Mainly engaged in: 

Research on brain-inspired computing, computer vision,  robot multimodal perception, and robot skill learning and development, etc.

Specific research work:

1. brain inspired perceptual cognitive computing, brain like pulse neural networks, etc;

2. research and application of vision detection, recognition and tracking algorithms for robots and UAVs;

3. research on robot skill learning and development based on reinforcement learning and continuous learning;

Korea University, South Korea                                                            Ph.D. 2010 – 2015.

Harbin Institute of Technology, China                                                  B.S. 2006 – 2010.                                

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences                                                                                            Associate Professor   2017- Pres.

Brain Inspired Intelligence Research Center, Institute of Automation, Chinese Academy of Sciences                                                                                                                                   Assistant Professor   2015-2017

[1]Y. F. Lu, J. W. Gao,  et.al, “A Cross-Scale and Illumination Invariance-Based Model for Robust Object Detection in Traffic Surveillance Scenarios,” IEEE Transactions on Intelligent Transportation Systems, 2023. 24(7): 6989-6999.

[2]Y. F. Lu, X. Yang, et.al, “A Novel Biologically-inspired Structural Model for Feature and Correspondence,” IEEE Transactions on Cognitive and Developmental Systems, 2023. 15(2): 844-854.

[3]Y. F. Lu, Q. Yu, et.al, “Cross Stage Partial Connections based Weighted Bi-directional Feature Pyramid and Enhanced Spatial Transformation Network for Robust Object Detection,” Neurocomputing, 2022. 513: 70-82.

[4]Y. F. Lu, W. J. Zhao, “What will the robots be like in the future?” National Science Review, 2019. 6(5): 1059–1061. 

[5]L.Y. Guo, Y. F. Lu*, et.al. Transformer-based Spiking Neural Networks for Multimodal Audio-Visual Classification, IEEE Transactions on Cognitive and Developmental Systems, DOI: 10.1109/TCDS.2023.3327081.

[6]Y. Li, Y. F. Lu*, et.al. Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model, Energy Reports, 2022, 13(8): 807-814.

[7]C. Ma, Y. F. Lu*, “Distributed Nonsynchronous Event-triggered State Estimation of Genetic Regulatory Networks with Hidden Markovian Jumping Parameters”, Mathematical Biosciences and Engineering, 2022, 19(12): 13878-13910. 

[8] Y. Li, Y. F. Lu*, et.al. "Electromagnetic Force Analysis of a Power Transformer under the Short-Circuit Condition," IEEE Transactions on Applied Superconductivity, 2021, 31(8): 1-3.

[9]Y. F. Lu, H. Z. Zhang, et.al, “Dominant Orientation Patch Matching for HMAX,” Neurocomputing, 2016. 193:155-166. 

[10]Y. F. Lu, T. Kang, et.al. “Enhanced hierarchical model of object recognition based on a novel patch selection method in salient regions,” Computer Vision, IET, 2015, 9(5): 663-672.

[11]Y. F. Lu, H. Qiao, et.al, “Image Recommendation based on a Novel Biologically Inspired Hierarchical Model,” Multimedia Tools and Applications, 2018, 77 (4):4323-4337.

[12]Y. F. Lu, L. H. Jia, et.al, “Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment,” International Journal on Wavelet, Multiresolution, and Information Processing,2019,17(2), 1940007.

[13]Y. F. Lu, M. Lim, et.al. “Extended Biologically Inspired Model for Object Recognition Based on Oriented Gaussian-Hermite Moment,” Neurocomputing, 2014. 139(2): 189-201. 

[14]H. Z. Zhang, Y. F. Lu, et.al, “B-HMAX: A fast Binary Biologically Inspired Model for Object Recognition,” Neurocomputing. 2016. 218: 242-250.

[15]J. Wang, Y. F. Lu*, et al. A novel CNN model for fine-grained classification with large spatial variants, International Conference on Intelligent Computing and Signal Processing, 2020.

[16]Y. F. Lu, H. Qiao, et.al, “A Novel Biologically Inspired Hierarchical Model for Image Recommendation,” 14th International Symposium on Neural Networks, Sapporo, Japan, 2017.

[17]Y. F. Lu, H. Z. Zhang, et.al. “A Novel Patch Selection Method in Salient Regions of Object recognition,” 30th Korean Conference of Institute of Control, Robotics and Systems, Seoul, South Korea, 2015.4.22-4.25.

[18]Y. F. Lu, A. X. Zhang, et.al. “Multi-Scale Scene Text Detection Based on Convolutional Neural Network,” Chinese Automation Congress, 2019.

[19]Y. F. Lu, H. Z. Zhang, et.al. Enhanced Hierarchical Model of Object Recognition Based on Saliency Map and Keypoint. Institute of Control, Robotics and Systems, 2015:53-54.

[20]W. Y. Zhang, Y. F. Lu*, et.al. “Convolutional Neural Networks on Apache Storm,” Chinese Automation Congress, 2019.

[21]B. C. Liu, Y. F. Lu*, et al. Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot, Chinese Automation Congress, 2021.

[22]J. Y. Chang, Y. F. Lu*, et al. Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system，International Conference on Intelligent Unmanned Systems，2020. 

[23]Y. Li, Y. F. Lu*, Dynamic Electromagnetic Force Analysis of a Power Transformer with Regulated Windings, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, 2020.

[24]Z. D. Sun, Y. F. Lu*, Underwater attached organisms intelligent detection based on an enhanced YOLO, IEEE International Conference on Electrical Engineering, Big Data and Algorithms, 2022, 1118-1122.

[25]Z. Y. Li, Y. F. Lu*, et al. Memory Mechanisms Based Few-shot Continual Learning Railway Obstacle Detection, The 5th China Symposium on Cognitive Computing and Hybrid Intelligence, 2023.

[26]D. Y. Lee, Y. F. Lu, et.al. "3-D Vision Based Local Obstacle Avoidance Method for Humanoid Robot," 2012 International Conference on Controls Automation and Systems, Jeju，South Korea, 2012.10.20-10.23.

[27]商迪，吕彦锋*，乔红，“受人脑中记忆机制启发的增量目标检测方法”，计算机科学，2023, 50 (2): 267-274.