Using fuzzy coding with qualitative data: example with subjective data in human-computer interaction

This article shows the role that fuzzy sets may play in the prospect of analysing qualitative data. To underline this role, a human-computer interaction (HCI) study is presented. The data coming from 20 experts concerns their judgment regarding 33 questions related to the use of HCI approaches in order to support interactive system development phases. Each response scale features three main modalities, that is Agree, Partially agree and Disagree. The dataset example is analysed using multiple correspondence analysis (MCA) with both crisp and fuzzy coding models where the intermediate modality, Partially agree, is removed and considered with ½ membership values to the two extreme modalities. A comparative analysis is performed and the discussion states the interest of fuzzy coding with several kinds of qualitative factors or measurement variables. With qualitative measurement variables (our example), the main drawback of fuzzy coding could be the information loss, which is counterbalanced by the possibility of having fewer modalities and therefore of simplifying the multivariate analysis.     

Quantifying data for group technology with weighted fuzzy features

The high potential of using group technology in manufacturing has attracted the interest of both practitioners and researchers. Group technology is based on clustering parts which have similar features. Very often it is very hard to quantify successfully data regarding these features. This is because in many real applications features are fuzzy. This paper identifies two types of fuzzy features: qualitative features, and quantitative ones with subjective meaning. The paper presents a methodology for quantifying the data that refer to the fuzzy features. The proposed methodology deals with crisp and fuzzy data in a unified manner. Finally, some clustering approaches which process the quantified features are also discussed     

Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation

Engineering design evaluation is characterized by imprecise (vague) importance and satisfaction levels of criteria, which are better treated as fuzzy variables rather than as subjective crisp variables. Nevertheless, operations on fuzzy numbers tend to increase unnecessarily the imprecision when they are combined for some applications, particularly, when a fuzzy-weighted average (FWA) is calculated. This paper proposes a novel method of operating on fuzzy numbers to obtain a fuzzy-weighted average of desirability levels during design evaluation. The method produces overall desirability levels less imprecise and more realistic than those of the conventional FWA. Furthermore, the balance points of these fuzzy numbers seem to be more credible than those of the FWA. An example is presented, which demonstrates the advantages of the method developed.     

智能座舱睡眠舒适度的实证研究

目的 基于生物力学原理,评价自动驾驶情景中座椅靠背和座面的多种组合对睡眠舒适度的影响,从而指导智能座舱的设计研发。方法 基于Tekscan的CONFORmat座椅压力分布测量系统,设计了座面角度(15°,25°,35°)和靠背角度(140°,150°,160°)的实验装置,获取了20位被试的客观多维数据与主观评价数据,随后应用人工神经网络构建了舒适度预测模型。结果 重复测量方差分析表明,随着座面平移角度的增加,可有效地均衡界面接触压力分布,座椅靠背角度为160°与座面角度35°的组合设计,其压力的加权平均值、总接触面积、界面压力分布指数、适应性等级与其他实验条件有显著性差异。结论 最后应用人工神经网络预测模型的精确验证,研究建议将35°的座面角度与160°的椅背应用于智能座舱的睡眠位置。     

A fuzzy logic based approach to reliability improvement estimation during product development

During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various design tools, experiments, and design review records and treated as fuzzy numbers or linguistic variables. Fuzzy reasoning method is used to combine and quantify the subjective information to map their impact on product reliability. The crisp output of the fuzzy reasoning process is treated as new evidence and incorporated into a Bayesian framework to update the reliability estimates. A case example is presented to demonstrate the proposed approach.     

A risk assessment methodology for incorporating uncertainties using fuzzy concepts

This paper proposes a new methodology for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. This paper also introduces new forms of fuzzy membership curves, designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments, since it is often difficult or even impossible to precisely estimate the occurrence rate of an event in terms of one single crisp probability.It is to be noted that simple linguistic variables such as ‘High/Low’ and ‘Good/Bad’ have the limitations in quantifying the various risks inherent in construction projects, but only represent subjective mental cognition adequately. Therefore, in this paper, the statements that include some quantification with giving specific value or scale, such as ‘Close to any value’ or ‘Higher/Lower than analyzed value’, are used in order to get over the limitations.It may be stated that the proposed methodology will be very useful for the systematic and rational risk assessment of construction projects.     

Modeling system behavior for risk and reliability analysis using KBARM

With advances in technology and the growing complexity of technological systems, the job of the reliability/system analyst has become more challenging as they have to study, characterize, measure and analyze the behavior of systems with the help of various traditional analytical (mathematical and statistical) techniques, which require knowledge of the precise numerical probabilities and component functional dependencies, information which is difficult to obtain. Even if data are available they are often inaccurate and are thus subject to uncertainty, i.e. historical records can only represent the past behavior and may be unable to predict the future behavior of the equipment. To cope with such situations, the knowledge‐based approximate reasoning methodologies (KBARMs) provide necessary help. Among them, the fuzzy and grey methodologies are the most viable and effective tools for coping with imprecise, uncertain and subjective information in a consistent and logical manner. In this paper, the authors present a methodological and structured approach (which makes use of both qualitative and quantitative techniques) to model, analyze and predict the failurebehavior of two units, namely the forming and press units of a paper machine, using KBARMs. Various system parameters such as repair time, failure rate, mean time between failures, availability and expected number of failures are computed to quantify the system behavior in terms of fuzzy, crisp and defuzzified values. Furthermore, a risk ranking approach based on fuzzy and grey relational analysis is discussed to prioritize various failure causes associated with the components in failure mode and effects analysis (FMEA). Copyright © 2007 John Wiley & Sons, Ltd.     

Quasi-quantitative measures of research performance: An assessment of construct validity and reliability

This paper argues that research performance is essentially a multidimensional concept which cannot be encapsulated into a single universal criterion. Various indicators used in quantitative studies on research performance at micro or meso-levels can be classified into two broad categories: (i) objective or quantitative indicators (e.g. counts of publications, patents, algorithms or other artifacts of research output) and (ii) subjective or qualitative indicators which represent evaluative judgement of peers, usually measured on Likert or semantic differential scales. Because of their weak measurement properties, subjective indicators can also be designated as quasi-quantitative measures. This paper is concerned with the factorial structure and construct validity of quasi-quantitative measures of research performance used in a large-scale empirical study carried out in India. In this study, a reflective measurement model incorporating four latent variables (R & D effectiveness, Recognition, User-oriented effectiveness and Administrative effectiveness) is assumed. The latent variables are operationalized through thirteen indicators measured on 5-point semantic differential scales. Convergent validity, discriminant validity and reliability of the measurement model are tested through LISREL procedure.     

键盘打字对颈部和上肢肌肉影响的分析研究

目的 分析键盘打字时工作台不同人因参数对颈部和上肢肌肉活动的影响,进而为长期处于键盘打字状态的用户提供打字建议.方法 设计不同人因变量实验(躯干姿势、键盘角度、显示器角度),对8名健康有经验的日常打字时长在4小时以上的被试进行颈部和上肢肌肉表面肌电(sEMG)和主观评测数据采集.每名被试进行8组20分钟的打字实验.结果 对sEMG和主观舒适度数据进行分析,发现躯干姿势、键盘角度、显示器角度均对颈部和上肢肌肉活动具有影响,而对主观数据均无影响.站姿且显示器为120°的情况下颈部肌肉活跃程度最低;和坐姿相比,站姿时上肢肌肉活动程度相对较弱,而手臂和手腕在一定支撑的情况下肌肉活动程度较低.结论 工作台的不同人因参数能够影响颈部和上肢肌肉的活动水平.该研究能够为长期处于键盘打字状态的用户提供打字建议,并为人机工作环境的优化设计提供指导方法.     

Multi-objective mixed integer programming and an application in a pharmaceutical supply chain

Multi-objective integer linear and/or mixed integer linear programming (MOILP/MOMILP) are very useful for many areas of application as any model that incorporates discrete phenomena requires the consideration of integer variables. However, the research on the methods for the general multi-objective integer/mixed integer model has been scant when compared to multi-objective linear programming with continuous variables. In this paper, an MOMILP is proposed, which integrates various conflicting objectives. We give importance to the imprecise nature of some of the critical factors used in the modelling that can influence the effectiveness of the model. The uncertainty and the hesitation arising from estimating such imprecise parameters are represented by intuitionistic fuzzy numbers. The MOMILP model with intuitionistic fuzzy parameters is first converted into a crisp MOMILP model, using appropriate defuzzification strategies. Thereafter, the MOMILP is transformed into a single objective problem to yield a compromise solution with an acceptable degree of satisfaction, using suitable scalarisation techniques such as the gamma-connective technique and the minimum bounded sum operator technique. The proposed solution method is applied to several test problems and a multi-objective pharmaceutical supply chain management model with self generated random data.