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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

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Control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence 38

ControlSynth Neural ODEs: Modeling Dynamical Systems with Gu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Mei, Wenjie Zheng, Dongzhe Li, Shihua The School of Automation and the Key Laboratory of MCCSE of the Ministry of Education Southeast University Nanjing China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals. They have advantages in learning and understanding the evolution of complex real dynamics. Many previous works have focused on NODEs in concise forms, while numerous physical systems taking straightforward forms, in fact, belong to their more complex quasi-classes, thus appealing to a class of general NODEs with high scalability and flexibility to model those systems. This, however, may result in intricate nonlinear properties. In this paper, we introduce ControlSynth Neural ODEs (CSODEs). We show that despite their highly nonlinear nature, convergence can be guaranteed via tractable linear inequalities. In the composition of CSODEs, we introduce an extra control term for learning the potential simultaneous capture of dynamics at different scales, which could be particularly useful for partial differential equation-formulated systems. Finally, we compare several representative NNs with CSODEs on important physical dynamics under the inductive biases of CSODEs, and illustrate that CSODEs have better learning and predictive abilities in these settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Dynamic Modeling of Strip Rolling Process Using Probabilistic Neural Network

Dynamic Modeling of Strip Rolling Process Using Probabilisti...

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18th IEEE International Conference on Advanced Motion Control, AMC 2024

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern University State Key Laboratory of Rolling and Automation Shenyang China Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342819

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model. © 2024 IEEE.

关键词： Deep neural networks

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Visible and Infrared Image Fusion for Object Detection: A Survey

Visible and Infrared Image Fusion for Object Detection: A Su...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Sun, Yuxuan Meng, Yuanqin Wang, Qingbo Tang, Minghua Shen, Tao Wang, Qingwang School of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Naval Research Academy Beijing China

ISBN: (纸本)9789819708543

Thanks to the advancements of Deep Learning (DL) Algorithms. DL-based object detection models has witnessed remarkable success in the past few years. By leveraging deep convolutional neural networks (CNNs) and other deep learning models, rich feature representations can be effectively extracted from visible (RGB) images for object detection. However, for challenging scenarios such as low-light conditions and haze, the performance of object detection using visible images is often not satisfactory. On the other hand, thermal camera, which is unaffected by lighting conditions, can penetrate through low-light and hazy environments to capture object images. However, infrared images lack edge and texture information of objects. Recognizing the complementary nature of visible and infrared images, researchers have explored the fusion of visible and infrared images for object detection, yielding promising research outcomes. This paper provides an analysis of the current research status of visible and infrared image fusion for object detection. Firstly, the fusion models are categorized into three kinds: pixel-level fusion, feature-level fusion, and decision-level fusion. Several models within each category are discussed. Furthermore, this paper summarizes five datasets that can be utilized for training RGB-Infrared object detection models and compares the experimental results of selected models on the KAIST dataset. Lastly, the paper concludes with a summary of existing challenges in the field and offers some reflections on future directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Dynamic performance and energy efficiency of reflective and insulative composite coating on building exterior wall

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Building Simulation 2023年第12期16卷 2245-2259页

作者： Guangpeng Zhang Huijun Wu Jia Liu Jianming Yang Huakun Huang Yujie Ding Lei Xie School of Civil Engineering Guangzhou UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies Guangzhou 510006China School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China

Reflective and insulative composite coatings are a new energy-saving material with high solar reflectance and extremely low thermal conductivity for *** optimization and impact of high solar reflectance and low thermal conductivity on the insulating capacity of walls remain *** work investigates the dynamic thermal performance and energy efficiency of a reflective and insulative composite coating in regions with hot summer and warm winter.A simplified thermal resistance-heat capacitance model of an exterior building wall is established to predict thermal *** dynamic temperature and heat flow of the wall are predicted to reduce heat loss through the interior surface of the wall and compared to the conventional *** specific impact of the thermal conductivity and solar reflectance of the coating on the heat loss is further investigated to minimize heat loss of the *** research shows that the composite coating shows better performance on adjusting outdoor climate change than the other *** with cement,it reduces the maximum temperature of the exterior surface of the wall by 7.45°C,and the heat loss through the interior surface of the wall by 38%.The heat loss is reduced with the increase of solar reflectance and the reduction of thermal *** results can provide a useful reference and guidance for the application of reflective and insulative composite coating on building exterior wall to promote their energy-saving use on building envelopes.

关键词： reflective and insulative composite coating exterior wall building insulation solar reflectance thermal conductivity

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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Coordinated Operation of Virtual Coupling High-Speed Trains Under a Novel Sequential Model Predictive Control Framework

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无人系统（英文） 2024年第2期12卷 359-373页

作者： Weiqi Bai Zixuan Zhang Xingtang Wu Yizhan Xiong Hairong Dong School of Automation Science and Electronic Engineering Beihang UniversityBeijing 100191P.R.China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijingP.R.China School of Control and Computer Engineering North China Electric Power UniversityBeijingP.R.China

This paper deals with the coordinated control problem of the multiple high-speeds operating under the virtual coupling mode with special consideration of the coupled safety inter-train distance constraints and other independent constraints affecting train ***-ering the inertial lag phenomenon within longitudinal dynamics,this study employs a third-order nonlinear control model to characterize train *** cope with the intricate running constraints explicitly,a centralized constrained model predictive(MPC)control problem is formulated for the regulation of the *** the framework of sequential solving approach,a novel distributed model predictive control(DMPC)algorithm is designed based on a distributed train running information exchange *** algorithm decomposes the centralized MPC problem into a sequence of local problems,allowing for sequential computation and meeting the real-time control ***,the algorithm mitigates solution conservatism arising from the distributed optimization mechanism by accounting for potential hypothetical control policies among a train's topological neighbors when designing its control *** feasibility and effectiveness of the proposed results are rigorously confirmed through theoretical analysis and illustrated by numerical experiment results.

关键词： Coordinated control multiple high-speed trains distributed model predictive control

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