基于态势评估的足球机器人决策模型

为了提高足球机器人中策略执行的有效性和针对性,提出了一种基于赛场正向态势评估和反向执行效果评估的有限状态自动机分层决策模型。该模型将赛场决策分为战术策略和具体任务,通过正向态势评估信息和任务执行效果的反馈信息进行综合量化,基于有限状态自动机建立策略和任务的转换关系,从而做出适合当前态势的战术策略。经仿真实验表明,该决策模型在很大程度上提高了策略决策的有效性和针对性,使得球队的整体攻防能力得到了相应的改善。     

基于AHP和MMTD的呼叫接纳控制方法AM-CAC

提出一种统一的呼叫接纳控制方法。应用层次分析过程(AHP)实现系统建模,根据网络运营目标,对于决策所依据的诸多准则之间的重要性关系进行定性分析和定量描述,从而确定各准则在决策中的影响力;应用中介真值程度度量(MMTD)方法统一量化各影响因素相对各准则的真值程度,通过配置合理参数,来适应异构网络在技术特性、性能目标上的差异。仿真结果表明,文中所提方法对于网络的运营目标和偏好具有很好的适应能力。     

电子对抗训练中通信干扰效果评估方法研究

研究电子对抗训练中通信干扰的基本原理,分析通信干扰的基本方程和评估通信干扰效果的基本准则,利用这一准则对语音模拟通信干扰效果进行评估,并得出结果。     

任务多变情况下技术团队隐性知识管理策略

很多以技术性活动为主的团队所执行的任务环境多变,无论是地形、气候或者是人员、设备的变化非常规问题地多发,会对其知识管理尤其是隐性知识管理带来一定影响。本文提出将承载着隐性知识的非结构化文档以数列集合的方式表达,能够便于管理和决策。     

Choosing knowledge management strategies by using a combined ANP and DEMATEL approach

Numerous companies are expecting their knowledge management (KM) to be performed effectively in order to leverage and transform the knowledge into competitive advantages. However, here raises a critical issue of how companies can better evaluate and select a favorable KM strategy prior to a successful KM implementation. The KM strategy selection is a kind of multiple criteria decision-making (MCDM) problem, which requires considering a large number of complex factors as multiple evaluation criteria. A robust MCDM method should consider the interactions among criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with all kinds of interactions systematically. Moreover, the Decision Making Trial and Evaluation Laboratory (DEMATEL) not only can convert the relations between cause and effect of criteria into a visual structural model, but also can be used as a way to handle the inner dependences within a set of criteria. Hence, this paper proposes an effective solution based on a combined ANP and DEMATEL approach to help companies that need to evaluate and select KM strategies. Additionally, an empirical study is presented to illustrate the application of the proposed method.     

软件质量方案模糊决策模型的研究

为了解决由于软件质量概念的模糊性而导致的软件质量方案决策困难的难题,从3个方面对软件质量方案模糊决策模型进行了研究.首先,采用L-R模糊数建立模糊权集,解决了经典决策方法存在的精确权集缺陷和模糊综合评价方法存在的伪模糊权集缺陷.其次,采用Bonissone方法简化了模糊运算.再者,采用模糊排序方法进行方案选择.从而全面提高了软件质量方案决策的准确性.最后,以一实例进行了验证.     

基于同构化信息融合的异构不确定多属性决策新模型与新算法

不确定多属性决策过程中,现有两大困难:(1)如何较好地表达和处理具有不确定性的属性评价信息;(2)如何将基于多样性评判准则的多准则评价结果进行信息融合,并获得更合理的综合评价结论.基于同构化思想,针对学术界最近才提出的一种能较好地处理具有多信息来源模糊信息的新数学模型"多值直觉模糊集模型",研究了多值直觉模糊集的隶属度与非隶属度的综合评判新课题与新方法;提出了兼有不确定语言型与区间型的异构风险型多属性决策新问题与新模型,构造了基于同构化信息融合的异构不确定多属性决策新模型与新算法.     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service

The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical factors for the adoption of a cloud computing service, replacing the subjective decision of the authors. The results of this study determined five critical factors, namely data access security, information transmission security, senior management support, fallback cloud management, and employee acceptance. Finally, the paper presents the findings and implications of the study.     

基于数据仓库技术的疫情决策支持系统的研究

为了疫情监测的网络化,信息化,标准化,及疫情预防控制信息系统的统一化,对疫情监测及预警展开研究,构建了一个以数据仓库技术为基础,结合联机分析处理技术的疫情监测决策支持系统。该系统通过建立数据仓库对来自不同数据源的疫情数据进行存储,采用联机分析处理技术对各级部门上传的疫情数据进行多角度、多侧面的分析,为决策人员做出决策提供强大的辅助支持。