• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.48-56
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• 2024

The application of artificial intelligence (AI) is becoming increasingly important to coping with air pollution. AI is effective in coping with it in various ways including air pollution forecasting, monitoring, and control, which is attracting a lot of attention. This attention has created high need for analyzing studies on AI and air pollution. To contribute for satisfying it, this study performed bibliometric analyses on the studies on AI and air pollution from 2012 to 2022 using the Web of Science database. This study analyzed them in various aspects such as the trend in the number of articles, the trend in the number of citations, the top 10 countries of origin, the top 10 research organizations, the top 10 research funding agencies, the top 10 journals, the top 10 articles in terms of total citations, and the distribution by languages. This study not only reports the bibliometric analysis results but also reveals the eight distinct features in the research steam in studies on AI and air pollution, identified from the bibliometric analysis results. They are expected to make a useful contribution for understanding the research stream in AI and air pollution.

• 한국컴퓨터정보학회논문지
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• 제25권12호
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• pp.35-44
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• 2020

본 연구에서는 주가 결정 방법이 주가 경향 예측에 미치는 영향을 확인하기 위한 분석을 수행한다. 주식시장에서 성공적인 투자를 위해서는 주가의 상승과 하락을 정확하게 예측하는 것이 큰 도움이 되므로 주가 경향 예측에 관해 많은 연구가 진행되고 있다. 예를 들어 근래에는 SNS나 뉴스의 내용을 텍스트 마이닝을 이용하여 분석하고, 이를 이용한 주가 등락의 예측 방법이 제안되었으며 다양한 기계학습 기법들이 활용되고 있다. 그러나 주가의 경향을 '상승' 또는 '하락'으로 결정하는 방법은 제대로 분석된 적 없으며 일반적으로 쓰던 방법을 답습하고 있다. 이에 본 논문에서는 주가 경향 결정 방법을 이동평균을 이용해 일반화하고 주가 경향 결정 방법이 예측 정확도에 미치는 영향을 분석한다. 분석 결과, 다음 날의 주가 경향을 예측하는 경우, 주가 경향 결정방법에 따라 예측 정확도가 47%까지 차이가 남을 발견하였다. 또한 경향 결정에 사용되는 기준값 윈도우의 크기와 예측의 정확도는 비례 관계이며, 대상값 윈도우의 크기와 정확도는 반비례 관례임을 알 수 있었다.

• 한국측량학회지
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• 제39권6호
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• pp.437-456
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• 2021

우리나라 원격탐사 분야에서는 2017년을 기점으로 딥러닝의 뛰어난 성능을 바탕으로 연구 성과를 나타내기 시작하여, 현재는 영상 전처리부터 활용까지 원격탐사의 거의 모든 분야에서 딥러닝을 적용하는 연구가 수행되고 있다. 원격탐사 분야에 적용된 딥러닝의 연구 동향 분석을 수행하기 위해, 2021년 10월까지 출판된 원격탐사 분야에 딥러닝이 적용된 국내 논문들을 수집하였다. 수집된 60여 편의 논문들을 바탕으로 딥러닝 네트워크 목적, 원격탐사 활용 분야, 원격탐사 영상 취득 탑재체별로 나누어 연구 동향 분석을 수행하였다. 또한, 논문에서 훈련자료 구축에 효과적으로 이용되었던 오픈소스데이터들을 정리하였다. 본 논문을 통해 현시점에서 딥러닝이 원격탐사 분야에 자리잡기 위해 해결해야 할 문제점들을 제시하면서, 향후 연구자들의 원격탐사 분야에 딥러닝 기술을 접목하기 위한 연구 방향을 설정하는 데 도움을 제공하고자 한다.

• 전자통신동향분석
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• 제34권1호
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.193-197
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• 2021

COVID-19로 인한 팬데믹 상황은 우리 사회의 사회적, 경제적, 심리적, 그리고 다른 모든 면에서 크고 작은 영향을 미치고 있다. 코로나 19 전파를 막기 위해 우리나라를 포함한 다양한 국가에서는 장기간의 가정 돌봄 및 원격 학습 체제에 들어갔다. 하지만 많은 나라에서 진행된 원격 학습 실험은 대면 교육을 원격 학습으로 대체할 수 있는지에 대한 문제가 제기되었다. 이에 본 연구에서는 원격 수업에 대한 언론 보도 내용을 바탕으로 여론, 사회 인식, 현장의 동향을 분석하였다. 이를 위해 본 연구에서는 원격 수업과 관련된 11개의 신문사 및 4개의 방송사의 기사, 2,600개를 수집하였다. 이 데이터를 바탕으로 키워드 트렌드 분석, 토픽모델링 분석, 감정 분석을 실시하였다.

• 공학교육연구
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• 제24권3호
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• pp.21-31
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• 2021

Flipped learning, which involves listening to lectures at home and performing dynamic group-based problem-solving activities in the classroom, is recently evaluated as a learner-centered teaching method, and interest and applications in engineering education are increasing. Therefore, this study aims to provide practical guidelines for successful application through empirical research analysis on the use of flipped learning in domestic engineering education. Through the selection criteria and keyword search, a systematic review of 36 articles was conducted. As a result of the analysis, flipped learning research in engineering education has increased sharply since 2016, focusing on academic journals and reporting its application cases and effects. Most of the research supported that flipped learning was effective not only for learners' learning activities(e.g., academic achievement, satisfaction, engagement, learning-flow, interaction), but also for individualized learning and securing sufficient practice time. It was often used in major classes with 15 to less than 50 students, especially in computer-related major courses. Most of them consisted of watching lecture videos, active learning activities, and lectures by instructors, and showed differences in management strategies for each class type. Based on the analysis results, suggestions for effective flipped learning management in future engineering education were presented.

• International journal of advanced smart convergence
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• 제8권1호
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• pp.166-175
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• 2019

Grit, a concept conceived in 2007 by Duckworth and others in the United States, is based on positive psychology that focuses on growth and development through individual strengths. Recently, "Grit", which means patience and enthusiasm for long-term goals, has emerged as a key factor of personality strength. In Korea, Joo-hwan Kim (2013) was the first to conceptualize and study the subject of Grit. However, there have been no overview studies that systematically summarize the overall trends and flow in the research of Grit so far. There have been 147 research papers on Grit published so far in Korea. The purpose of this study was to conduct trend analysis on the subject of Grit by analyzing forty-three (43) master's and doctoral dissertations, thus presenting the direction of future research on Grit through careful analysis. In the studies conducted, it was found that Grit is a very significant variable linked to self-efficacy. It is also a subjective belief that can help an individual achieve his/her educational goals, and go through failure resynchronization. In addition, Grit is very significant as a practical core indicator of how fusion talent is fostered for the fourth industrial revolution. Therefore, there is a need for more in-depth research from the viewpoints of workplace learning, experiential learning, or informal learning, as well as research into Grit characteristics.

• 패션비즈니스
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• 제27권5호
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• pp.121-134
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• 2023

With the development of information and communication technology, interest in new educational approaches that can enhance the learning performance of learners with improved information literacy skills is increasing, and universities are actively promoting educational innovation to foster the talents required by society. In the field of fashion studies education, which is closely related to the fashion industry, there is a strong need to develop field-linked educational programs that reflect the trends in the industry and changes in the educational system. The purpose of this study was to introduce industry-coupled problem-based learning (IC-PBL) to the course "Understanding Fashion Consumption Trends" for non-fashion majors to reflect the current needs and strengthen the educational effectiveness of the learners through a survey. A seven-step curriculum (introduction to the class, practitioner's problem, learner's problem analysis, organizing concepts related to variables, information collection and scenario writing, presentation and scenario proposal, and evaluation) not only enhanced learners' understanding of fashion consumption trends and the fashion industry but also greatly amplified learners' satisfaction with the class. The results of the survey showed that the seven-step curriculum was effective in increasing learners' self-directed learning ability, problem-solving ability, and confidence in learning. Self-directed learning ability was stronger than other factors, consistent with the core principle of problem-based learning to empower learners to take the initiative and promote self-directed learning. Each factor analyzed was positively correlated.

• Educational Technology International
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• 제20권2호
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• pp.169-194
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• 2019

This study aims to compare and analyze the trends of research on mobile learning conducted in Korea and China to suggest future directions and multifaceted subject areas in its study field. To achieve this purpose, 620 Chinese papers from CNKI (CSSCI and CSCD) database and 205 Korean papers from RISS database (KCI and KCI candidate) published between 2009 and 2018 were selected to be analyzed through applying the frequency analysis and visualized semantic network analysis. The criteria for analysis used in this study are four types: publication years, research subjects, research methods, and keywords. The results of this study are as follows. Firstly, in relation to the year of publication, Korea entered the peak of mobile learning research in 2016 (33 papers), and China reached high publications (94 papers) in 2017. Secondly, with regard to the research subjects, the most frequently studied subjects in Korea and China were targeted to college students, followed by general adult groups. Thirdly, in terms of research methods, quantitative research accounted for a high proportion in Korea, but in China, literature research showed a high frequency. Fourthly, the high frequency keywords appearing in mobile learning research of the two countries were mainly reflected in language learning. Based on the findings, several directions of future research for both countries were suggested.