武妍教师主页

个人简介 Personal Profile 武妍，博士，同济大学电子与信息工程学院计算机科学与技术系，教授，博士生导师。2000年10月－2003年1月在复旦大学电子科学与技术博士后流动站进行科研工作。长期从事人工智能相关方向的教学和研究工作，主要研究方向和领域包括神经网络、深度学习、模式识别、计算机视觉以及自动驾驶等。主持和重点参与了多项国家重点研发计划、国家自然科学基金、上海市重点学科、上海市自然科学基金、铁道部基金、上海市博士后基金等项目。在国内外重要的学术刊物和学术会议上发表了150多篇学术研究论文，其中90多篇被SCI/EI收录，参与编著论著一部、主编教材两部。主要讲授课程包括《人工智能原理》、《机器学习》、《计算智能技术》、《数据结构》等。 研究方向Research Directions 人工智能,深度学习,自动驾驶 2. 机电结构优化与控制 研究内容：在对机电结构进行分析和优化的基础上，运用控制理论进行结构参数的调整，使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容：以仿生结构为研究对象，运用连续体结构拓扑优化设计理论和方法，对多相仿生结构(机构)材料进行2. 机电结构优化与控制 研究内容：在对机电结构进行分析和优化的基础上，运用控制理论进行结构参数的调整，使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容：以仿生结构为研究对象，运用连续体结构拓扑优化设计理论和方法，对多相仿生结构(机构)材料进行整体布局设计。 整体布局设计。 科研项目 目前承担的国家级科研项目：[1] 2021/12－2024/11，国家重点研发计划课题, 2021YFB2501104,基于地图的车-路-云协同感知，子课题负责人[2] 2020/01-2023/12：国家自然科学基金项目(联合基金)，U19A2069，冰雪环境下汽车智能驾驶决策与人车协同控制的关键技术研究，参加 研究成果 2010年以来已发表论文：[1] Yan Wu, Yujun Liao, Wei Jiang, Junqiao Zhao, Feilin Liu, Yujian Mo, CLSD Continual learning for lane line segmentation across domains, International Conference on Intelligent Transportation Engineering, IEEE, 2022. pp. 580-585[2] Xinneng Yang, Yan Wu, Junqiao Zhao, Feilin Liu, Yujun Liao, Yujian Mo, Efficient Adaptive Upsampling Module for Real-time Semantic Segmentation, IJPRAI, 2022 [3] Feilin Liu, Yan Wu, Xinneng Yang, Yujian Mo, Yujun Liao, Road Friction Coefficient Estimation via Weakly Supervised Semantic Segmentation and Uncertainty Estimation, IJPRAI, 2022[4] Yujun Liao, Yan Wu, Yujian Mo, Feilin Liu, Yufei He, Junqiao Zhao, UPC-Faster-RCNN: A Dynamic Self-Labeling Algorithm for Open-Set Object Detection Based on Unknown Proposal Clustering, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems （MFI ）, 2022. pp. 1-6[5] Feilin Liu, Yan Wu, Xinneng Yang, Yujian Mo, Yujun Liao, Identification of Winter Road Friction Coefficient Based on Multi-task Distillation Attention Network, Pattern Analysis and Applications, 2022, 25(2): 441-449[6] Yujian Mo, Yan Wu, Xinneng Yang, Feilin Liu, Yujun Liao, Review the state-of-art technologies of semantic segmentation based on deep learning, Neurocomputing, 2022， 493：626-646[7] Hongtu Zhou, Xinneng Yang, Junqiao Zhao, Enwei Zhang, Lewen Cai,Chen Ye, Yan Wu, Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN, CVCI’ 2022[8] Linting Guan, Yan Wu, Reduce the Difficulty of Incremental Learning with Self-Supervised Learning, IEEE Access, 2021, 9: 128540-128549[9] Yan Wu, Feilin Liu, Wei Jiang, Xinneng Yang, Multi Spatial Convolution Block for Lane Lines Semantic Segmentation, ICIC 2021, pp.31-41[10] Xinneng Yang, Yan Wu, Junqiao Zhao, Feilin Liu, GPU-Efficient Dense Convolutional Network for Real-time Semantic Segmentation, ICRA 2021，pp.553-570 [11] JunmingZhang, Yan Wu, Competition convolutional neural network for sleepstage classification, Biomedical Signal Processing and Control, 2021, 64:102318[12] GuodongZhao,Yan Wu, An Efficient Kernel Based-Feature Extraction Using a Pull-PushMethod, Applied softcomputing, 2020, 96:106584－1-106584－12[13] Xinneng Yang, Yan Wu, Junqiao Zhao, Feilin Liu, Dense Dual-Path Network for Real-time Semantic Segmentation，The 15th Asian Conference on Computer Vision (ACCV), 2021, LNCS 12622, pp. 1–18[14] Yan Wu, FeilinLiu, Linting Guan, Xinneng Yang, A survey of vision-based road parameterestimating methods, ICIC 2020, LNAI 12465, pp.314-325 [15] Wei Jiang, Yan Wu, DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block, International Conference on Robotics and Automation, Montreal, Canada, 2019, pp.5887-5862[16] Guodong Zhao, Yan Wu,Efficient Large Margin-Based Feature Extraction,Neural Processing Letter, Neural Processing Letters, 2019, 50:1257–1279 [17] Tao Yang, Yan Wu, Junqiao Zhao, Linting Guan, Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions, Cognitive Systems Research, 2019, 53:20-30[18] Junming Zhang, Yan Wu, Complex-valued unsupervised convolutional neural networks for sleep stage classification, Computer Methods and Programs in Biomedicine, 2018,164: 181-191[19] Linting Guan, Yan Wu, Junqiao Zhao and Chen Ye, Learn to Detect Objects Incrementally, In , IEEE IV2018, Changshu, Jiangsu, China, 2018, pp.403-408[20] Yan Wu, Tao Yang, Junqiao, Zhao*, Linting Guan and Wei Jiang, VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View, In , IEEE IV2018, Changshu, Jiangsu, China, 2018, pp.1767-1772[21] JunqiaoZhao, Chen Ye, Yan Wu, Linting Guan, Lewen Cai, Lu Sun, Tao Yang, Xudong He,Jun Li, Yongchao Ding, Xinglian Zhang, Xinchen Wang, Jinglin Huang, EnweiZhang, Yewei Huang, Wei Jiang, Shaoming Zhang, Lu Xiong and Tiantian Feng, TiEV: The Tongji Intelligent Electric Vehicle in theIntelligent Vehicle Future Challenge of China, 2018 IEEE International Conference on Intelligent Transportation Systems(ITSC), 2018, pp.1303-1309[22] Linting Guan, Yan Wu, Junqiao Zhao, SCAN: Semantic Context Aware Network for Accurate Small Object Detection, International Journal of Computational Intelligent Systems, 2018, 11:951-961[23] Junming Zhang, Yan Wu, Automatic Sleep Stage Classification of Single-Channel EEG by Using Complex-Valued Convolutional Neural Network, Biomedizinische Technik/Biomedical Engineering , 2018,63(2):177-190[24] Junming Zhang, Yan Wu, A New Method for Automatic Sleep Stage Classification, IEEE Transactions on Biomedical Circuits and Systems, 2017, 11(5):1097-1110[25] Yan Wu, Wei Jiang, Jiqian Li, Tao Yang, Speeding up Dilated Convolution Based Pedestrian Detection with Tensor Decomposition, ICIC 2017, Part Ⅲ, LNAI 10363, pp.117-127[26] Yan Wu, Tao Yang, Junqiao Zhao, Linting Guan, Jiqian Li, Fully Combined Convolutional Network with Soft Cost Function for Traffic Scene Parsing, ICIC 2017,PartⅠ , LNCS 10361, pp.725-731[27] Jiqian Li, Yan Wu, Junqiao Zhao, Linting Guan, Chen Ye, Tao Yang, Pedestrian Detection with Dilated Convolution, Region Proposal Network and Boosted Decision Trees, IJCNN 2017, 2017, pp.4052-4057 [28] Yan Wu, Jiqian Li, Jin Bai, Multiple Classifiers Based Feature Fusion for RGB-D Object Recognition, International Journal of Pattern Recognition and Artificial Intelligence,  2017, 31(5):1750014-1-1750014-19 [29] Guodong Zhao, Yan Wu, Gene Subset Selection for Cancer Classification Using Weight Local Modularity, Scientific Reports, 2016, 6:34759-34774 [30] Junming Zhang, Yan Wu, Jing Bai, Fuqiang Chen, Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers, Transactions of the Institute of Measurement and Control, 2016,38(4):435-451[31] Jiqian Li, Yan Wu, Junming Zhang, Guodong Zhao, A Novel Method to Fix Numbers of Hidden Neurons in Deep Neural Networks, 2015 8th International Symposium on Computational Intelligence and Design(ISCID), 2015, Hangzhou, China[32] 沈宏，武妍，基于目标特征库的多假设跟踪算法优化，数字技术与应用，2015，7：128－130[33]Fuqiang Chen, Yan Wu, Improving Image Recognition by Hierarchical Model and Denoising, 2015 International Conference on Natural Computation (ICNC), 2015, Zhangjiajie, China[34]Jing Bai, Yan Wu, Junming Zhang, Fuqiang Chen, Subset based deep learning for RGB-D object recognition, Neurocomputing, 2015,165:280-292[35]Guodong Zhao, Yan Wu, Fuqiang Chen, Jing Bai, Effective Feature Selection Using Feature Vector Graph For Classification, Neurocomputing, 2015, 151: 376-389[36]Bai J, Wu Y. SAE-RNN Deep Learning for RGB-DBased Object   Recognition, IntelligentComputing Theory, Springer International Publishing, 2014, pp.235-240. [37] Chen F.Q, Wu Y, Zhao G.D, Zhang J.M,Zhu M, Bai J. Contractive De-noising Auto-Encoder, Intelligent ComputingTheory, Springer International Publishing, 2014, pp.776-781[38] 朱明，武妍，基于深度网络的图像处理的研究，电子技术与软件工程，2014, 31：101-102[39] Ming Zhu, Yan Wu.  ANovel Deep Model for Image Recognition. 5th IEEE InternationalConference on Software Engineering and Service Sciences, 2014, pp.373-376[40] Yuanfang Ren, Yan Wu, ConvolutionalDeep Belief Networks for Feature Extraction of EEG Signal, 2014 InternationalJoint Conference on Neural Network(IJCNN) , 2014, pp.2850－2853[41] Rui Zhao, Zhihua Wei, Yan Wu, Cairong Zhao,Duoqian Miao, Bayes Network based CollaboratingControl Algorithm in Active Multi-Camera Network with Applications to ObjectTracking, Mathematical Problems in Engineering, 2014, DOI:  http://dx.doi.org/10.1155/2014/219367[42] Chen Fuqiang, Wu Yan,Bu Yude, Zhao Guodong Spectral Classification Using Restricted BoltzmannMachine, Publications of the Astronomical Society of Australia, 2014, DOI:http://dx.doi.org/10.1017/pasa.2013.38[43] Yuanfang Ren, YanWu,Yanbin Ge, A Co-training Algorithm for EEG Classificationwith Biomimetic Pattern Recognition and Sparse Representation, Neurocomputing,2014, 137: 212-222[44] Guodong Zhao, Yan Wu,Yuanfang Ren, Ming Zhu, EAMCD: An Efficient Algorithm based on Minimum CouplingDistance for community identification in complex networks, The European Physical Journal B, 2013,36(1): 14 DOI:10.1140/epjb/e2012-30697-5 [45] Yuanfang Ren, YanWu, Anefficient algorithm for high-dimensional function optimization, Soft Computing,2013, 17(6): 995-1004[46] Yan Wu, Yanbin Ge, A Novel Method for Motor Imagery EEG AdaptiveClassification Based Biomimetic Pattern Recognition, Neurocomputing, 2013,116:280-290 [47] Rui Zhao, Zhihua Wei, DuoqianMiao, Yan Wu, Lin Mei, Semi-supervised Vehicle Recognition: An ApproximateRegion Constrained Approach, Rough Setsand Knowledge Technology, Lecture Notes in Computer Science,Volume7414, 2012, pp. 161-166[48]Yanbin Ge, Yan Wu, A NewHybrid Method with Biomimetic Pattern Recognition and Sparse Representation forEEG, CCIS 304,P212-217, ICIC 2012[49]Rui Zhao, YanWu, Junbo Zhu, Zhihua Wei, Efficient Vehicle Identification UsingMPEG-7 Color Layout Descriptor,2011 IEEE International conference onSupernetworks and System Management, 2011. pp.128-131[50] 武妍,徐凯，基于增量半监督仿生模式识别的运动想象脑电识别,中国生物医学工程学报，2011，30（6）：878－884[51] Yanbin Ge, Yan Wu, Towards Adaptive Classification of MotorImagery EEG Using Biomimetic Pattern Recognition, LNCS 6819, ICIC 2011, pp.455-460[52] 耿辉，武妍，基于动态入侵的自适应遗传算法研究，计算机工程与应用，2011，47（7）：40-42，102[53] 梁旭东，武妍, 基于邻域特征和聚类的图像分割方法，计算机工程，2011，37（3）：201-203[54] 顾翼，武妍, 基于结构知识的手写体汉字合成方法，计算机工程，2011，37（3）：266-268[55] Yan Wu, Hui Geng,Xiao-Yue Bian, A new method of signature verification based on biomimeticpattern recognition theory, The 2nd International Conference onBiomedical Engineering and Computer Science, Wuhan, China, 2011, pp.357-360[56] Yan Wu, Bing Xu, Xiao-Yue Bian, An improved PCNNmodel and a new removing algorithm of salt and pepper noise, 2010 Secondinternational conference on computational intelligence and natural computing,Wuhan, China, 2010, pp.178-182[57] 王改良，武妍，基于仿生模式识别理论的声调识别，计算机应用，2010，30（10）：2709－2711[58] Xu Kai, Wu Yan，Motor Imagery EEG Recognition Based OnBiomimetic Pattern Recognition，20103rd International Conference on Biomedical Engineering andInformatics(BMEI'10)，Yantai，China， 2010，pp. 955-959[59] 王改良，武妍，用入侵的自适应遗传算法训练人工神经网络，红外与毫米波学报，2010，29（2）：136-139 [60] 卞晓月，武妍，基于CT图像的肺实质细分割综合方法，重庆邮电大学学报（自然科学版），2010，22（5）：665-668授权专利：发明专利：武妍、莫宇剑、刘飞麟，一种自动驾驶路面摩擦系数预测方法、电子设备及介质，ZL202110718997.4 学生信息 当前位置：教师主页 > 学生信息 入学日期 所学专业 学号 学位 招生信息 当前位置：教师主页 > 招生信息 招生学院 招生专业 研究方向 招生人数 推免人数 考试方式 招生类别 招生年份