Owing to the ubiquitous use of smartphones by soldiers, military researchers have an increasing interest in potentially problematicside effects such as smartphone overdependence. This raises a question regarding the p...Owing to the ubiquitous use of smartphones by soldiers, military researchers have an increasing interest in potentially problematicside effects such as smartphone overdependence. This raises a question regarding the psychological mechanisms underlying thepotentially self-damaging use of smartphones. Here, we address this question by analyzing how heterogeneity in commander’sgood leadership explains subordinate soldiers’ differences in self-control and smartphone use. Specifically, we found thatsubordinate soldiers who thought their commander's leadership was good were self-regulated, less dependent on smartphones,less stressed, and finally had good mental health. This result indicates that commander’s good leadership can be used toestimate whether subordinate soldiers exert control over their impulses and use their smartphones properly. Thus, the currentfindings help to identify external factors that lead to a better understanding of problematic smartphone use and can potentiallyhelp to design appropriate preventive mechanisms or interventions that target commander’s good leadership.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ...Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.展开更多
Cicero tried to take advantage of the War of Mutina in spring 44 BC,to vilify Mark Antony who,in his view,was the mortal foe to the res publica libera.In order to achieve his aim,Cicero used his letter-exchange with t...Cicero tried to take advantage of the War of Mutina in spring 44 BC,to vilify Mark Antony who,in his view,was the mortal foe to the res publica libera.In order to achieve his aim,Cicero used his letter-exchange with the military commanders in North and West Italy as well as in Africa to exert his infl uence and assure their alliance against Antony.Cicero employed various tactics in his letters to persuade and integrate those commanders into his framework.Those tactics functioned for some time during the war but failed eventually.Due to the formidable alliance of the Caesarian party and the still scattered powers of the Republicans,Cicero’s maneuver to remove Antony only by means of political advocacy failed.展开更多
With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation ...With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation of synthetic attacks,including XSS(Cross-Site Scripting),SQL Injections,and Command Injections.A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence.The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks.This approach not only improves the model’s precision and dependability but also serves as a practical resource for cybersecurity professionals to enhance the security of web applications.The methodology and structured implementation underscore the importance and potential of advanced language models in cybersecurity,illustrating their effectiveness in generating high-quality synthetic data for penetration testing purposes.The research results demonstrate that this approach enables the identification of vulnerabilities that traditional methods may not uncover,providing deeper insights into potential threats and enhancing overall security measures.The performance evaluation of the model indicated satisfactory results,while further hyperparameter optimization could improve accuracy and generalization capabilities.This research represents a significant step forward in improving web application security and opens new opportunities for the use of LLMs in security testing,thereby contributing to the development of more effective cybersecurity strategies.展开更多
The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper.First,a comprehensive nonlinear mathematical model that encompasses ...The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper.First,a comprehensive nonlinear mathematical model that encompasses both matched and mismatched disturbances is formulated.Due to the fact that only position information can be measured,a linear Extended State Observer(ESO)is introduced to estimate unknown states and matched disturbances,while a dedicated disturbance observer is constructed to estimate mismatched disturbances.Different from the traditional observer results,the design of the disturbance observer used in this study is carried out under the constraint of output feedback.Furthermore,an output feedback nonlinear controller is proposed leveraging the aforementioned observers to achieve accurate trajectory tracking.To mitigate the inherent differential explosion problem of the traditional backstepping framework,a finite-time stable command filter is incorporated.Simultaneously,considering transient filtering errors,a set of error compensation signals are designed to counter their negative impact effectively.Theoretical analysis affirms that the proposed control strategy ensures the boundedness of all signals within the closed-loop system.Additionally,under the specific condition of only time-invariant disturbances in the system,the conclusion of asymptotic stability is established.Finally,the algorithm’s efficacy is validated through comparative experiments.展开更多
学报简介:《指挥与控制学报》(Journal of Command and Control)是中国指挥与控制学会会刊,由北方自动控制技术研究所和中国指挥与控制学会共同主办,2015年创刊。现为中文核心期刊、CSCD核心期刊、中国科技核心期刊、RCCSE中国权威学术...学报简介:《指挥与控制学报》(Journal of Command and Control)是中国指挥与控制学会会刊,由北方自动控制技术研究所和中国指挥与控制学会共同主办,2015年创刊。现为中文核心期刊、CSCD核心期刊、中国科技核心期刊、RCCSE中国权威学术期刊(A+)等。展开更多
学报简介:《指挥与控制学报》(Journal of Command and Control)是中国指挥与控制学会会刊,由北方自动控制技术研究所和中国指挥与控制学会共同主办,2015年创刊。现为中文核心期刊、CSCD核心期刊、中国科技核心期刊、RCCSE中国权威学术...学报简介:《指挥与控制学报》(Journal of Command and Control)是中国指挥与控制学会会刊,由北方自动控制技术研究所和中国指挥与控制学会共同主办,2015年创刊。现为中文核心期刊、CSCD核心期刊、中国科技核心期刊、RCCSE中国权威学术期刊(A+)等。展开更多
学报简介:《指挥与控制学报》(Journal of Command and Control)是中国指挥与控制学会会刊,由北方自动控制技术研究所和中国指挥与控制学会共同主办,2015年创刊。现为中文核心期刊、CSCD核心期刊、中国科技核心期刊、RCCSE中国权威学术...学报简介:《指挥与控制学报》(Journal of Command and Control)是中国指挥与控制学会会刊,由北方自动控制技术研究所和中国指挥与控制学会共同主办,2015年创刊。现为中文核心期刊、CSCD核心期刊、中国科技核心期刊、RCCSE中国权威学术期刊(A+)等。展开更多
Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in ...Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.展开更多
基金supported by 2023 Research Fund of Korea Military Academy(Hwarangdae Research Institute,RN:2023B1012).
文摘Owing to the ubiquitous use of smartphones by soldiers, military researchers have an increasing interest in potentially problematicside effects such as smartphone overdependence. This raises a question regarding the psychological mechanisms underlying thepotentially self-damaging use of smartphones. Here, we address this question by analyzing how heterogeneity in commander’sgood leadership explains subordinate soldiers’ differences in self-control and smartphone use. Specifically, we found thatsubordinate soldiers who thought their commander's leadership was good were self-regulated, less dependent on smartphones,less stressed, and finally had good mental health. This result indicates that commander’s good leadership can be used toestimate whether subordinate soldiers exert control over their impulses and use their smartphones properly. Thus, the currentfindings help to identify external factors that lead to a better understanding of problematic smartphone use and can potentiallyhelp to design appropriate preventive mechanisms or interventions that target commander’s good leadership.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金supported by the Natural Science Foundation of Shaanxi Province(2023-JC-QN-0728)the China Postdoctoral Science Foundation(2021M693942)。
文摘Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.
文摘Cicero tried to take advantage of the War of Mutina in spring 44 BC,to vilify Mark Antony who,in his view,was the mortal foe to the res publica libera.In order to achieve his aim,Cicero used his letter-exchange with the military commanders in North and West Italy as well as in Africa to exert his infl uence and assure their alliance against Antony.Cicero employed various tactics in his letters to persuade and integrate those commanders into his framework.Those tactics functioned for some time during the war but failed eventually.Due to the formidable alliance of the Caesarian party and the still scattered powers of the Republicans,Cicero’s maneuver to remove Antony only by means of political advocacy failed.
基金supported by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia,and these results are parts of Grant No.451-03-66/2024-03/200132 with the University of Kragujevac-Faculty of Technical Sciences Cacak.
文摘With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation of synthetic attacks,including XSS(Cross-Site Scripting),SQL Injections,and Command Injections.A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence.The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks.This approach not only improves the model’s precision and dependability but also serves as a practical resource for cybersecurity professionals to enhance the security of web applications.The methodology and structured implementation underscore the importance and potential of advanced language models in cybersecurity,illustrating their effectiveness in generating high-quality synthetic data for penetration testing purposes.The research results demonstrate that this approach enables the identification of vulnerabilities that traditional methods may not uncover,providing deeper insights into potential threats and enhancing overall security measures.The performance evaluation of the model indicated satisfactory results,while further hyperparameter optimization could improve accuracy and generalization capabilities.This research represents a significant step forward in improving web application security and opens new opportunities for the use of LLMs in security testing,thereby contributing to the development of more effective cybersecurity strategies.
基金supported by the National Key R&D Program of China(No.2021YFB2011300)the Special Funds Project for the Transformation of Scientific and Technological Achievements of Jiangsu Province,China(No.BA2023039)+1 种基金the National Natural Science Foundation of China(No.52075262)the Fundamental Research Funds for the Central Universities,China(No.30922010706).
文摘The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper.First,a comprehensive nonlinear mathematical model that encompasses both matched and mismatched disturbances is formulated.Due to the fact that only position information can be measured,a linear Extended State Observer(ESO)is introduced to estimate unknown states and matched disturbances,while a dedicated disturbance observer is constructed to estimate mismatched disturbances.Different from the traditional observer results,the design of the disturbance observer used in this study is carried out under the constraint of output feedback.Furthermore,an output feedback nonlinear controller is proposed leveraging the aforementioned observers to achieve accurate trajectory tracking.To mitigate the inherent differential explosion problem of the traditional backstepping framework,a finite-time stable command filter is incorporated.Simultaneously,considering transient filtering errors,a set of error compensation signals are designed to counter their negative impact effectively.Theoretical analysis affirms that the proposed control strategy ensures the boundedness of all signals within the closed-loop system.Additionally,under the specific condition of only time-invariant disturbances in the system,the conclusion of asymptotic stability is established.Finally,the algorithm’s efficacy is validated through comparative experiments.
基金Supported by Zhejiang Provincial Key Research and Development Program(Grant No.2021C04015)。
文摘Learning from demonstration is widely regarded as a promising paradigm for robots to acquire diverse skills.Other than the artificial learning from observation-action pairs for machines,humans can learn to imitate in a more versatile and effective manner:acquiring skills through mere“observation”.Video to Command task is widely perceived as a promising approach for task-based learning,which yet faces two key challenges:(1)High redundancy and low frame rate of fine-grained action sequences make it difficult to manipulate objects robustly and accurately.(2)Video to Command models often prioritize accuracy and richness of output commands over physical capabilities,leading to impractical or unsafe instructions for robots.This article presents a novel Video to Command framework that employs multiple data associations and physical constraints.First,we introduce an object-level appearancecontrasting multiple data association strategy to effectively associate manipulated objects in visually complex environments,capturing dynamic changes in video content.Then,we propose a multi-task Video to Command model that utilizes object-level video content changes to compile expert demonstrations into manipulation commands.Finally,a multi-task hybrid loss function is proposed to train a Video to Command model that adheres to the constraints of the physical world and manipulation tasks.Our method achieved over 10%on BLEU_N,METEOR,ROUGE_L,and CIDEr compared to the up-to-date methods.The dual-arm robot prototype was established to demonstrate the whole process of learning from an expert demonstration of multiple skills and then executing the tasks by a robot.
