基本信息
陈亚冉  女  硕导  中国科学院自动化研究所
电子邮件: chenyaran2013@ia.ac.cn
通信地址: 北京市海淀区中关村东路95号
邮政编码:

研究领域

深度强化学习;基于大模型的机器人任务规划;机器人交互感知与控制;

招生信息

计算机专业、自动化专业、人工智能相关专业


招生专业
081101-控制理论与控制工程
081104-模式识别与智能系统

教育背景

2013-09--2018-07   中国科学院自动化研究所   博士学位
2009-09--2013-07   哈尔滨工程大学   学士学位

工作经历

2018.07-2022.10,中国科学院自动化研究所,助理研究员

2022.11-现在,中国科学院自动化研究所,副研究员

专利与奖励

   
奖励信息
(1) IEEE Transactions on Cognitive and Developmental Systems期刊论文2020年度优秀论文奖, 特等奖, 其他, 2020
(2) ICRA RoboMaster全球人工智能挑战赛感知、路径规划和决策三项比赛冠军, 特等奖, 其他, 2020
(3) 中国“AI+”创新创业大赛总决赛一等奖, 一等奖, 其他, 2019
(4) 中国智能车未来挑战赛-复杂交通环境认知基础能力离线测试-前方车辆距离检测一等奖,前方车辆检测一等奖, 特等奖, 其他, 2017
专利成果
( 1 ) 面向智能驾驶的移动目标轨迹预测方法、系统、装置, 发明专利, 2020, 第 1 作者, 专利号: CN111597961A

( 2 ) 智能驾驶车道保持方法及系统, 专利授权, 2020, 第 4 作者, 专利号: CN109466552B

( 3 ) 基于光流和卡尔曼滤波的多目标追踪方法、系统、装置, 发明专利, 2019, 第 1 作者, 专利号: CN110415277A

( 4 ) 面向驾驶辅助系统的危险目标检测方法、装置, 发明专利, 2018, 第 2 作者, 专利号: CN107609483A

科研活动

   
科研项目
( 1 ) 适配硬件和任务的One-shot神经网络架构搜索, 负责人, 国家任务, 2021-01--2023-12
( 2 ) 室外复杂视觉条件下的机器人感知和目标识别, 参与, 国家任务, 2022-01--2025-12
( 3 ) 多智能体深度强化学习***, 参与, 中国科学院计划, 2021-07--2025-06

发表论文

1].  Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao*, “BNAS-v2: memory-efficient and performance-collapse-prevented broad neural architecture search,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022,DOI: 10.1109/TSMC.2022.3143201

[2].  Yaran Chen, Ruiyuan Gao, Fenggang Liu, Dongbin Zhao*, “ModuleNet: knowledge-inherited neural architecture search,” IEEE Transactions on Cybernetics, DOI 10.1109/TCYB.2021.3078573. (SCI Q1, IF 11.448)

[3].  Haoran Li, Yaran Chen, Qichao Zhang, Dongbin Zhao, “BiFNet: bidirectional fusion network for road segmentation,” IEEE Transactions on Cybernetics, DOI 10.1109/TCYB.2021.3105488. (SCI Q1, IF 11.448)

[4].  Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao*, Z. Q. Sun, P. C. L. Chen, “BNAS: neural architecture search using broad scalable architecture,” IEEE Transactions on Neural Networks and Learning Systems, 2021, DOI 10.1109/TNNLS.2021.3067028. (SCI Q1, IF 10.451)

[5].  Nannan Li, Yu Pan, Yaran Chen*, Zixiang Ding, Dongbin Zhao, Zenglin Xu, “Heuristic rank selection with progressively searching tensor ring network,” Complex & Intelligent Systems, 2021, DOI: 10.1007/s40747-021-00308-x. (SCI Q2, IF 4.927)

[6].  Yi Lu, Yaran Chen, Dongbin Zhao*, Bao Liu, Zhichao Lai, Jianxin Chen, “CNN-G: convolutional neural network combined with graph for image segmentation with theoretical analysis,” IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 631 – 644, Sept. 2021. (热点论文) (SCI Q3, IF 2.98/2019)

[7].  Yi Lu, Yaran Chen, Dongbin Zhao*, Dong Li, “Graph neural network-based inference in a Markov network for visual navigation,” Neurocomputing, vol. 421, pp. 140-150, 2021. DOI: 10.1016/j.neucom.2020.07.091 (SCI Q2, IF 5.19/2019).

[8].  Yaran Chen, Haoran Li, Ruiyuan Gao, Dongbin Zhao*, “Boost 3D object detection via point clouds segmentation and fused 3D GIoU-L1 loss,” IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TG.2020.3022698. (SCI Q1, IF 8.793)

[9].  Xiaodong Zhao, Yaran Chen*, Jin Guo and Dongbin Zhao, “A spatial-temporal LSTM model for human trajectory prediction,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 4, pp. 965-974, July 2020. DOI: 10.1109/JAS.2020.1003228. (SCI Q2 IF 5.13)

[10]. Dong Li, Dongbin Zhao, Qichao Zhang, Yaran Chen, Reinforcement learning and deep learning based lateral control for autonomous driving, IEEE Computational Intelligence Magazine, vol. 14, no. 2, pp. 83 – 98, 2019.

[11]. Yaran Chen, Dongbin Zhao*, Le Lv, Qichao Zhang, “Multi-task learning for dangerous object detection in autonomous driving”, Information Sciences, vol. 432, pp. 559-571, 2018. DOI: 10.1016/j.ins.2017.08.035.

[12]. Dongbin Zhao , Yaran Chen, Le Lv, “Deep reinforcement learning with visual attention for vehicle classification,” IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 4, pp. 356-367, 2017. (Outstanding Paper Award)

[13]. Xin Liu, Zixiang Ding, Nannan Li, Yaran Chen, Dongbin Zhao, “EGCN: ensemble graph convolutional network for neural architecture performance prediction,” 8th IEEE International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), Dec. 11-12, 2021, Beijing, China.

[14]. Zhisong Zhang, Yaran Chen, Haoran Li and Qichao Zhang, “IA-CNN: A generalised interpretable convolutional neural network with attention mechanism,” IEEE/INNS International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, July 18 -22, 2021.

[15]. Junwen Chen, Yi Lu, Yaran Chen, Dongbin Zhao, Zhonghua Pang, “ContourRend: a segmentation method for improving contours by rendering,” M. Han et al. (Eds.): ISNN 2020, LNCS 12557, pp. 251–260, 2020.

[16]. Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, Device placement optimization for deep neural networks via one-shot model and reinforcement learning, Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI) – Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Canberra, Australia, Dec. 1 - 4, 2020.

[17]. Nannan Li, Yaran Chen, Zixiang Ding and Dongbin Zhao, “Shift-invariant convolutional network search,” The International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, July 19 -24, 2020.

[18]. Haoran Li, Qichao Zhang, Dongbin Zhao and Yaran Chen, “RailNet: an information aggregation network for rail track segmentation,” The International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, July 19 -24, 2020.

[19]. Zixiang Ding, Yaran Chen, Nannan Li and Dongbin Zhao, “Simplified space based neural architecture search,” Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI) – Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Xiamen, China, Dec.6 - 9, 2019

[20]. Nannan Li, Yaran Chen Zixiang Ding Dongbin Zhao, “Multi-objective neural architecture search for light-weight model,” Chinese Automation Congress, Hangzhou, Nov. 22-24, 2019, accepted.

[21]. Yaran Chen, Dongbin Zhao, “Deep Kalman filter with optical flow for multiple object tracking,” IEEE International Conference on Systems, Man and Cybernetics, Bari, Italy, Oct. 6-9, 2019.

[22]. Hao Su, Yaran Chen, Shiwen Tong and Dongbin Zhao, “Real-time multiple object tracking based on optical flow,” The 9th International Conference on Information Science and Technology (ICIST), Hulunbuir, Inner Mongolia, China during August 2-5, 2019.

[23]. Junjie Wang, Qichao Zhang, Dongbin Zhao and Yaran Chen, “Lane change decision-making through deep reinforcement learning with rule-based constraints,” The International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14 -19, 2019.

[24]. Yi Lu, Yaran Chen, Dongbin Zhao, Jianxin Chen, “Graph-FCN for image semantic segmentation,” In: Lu H., Tang H., Wang Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science, vol 11554, pp. 97-105, Springer, Cham

[25]. Haoran Li, Xiaolei Zhou, Yaran Chen, Dongbin Zhao, “Comparison of 3D object detection based on LiDAR point cloud,” IEEE Data Driven Control and Learning Systems Conference (DDCLS), Dali, China, May 25-27, 2019.

[26]. Yi Lu, Yaran Chen, Dongbin Zhao, “Hybrid deep learning based moving object detection via motion prediction,” Chinese Automation Congress (CAC 2018), Xi’an, China, Nov. 30-Dec.2, 2018, pp. 1862-1867.

[27]. Haoran Li, Dongbin Zhao, Yaran Chen, Qichao Zhang, “An efficient network for lane segmentation,” International Conference on Cognitive Systems and Information Processing (ICCSIP 2018), Beijing, China, Nov.29 - 31, 2018.

[28]. Yaran Chen, Dongbin Zhao, Haoran Li, Dong Li and Ping Guo, “A temporal-based deep learning method for multiple objects detection in autonomous driving,” The International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, July 8 – 13, 2018.

[29]. Dong Li, Dongbin Zhao, Yaran Chen and Qichao Zhang, “DeepSign: deep learning based rraffic sign recognition,” The International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, July 8 – 13, 2018. (Best Paper Award Final List).

[30]. Yaran Chen, Dongbin Zhao and Haoran Li, Deep kalman filter with optical flow for multiple object tracking, IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019), 06-09 October Bari, Italy, 2019.

[31]. Yaran Chen, Dongbin Zhao, “Multi-task learning with Cartesian product-based multi-objective combination for dangerous object detection”, F. Cong et al. (Eds.): ISNN 2017, Part I, LNCS 10261, pp. 28–35, 2017.

[32]. Yaran Chen, Dongbin Zhao, Le Lv, Chengdong Li, “A visual attention based convolutional neural network for image classification,” Proceedings of the 12th World Congress on Intelligent Control and Automation (WCICA 2016), Guilin, China, July 12-15, 2016, pp.764-769. (Steve and Rosalind Hsia Best Biomedical Paper Award List)

[33]. Dongbin Zhao, Yuanheng Zhu, Le Lv, Yaran Chen and Qichao Zhang ‘Convolutional Fitted Q Iteration For Vision-Based Control Problems’; IJCNN 2016.(EI)

[34]. Dongbin Zhao, Le Lv, Yaran Chen, and Yuanheng Zhu. ‘Winner-Take-All Autoencoder Based Image Clustering’; The 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016) .(EI)