Establishment of an Aggregation Model Associated with Instrument Interface Design Based on Kansei Factors of Electric Vehicle Drivers

Wu, Yixiang; Kang, Xinhui

doi:10.1155/2020/8315641

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Through the use of questionnaire surveys and in-depth interviews with relevant experts, the authors collected the current society's requirements for disinfection and epidemic prevention robots and then selected, decomposed, combined, and built the requirement level model [20]. In contrast with the traditional AHP analytic hierarchy process, by adopting the fuzzy AHP method, one can optimize the complicated consistency test and maintain the primacy of data [21,22]. The fuzzy AHP tool has better accuracy and objectivity in complex evaluation systems to calculate the demand weights of disinfection and epidemic prevention robots in all aspects.…”

Section: Design Processmentioning

confidence: 99%

Design of a Disinfection and Epidemic Prevention Robot Based on Fuzzy QFD and the ARIZ Algorithm

2022

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In the present era, against the background of normalized measures of epidemic prevention and control, a multi-function intelligent robot that can completely replace human beings in disinfection and epidemic prevention work needs to be designed in order to contain the spread of viruses at the source, to efficiently and conveniently serve the controlled regions, and to meet the needs of the current context. Existing disinfection robot designs only address the problem of disinfection; there is no disinfection robot that can achieve full-domain and full-space disinfection (including stairs), as well as having multiple epidemic prevention functions. The fuzzy QFD tool and the ARIZ algorithm were used here to solve the problems related to disinfection robots. By using fuzzy QFD as the research framework and combining it with fuzzy AHP to calculate the weight of demand for the disinfection and epidemic prevention robot, the authors further translated the demand into the technical and part characterization of the robot. The conflicts in the fuzzy QFD model were solved using the ARIZ algorithm. Finally, based on the solving principle of the ARIZ algorithm and the qualitative and quantitative conclusions of fuzzy QFD, a robot for disinfection and epidemic prevention was designed in a comprehensive manner. A robot that can completely replace human beings in the tasks of disinfection and epidemic prevention was designed in this work, solving the problems associated with the robot’s ability to climb stairs and realizing the innovation of the robot’s comprehensive disinfection, distribution of materials, real-time monitoring, temperature measurement, and other functions. This study provides a theoretical reference for the design of related products.

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Section: Design Processmentioning

confidence: 99%

Design of a Disinfection and Epidemic Prevention Robot Based on Fuzzy QFD and the ARIZ Algorithm

2022

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“…Expert interview Kansei evaluation Function evaluation Filter product design elements Synthesis is paper EGM GRA NN Wu and Cheng [16] EGM Fuzzy Kano, fuzzy AHP QT-I Wu and Kang [23] EGM Fuzzy AHP Kang et al [5] EGM Fuzzy Kano, fuzzy AHP Fuzzy QFD Kang et al [8] EGM QT-I Kang et al [24] Focus group Fuzzy Delphi FWARM Chen and Li [10] EGM QT-I Shen [13] EGM QT-I Zhang et al [15] EGM ANP Wang and Hsueh [25] AHP, Kano model DEMATEL Wang [26] AHP, TRIZ QFD Wang [27] Focus group TRIZ, FCPR RST Notes. FWARM: fuzzy weighted association rule mining; TRIZ: theory of inventive problem solving; DEMATEL: decision-making trial and evaluation laboratory; RST: rough set theory; FCPR: fuzzy cognitive pairwise rating.…”

Section: Literaturementioning

confidence: 99%

Combining Grey Relationship Analysis and Neural Network to Develop Attractive Automobile Booth Design

Kang

2020

Computational Intelligence and Neuroscience

Self Cite

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Miryoku engineering is a design concept based on customer preferences, with the goal of creating attractive products or spaces. However, traditional Miryoku engineering faces two main issues: (1) the upper Kansei factor ranks the weights by the number of mentions, but it does not represent the importance of customers; (2) the mapping connection between the upper Kansei factor and the lower specific conditions adopts a statistical analysis method, which easily leads to the omission of key information. With the development of computer-based artificial intelligence, it repeatedly simulates human thinking with simple calculation rules, which has the advantages of fewer errors and faster speed. Therefore, on the three-level evaluation grid diagram platform established by Miryoku engineering, this paper first uses grey relationship analysis to comprehensively evaluate the priority order of Kansei words. Secondly, for the key Kansei factors, a morphological deconstruction table that connects the original reasons and specific conditions is established. Orthogonal design is used to screen representative combinations of design elements and create sample models by using the 3D software. Finally, the neural network was used to establish a mapping function between the key Kansei factors and the representative product design elements, and based on this, the most perceptually attractive product design was discovered. As a case study, the automobile booth was used to validate the effectiveness of the proposed method and significantly improve exhibitor design decisions and attendees’ satisfaction.

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“…Users, designers, and products can be organically combined using the KE method, thus boosting user satisfaction and design efficiency. Wu et al [29] and Xue et al [30] proposed a comprehensive decision system for design. The importance of each design element in the system can be quantified based on the user's perceptions, combined with evaluation and analysis methods, such as fuzzy computing and quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Virtual Reality Visualization Interface Based on Kansei Engineering Image Space Research

Tang

et al. 2020

Symmetry

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To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.

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Establishment of an Aggregation Model Associated with Instrument Interface Design Based on Kansei Factors of Electric Vehicle Drivers

Cited by 3 publications

References 29 publications

Design of a Disinfection and Epidemic Prevention Robot Based on Fuzzy QFD and the ARIZ Algorithm

Design of a Disinfection and Epidemic Prevention Robot Based on Fuzzy QFD and the ARIZ Algorithm

Combining Grey Relationship Analysis and Neural Network to Develop Attractive Automobile Booth Design

Optimal Design of Virtual Reality Visualization Interface Based on Kansei Engineering Image Space Research

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