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1. FViT: A Focal Vision Transformer with Gabor Filter

Author

Shi, Yulong, Sun, Mingwei, Wang, Yongshuai, Wang, Rui, Sun, Hui, and Chen, Zengqiang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Vision transformers have achieved encouraging progress in various computer vision tasks. A common belief is that this is attributed to the competence of self-attention in modeling the global dependencies among feature tokens. Unfortunately, self-attention still faces some challenges in dense prediction tasks, such as the high computational complexity and absence of desirable inductive bias. To address these issues, we revisit the potential benefits of integrating vision transformer with Gabor filter, and propose a Learnable Gabor Filter (LGF) by using convolution. As an alternative to self-attention, we employ LGF to simulate the response of simple cells in the biological visual system to input images, prompting models to focus on discriminative feature representations of targets from various scales and orientations. Additionally, we design a Bionic Focal Vision (BFV) block based on the LGF. This block draws inspiration from neuroscience and introduces a Multi-Path Feed Forward Network (MPFFN) to emulate the working way of biological visual cortex processing information in parallel. Furthermore, we develop a unified and efficient pyramid backbone network family called Focal Vision Transformers (FViTs) by stacking BFV blocks. Experimental results show that FViTs exhibit highly competitive performance in various vision tasks. Especially in terms of computational efficiency and scalability, FViTs show significant advantages compared with other counterparts. Code is available at https://github.com/nkusyl/FViT, Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2024

5. EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

Author

Shi, Yulong, Sun, Mingwei, Wang, Yongshuai, Ma, Jiahao, and Chen, Zengqiang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Thanks to the advancement of deep learning technology, vision transformers has demonstrated competitive performance in various computer vision tasks. Unfortunately, vision transformers still faces some challenges such as high computational complexity and absence of desirable inductive bias. To alleviate these issues, we propose a novel Bi-Fovea Self-Attention (BFSA) inspired by the physiological structure and visual properties of eagle eyes. This BFSA is used to simulate the shallow and deep fovea of eagle vision, prompting the network to learn the feature representation of targets from coarse to fine. Additionally, we design a Bionic Eagle Vision (BEV) block based on BFSA. It combines the advantages of convolution and introduces a novel Bi-Fovea Feedforward Network (BFFN) to mimic the working way of biological visual cortex processes information in hierarchically and parallel. Furthermore, we develop a unified and efficient pyramid backbone network family called Eagle Vision Transformers (EViTs) by stacking BEV blocks. Experimental results show that EViTs exhibit highly competitive performance in various computer vision tasks such as image classification, object detection and semantic segmentation. Especially in terms of performance and computational efficiency, EViTs show significant advantages compared with other counterparts. Code is available at https://github.com/nkusyl/EViT, Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2023

7. Distributed Nash Equilibrium Seeking Algorithm for Aggregative Games with Time-Varying Directed Communication Networks

Author

Zhu, Rui, Wang, Fuyong, Liu, Zhongxin, Chen, Zengqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Hua, Yongzhao, editor, Liu, Yishi, editor, and Han, Liang, editor

Published
2024
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8. Intelligent Trajectory Tracking Control of Unmanned Parafoil System Based on SAC Optimized LADRC

Author

Zheng, Yuemin, Tao, Jin, Sun, Qinglin, Yang, Jinshan, Sun, Hao, Sun, Mingwei, Chen, Zengqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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9. Adaptive Load Frequency Control and Optimization Based on TD3 Algorithm and Linear Active Disturbance Rejection Control

Author

Zheng, Yuemin, Tao, Jin, Sun, Qinglin, Sun, Hao, Sun, Mingwei, Chen, Zengqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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10. Output tracking based on extended observer for nonlinear uncertain systems

Author

Wang, Xinhua, Chen, Zengqiang, and Yuan, Zhuzhi

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

A high-gain extended observer is designed for a class of nonlinear uncertain systems. This observer has the ability of estimating system uncertainty, and it can be used to estimate the derivatives of signal up to order n. The controller based on this extended observer can make the tracking error and its derivatives converge to zero rapidly even when uncertainties and disturbances exist. The result of simulation indicates that this method has satisfactory control performance for nonlinear uncertain systems., Comment: 6 pages, 1 figure, published in Control and Decision, 19(10), 2004, pp. 1113-1116

Published
2023

11. Probabilistic Framework of Howard's Policy Iteration: BML Evaluation and Robust Convergence Analysis

Author

Wang, Yutian, Ni, Yuan-Hua, Chen, Zengqiang, and Zhang, Ji-Feng

Subjects
Mathematics - Optimization and Control
Abstract

This paper aims to build a probabilistic framework for Howard's policy iteration algorithm using the language of forward-backward stochastic differential equations (FBSDEs). As opposed to conventional formulations based on partial differential equations, our FBSDE-based formulation can be easily implemented by optimizing criteria over sample data, and is therefore less sensitive to the state dimension. In particular, both on-policy and off-policy evaluation methods are discussed by constructing different FBSDEs. The backward-measurability-loss (BML) criterion is then proposed for solving these equations. By choosing specific weight functions in the proposed criterion, we can recover the popular Deep BSDE method or the martingale approach for BSDEs. The convergence results are established under both ideal and practical conditions, depending on whether the optimization criteria are decreased to zero. In the ideal case, we prove that the policy sequences produced by proposed FBSDE-based algorithms and the standard policy iteration have the same performance, and thus have the same convergence rate. In the practical case, the proposed algorithm is still proved to converge robustly under mild assumptions on optimization errors., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2022

25. Strong current-state and initial-state opacity of discrete-event systems

Author

Han, Xiaoguang, Zhang, Kuize, Zhang, Jiahui, Li, Zhiwu, and Chen, Zengqiang

Subjects
Computer Science - Cryptography and Security
Abstract

Opacity, as an important property in information-flow security, characterizes the ability of a system to keep some secret information from an intruder. In discrete-event systems, based on a standard setting in which an intruder has the complete knowledge of the system's structure, the standard versions of current-state opacity and initial-state opacity cannot perfectly characterize high-level privacy requirements. To overcome such a limitation, in this paper we propose two stronger versions of opacity in partially-observed discrete-event systems, called \emph{strong current-state opacity} and \emph{strong initial-state opacity}. Strong current-state opacity describes that an intruder never makes for sure whether a system is in a secret state at the current time, that is, if a system satisfies this property, then for each run of the system ended by a secret state, there exists a non-secret run whose observation is the same as that of the previous run. Strong initial-state opacity captures that the visit of a secret state at the initial time cannot be inferred by an intruder at any instant. Specifically, a system is said to be strongly initial-state opaque if for each run starting from a secret state, there exists a non-secret run of the system that has the same observation as the previous run has. To verify these two properties, we propose two information structures using a novel concurrent-composition technique, which has exponential-time complexity $O(|X|^4|\Sigma_o||\Sigma_{uo}||\Sigma|2^{|X|})$, where $|X|$ (resp., $|\Sigma|$, $|\Sigma_o|$, $|\Sigma_{uo}|$) is the number of states (resp., events, observable events, unobservable events) of a system., Comment: 10 pages, 11 figures

Published
2021

31. Landing Point Control Technology of Parafoil System Based on Sliding Mode Control in a Complex Environment

Author

Lu, Weitao, Sun, Hao, Sun, Qinglin, Chen, Zengqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, Fu, Yongling, editor, and Wang, Jiqiang, editor

Published
2023
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32. Research on Intelligent Collaboration and Obstacle Avoidance Control of Multiple Parafoils System

Author

Yang, Jinshan, Sun, Qinglin, Sun, Hao, Zheng, Yuemin, Chen, Zengqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, Fu, Yongling, editor, and Wang, Jiqiang, editor

Published
2023
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33. State Estimation and Fusion for Multisensor Systems with False Data Injection Attacks and Non-Gaussian Noises

Author

Hu, Jinhao, Wang, Fuyong, Liu, Zhongxin, Chen, Zengqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, Fu, Yongling, editor, and Wang, Jiqiang, editor

Published
2023
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34. Observer-Based Leader-Following Consensus of Heterogeneous Multi-agent Systems with Intermittent Communication

Author

Sader, Malika, Wu, Yueyang, Wang, Fuyong, Liu, Zhongxin, Chen, Zengqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Ren, Zhang, editor, Wang, Mengyi, editor, and Hua, Yongzhao, editor

Published
2023
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35. Research on Intelligent Maneuvering Decision-Making in Close Air Combat Based on Deep Q Network

Author

Zhang Tingyu, Sun Mingwei, Wang Yongshuai, Chen Zengqiang

Subjects
air combat, autonomous maneuvering decision-making, deep reinforcement learning, dqn, reward function, intelligent maneuver, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Aiming at the problem of UCAV maneuvering decision-making in close air combat, the design of reinforcement learning reward function and the selection of hyper-parameters are studied based on the framework of deep Q network algorithm. For the sparse reward problem in reinforcement learning, an auxiliary reward function that considers angle, range, altitude and speed factors is used to describe the air combat mission accurately and guide the learning direction of the agent correctly. Meanwhile, aiming at the problem of applying reinforcement learning hyper-parameter selection, the influence of learning rate, the number of network nodes and network layers on the decision-making system is explored, and a good range of parameter selection is given, which provides a reference for the following research on parameter selection. The simulation results show that the trained agent can learn the optimal maneuver strategy in different air combat situations, but it is sensitive to reinforcement learning hyper-parameters.

Published
2023
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