• Fashion & Textile Research Journal
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• v.19 no.6
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• pp.786-794
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• 2017

The purpose of this study is to analyze and classify breast shape of women aged 11~15 using 3D body scan data. In this study, 250 women's body scans were selected from the 6th Size Korea dataset, and 30 items from each of the scan were measured using RapidForm XOR 3 program. The principal component analysis and cluster analysis were conducted using statistical program SPSS 17.0. The five principal components were identified; breast drooping and breast capacity, size from chest to under bust area, breast protrusion, breast height, and under breast angle & outer distance of breast. As the results of cluster analysis, woman's breast types were classified into four types. The breast type 1 was protrusion type (25.1%) which is considered as the breast maturity stage. The breast type 2 had the most drooped breast covering a large area (20.2%). The breast type 3 had the least prominent breast with a highest nipple point, which was considered as the early breast development stage (38.9%). The breast type 4 had the obesity of the chest and breast circumferences with the slightly prominent and the least drooped breast (15.8%). This study can provide fundamental information to develop sizing system and brassiere pattern for junior girls.

• Human Ecology Research
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• v.56 no.2
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• pp.109-122
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• 2018

Cross-border online shopping is an example of non-mobile transnational consumption behavior that has become more popular over the last decade due to the development of technology and transportation. Based on the Theory of Reasoned Action(TRA), this study proposed and tested the hypothesized model that would explain the relationships among consumption beliefs, attitudes toward cross-border online shopping, subjective norms, and purchase intention. Consumption beliefs were measured by global consumption orientation, consumer orientation, and global brand beliefs. In addition, subjective norms included two types: online and offline norms. Descriptive statistics and path analysis were employed for the analysis of the dataset of 174 participants. As a result, the hypothesized model was generally supported. Consistent with the hypothesis, global consumption orientation and global brand beliefs were positively related to positive attitudes toward cross-border online shopping but negatively associated with consumer ethnocentrism. Offline subjective norms positively predicted both the attitudes and purchase intention whereas online subjective norms only predicted purchase intention. The results reflected that TRA was applicable to the intention of cross-border online shopping in a current on-line shopping context. We also discussed the practical applications and limitations of the study.

• The Journal of Industrial Distribution & Business
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• v.12 no.8
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• pp.33-39
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• 2021

Purpose: The influence of age on time preference is not identified in the usual cross-sectional analysis. This study aims to test whether age affects time preference after controlling for the effects of individual heterogeneity including cohort effects. Research design, data and methodology: Drawing on a nationally representative panel dataset of Indonesians, we estimate the effects of age on time preference after controlling for unobserved individual heterogeneity as well as potential cohort effects. We measure time preference exploiting information on two sets of multiple price lists: one for a one-year delay, and the other for a five-year delay. Results: When we controlled for time-invariant individual characteristics, including birth cohort effects in a fixed effects model, the older men and women were more patient in a linear fashion, particularly when the delay was longer. To highlight the importance of controlling for individual fixed effects, we repeated the specification without controlling for individual fixed effects in OLS or censored maximum likelihood regression; we found no relation between age and impatience in men or women and for a one or five-year delay. Conclusions: The older men and women are more patient, and time preferences are correlated with unobserved individual heterogeneity.

• International Journal of Contents
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• v.18 no.3
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• pp.11-20
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• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.1
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• pp.171-190
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• 2023

This study aims to develop algorithms for automatic extraction landmarks from the lower body of women aged 20-54 using the Grasshopper programming language, based on 3D scan data in the 8th SizeKorea dataset. First, 11 landmarks were defined using the morphological features of 3D body surfaces and clothing applications, from which automatic landmark extraction algorithms were developed. To verify the accuracy of the algorithm, this study developed an additional algorithm that could automatically measure 16 items, and algorithm-derived measurements and SizeKorea measurements were compared using paired t-test analysis. The statistical differences between the scan-derived measurements and the SizeKorea measurements were compared, with an allowable tolerance of ISO 20685-1:2018. This study found that the algorithm successfully identified most items except for the crotch point and gluteal fold point. In the case of landmarks with significant differences, the algorithms were modified. This study was significant because scan editing, landmark search, and measurement extraction were successfully performed in one interface, and the developed algorithm has a high efficiency and strong adaptability.

• ETRI Journal
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• v.46 no.2
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• pp.277-289
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• 2024

In this paper, we describe a neural network-based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi-item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth-related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate-based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one-to-many mapping problem of multi-item recommendations. A fashion-domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi-item recommendation results, which is different from traditional accuracy evaluation.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.315-334
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• 2013

This study performs opinion mining of newspaper articles, based on topics extracted by topic modeling. We analyze the attitudes of the news media towards a major issue of 'presidential election', assuming that newspaper partisanship is a kind of opinion. We first extract topics from a large collection of newspaper texts, and examine how the topics are distributed over the entire dataset. The structure and content of each topic are then investigated by means of network analysis. Finally we track down the chronological distribution of the topics in each of the newspapers through time serial analysis. The result reveals that both the liberal newspapers and the conservative newspapers exhibit their own tendency to report in line with their adopted ideology. This confirms that we can count on opinion mining technique based on topics in order to analyze opinion in a reliable fashion.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.469-486
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• 2018

We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images in the context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploits only the lowest kind of local information through the global histogram. However, such global Histograms reflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which uses local histograms of the LBP, was one of few tentative to catch higher level textural information. We believe that important local and useful information in between the two levels is just ignored by the two schemas. The newly developed method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as much local information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present in grayscale images extracted by LBP through a complex structure. The used framework is comprised of a multitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. The framework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic forms allow targeting texture directionality, which is a very useful property in texture characterization. In addition, the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared to published works including the very effective DLEP. Results show significant higher or comparable performance of GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.

• International Journal of Human Ecology
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• v.12 no.1
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• pp.87-99
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• 2011

Purpose- The purpose of this study was to compare the fit of two prototype liquid cooled vests using a 3D body scanner and accompanying software. The objectives of this study were to obtain quantitative measurements of ease values, and to use these data to evaluate the fit of two cooling vests in active positions and to develop methodological protocol to resolve alignment issues between the scans using software designed for the alignment of 3D objects. Design/methodology/approach- Garment treatments and body positions were two independent variables with three levels each. Quantitative dataset were dependent variables, and were manipulated in 3x3 factorial designs with repeated measures. Scan images from eight subjects were used and ease values were obtained to compare the fit. Two different types of analyses were conducted in order to compare the fit using t-test; those were radial mean distance value analysis and radial distance distribution rate analysis. Findings- Overall prototype II achieved a closer fit than prototype I with both analyses. These were consistent results with findings from a previous study that used a different approach for evaluation. Research limitations/implications- The main findings can be used as practical feedback for prototype modification/selection in the design process, making use of 3D body scanner as an evaluation tool. Originality/value- Methodological protocols that were devised to eliminate potential sources of errors can contribute to application of data from 3D body scanners.