Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain f...Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.展开更多
Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the pr...Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.展开更多
INTRODUCTION Reports indicating that culturally and linguistically diverse(CALD)people-often with migrant backgrounds-in Australia and New Zealand are more likely to be placed in compulsory community treatment(CCT)hav...INTRODUCTION Reports indicating that culturally and linguistically diverse(CALD)people-often with migrant backgrounds-in Australia and New Zealand are more likely to be placed in compulsory community treatment(CCT)have rightlyraised concernsthat such action might be discriminatory.展开更多
An appropriate spatial structure of a power battery supply network is crucial for the specialization and scale development of key components in new energy vehicles, accelerating the transformation and upgrade of the i...An appropriate spatial structure of a power battery supply network is crucial for the specialization and scale development of key components in new energy vehicles, accelerating the transformation and upgrade of the industry. This paper investigates the cooperative relationships among supply chain enterprises from the perspective of complex networks.Employing methodologies such as the gravity model and Moran's I analysis, it explores the spatial structural characteristics and correlation patterns of the power battery supply network in China and discusses the influencing factors using the quadratic assignment procedure,revealing the mechanisms behind the differences in the spatial distributions of the power battery supply network. The results indicate that the distribution of power battery enterprises is densely concentrated in the eastern and southern regions, whereas the western region has a sparse distribution. The spatial supply network consists of a four-tier linkage system, encompassing 135 prefecture-level cities, with Chongqing, Shanghai, Nanjing, and other cities particularly prominent. Overall, the degree of agglomeration is low, with coastal cities dominating the landscape and inland cities serving as complementary regions. Most areas are characterized as insignificant or low-high regions, and the regional linkage effect of core cities is not pronounced. There is a notable lack of significance and high spatial heterogeneity.Four types of factors—spatial factors, market factors, agglomeration economies, and innovation levels—jointly influence and shape the spatial structure of the power battery supply network.展开更多
With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,whic...With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.展开更多
The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively stu...The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively studied across various domains such as land,sea,air,space,and electronics,the MTA problem has led to the emergence of numerous models and algorithms.To delve deeper into this field,this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software.The analysis includes examining keyword clustering,co-occurrence,and burst,with visual representations of the results.Following this,the paper provides an overview of current classification and modeling techniques for addressing the MTA problem,distinguishing between static multi-target assignment(SMTA)and dynamic multi-target assignment(DMTA).Subsequently,existing solution algorithms for the MTA problem are reviewed,generally falling into three categories:exact algorithms,heuristic algorithms,and machine learning algorithms.Finally,a development framework is proposed based on the"HIGH"model(high-speed,integrated,great,harmonious)to guide future research and intelligent weapon system development concerning the MTA problem.This framework emphasizes application scenarios,modeling mechanisms,solution algorithms,and system efficiency to offer a roadmap for future exploration in this area.展开更多
Multi-band optical networks are a potential technology for increasing network capacity.However,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck res...Multi-band optical networks are a potential technology for increasing network capacity.However,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical networks.To overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength differences.Therefore,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical networks.Our objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not exceeded.Next,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation algorithm.Finally,we conduct simulations under different test conditions.The simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to mi...To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to minimize total time expenditure is constructed.It incorporates parking search time,walking time,and departure time,focusing on short-term parking features.Then,the information dimensions that the parking lot can obtain are evaluated,and three assignment strategies based on three types of regression models-linear regression(LR),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP)-are proposed.A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection,model training,and case studies.Finally,the performance of the three strategies is com-pared,revealing that the XGBoost-based strategy performs the best in case parking lots,which reduces time expendi-ture by 29.3%and 37.2%,respectively,compared with the MLP-based strategy and LR-based strategy.This method offers diverse options for practical parking manage-ment.展开更多
As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UA...As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.展开更多
Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonline...Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonlinear dynamics,parameter variations,and unmeasurable external disturbances,particularly load torquefluctuations.This study proposes an enhanced Interconnection and Damp-ing Assignment Passivity-Based Control(IDA-PBC)scheme,formulated within the port-controlled Hamiltonian(PCH)framework,to address these limitations.A nonlinear disturbance observer is embedded to estimate and compensate,in real time,for lumped mis-matched disturbances arising from parameter uncertainties and external loads.Additionally,aflatness-based control strategy is employed to generate the desired current references within the nonlinear drive system,ensuring accurate tracking of time-varying speed commands.This integrated approach preserves the system’s energy-based structure,enabling systematic stability analysis while enhancing robustness.The proposed control architecture also maintains low complexity with a limited number of tunable parameters,facilitating practical implementation.Simulation and experimental results under various operating conditions demonstrate the effectiveness and robustness of the proposed method.Comparative analysis with conventional proportional-integral(PI)control and standard IDA-PBC strategies confirms its capability to handle disturbances and maintain dynamic performance.展开更多
Frontiers of Engineering Management,after two years’preparation,came,in spring2014,to offer its readers,with the mission and the editor inclination to promote the advancement of engineering management,so as to propel...Frontiers of Engineering Management,after two years’preparation,came,in spring2014,to offer its readers,with the mission and the editor inclination to promote the advancement of engineering management,so as to propel the advancement of engineering science and technology.It is a general consensus of the scientific communities that engineering science is展开更多
Following publication of the original article[1],the authors reported a typesetting error in the authors’affiliations.Due to a typesetting error,the affiliations of authors Shaoyi Du and Lin Wang were mistakenly assi...Following publication of the original article[1],the authors reported a typesetting error in the authors’affiliations.Due to a typesetting error,the affiliations of authors Shaoyi Du and Lin Wang were mistakenly assigned.展开更多
City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordi...City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.展开更多
The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment pro...The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.展开更多
With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data ...With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.展开更多
足球比赛场景中球员居多、足球目标偏小且移动速度快,足球检测识别难度很大。为了解决这一问题,提出一种基于改进的YOLOv5的足球检测方法,增加使用了OTA(Optimal Transport Assignment)损失函数来优化模型提高对足球目标的识别精度,最后...足球比赛场景中球员居多、足球目标偏小且移动速度快,足球检测识别难度很大。为了解决这一问题,提出一种基于改进的YOLOv5的足球检测方法,增加使用了OTA(Optimal Transport Assignment)损失函数来优化模型提高对足球目标的识别精度,最后在Roboflow的足球数据集上进行训练,对足球比赛场景下的足球进行目标检测实现足球识别。根据实验可以得出结论:改进后的YOLOv5算法的足球识别不仅提高了足球的识别性能与精度,而且有效地提高了检测速度,具有更好的识别性能。展开更多
基金supported by the National Natural Science Foundation of China(62173175,61873033)the Shandong Provincial Natural Science Foundation(ZR2024MF032)。
文摘Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
基金supported by the National Natural Science Foundation of China,No.62276089。
文摘Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.
文摘INTRODUCTION Reports indicating that culturally and linguistically diverse(CALD)people-often with migrant backgrounds-in Australia and New Zealand are more likely to be placed in compulsory community treatment(CCT)have rightlyraised concernsthat such action might be discriminatory.
基金Humanities and Social Sciences Project of the Ministry of Education of China,No.23YJCZH195Scientific Research Program funded by the Shaanxi Provincial Education Department,No.20JK0358, No.21JK0908+2 种基金Natural Science Basic Research Program of Shaanxi,No.2024JC-YBQN-0738, No.2023-JC-QN-0560Shaanxi Provincial Education Science Planning Project,No.SGH23Y2484China Logistics Society&China Federation of Logistics and Purchasing Research Fund,No.2023CSLKT3-220。
文摘An appropriate spatial structure of a power battery supply network is crucial for the specialization and scale development of key components in new energy vehicles, accelerating the transformation and upgrade of the industry. This paper investigates the cooperative relationships among supply chain enterprises from the perspective of complex networks.Employing methodologies such as the gravity model and Moran's I analysis, it explores the spatial structural characteristics and correlation patterns of the power battery supply network in China and discusses the influencing factors using the quadratic assignment procedure,revealing the mechanisms behind the differences in the spatial distributions of the power battery supply network. The results indicate that the distribution of power battery enterprises is densely concentrated in the eastern and southern regions, whereas the western region has a sparse distribution. The spatial supply network consists of a four-tier linkage system, encompassing 135 prefecture-level cities, with Chongqing, Shanghai, Nanjing, and other cities particularly prominent. Overall, the degree of agglomeration is low, with coastal cities dominating the landscape and inland cities serving as complementary regions. Most areas are characterized as insignificant or low-high regions, and the regional linkage effect of core cities is not pronounced. There is a notable lack of significance and high spatial heterogeneity.Four types of factors—spatial factors, market factors, agglomeration economies, and innovation levels—jointly influence and shape the spatial structure of the power battery supply network.
基金supported by the National Natural Science Foundation of China under Grant No.62072475 and No.62302062in part by the Hunan Provincial Natural Science Foundation of China under Grant Number 2023JJ40081。
文摘With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.
基金the financial support provided by the National Natural Science Foundation of China(NSFC)(Grant No.62173274)the National Key R&D Program of China(Grant No.2019YFA0405300)+4 种基金the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045)the Practice and Innovation Funds for Graduate Students of Northwestern Polytechnical University(Grant No.PF2023046)the Open Research Subject of State Key Laboratory of Intelligent Game(Grant No.ZBKF-24-01)the Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the China Postdoctoral Science Foundation(Grant No.2024M754304)。
文摘The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively studied across various domains such as land,sea,air,space,and electronics,the MTA problem has led to the emergence of numerous models and algorithms.To delve deeper into this field,this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software.The analysis includes examining keyword clustering,co-occurrence,and burst,with visual representations of the results.Following this,the paper provides an overview of current classification and modeling techniques for addressing the MTA problem,distinguishing between static multi-target assignment(SMTA)and dynamic multi-target assignment(DMTA).Subsequently,existing solution algorithms for the MTA problem are reviewed,generally falling into three categories:exact algorithms,heuristic algorithms,and machine learning algorithms.Finally,a development framework is proposed based on the"HIGH"model(high-speed,integrated,great,harmonious)to guide future research and intelligent weapon system development concerning the MTA problem.This framework emphasizes application scenarios,modeling mechanisms,solution algorithms,and system efficiency to offer a roadmap for future exploration in this area.
基金supported in part by the National Natural Science Foundation of China under Grants U21B2005,62201105,62331017,U24B20134,62222103,and 62025105in part by the Chongqing Municipal Education Commission under Grants KJQN202400621,KJQN202100643,and KJZDK202400608+1 种基金in part by the China Postdoctoral Science Foundation under Grant 2021M700563in part by the Chongqing Postdoctoral Funding Project under Grant 2021XM3052。
文摘Multi-band optical networks are a potential technology for increasing network capacity.However,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical networks.To overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength differences.Therefore,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical networks.Our objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not exceeded.Next,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation algorithm.Finally,we conduct simulations under different test conditions.The simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金The National Natural Science Foundation of China(No.52302388)the Natural Science Foundation of Jiangsu Province(No.BK20230853).
文摘To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs,a mixed parking demand assignment method based on regression modeling is proposed.First,an optimal model aiming to minimize total time expenditure is constructed.It incorporates parking search time,walking time,and departure time,focusing on short-term parking features.Then,the information dimensions that the parking lot can obtain are evaluated,and three assignment strategies based on three types of regression models-linear regression(LR),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP)-are proposed.A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection,model training,and case studies.Finally,the performance of the three strategies is com-pared,revealing that the XGBoost-based strategy performs the best in case parking lots,which reduces time expendi-ture by 29.3%and 37.2%,respectively,compared with the MLP-based strategy and LR-based strategy.This method offers diverse options for practical parking manage-ment.
基金supported by the National Natural Science Foundation of China (No. 62073267)。
文摘As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
基金supported in part by an International Research Partnership“Electrical Engineering-Thai French Research Center(EE-TFRC)”under the project framework of the Lorraine Universite´d’Excellence(LUE)in cooperation between Universite´de Lorraine(France)and King Mongkut’s University of Technology North Bangkok(year 2021-2024/2025-28)by the National Research Council of Thailand(NRCT)under Research Team Promotion Grant(Senior Research Scholar Program)under Grant No.N42A 680561by the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation under Research project Grant No.B41G680025.
文摘Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonlinear dynamics,parameter variations,and unmeasurable external disturbances,particularly load torquefluctuations.This study proposes an enhanced Interconnection and Damp-ing Assignment Passivity-Based Control(IDA-PBC)scheme,formulated within the port-controlled Hamiltonian(PCH)framework,to address these limitations.A nonlinear disturbance observer is embedded to estimate and compensate,in real time,for lumped mis-matched disturbances arising from parameter uncertainties and external loads.Additionally,aflatness-based control strategy is employed to generate the desired current references within the nonlinear drive system,ensuring accurate tracking of time-varying speed commands.This integrated approach preserves the system’s energy-based structure,enabling systematic stability analysis while enhancing robustness.The proposed control architecture also maintains low complexity with a limited number of tunable parameters,facilitating practical implementation.Simulation and experimental results under various operating conditions demonstrate the effectiveness and robustness of the proposed method.Comparative analysis with conventional proportional-integral(PI)control and standard IDA-PBC strategies confirms its capability to handle disturbances and maintain dynamic performance.
文摘Frontiers of Engineering Management,after two years’preparation,came,in spring2014,to offer its readers,with the mission and the editor inclination to promote the advancement of engineering management,so as to propel the advancement of engineering science and technology.It is a general consensus of the scientific communities that engineering science is
文摘Following publication of the original article[1],the authors reported a typesetting error in the authors’affiliations.Due to a typesetting error,the affiliations of authors Shaoyi Du and Lin Wang were mistakenly assigned.
基金Under the auspices of the National Natural Science Foundation of China (No.72273151)。
文摘City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.
基金supported by the Basic Scientific Research Business Expenses of Central Universities(3072022QBZ0806)。
文摘The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.[42275147].
文摘With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.
文摘足球比赛场景中球员居多、足球目标偏小且移动速度快,足球检测识别难度很大。为了解决这一问题,提出一种基于改进的YOLOv5的足球检测方法,增加使用了OTA(Optimal Transport Assignment)损失函数来优化模型提高对足球目标的识别精度,最后在Roboflow的足球数据集上进行训练,对足球比赛场景下的足球进行目标检测实现足球识别。根据实验可以得出结论:改进后的YOLOv5算法的足球识别不仅提高了足球的识别性能与精度,而且有效地提高了检测速度,具有更好的识别性能。
