• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.331-343
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• 2002

This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1440-1453
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• 2011

Proper consumer segmentation is receiving more attention from industry professionals as markets become more diverse and consumer-centered. Researchers have recognized the limitations of the traditional cluster analysis technique and this research study analyzes market segmentation using Mixture-model or latent-class segmentation. This study used a questionnaire to determine the characteristics of clothing shoppers using a new technique that proved its superiority over traditional techniques. Questions included items measuring fashion shopping behavior, store choice criteria, apparel consumption styles, price perception by product type, and demographic characteristics. Data were collected from 1074 males and females in their 20s and 30s through an online survey. SPSS 16.0 and Latent GOLD 4.0 were used to analyze the data. The ideal typology of clothing shoppers using the Mixture-model were: 'brand loyalty orientated group', 'group of conservative late 30s', 'group of pleasure-emotion early 20s', 'value oriented consumer product with high-income group', 'group of eco/symbol oriented consumer', and 'group of utility/goal oriented male consumer'. This study showed differences in fashion product purchasing behavior by conducting market segmentation for clothing shoppers using the Mixture-model.

• Journal of Distribution Science
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• v.22 no.10
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• pp.43-54
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• 2024

Purpose: Market competition in the fashion industry is intensifying, pushing brands to strive for consumer preference and market leadership. Mobile fashion applications have emerged as key distribution channels, with live streaming being a common feature for product distribution and consumer loyalty. Therefore, this study will analyze the loyalty model in live streaming on mobile fashion applications by integrating the quality loyalty model, parasocial relationships, and uses and gratifications (U&G) theory. Research design, data and methodology: Data were collected from a survey of 427 respondents who are customers of a fashion product that had been purchased through live streaming on a mobile fashion application and processed using the PLS-SEM method Results: The results of the study show that the live streamer and product quality significantly influence satisfaction and loyalty. Conversely, AR content and live streaming content do not directly influence loyalty but have an indirect effect through satisfaction Conclusions: this study is the first to model loyalty in mobile fashion applications by integrating the quality loyalty model, parasocial relationship, and U&G theory. Practically, fashion companies are advised to conduct live streaming by considering aspects of content, live streamer, and product quality to enhance satisfaction and loyalty.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.82-92
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• 1998

An international standard for the product model data, STEP, and a standard for the distributed object technology, CORBA, will play a very important role in the future manufacturing environment. These two technologies provide background for the sharing of product data and the integration of applications on the network. This paper describes a prototype CAD/CAE environment that is integrated on the network by STEP and CORBA. Several application servers and client software were developed to verify the proposed concept. The present CAD/CAE environments are composed of several individual software components which are not tightly integrated. They also do not utilize the rapidly expanding network and object technologies for the collaboration in the product design process. In the design process in a large organization, sharing of application resources, design data and analysis data through the network will greatly enhance the productivity. The integration between applications can be supported by two key technologies, CORBA(Common Object Request Broker Architecture) and STEP(Standard for the Exchange of Product Model Bata). The CORBA provides interoperability between applications on different machines in heterogeneous distributed environments and seamlessly interconnects distributed object systems. Moreover, if all the data in the CAD/CAE environment are based on the STEP, then we can exclude all the data conversion problems between the application systems.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.1-6
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• 2014

Many companies have tried to adopt Software Product Line Engineering for improving the quality and productivity of information systems and software product. There are several models defined in software product line methodology and each model has different abstraction level. Therefor it is important to maintain the traceability and consistency between models. In this paper, consistency checking rules are suggested by traceability matrix of work products.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Journal of Ship and Ocean Technology
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• v.9 no.3
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• pp.23-32
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• 2005

Ship STEP(Standard for the Exchange of Product Model Data) which is composed of AP 215 (Ship Arrangement), AP 216(Ship Hull Form), AP 218 (Ship Structure), has been developed more than last 10 years and it is now at the stage just before IS(International Standard). It is expected that ship STEP would be used for the seamless data exchange among various CAD/CAM/CAE systems of shipbuilding process. In this paper the huge and complicated data structure of ship STEP is briefly reviewed at the level of ARM(Application Reference Model) and some abstract test cases which will be included as part of the standards are introduced. Basically ship STEP has common data model to be used without losing compatibility among those three different ship AP's, and it is defined as the modeling framework. Typical cases of data exchange during shipbuilding process, such as hull form data exchange between design office and model basin, midship structure data between shipbuilding yard and classification society are reviewed and STEP physical data are generated using commercial geometric modeling kernel. Test cases of ship arrangement at initial design stage and hydrodynamic data of crude oil carrier are also included.

• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.274-283
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• 2005

Product data quality (PDQ) is a real and significant issue in today's manufacturing environment. In the Korean automotive industry, much of the design work takes place with the support of software tools, such as CAD systems. Although many designers frequently encounter quality problems regarding product data, there is no investigation into the present state of CAD usage and PDQ activities before. The Korean automotive industry is responsible for about 11.1 percent of the total value of manufactured goods in Korea and 7.9 percent of employment in manufacturing. A study performed by the Research Triangle Institute showed that imperfect interoperability imposes at least $\$1$ billion per year on the members of the U.S. automotive supply chain. The trends toward concurrent engineering and out-sourcing have elevated the importance of high-quality product data and efficient product data exchange. This paper shows the results from a survey of PDQ conducted on seven 1st tiers among members of the Korean automotive supply chain.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of the Korean Society of Food Culture
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• v.21 no.4
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• pp.414-419
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• 2006

The purpose of this study was to measure the factor influencing tourist preferences for leaf mustard iimchi. Among 250 questionnaires, 230 questionnaires were utilized for the analysis. Frequencies, conjoint model, max. utility model, BTL model, Logit model, K-means cluster analysis, and one-way ANOVA analysis were used for this study. The findings from this study were as follows. First, the Pearson's R and Kendall's tau statistics showed that the model fitted the data well. Second, it was found that total respondents and three clusters regarded taste and price as the very important factor. Third, it was found that the first cluster most preferred product with light red color, plain package, and mild taste sold at a cheap price in factory. The second cluster most preferred product with light red color, plain package, and moderately pungent taste sold at a expensive price in factory. The third cluster most preferred product with dark red color, shaped package, and highly pungent taste sold at a cheap price in factory. Fourth, it was found that the first cluster most preferred simulation product with light red color, shaped package, and mild taste sold at a cheap price in factory. The second cluster most preferred simulation product with light red color, shaped package, and moderately pungent taste sold at a cheap price in factory. The third clutter most preferred simulation product with dark red color, shaped package, and highly pungent taste sold at a cheap price in factory.