学术论文(带*为通信作者) [1]Rui Yang,Kaoru Ota, Mianxiong Dong, Xiaojun Wu. Semantic layout-guided diffusion model for high-fidelity image synthesis in "The Thousand Li of Rivers and Mountains", DOI: 10.1016/j.eswa.2024.125645 [2]Rui Yang, Xiaojun Wu, and Shengfeng He. MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention. IEEE Transactions on Visualization and Computer Graphics. 2024.3502395. DOI: 10.1109/TVCG. [3]Rui Yang, Honghong Yang, Li Zhao, Qin Lei, Mianxiong Dong, Kaoru Ota, Xiaojun Wu, Ref2Sketch-SA: One-Shot Reference-based Structure-Aware Image to Sketch Synthesis. The 39th AAAI Conference on Artificial Intelligence (AAAI 2025), Philadelphia, PA, USA, February 25-March 4, 2025. [4]YANG Honghong, SHANG Junchao, LI Jingjing, ZHANG Yumei, and WU Xiaojun. Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint[J], Chinese Journal of Electronics. 2022(002):031 [5]Honghong Yang, Longfei Guo, Xiaojun Wu, Yumei Zhang. Scale‑aware attention‑based multi‑resolution representation for multi‑person pose estimation[J]. Multimedia Systems.2022(1):28 [6] Sun W , Su Y , Wu X ,et al.EEG denoising through a wide and deep echo state network optimized by UPSO algorithm[J]. Applied Soft Computing, 2021, 105(3):107149.DOI:10.1016/j.asoc.2021.107149. [7] Zhu Y, Zhao L, Zhang Y, Wu X. Receiver selection for multi-target tracking in multi-static Doppler radar systems[J].EURASIP Journal on Advances in Signal Processing, 2021, 2021(1):1-16.DOI:10.1186/s13634-021-00826-3. [8] Honghong Yang, Jinming Wen, Xiaojun Wu, et al. An Efficient Edge Artificial Intelligence MultiPedestrian Tracking Method With Rank Constraint[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019(15)7 [9] Zhang Y, Bai s, Lu g, Wu x. Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction[J]. Chinese Journal of Electronics, 2019,28(01):131-139. [10] Li J, Zhang Q, Zhang Y, Wu X, et al. Hidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals[J]. Chinese Journal of Electronics, 2018, 27(6):1221-1228. [11] Jiang T , Zhang Y , Wu X ,et al. Single Image Super Resolution via a Refined Densely Connected Inception Network[C]//International Conference on Image Processing.IEEE, 2018.DOI:10.1109/icip.2018.8451441. [12]Jiang, Tao, Wu, Xiaojun, Yu, Zhang, Shui, Wuyang, Lu, Gang,ect al. Recursive Inception Network for Super-Resolution[C]//24th International Conference on Pattern Recognition, ICPR 2018. IEEE 2018.DOI:10.1109/ICPR.2018.8546059 [13] Gang L, ZHou M, Wang X, Li X, Wu X, Zhang Y. Principles of the Complete Voronoi Diagram Localization.IEEE transactions on mobile computing, 2016, 15(8). [14] Yuping Su; Ying Li; Xiaojun Wu*; Lei Liu. Outage performance for amplify-and-forward two-hop multiple-access channel with noisy relay and interference-limited destination. IET COMMUNICATIONS.2018,12(2):205-213, DOI:10.1049/iet-com.2017.0315(SCI) [15] Jingjing Li; Yumei Zhang; Jiayu Man; Yun Zhou; Xiaojun Wu∗. SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks.Physica A, 2017,468.740-749,DOI:10.1016/j.physa.2016.11.126(SCI) [16] Lei Yang; Junxi Zhang; Xiaojun Wu∗; Yumei Zhang; Jingjing Li. A chaotic time series prediction model for speech signal encoding based on genetic programming. Applied Soft Computing. 2016, 38. 754-761.DOI:10.1016/j.asoc.2015.10.003(SCI) [17] Zhang Yu-Mei; Hu Xiao-Jun; Wu Xiao-Ju*; Bai Shu-lin; Lu Gang. Volterra prediction model for speech signal series. ACTA PHYSICA SINICA, 2015, 64(20). 117-129. DOI: 10.7498/aps.64.200507(SCI) [18]陈龙杰,张钰,张玉梅,吴晓军*.基于多注意力多尺度特征融合的图像描述生成算法[J].计算机应用,2019,39(02):354-359. [19] Tao Jiang; Yu Zhang*; Xiaojun Wu*; Rao Yuan; Mingquan Zhou. Single Image Super-Resolution via Squeeze and Excitation Network. The 29TH The British Machine Vision Conference (BMVC). 2018.09.03. Newcastle, UK.(EI) [20] Tao Jiang, Yu Zhang*, Xiaojun Wu*, Gang Lu, Fei Hao, Yumei Zhang, Single Image Super Resolution via a Refined Densely Connected Inception Network[C]//2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018: 3588-3592. |
发明专利 1. 张玉梅, 李丽娜, 吴晓军, 李华芳, 沈佳怡. 融合密集残差和注意力机制的脑电时空去噪方法, [国内发明:ZL202210878058.0] 2. 张玉梅, 廖胜利, 吴晓军, 杨红红, 杨小蕊, 李丽娜. 基于卷积-长短期记忆网络音乐脑电时空特征分类方法. [国内发明:ZL202110940695.1] 3. 张玉梅,李丛, 吴晓军, 杨红红. 基于残差生成对抗网络的脑电信号去噪方法. [国内发明:ZL202110666391.0] 4. 吴晓军, 李菁菁, 张玉梅, 杨红红. 基于R-LSTM模型的脑电信号情绪识别方法. [国内发明:ZL202011093477.0] 5. 李鹏, 崔苑茹, 刘宏, 王小明, 吴晓军, 李黎, 张立臣.一种基于机会网络缓存共享的协作小组资源调度方法. [国内发明:ZL202010831287.8] 6. 李鹏,刘宏,王小明, 吴晓军, 张立臣, 李黎, 郭龙江. 校园协作学习环境下机会网络信息快速扩散方法. [国内发明: ZL201910627354.1] 7. 吴晓军, 孙维彤, 张玉梅, 路纲. 基于一维残差卷积神经网络的脑电信号去噪方法. [国内发明: ZL201811650041.x] 8. 张玉梅, 戎宇莹,,吴晓军, 李丛. 基于代理模型Volterra建模的脑电信号编码解码方法. [国内发明:ZL201811566452.0] 9. 吴晓军, 马悦,一种人体行为的识别分类方法. [国内发明:ZL201210330136.X] 10. 吴晓军, 马悦, 良梓, 学生学习行为采集与分析系统及其方法. [国内发明:ZL201210076652.4] 11. 吴晓军, 王颖. 温度数据采集装置及其存储与传输方法。 [发明专:ZL.2011.1.0053982.7] |
获奖 [1] 移动网络中信息传播过程调控动力学理论与优化方法,陕西高校科学技术一等奖, 2023 [2] 移动无线自组织网络中数据高效可靠性传输理论和关键技术研究, 陕西省自然科学二等奖, 2020 [3] 移动无线自组织网络中数据高效可靠传输理论和关键技术研究, 陕西高校科学技术一等奖,2018. [4] 网络温湿度采集系统关键技术及应用研究, 陕西高校科学技术二等奖, 2014. [5] 网络采集系统关键技术及应用研究, 陕西省科学技术三等奖, 2015. 指导学生获奖 [1]2019第十届亚洲青少年机器人锦标赛活动 VEX U 工程挑战赛——大学组,亚军,2019. [2]2019第十届亚洲青少年机器人锦标赛活动 VEX U 工程挑战赛——大学组,最佳设计奖,2019. [3]2019第十届亚洲青少年机器人锦标赛活动 VEX U 工程挑战赛——大学组,二等奖,2019. [4]2018-2019VEX机器人亚洲公开赛,一等奖,2019. [5]2018-2019VEX机器人亚洲公开赛,设计奖,2019. [6]第十四届全国大学生“恩智浦”杯智能汽车竞赛,西部赛区双车组二等奖,2019. [7]第十四届全国大学生“恩智浦”杯智能汽车竞赛,西部赛区四轮组二等奖,2019. |