In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dyna...In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.展开更多
To study multi-radio multi-channel （MR-MC） Ad Hoc networks based on 802.11, an efficient cross-layer routing protocol with the function of joint channel assignment, called joint channel assignment and cross-layer ro...To study multi-radio multi-channel （MR-MC） Ad Hoc networks based on 802.11, an efficient cross-layer routing protocol with the function of joint channel assignment, called joint channel assignment and cross-layer routing （JCACR）, is presented. Firstly, this paper introduces a new concept called channel utilization percentage （CUP）, which is for measuring the contention level of different channels in a node’s neighborhood, and deduces its optimal value for determining whether a channel is overloaded or not. Then, a metric parameter named channel selection metric （CSM） is designed, which actually reffects not only the channel status but also corresponding node’s capacity to seize it. JCACR evaluates channel assignment by CSM, performs a local optimization by assigning each node a channel with the smaller CSM value, and changes the working channel dynamically when the channel is overloaded. Therefore, the network load balancing can be achieved. In addition, simulation shows that, when compared with the protocol of weighted cumulative expected transfer time （WCETT）, the new protocol can improve the network throughput and reduce the end-to-end average delay with fewer overheads.展开更多
This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new netw...This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new network model. With this net-work model, the multi-channel wireless network is divided into several subnets according to the num-ber of channels. Based on this, we present a link allocation algorithm with time complexity O（l^2）to al-locate all links to subnets. This link allocation algo-rithm adopts conflict matrix to minimize the network contention factor. After all links are allocated to subnets, the rate assignment algorithm to maximize a fairness utility in each subnet is presented. The rate assignment algorithm adopts a near-optirml al-gorithm based on dual decomposition and realizes in a distributed way. Simulation results demonstrate that, compared with IEEE 802.11b and slotted see-ded channel hopping algorithm, our algorithm de-creases network conflicts and improves the net-work throughput significantly.展开更多
The major challenge faced by wireless mesh networks is the capacity reduction caused by interference,and interference can be reduced or even eliminated through appropriate channel assignment.This article presents a co...The major challenge faced by wireless mesh networks is the capacity reduction caused by interference,and interference can be reduced or even eliminated through appropriate channel assignment.This article presents a comprehensive survey of channel assignment schemes for multicast in multi-radio multi-channel wireless mesh networks.We analyze the state-of-the-art channel assignment schemes for multicast and provide comprehensive taxonomy of the latest work.In general,we classify the channel assignment schemes for multicast into two types,that is,sequential multicast routing and channel assignment(SMRCA)and joint multicast routing and channel assignment(JMRCA).Detailed review of channel assignment schemes in each category is provided.Possible future research directions and corresponding solutions are also explored to motivate research interests in the field of channel assignment for multicast in wireless mesh networks.展开更多
Employing multiple channels in wireless multihop networks is regarded as an effective approach to increas-ing network capacity. This paper presents a centralized quasi-static channel assignment for multi-radio multi-c...Employing multiple channels in wireless multihop networks is regarded as an effective approach to increas-ing network capacity. This paper presents a centralized quasi-static channel assignment for multi-radio multi-channel Wireless Mesh Networks (WMNs). The proposed channel assignment can efficiently utilize multiple channels with only 2 radios equipped on each mesh router. In the scheme, the network end-to-end traffics are first modeled by probing data at wireless access points, and then the traffic load between each pair of neighboring routers is further estimated using an interference-aware estimation algorithm. Having knowledge of the expected link load, the scheme assigns channels to each radio with the objective of mini-mizing network interference, which as a result greatly improves network capacity. The performance evalua-tion shows that the proposed scheme is highly responsive to varying traffic conditions, and the network per-formance under the channel assignment significantly outperforms the single-radio IEEE 802.11 network as well as the 2-radio WMN with static 2 channels.展开更多
Capacity reduction is a major problem faced by wireless mesh networks. An efficient way to alleviate this problem is proper channel assignment. Current end-toend channel assignment schemes usually focus on the case wh...Capacity reduction is a major problem faced by wireless mesh networks. An efficient way to alleviate this problem is proper channel assignment. Current end-toend channel assignment schemes usually focus on the case where channels in distinct frequency bands are assigned to mesh access and backbone, but actually backbone network and access network can use the same IEEE 802.11 technology. Besides, these channel assignment schemes only utilize orthogonal channels to perform channel assignment, and the resulting network interference dramatically degrades network performance. Moreover, Internet-oriented traffic is considered only, and peerto-peer traffic is omitted, or vice versa. The traffic type does not match the practical network. In this paper, we explore how to exploit partially overlapped channels to perform endto-end channel assignment in order to achieve effective end-to-end flow transmissions. The proposed flow-based end-to-end channel assignment schemes can conquer the limitations aforementioned. Simulations reveal that loadaware channel assignment can be applied to networks with stable traffic load, and it can achieve near-optimal performance; Traffic-irrelevant channel assignment is suitable for networks with frequent change of traffic load,and it can achieve good balance between performance and overhead. Also, partially overlapped channels' capability of improving network performance is situation-dependent, they should be used carefully.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
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
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
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
Cooperative task assignment is one of the key research focuses in the field of unmanned aerial vehicles(UAVs). In this paper, an energy learning hyper-heuristic(EL-HH) algorithm is proposed to address the cooperative ...Cooperative task assignment is one of the key research focuses in the field of unmanned aerial vehicles(UAVs). In this paper, an energy learning hyper-heuristic(EL-HH) algorithm is proposed to address the cooperative task assignment problem of heterogeneous UAVs under complex constraints. First, a mathematical model is designed to define the scenario, complex constraints, and objective function of the problem. Then, the scheme encoding, the EL-HH strategy, multiple optimization operators, and the task sequence and time adjustment strategies are designed in the EL-HH algorithm. The scheme encoding is designed with three layers: task sequence, UAV sequence, and waiting time. The EL-HH strategy applies an energy learning method to adaptively adjust the energies of operators, thereby facilitating the selection and application of operators. Multiple optimization operators can update schemes in different ways, enabling the algorithm to fully explore the solution space. Afterward, the task order and time adjustment strategies are designed to adjust task order and insert waiting time. Through the iterative optimization process, a satisfactory assignment scheme is ultimately produced. Finally, simulation and experiment verify the effectiveness of the proposed algorithm.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
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
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
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
Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interfere...Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.展开更多
Multiple channels are available for use in IEEE 802.11.Multiple channels can increase the available network's capacity,and how to efficiently assign these available channels to optimize the network performance is ...Multiple channels are available for use in IEEE 802.11.Multiple channels can increase the available network's capacity,and how to efficiently assign these available channels to optimize the network performance is a challenge.We survey current techniques to solve the problem,and category the techniques single-radio,multi-radio,cross-layer multi-channel assignment.This paper also discusses some interesting issues at last.展开更多
A channel assignment algorithm with awareness of link traffic is proposed in multi-radio multi-channel wireless mesh networks. First, the physical interference model based on the signal-to-interference-plus-noise rati...A channel assignment algorithm with awareness of link traffic is proposed in multi-radio multi-channel wireless mesh networks. First, the physical interference model based on the signal-to-interference-plus-noise ratio and successful transmission condition is described. The model is more suitable for a wireless communication environment than other existing models. Secondly, a pure integer quadratic programming （PIQP） model is used to solve the channel assignment problem and improve the capacity of wireless mesh networks. Consequently, a traffic- aware static channel assignment algorithm（TASC） is designed. The algorithm adopts some network parameters, including the network connectivity, the limitation of the number of radios and the successful transmission conditions in wireless communications. The TASC algorithm can diminish network interference and increase the efficiency of channel assignment while keeping the connectivity of the network. Finally, the feasibility and effectivity of the channel assignment solution are illustrated by the simulation results. Compared witb similar algorithms, the proposed algorithm can increase the capacity of WMNs.展开更多
Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for t...Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for the two drugs were performed and confirmed by theevidence of J_(HF) and J_(CF). Conclusion The structures of amlodipine and risperidone wereconfirmed by careful analysis of regular 1D and 2D NMR spectroscopy.展开更多
A discrete time stochastic traffic assignment model is proposed. The model provides a discrete time description of the variations of flows on a road network during a day or a peak period. The congestion effect at li...A discrete time stochastic traffic assignment model is proposed. The model provides a discrete time description of the variations of flows on a road network during a day or a peak period. The congestion effect at links and link junctions are taken into account. The first in first out principle is enforced on all links at all periods of the day. A stochastic user equilibrium assignment is achieved when the tripmaker is unable to find better travel alternatives. A computational procedure is also presented.展开更多
基金supported by the National Natural Science Foundation of China(61702528,61806212,62173336)。
文摘In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.
基金supported by the National Natural Science Foundationof China (60873195 61070220)+3 种基金the Natural Science Foundation of Anhui Province (070412049)the Outstanding Young Teacher Foundation of Anhui Higher Education Institutions of China (2009SQRZ167)the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2009B114)the Open Project Program of Engineering Research Center of Safety Critical Industry Measure and Control Technology (SCIMCT0802)
文摘To study multi-radio multi-channel （MR-MC） Ad Hoc networks based on 802.11, an efficient cross-layer routing protocol with the function of joint channel assignment, called joint channel assignment and cross-layer routing （JCACR）, is presented. Firstly, this paper introduces a new concept called channel utilization percentage （CUP）, which is for measuring the contention level of different channels in a node’s neighborhood, and deduces its optimal value for determining whether a channel is overloaded or not. Then, a metric parameter named channel selection metric （CSM） is designed, which actually reffects not only the channel status but also corresponding node’s capacity to seize it. JCACR evaluates channel assignment by CSM, performs a local optimization by assigning each node a channel with the smaller CSM value, and changes the working channel dynamically when the channel is overloaded. Therefore, the network load balancing can be achieved. In addition, simulation shows that, when compared with the protocol of weighted cumulative expected transfer time （WCETT）, the new protocol can improve the network throughput and reduce the end-to-end average delay with fewer overheads.
基金This work was supported by the National Natural Science Foundation of China under Cxant No. 60902010 the Research Fund of State Key Laboratory of Mobile Communications un-der Crant No. 2012A03.
文摘This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new network model. With this net-work model, the multi-channel wireless network is divided into several subnets according to the num-ber of channels. Based on this, we present a link allocation algorithm with time complexity O（l^2）to al-locate all links to subnets. This link allocation algo-rithm adopts conflict matrix to minimize the network contention factor. After all links are allocated to subnets, the rate assignment algorithm to maximize a fairness utility in each subnet is presented. The rate assignment algorithm adopts a near-optirml al-gorithm based on dual decomposition and realizes in a distributed way. Simulation results demonstrate that, compared with IEEE 802.11b and slotted see-ded channel hopping algorithm, our algorithm de-creases network conflicts and improves the net-work throughput significantly.
基金supported by the National Natural Science Foundation of China under Grants No.61373124
文摘The major challenge faced by wireless mesh networks is the capacity reduction caused by interference,and interference can be reduced or even eliminated through appropriate channel assignment.This article presents a comprehensive survey of channel assignment schemes for multicast in multi-radio multi-channel wireless mesh networks.We analyze the state-of-the-art channel assignment schemes for multicast and provide comprehensive taxonomy of the latest work.In general,we classify the channel assignment schemes for multicast into two types,that is,sequential multicast routing and channel assignment(SMRCA)and joint multicast routing and channel assignment(JMRCA).Detailed review of channel assignment schemes in each category is provided.Possible future research directions and corresponding solutions are also explored to motivate research interests in the field of channel assignment for multicast in wireless mesh networks.
文摘Employing multiple channels in wireless multihop networks is regarded as an effective approach to increas-ing network capacity. This paper presents a centralized quasi-static channel assignment for multi-radio multi-channel Wireless Mesh Networks (WMNs). The proposed channel assignment can efficiently utilize multiple channels with only 2 radios equipped on each mesh router. In the scheme, the network end-to-end traffics are first modeled by probing data at wireless access points, and then the traffic load between each pair of neighboring routers is further estimated using an interference-aware estimation algorithm. Having knowledge of the expected link load, the scheme assigns channels to each radio with the objective of mini-mizing network interference, which as a result greatly improves network capacity. The performance evalua-tion shows that the proposed scheme is highly responsive to varying traffic conditions, and the network per-formance under the channel assignment significantly outperforms the single-radio IEEE 802.11 network as well as the 2-radio WMN with static 2 channels.
基金supported by the National Natural Science Foundation of China under Grants No.61373124
文摘Capacity reduction is a major problem faced by wireless mesh networks. An efficient way to alleviate this problem is proper channel assignment. Current end-toend channel assignment schemes usually focus on the case where channels in distinct frequency bands are assigned to mesh access and backbone, but actually backbone network and access network can use the same IEEE 802.11 technology. Besides, these channel assignment schemes only utilize orthogonal channels to perform channel assignment, and the resulting network interference dramatically degrades network performance. Moreover, Internet-oriented traffic is considered only, and peerto-peer traffic is omitted, or vice versa. The traffic type does not match the practical network. In this paper, we explore how to exploit partially overlapped channels to perform endto-end channel assignment in order to achieve effective end-to-end flow transmissions. The proposed flow-based end-to-end channel assignment schemes can conquer the limitations aforementioned. Simulations reveal that loadaware channel assignment can be applied to networks with stable traffic load, and it can achieve near-optimal performance; Traffic-irrelevant channel assignment is suitable for networks with frequent change of traffic load,and it can achieve good balance between performance and overhead. Also, partially overlapped channels' capability of improving network performance is situation-dependent, they should be used carefully.
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.
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funded by the National Natural Science Foundation of China (Grant No.62203217)the Jiangsu Province Basic Research Program Natural Science Foundation (Grant No.BK20220885)+3 种基金the Hong Kong,Macao and Taiwan Science and Technology Cooperation Project of Special Foundation in Jiangsu Science and Technology Plan (Grant No.BZ2023057)the Fundamental Research Funds for the Central Universities (Grant No.NJ2024012)the China Postdoctoral Science Foundation (Grant No.GZC20242230)the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No.KYCX24_0586)。
文摘Cooperative task assignment is one of the key research focuses in the field of unmanned aerial vehicles(UAVs). In this paper, an energy learning hyper-heuristic(EL-HH) algorithm is proposed to address the cooperative task assignment problem of heterogeneous UAVs under complex constraints. First, a mathematical model is designed to define the scenario, complex constraints, and objective function of the problem. Then, the scheme encoding, the EL-HH strategy, multiple optimization operators, and the task sequence and time adjustment strategies are designed in the EL-HH algorithm. The scheme encoding is designed with three layers: task sequence, UAV sequence, and waiting time. The EL-HH strategy applies an energy learning method to adaptively adjust the energies of operators, thereby facilitating the selection and application of operators. Multiple optimization operators can update schemes in different ways, enabling the algorithm to fully explore the solution space. Afterward, the task order and time adjustment strategies are designed to adjust task order and insert waiting time. Through the iterative optimization process, a satisfactory assignment scheme is ultimately produced. Finally, simulation and experiment verify the effectiveness of the proposed algorithm.
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.
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 by the National Natural Science Foundation of China(62201602)。
文摘Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.
基金spported by the National Natural Science Foundation of China ( No. 60973139, 60773041 )Postdoctoral Foundation (No. 0801019C,20090451240, 20090451241)+2 种基金Science&Technology Innovation Fund for Higher Education Institutions of Jiangsu Province( No.CX09B_153Z,CX08B-086Z )Six Projects Sponsoring Talent Summits of Jiangsu Province(No. 2008118)the project of NJUPT(No.NY207135)
文摘Multiple channels are available for use in IEEE 802.11.Multiple channels can increase the available network's capacity,and how to efficiently assign these available channels to optimize the network performance is a challenge.We survey current techniques to solve the problem,and category the techniques single-radio,multi-radio,cross-layer multi-channel assignment.This paper also discusses some interesting issues at last.
基金The National Basic Research Program of China(973Program)(No.2009CB320501)the Natural Science Foundation of Jiangsu Province(No.BK2010414)+1 种基金China Postdoctoral Science Foundation(No.20100480071)Specialized Research Fund for the Doctoral Program of Higher Education(No.20090092120029)
文摘A channel assignment algorithm with awareness of link traffic is proposed in multi-radio multi-channel wireless mesh networks. First, the physical interference model based on the signal-to-interference-plus-noise ratio and successful transmission condition is described. The model is more suitable for a wireless communication environment than other existing models. Secondly, a pure integer quadratic programming （PIQP） model is used to solve the channel assignment problem and improve the capacity of wireless mesh networks. Consequently, a traffic- aware static channel assignment algorithm（TASC） is designed. The algorithm adopts some network parameters, including the network connectivity, the limitation of the number of radios and the successful transmission conditions in wireless communications. The TASC algorithm can diminish network interference and increase the efficiency of channel assignment while keeping the connectivity of the network. Finally, the feasibility and effectivity of the channel assignment solution are illustrated by the simulation results. Compared witb similar algorithms, the proposed algorithm can increase the capacity of WMNs.
文摘Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for the two drugs were performed and confirmed by theevidence of J_(HF) and J_(CF). Conclusion The structures of amlodipine and risperidone wereconfirmed by careful analysis of regular 1D and 2D NMR spectroscopy.
文摘A discrete time stochastic traffic assignment model is proposed. The model provides a discrete time description of the variations of flows on a road network during a day or a peak period. The congestion effect at links and link junctions are taken into account. The first in first out principle is enforced on all links at all periods of the day. A stochastic user equilibrium assignment is achieved when the tripmaker is unable to find better travel alternatives. A computational procedure is also presented.
