基于模糊综合评价的电动汽车充电安全风险评估

为了定量评估电动汽车充电过程中的安全水平,对电动汽车充电过程中诱发充电安全事故的因素进行了分析,并采用模糊综合评价方法设计了电动汽车充电安全风险评估方案;提出了一种综合赋权的方法来改进灰色关联度主观赋权存在的指标权重不确定的问题,保证了评估结果的合理性与可靠性;首先统计分析指标诱发充电事故的频率获得客观权重,然后经过基于灰色关联度的专家经验判断得到主观权重,最后通过标准离差求出主观权重与客观权重的分配系数;该方法既保留了主观权重中专家的权威性,又充分体现了数据本身所具有的客观特征;其次,将充电安全划分为5个等级并且设定合理的阈值,构建梯形分布的隶属度函数;经过对某充电站充电数据的分析,验证了该风险评估方案的可行性和正确性。     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before.Given a task and a graph of experts where each expert possesses some skills,we tend to find an effective team of experts who are able to accomplish the task.This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is,considering each expert has the minimum required level of skill.Here,we generalize the problem in multiple perspectives.First,a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors.Second,the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them.By considering the groups,search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members.Third,the existing RarestFirst algorithm is extended to more generalized version,and finally the cost definition is customized to improve the efficiency of selected team.Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness,our proposed framework is achieved well in practice.     

Inter-rater reliability of query/probe-based techniques for measuring situation awareness

Query- or probe-based situation awareness (SA) measures sometimes rely on process experts to evaluate operator actions and system states when used in representative settings. This introduces variability of human judgement into the measurements that require inter-rater reliability assessment. However, the literature neglects inter-rater reliability of query/probe-based SA measures. We recruited process experts to provide reference keys to SA queries in trials of a full-scope nuclear power plant simulator experiment to investigate the inter-rater reliability of a query-based SA measure. The query-based SA measure demonstrated only ‘moderate’ inter-rater reliability even though the queries were seemingly direct. The level of agreement was significantly different across pairs of experts who had different levels of exposure to the experiment. The results caution that inter-rater reliability of query/probe-based techniques for measuring SA cannot be assumed in representative settings. Knowledge about the experiment as well as the domain is critical to forming reliable expert judgements.     

SNA-based multi-criteria evaluation of multiple construction equipment: A case study of loaders selection

Selecting the proper construction equipment is a challenging task owing to a wide range of available types as well as a host of criteria to be considered during decision making. To deal with this, a heterogeneous group decision-making framework to evaluate multiple purchasing choices of construction equipment is proposed with two data forms, i.e., 2-dimension uncertain linguistic variables (2DULVs) and real numbers. Firstly, a novel way to derive weights of experts by social network analysis (SNA) is applied considering trust degrees among experts in a social trust network. Secondly, after evaluation index system is established, 2DULVs that include both the linguistic evaluations on alternatives and decision makers’ appraisals on the given evaluation results are applied to represent subjective fuzzy evaluation information, while real numbers are used to represent quantitative values. Thirdly, 2-dimension uncertain linguistic power generalized weighted aggregation (2DULPGWA) operator is applied to aggregate evaluation values among experts. Fourthly, analytic hierarchy process (AHP) and entropy method are utilized to derive combined weights of sub-criteria before the rank can be obtained by the evaluation based on distance from average solution (EDAS) method. Finally, a case study to evaluate multiple choices of loaders is proposed and a comparative analysis is conducted to prove the effectiveness of the proposed framework. This study provides a meaningful guidance for the optimal selection among various types of construction equipment.     

基于贪心搜索的分组项目评审专家遴选方法

在科技项目评审环节中,往往每组同时有多个项目和多位评审专家,其中每个项目都有其所覆盖的专业领域,而每位专家又有其所研究的专业领域,如何科学且自动地根据待评审科技项目所涵盖的专业领域,从候选专家库中找出合适的评审专家组合团体具有很实际的研究意义.对此本文提出了一种基于贪心算法的科技项目评审专家多重匹配模型,该模型应用于已经建立起关联的“项目-领域”与“专家-领域”两个相关性矩阵上,通过分别计算科技项目及评审专家团体所对应于专业领域上的离散分布,并利用合适的评价函数综合衡量待审科技项目与评审专家组合之间的匹配度,最终求出最优的评审专家组合来作为该期科技项目的最终评审专家团体.本文通过使用电力行业数据集进行多次实验,结果表明该模型能有效地进行科技项目评审专家的匹配,并具有较高的合理性与准确性,在解放人力成本提高评审工作效率的同时,也杜绝了传统人工遴选专家过程中所出现纰漏以及评审不公现象的发生.     

Ranking user authority with relevant knowledge categories for expert finding

The problem of expert finding targets on identifying experts with special skills or knowledge for some particular knowledge categories, i.e. knowledge domains, by ranking user authority. In recent years, this problem has become increasingly important with the popularity of knowledge sharing social networks. While many previous studies have examined authority ranking for expert finding, they have a focus on leveraging only the information in the target category for expert finding. It is not clear how to exploit the information in the relevant categories of a target category for improving the quality of authority ranking. To that end, in this paper, we propose an expert finding framework based on the authority information in the target category as well as the relevant categories. Along this line, we develop a scalable method for measuring the relevancies between categories through topic models, which takes consideration of both content and user interaction based category similarities. Also, we provide a topical link analysis approach, which is multiple-category-sensitive, for ranking user authority by considering the information in both the target category and the relevant categories. Finally, in terms of validation, we evaluate the proposed expert finding framework in two large-scale real-world data sets collected from two major commercial Question Answering (Q&A) web sites. The results show that the proposed method outperforms the baseline methods with a significant margin.     

基于Hopfield神经网络的企业竞争力评价模型

在专家评价数据基础上,针对企业竞争力评价的非线性特征以及竟争力评估体系中存在的诸多非定量因素,建立企业竞争力评价的Hopfield神经网络模型。评价模型可以根据专家打分对已有评价体系中的主观性因素进行量化,并利用神经网络的联想与记忆功能进行量化评价的计算。该模型较已有的评价体系具有更高的计算效率。利用Hopfield神经网络进行企业竞争力评价的方法可操作性强,可以克服传统评价方法主观性过强的缺点,具有较广泛的实用性。     

社会网络环境下基于分布式信任的在线评价方法

在线评价是社会网络环境下的一种新型多属性群决策问题.首先,为处理在线评价过程的不确定性,定义了分布式信任的运算法则、集成算子和排序方法等;其次,提出一种分布式信任社会网络分析方法,用来分析专家间的信任关系,并计算出每位专家的信任权重;再次,基于在线评价信息,结合最大和最小平均加权距离来综合确定未知的属性权重,并利用相对贴近度对备选方案进行排序,进而提出一种基于分布式信任的扩展TOPSIS法;最后,通过实例分析表明所提出方法的可行性.     

信任网络下的TODIM群体决策方法

针对在群体决策中如何利用专家之间的社会关系和决策专家的有限理性的问题,提出一种信任网络下的TODIM群体决策方法。首先,根据专家讨论次数,在每一次讨论中,每个专家会根据信任接受程度参考信任者的决策矩阵,并通过信息交互和协商修改决策矩阵;其次,当达到设定的专家讨论次数时,计算最终的群体决策矩阵;最后,分别运用信任网络下的TODIM群体决策方法和TODIM群体决策方法计算各方案排序。对所得结果进行对比分析,并对专家讨论次数和信任接受程度进行灵敏度分析。案例分析结果表明,信任网络下的TODIM群体决策方法能充分结合信任网络,保证了决策过程中的多阶段信息交互和反馈过程,并在对比分析和灵敏度分析上优于对比方法。     

面向风险评估的专家权重自适应调整方法

信息资产评估是信息安全风险评估技术重要的研究内容之一。目前,其在资产评估中主要采用专家评估与专家权重相结合的评估量化方法,然而该方法在实际应用中却面临如何科学确定专家权重以降低偏差较大评估意见对整体评估结果影响的问题。针对该问题,提出了一种基于专家偏离度的权重自适应调整评估方法,能够合理地减小专家主观性给出的异常评估值对评估的影响。最后实现算法并通过 实验 验证算法的有效性。结果表明该方法能够合理减小异常评估值对评估的影响。     

考虑群体信任的网络安全态势多维偏好评级方法

现有网络安全态势评级方法难以同时兼顾专家间信任信息、偏好信息以及客观异质信息等多源信息,为此,建立基于群体信任的网络安全态势多维偏好评级模型.在描述网络安全态势多维偏好评级问题的基础上,提出社会网络中信任信息不确定程度度量方法,进一步建立一种考虑单链信任信息不确定性和内部差异性、多链间信任非补偿性和内部差异性的不完整信任网络构造模型,据此确定专家权重;定义基于级别特征值的客观排序、专家偏好的群体一致性程度和群体非一致性程度,进一步建立包含专家信任信息、偏好信息和评级信息等在内的网络安全态势评级多维偏好分段变权综合优化模型,获得基于二元语义的网络安全态势等级和等级区分度.所提出方法为构建和检验网络安全态势评级提供理论依据,有助于研发先进的网络安全态势评级系统,提高网络安全防护能力.     

基于信任关系和互补判断矩阵的群决策方法

随着信息和网络技术的不断发展,基于社会网络的群决策问题受到越来越多研究者的关注.针对社会网络环境下模糊互补判断矩阵的群决策问题,研究群体共识调整过程和方案选择方法.首先,融合决策者之间的社会关系、身份地位、知识能力3个方面信息来构建决策者两两之间的信任关系;其次,提出一种尽可能减少元素间共识补偿的共识度度量方法,在此基础上建立基于信任关系的共识调整模型,并从理论上证明该模型的有效性;最后通过信任关系矩阵的特征向量中心度分别求出专家的重要性权重,用以集结专家的偏好信息和对方案进行排序选择,算例分析表明了所提出方法的有效性.     

专家交互情境下不完备群组DEMATEL决策方法

为克服现有群组DEMATEL存在的尚未考虑群组专家之间的信息交互、对于不完备专家判断信息的推断机理不明确等缺陷,基于社交网络中的信任关系理论和凝聚层次聚类理论,通过给出不完备群组DEMATEL初始直接影响矩阵残缺值的推断方法和专家交互情境下群组DEMATEL直接影响矩阵信息修正方法,提出专家交互情境下不完备群组DEMATEL决策方法的实现步骤.最后,通过算例对比分析反映出通过专家多轮次交互,群组专家之间的共识度以及决策结果的可靠性持续提升,从而验证了所提出方法的科学性和可行性.     

Social Network Decision Making with Linguistic Trustworthiness–Based Induced OWA Operators

Classic aggregation operators in group decision making such as the ordered weighted averaging (OWA), induced ordered weighted averaging (IOWA), C‐IOWA, P‐IOWA, and I‐IOWA have shown to be successful tools to provide flexibility in the aggregation of preferences. However, these operators do not take advantage of information related to the interaction between experts. Experts involved in a group decision‐making problem may have developed opinions about the reliability of other experts' judgments, either because they have previous history of interaction with each other or because they have knowledge that informs them on the reliability of other colleagues in the group in solving decision‐making problems in the past. In this paper, and within the framework of social network decision making, we present three new social network analysis based IOWA operators that take advantage of the linguistic trustworthiness information gathered from the experts' social network to aggregate the social group preferences. Their use is analysed with simple but illustrative examples.     

The quality of expert probability judgement: issues and analysis

Abstract: Many decision-aiding technologies require valid probability judgements to be elicited from domain experts. But how valid are experts' probability judgements? We describe two approaches to the assessment of quality of probability judgement—calibration and coherence—and review the research findings following from these two approaches. In many cases, expert probability judgement has been found to lack validity and this sub-optimality has largely been attributed to computational errors on the part of the expert. The preferred solution to poor validity in probability judgement has therefore been to reduce the amount of computation performed by the expert. Complex probabilities can be calculated mechanically from simple probability judgements elicited from the expert. We present evidence which suggests that this recomposition technique doesn't guarantee valid probabilities. Our explanation for this finding is that there are various problems concerned with eliciting even the simple probabilities which are necessary for subsequent recomposition. We conclude by proposing some solutions to these elicitation problems which should help ensure that probability judgements of increased validity are available to those attempting to capture subjective assessments for input into decision support systems.     

MotionGlyphs: Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior

Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio-temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node-link diagrams, resulting in issues of node-overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio-temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio-temporal networks of collective animal behavior.     

A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.      

基于信任关系的TODIM群体多属性决策方法

针对现有多属性群体决策方法较少考虑社会网络和决策者有限理性因素的影响,考虑到社会网络中的信任关系,提出了基于信任关系的TODIM(TOmada de decis?o interativa multicritério)群体多属性决策方法.根据决策专家之间的信任关系,计算出信任网络中的领导者、信任关系矩阵以及评价矩阵等.专...     

基于可拓理论的网络安全评估研究

针对目前网络安全评估算法存在的不足,提出了一种基于可拓模糊层次分析的安全评估算法。该算法在模糊层次分析法的基础上,结合可拓理论,将调查问卷表收集到的指标值映射到可拓区间中,通过相对隶属度的计算,构建了新的判决矩阵,利用模糊层次分析法计算各指标的综合权重。通过对相对隶属度的加权排序,得到网络安全评估值。通过实例分析,说明该方法能综合考虑专家打分的权威性和调查问卷的普遍性,提高了网络安全评估的准确性和有效性。