• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.1-9
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• 2015

As the use of software is more wider in the safety-critical nuclear fields, so study to improve safety and quality of the software has been actively carried out for more than the past decade. In the nuclear power plant, nuclear man-machine interface systems (MMIS) performs the function of the brain and neural networks of human and consists of fully digitalized equipments. Therefore, errors in the software for nuclear MMIS may occur an abnormal operation of nuclear power plant, can result in economic loss due to the consequential trip of the nuclear power plant. Verification and validation (V&V) is a software-engineering discipline that helps to build quality into software, and the nuclear industry has been defined by laws and regulations to implement and adhere to a through verification and validation activities along the software lifecycle. V&V is a collection of analysis and testing activities across the full lifecycle and complements the efforts of other quality-engineering functions. This study propose a methodology based on V&V activities and related tool-chain to improve quality for software in the nuclear power plant. The optimized methodology consists of a document evaluation, requirement traceability, source code review, and software testing. The proposed methodology has been applied and approved to the real MMIS project for Shin-Hanul units 1&2.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.99-105
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• 2017

With the advancement of Information and Communication Technology (ICT), the wearable-device industry is growing at a rapid pace in line with the hyper-connected society of people-to-things and things-to-things network connections. International Data Corporation (IDC), a market research institute, estimates that the wearable-device industry will grow rapidly by 2020, despite not yet attracting a popular response. This study investigates the trend of the wearable-device industry and draws implications for product and service development through user experience analysis. The subject of analysis was smart bands and the data generated from product review were collected and analyzed. As a result, user experience could extract utility, usability, aesthetics, value, and reliability, and polarity was analysed and visualized in the extracted data. The study results reveal that current wearable-devices are expensive, that users cannot receive useful information from the long-term viewpoint since the analysis of accumulated data remains focused on functional development, and that they are recognized as a fashion item or an accessory. These factors hinder the continuous usage, motivation and market spread of the product. In a future follow-up study, we will conduct a comparative study on bands and watches by analyzing the second smart watch.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.470-477
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• 2016

This study analyzed the research trends concerning the use of smart devices by young children, such as smart phones, tablet PCs, interactive whiteboards and teacher assistant robots, which has begun to be mentioned relatively recently, and attempted to analyze the characteristics of the research trends and provide guidelines for the direction of future research. A search of articles related to the use of electronic media by young children using an Online Search DB revealed a total of 192 research papers, which were analyzed according to the subject of research, teaching-learning method, area of development and area of activity. It was found that the teaching-learning method, teacher education and professionalism were highly prevalent in the subject of research; the education method integrating play activity with literature activity were highly prevalent in the teaching-learning method; language development and social development were highly prevalent in the area of development; and language activity and social activity were highly prevalent in the area of activity.

• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.8-16
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• 2023

The importance of next-generation packaging technologies is being emphasized as a solution as the miniaturization of devices reaches its limits. To address the bottleneck issue, there is an increasing need for 2.5D and 3D interconnect pitches. This aims to minimize signal delays while meeting requirements such as small size, low power consumption, and a high number of I/Os. Hybrid bonding technology is gaining attention as an alternative to conventional solder bumps due to their limitations such as miniaturization constraints and reliability issues in high-temperature processes. Recently, there has been active research conducted on SiCN to address and enhance the limitations of the Cu/SiO₂ structure. This paper introduces the advantages of Cu/SiCN over the Cu/SiO₂ structure, taking into account various deposition conditions including precursor, deposition temperature, and substrate temperature. Additionally, it provides insights into the core mechanisms of SiCN, such as the role of Dangling bonds and OH groups, and the effects of plasma surface treatment, which explain the differences from SiO₂. Through this discussion, we aim to ultimately present the achievable advantages of applying the Cu/SiCN hybrid bonding structure.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.263-276
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• 2024

Since the first introduction of one-dimensional transient storage modeling in the field of solute transport analysis in rivers, its application has notably expanded for various purposes, including for hydrology and geobiology over the past few decades. Despite strides in refining transient storage models, there remain unresolved challenges in simplifying complex river transport dynamics into concise formulas and a limited set of parameters. This review paper is dedicated to cataloging and assessing existing transient storage models, outlining the difficulties associated with model structures, parameters, and data, and suggesting directions for future research. We seek to enhance understanding of transient storage by highlighting the importance of continuously evaluating residence time distribution modeling, integrating hydrodynamic models, and using data with minimal assumptions. This paper would contribute to advance our comprehension of the transient storage process, offering insights into sophisticated modeling techniques, pinpointing uncertainty in parameters, and suggesting the necessary avenues for further study.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.