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

Generation of product design using GAN based on customer's kansei evaluation

In recent years, deep learning has attracted much attention and various techniques have been proposed. GAN (Generative adversarial networks) is one such method. GAN uses images as the training set and learns to generate new images that are indistinguishable from the training set. In this study, A GAN-based design method that generates new products from the images of the customer's favorite products is proposed. The product images that customers evaluated as preferable are used as the training set of GAN. If the GAN fulfills its capabilities properly, the images generated from a customer's favorite product are more likely to be preferred by the customer. In the case study, the proposed method was applied to chair design. The generated chair images were first evaluated in terms of image quality, and then evaluated by subjects

Comparison of elegance of Japanese and European jackets

Purpose - The purpose of this paper is to investigate factors affecting the impression of elegance of a jacket's appearance. Design/methodology/approach - A questionnaire survey on the impression of jacket appearance was conducted using images of jackets of Japanese and European brands. A paired comparison and sensory test were carried out for four Japanese and European jackets. To explain different jacket appearances, the jacket patterns and silhouettes were investigated from an engineering point of view. Findings - Most Japanese subjects responded that European jackets in images were more elegant and characteristic of European style. In a comparison of jacket silhouettes, Jacket 1 (European brand) was evaluated as the most elegant. The waist and bust parts of Jacket 1 had three-dimensional shape whereas the Japanese jackets had planar and rectilinear shapes. This was due to the difference in the waist darts and curved lines in the patterns. Jacket appearance in terms of elegance is thus mainly affected by the waist and bust shapes, which are affected by darts and lines in the patterns. Originality/value - This is pioneering research on the elegance of garment appearance from an engineering point of view, using actual clothing. The comparison results for commercial jackets will be valuable to the apparel industry.ArticleINTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY. 27(4):506-522 (2015)journal articl

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

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)

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

Pengaruh Fitur Warna pada Klasifikasi Impresi Citra Batik Indonesia Menggunakan Probabilistic Neural Network

Penelitian tentang batik Indonesia dan impresi, masing-masing secara terpisah telah dilakukan. Penelitian tentang klasifikasi citra batik juga telah banyak dilakukan. Impresi yang merupakan perasaan psikologis seseorang terhadap suatu produk dapat menjadi label kelas dalam pengelompokan citra batik. Penelitian ini memiliki tujuan mengetahui seberapa besar pengaruh fitur warna yang diekstraksi dari sebuah citra batik dan sistem klasifikasinya berdasarkan impresi. Praproses klasifikasi dengan melakukan ekstraksi fitur tekstur, dan bentuk dari citra batik yang selanjutnya digunakan sebagai input sistem klasifikasi. Untuk memperoleh fitur tekstur digunakan metode transformasi fourier hingga didapat nilai amplitudo dan sudutnya dalam domain frekuensi. Selanjutnya menggunakan filter Gabor untuk mendapatkan fitur bentuk. Selanjutnya fitur tersebut akan disederhanakan dimensinya menggunakan Singular Value Decomposition (SVD). Pada proses klasifikasi citra, digunakan metode Probabilistic Neural Network (PNN) dengan input berupa fitur yang telah disederhanakan dimensinya dan output yang dihubungkan ke kelas impresi. Dari serangkaian pengujian, dihasilkan klasifikasi terhadap citra batik testing yang hasilnya hampir sama dengan penelitian sebelumnya, bahkan dapat dikatakan stabil pada proses uji tanimoto distance-nya. Pada proses reduksi dengan jumlah informasi fitur yang ditentukan sebesar 90% dari nilai awal, diperoleh total vektor fitur sebesar 11 dimensi, lebih kecil dari penelitian sebelumnya sebesar 14 dimensi (yang termasuk didalamnya terdapat fitur warna) dengan hasil nilai akurasi Tanimoto distance sebesar 0,27. Hal berbeda 0,01 dari penelitian sebelumnya sebesar 0,26 untuk 81 citra training dan testing. Dengan demikian dapat disimpulkan bahwa vektor fitur warna yang diusulkan pada penelitian sebelumnya tidak mempunyai pengaruh yang signifikan pada proses klasifikasi

Multimodal sequential fashion attribute prediction

We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental dataset