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

Design aesthetics recommender system based on customer profile and wanted affect

Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like the product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect

Design aesthetics recommender system based on customer profile and wanted affect

Product recommendation systems have been instrumental in online commerce since the early days. Their development is expanded further with the help of big data and advanced deep learning methods, where consumer profiling is central. The interest of the consumer can now be predicted based on the personal past choices and the choices of similar consumers. However, what is currently defined as a choice is based on quantifiable data, like product features, cost, and type. This paper investigates the possibility of profiling customers based on the preferred product design and wanted affects. We considered the case of vase design, where we study individual Kansei of each design. The personal aspects of the consumer considered in this study were decided based on our literature review conclusions on the consumer response to product design. We build a representative consumer model that constitutes the recommendation system's core using deep learning. It asks the new consumers to provide what affect they are looking for, through Kansei adjectives, and recommend; as a result, the aesthetic design that will most likely cause that affect.Comment: 11 pages, 10 figures, peer-reviewed at the KEER 2022 conferenc

Infrared Spectral Energy Distribution of Galaxies in the AKARI All Sky Survey: Correlations with Galaxy Properties, and Their Physical Origin

We have studied the properties of more than 1600 low-redshift galaxies by utilizing high-quality infrared flux measurements of the AKARI All-Sky Survey and physical quantities based on optical and 21-cm observations. Our goal is to understand the physics determining the infrared spectral energy distribution (SED). The ratio of the total infrared luminosity L_TIR, to the star-formation rate (SFR) is tightly correlated by a power-law to specific SFR (SSFR), and L_TIR is a good SFR indicator only for galaxies with the largest SSFR. We discovered a tight linear correlation for normal galaxies between the radiation field strength of dust heating, estimated by infrared SED fits (U_h), and that of galactic-scale infrared emission (U_TIR ~ L_TIR/R^2), where R is the optical size of a galaxy. The dispersion of U_h along this relation is 0.3 dex, corresponding to 13% dispersion in the dust temperature. This scaling and the U_h/U_TIR ratio can be explained physically by a thin layer of heating sources embedded in a thicker, optically-thick dust screen. The data also indicate that the heated fraction of the total dust mass is anti-correlated to the dust column density, supporting this interpretation. In the large U_TIR limit, the data of circumnuclear starbursts indicate the existence of an upper limit on U_h, corresponding to the maximum SFR per gas mass of ~ 10 Gyr^{-1}. We find that the number of galaxies sharply drops when they become optically thin against dust-heating radiation, suggesting that a feedback process to galaxy formation (likely by the photoelectric heating) is working when dust-heating radiation is not self-shielded on a galactic scale. Implications are discussed for the M_HI-size relation, the Kennicutt-Schmidt relation, and galaxy formation in the cosmological context.Comment: 29 pages including 28 figures. matches the published version (PASJ 2011 Dec. 25 issue). The E-open option was chosen for this article, i.e., the official version available from PASJ site (http://pasj.asj.or.jp/v63/n6/630613/630613-frame.html) without restrictio

The BRCT domain from the large subunit of human Replication Factor C

The work described in this thesis deals with characterization of DNA binding by the BRCT domain of the large subunit of RFC. Replication Factor C (RFC) is a five protein complex involved in initiating and regulating new DNA synthesis. The first half of the thesis describes region of the RFC and structural determinants of DNA required for productive protein-DNA interaction. The second half describes three-dimensional structure determination of the protein-DNA complex, which consists of the BRCT region of the RFC and doubled stranded DNA. The resulting structure based on the data from NMR and mutagenesis reveals structural conservations of few amino acids among the members of BRCT domain superfamily, which are known to bind either phosphorylated peptide or DNA. The work may help us to identify other potential DNA binding BRCT domains.UBL - phd migration 201