In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understandi...The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
An increase in the use of the gun barrel will cause wear of the inner wall,which reduces the muzzle velocity and the spin rate of the projectile.The off-bore flight attitude and trajectory of the projectile also chang...An increase in the use of the gun barrel will cause wear of the inner wall,which reduces the muzzle velocity and the spin rate of the projectile.The off-bore flight attitude and trajectory of the projectile also change,affecting the shooting power and the accuracy.Exterior ballistic data of a high-speed spinning projectile are required to study the performance change.Therefore,based on the barrel’s accelerated life test,the whole process of projectile shooting is reproduced using numerical simulation technology,and key information on the ballistic performance change at each shooting stage are acquired.Studies have shown that in the later stages of barrel shooting,the accuracy of shooting has not decreased significantly.However,it is found that the angle of attack of the projectile increases as the wear of the barrel increases.The maximum angle of attack reaches 0.106 rad when the number of shots reaches 4300.Meanwhile,elliptical bullet hole has appeared on the target at this shooting stage.Through combining external ballistic theory with simulation results,the primary reason of this phenomenon is found to be a significant decrease in the muzzle spin rate of the projectile.At the end of the barrel life,the projectile muzzle spin rate is 57.5%lower than that of a barrel without wear.展开更多
Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially in...Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.展开更多
Battlefield situation awareness is equivalent to the observation and orientation part of the Observation-Orientation-Decision-Action(OODA)loop.It is the key to transforming information advantage into decision-making a...Battlefield situation awareness is equivalent to the observation and orientation part of the Observation-Orientation-Decision-Action(OODA)loop.It is the key to transforming information advantage into decision-making advantages and an essential support for agile command decision-making.With the rapid rise and widespread application of various advanced intelligent technologies and model algorithms in the military field,traditional situation awareness technology and capacity building are facing great challenges.Relying solely on manual processing of battlefield situational data is no longer sufficient to meet the changing needs of the battlefield,nor can it provide commanders with real-time,accurate,efficient,and reliable situational information.Therefore,it is necessary to use highperformance hardware facilities and artificial intelligence technology to process massive battlefield data and provide an important reference basis for commanders to implement decision-making behaviors.展开更多
Electronic warfare is a modern combat mode,in which predicting digital material consumption is a key for material requirements planning(MRP).In this paper,we introduce an insensitive loss function(ε) and propose a ε...Electronic warfare is a modern combat mode,in which predicting digital material consumption is a key for material requirements planning(MRP).In this paper,we introduce an insensitive loss function(ε) and propose a ε-SVR-based prediction approach.First,we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set,and preprocess it to construct the sample space.Subsequently,we establish the ε-SVR-based prediction model based on "wartime influencing factors-material consumption" and perform model training.In case study,we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach.The results illustrate its higher accuracy and more convenience compared with other current approaches.Taking data acquisition controller prediction as an example,our model has better prediction performance(RMSE=0.575 7,MAPE(%)=12.037 6 and R^2=0.996 0) compared with BP neural network model(RMSE=1.272 9,MAPE(%)=23.577 5 and R^2=0.980 3) and GM(1,1) model(RMSE=2.095 0,MAPE(%)=24.188 0 and R^2=0.946 6).The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.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
Various application domains require the integration of distributed real-time or near-real-time systems with non-real-time systems.Smart cities,smart homes,ambient intelligent systems,or network-centric defense systems...Various application domains require the integration of distributed real-time or near-real-time systems with non-real-time systems.Smart cities,smart homes,ambient intelligent systems,or network-centric defense systems are among these application domains.Data Distribution Service(DDS)is a communication mechanism based on Data-Centric Publish-Subscribe(DCPS)model.It is used for distributed systems with real-time operational constraints.Java Message Service(JMS)is a messaging standard for enterprise systems using Service Oriented Architecture(SOA)for non-real-time operations.JMS allows Java programs to exchange messages in a loosely coupled fashion.JMS also supports sending and receiving messages using a messaging queue and a publish-subscribe interface.In this article,we propose an architecture enabling the automated integration of distributed real-time and non-real-time systems.We test our proposed architecture using a distributed Command,Control,Communications,Computers,and Intelligence(C4I)system.The system has DDS-based real-time Combat Management System components deployed to naval warships,and SOA-based non-real-time Command and Control components used at headquarters.The proposed solution enables the exchange of data between these two systems efficiently.We compare the proposed solution with a similar study.Our solution is superior in terms of automation support,ease of implementation,scalability,and performance.展开更多
The purpose of the study was to investigate and illustrate the challenges faced by performers and audiences during Ateso oral narratives in Ateso speaking communities in Uganda. The study used ethnographic and discurs...The purpose of the study was to investigate and illustrate the challenges faced by performers and audiences during Ateso oral narratives in Ateso speaking communities in Uganda. The study used ethnographic and discurssive analyses methods of research. The topic was Audience-Performer Interface as a Battlefield of Expression： A Study of Ateso Oral Narratives. Ethnographic method of study was used in Ateso speaking communities of Serere, Ngora, Bukedea and Pallisa districts of Uganda. The author stayed with communities for four to seven days in 2009, 2010 and 2011. The study analysed the interpretational dimensions of the oral narrative episodes. Questionnaires and focused group discussions were used to solicit data from a total of 20 （33.3%） out of 60 persons. The study saw that there was dire need to revive the cultural media of communication in Teso. In Serere, Bukedea and Ngora there was more of unpleasant intrusion than in Pallisa and Serere. Performers should consider their audiences complementary to the narration and establish rapport. Audiences should appreciate the efforts of the narrators to keep the cultural norm of story-telling alive in Teso. The Ministry of Education and Sports in Uganda should encourage local languages at all levels of education.展开更多
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
The prevailing narrative instructs us that humane treatment of captured enemy fighters is down to white knights from the western parts of the European continent with their codes of chivalry, or alternatively, the Swis...The prevailing narrative instructs us that humane treatment of captured enemy fighters is down to white knights from the western parts of the European continent with their codes of chivalry, or alternatively, the Swiss businessman Henri Dunant. This contribution challenges that narrative for overlooking, or being ignorant of, the way that societies around the world have approached the matter of the captured enemy fighter. Traces of some of the critical principles about humane treatment that we see in our present law can actually be found in much older societies from outside of Europe. A more accurate and representative way of understanding humanitarianism in the treatment of captured enemy fighters can and must be crafted, with the prevailing Euro-centric account balanced with practices, cultures and faiths from elsewhere. The quest to achieve more humane treatment in armed conflict is first and foremost a battle of the intellect. Narratives and conceptualisations that are more inclusive, recognising and appreciating of the ways of the rest of the world are likely to be more effective in communicating humanitarian ideals. This work adopts a new method of approaching the richness and diversity of the treatment of captured enemy fighters over time and space. This new framework of analysis uses six cross-cutting themes to facilitate a broader international and comparative perspective, and develop a more sophisticated level of understanding. The first theme is how older and indigenous societies approached the matter of captured enemy fighters. The second focuses on religions of the world, and what they teach or require. The third section examines the matter of martial practices and codes of ethics for combatants in certain societies. The fourth category engages with colonisation and decolonisation, and regulation （or non-regulation） of the treatment of captives of war. Fifth is the issue of modernisation and the impact it has had on armed forces and fighters, including on the treatment of captives. The final issue is the shift towards formalised agreements, beginning with the first bilateral agreements and then the multilateral codification exercise that began in the mid-19th century and continues to this day. This framework for analysis leads into a final chapter, presenting a fresh and holistic view on the evolution of prisoner of war protections in the international order. It provides a different way of looking at International Humanitarian Law, starting with this effort at a global understanding of the treatment of captured enemy fighters.展开更多
The efficiency of carrier-based aircraft support operation scheduling critically impacts aircraft carrier operational effectiveness by determining sortie generation rates,yet faces significant challenges in complex de...The efficiency of carrier-based aircraft support operation scheduling critically impacts aircraft carrier operational effectiveness by determining sortie generation rates,yet faces significant challenges in complex deck environments characterized by resource coupling,dynamic constraints,and highdimensional state-action spaces.Traditional optimization algorithms and vanilla reinforcement learning(RL)struggle with computational inefficiency,sparse rewards,and adaptability to dynamic scenarios,while human expert systems are constrained by the quality of expert knowledge,and poor expert guidance may even have a negative impact.To address these limitations,this paper proposes a human experience-guided actor-critic reinforcement learning framework that synergizes domain expertise with adaptive learning.First,a dynamic Markov decision process(MDP)model is developed to rigorously simulate carrier deck operations,explicitly encoding constraints on positions,resources,and collision avoidance.Building upon this foundation,a human experience database is constructed to enable real-time pattern-matching-based intervention during agent-environment interactions,dynamically correcting wrong actions to avoid catastrophic states while refining exploration efficiency.Finally,the policy and value network objectives are reshaped to incorporate human intent through hybrid reward functions and adaptive guidance weighting,ensuring balanced integration of expert knowledge with RL's exploration capabilities.Extensive simulations across three scenarios demonstrate superior performance compared to state-of-the-art methods and maintain robustness under suboptimal human guidance.These results validate the framework's ability to harmonize human expertise with adaptive learning,offering a practical solution for real-world carriers.展开更多
Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human f...Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.展开更多
On September 3,2025,heads turned to Beijing for a grand military parade marking the 80th anniversary of the victory in the Chinese People’s War of Resistance Against Japanese Aggression and the World Anti-Fascist War...On September 3,2025,heads turned to Beijing for a grand military parade marking the 80th anniversary of the victory in the Chinese People’s War of Resistance Against Japanese Aggression and the World Anti-Fascist War.Such events often highlight military strength,but this commemoration sought to project a deeper message:remembrance of sacrifice,recognition of the immense costs of conflict,and a renewed commitment to peace.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
National awakening and the cultivation of national defense consciousness among the populace are deeply interconnected,forming a central narrative theme in Chinese films about the War of Resistance Against Japanese Agg...National awakening and the cultivation of national defense consciousness among the populace are deeply interconnected,forming a central narrative theme in Chinese films about the War of Resistance Against Japanese Aggression.The collective historical memory of the Chinese people’s wartime experience,which embodies the awakening of public defense consciousness,provides a significant historical reference for contemporary national defense development.This paper takes the film The Eight Hundred as a case study,analyzing its dialogues and scenes to trace the process through which national defense consciousness awakens among ordinary people.It concludes that such awakening is an inevitable outcome shaped by moral exemplars and cultural heritage.As an important medium of cultural transmission,War of Resistance-themed films not only construct and perpetuate national defense awareness through historical narratives but also inspire contemporary society to strengthen its defense consciousness through cinematic representation.Furthermore,these films serve as a window for international cross-cultural dialogue.Especially on the 80th anniversary of the victory in the War of Resistance,they showcase to the world the spiritual strength and national resilience of the Chinese people in resisting foreign aggression.展开更多
Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges...Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.展开更多
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,project number MoE-IF-UJ-R2-22-20772-1.
文摘The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
基金supported by the National Nature Science Foundation of China(61304223)the Aeronautical Science Foundation of China(2016ZA52009)the Research Fund for the Doctoral Program of Higher Education of China(20123218120015)
基金financially supported by Beijing Key Laboratory for Corrosion-Erosion and Surface Technology,Beijing Municipal Education Commission Project(SYS100080419)。
文摘An increase in the use of the gun barrel will cause wear of the inner wall,which reduces the muzzle velocity and the spin rate of the projectile.The off-bore flight attitude and trajectory of the projectile also change,affecting the shooting power and the accuracy.Exterior ballistic data of a high-speed spinning projectile are required to study the performance change.Therefore,based on the barrel’s accelerated life test,the whole process of projectile shooting is reproduced using numerical simulation technology,and key information on the ballistic performance change at each shooting stage are acquired.Studies have shown that in the later stages of barrel shooting,the accuracy of shooting has not decreased significantly.However,it is found that the angle of attack of the projectile increases as the wear of the barrel increases.The maximum angle of attack reaches 0.106 rad when the number of shots reaches 4300.Meanwhile,elliptical bullet hole has appeared on the target at this shooting stage.Through combining external ballistic theory with simulation results,the primary reason of this phenomenon is found to be a significant decrease in the muzzle spin rate of the projectile.At the end of the barrel life,the projectile muzzle spin rate is 57.5%lower than that of a barrel without wear.
基金supported by the National Natural Science Foundation of China(Nos.61771027,61071139,61471019,61671035)supported in part under the Royal Society of Edinburgh-National Natural Science Foundation of China(RSE-NNSFC)Joint Project(2017–2019)(No.6161101383)with China University of Petroleum(Huadong)partially supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/I009310/1,EP/M026981/1)
文摘Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
文摘Battlefield situation awareness is equivalent to the observation and orientation part of the Observation-Orientation-Decision-Action(OODA)loop.It is the key to transforming information advantage into decision-making advantages and an essential support for agile command decision-making.With the rapid rise and widespread application of various advanced intelligent technologies and model algorithms in the military field,traditional situation awareness technology and capacity building are facing great challenges.Relying solely on manual processing of battlefield situational data is no longer sufficient to meet the changing needs of the battlefield,nor can it provide commanders with real-time,accurate,efficient,and reliable situational information.Therefore,it is necessary to use highperformance hardware facilities and artificial intelligence technology to process massive battlefield data and provide an important reference basis for commanders to implement decision-making behaviors.
基金funded by National Natural Science Foundation of China(grant number 61473311,70901075)Natural Science Foundation of Beijing Municipality(grant number 9142017)military projects funded by the Chinese Army。
文摘Electronic warfare is a modern combat mode,in which predicting digital material consumption is a key for material requirements planning(MRP).In this paper,we introduce an insensitive loss function(ε) and propose a ε-SVR-based prediction approach.First,we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set,and preprocess it to construct the sample space.Subsequently,we establish the ε-SVR-based prediction model based on "wartime influencing factors-material consumption" and perform model training.In case study,we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach.The results illustrate its higher accuracy and more convenience compared with other current approaches.Taking data acquisition controller prediction as an example,our model has better prediction performance(RMSE=0.575 7,MAPE(%)=12.037 6 and R^2=0.996 0) compared with BP neural network model(RMSE=1.272 9,MAPE(%)=23.577 5 and R^2=0.980 3) and GM(1,1) model(RMSE=2.095 0,MAPE(%)=24.188 0 and R^2=0.946 6).The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.
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Various application domains require the integration of distributed real-time or near-real-time systems with non-real-time systems.Smart cities,smart homes,ambient intelligent systems,or network-centric defense systems are among these application domains.Data Distribution Service(DDS)is a communication mechanism based on Data-Centric Publish-Subscribe(DCPS)model.It is used for distributed systems with real-time operational constraints.Java Message Service(JMS)is a messaging standard for enterprise systems using Service Oriented Architecture(SOA)for non-real-time operations.JMS allows Java programs to exchange messages in a loosely coupled fashion.JMS also supports sending and receiving messages using a messaging queue and a publish-subscribe interface.In this article,we propose an architecture enabling the automated integration of distributed real-time and non-real-time systems.We test our proposed architecture using a distributed Command,Control,Communications,Computers,and Intelligence(C4I)system.The system has DDS-based real-time Combat Management System components deployed to naval warships,and SOA-based non-real-time Command and Control components used at headquarters.The proposed solution enables the exchange of data between these two systems efficiently.We compare the proposed solution with a similar study.Our solution is superior in terms of automation support,ease of implementation,scalability,and performance.
文摘The purpose of the study was to investigate and illustrate the challenges faced by performers and audiences during Ateso oral narratives in Ateso speaking communities in Uganda. The study used ethnographic and discurssive analyses methods of research. The topic was Audience-Performer Interface as a Battlefield of Expression： A Study of Ateso Oral Narratives. Ethnographic method of study was used in Ateso speaking communities of Serere, Ngora, Bukedea and Pallisa districts of Uganda. The author stayed with communities for four to seven days in 2009, 2010 and 2011. The study analysed the interpretational dimensions of the oral narrative episodes. Questionnaires and focused group discussions were used to solicit data from a total of 20 （33.3%） out of 60 persons. The study saw that there was dire need to revive the cultural media of communication in Teso. In Serere, Bukedea and Ngora there was more of unpleasant intrusion than in Pallisa and Serere. Performers should consider their audiences complementary to the narration and establish rapport. Audiences should appreciate the efforts of the narrators to keep the cultural norm of story-telling alive in Teso. The Ministry of Education and Sports in Uganda should encourage local languages at all levels of education.
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
文摘The prevailing narrative instructs us that humane treatment of captured enemy fighters is down to white knights from the western parts of the European continent with their codes of chivalry, or alternatively, the Swiss businessman Henri Dunant. This contribution challenges that narrative for overlooking, or being ignorant of, the way that societies around the world have approached the matter of the captured enemy fighter. Traces of some of the critical principles about humane treatment that we see in our present law can actually be found in much older societies from outside of Europe. A more accurate and representative way of understanding humanitarianism in the treatment of captured enemy fighters can and must be crafted, with the prevailing Euro-centric account balanced with practices, cultures and faiths from elsewhere. The quest to achieve more humane treatment in armed conflict is first and foremost a battle of the intellect. Narratives and conceptualisations that are more inclusive, recognising and appreciating of the ways of the rest of the world are likely to be more effective in communicating humanitarian ideals. This work adopts a new method of approaching the richness and diversity of the treatment of captured enemy fighters over time and space. This new framework of analysis uses six cross-cutting themes to facilitate a broader international and comparative perspective, and develop a more sophisticated level of understanding. The first theme is how older and indigenous societies approached the matter of captured enemy fighters. The second focuses on religions of the world, and what they teach or require. The third section examines the matter of martial practices and codes of ethics for combatants in certain societies. The fourth category engages with colonisation and decolonisation, and regulation （or non-regulation） of the treatment of captives of war. Fifth is the issue of modernisation and the impact it has had on armed forces and fighters, including on the treatment of captives. The final issue is the shift towards formalised agreements, beginning with the first bilateral agreements and then the multilateral codification exercise that began in the mid-19th century and continues to this day. This framework for analysis leads into a final chapter, presenting a fresh and holistic view on the evolution of prisoner of war protections in the international order. It provides a different way of looking at International Humanitarian Law, starting with this effort at a global understanding of the treatment of captured enemy fighters.
基金supported by funding from the National Natural Science Foundation of China(Grant Nos.62325602,62406292,62302459,62406293,and 62036010)。
文摘The efficiency of carrier-based aircraft support operation scheduling critically impacts aircraft carrier operational effectiveness by determining sortie generation rates,yet faces significant challenges in complex deck environments characterized by resource coupling,dynamic constraints,and highdimensional state-action spaces.Traditional optimization algorithms and vanilla reinforcement learning(RL)struggle with computational inefficiency,sparse rewards,and adaptability to dynamic scenarios,while human expert systems are constrained by the quality of expert knowledge,and poor expert guidance may even have a negative impact.To address these limitations,this paper proposes a human experience-guided actor-critic reinforcement learning framework that synergizes domain expertise with adaptive learning.First,a dynamic Markov decision process(MDP)model is developed to rigorously simulate carrier deck operations,explicitly encoding constraints on positions,resources,and collision avoidance.Building upon this foundation,a human experience database is constructed to enable real-time pattern-matching-based intervention during agent-environment interactions,dynamically correcting wrong actions to avoid catastrophic states while refining exploration efficiency.Finally,the policy and value network objectives are reshaped to incorporate human intent through hybrid reward functions and adaptive guidance weighting,ensuring balanced integration of expert knowledge with RL's exploration capabilities.Extensive simulations across three scenarios demonstrate superior performance compared to state-of-the-art methods and maintain robustness under suboptimal human guidance.These results validate the framework's ability to harmonize human expertise with adaptive learning,offering a practical solution for real-world carriers.
基金supported by the National Natural Science Foundation of China(61573017)the Doctoral Foundation of Air Force Engineering University(KGD08101604)
文摘Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.
文摘On September 3,2025,heads turned to Beijing for a grand military parade marking the 80th anniversary of the victory in the Chinese People’s War of Resistance Against Japanese Aggression and the World Anti-Fascist War.Such events often highlight military strength,but this commemoration sought to project a deeper message:remembrance of sacrifice,recognition of the immense costs of conflict,and a renewed commitment to peace.
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.
文摘National awakening and the cultivation of national defense consciousness among the populace are deeply interconnected,forming a central narrative theme in Chinese films about the War of Resistance Against Japanese Aggression.The collective historical memory of the Chinese people’s wartime experience,which embodies the awakening of public defense consciousness,provides a significant historical reference for contemporary national defense development.This paper takes the film The Eight Hundred as a case study,analyzing its dialogues and scenes to trace the process through which national defense consciousness awakens among ordinary people.It concludes that such awakening is an inevitable outcome shaped by moral exemplars and cultural heritage.As an important medium of cultural transmission,War of Resistance-themed films not only construct and perpetuate national defense awareness through historical narratives but also inspire contemporary society to strengthen its defense consciousness through cinematic representation.Furthermore,these films serve as a window for international cross-cultural dialogue.Especially on the 80th anniversary of the victory in the War of Resistance,they showcase to the world the spiritual strength and national resilience of the Chinese people in resisting foreign aggression.
基金co-supported by the National Natural Science Foundation of China(Nos.61806219,61876189 and 61703426)the Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Nos.20190108 and 20220106)the Innvation Talent Supporting Project of Shaanxi,China(No.2020KJXX-065)。
文摘Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.
