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Robust Transmission Design for Covert Satellite Communication systems With Dual-CSI Uncertainty

IEEE Internet of Things Journal 2025年第12期12卷 21892-21903页

作者： Jia, Huaiqi Wang, Ying Wu, Wen Yuan, Jun Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Frontier Research Center Shenzhen518000 China Joint Laboratory of Technology and Applications on Frequency Orbital Space Information Networks China Satellite Network System Research Institute Co Ltd Beijing100070 China

In this paper, we investigate a novel covert transmission scheme for satellite communication systems. Specifically, the satellite employs a rate-splitting multiple access technique to covertly transmit messages to multiple users and simultaneously transmit jamming signals, thereby enhancing the covert rate and robustness while avoiding detection by a warden. Due to the long propagation delay and lack of the warden's precise location information, acquiring perfect channel state information (CSI) between the satellite, covert users, and the warden is difficult. To this end, we establish a dual-CSI uncertainty model, which incorporates phase and norm-bounded uncertainty for the covert and wiretap channels to characterize the actual CSI. In addition, we formulate a stochastic optimization problem with the objective of maximizing the minimum covert rate while adhering to covert communication constraints. The optimization problem is non-convex and difficult to solve directly due to the dual-CSI uncertainty and the coupled nature of the variables. To solve the problem, we reformulate the original problem into a series of convex optimization problems by utilizing semidefinite relaxation, fractional programming, and the S-procedure methods. Then, we propose a robust Common Rate Allocation and Beamforming (CRAB) algorithm to obtain near-optimal solutions for the beamforming vectors and common rate allocation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms baseline schemes in terms of both covert rate and robustness. © 2014 IEEE.

关键词： Satellite communication systems

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Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization

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Intelligent and Converged Networks 2023年第4期4卷 273-279页

作者： Saad-Eddine Chafi Younes Balboul Mohammed Fattah Said Mazer Moulhime El Bekkali the Laboratory of Artificial Intelligence Data Sciences and Emerging SystemsNational School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco the LIA Laboratory Moulay Ismail UniversityMeknes 50050Morocco

Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization *** Algorithm(GA)is widely popular due to its logical approach,broad applicability,and ability to tackle complex issues encountered in engineering ***,GA is known for its high implementation cost and typically requires a large number of *** the other hand,Particle Swarm optimization(PSO)is a relatively new heuristic technique inspired by the collective behaviors of real *** GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches *** they are often seen as competitors,their efficiency heavily relies on the parameter values chosen and the specific optimization problem at *** this study,we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud *** extensive experiments and performance evaluations,the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim *** comparative analysis sheds light on the strengths and limitations of each algorithm,providing valuable insights for researchers and practitioners in the field.

关键词： particle swarm optimization genetic algorithm performance evaluation edge and fog cloud FogWorkflowSim

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Deep-Learning-Coupled Numerical optimization Method for Designing Geometric Structure and Insertion-Withdrawal Force of Press-Fit Connector

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Acta Mechanica Solida Sinica 2025年第1期38卷 78-90页

作者： Mingzhi Wang Bingyu Hou Weidong Wang School of Mechano-Electronic Engineering Xidian UniversityXi’an710071China CityU-Xidian Joint Laboratory of Micro/Nano Manufacturing Shenzhen518057China

The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive *** paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization *** ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model *** numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit *** Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization *** objective function is established,the inverse problem is solved,and effectiveness is *** Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic *** Pareto solution sets of insertion-withdrawal forces are obtained,and results are *** established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.

关键词： Press-fit connector Compliant pin Insertion-withdrawal force optimization design Neural network model

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Improved Proximal Policy optimization Algorithm for Sequential Security-constrained Optimal Power Flow Based on Expert Knowledge and Safety Layer

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Journal of Modern Power systems and Clean Energy 2024年第3期12卷 742-753页

作者： Yanbo Chen Qintao Du Honghai Liu Liangcheng Cheng Muhammad Shahzad Younis State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources and School of Electrical&Electronic Engineering North China Electric Power UniversityBeijing 102206China School of Engineering Xining UniversityXining 810008Chinaand Key Laboratory of Smart Operation of New Energy Power SystemMinistry of EducationQinghai UniversityXining 810016China China Electric Power Research Institute Nanjing 210003China National University of Sciences and Technology Islamabad44000Pakistan

In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization *** has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing ***,existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power ***,we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety layer to determine active power dispatch strategy,voltage optimization scheme of the units,and charging/discharging dispatch of energy storage *** expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable ***,to avoid line overload,we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF *** results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

关键词： Sequential security-constrained optimal power flow(SCOPF) expert experience safety layer renewable energy safe reinforcement learning

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Comparative Analysis of PV Module Models Using AI-Based Metaheuristic optimization Techniques

Comparative Analysis of PV Module Models Using AI-Based Meta...

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International Conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2024

作者： Messini, El Mouatez Billah Bourek, Yacine Ammari, Chouaib LAGE Laboratory Faculty of Applied Sciences University of Ouargla Ouargla30000 Algeria Electrical Engineering Department Faculty of Applied Sciences Ouargla University Road Ghardaia Ouargla30000 Algeria Sonatrach - Institut Algérien du Pétrole - École de Hassi Messaoud BP 54 – Hassi Messaoud Ouargla30001 Algeria Department of renewable energy Faculty of Hydrocarbons Renewable Energy Science Earth and Universe University Kasdi Merbah-Ouargla Ouargla Algeria

ISBN: (纸本)9783031803000

For successful modelling of PV system characteristics, an accurate PV module model is necessary. This research, which investigates and compares three PV module models: the single diode model (SDM), the double diode model (DDM), and the triple diode model (TDM), employs modern AI-based methods. Metaheuristic optimization techniques have solved the difficulty of obtaining parameters for the equivalent circuit of a solar module. Two artificial intelligence methods, Genetic Algorithm (GA) and Particle Swarm optimization (PSO), are employed to achieve the optimal solution for error minimization between measured and estimated data using the three models. The optimization process involves minimizing the Root Mean Squared Error (RMSE) between measured and estimated data. A convergence comparison between GA and PSO after 50 runs demonstrates that both techniques effectively extract parameters for all three models. The optimization findings demonstrate accurate modelling with RMSE results as follows: for the SDM, 5.49% (GA) and 1.02% (PSO);for the DDM, 3.91% (GA) and 0.79% (PSO);and for the TDM, 4.94% (GA) and 0.94% (PSO). These results validate the effectiveness of AI-based methods for parameter extraction, with the DDM with PSO method showing superior accuracy and timing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Resource Allocation for Space-Air-Ground Integrated Networks:A Comprehensive Review

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Journal of Communications and Information Networks 2024年第1期9卷 1-23页

作者： Hui Liang Zhiqing Yang Guobin Zhang Hanxu Hou School of Electrical Engineering and Intelligentization Dongguan University of TechnologyDongguan 523808China Peng Cheng Laboratory Shenzhen 518055China School of Electronics and Information Engineering Shenzhen UniversityShenzhen 518061China School of Electrical Engineering and Intelligentization Dongguan University of TechonologyDongguan 523808China School of Information and Communication Engineering Guangzhou Maritime UniversityGuangzhou 510725China

The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.

关键词： space-air-ground integrated networks resource allocation 6G optimization theory game theory artificial intelligence

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Review on operation control of cold thermal energy storage in cooling systems

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Energy and Built Environment 2025年第3期6卷 509-523页

作者： Wang, Huan Xie, Baoshan Li, Chuanchang Key Laboratory of Renewable Energy Electric-Technology of Hunan Province School of Energy and Power Engineering Changsha University of Science and Technology Changsha410114 China

Cold storage technology is useful to alleviate the mismatch between the cold energy demand and supply. The integration of cold energy storage in cooling system is an effective approach to improve the system reliability and performance. This review provides an overview and recent advances of the cold thermal energy storage (CTES) in refrigeration cooling systems and discusses the operation control for system optimization. Firstly, the composition and principles of cooling systems coupled with CTES are presented. Special attention was paid to cold storage medium of phase change material (PCM) with high energy density and stable phase change temperature. Then, based on the classification of driven energy, the different applications of passive or active cooling systems with CTES are classified, including building cooling, cold chain logistics, and other refrigeration systems. Most importantly, the operation control which is necessary to performance optimization is presented, including operational control strategies, cold load predictions, and economic evaluation methods. Three types of operational control strategies are summarised using water storage and cooling system as an example. Two types of cold load predictions, parametric regression and artificial neural network method, are introduced. Three aspects of economic costs are summarized in terms of initial equipment investment cost, operational cost, and life-cycle cost are summarized. Finally, an outlook on the development of cooling systems using CTES is given. © 2024

关键词： Cooling systems

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Scenario Modeling-Aided AP Placement optimization Method for Indoor Localization and Network Access

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China Communications 2024年第3期21卷 37-50页

作者： Pan Hao Chen Yu Qi Xiaogang Liu Meili School of Mathematics and Statistics Xidian UniversityXi’an 710126China Science and Technology on Near-Surface Detection Laboratory Wuxi 214035China School of Software Nanchang Hangkong UniversityNanchang 330063China

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much *** placement of access points(APs)significantly influences localization accuracy and network ***,the indoor scenario and network access are not fully considered in previous AP placement optimization *** study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network *** order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction *** that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP ***,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

关键词： indoor localization MOPSO network access RSS prediction

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Learning to Branch in Combinatorial optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Process in laser drilling of deep microholes without taper on metal materials

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Science China(Technological Sciences) 2024年第1期67卷 37-59页

作者： FAN RunZe MEI XueSong CUI JianLei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710054China

Deep microhole machining is currently a prominent research area within the aerospace field,encompassing blade film cooling and fuel injection control ***,taper defects in metal materials may lead to performance degradation or even structural damage over a component’s *** and helical drilling,facilitated by ultrashort pulse lasers,have proven more suitable for achieving high-precision,deep holes in metal ***,excessive repetition rates can also result in severe thermal *** methods are commonly employed for controlling taper,including parameter optimization,assistance,and secondary *** laser beam drilling is widely utilized and has been integrated into relevant machining systems for commercial *** deep microholes include film cooling holes and injection *** drilling is a potential machining method for new materials in the aerospace *** laser drilling processing has been studied,numerous related scientific challenges and technical difficulties must be addressed before practical implementation.

关键词： aerospace laser drilling deep microhole taper control

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