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.     

基于动态贝叶斯网络的社交圈归属匹配模型

为了帮助社交网中新成员寻找与之最为合适的社交圈,尝试采用动态贝叶斯网（DBN）理论解决社交网分析应用中成员（节点）与社交圈（集合）的匹配问题。将圈内成员个人的多项兴趣爱好程度作为描述社交圈基本属性的特征向量,对每一类圈子建立了带有辅助信息形式的DBN模型,求解最大输出概率即为最佳匹配对象。结果表明,在客观测试和主观评价两方面,该模型都收到了较为满意的结果。     

Multi-objective routing algorithms for low-earth orbit satellite network

The low-earth orbit (LEO) satellite network, composed of a large number of satellite nodes, is a hot research topic at present. Due to the characteristics of the large-scale LEO satellite network, such as many satellite nodes, short orbit period, large dynamic change of topology, and unstable link-state, its communication quality of service (QoS) requirements are difficult to meet. Aiming at this problem, various factors that may affect data transmission are first analyzed. The network link selection problem is modeled as a multi-constraint optimization decision problem, a routing mathematical model based on linear programming (LP) is designed, and its solution is solved. Aiming at the problem of limited onboard computing resources, a multi-object optimization Dijkstra algorithm (MOODA) is designed. The MOODA finds the optimal path according to the comprehensive performance of the link. It solves the problems of poor comprehensive QoS performance and the low degree of load balancing of the paths found by the Dijkstra algorithm. The simulation results show that the paths found by the two algorithms have good QoS, robustness, and load balancing performance.     

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.     

基于海量数据分析的煤矿生产辅助决策支持系统的设计

以某煤矿自动化系统的建设为例,提出了一种基于海量数据分析的煤矿生产辅助决策支持系统的设计方案。该系统通过融合全矿井各传感器采集的数据,进行分系统的汇聚统计分析以及全系统的集成统计分析,可对煤矿生产过程中各机电设备的健康状况、故障、运行能耗作出判断,从而实现对煤矿生产的辅助决策支持。实际应用表明,该系统运行良好,提高了煤矿生产效率和管理水平。     

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。     

Agent decision-making in open mixed networks

Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.     

基于最大隶属度的区间概率灰色随机多准则决策方法

定义了一种灰色隶属函数.针对概率为区间数、准则值为区间灰数的灰色随机多准则决策问题,提出一种基于最大隶属度的决策方法.首先,运用区间数可能度排序向量将区间概率转化为点概率,并将其转化为无风险决策问题;然后,计算各方案在负理想方案到正理想方案上的灰色隶属度,并计算各方案准则值的相对灰度,进而根据灰色隶属度和相对灰度大小对方案进行排序;最后,通过算例验证了该方法的可行性和有效性.