Developing a Labeled Affective Magnitude scale and Fuzzy Linguistic scale for tactile feeling

Akay, Diyar; Duran, Burcu Uzgur; Duran, Engin; Henson, Brian; Boran, Fatih Emre

doi:10.1002/hfm.20866

Cited by 9 publications

(4 citation statements)

References 90 publications

(118 reference statements)

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“…A questionnaire and field investigation is adopted, and a decision-making group composed of designers and managers is set up for the project. After complete discussion by the decision-making group, the following customer requirements are determined, as shown in Table 1: The psychological research shows that, due to the limited short-term memory ability, human beings can reasonably remember 7 ± 2 items, so it is relatively safe to use up to 5 items in the fuzzy set [41]. Then, the fuzzy number is used to establish the corresponding linguistic terms to avoid directly assigning equidistant values [42].…”

Section: Resultsmentioning

confidence: 99%

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Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method

et al. 2021

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In concept design, effective decision making and management of schemes can shorten the design cycle and improve product quality. The decision maker (DM)’s confidence is one of the critical factors affecting the conceptual design evaluation. Although many studies use quantitative linguistic evaluation for design scheme decision-making, which improves product conceptual design decision-making efficiency and effectiveness, few studies consider the confidence level of a decision. A conceptual design evaluation method based on Z-numbers is proposed to solve this problem, considering the customer requirements and the DM’s confidence. Firstly, the evaluation criteria are determined by analyzing customer requirements; then, the fuzzy analytic hierarchy process in the Z-numbers environment (Z-AHP) is used to determine the criteria weight; Finally, the fuzzy technique for order preference by similarity to ideal solution method in the Z-numbers environment (Z-TOPSIS) is used to evaluate the design schemes to obtain the optimal scheme. The proposed method is applied to the selection of the design scheme of the waste containers in the kitchen. The results show that considering the DM’s self-confidence can achieve a more reasonable and practical evaluation of the conceptual design scheme, and it is easier to obtain the best scheme.

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Section: Resultsmentioning

confidence: 99%

“…The psychological research shows that, due to the limited short-term memory ability, human beings can reasonably remember 7 ± 2 items, so it is relatively safe to use up to 5 items in the fuzzy set [41]. Then, the fuzzy number is used to establish the corresponding linguistic terms to avoid directly assigning equidistant values [42].…”

Section: C3mentioning

confidence: 99%

Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method

et al. 2021

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“…These design attributes are predefined in an existing list. Often, evaluators are prompted to indicate a position on the Likert Scale between contrasting attributes [45,46]. In this work, the participants were asked to rate the response of various vase designs by selecting five degrees of affect (strongly disagree, disagree, neutral, agree, and strongly agree) in 12 affective adjectives, namely: Feminine, Emotional, Delicate, Elegant, Technological, Strong, Gentle, Traditional, Loud, Stable, Practical, and Luxurious.…”

Section: Individual Design Perceptionmentioning

confidence: 99%

A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm

Benaissa,

Kobayashi,

Kinoshita

et al. 2023

Axioms

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This paper presents a novel approach for individual design perception modeling using the YUKI algorithm-trained Fuzzy Inference System. The study focuses on understanding how individuals perceive design based on personality traits, particularly openness to experience, using the YUKI algorithm and Fuzzy C-means clustering algorithm. The approach generates several Sugeno-type Fuzzy Inference System models to predict design perception, to minimize the Root Mean Squared Error between the model prediction and the actual design perception of participants. The results demonstrate that the suggested method offers more accurate predictions compared to the traditional Fuzzy C-means Fuzzy Inference System and Deep Artificial Neural Networks, and the Root Mean Square deviation for individual design perceptions falls within a satisfactory range of 0.84 to 1.32. The YUKI algorithm-trained Fuzzy Inference System proves effective in clustering individuals based on their level of openness, providing insights into how personality traits influence design perception.

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“…The well-known CT scale is the Computational Thinking Scale (CTs) developed by Korkmaz et al (2017), in which 29 items are scored on a 5-point Likert scale by participants according to their attitude. However, the scale lacks more details of thinking because the complexity of thinking makes it difficult to quantify accurately (Dong et al, 2009), and attitude words such as "agree" and "uncertain" inherently contain fuzziness (Akay et al, 2021). In addition, five sub-abilities from CTs are equally important but the contribution of indexes is usually different in multiple criteria evaluation.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-criteria decision making model using Pythagorean fuzzy set and evolutionary algorithm for computational thinking assessment

Zhao,

Lei,

Luo

et al. 2023

Preprint

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Computational thinking (CT) is one of the essential skills for students in the 21st century, which has been widespread around the world. Therefore, the assessment of CT has become a hot topic. Increasing efforts to integrate CT assessment across all disciplinary backgrounds have lately progressed. The scale assessment is the most representative assessment tool. However, it cannot adequately reflect students’ preferences and ideas. The present study was aimed at proposing an improved scale-based assessment method, which could collect students’ fuzzy preferences with the support of Pythagorean fuzzy set (PFS). This method treats CT assessment as a multi-criteria decision-making (MCDM) problem. In the PFS environment, the whale optimization algorithm (WOA) is used for criteria weight computation, and students’ CT along with five sub-abilities are classified into five levels using the adapted Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. A total of 24 eighth-grade students participated in the CT assessment. And the experimental results indicate the feasibility of the method. Two sensitivity analyses were conducted to verify the effectiveness of the model. Furthermore, a correlation analysis between the method, traditional scales method, and teacher evaluations also confirmed it.

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Developing a Labeled Affective Magnitude scale and Fuzzy Linguistic scale for tactile feeling

Cited by 9 publications

References 90 publications

Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method

Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method

A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm

A novel multi-criteria decision making model using Pythagorean fuzzy set and evolutionary algorithm for computational thinking assessment

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