Bo Li

Northwestern Polytechnical University

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i-10 index

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Co-Authors

Zhongjiang Yan

Northwestern Polytechnical University

Mao Yang

Northwestern Polytechnical University

Daqing Chen

London South Bank University

Shiyang Liang

Clemson University

Kaifang Wan

Northwestern Polytechnical University

Xiaoya Zuo

Northwestern Polytechnical University

Qiao Qu

China Medical University

Bo Yang

Northwestern Polytechnical University

E S Neretin

Moscow Aviation Institute

Qian Zhang

Huazhong University of Science and Technology

Cooperative Institutions

Northwestern Polytechnical University

220

Chinese Academy of Sciences

103

Zhejiang University

University of Chinese Academy of Sciences

Shanghai Jiao Tong University

Tsinghua University

Huazhong University of Science and Technology

Beihang University

Xidian University

Harbin Institute of Technology

Author Statistics

Papers

Citation

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Research Field

A learning-based flexible autonomous motion control method for UAV in dynamic unknown environments

Journal of Systems Engineering and Electronics (2021)

Kaifang Wan Bo Li Xiaoguang Gao Zijian Hu Zhi-peng Yang

This paper presents a deep reinforcement learning (DRL)-based motion control method to provide unmanned aerial vehicles (UAVs) with additional flexibility while flying across dynamic unknown environments autonomously. This method is applicable in both military and civilian fields such as penetration and rescue. The autonomous motion control problem is addressed through motion planning, action interpretation, trajectory tracking, and vehicle movement within the DRL framework. Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem. An improved Lyapunov guidance vector field (LGVF) method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV. In contrast to conventional motion-control approaches, the proposed methods directly map the sensorbased detections and measurements into control signals for the inner loop of the UAV, i.e., an end-to-end control. The training experiment results show that the novel DRL algorithms provide more than a 20% performance improvement over the state-of-the-art DRL algorithms. The testing experiment results demonstrate that the controller based on the novel DRL and LGVF, which is only trained once in a static environment, enables the UAV to fly autonomously in various dynamic unknown environments. Thus, the proposed technique provides strong flexibility for the controller.

10.23919/jsee.2021.000126

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Citations (17)

Intelligent video surveillance for real-time detection of suicide attempts

Pattern Recognition Letters (2018)

Wassim Bouachir Rafik Gouiaa Bo Li Rita Noumeir

RGB color model

Odds

10.1016/j.patrec.2018.03.018

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Citations (33)

UAV Target Tracking Method Based on Deep Reinforcement Learning

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI) (2022)

Haohui Zhang Pingkuan He Ming Zhang Daqing Chen E S Neretin

This study proposes a UAV target tracking method using reinforcement learning algorithm combined with Gate Recurrent Unit (GRU) to promote UAV target tracking and visual navigation in complex environment. Firstly, an algorithm Twins Delayed Deep Deterministic policy gradient algorithm (TD3) using deep reinforcement learning and the GRU gated loop unit are introduced. The unit is then added to the neural network to process continuous time data, and the algorithm TD3 is adopted to train the model so that it can drive the UAV to make autonomous flight decisions and accomplish target tracking. The proposed method is verified on the AirSim simulation platform.

Tracking (education)

10.1109/iccsi55536.2022.9970588

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Citations (2)

Distributionally Invariant Learning: Rationalization and Practical Algorithms

arXiv (Cornell University) (2022)

Jiashuo Liu Jiayun Wu Jie Peng Zheyan Shen Bo Li

Enhancing the stability of machine learning algorithms under distributional shifts is at the heart of the Out-of-Distribution (OOD) Generalization problem. Derived from causal learning, recent works of invariant learning pursue strict invariance with multiple training environments. Although intuitively reasonable, strong assumptions on the availability and quality of environments are made to learn the strict invariance property. In this work, we come up with the ``distributional stability" notion to mitigate such limitations. It quantifies the stability of prediction mechanisms among sub-populations down to a prescribed scale. Based on this, we propose the learnability assumption and derive the generalization error bound under distribution shifts. Inspired by theoretical analyses, we propose our novel stable risk minimization (SRM) algorithm to enhance the model's stability w.r.t. shifts in prediction mechanisms ($Y|X$-shifts). Experimental results are consistent with our intuition and validate the effectiveness of our algorithm. The code can be found at https://github.com/LJSthu/SRM.

rationalization

10.48550/arxiv.2206.02990

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Citations (1)

Research and Simulation of Physical Layer Abstraction Model for Next Generation WiFi Integrated Simulation

Springer eBooks (2020)

Kun Zhang Bo Li Mao Yang Zhongjiang Yan Yang Qi

PHY

Abstraction

Abstraction layer

10.1007/978-3-030-44751-9_39

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Citations (0)

Unsaturated traffic oriented spatial clustering multiusers MAC protocol for the next generation WLAN

International Journal of Internet Protocol Technology (2021)

Yong Li Bo Li Mao Yang

The next generation Wireless Local Area Network (WLAN) needs to significantly improve area throughput in high-dense deployment scenarios. Facing to the interference spread problem of OFDMA MAC, some existing studies introduce Spatial Clustering Group-based OFDMA (SCG-OFDMA) protocol, which enables geographically close nodes to send uplink OFDMA data simultaneously. However, SCG-OFDMA assumes that all the nodes have saturated traffic, which cannot be directly extended to the unsaturated traffic case. In this paper, an unsaturated traffic oriented Spatial Clustering Multiusers MAC (SCM-MAC) protocol is proposed. Specifically, Spatial Clustering Multiusers (SCM) discovery process is designed. After that, whether the cluster heads have traffic or not, they contend channel for their group members. Moreover, theoretical analysis of the optimal access radius is derived. Simulation results are in agreement with the theoretical analysis, and show that the area throughput of SCM-MAC is 34% and 50% higher than that of IEEE 802.11ax and OMAX protocol, respectively.

10.1504/ijipt.2021.116255

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Citations (1)

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Defence Technology (2020)

Bo Li Zhi-peng Yang Daqing Chen Shiyang Liang Hao Ma

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this paper, aiming to address the control problem of maneuvering target tracking and obstacle avoidance, an online path planning approach for UAV is developed based on deep reinforcement learning. Through end-to-end learning powered by neural networks, the proposed approach can achieve the perception of the environment and continuous motion output control. This proposed approach includes: (1) A deep deterministic policy gradient (DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs; (2) An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning; and (3) An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG. The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Tracking (education)

Transfer of learning

10.1016/j.dt.2020.11.014

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Citations (95)

Editorial: MAC for the Next Generation Networks in Unlicensed Band

Mobile Networks and Applications (2019)

Bo Li Lijun Qian Daji Qiao Shihai Shao

10.1007/s11036-018-1199-4

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Citations (0)

Decision-making method for the optimal coordination of fire and electronic warfare based on integrative effectiveness

Systems engineering and electronics (2007)

Bo Li

A novel decision-making method is presented for optimal coordination of hardkill and softkill systems.From a set of candidate schemes,the method seeks such an attack scheme that gives the maximal integrative combat effectiveness.To measure the integrative combat effectiveness of such integrated systems,a new evaluation model is developed by introducing the concept of capability matrix,availability matrix,weight matrix and association matrix.These matrices involve four key factors of air combat: information on battlefield,maneuverability,electronic interference and fire.The proposed method is tested and the relevant simulation results are given to show its efficiency.

Battlefield

Matrix (chemical analysis)

Operational effectiveness

Source

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Citations (3)

Denoising Convolutional Autoencoder Based B-mode Ultrasound Tongue Image Feature Extraction

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)

Bo Li Kele Xu Dawei Feng Haibo Mi Huaimin Wang

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://github.com/DeePBluE666/Source-code1.

Autoencoder

Feature (linguistics)

10.1109/icassp.2019.8682806

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Citations (21)