，入选安徽省学术与技术带头人。
4.2023年，入选安徽工业大学佳山学者。
5.2020年，安徽省自然科学奖三等奖：生物行为启发的智能计算方法及应用。
6.2017年，入选安徽工业大学青年拔尖人才培养计划（A类）。
7.2016年，马鞍山市侨届先进个人。
8.2016年，入选教育部/安徽省留学回国人员创新创业扶持计划。
9.2016年，入选安徽省高校优秀青年人才重点项目。
10.2014年，入选安徽省学术与技术带头人后备人选。
11.2007年，中国科学院院长优秀奖。
12.2006年，中国科学技术大学光华奖。
