• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.65-77
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• 2024

This study aimed to propose effective ways to integrate CLO into educational settings by conducting a comparative analysis of pattern functions in YUKA and CLO, specifically focusing on skirt prototypes and variations. CLO, being a 3D virtual sample CAD tool, is mainly used in education to facilitate the creation of 3D virtual clothing. In order to explore the applicability of CLO's pattern functions in pattern education, CAD education experts were asked to produce two types of skirt prototypes and two skirt variations. Subsequently, in-depth interviews were conducted. In addition, the skirt pattern creation process was recorded on video and used for comparative analysis of YUKA and CLO pattern functions. The comparison revealed that CLO provides the pattern tools necessary for drafting skirt prototypes. The learning curve for acquiring the skills necessary for drafting and transforming skirt prototypes was found to be relatively shorter for CLO compared to YUKA. In addition, due to CLO's surface-based pattern drawing method, it is difficult to move or copy only specific parts of the outline, and there are some limitations in drawing right angle lines. In the pattern transformation process, CLO's preview function proved to be advantageous, and it was highly rated on user convenience due to the intuitive UI. Thus, CLO shows promise for pattern drafting education and is deemed to have high scalability as it is directly linked to 3D virtual clothing.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.57-84
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• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.8-14
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• 2020

The objective of this research is to investigate how implementation of smart factory systems(SFS) effects management accounting(MA). The results based on data collected from 108 Korea small and medium enterprises(SME) confirmed that SFS implementation caused significant MA changes. Estimated regression models revealed that the most important SFS characteristic were the analytical capabilities since it positively influenced MA changes in four dimensions: internal reporting, budgeting, application of modern accounting techniques and MA employee's job. In the segment of budgeting, the quality of implementation of specialized bedgeting software had significant and positive influence. The only negative correlation founded was the one between the uncertainty of business environment and adoption of modern accounting techniques. Results from this study provide that SME should put special focus on implementation of business analytics modules in order to achieve comprehensive benefits in MA prctices.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1115-1121
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• 2018

In this paper, we have studied a data analysis method by deep learning to predict learning achievements based on accumulated data in cyber university learning management system. By predicting learner's academic achievement, it can be used as a tool to enhance learner's learning and improve the quality of education. In order to improve the accuracy of prediction of learning achievements, the autoencoder based attendance prediction method is developed to improve the prediction performance and deep learning algorithm with ongoing evaluation metrics and predicted attendance are used to predict the final score. In order to verify the prediction results of the proposed method, the final grade was predicted by using the evaluation factor attendance data of the learning process. The experimental result showed that we can predict the learning achievements in the middle of semester.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.109-120
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• 2020

The supply chain without digitization is just a series of discrete, siloed steps taken through marketing, product development, manufacturing, and logistics, and finally into the hands of the customer. Digitization brings down those walls, and the chain becomes a completely integrated network fully transparent to all the parties involved. The ulitimate goals of digitizatized supply chain management are velocity and visibility. This network will depend on a number of key technologies including integrated planning and execution systems, supply chain analytics, autonomous logistics, smart warehousing and factory, etc, enabling companies to react to disruptions in the supply chain, and even anticipate them, by fully modeling the network, creating "what-if" scenarios, and adjusting the supply chain in real time as conditions change. This paper presents a number of studies on digitalization of supply chains and provides a discussion on issues raised in the process of technology adoption. Implications of the study findings are also provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2308-2314
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• 2006

Internet Technology allowed general commerce to have proceeded actively, and now it is sharply evolving into Mobile Commerce. Mobile Commerce came into an inheritance of the Electronic Commerce, thought the feature to be different form is various as well. Many researches that analyzed the feature of Electronic Commerce were carried out but those were processed according to its location and field only. This study extracted and analyzed the factor that considered the several positions for Mobile Commerce so that it can reflect diverse the demands from those related to Mobile Commerce. It sought the new characteristic which is different from the Electronic Commerce, and took account of the successful factor for Mobile Commerce which includes the position in a user, a developer and an operator. In addition AHP (Analytics Hierarchy Process) was used in order to evaluate the subject characteristic applied to each related to through more objective methods. The analysis results identified in this study such as the quality trust and understandability for an information duality might be the one of the chief elements of success in Mobile Commerce which applies at the present.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3384-3389
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• 2009

There are two major problems with current National GAP system. One was false in traceability record because it was written or inputted by farmers or distributers and no other measures to check the accuracy was valid. The other was incapability of tracking back and recalling the contaminated agricultural products. For solving these matters, IT convergence model which combined information technology with agricultural experience is elaborated. In IT convergence model, video analytic system classifies every activity depending on the pre-programmed farming process and create the traceability data automatically. Also real time trace system based on USN would solve the problem of tracking back. This system transmits the present location and monitors data of agricultural products from farm to table at all times.

• The Journal of Korean Association of Computer Education
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• v.23 no.1
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• pp.77-90
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• 2020

The purpose of the study is to provide educational implications for more effective Problem-based learning(PBL) by investigating students' learning types based on their online learning behaviors. A total of 1,341 students participated in the study, and they engaged in a six-week-long PBL program run by K University. For the study, participants' online activity data were collected. From the data, a total of 48 variables that represent their various online learning behaviors were extracted. Based on the variables, hierarchical cluster analysis was conducted to analyze learning types. Also, the differences in learning characteristics and achievements were investigated by considering types of learning. As a result, the learning types in online PBL were classified as 'high-level participation (cluster 1)', 'medium-level participation (cluster 2)', and 'low-level participation (cluster 3)'. In addition, the achievement level was found to be highest in 'high-level participation (cluster 1)' and lowest in 'low-level participation (cluster 3)'. Based on the results, the implications for improving online PBL were suggested.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.123-134
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• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.