The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to...The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.展开更多
According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and genera...According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.展开更多
The Short Happy Life of Francis Macomber is a quintessential Hemingway tale of one man's attempt to overcome an in ternal struggle by mastering the external world. Francis Macomber discovers his own bravery and st...The Short Happy Life of Francis Macomber is a quintessential Hemingway tale of one man's attempt to overcome an in ternal struggle by mastering the external world. Francis Macomber discovers his own bravery and strength when he ignores his self-consciousness and relies on instinct. This essay will examine Hemingway's code and how it confronts nada thus analyze Macomber's change from innocent to suffering to aware.展开更多
In this paper, as a new contribution to the tensor-centric warfare (TCW) series [1] [2] [3] [4], we extend the kinetic TCW-framework to include non-kinetic effects, by addressing a general systems confrontation [5], w...In this paper, as a new contribution to the tensor-centric warfare (TCW) series [1] [2] [3] [4], we extend the kinetic TCW-framework to include non-kinetic effects, by addressing a general systems confrontation [5], which is waged not only in the traditional physical Air-Land-Sea domains, but also simultaneously across multiple non-physical domains, including cyberspace and social networks. Upon this basis, this paper attempts to address a more general analytical scenario using rigorous topological methods to introduce a two-level topological representation of modern armed conflict;in doing so, it extends from the traditional red-blue model of conflict to a red-blue-green model, where green represents various neutral elements as active factions;indeed, green can effectively decide the outcomes from red-blue conflict. System confrontations at various stages of the scenario will be defined by the non-equilibrium phase transitions which are superficially characterized by sudden entropy growth. These will be shown to have the underlying topology changes of the systems-battlespace. The two-level topological analysis of the systems-battlespace is utilized to address the question of topology changes in the combined battlespace. Once an intuitive analysis of the combined battlespace topology is performed, a rigorous topological analysis follows using (co)homological invariants of the combined systems-battlespace manifold.展开更多
O2O industry is surging in 2015,while 020 companies face fiercer competition.Though those startups got several rounds of financing,they still need to solve different problems one by one and move on.1Vicious competitio...O2O industry is surging in 2015,while 020 companies face fiercer competition.Though those startups got several rounds of financing,they still need to solve different problems one by one and move on.1Vicious competitionHunter poaching bears the brunt to become the most severe challenge for O2O entrepreneurs.展开更多
The widespread application of new technologies,while empowering women with new opportunities,might also put them at disadvantage.For example,in comparison with males,the application of AI might be more likely to cost ...The widespread application of new technologies,while empowering women with new opportunities,might also put them at disadvantage.For example,in comparison with males,the application of AI might be more likely to cost them their jobs.Meanwhile,women are missing out the opportunity to participate in the policy-making process–they are absent from the table.If no change is made in the current policies,we will miss the goal to achieve gender equality,the fifth of the 17 Sustainable Development Goals set for UN’s 2030 Agenda for Sustainable Development,warned elite women scientists with the Organization for Women in Science for the Developing World(OWSD).What shall be done now?How can we make a difference?They are in action to help.展开更多
Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,si...Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.展开更多
基金the Project of National Natural Science Foundation of China(Grant No.62106283)the Project of National Natural Science Foundation of China(Grant No.72001214)to provide fund for conducting experimentsthe Project of Natural Science Foundation of Shaanxi Province(Grant No.2020JQ-484)。
文摘The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.
基金supported by the Military Scentific Research Project(41405030302,41401020301).
文摘According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.
文摘The Short Happy Life of Francis Macomber is a quintessential Hemingway tale of one man's attempt to overcome an in ternal struggle by mastering the external world. Francis Macomber discovers his own bravery and strength when he ignores his self-consciousness and relies on instinct. This essay will examine Hemingway's code and how it confronts nada thus analyze Macomber's change from innocent to suffering to aware.
文摘In this paper, as a new contribution to the tensor-centric warfare (TCW) series [1] [2] [3] [4], we extend the kinetic TCW-framework to include non-kinetic effects, by addressing a general systems confrontation [5], which is waged not only in the traditional physical Air-Land-Sea domains, but also simultaneously across multiple non-physical domains, including cyberspace and social networks. Upon this basis, this paper attempts to address a more general analytical scenario using rigorous topological methods to introduce a two-level topological representation of modern armed conflict;in doing so, it extends from the traditional red-blue model of conflict to a red-blue-green model, where green represents various neutral elements as active factions;indeed, green can effectively decide the outcomes from red-blue conflict. System confrontations at various stages of the scenario will be defined by the non-equilibrium phase transitions which are superficially characterized by sudden entropy growth. These will be shown to have the underlying topology changes of the systems-battlespace. The two-level topological analysis of the systems-battlespace is utilized to address the question of topology changes in the combined battlespace. Once an intuitive analysis of the combined battlespace topology is performed, a rigorous topological analysis follows using (co)homological invariants of the combined systems-battlespace manifold.
文摘O2O industry is surging in 2015,while 020 companies face fiercer competition.Though those startups got several rounds of financing,they still need to solve different problems one by one and move on.1Vicious competitionHunter poaching bears the brunt to become the most severe challenge for O2O entrepreneurs.
文摘The widespread application of new technologies,while empowering women with new opportunities,might also put them at disadvantage.For example,in comparison with males,the application of AI might be more likely to cost them their jobs.Meanwhile,women are missing out the opportunity to participate in the policy-making process–they are absent from the table.If no change is made in the current policies,we will miss the goal to achieve gender equality,the fifth of the 17 Sustainable Development Goals set for UN’s 2030 Agenda for Sustainable Development,warned elite women scientists with the Organization for Women in Science for the Developing World(OWSD).What shall be done now?How can we make a difference?They are in action to help.
基金supported in part by the National Natural Science Foundation of China under Grant No.61973055Fundamental Research Funds for the Central Universities under Grant No.ZYGX2020J011Regional Innovation Cooperation Funds of Sichuan under Grant No.2024YFHZ0089.
文摘Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.