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Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

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Chinese Journal of Aeronautics 2024年第5期37卷 27-38页

作者： Tongyan WU Haibin DUAN Yanming FAN State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China AVIC Shenyang Aircraft Design and Research Institute Shenyang 110035China

Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and *** detection based on image intrinsic cues can achieve fast detection,but with poor *** studies reveal that optimization-based methods provide accurate and quick solutions for saliency *** paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue *** can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and *** results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic *** algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

关键词： Autonomous aerial refueling Drones hybrid pigeon-inspired optimization Color opponent Saliency detection Saliency aggregation

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Research on digital twin modelling technique for±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding

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High Voltage 2025年第2期10卷 294-304页

作者： Hao Luo Li Cheng Pengyong Yi Jiuyi Wang Xuetong Zhao Lijun Yang Ruijin Liao State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina Tsinghua University Shenzhen International Graduate School ShenzhenChina

Digital twin(DT)modelling is a prerequisite for the successful application of DT technology in the power ***,traditional scene modelling methods are costly,time-consuming,focus on overall features and lack real-time updates,hindering the interaction between DT models and physical power equipment ***,a scene DT modelling technique focusing on local features in risk areas and real-time updates is urgently ***,real-time modelling of the±800 kV converter transformer is achieved by improving the neural radiation field based on a hybrid attention mechanism and multiresolution hash *** to traditional methods,modelling time is reduced from hours to 1 min without professional equipment or manual *** model quality is more concerned with local features of risk areas in transformers while ensuring the overall scene,and the accuracy is improved by about 6%,realising the real-time modelling of transformers and the DT of scenes.

关键词： risk areas kv converter transformer hybrid attention mechanism multiresolution hash encoding real time modelling scene modelling digital twin scene modelling methods

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hybrid Intelligent optimization of Nonlinear Switched systems With Guaranteed Feasibility

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5244-5257页

作者： Li, Huan Fu, Jun Chai, Tianyou Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang100819 China

To address the challenge of globally optimal control of path-constrained switched systems, a hybrid intelligent dynamic optimization method is proposed by combining the biobjective particle swarm optimization (PSO) method and a gradient descent method, which simultaneously obtains globally optimal switching instants and input and guarantees rigorous satisfaction of the path constraints over the continuous time horizon. First, the path constraint of switched systems is discretized into multiple point constraints, and then the right-hand side of the path constraint (≤ 0) is substituted with a negative value (≤-ϵ). Second, the single-objective constrained dynamic program of switched systems is transformed into a biobjective unconstrained dynamic program where each particle intelligently adjusts its objectives to detect the global optimum area satisfying the constraints, depending on its current position in the search space by the search mechanism of PSO. Third, the deterministic optimization method is deployed in the detected global optimum area to locate a feasible solution satisfying the Karush-Kuhn- Tucker (KKT) conditions to a specified tolerance of dynamic optimization of switched systems. Moreover, it is proved that the hybrid intelligent dynamic optimization method can obtain the optimal solution satisfying the first-order approximation KKT conditions within a finite number of iterations. Finally, the results of numerical simulations show the effectiveness of the presented method in terms of improving the solution accuracy and guaranteeing rigorous satisfaction of the path constraint. Impact Statement-The optimization methods of pathconstrained switched systems can be broadly categorized into two groups: deterministic optimization and heuristic optimization methods. Deterministic optimization methods can guarantee that the path constraint is feasible within a finite number of iterations, but the obtained solution can easily fall into a local optimum. On the cont

关键词： Heuristic algorithms

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Constraint-Feature-Guided Evolutionary Algorithms for Multi-Objective Multi-Stage Weapon-Target Assignment Problems

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Journal of systems Science & complexity 2025年第3期38卷 972-999页

作者： WANG Danjing XIN Bin WANG Yipeng ZHANG Jia DENG Fang WANG Xianpeng School of Automation Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University

The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2). Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment(CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms(CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks(i.e., NSGA-Ⅱ, NSGA-Ⅲ, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover,mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover,a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-Ⅱ among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-Ⅱ.

关键词： constrained multi-objective optimization problem constraint handling Evolutionary algorithms weapon-target assignment

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A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

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Journal of systems Engineering and Electronics 2024年第5期35卷 1219-1230页

作者： XU Changyi WANG Yun DUAN Yiman ZHANG Chao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and EngineeringDalian University of TechnologyDalian 116024China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China

Discrete event system(DES)models promote system engineering,including system design,verification,and *** advancement in manufacturing technology has endowed us to fabricate complex industrial ***,the adoption of advanced modeling methodologies adept at handling complexity and scalability is ***,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the *** this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the *** traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone *** with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new ***,this general modeling approach has wide potential for future complex and intelligent industrial systems.

关键词： industrial complex system operation specifying Boolean semantic discrete event system(DES)theory intelligent operation

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Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang Nian-Tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and TechnologyQingdao266590ShandongChina Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao266071ShandongChina Transportation Institute of Inner Mongolia University Hohhot010000Inner MonggolChina Key Laboratory of Gas Hydrate Qingdao Institute of Marine GeologyMinistry of Natural ResourcesQingdao266237ShandongChina

Conventional machine learning(CML)methods have been successfully applied for gas reservoir *** prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly *** used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data *** contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw ***,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir ***-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction *** constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML *** prediction results obtained are better than those of a single CNN model or APSO-LSSVM *** the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization *** the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction *** constructed CNN-APSO-LSSVM model had lower errors and a

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

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Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous hybrid Flow Shops with Blocking Constraints

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Engineering 2025年第3期46卷 278-291页

作者： Xueyan Sun Weiming Shen Jiaxin Fan Birgit Vogel-Heuser Fandi Bi Chunjiang Zhang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Institute of Automation and Information Systems Technical University of MunichGarching 85748Germany

This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision *** FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data *** proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning *** experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.

关键词： Multi-objective Markov decision process Multi-agent deep reinforcement learning Proximal policy optimization Distributed hybrid flow-shop scheduling Blocking constraints

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A Fully Distributed hybrid Control Framework For Non-Differentiable Multi-Agent optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1792-1800页

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Yue Wei the Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the School of Electronic and Information Engineering Tongji UniversityShanghai 200082 the Peng Cheng Laboratory Shenzhen 518055China

This paper develops a fully distributed hybrid control framework for distributed constrained optimization *** individual cost functions are non-differentiable and *** on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization *** proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump *** the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different *** simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

关键词： Distributed algorithm hybrid framework multiagent network non-differentiable optimization

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Offline Reinforcement Learning with Constrained hybrid Action Implicit Representation Towards Wargaming Decision-Making

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Tsinghua Science and Technology 2024年第5期29卷 1422-1440页

作者： Liwei Dong Ni Li Guanghong Gong Xin Lin School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191ChinaState Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijing 100191Chinaand also with Zhongguancun LaboratoryBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191Chinaand also with State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijing 100191China

Reinforcement Learning(RL)has emerged as a promising data-driven solution for wargaming ***,two domain challenges still exist:(1)dealing with discrete-continuous hybrid wargaming control and(2)accelerating RL deployment with rich offline *** RL methods fail to handle these two issues simultaneously,thereby we propose a novel offline RL method targeting hybrid action space.A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent *** allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid ***,a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution *** method demonstrates superior performance and generality across different tasks,particularly in typical realistic wargaming scenarios.

关键词： offline Reinforcement Learning(RL) wargaming decision-making hybrid action space

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Numerical modelling and simulation analysis of wind blades:a critical review

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Clean Energy 2024年第1期8卷 261-279页

作者： Ibrahim Alrowwad Xiaojia Wang Ningling Zhou Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing 210096China

Wind energy has emerged as a promising renewable energy source and wind turbine technology has developed rapidly in recent *** wind turbine performance depends heavily on the design and optimization of wind *** work offers a critical evaluation of the state of the art in the field of numerical modelling and simulation analysis,which have become crucial for the design and optimization of wind *** evaluation of the literature includes considerable research on the application of numerical methods for the structural and aerodynamic performance of wind blades under various operating situations,as well as for analysis and optimization of wind *** article illustrates how numerical techniques can be used to analyse wind blade performance and maximize design *** study of blade performance under various wind conditions has also been made possible through the use of simulation analysis,thus enhancing the efficiency and dependability of wind *** in wind turbine efficiency and dependability,and ultimately the move towards a more sustainable energy future,will be greatly helpful for the development of numerical modelling and simulation techniques.

关键词： wind blades simulations optimization numerical analysis mathematical modelling performance

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