Task assignment in ground-to-air confrontation based on multiagent deep reinforcement learning

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.

Multi-style Chord Music Generation Based on Artificial Neural Network

With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.

Research on virtual entity decision model for LVC tactical confrontation of army units

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.

From Innocence to Suffering to Awareness:Nada Confrontation in Francis Macomber

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.

Tensor-Centric Warfare V: Topology of Systems Confrontation

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.

On the Confrontation Between Masculinism and Feminism in The Great Gatsby

In The Great Gatsby, Fitzgerald depicts the conflicts and contradictions between men and women about society, family, love, and money, literally mirroring the patriarchal society constantly challenged by feminism in the 1920s of America. This paper intends to compare the features of masculinism and feminism in three aspects： gender, society, and morality. Different identifications of gender role between men and women lead to female protests against male superiority and pursuits of individual liberation. Meanwhile, male unshaken egotism and gradually expanded individualism of women enable them both in lack of sound moral standards. But compared with the female, male moral pride drives them with much more proper moral judge, which reflects Fitzgerald＇s support of the masculine society. Probing into the confrontation between masculinism and feminism, it is beneficial for further study on how to achieve equal coexistence and harmony between men and women.

Research on Cyberspace Attack and Defense Confrontation Technology

This paper analyzes the characteristics of Interact space and confrontation, discussed on the main technology of network space attack and defense confrontation. The paper presents the realization scheme of network space attack defense confrontation system, and analyzes its feasibility. The technology and the system can provide technical support for the system in the network space of our country development, and safeguard security of network space in China, promote the development of the network space security industry of China, it plays an important role and significance to speed up China＇ s independent controllable security products development.

糖尿病患者自我厌恶现状及影响因素分析

目的了解糖尿病患者自我厌恶现状及其影响因素,为医护人员采取针对性干预改善糖尿病患者心理健康提供参考。方法采用一般情况调查表、自我厌恶量表、医学应对问卷和领悟社会支持量表对274例2型糖尿病患者进行调查。结果糖尿病患者自我厌恶得分(35.46±6.39)分,年龄、糖尿病相关并发症、回避应对、领悟社会支持总分是自我厌恶的主要影响因素(均P<0.05),可预测总变异的49.6%。结论糖尿病患者自我厌恶为中等偏高水平,护理人员应加强对糖尿病患者自我厌恶水平的评估和筛查,加强对年轻、有糖尿病相关并发症的患者的干预,帮助患者减少其回避行为,并增强其领悟社会支持水平,从而降低其自我厌恶水平。

攻防对抗视角下的网络安全主动防御体系研究

为了解决纵深防御体系中防御能力固化、灵活性不足等问题,从攻防对抗视角出发,提出一种网络安全主动防御体系。首先,基于情报收集、监测预警、防御指挥和防御行动等要素的详细描述,设计了用于规划主动防御系统的能力组成框架;其次,选择网络杀伤链、ATT&CK知识库和OODA循环法则,设计了一个以攻助防的防御模型,并分析了引入博弈论进行防御算法设计的优势;最后,以实现为目标,构想了一个典型应用场景,以期为安全能力的体系化设计提供有益借鉴。

对抗性话语的底层概念及研究方法

国际地缘政治场域中错综复杂的矛盾与利益冲突已投射、产生一种新的频繁显现的话语方式——对抗性话语。本文从梳理哲学中的“矛盾”概念开始,然后系统讨论对抗性话语的界定特征及实证研究方法。研究揭示,毛泽东论述的对抗性矛盾是现今对抗性话语最重要和最直接的哲学概念基础,而维特根斯坦的语言游戏论可为对抗性话语研究提供分析工具。文章继而从话语动因、目标受众、话语方式与策略、评价标准等向度讨论话语对抗双方的攻防、博弈和较量特征。方法论上,本文主张采用基于语料库与人工智能技术相结合的路径,强调数据整体解释性与局部语境下意义识解的协同要义。

“谢谢”的行为反语功能

本文讨论“谢谢”类施为短句回应抵触性引发行为、实施言语行为对抗的隐性否定新用法,即行为反语。“谢谢”的行为对抗功能,朝着交际非当面、回应对象非可控角度发展。从日常对话向网络虚拟对话演进,其行为对抗强度、负面情绪态度发生强化,形成“我谢谢你”等负面对抗性固化表达。“谢谢”的行为对抗功能填补了不礼貌抵触类行为配对的交际空位,满足了互动平衡中“文明对抗”的交际需要。

无人集群博弈对抗系统仿真验证及决策关键技术综述

无人集群博弈对抗是一种新兴的作战样式,在智能化战争扮演着至关重要的作用,其核心是自主生成博弈对抗决策序列,为集群“赋能”。分析了无人集群博弈对抗系统仿真验证的进展;从基于专家系统和博弈论的技术、基于群体智能和优化理论的技术,以及基于神经网络和强化学习的技术三个方面论述了自主决策关键技术,以及课题组在自主决策上开展的相关工作;提出了无人集群博弈对抗的发展方向。

军事系统的复杂性对抗

阐释军事复杂系统的内涵,总结未来作战的复杂性来源,分析军事系统的典型复杂性特征及度量。军事系统的复杂性对抗,即将复杂性作为一种给对手制造多重困境的武器用于军事对抗,旨在最小化自身复杂性的同时最大化对手所面临的复杂性。通过采取特定行动塑造利于己方的态势,利用复杂自适应特点针对对手施加复杂攻击手段,从复杂性视角来指导未来作战。军事系统是一类特殊的复杂系统。未来作战正向全域拓展,作战资源要素规模显著增长、资源调度及决策明显加速,使得军事系统的复杂性急剧增长。发展迷雾混淆意图、快变抑慢变、分布对集中、多域制单域等复杂性对抗途径,可创造形成军事系统决策优势,达到以复杂性制胜的作战目的。

边缘对抗结合层次门控卷积的人脸修复研究

针对目前人脸缺损图像修复存在边缘模糊和失真等问题,提出了一种两阶段层次门控卷积网络(Hierarchical gated convolutional network,HGCN),并将其与边缘对抗网络相结合用于人脸图像修复。首先,采用边缘对抗网络生成边缘图像。其次,将边缘图、掩模和缺损图像作为输入,训练HGCN的GAN模型以修复缺损人脸图像。HGCN网络采用门控卷积取代传统卷积,并引入了扩张卷积,网络的主体由粗修复模块和精修复模块组成。在粗修复模块中,编码器和解码器网络结构用于粗修复;在精细修复模块中,引入注意力机制来增强特征提取能力,进一步细化修复结果。实验使用Celeba-HQ数据集和NVIDIA不规则掩码数据集作为训练数据集,采用门控卷积网络和注意力机制网络作为实验对比模型,PSNR、 SSIM和MAE作为实验评估指标。实验结果表明,对于缺损区域小于20%的人脸图像,所提出的网络在上述三个指标上优于两种比较网络,而对于缺失区域大于20%的图像,所提出的网络与两种比较方法性能指标接近。在视觉效果方面,所提出的方法在细节上也优于两种对比网络。因而,所提出的网络可以明显提高图像修复效果,尤其是对图像细节的修复效果。

42个草菇栽培菌株对绿色木霉和腐食酪螨的抗性鉴定

采用平板对峙法和平板接螨法,初步鉴定了42个草菇栽培菌株对绿色木霉和腐食酪螨的抗性。结果表明,供试菌株对绿色木霉和腐食酪螨的抗性存在极显著差异。绿色木霉对42个草菇菌株菌丝的生长抑制率为1.6%~55.0%,其中有12株抑制率低于20%,占比28.6%,以V01、C1和C43个菌株对绿色木霉的抗性最强;有13个菌株在与绿色木霉对峙培养3天后产生了明显的拮抗线。腐食酪螨对供试菌株的为害级别在3.25~9.0,产生抗、中抗、感和高感反应,其中以C1、C3和C4等10个菌株对腐食酪螨的抗性较强,占比23.8%。最终从42个供试菌株中初步筛选出同时具有绿色木霉抗性和螨虫抗性的菌株2个(C1、C4),为草菇多抗品种选育及病虫害绿色防治提供了基础材料。

基于条件生成对抗网络的实验室红外监控系统

实验室作为科研基地,其中存储了很多珍贵的资料和危险物品,因此,进行安全监控是十分必要的。在此背景下,设计一种基于条件生成对抗网络的实验室红外监控系统。借助B/S三层架构设计系统框架,分为采集层、网络层和应用层,将MySQL作为支撑,设计数据库。以STC89C52单片机为核心,主控各个外围硬件,搭建系统硬件。以ZigBee技术为基础组建系统的无线传输网络,用于远程传输。设计监控系统功能单元,通过采集单元采集实验室红外图像并利用预处理单元对实验室红外图像实施去噪、亮度调节、图像分割预处理,基于条件生成对抗网络(cGAN)设计异常识别单元,判断监控图像中是否存在异常情况并以此为依据进行预警。结果表明:系统对异常的监控结果与真实情况一致,由此证明了所设计的监控系统性能较高。