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.

Fast mode decision for Hierarchical-B pictures in scalable video coding

To decrease the computational complexity of adaptive inter-layer prediction and improve the encoding efficiency in sealable video coding, a mode decision algorithm is proposed by exploiting the part of used candidate modes of the co-located reference macrobloeks for Hierarchical-B pictures. This scheme reduces the amount of the candidate modes to generate a dynamic list for the current encoding macroblock according to the statistical information derived from the co-located reference macroblocks in different temporal levels. The experimental results show that this fast algorithm reduces approximately 31% encoding time on average with the negligible loss of encoding performance.

Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

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。

基于近似动态规划的多级火箭全程任务决策

针对火箭发生推力下降故障下的任务决策问题,提出了一种基于近似动态规划的多级火箭全程任务决策方法。首先,通过设置初始状态集合、决策选项、奖励函数、Q函数迭代方法等,建立了火箭任务决策分层强化学习模型,得到对火箭后续飞行进行评价的“评价网络”;然后利用基于凸优化的在线能力评估和轨迹规划方法,得到近似动态规划原理中的“决策生成”模块;最后,通过两者结合完成对火箭故障下后续飞行中连续轨迹和各级飞行段离散轨道根数等的决策。仿真结果表明该方法能够在非致命推力下降故障下实现火箭全程飞行任务决策并给出飞行轨迹。

基于DEMATEL-ISM的高校实验教师队伍发展影响因素关联测度

为识别高校实验教师队伍发展影响因素及其关联关系,从个人层面、组织层面以及社会层面3个维度建立了高校实验教师发展影响因素集,应用决策实验室分析法(DEMATEL)测算出实验教师发展影响因素的中心度、原因度、影响度以及被影响度,通过解释结构模型(ISM)方法构建了实验教师发展影响因素的4级层次递阶结构,进而揭示了实验教师发展影响因素之间的深层次逻辑关联及其作用机理。研究结果表明,人事政策与管理体制、行业与产业需求、教研资源投入、科技创新环境以及技术更新与迭代是影响实验教师队伍发展的原因类因素,而实践创新能力、实验室建设与管理水平、实验教学经验、学术水平以及职称等级是影响实验教师队伍发展的结果类因素。本研究可为高校实验教师队伍发展改革提供决策支持。

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。

Ethical decision-making in older drivers during critical driving situations: An online experiment

The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios.204 participants from North America,grouped into two age groups(18–30 years and 65 years and above),were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem.Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment.Bayesian hierarchical models were used to analyze participants’responses,response time,and acceptability of utilitarian ethical decision-making.The results showed significant pedestrian placement,age,and time-to-collision(TTC)effects on participants’ethical decisions.When pedestrians were in the right lane,participants were more likely to switch lanes,indicating a utilitarian approach prioritizing pedestrian safety.Younger participants were more likely to switch lanes in general compared to older participants.The results imply that older drivers can maintain their ability to respond to ethically fraught scenarios with their tendency to switch lanes more frequently than younger counterparts,even when the tasks interacting with an automated driving system.The current findings may inform the development of decision algorithms for intelligent and connected vehicles by considering potential ethical dilemmas faced by human drivers across different age groups.

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。