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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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A Novel Relocating Recognition Algorithm in Overlapping Region for Multi-UAVs Cluster

A Novel Relocating Recognition Algorithm in Overlapping Regi...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Li, Chen Qi, Xuelei Zhao, Juntao Liao, Xuanhong Lin, Zitian Ma, Hongjun Liang, Xinkai College of Information Science and Engineering Northeastern University Shenyang China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong High School Attached to Northeast Normal University International Curriculum Center Changchun China School of Automation Science and Engineering South China University of Technology Guangzhou China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

How improve the mapping efficiency and location accuracy of the multi-UAV cluster based on the distributed SLAM technology is a significant problem in overlapping regions. Therefore, this paper mainly proposes a novel relocation recognition algorithm for the multi-UAV cluster. First, the autonomous localization algorithm based on visual-inertial fusion is proposed. Then, the algorithm for vocabulary updating and relocating is designed. Finally, practical testing is implemented to verify and improve the efficiency of exploration ability and the accuracy of the autonomous navigation and positioning in the complex overlapping regions. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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control of General Linear Time-Delay systems: Applications of the Kronecker-Seuret Decomposition

arXiv

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arXiv 2022年

作者： Feng, Qian Zheng, Wei Xing Xiao, Feng Wang, Xiaoyu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

We consider the design of stabilizing feedback controllers for autonomous linear time-delay system ẋ(t) = Xi=0ν Aix(t − ri) + Z−0r Ae(τ)x(t + τ)dτ + Xi=0ν Biu(t − ri) + Z−0r Be(τ)u(t + τ)dτ + D1w(t), z(t) = Xi=0ν Cix(t − ri) + Z−0r Ce(τ)x(t + τ)dτ + Xi=0ν Biu(t − ri) + Z−0r Be (τ)u(t + τ)dτ + D2w(t), x(t0 + θ) = ψ(θ), ∀θ ∈ [−r, 0], r = rν > · · · > r2 > r1 > r0 = 0, ν ∈ N under dissipative constraints expressed by quadratic forms, where matrix-valued functions Ae(·), Ce(·), Be(·), Be (·) can contain finite but unbounded number of square integrable functions. Our result is derived from an application of the Kronecker-Seuret decomposition in conjunction with the construction of Krasovskiı̆ functionals, where the final solution is expressed as a set of convex semidefinite programming problems. All functions in Ae(·), Ce(·), Be(·), Be (·) are explicitly addressed without requiring the use of operator or C0-semigroup theory. Moreover, the resulting optimization sufficient conditions can be efficiently computed by numerical solvers that are accessible to the general public, without requiring that the stabilizability and detectability of the open-loop system are known. We present the test results of two challenging examples, which could not be efficiently tackled by existing methods, to demonstrate the effectiveness of our methodology. The main objective of this study is to develop a non-conservative and numerically efficient framework for the mathematical control problem under investigation, designed to be accessible to engineers without expertise in operator theory and functional analysis. Additionally, this article briefly reviews recent research advancements in the mathematical control of linear time-delay systems, which could be useful to both emerging and experienced researchers in this field. Copyright © 2022, The Authors. All rights reserved.

关键词： State feedback

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Optimal Action Strategy approach for Desert Crossing Based on Dynamic Programming

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Procedia computer Science 2021年 188卷 144-154页

作者： Xingrong Fan Dian Meng Chongqing Engineering Laboratory for Detection Control and Integrated System Chongqing Technology and Business University Chongqing and 400067 China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing and 400067 China

It is a novel but challenging task that simulated desert crossing strategy for the game players under the specified conditions. This study is aimed to develop an optimal action strategy approach for desert crossing based on dynamic programming. According to the game rules, a multi-stage decision optimization model was first established to maximize the remaining funds of players within given game time. Then, a simulation-based dynamic programming algorithm was designed to solve the optimization model, and the optimal action strategy was obtained by backtracking crossing routes. Finally, two experimental case studies with different initial conditions were used to validate the model. In comparison with the benchmark exhaustive search algorithm, the proposed approach has higher performance and efficiency. The case studies confirm that the method can obtain the optimal action strategy under different game maps and initial resources.

关键词： Crossing desert Action strategy Dynamic programming Multi-stage decision computer simulation

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M2-STGN: A Missing Data-Tolerant Learning Model for Condition Monitoring of Wind Turbines

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jian Xu Yuguang Niu Ming Du Jun Yao School of Control and Computer Engineering North China Electric Power University Beijing China Beijing Huairou Laboratory Beijing China School of Control and Computer Engineering and the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

The field data of supervisory control and data acquisition (SCADA) systems are commonly missing, which increases the difficulty of feature extraction and reduces the effective utilization of data. This negatively affects the reliability and integrity of wind turbine condition monitoring (WTCM). To address the interference caused by missing data during feature extraction, this article proposes a multiscale masked spatial–temporal graph networks (M 2 -STGNs) model, mainly composed of multiscale spatial and masked temporal feature learning modules. Employing graph attention networks (GAT) with different heads, spatial features of diverse granularity are extracted from multiscale images in parallel. Then, based on the absence of data and the degree of temporal correlation, a temporal masked attention module is specially designed to calculate differentiated attention scores. By collaborating with temporal convolutional networks (TCNs), masked temporal features can be extracted from non-missing data. Therefore, the proposed model adapts to complex and changeable missing proportions and provides robust spatial–temporal features for WTCM tasks. Furthermore, to address missed alarms caused by the discontinuous WTCM process, a fault soft judgment strategy is proposed to enhance the sensitivity of fault detection. Two real-world SCADA datasets are utilized to validate the proposed model and strategy.

关键词： Feature extraction Imputation Data models Data mining Time series analysis Sensors SCADA systems Condition monitoring Computational modeling Representation learning

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Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

arXiv

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

arXiv

引用

arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

arXiv

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arXiv 2023年

作者： Nie, Chang Wang, Guangming Liu, Zhe Cavalli, Luca Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Department of Computer Science ETH Zürich Switzerland Microsoft Mixed Reality AI Zürich Lab Switzerland

Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://***/IRMVLab/RLSAC. © 2023, CC BY.

关键词： Reinforcement learning

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SvTI: SFC-driven Queue Injection in Virtual Time-sensitive Network via Augmented Topology

SvTI: SFC-driven Queue Injection in Virtual Time-sensitive N...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yajing Zhang Qimin Xu Cailian Chen Mingyan Li Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China College of Computer Science Chongqing University Chongqing P. R. China

ISBN: (纸本)9781665435413

Driven by the coexisted heterogeneous communication protocols and diverse quality of service (QoS) requirements of industrial applications, time-sensitive networking (TSN) has been proposed as a promising and unified standard to provide deterministic transmission. However, the traffic scheduling of the TSN network should be orchestrated in a coordinated manner, which is challenging to respond to dynamic applications rapidly. For this, network function virtualization (NFV) has been introduced to eliminate the tight coupling between functions and devices, thus enabling a more flexible network resource allocation. However, NFV cannot be simply applied in a TSN network due to the inherent complex characteristics of TSN. To address this issue, we develop SvTI, a service function chain (SFC) driven queue injection scheme in the NFV-enabled TSN network. The basic idea of SvTI is to jointly model virtual network functions (VNFs) embedding and the TSN multi-queue characteristic by constructing an ordered augmented topology according to the SFC. Thus, the VNF embedding and TSN queue injection problem can be transformed into a shortest path routing problem. Besides, considering the QoS requirements of applications, a TSN queue-based topology division mechanism is proposed to simplify the augmented topology further and improve the algorithm efficiency. Simulation results show that our SvTI obtains a smaller graph size than other works and is more scalable.

关键词： Network topology Service function chaining Simulation Quality of service Dynamic scheduling Routing Topology

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