![]()
- [1] Jinsheng Xiao, Yuanxu Wu, Yunhua Chen, Shurui Wang, Zhongyuan Wang, Jiayi Ma, LSTFE-Net: Long Short-Term Feature Enhancement Network for Video Small Object Detection, The 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2023),14613-14622, June 18-22, Vancouver Canada..
- [2] Jinsheng Xiao, Haowen Guo, Jian Zhou, Tao Zhao, Qiuze Yu, Yunhua Chen, Zhongyuan Wang, Tiny object detection with context enhancement and feature purification, Expert Systems with Applications 211 (2023),118665:1-10..
- [3] Chen,Y., Xiong, Z., Feng, R., Chen, P., & Xiao, J., RMPE: Reducing Residual Membrane Potential Error for Enabling High-Accuracy and Ultra-low-latency Spiking Neural Networks. The 2023 International Conference on Neural Information Processing(ICONIP2023), 81-93. November 20-23, Changsha, China..
- [4] Chen,Y., Bai, W., Huang, Q., & Xiao, J. Efficient Motion Symbol Detection and Multikernel Learning for AER Object Recognition.IEEE Transactions on Cognitive and Developmental Systems,2022,14(4):1544-1552..
- [5] Yunhua Chen and Yingchao Mai and Ren Feng and Jinsheng Xiao, "An adaptive threshold mechanism for accurate and efficient deep spiking convolutional neural networks." Neurocomputing 469 (2022): 189-197..
- [6] Weijie Bai, Yunhua Chen, Ren Feng, Yuliang Zheng. Accurate and Efficient Frame-based Event Representation for AER Object Recognition. The 2022 International Joint Conference on Neural Networks (IJCNN2022). 2022.7.18-2022.7.23, Padua, Italy.
- [7] Junfeng Lei, Yuxuan Dong, Tao Zhao, Jinsheng xiao, Yunhua Chen(通讯作者), Bijun Li, Novel shrinking residual convolutional neural network for efficient accurate stereo matching, J. Vis. Commun. Image R. 72 (2020) 102872..
- [8] Yunhua Chen, Yingchao Mai, Jingshen Xiao, Ling Zhang. Improving Antinoise Ability of DNNs Via a Bio-inspired Noise Adaptive Activation Function Rand Softplus[J]. Neural Computation,2019,31(6): 1215-1233. .
- [9] Yunhua Chen, Jin Du, Qian Liu, Ling Zhang, and Yanjun Zeng. Robust and Energy Efficient Expression Recognition Based on Improved Deep ResNets[J]. Biomedizinische Technik / Biomedical Engineering, 2019,64(5)：1-10. .
- [10] Junlan Dong, Ling Zhang, Yunhua Chen, Wenchao Jiang. Occlusion expression recognition based on non-convex low-rank double dictionaries and occlusion error model[J]. Signal Processing: Image Communication, 76 (2019) 8–88. .
- [11] Junfeng Lei, Shangyue Zhang, Wentao Zou,Jinsheng Xiao, Yunhua Chen, HaiGang Sui. Single image rain removal based on depth of field and sparse coding[C], 24th International Conference on Pattern Recognition (ICPR),Beijing, China, August 20-24, 2018..
- [12] Jinsheng Xiao , Wentao Zou , Yunhua Chen , Wen Wang , Junfeng Lei. Single image rain removal based on depth of field and sparse coding[J], Pattern Recognition Letters ，116 (2018) 212–217. .
- [13] Yunhua Chen, Jin Du, QianLiu, and Bi Zeng, Robust Expression Recognition using ResNet with a Biologically-plausible Activation Function[C], the 8th Pacific-Rim Symposium on Image and Video Technology , 2017.1120-2017.1124, Wuhan, China. .
- [14] Luo Yuan, Zhang Ling,Chen Yunhua, Jiang Wenchao. Facial Expression Recognition Algorithm based on Reverse Co-salient Regions (RCSR) Features [C]. Proceedings of the 4th International Conference on Information Science and Control Engineering (ICISCE), 2017, 11: 326-329..
- [15] Yunhua Chen, Weijian Liu, Ling Zhang, Mingyu Yan, Yanjun Zeng, Hybrid Facial Image Feature Extraction and Recognition for Non-invasive Chronic Fatigue Syndrome Diagnosis[J], Computers in Biology and Medicine, 2015, 64(5):30-39..
- [16] Weijian Liu, Zeqi Chen, Yunhua Chen, Ruohe Yao, An -BTV Regularization Algorithm for Super-resolution[C], 2015 IEEE International Conference on Computer Science and Network Technology(ICCSNT 2015), 19-20 December 2015, Harbin, China. .
- [17] 冯忍, 陈云华, 熊志民, 陈平华. 自校准首脉冲时间编码神经元模型[J]. 计算机科学, 2024, 51(3): 244-250..
- [18] 郑宇亮，陈云华，白伟杰，陈平华．融合事件数据和图像帧的车辆目标检测[J/OL]．计算机应用.https://link.cnki.net/urlid/51.1307.tp.20230810.1310.002.
- [19] 曾建新，陈云华，李炜奇，陈平华．跨脉冲传播的深度脉冲神经网络训练方法[J/OL]．计算机应用研究. https://doi.org/10.19734/j.issn.1001-3695.2023.11. 0562.
- [20] 兰浩鑫，陈云华. 基于脉冲频率与输入电流关系的SNN训练算法,计算机工程与应用，2022,58(10):87-92..
- [21] 陈运享，冯忍，陈云华.具有混合奖惩信号的脉冲时间依赖可塑性算法，微电子学与计算机，2022,39(9):20-25..
- [22] 吴一凡,陈云华,张灵,陈平华.一种非局部连接的抗噪性储备池构建方法,控制理论与应用. 2020,37(6):1413-1418..
- [23] 黄庆坤,陈云华,张灵,兰浩鑫. 时域感兴趣区域精确定位与膜电位多核调整的动态视觉传感器数据分类,控制理论与应用. 2020,37(8):1837-1845..
- [24] 麦应潮，陈云华, 张灵. 具有生物真实性的强抗噪性神经元激活函数[J]. 计算机科学，2019,46(7):206-210..
- [25] 杜进, 陈云华, 张灵, 麦应潮. 基于改进深度残差网络的低功耗表情识别[J]. 计算机科学，2018, 45(9): 301-307,319..
- [26] 杜力凯，张灵，陈云华。一种改进的ZigBee路由优化算法, 计算机科学, 2015, 42(5): 153-156, 168 .
- [27] 董亮(学生)，张灵，陈云华，基于限制广播的ZigBee分布式动态能量均衡协议，传感技术学报，2014，27(8): 1120-1124，.
- [28] 陈云华，张灵，丁伍洋，严明玉，精神疲劳实时监测中多面部特征时序分类模型，中国图象图形学报，2013，18（08）：953-960.
- [29] 陈云华，余永权，张灵，丁伍洋，基于面部视觉特征的精神疲劳可拓辨识模型，计算机科学，2013，40（02）：284-288.
- [30] 陈云华，张灵，严明玉，面向精神疲劳监测的实用虹膜定位方法，微电子学与计算机，2013，30（01）：17-20 .
- [31] 陈云华, 陈平华, 精神疲劳识别的可拓模型与策略生成, 计算机科学, 2015, 42(5): 300-304. .
- [32] 高素文，张灵，陈云华，丁伍洋，基于多策略融合的亚像素精度立体匹配研究，计算机应用与软件，2014, 31(4):231-234..
- [33] 谢国波，陈云华，张灵，丁伍洋，基于嘴巴特征点曲线拟合的哈欠检测，计算机工程与科学，2014，36（04）：731-736.
- [34] 丁伍洋，陈云华，张灵，高素文，基于眼动参数云融合模型的疲劳检测方法，计算机工程与设计，2013（11）：4067-4071.
- [35] 向文,张灵,陈云华,姬秋敏. 结合结构自相似性和卷积网络的单幅图像超分辨率[J].计算机应用,2018,38(03):854-858..
- [36] 向文, 张灵,陈云华,姬秋敏. 基于结构自相似性和形变块特征的单幅图像超分辨率算法[J].计算机应用,2019,39(01):275-280..
- [37] 姬秋敏,张灵,罗源,陈云华,向文.基于多帧间区域性光流特征的精神疲劳检测[J].计算机工程与设计,2018,39(08):2582-2586..
- [38] 姬秋敏,张灵,陈云华,麦应潮,向文,罗源.基于视觉机制与协同显著性的自发式表情识别[J].计算机工程与设计,2019,40(06):1741-1746..
- [39] 罗源,张灵,陈云华,朱思豪,田小路. 基于层次结构化字典学习的人脸表情识别[J]. 计算机应用研究, 2017,34(11):3514-3517..
- [40] 罗源,张灵,陈云华,曾碧,姜文超.一种提取反向协同显著区域特征的表情识别算法[J].小型微型计算机系统,2018,39(07):1585-1589..
- [41] 董俊兰,张灵,陈云华.自适应加权融合显著性结构张量和LBP的表情识别[J].计算机工程与应用,2019,55(17):185-190..
- [42] 董俊兰,张灵,陈云华,姜文超.基于非凸低秩分解双字典误差模型的遮挡表情识别[J].小型微型计算机系统,2019,40(07):1538-1543..
- [43] 张哲源,张灵,陈云华.结合分块LBP与投影字典对学习的表情识别[J].计算机工程与应用,2019,55(12):149-154..
- [44] 文国成,曾碧,陈云华.一种适用于激光SLAM大尺度地图的闭环检测方法[J].计算机应用研究,2018,35(06):1724-1727+1732..
- [45] 曾碧,任万灵,陈云华.基于CycleGAN的非配对人脸图片光照归一化方法[J].广东工业大学学报,2018,35(05):11-19..
- [46] 叶向荣,刘怡俊,陈云华,熊炯涛. 基于L_(1/2)自适应稀疏正则化的图像重建算法[J].广东工业大学学报,2017,34(06):43-48. .
- [47] 朱思豪,张灵,罗源,陈云华.基于spin image的人脸点云特征定位[J].计算机工程与设计,2017,38(08):2209-2212..
- [48] 袁嘉杰,张灵,陈云华. 基于注意力卷积模块的深度神经网络图像识别[J]. 计算机工程与应用,2019,55(08):9-16..
- [49] 赵锐,陈云华,邓九英.一种改进的双群进化规划算法[J].计算机工程,2010,36(18):21-23..
- [50] 陈云华,曾碧,曾洪周.基于J2EE与ArcGIS的测绘成果管理系统研究与实现[J].微电子学与计算机,2011,28(01):83-87+91. .
个人信息
- 副教授
硕士生导师
- 教师拼音名称：Chen Yunhua
- 入职时间：2003-07-10
- 所在单位：计算机学院
- 学历：博士研究生毕业
- 联系方式：5350299@qq.com
- 学位：工学博士学位
- 职称：副教授
- 在职信息：在职
- 学科：计算机应用技术
其他联系方式
- 邮箱:b76a8c69f3dbd5b0558dc144a55a4e83488ee4a89c11fcbb1018f85bffb31f231f4c78c7a53827d639396bef5464d715d9133a32e916d35b84d480145d317ccfe2afaa7d30dbd25b75b91e71842225565eb72f451d6460ce550750a6a13dde7d32bb883fb1fe1dabfb19cc5d00ca738463d21eb18ae898a6171a9715f1c5ae11
- 广东工业大学网络信息与现代教育技术中心电话：020-39323866 版权所有@ 2020-2021保留所有权利
粤ICP备05008833号
手机版