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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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Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition

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IEEE Transactions on Image Processing 2023年 32卷 2719-2733页

作者： Liu, Wenxuan Zhong, Xian Zhou, Zhuo Jiang, Kui Wang, Zheng Lin, Chia-Wen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan430070 China Peking University School of Electronics Engineering and Computer Science Beijing100091 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University School of Computer Science Wuhan430072 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://***/51cloud/DRDN. © 1992-2012 IEEE.

关键词： Economic and social effects

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel Robot 11

Experimental Study on Motion Controller based on NN-ARX and ...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Jalali, Hasan Damavandi, Hossein Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran University of Tehran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers. © 2023 IEEE.

关键词： Controllers

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Towards Simpler Modelling Expressions for the Mechanical Characterization of Soft Materials

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Micro and Nanosystems 2024年第3期16卷 172-182页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Stylianou, Andreas Chliveros, Georgios Faculty of Engineering and Architecture Metropolitan College Athens Greece BioNanoTec LTD Nicosia Cyprus Radar Systems and Remote Sensing Lab School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Department of Mechanical Engineering Faculty of Engineering School of Sciences European University Cyprus Nicosia2404 Cyprus

Aims: The aim of this paper is to develop a new, simple equation for deep spherical indentations. Background: The Hertzian theory is the most widely applied mathematical tool when testing soft materials because it provides an elementary equation that can be used to fit force-indentation data and determine the mechanical properties of the sample (i.e., its Young’s modulus). However, the Hertz equation is only valid for parabolic or spherical indenters at low indentation depths. For large indentation depths, Sneddon’s extension of the Hertzian theory offers accurate force-indentation equations, while alternative approaches have also been developed. Despite ongoing mathematical efforts to derive new accurate equations for deep spherical indentations, the Hertz equation is still commonly used in most cases due to its simplicity in data processing. Objective: The main objective of this paper is to simplify the data processing for deep spherical indentations, primarily by providing an accurate equation that can be easily fitted to force-indentation data, similar to the Hertzian equation. Methods: A simple power-law equation is derived by considering the equal work done by the indenter using the actual equation. Results: The mentioned power-law equation was tested on simulated force-indentation data created using both spherical and sphero-conical indenters. Furthermore, it was applied to experimental force-indentation data obtained from agarose gels, demonstrating remarkable accuracy. Conclusion: A new elementary power-law equation for accurately determining Young’s modulus in deep spherical indentation has been derived. © 2024 Bentham Science Publishers.

关键词： Spheres

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Advanced extended-term simulation approach with flexible quas-isteady-state and dynamic semi-analytical simulation engines

iEnergy

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iEnergy 2022年第1期1卷 124-132页

作者： Rui Yao Dongbo Zhao A.P.Sakis Meliopoulos Chanan Singh Joydeep Mitra Feng Qiu Division of Energy Systems Argonne National LaboratoryLemontIL 60439USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege Sta-tionTX 77843USA Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI 48824USA

Power system simulations that extend over a time period of minutes,hours,or even longer are called extendedterm *** power systems evolve into complex systems with increasing interdependencies and richer dynamic behaviors across a wide range of timescales,extendedterm simulation is needed for many power system analysis tasks(e.g.,resilience analysis,renewable energy integration,cascading failures),and there is an urgent need for efficient and robust extendedterm simulation *** conventional approaches are insufficient for dealing with the extendedterm simulation of multitimescale *** paper proposes an extendedterm simulation approach based on the semianalytical simulation(SAS)*** accuracy and computational efficiency are backed by SAS's high accuracy in eventdriven simulation,larger and adaptive time steps,and flexible switching between fulldynamic and quasisteadystate(QSS)*** used this proposed extendedterm simulation approach to evaluate bulk power system restoration plans,and it demonstrates satisfactory accuracy and efficiency in this complex simulation task.

关键词： Extended-term simulation resilience multi-timescale simulation event-driven simulation dynamics quasi-steady-state(QSS) hybrid simulation holomorphic embedding(HE) semi-analytical simulation(SAS)

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kwag, Cheolsu Kim, Euntaek Lee, Yongsuk Jung, Seungmin Hwang, Sung Soo Ryu, Hwangryol Moon, Youngmin Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Research Institute of Industrial Science and Technology Smart Engineering Research Laboratory Pohang Korea Republic of

ISBN: (纸本)9798331510756

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models. © 2025 IEEE.

关键词： Digital elevation model

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Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 766-778页

作者： Dongliang Xiao Mohamed Kareem AlAshery Wei Qiao IEEE Power and Energy Systems Laboratory Department of Electrical and Computer EngineeringUniversity of Nebraska-LincolnLincolnNE 68588-0511USA School of Electric Power South China University of TechnologyGuangzhou 510641China

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding,which is a kind of financial tool available in most electricity markets of the United *** the proposed model,virtual bidding is used to improve the wind power producer’s market power in the dayahead(DA)market by trading at multiple buses,which are not limited to the locations of the wind *** optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization *** upperlevel problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk(CVa R)for risk *** lower-level problem represents the clearing process of the DA *** using the Karush-Kuhn-Tucker(KKT)conditions,duality theory,and big-M method,the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints(MPEC)model and then a mixed-integer linear programming(MILP)model,which can be solved by existing commercial *** reduce the computational cost of solving the proposed stochastic optimization model for large systems,a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual *** studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual *** impacts of the transmission limits,wind unit location,risk aversion parameters,wind power volatility,and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

关键词： Bi-level optimization electricity market risk management stochastic optimization virtual bidding wind power

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Identification of the Worst-case Static Voltage Stability Margin Interval of AC/DC Power System Considering Uncertainty of Renewables

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CSEE Journal of Power and Energy systems 2024年第3期10卷 974-987页

作者： Wanbin Liu Shunjiang Lin Yuerong Yang Mingbo Liu Qifeng Li School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China Guangdong Key Laboratory of Clean Energy Technology South China University of TechnologyGuangzhou 511458China Department of Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816USA CSEE IEEE

Calculation of static voltage stability margin(SVSM)of AC/DC power systems with lots of renewable energy sources(RESs)integration requires consideration of uncertain load growth and renewable energy generation *** paper presents a bi-level optimal power flow(BLOPF)model to identify the worst-case SVSM of an AC/DC power system with line commutation converter-based HVDC and multi-terminal voltage sourced converter-based HVDC transmission *** of uncertain load growth’s hypercone model and control mode switching of DC converter stations are considered in the BLOPF ***,uncertain RES output fluctuations are described as intervals,and two three-level optimal power flow(TLOPF)models are established to identify interval bounds of the system worst-case *** two TLOPF models are both transformed into max–min bi-level optimization models according to independent characteristics of different uncertain ***,transforming the inner level model into its dual form,max–min BLOPF models are simplified to single-level optimization models for direct *** results on the modified IEEE-39 bus AC/DC case and an actual large-scale AC/DC case in China indicate correctness and efficiency of the proposed identification method.

关键词： AC/DC power system control mode switching convex relaxation dual programming multi-level optimization optimal power flow static voltage stability margin

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Topological Photonic Molecule with Time-Varying Modulations

Topological Photonic Molecule with Time-Varying Modulations

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2023 Photonic Networks and Devices, Networks 2023 in Advanced Photonics Congress - Part of Advanced Photonics Congress 2023

作者： Suh, Joseph Kim, Gyunghun Park, Hyungchul Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose dynamic modulations of a photonic molecule to achieve topological properties of light. We investigate the Hall transport in synthetic dimensions and the system modulation strategy to demonstrate the pumping... 详细信息

ISBN: (纸本)9781957171265

关键词： Molecules

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