基于云理论和信息融合理论的系统性能评估方法研究

现代系统结构复杂,其性能评估需要从多个层次综合评判形成合理可信的最终评估结果,评估过程大量存在多源不确定信息。不确定性的存在增加了评估过程中定性与定量综合集成的难度。云理论和信息融合理论在不确定信息和多源信息的处理上各有优点,在功能上具有一定的互补性。两者相结合能够增强对信息的处理能力,挖掘出数据中更有价值的隐含信息。本文创新地将二者结合起来,提出了新的系统性能评估模型和方法,较好地融合了影响系统性能的多源信息,充分利用和集成了信息的模糊性和随机性,完成定性和定量之间的映射。这种方法能有效地处理各多源信息的不确定性,具有科学性,同时最终得到的评价结果是语言评判值,具有直观性。该方法是对信息融合理论和云理论联合使用的探索。     

基于结构熵权 TOPSIS 法的产品设计方案评估研究

为了提高企业在产品设计方案评估阶段进行合理决策的效率,提出了一种基于结 构熵权双基点法(TOPSIS)的产品设计方案评估方法。在评估流程中,首先确定产品设计方案评 价指标,专家对各评价指标进行重要度排序,在得到了“典型排序”后经过结构熵计算和“认识盲 度”计算,得出各评价指标的权重,以替代双基点法中专家主观设定的权重系数;其次,构建设 计方案各指标加权标准决策的矩阵,进而求得各指标的理想解,基于以上可得到各设计方案距 离理想解的相对贴近度,经对比后确定设计方案的排序情况,从而得到各方案的优劣。最后, 将该评估流程应用于录音笔设计方案评估的决策中,验证了该方法的可行性与有效性。     

基于云模型的区间直觉模糊数多属性群决策

针对属性值为区间直觉模糊数的多属性群决策问题,考虑到模糊性和随机性对群决策过程及结果的影响,本研究将利用云模型理论结合区间直觉模糊数的特征,运用灰色关联系数法和信息熵理论确定专家和属性权重,通过信息集结构建综合评价云模型.不同于传统的区间直觉模糊数的排序方法,本研究利用云模型的3En规则将区间直觉模糊数进行云转换并通过云相似度确定方案的综合评价值和犹豫度,然后对决策方案进行比较分析.研究结果表明:该方法能够科学有效地进行决策,进而为决策方提供科学依据.     

基于指标结构基因模型的评价驱动型产品设计

针对产品方案设计中用户反馈信息处理、转化问题,提出了交互式产品设计方法,建立了以用户交互式评价为核心的"指标-结构-基因"产品设计模型。该模型应用模糊集与粗糙集理论处理用户评价信息;利用灰色系统理论,应用层次分析法建立评价指标体系,实现由指标体系到产品结构的转换;采用遗传算法,通过产品结构基因编码,根据用户交互式评价,获得产品进化设计方案。以某企业婴儿手推车产品方案设计为例,阐述了应用该方法进行产品方案设计的一般步骤。     

A new approach for product evaluation based on integration of EEG and eye-tracking

Exploiting biometric measures, especially neurophysiological data of evaluator for product evaluation is advantageous at avoiding bias and subjectivity in expert scoring process. This paper proposes an approach that integrates electroencephalograph (EEG) and eye-tracking (ET) data in a new way to derive multi-faceted supportive information for product evaluation. Firstly, emotion recognition from EEG signals of evaluator is carried out with a spatial–temporal neural network. Then, based on correlations between emotions and preferential judgement, general customer preference toward product design scheme is inferred from emotions by fuzzy system. Finally, general preference is integrated with ET data at application-level to quantify fine-grained customer preferences toward design modules and visual attractiveness. This approach is verified with a case study which evaluates six designs of frontal area of automotive interior, and valuable supportive information for design decision-making is yielded. Also, comprehensive analysis is conducted and the results verify the effectiveness of proposed approach.     

基于D-S理论的可信云服务评价方法研究

云制造环境下,用户选择云服务资源时往往陷入信息量巨大以及信任缺失的困境。针对如何选取可信云服务的问题,提出了一种基于D-S理论的云服务评价方法。选取六个云服务指标作为可信特征集;通过获取定性定量指标评价信息,求解指标的评价等级隶属度;进而获得评价指标效用偏好度作为基本概率分配值;引入各云服务评价指标的权重修正基本概率分配以减少证据合成过程中的冲突;应用D-S理论合成规则对可信云服务的证据进行合成;计算各云服务的信任区间并将其进行偏序关系的对比,实现了云服务的选择。以某汽车零部件制造商选择云服务为例,验证所提方法的可行性和有效性。     

Research on multi-objective decision-making under cloud platform based on quality function deployment and uncertain linguistic variables

With developments in cloud computing and big data, the term “cloud” has become a household name owing to its characteristics of smartness and connectedness. Cloud-based platforms are associated with people. However, multi-objective decision-making problem in cloud platforms has not been extensively studied. There is no consensus on the reasonable and effective implementation of the decision-making model to select the optimum design scheme in a cloud platform and its establishment to objectively evaluate its significance. In this study, to efficiently and accurately realize the optimal selection of design scheme, a fuzzy evaluation mechanism in handling the vagueness and uncertainty is established, with the advantage of quality function deployment and strength Pareto evolutionary algorithm in decision analysis. A case study is provided to validate the proposed approach in cloud platforms. To reveal the advantages of the proposed method, it was compared with other methods such as the requirement-scenario-experience evaluation framework and a rough number-based integrated method. MATLAB programming was used for this comparison and accuracy certification. The result shows that the proposed model is more efficient and dynamic and provided users with multidimensional evaluation based on open and distributed resources system.     

交互式遗传算法在室内布局设计中的应用

室内平面设计是在房地产开发商交付的毛坯房进行装修过程中首当其冲的步骤, 符合用户个性化需求的平面图设计是典型的隐性优化问题. 室内设计工程师与用户之间的沟通很难准确获取用户需求, 且成本极高. 基于最优法则理论, 通过交互式遗传算法中的人机交互模式, 用主观评价替代繁琐的适应值函数设计来解决上述挑战. 实验表明, 该算法能有效满足平面图设计过程中客户的个性化需求, 改进的交互式遗传算法将用户的主观评价引入到传统的设计流程, 让用户真正参与到设计工作中.     

面向工业造型设计的产品设计定位方法研究

针对传统的设计定位对市场、用户等人文因素不能量化处理,需求分析结果不能满足后续工业造型设计要求,该文综合决策论方法、设计风格的语义描述方法,在产品设计定位中融入用户偏好等心理因素,以及市场变化等动态因素,以基于市场调查的市场细分的目标市场从上向下驱动和基于用户数据库的自下而上的数据驱动方式结合的方式,使产品市场定位更为准确、实用,后续设计所需的产品描述更为完备。同时根据该方法,构建了相应的软件系统,为更系统地将设计学方法知识与计算机系统结合,创造实用的CAID系统创造了条件。     

产品形态多意象蛛网灰靶决策模型

针对概念设计中多意象设计方案决策困难的现象,提出了一种产品形态多意象蛛网灰靶决策方法。首先,运用感性工学相关方法获取设计主体的认知数据,结合熵权法及博弈论思想构建基于设计主体认知数据的综合评价模型,并根据各意象的设计主体综合评价数据确定各意象的权重关系;其次,通过人工选择的方式从产品形态进化系统中选择多个进化方案,运用蛛网图表征各进化方案的意象关系,构建多意象蛛网灰靶决策模型,计算决策系数,对其进行比较排序,得到符合设计主体认知的相对最优方案;最后,应用灰色关联分析法验证该决策模型的可行性。结果表明,该模型能够帮助设计师在设计决策阶段快速、准确地确定符合多设计主体认知的多意象方案,为产品方案的多意象决策提供了新的理论和方法。     

Sicheres und nachhaltiges Benchmarking in der Cloud

Cloud computing entails a novel security threat: The cloud service provider is entrusted with the data of all its customers. This may not be sustainable for highly confidential data. Encryption, or more generally cryptography, may provide a solution by computing on data encrypted by the customers. While this solution is theoretically appealing, it raises a number of research questions in information system design. Using the example of collaborative benchmarking the author presents and evaluates an exemplary design and implementation of a cloud application that operates only on encrypted data, thus protecting the confidentiality of the customer??s data against the cloud service provider. The cloud application computes common statistics for benchmarking without disclosing the individual key performance indicators. Benchmarking is an important process for companies to stay competitive in today??s markets. It allows them to evaluate their performance against the statistics of their peers and implement targeted improvement measures.     

The evaluation for perceived quality of products based on text mining and fuzzy comprehensive evaluation

With the growth of the Internet and electronic commerce, more and more customers browse online reviews to understand products and service reputation. Online reviews can provide decision support for customers to purchase a product that is to their satisfaction. Manufacturers can also mine and analyze valuable information in favor of design and production from online reviews. Customer satisfaction is mainly determined by perceived quality of products. Hence, this study establishes a new method to evaluate the perceived quality by combining text mining with a fuzzy comprehensive evaluation method. The new evaluation method offers ideas and methods for future work to combine text mining technology with traditional evaluation methods. Customers can also make better purchase decisions and manufacturers design and manufacture better products by using this evaluation method.     

Incorporating affective customer needs for luxuriousness into product design attributes

In a highly competitive market, customers' product affection is a critical factor to product success. However, understanding customers' affective needs is difficult to grasp; product design practitioners often misunderstand what customers really want. In this study we report our experience in developing and using an affective design framework that identified critical affective features customers have on products and are systematically incorporated into product design attributes. To identify key affective features such as luxuriousness, we utilized the Kansei engineering methodology. This approach consists of three steps: (1) selecting related affective features and product design attributes through a comprehensive literature survey, expert panel opinion, and focus group interviews; (2) conducting evaluation experiments; and (3) developing Kansei models using multivariate statistical analysis and analyzing critical product design attributes. To demonstrate applicability of the proposed affective design framework, 30 customers and 30 product design practitioners participated in an evaluation experiment for car crash pads, and 44 customers and 20 designers participated in an evaluation experiment for two interior room products (wallpapers and flooring materials). The evaluation experiments were conducted via systematically developed questionnaires consisting of a 7‐point semantic differential scale and a 100‐point magnitude estimation scale. The results of the experiments were analyzed using principal component regression and quantification theory type I method. Using the analyzed survey data, the relationship between luxuriousness and related affective features and product design attributes were identified. This relationship indicated that there was a significant difference in the perception of luxuriousness between customers and designers. Consequently, it is expected that the results of this study could provide a foundation for developing affective products. © 2009 Wiley Periodicals, Inc.     

An optimum design selection approach for product customization development

Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert’s group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.     

基于用户心理行为的云制造知识服务优选决策

为提高云制造环境下知识服务的选择质量,提出一种基于用户心理行为的云制造知识服务优选决策方法。基于云制造知识服务的特性分析,建立云制造知识服务优选评价指标体系;运用粗糙集理论,对各评价指标进行初始权重分配,并根据用户多属性偏好对初始权重进行调整,以确保权重分配的合理性;在此基础上,结合用户在决策过程中的心理行为分析和后悔理论,构建云制造知识服务的感知价值矩阵,并计算各知识服务的综合感知价值,以获得最优云制造知识服务。通过一个应用实例,验证此方法的有效性和实用性。     

A rough set-based interval-valued intuitionistic fuzzy conceptual design decision approach with considering diverse customer preference distribution

Conceptual design evaluation plays a crucial role in new product development (NPD) and determines the quality of downstream design activities. Currently, most existing methods focus on fuzzy quantitative the evaluation information of multi-objectives in conceptual schemes selection. However, the above process ignores the various customers' preferences for each scheme under the evaluation objective, causing inconsistent preference weights in the various schemes, which cannot guarantee the market value of the optimal scheme. Furthermore, the ambiguous attitude from experts in the early design stage is not well taken into account. To this end, a conceptual scheme decision model with considering diverse customer preference distribution based on interval-valued intuitionistic fuzzy set (IVIFS) is proposed. The model is divided into three parts. Firstly, the initial decision matrix of multi-experts concerning the qualitative and quantitative design attributes is constructed based on intuitionistic fuzzy sets, and then the IFS decision matrix with interval boundaries is formed by using rough set technology. Secondly, the mapping model of design attribute to customer preference is constructed, and then the demand preference strategy implied by design attribute is judged. Thirdly, based on the demand preference strategy, the preferences’ weights for each scheme are calculated. Next, integrating the evaluation data with the same preference in the scheme, the comprehensive satisfaction of the scheme is obtained through IVIFS weighted aggregation operator, and then the optimal scheme is decided. Eventually, a case study of mobile phone form feature schemes is further employed to verify the proposed decision model, and results are sensitivity analyzed and compared.     

考虑决策者心理行为的软件质量评价方法

针对软件质量评价方法中缺乏考虑决策者心理行为的问题,提出一种基于区间二元语义的交互式多准则决策（TODIM）软件质量评价方法。首先,通过区间二元语义表征专家对软件质量的评价信息;其次,利用主观赋权法和逼近理想解排序法（TOPSIS）分别计算软件质量属性的主客观权重,并在此基础上通过组合赋权法获取软件质量属性的综合权重;然后,为更好地刻画专家在软件质量评价过程中的心理行为,将TODIM引入软件质量评价中。利用TODIM方法对高速铁路调度系统的助理调度员终端进行软件质量评价,结果表明铁路软件供应商提供的第3款助理调度员终端软件的占优度值最高,质量最优。将该方法与后悔理论方法以及II类偏好序结构排序法（PROMETHEE-II）进行对比分析,所得结果表明三种方法在质量最优软件的选取上具有一致性,然而三者的总体排序有所差异,说明所构建方法在描述多准则的交互关系和决策者的心理行为上具有较强的优越性。     

云存储环境下移动终端信息隐匿方法研究

为了提高云存储环境下移动终端信息存储的安全性,需要进行信息隐匿算法设计,提出一种基于双曲椭圆线性编码加密的云存储环境下移动终端信息隐匿方法。构建移动终端信息编码数字密钥,采用双线性映射方法进行云存储环境下移动终端信息隐匿编码的统计量设计,采用比特序列分块匹配方法构建移动终端信息加密协议,结合混沌编码方案进行信息隐写过程中的块内频数检测设计,采用双曲椭圆线性编码加密方法提高云存储环境下移动终端信息隐匿的深度,根据随机性检测结果实现云存储环境下移动终端信息连续码元隐匿设计。仿真结果表明,采用该方法进行云存储环境下移动终端信息隐匿的加密性能较好,抗攻击能力较强,提高了数据存储的安全性。     

云模型与VIKOR集成的多属性群决策方法

针对以往定性概念量化过程中只考虑到模糊性这一不确定性因素的不足,采用云模型从模糊性和随机性两个方面实现语义评价变量的量化,并对专家组的语义评价信息进行集结。针对以往云差异性度量方法的不足,基于云模型构成的本质特点,从云滴分布的角度提出了云距离测度算法,进而提出了云相似度算法。考虑到决策指标间相互影响关系,将云模型与决策试验与实验评估法（Decision-Making Trial and Evaluation Laboratory,DEMATEL）相结合,采用云DEMATEL法对专家主观评价给出的指标初始重要度进行修正,进而计算得到指标权重。采用云距离算法计算备选方案与正、负理想解间的距离,并最终由云VIKOR求得备选方案的妥协解。最后以实例验证了所提方法的可行性和有效性。     

灰色关联分析与云模型集成的方案评价方法*

针对传统方案评价方法中大都没有考虑评价信息随机性的情况,运用云模型建立云评价标度。针对传统客观属性权重确定方法主要依据属性值分布,没有考虑到指标间的相互影响关系,将熵值法和DEMATEL结合计算指标权重,其中采用云距离测度算法直接计算定性评价间的距离,避免了定性云转化为定量值时的信息损失。采用灰色关联分析处理云评价指标,对方案进行排序。利用方案总体云评价绘制云图,定性比较各方案优劣。最后以实例验证了所提方法的有效性。