基于云模型的军事航空通信频谱共享信任机制研究

针对军事航空通信频谱共享的特殊性,提出一种基于云模型的频谱共享信任机制。通过对信任的相关理论进行研究,引入云模型理论,构建了基于云模型的频谱共享信任体系结构,确立了频谱共享信任体系中的信任交互关系。运用云模型进行信任评价,实现了将频谱共享中模糊定性的信任属性向精确定量的信任等级的转换,较好地解决了频谱共享信任关系建立等系列问题。设计了信任评价实验,验证了信任评价模型的有效性和实用性,为基于信任机制的频谱共享策略实施提供了有效的支撑。     

情感词典构建综述

文本情感分析是近年来迅速兴起的一个研究课题,具有显著的研究价值和应用价值。情感词典的构建在情感分析任务中发挥着越来越重要的影响力。该文对情感词典构建的研究进展进行了总结。首先重点介绍了情感词典构建的研究现状,将其归纳为四种方法,即基于启发式规则的方法、基于图的方法、基于词对齐模型的方法以及基于表示学习的方法,并对每种方法进行介绍和分析;然后对一些常见的语料库、词典资源以及评测组织进行介绍;最后,对情感词典的构建进行了总结,并对发展趋势进行了展望。
     

P2P网络中基于动态贝叶斯网络可信度量模型

本文针对P2P网络中节点匿名、动态导致安全性下降,现有信任度模型计算动态性不足等问题,提出了基于动态贝叶斯网络的可信度量计算模型,模型依据历史交互记录数据,从直接信任度与推荐信任度两个方面进行信任度量计算,并考虑到时效性与恶意节点等问题,引入时效因子与惩罚因子.最后通过仿真实验验证了模型的有效性与可行性.     

面向旅游电子商务的多目标复合评估及优化推荐算法

推荐算法在电子商务系统中具有良好的应用前景,受到了越来越多关注和重视,逐渐成为了电子商务领域的研究热点.在介绍目前主流的推荐算法的基础上,结合电子商务实际应用需求,提出了多目标复合评估及优化推荐算法.并基于途牛旅游网数据,与一般推荐算法进行比较,验证了该算法的有效性,从而为多目标复合评估及优化推荐系统的研究提供了新思路与新方法.     

医院信息系统综合评价体系模型

为推动医院信息化建设工作的规范化,加强医院信息化建设的管理、监督、指导和评审工作,构建出一个具有广泛代表性的医院信息系统评价体系模型,使得我区医院信息化进程能够持续健康的发展. 我们运用一种改进的层次分析法原理,对指标体系中的每一个单一指标进行权重求解,从而得出一个可以求解出系统性能测试和功能测试总得分的指标体系,对医院信息系统进行综合评价. 通过该模型对新疆医科大学两家附属医院信息系统进行计算评分,最终科学有效地评价了该两家医院的信息系统,找出医院信息化工作的不足之处并给出了科学的改进建议.     

Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization

Histogram equalization is an effective technique to boost image quality and contrast enhancement. However, in some cases the increase in image contrast by traditional histogram equalization exceeds the desired amount Which damages the image properties and wanes its natural look. Histogram division and performing a separate equalization for each sub-histogram is one of the presented solutions. The dividing method and determining the number of sub-histograms are the main problems directly affecting the output image quality. In this study, a method is introduced for automatic determination of the number of sub-histograms and density based histogram division leading to appropriate output with no need for parameter setting. Each main peak is in a separate section. Image contrast is increased with no loss of image specifications through determining the number of sub-histograms based on the number of main peaks. The introduced histogram equalization approach consists of three stages. The first stage, using histogram analysis, produces an automated estimate of number of clusters for image brightness levels. The second, clusters the image brightness levels, and using the provided transfer function, the final stage includes contrast enhancement for each individual cluster separately. The results of the proposed approach demonstrate not only clearer details along with a boost in contrast, but also noticeably more natural appearance in the images.     

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.     

Robust optimization using Bayesian optimization algorithm: Early detection of non-robust solutions

Probabilistic robustness evaluation is a promising approach to evolutionary robust optimization; however, high computational time arises. In this paper, we apply this approach to the Bayesian optimization algorithm (BOA) with a view to improving its computational time. To this end, we analyze the Bayesian networks constructed in BOA in order to extract the patterns of non-robust solutions. In each generation, the solutions that match the extracted patterns are detected and then discarded from the process of evaluation; therefore, the computational time in discovering the robust solutions decreases. The experimental results demonstrate that our proposed method reduces computational time, while increasing the robustness of solutions.     

Adaptive linguistic weighted aggregation operators for multi-criteria decision making

In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria weights, we introduce a method to deduce secondary criteria weights from the criteria evaluations, which reflect the role of the different criteria in discriminating among the alternatives. The properties of the proposed operators are investigated. An approach for the application of the said operators in a group multi-criteria decision making problem is presented. Following the same, the proposed operators are applied in a case study on supplier selection. The empirical validation of the proposed operators is performed on a set of 12 real datasets.Note: All usages of he, him, his in the paper, also refer to she, and her.     

基于灰色聚类的系统群安全评估方法

目前大多数信息系统的安全评估工作基于某个单一系统,根据其各个属性分层评估其安全状态,而面向系统群来评价信息系统安全等级的方法较少。针对信息系统的安全需求,提出一种基于系统群的安全等级评估方法。考虑到灰色系统理论中的灰色聚类分析方法采用白化权函数将一些评价对象分为几个等级,适合于系统结构复杂、不确定性高、有效信息缺乏的系统分析,文中使用白化权函数来确定信息系统的安全评估等级。首先,从参与评价的信息系统的角度,采用高效快速的K-均值聚类算法确定白化权函数;其次,运用白化权函数得出各个信息系统的灰级灰色聚类系数;最后,得出各个信息系统的安全状态等级。实例结果表明,该方法可快速有效地对信息系统群的安全等级进行评估。