• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.731-736
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• 2023

Recently, the medical field has been applying mandatory Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) systems that computerize and manage medical records, and distributing them throughout the entire medical industry to utilize patients' past medical records for additional medical procedures. However, the conversations between medical professionals and patients that occur during general medical consultations and counseling sessions are not separately recorded or stored, so additional important patient information cannot be efficiently utilized. Therefore, we propose an electronic medical record system that uses speech recognition and natural language processing deep learning to store conversations between medical professionals and patients in text form, automatically extracts and summarizes important medical consultation information, and generates electronic medical records. The system acquires text information through the recognition process of medical professionals and patients' medical consultation content. The acquired text is then divided into multiple sentences, and the importance of multiple keywords included in the generated sentences is calculated. Based on the calculated importance, the system ranks multiple sentences and summarizes them to create the final electronic medical record data. The proposed system's performance is verified to be excellent through quantitative analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of The Geomorphological Association of Korea
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• v.18 no.4
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• pp.59-78
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• 2011

In this study, the tidal flat research history of South Korea was organized by type and period. South Korea's tidal flat research history was largely divided into four fields: sediment research of tidal flat, research using satellite imagery, research on the Quaternary environment change and tidal flats, and ecological research on tidal flats. The comprehensive review of the South Korean tidal flat research history showed that tidal flats had been researched on since the period of Japanese colonial period, but most of the past studies were related to fisheries. Then, in the 1960s, the studies started to focus on the reclaimed land created through reclamation projects. The research on tidal flats from a geomorphological perspective fundamentally started in the 1970s, and the importance of tidal flats became more widely known in the 1980s. Most of the studies then were about the sedimentary environment and the form of landform, the ecosystem, and morphological changes. Since the 2000s, research has been carried out on satellite imagery data together with field survey, to continuously monitor the changes in the sedimentary facies of tidal flats, and in the sedimentary environment. There have been many academic studies on the geographic field of tidal flats, but the research performance on tidal flats in terms of geomorphology is still a blue ocean that has been touched by only a few. Therefore, it is hoped that various studies on Korean tidal flats will be conducted by geomorphologists in the future, and that such area will be established as an important field of study in geomorphology.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.207-228
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• 2023

After COVID-19, organizations have widely adopted platforms such as zoom or developed their proprietary online real-time systems for remote work, with recent forays into incorporating the metaverse for meetings and publicity. While ongoing studies investigate the impact of avatar customization, expansive virtual environments, and past virtual experiences on participant satisfaction within virtual reality or metaverse settings, the utilization of the metaverse as a dedicated workspace is still an evolving area. There exists a notable gap in research concerning the factors influencing the performance of the metaverse as a workspace, particularly in non-immersive work-type metaverses. Unlike studies focusing on immersive virtual reality or metaverses emphasizing immersion and presence, the majority of contemporary work-oriented metaverses tend to be non-immersive. As such, understanding the factors that contribute to the success of these existing non-immersive metaverses becomes crucial. Hence, this paper aims to empirically analyze the factors impacting personal outcomes in the non-immersive metaverse workspace and derive implications from the results. To achieve this, the study adopts the Embodied Social Presence (ESP) model as a theoretical foundation, modifying and proposing a research model tailored to the non-immersive metaverse workspace. The findings validate that the impact of presence on task engagement and task involvement exhibits a moderating effect based on the metaverse platform used. Following interviews with participants engaged in non-immersive metaverse workplaces (specifically Gather Town and Ifland), a survey was conducted to gather comprehensive insights.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.111-120
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• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• Journal of Powder Materials
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• v.31 no.2
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• pp.169-179
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• 2024

Colloidal quantum dot (QDs) have emerged as a crucial building block for LEDs due to their size-tunable emission wavelength, narrow spectral line width, and high quantum efficiency. Tremendous efforts have been dedicated to improving the performance of quantum dot light-emitting diodes (QLEDs) in the past decade, primarily focusing on optimization of device architectures and synthetic procedures for high quality QDs. However, despite these efforts, the commercialization of QLEDs has yet to be realized due to the absence of suitable large-scale patterning technologies for high-resolution devices., This review will focus on the development trends associated with transfer printing, photolithography, and inkjet printing, and aims to provide a brief overview of the fabricated QLED devices. The advancement of various quantum dot patterning methods will lead to the development of not only QLED devices but also solar cells, quantum communication, and quantum computers.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.415-436
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• 2023

In this study, we aimed to examine the level of implementation of the second comprehensive plan for promoting academic libraries (2019-2023) by analyzing key statistics of academic libraries and gathering perceptions from library staff. We analyzed the changes in major statistical indicators of libraries over the past five years. Additionally, we surveyed library staff to understand their overall perceptions of the plan and their attitudes towards the 17 sub-tasks outlined in it. The analysis of 369 survey responses revealed several key findings. Firstly, most respondents comprehended the plan well and frequently utilized it for developing their libraries' development and implementation plans. Secondly, the IPA results indicated that regardless of the type of university, there should be a continuous focus on facility improvement, teaching-learning support, and expanding access to academic resources. Efforts to develop library policies and strengthen human and financial resources were identified as crucial. Thirdly, four-year universities particularly emphasized the importance of expanding access to international academic resources compared to junior colleges. Conversely, junior colleges perceived foundational skill-building programs and inclusive services as more significant than four-year universities. The application of the IPA diagonal model revealed that the performance levels of all sub-tasks were lower than their perceived importance levels, suggesting the need for strategies to enhance effectiveness in future comprehensive plan formulation.

• The Mathematical Education
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• v.63 no.1
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• pp.35-61
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• 2024

The purpose of this study is to examine changes in Korean middle school students' affective attitudes toward mathematics over the past 5 cycles of TIMSS. To this end, we first analyzed the changes in students' affective attitudes towards mathematics in five major countries, and then analyzed the changes in Korean students' affective attitudes toward mathematics by item. As a result of the study, there were positive changes in Korean students' interest, confidence, and value perception of mathematics during the recent 5 cycle of TIMSS. Korean male students' affective attitude toward mathematics is higher than that of female students, and the gender gap has been increasing recently. There was a large difference in the affective attitudes toward mathematics among Korean students, depending on their achievement level, and in particular, the affective attitudes toward mathematics of students at the lower achievement level remained significantly low. Item-level analysis revealed a decrease in Korean students' awareness of the necessity of mathematics in daily life. Based on these results, we discussed the implications for cultivating Korean students' affective attitudes. It is hoped that the results of this study will be meaningfully used as basic data for examining the performance of mathematics education in Korea and contribute to developing measures to foster students' positive attitudes toward mathematics.