With the grain yield accounting for 20% of the whole country, the north- east China is a strategic region for ensuring national grain security and also a most centralized region of large grain farmers. Through a sampl...With the grain yield accounting for 20% of the whole country, the north- east China is a strategic region for ensuring national grain security and also a most centralized region of large grain farmers. Through a sampling survey of large grain farmers in 15 counties and cities of northeast China, with the aid of SPSS and AMOS software, using multiple regression analysis and structural equation modeling, this paper made a quantitative analysis on the influence of the subjective and ob- jective factors of large grain farmers on their large-scale management. The results showed that the age structure, educational level, family operating capital, yield ex- pectation and protective farming awareness of large grain farmers are the positive factors influencing their large scale operation due to agricultural subsidy policy. By comparison, the number of agricultural machinery and equipment owned by family, regional labor force, expectation for future income, and expectation for contractual scale become negative factors influencing large-scale operation of large grain farm- ers because of agricultural policies. When the future expectation, self conditions, family endowment, and operation conditions of large grain farmers increase one unit, their large scale operation motivation will increase by 0.692, 0.689, 0.487 and 0.363 units respectively. Thus, increasing the future expectation and self conditions of large grain farmers is a key factor for promoting large scale operation of farmland.展开更多
As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerou...As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.展开更多
Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge a...Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge and efficient problem-solving is introduced,and a recommendation method for UAV swarm operation strategies based on CBR is proposed.Firstly,we design a universal framework for UAV swarm operation strategies from three dimensions:operation effectiveness,time,and cost.Secondly,based on the representation of operation cases,certain,fuzzy,interval,and classification attribute similarity calculation methods,as well as entropybased attribute weight allocation methods,are suggested to support the calculation of global similarity of cases.This method is utilized to match the source case with the most similarity from the historical case library,to obtain the optimal recommendation strategy for the target case.Finally,in the form of red blue confrontation,a UAV swarm operation strategy recommendation case is constructed based on actual battle cases,and a system simulation analysis is conducted.The results show that the strategy given in the example performs the best in three evaluation indicators,including cost-effectiveness,and overall outperforms other operation strategies.Therefore,the proposed method has advantages such as high real-time performance and interpretability,and can address the issue of recommending UAV swarm operation strategies in complex battlefield environments across both online and offline modes.At the same time,this study could also provide new ideas for the selection of UAV swarm operation strategies.展开更多
Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Al...Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Although numerous methods exist,automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging.Given this,we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm,which aims to effectively utilize sparse knowledge for designing diversified genetic operators,and automatically select appropriate genetic operators for various problems or different situations within the same optimization process.Specifically,three sparsity-aware genetic operators are designed by utilizing sparsity statistic,sparsity clustering,and sparsity logic operation.They possess distinct advantages in terms of convergence speed,solution quality,and diversity.Furthermore,the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population.The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems.Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.展开更多
At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a pr...At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a project of "973 Program". Mr. Zhou, the chief engineer of China Electric Power Research Institute(CEPRI) and an academician of Chinese Academy of Sciences, is the chief scientist in charge of this research project.展开更多
The level of personnel operation ability determines the expected effectiveness of large-scale complex equipment. Firstly, this paper constructs the personnel operational ability evaluation index system and analyzes th...The level of personnel operation ability determines the expected effectiveness of large-scale complex equipment. Firstly, this paper constructs the personnel operational ability evaluation index system and analyzes the data source of index. Secondly, the weight of index is determined and the fuzzy comprehensive evaluation model is proposed. Finally, results of instance analysis show that the evaluation model is feasible and effective.展开更多
The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the...The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.展开更多
The maneuvering of a large-scale unmanned aerial vehicle(UAV)swarm,notable for flexible flight with collisionfree,is still challenging due to the significant number of UAVs and the compact configuration of the swarm.I...The maneuvering of a large-scale unmanned aerial vehicle(UAV)swarm,notable for flexible flight with collisionfree,is still challenging due to the significant number of UAVs and the compact configuration of the swarm.In light of this problem,a novel parallel control method that utilizes space and time transformation is proposed.First,the swarm is decomposed based on a grouping-hierarchical strategy,while the distinct flight roles are assigned to each UAV.Then,to achieve the desired configuration(DCF)in the real world,a bijection transformation is conducted in the space domain,converting an arbitrarily general configuration(GCF)into a standard configuration(SCF)in the virtual space.Further,to improve the flexibility of the swarm,the time scaling transformation is adopted in the time domain,which ensures the desired prescribed-time convergence of the swarm independent of initial conditions.Finally,simulation results demonstrate that collision-free maneuvering,including formation changes and turning,can be effectively and rapidly achieved by the proposed parallel control method.Overall,this research contributes a viable solution for enhancing cooperation among largescale UAV swarms.展开更多
As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has a...As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has attracted much attention.Air-to-ground(A2G)propagation channel models vary in different scenarios,requiring accurate models for designing and evaluating UAV communication links.Unlike terrestrial models,A2G channel models lack detailed investigation.Therefore,this paper provides an overview of existing A2G channel measurement campaigns,different types of A2G channel models for various environments,and future research directions for UAV airland channel modeling.This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights nonsuburban scenarios requiring consideration in future modeling efforts.展开更多
To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious op...To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious operations,this paper proposes an integrated framework combining complex system network modeling with dynamic adversarial simulation for evaluating mission-critical system-of-systems(SoS).Specifically,the contribution rate of unmanned aerial vehicles(UAVs)to the amphibious joint landing SoS(AJLSoS)is quantified.Firstly,a standardized network topology model is developed using operation loop theory,systematically characterizing node functionalities and their interdependencies.Secondly,the ideal Lanchester equation is augmented according to the model’s static operational capability,and an amphibious operational simulation model is constructed based on the modified equation,enabling dynamic simulation of force attrition and engagement duration as key performance indicators of AJLSoS.To validate the theoretical framework,a battalion-level amphibious campaign scenario is developed to compute effectiveness metrics across multiple control scenarios and the contribution rate of UAVs to AJLSoS is analyzed.This study not only provides actionable insights for operational mission planning of UAVs in the context of amphibious operations but also demonstrates high adaptability to diverse operational contexts.展开更多
Unmanned Aerial Vehicle(UAV)-aided communication,prized for its network reconfigurability,operational flexibility,and cost-effectiveness,is a key enabler of the low-altitude economy.However,the high possibilities of l...Unmanned Aerial Vehicle(UAV)-aided communication,prized for its network reconfigurability,operational flexibility,and cost-effectiveness,is a key enabler of the low-altitude economy.However,the high possibilities of line-of-sight links and the broadcast nature of air-ground UAV communications make it vulnerable and prone to eavesdropping by malicious nodes.展开更多
文摘With the grain yield accounting for 20% of the whole country, the north- east China is a strategic region for ensuring national grain security and also a most centralized region of large grain farmers. Through a sampling survey of large grain farmers in 15 counties and cities of northeast China, with the aid of SPSS and AMOS software, using multiple regression analysis and structural equation modeling, this paper made a quantitative analysis on the influence of the subjective and ob- jective factors of large grain farmers on their large-scale management. The results showed that the age structure, educational level, family operating capital, yield ex- pectation and protective farming awareness of large grain farmers are the positive factors influencing their large scale operation due to agricultural subsidy policy. By comparison, the number of agricultural machinery and equipment owned by family, regional labor force, expectation for future income, and expectation for contractual scale become negative factors influencing large-scale operation of large grain farm- ers because of agricultural policies. When the future expectation, self conditions, family endowment, and operation conditions of large grain farmers increase one unit, their large scale operation motivation will increase by 0.692, 0.689, 0.487 and 0.363 units respectively. Thus, increasing the future expectation and self conditions of large grain farmers is a key factor for promoting large scale operation of farmland.
基金This work was supported by National Key R&D Program of China under Grant 2018YFB1800802in part by the National Natural Science Foundation of China under Grant No.61771488,No.61631020 and No.61827801+1 种基金in part by State Key Laboratory of Air Traffic Management System and Technology under Grant No.SKLATM201808in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province under No.KYCX190188.
文摘As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.
基金supported by the National Natural Science Foundation of China(72101263)the Natural Science Foundation of Hunan Province(2023JJ40677).
文摘Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge and efficient problem-solving is introduced,and a recommendation method for UAV swarm operation strategies based on CBR is proposed.Firstly,we design a universal framework for UAV swarm operation strategies from three dimensions:operation effectiveness,time,and cost.Secondly,based on the representation of operation cases,certain,fuzzy,interval,and classification attribute similarity calculation methods,as well as entropybased attribute weight allocation methods,are suggested to support the calculation of global similarity of cases.This method is utilized to match the source case with the most similarity from the historical case library,to obtain the optimal recommendation strategy for the target case.Finally,in the form of red blue confrontation,a UAV swarm operation strategy recommendation case is constructed based on actual battle cases,and a system simulation analysis is conducted.The results show that the strategy given in the example performs the best in three evaluation indicators,including cost-effectiveness,and overall outperforms other operation strategies.Therefore,the proposed method has advantages such as high real-time performance and interpretability,and can address the issue of recommending UAV swarm operation strategies in complex battlefield environments across both online and offline modes.At the same time,this study could also provide new ideas for the selection of UAV swarm operation strategies.
基金supported by the National Natural Science Foundation of China(Nos.62303013,62276001,and U21A20512).
文摘Sparse Large-scale Multi-objective Optimization Problems(sparse LMOPs)widely exist in various optimization applications,such as neural network training,portfolio optimization,and feature selection of classification.Although numerous methods exist,automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging.Given this,we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm,which aims to effectively utilize sparse knowledge for designing diversified genetic operators,and automatically select appropriate genetic operators for various problems or different situations within the same optimization process.Specifically,three sparsity-aware genetic operators are designed by utilizing sparsity statistic,sparsity clustering,and sparsity logic operation.They possess distinct advantages in terms of convergence speed,solution quality,and diversity.Furthermore,the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population.The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems.Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.
文摘At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a project of "973 Program". Mr. Zhou, the chief engineer of China Electric Power Research Institute(CEPRI) and an academician of Chinese Academy of Sciences, is the chief scientist in charge of this research project.
基金supported by the Natural Science Foundation of China(71704184)Projects of the of the National Social Science Foundation of China(15GJ003-245)Science Foundation of Equipment Research(JJ20172A05095)
文摘The level of personnel operation ability determines the expected effectiveness of large-scale complex equipment. Firstly, this paper constructs the personnel operational ability evaluation index system and analyzes the data source of index. Secondly, the weight of index is determined and the fuzzy comprehensive evaluation model is proposed. Finally, results of instance analysis show that the evaluation model is feasible and effective.
基金supported by the National Natural Science Foundation of China(with Granted Number 72271239,grant recipient P.J.)Research on the Design Method of Reliability Qualification Test for Complex Equipment Based on Multi-Source Information Fusion.https://www.nsfc.gov.cn/.
文摘The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.
基金supported in part by the National Natural Science Foundation of China(62373302,62333009,61973253,62273283).
文摘The maneuvering of a large-scale unmanned aerial vehicle(UAV)swarm,notable for flexible flight with collisionfree,is still challenging due to the significant number of UAVs and the compact configuration of the swarm.In light of this problem,a novel parallel control method that utilizes space and time transformation is proposed.First,the swarm is decomposed based on a grouping-hierarchical strategy,while the distinct flight roles are assigned to each UAV.Then,to achieve the desired configuration(DCF)in the real world,a bijection transformation is conducted in the space domain,converting an arbitrarily general configuration(GCF)into a standard configuration(SCF)in the virtual space.Further,to improve the flexibility of the swarm,the time scaling transformation is adopted in the time domain,which ensures the desired prescribed-time convergence of the swarm independent of initial conditions.Finally,simulation results demonstrate that collision-free maneuvering,including formation changes and turning,can be effectively and rapidly achieved by the proposed parallel control method.Overall,this research contributes a viable solution for enhancing cooperation among largescale UAV swarms.
基金supported by the National Natural Science Foundation of China under Grant No.42176190Fundamental Research Funds for the Central Universities,CHD under Grant Nos.300102243401 and 300102244203Research Funds for the Interdisciplinary Projects,CHU under Grant Nos.300104240912 and 300104240922。
文摘As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has attracted much attention.Air-to-ground(A2G)propagation channel models vary in different scenarios,requiring accurate models for designing and evaluating UAV communication links.Unlike terrestrial models,A2G channel models lack detailed investigation.Therefore,this paper provides an overview of existing A2G channel measurement campaigns,different types of A2G channel models for various environments,and future research directions for UAV airland channel modeling.This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights nonsuburban scenarios requiring consideration in future modeling efforts.
文摘To overcome the limitations of conventional approaches that adopt monolithic architectures and overlook critical dynamic interactions in evaluating combat effectiveness and subsystem contributions within amphibious operations,this paper proposes an integrated framework combining complex system network modeling with dynamic adversarial simulation for evaluating mission-critical system-of-systems(SoS).Specifically,the contribution rate of unmanned aerial vehicles(UAVs)to the amphibious joint landing SoS(AJLSoS)is quantified.Firstly,a standardized network topology model is developed using operation loop theory,systematically characterizing node functionalities and their interdependencies.Secondly,the ideal Lanchester equation is augmented according to the model’s static operational capability,and an amphibious operational simulation model is constructed based on the modified equation,enabling dynamic simulation of force attrition and engagement duration as key performance indicators of AJLSoS.To validate the theoretical framework,a battalion-level amphibious campaign scenario is developed to compute effectiveness metrics across multiple control scenarios and the contribution rate of UAVs to AJLSoS is analyzed.This study not only provides actionable insights for operational mission planning of UAVs in the context of amphibious operations but also demonstrates high adaptability to diverse operational contexts.
文摘Unmanned Aerial Vehicle(UAV)-aided communication,prized for its network reconfigurability,operational flexibility,and cost-effectiveness,is a key enabler of the low-altitude economy.However,the high possibilities of line-of-sight links and the broadcast nature of air-ground UAV communications make it vulnerable and prone to eavesdropping by malicious nodes.
