坦克兵力行为智能体模型研究

为了有效模拟兵力物理行为和智能行为的不同特性,提出了一个复合结构坦克兵力智能体模型,该模型综合了反应式行为和慎思行为两方面的优点。采用层次化组合方法建立了基于行为模式的兵力物理行为模型,使兵力智能体能对动态战场环境作出灵活的反应。结合兵力不同类型任务的分析,设计了基于规划的兵力智能行为模拟方法,使兵力智能体能按照战术原则进行作战任务规划。     

The Serbian defence institution: the need for clear policy guidance to improve the Serbian defence planning,management, and command processes

ABSTRACT

The uncritical layering of western liberal defence governance norms and concepts on top of existing legacy concepts has impeded achieving coherent military capabilities and capacities when Serbia’s political and military leadership tried to reform the defence system using Western benchmarking principles and Western countries’ best practices. The process of this change has been more valuable than its actual output, as defined by increased capabilities. Such outcomes should be reflective of policy guidance, and can be thought of as closing the trinity loop of a defence planning system: plans, money and execution. This article addresses two key functional areas of the Serbian defence institution. First, it assesses the current state of defence planning to discern its strengths and weaknesses to ascertain if plans are tied to financial decision-making. Second, a full examination of current Serbian defence management is conducted to discern whether weaknesses exist that distract from producing operational capabilities. Both areas are analyzed thoroughly and some solutions for change are proposed. Also, the article analyzes the appearance of two negative phenomena in the planning process – economization and managerialism.     

Optimal motion planning in rapid‐fire combat situations with attacker uncertainty

This article provides a modeling framework for quantifying cost and optimizing motion plans in combat situations with rapid weapon fire, multiple agents, and attacker uncertainty characterized by uncertain parameters. Recent developments in numerical optimal control enable the efficient computation of numerical solutions for optimization problems with multiple agents, nonlinear dynamics, and a broad class of objectives. This facilitates the application of more realistic, equipment‐based combat models, which track both more realistic models, which track both agent motion and dynamic equipment capabilities. We present such a framework, along with a described algorithm for finding numerical solutions, and a numerical example.     

权值编码遗传算法在单机任务规划中的应用

任务规划是在任务有不同威胁程度,资源消耗和战术价值条件下,寻找总战术价值最高,而又不超出约束的任务链,这对飞机完成任务中具有重要作用,针对机载资源有限、任务有不同威胁程度和战术价值,提出了基于权值编码遗传算法的任务规划方法,该算法通过对原始问题的偏离和启发式解码来得到最优的可完成任务子集.仿真结果验证了算法的有效性.     

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.     

试论战略数据规划及其概念基础

讨论了当前人们对数据规划概念的不同理解 ,在分析信息、系统等概念的基础上 ,提出了一个明确的面向整个组织的数据规划概念 ,给出了一种基于这一概念的新的数据规划方法的框架。     

军事能力到装备系统的双层规划模型及其求解算法

阐述了基于能力的军事规划新思路,提出了从军事能力到系统项目的两层规划模型。该模型上层以效果和效果-成本比值最大化为目标,通过资本规模来约束下层决策。下层以各种能力最大化为目标,在成本约束下寻求不同系统项目的最佳组合,并将结果返回上层影响最终的决策。应用了多目标遗传算法来求解该规划模型。算例结果表明,所建立的双层规划模型及其求解算法对于国防资源分配问题是有效的。     

基于程序状态变迁的ASLR机制脆弱性分析方法

地址随机化ASLR是一种针对控制流劫持漏洞的防御机制。已有的漏洞自动分析与利用技术缺少对地址随机化机制影响的分析,导致生成的测试用例在实际环境中的运行效果受到极大限制。针对地址随机化的缺陷及其绕过技术的特点,本文提出了一种地址随机化脆弱性分析方法。该方法使用有限状态机描述程序运行路径中各关键节点的状态;针对常见的内存泄漏与控制流劫持场景建立约束条件;通过求解内存泄露状态约束与控制流劫持状态约束的兼容性,分析地址随机化机制在特定场景下的脆弱性。实验结果显示,该方法可有效检测通过内存泄漏导致的地址随机化绕过及控制流劫持攻击,实现自动化的地址随机化脆弱性分析,提高针对软件安全性分析的效率。     

多模块ROP碎片化自动布局方法

返回导向式编程(Return Oriented Programming, ROP)是一种可有效绕过数据执行保护机制的技术。ROP通过搜索内存代码区中合适的汇编指令片段,可组成一段执行特定功能的程序。已有的ROP自动构造技术只考虑ROP链的功能实现,而忽视了ROP链布局对程序内存可控性的要求,导致自动生成的ROP链实用性低。为解决该问题,提出了一种基于符号执行的多模块ROP碎片化自动布局方法。该方法在ROP自动构造Q框架的基础上,以模块为单位对ROP链进行切片;使用符号执行工具S2E,对控制流劫持状态下的程序内存状态进行动态分析;为各ROP模块匹配相应的可控内存区域,构造碎片化布局的ROP链。实验证明,相比已有技术,该方法生成的ROP链有效降低了对程序内存可控性的要求。     

基于深度神经网络的无线覆盖预测算法*

为保证新一代移动无线网络能够根据实时覆盖情况动态地调节小区天线参数,需要实现高效且准确的无线覆盖预测。传统的求解方法通过精确的场强预测来判断天线参数的优劣,虽然精度很高但需要大量的计算资源,无法满足5G和后5G移动网络通过实时覆盖预测进行射频参数动态调整的实际需求。现采用基于深度神经网络的算法对给定天线参数的覆盖效果进行预测,以取代对目标区域的精确场强预测。数值结果表明,该方法能够在保持计算准确性的同时显著减少计算量,为5G动态网络规划提供基础性参考数据。