About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Text analytics on MOOCs. A comprehensive analysis of emotions

The value of diversity in education is highly emphasized in recent years, particularly in the wake of the COVID-19 pandemic, by many scholars. Massive open online courses (MOOCs) have aided the evolution of online learning by broadening the range of learning opportunities available. They have gained popularity, especially in higher education by providing unlimited access to lectures and rich learning materials by renowned and respected academics in a wide variety of areas, with no restrictions and at very low fees. Furthermore, learners' motivations for enrolling in a MOOC may vary depending on their choices for the course's instructional design as well as their emotions. Knowing this, the development of more effective online courses that address affective concerns would appeal to a wider audience and improve the learning experience. This research aims to uncover the emotional characteristics of MOOCs to better understand why learners choose a specific course among hundreds of options available on MOOC sites. For extracting the learners' emotions from user reviews, the study used Kansei Engineering approach, which is enhanced with text analytics techniques. The research methodology entails gathering reviews from MOOCs and analyzing them using natural language processing (NLP) techniques to discover Kansei words that characterize MOOCs, notably for courses in the discipline of Data Science. The expected output of this study is a Kansei corpus for online courses in this discipline

A STEP TOWARD AN INTELLIGENT AND INTEGRATED COMPUTER-AIDED DESIGN OF APPAREL PRODUCTS

An apparel product (or “apparel”) is a human product. The design of an apparel product (or “apparel design”) should share many features of general product design and be conducted with a high degree of systematics and rationality. However, the current practice of apparel design is relatively more experience-based and ad-hoc than it should be. Besides, computer support to apparel design is quite limited in that there are several software systems available for supporting apparel design but they are isolated. Two reasons may explain this above situation: (1) absence of the ontology of apparel and apparel design, and (2) absence of a systematic and rational apparel design process. Furthermore, apparel is a specialized type of product in that all three inherent requirements (i.e., function, comfort related to ergonomics, and pleasure related to aesthetics) are equally important, especially the latter, which creates positive affects in the human wearer. In general, knowledge of how to design an apparel product for pleasure/affects is missing from the current design. The general motivation for the research conducted in this thesis is to locate and articulate this “missing knowledge” in order to advance design technology including computer-aided design for modern apparel products. The specific objectives of the research presented in this thesis are: (1) development of a model for the ontology of apparel or apparel system so that all basic concepts and their relationships related to the apparel system are captured; (2) development of a systematic design process for apparel that captures all the inherent characteristics of design, namely iteration and open-endedness; and (3) development of a computer-aided system for affective design for apparel, whereby human feeling once described can be computed with the result that an apparel product meets the wearer’s “feeling needs” (functional and ergonomic needs are assumed to be satisfied or not the concern of this thesis). There are several challenges to achieving the foregoing objectives. The first of these is the understanding of ontology for apparel and apparel design, given that there are so many types of apparel and ad-hoc apparel design processes in practice. The second challenge is the generalization and aggregation of the various ad-hoc apparel design processes that exist in practice. Third is the challenge presented by imprecise information and knowledge in the aspect of human’s affect. All three above challenges have been tackled and answered in this thesis. The first challenge is tackled with the tool of data modeling especially semantic-oriented data modeling. The second challenge is tackled with the general design theory such as general design phase theory, axiomatic design theory, and FCBPSS knowledge architecture (F: function, C: context, B: behavior, P: principle, SS: state and structure). The third challenge is tacked with the data mining technique and subjective rating technique. Several contributions are made with this thesis. First is the development of a comprehensive ontology model for apparel and apparel design that provides a basis for computer-aided design and manufacturing of apparel in the future. Second is the development of a general apparel design process model that offers a reference model for any specific apparel design process. Third is the provision of new “data mining” technology for acquiring words in human language that express affects. It should be noted that this technology is domain-independent, and thus it is applicable to any other type of product for affective design. The final contribution is the development of a method for searching apparel design parameters which describe an apparel product meeting a wearer’s required feelings described by “feeling words”. The database of words and the algorithm can be readily incorporated into commercial software for computer aided design of apparel products with the new enabler (i.e., design for affect or feeling)

Web-based virtual learning environment (EmoViLe) with emotive interface feature

This study attempts to obtain the user requirements for the EmoViLe during the data collection and to identify the user requirement for the EmoViLE. This VLE will be benefited to the student by improving the user experience and increasing their engagement when using the VLE, and lecturer in helping them to understand their student better and to come out with a better future plan for their learning assessment. The methodology in doing this study are Phase 1 which involved the initial study on the previous work, Phase 2 is the data collection on the user requirement, Phase 3 which involves the design process of the VLE, Phase 4 is the development phase of the VLE and Phase 5 is the testing on the prototype of the VLE. The expected outcome is a prototype of the VLE with the emotive interface feature that can contribute for the improvement and betterment on teaching and learning process between students and lecturer.Keywords: emotions; virtual learning environment; web-based teaching; web-based learning dashboard; Kansei engineering

Kansei engineering with online review mining methodology for robust service design

Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers’ emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE’s flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei’s validity and the proposed solution’s robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework