Study on group air to ground attack-defends hierarchical dynamic decision-making

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attackdefends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies, dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle,

Discrete decision model and multi-agent simulation of the Liang Zong two-chain hierarchical organization in a complex project

Different from the organization structure of complex projects in Western countries, the Liang Zong hierarchical organization structure of complex projects in China has two different chains, the chief-engineer chain and the general-director chain,to handle the trade-off between technical and management decisions. However, previous works on organization search have mainly focused on the single-chain hierarchical organization in which all decisions are regarded as homogeneous. The heterogeneity and the interdependency between technical decisions and management decisions have been neglected. A two-chain hierarchical organization structure mapped from a real complex project is constructed. Then, a discrete decision model for a Liang Zong two-chain hierarchical organization in an NK model framework is proposed. This model proves that this kind of organization structure can reduce the search space by a large amount and that the search process should reach a final stable state more quickly. For a more complicated decision mechanism, a multi-agent simulation based on the above NK model is used to explore the effect of the two-chain organization structure on the speed, stability, and performance of the search process. The results provide three insights into how, compared with the single-chain hierarchical organization, the two-chain organization can improve the search process: it can reduce the number of iterations efficiently; the search is more stable because the search space is a smoother hill-like fitness landscape; in general, the search performance can be improved.However, when the organization structure is very complicated, the performance of a two-chain organization is inferior to that of a single-chain organization. These findings about the efficiency of the unique Chinese-style organization structure can be used to guide organization design for complex projects.

Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.

Optimal Distribution of N-Team Interacting Decision Makers with Hierarchical Command Inputs That Are Predicated on Order Statistics

The spectral analysis of simulated N-team of interacting decision makers with bounded rationality constraints of Oladejo, which assumes triangular probability density function of command inputs is hereby restructured and analysed, to have hierarchical command inputs that are predicated on order statistics distributions. The results give optimal distributions.

Sequential maneuvering decisions based on multi-stage influence diagram in air combat

A multi-stage influence diagram is used to model the pilot＇s sequential decision making in one on one air combat. The model based on the multi-stage influence diagram graphically describes the elements of decision process, and contains a point-mass model for the dynamics of an aircraft and takes into account the decision maker＇s preferences under uncertain conditions. Considering an active opponent, the opponent＇s maneuvers can be modeled stochastically. The solution of multistage influence diagram can be obtained by converting the multistage influence diagram into a two-level optimization problem. The simulation results show the model is effective.

Expansion and Application of Mencius Decision-making Thought in Industrial Decision

Applying Mencius decision-making thought and combing dialectical theories such as the relationship between intrinsic and extrinsic factors and relationship between primary and secondary contradictions and between primary and secondary aspects of contradictions,at the same time analyzing the decision-making factors which based on the industry point of view,this paper presents Mencius decision-making circle and countermeasure classification chart.It takes brief analysis by SWOT method,and studies development strategies of cut roses industry in Hainan Province using Mencius decision-making circle.This method shows stronger and clearer hierarchy,so it will be favorable for decision classification.

Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision

This study focuses on a robot vision localization method for coping with the operational task ofautomatic nasal swab sampling. The application is important in the detection and epidemic prevention of CoronaVirus Disease 2019 (COVID-19) to alleviate the large-scale negative impact of individuals suffering from pneumoniaowing to COVID-19. In this method, the idea of a hierarchical decision network is used to consider the stronginfectious characteristics of the COVID-19, which is followed by processing the robot behavior constraint condition.The visual navigation and positioning method using a single-arm robot for sampling is also planned, whichconsiders the operation characteristics of medical staff. In the decision network, the risk factor for potentialcontact infection caused by swab sampling operations is established to avoid the spread among personnel. A robotvisual servo control with artificial intelligence characteristics is developed to achieve a stable and safe nasal swabsampling operation. Experiments demonstrate that the proposed method can achieve good vision positioning forthe robots and provide technical support for managing new major public health situations.

基于改进DEMATEL-ISM的钢桁梁桥施工风险影响因素研究

为提高钢桁梁桥施工过程中的安全管理,对钢桁梁桥施工中的风险因素进行分析,提出了一种基于改进DEMATEL-ISM的评价方法,首先引入三角模糊数对DEMATEL法中的直接影响矩阵进行改进,从而去模糊化以减少专家评价的主观性,再通过DEMATEL法分析各影响因素的影响度、被影响度、中心度与原因度并加以排序,对得到的主要影响因素进行初步分析,利用ISM法对筛选出的影响因素进行层次划分,得到直接层影响因素、过渡层影响因素与本质影响因素,并针对分析得到的5个本质影响因素提出深入的预防预警措施,可为钢桁梁桥施工方向的安全管理提供针对性措施,为事故预防提供一定的参考依据。

基于层次分析和关联规则的陆路交通工程间接勘察技术方案决策方法

勘察技术方案的决策是开展陆路交通工程勘察的首要工作,然而在不同的勘察阶段,由于成本、效率、效果等需求不同,对勘察技术及手段的要求也不同。现阶段勘察技术的选取长期单纯地依赖从业者经验,导致其无法满足信息化、智能化勘察的需求;同时,实用的新型勘察技术由于缺乏科学有效的决策机制,导致工程实践中无法及时融入。因此,需要建立科学的决策机制,结合新技术,制定更加精准、高效的勘察方案。将层次分析法和关联规则进行融合,提出了一种基于两者的间接勘察技术方案决策方法。首先,根据勘察技术本身具备的特性和目标地区地层参数进行定量分析,确定主要的勘察类别;然后,利用层次分析法选取该类别中满足勘察准确性、技术可靠性、经济合理性的最佳勘察技术;最后,运用关联规则围绕最佳勘察技术补齐在应用中存在的短板,综合新型勘察技术的应用实践结果,构建间接勘察技术组合方案。通过本决策方法的应用,极大程度地减小了勘察技术选择的随意性,据此建立的间接勘察技术组合方案符合现场需求和实践经验,证明了本方法的可行性。

基于兵棋推演的空战编组对抗智能决策方法

基于兵棋研究的空战编组对抗方法主要使用规则或运筹等手段,存在假设不够合理、建模不准确、应变性差等缺陷。强化学习算法可以根据作战数据自主学习编组对抗策略,以应对复杂的战场情况,但现有强化学习对作战数据要求高,当动作空间过大时,算法收敛慢,且对仿真平台有较高的要求。针对上述问题,提出了一种融合知识数据和强化学习的空战编组对抗智能决策方法,该决策方法的输入是战场融合态势,使用分层决策框架控制算子选择并执行任务,上层包含使用专家知识驱动的动作选择器,下层包含使用专家知识和作战规则细化的避弹动作执行器、侦察动作执行器和使用强化学习算法控制的打击动作执行器。最后基于典型作战场景进行实验,验证了该方法的可行性和实用性,且具有建模准确、训练高效的优点。

众包制造环境下协同产品族设计与延迟决策的主从关联优化

针对众包制造环境下产品族设计与延迟决策之间的协同优化问题,建立了以产品族设计为主、延迟决策为从的混合整数非线性双层规划模型。模型上层由制造商设计产品族架构以最大化自身的期望利润,下层由多个分销商优化延迟产品模块的类型以最大化各自的期望利润,且各分销商之间的决策具有相互独立性。考虑到延迟产品模块事先不确定,提出虚拟延迟结构将该优化问题具体化,并证明需要被延迟的产品模块。开发了一个双层嵌套遗传算法对模型进行求解。用智能冰箱产品族延迟案例验证了所提模型和算法的有效性,并对所提延迟偏好参数进行了灵敏度分析。

考虑威胁区通行概率的变体飞行器轨迹规划

针对威胁区交叉重叠且全覆盖飞行路径的变体飞行器轨迹规划问题,提出了一种考虑威胁区通行概率和变外形参数优化的轨迹规划方法。基于分层强化学习思想,通过设置飞行环境集合、决策选项、代价函数、Q函数以及选项内的策略等,建立了变体飞行器路径决策的分层强化学习模型;通过训练得到的评价网络,能够结合威胁区通行概率对实际的场景进行路径决策;根据飞行器可变外形的特点,优化所得决策结果的参数,得到全过程的通行轨迹和外形形态。仿真结果表明,该方法能够根据实际情况实时决策飞行路径,经过优化后得到全过程的优化轨迹和飞行形态。

集成灾害系统和D/I模型的岩溶区燃气管道致灾演化层次结构分析

为进一步厘清岩溶区燃气管道多因素致灾演化的层次结构,基于灾害系统理论将其解构为孕灾环境、灾害因子、承灾体和灾情4大子系统,结合决策实验室分析法和解释结构模型分析多因素致灾演化的逻辑层次和因果递进关系,建立多级递阶结构模型并明确风险管控要点。研究结果表明:灾害系统风险因素分布于多阶层级,形成差异化作用路径和异质性影响效应;岩溶塌陷长度和宽度等表层直接因素对致灾演化造成直接影响,是风险管控第1要务;岩溶覆盖层特征、地形地貌等中层间接因素在灾害系统中起承上启下作用;构造条件、火源等深层根本因素位于多阶结构底层,对其管控能从本质上有效遏制灾害演化发展。研究结果可为岩溶地质隐患区燃气管道工程提供防灾减灾参考。

基于区块链的园区碳排放可信监测模型

构建了基于能源电力区块链的园区碳排放可信监测数字模型。首先,利用区块链防篡改技术保障监测数据的可信存证要求,相关接入实体指标全部由联盟链进行身份认证和权限控制,避免了数据遗失以及主体以外其他人恶意篡改的风险;其次,在具体的监测指标融合过程中,结合层次分析法,构建碳排放评价指标融合策略,对能源电力相关指标数据结合相似性聚类算法进行多源在线融合;最后,基于局部异常因子算法(local outlier factor,LOF)实现指标数据长周期异常离群检测,一定程度上解决数据畸变和错报自筛难题。

国家级决策气象服务满意度指标及应用研究

为了提升国家级决策气象服务能力,构建国家级决策气象服务满意度评价的6个特性和23个基础指标,采用层次分析法确定指标权重,利用满意度模型计算用户满意度,应用波士顿矩阵分析发展潜力。基于2022年1月—6月国家级决策气象服务的满意度调查数据,发现国家级决策气象服务的满意度为0.939,6个特性指标中准确性的满意度最低,23个基础指标中有19个指标的满意度大于0.9;6个特性指标中收获项目有3个,培育项目有1个,改进项目有2个;23个基础指标中收获项目有9个,培育项目有6个,改进项目有8个。

融合多层次决策信息的视觉解释方法

视觉解释方法是深度神经网络可解释领域的热门研究课题,但现有方法未能有效利用多层次决策信息导致视觉解释效果差。针对此问题,提出一种融合多层次决策信息的视觉解释方法。挖掘特征图中高细粒度局部层次的决策信息生成一组与决策结果相关性强的加权特征图,采用定序分组方式对其合并,获取一组低冗余度掩码;采取模糊边界和积分方法对掩码进行处理,基于全局层次的决策贡献并行计算分组掩码重要度分数,提高了算法对全局决策信息的敏感性和算法速度。通过消融实验确定了算法的最优参数组合,并在ImageNet数据集上与现有的先进视觉解释方法进行了定性和定量比较。实验结果表明:该方法通过结合多层次决策信息,在置信度测试和定位测试中取得了更好的视觉解释结果,且耗时达到68 ms。

基于朴素贝叶斯的配电终端数据联合分层加密

为完善配电终端数据密文模板,缩短配电终端数据加密耗时,提出了基于朴素贝叶斯的配电终端数据联合分层加密算法。根据朴素贝叶斯模型建立决策树组织,联合已输出的配电终端数据求解挖掘深度指标的具体数值,完成对配电终端数据的处理。按照数据分层标准,创建密钥文本模板,再通过计算加密系数的方式,实现基于朴素贝叶斯的配电终端数据联合分层加密。对比实验结果表明,在朴素贝叶斯模型的作用下,配电终端数据输出量达到9×109MB时,所需加密时长仅为27.3 ms,利用该方法定义密文模板的完善程度明显更高,加密耗时较短。

基于GEM-AHP-可拓学高校图书馆用户满意度分析

为探索图书馆用户满意度影响因素,实现高校图书馆综合服务水平提升,推动高校图书馆发展,运用群组决策特征根法(GEM)对初选指标进行筛选,建立关键指标体系并构建AHP判断矩阵。运用AHP确定满意度因素权重排序,按其权重值排序进行有针对性的措施,运用可拓学确定各因素满意度等级。研究结果表明,在高校图书馆用户满意度指标体系中,图书馆环境与读者空间方面表现良好,图书馆资源、图书馆服务、基础设备、图书管理员方面存在一定的问题。

基于熵权决策的Halbach直线发电机分层优化设计

为了实现直驱式波浪发电装置的高功率密度及高转化效率,针对Halbach阵列直线发电机,提出了一种基于熵权多属性决策的分层优化设计方法。在分析电机基本原理、发电性能以及试验验证的基础上,以开环电动势幅值、电压波形畸变率为目标,采用灵敏度方法筛选关键变量,建立分层优化模型,进一步结合NSGA-II优化方法开展多目标优化设计;最后,提出通过熵权多属性决策TOPSIS算法对pareto非劣解集开展决策评估,获得电机结构尺寸的最优解。结果表明,电机优化前后电动势幅值提升了13.7%;THD降低了1.06%,验证了所提出的优化设计方法的高效性、可行性,以及决策方法的科学客观性。