• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.383-383
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• 2023

오늘날 수문학 분야에서는 유역에 대한 강우-유출 시뮬레이션을 머신 러닝(ML: Machine Learning)을 활용하여 다양한 연구를 실행하고 있다. 본 연구에서는 시간별 강우-유출 예측 모델인 GR4H(Génie Rural à 4 paramètres Horaires)를 사용하여 충주댐 유역을 대상으로 연구를 수행하였다. 유역의 속성에 따라서 모델의 성능이 어떻게 달라지는지 비교하여 특성에 맞는 모델을 알아내고. 또한 이 과정에서 기상 및 유출 데이터의 보정 길이를 가지고 어느 정도의 데이터 기간이 모델에서 좋은 성능을 보이는지 파악하였다. 뿐만 아니라 모델에 필요한 선행기간의 데이터가 있는 경우와 없는 경우를 비교하여 어떠한 차이를 보이는지, 그리고 선행기간은 얼마나 필요한지 연구를 통하여 알아냈다. 본 연구를 통하여 충주댐 유역에 대한 모델의 적용성 및 성능을 파악하고 수문 모형 구축에 제한이 있는 유역에 대해서도 사용이 가능한지 판단한다. 실험 유역의 관측 값을 모델에 입력한 후 각 모델에 해당하는 매개변수의 최적값을 찾아내는 과정을 거쳐 시뮬레이션을실 행했다. 본 연구에서 사용한 강우-유출 모델인 GR4H는 프랑스의 INRAE-Antony(Institut National de la recherche agronomique-Antony)에서 만들어진 airGR의 일종으로, 시간별 강우-유출 예측을 위해 개발된 공정 기반(process-based)의 집중적, 개념적 수문학 모델이다. 4개의 매개변수(parameter)가 있으며 이는 유역의 특정 속성을 나타낸다. GR4H를 시뮬레이션 하는 과정에서 매개변수의 최적화를 위해 적절한 보정 길이를 파악하여야 한다. 이러한 과정은 4년, 5년, 6년 등 1년씩 데이터의 양을 늘려가며 매개변수를 최적화한다. 이 과정에서 기상 및 유출 데이터의 적절한 보정 길이를 찾아낸다. 시뮬레이션을 통해 얻은 데이터를 관측 값과 비교하여 모델의 성능을 평가하고 다른 관측 값을 통해 시뮬레이션을 실행하여 검증을 거친다.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Industrial Convergence
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• v.21 no.5
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• pp.1-13
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• 2023

Carbon neutrality is the concept of reducing greenhouse gases emitted by human activities and making actual emissions zero through removal of remaining gases. It is also called "Net-Zero" and "carbon zero". Korea has declared a "2050 Carbon Neutrality policy" to cope with the climate change crisis. Various carbon reduction legislative processes are underway. Since carbon neutrality requires changes in industrial technology, it is important to prepare a system for carbon zero. This paper aims to understand the status and trends of global carbon neutrality technology. Therefore, ROK's web platform "www.naver.com." was selected as the data collection scope. Korean online articles related to carbon neutrality were collected. Carbon neutrality technology trends were analyzed by future signal methodology and Word2Vec algorithm which is a neural network deep learning technology. As a result, technology advancement in the steel and petrochemical sectors, which are carbon over-release industries, was required. Investment feasibility in the electric vehicle sector and technology advancement were on the rise. It seems that the government's support for carbon neutrality and the creation of global technology infrastructure should be supported. In addition, it is urgent to cultivate human resources, and possible to confirm the need to prepare support policies for carbon neutrality.

• Journal of The Korean Association of Information Education
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• v.19 no.3
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• pp.345-360
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• 2015

This study was designed to implement Activity-Orientated Career Education for college students, using the web-based interface. A draft web interface was designed by Rapid Prototyping and was thereafter taken to experts to review and determine the designs, in order to achieve the purpose of the study. Firstly, the sequence of activities of career education was run in accordance with the systematic order of Action Learning, in order to understand the overall design direction. Secondly, the design of the study may result in virtuous cycle-to-cycle. According to two major design directions, on the learning environment platform, the interface consisted of, knowing who I am; knowing who others think I am, knowing the job world; career decisions; career action plans; action planning; individual and co-evaluation; reflection; etc. These steps were designed to be implemented in accordance with strong interactivity of Wiki and Web 2.0 designs. This study was able to implement career education experiences centered around the activities of students at university, as well as experts, in order to increase the accessibility of Career Based Education on the web.

• Journal of Practical Engineering Education
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• v.11 no.1
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• pp.1-8
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• 2019

Virtual Training is an educational exercise in which the environment or the situation is virtually implemented for specific training and proceed like a real situation. In recent years, the virtual reality technology has developed rapidly, and the demand for experiencing situation that are not directly experienced in the real world is increasing more and more in virtual reality. Particularly, there is an increasing demand of contents for hands-on training and virtual training for equipment training that replaces high-risk and high-cost industry training. The virtual training contents have been developed and utilized for the purpose of technical training. However, it is known that virtual training is more effective when it is used as a supplementary training material or combined with e-learning contents rather than replacing one training course with virtual training contents because purpose and effect are different from general technical training course. In this study, we explored the development method for effective utilization of electrohydraulic servo control process, which is the virtual reality contents developed in 2017 in combination with e-learning contents. In addition, in order to establish a teaching and learning strategy, we actually develop and operate a case studies using virtual training contents. Surveys and case studies are conducted to investigate the effects of teaching and learning strategies applied in the classroom on students and their educational usefulness.

• Information Systems Review
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• v.25 no.1
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• pp.27-46
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• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.19-31
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• 2016

With the development of information and communication technologies, a new paradigm in higher education is required. Accordingly, establishing a smart campus has emerged as an important issue in universities worldwide. This study aims to discuss key issues and to provide useful academical and practical implications on smart campus by reviewing related literatures. For this purpose, this study examined recent literatures on smart campus by four research perspectives; 1) learning/knowledge-centric approach, 2) technology-centric approach, 3) integrated approach, and 4) user-centric approach, then developed smart campus framework. Smart campus user criteria contained members of university as well as local community and business stockholders. Smart campus framework presented specific service areas each belongs to smart education, smart life and smart administration domains and motivating factors of using smart campus. Moreover, by considering key issues and problems raised in previous studies, this study suggested practical implications for successful development of smart campus.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.246-254
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• 2010

Recent changes in medical environment are based on technology such as internet. As most hospitals require the change and the adaptation of medical, medical information experts are more needed. In this study, we developed web based PBL model and applied it to the students majoring in medical information system. The developed web-PBL model focuses on learner's online learning activities to enhance collaborative learning and self-directed learning by using online learning tools. At the result of the research, We found that the students' course evaluation somewhat increased compared to the previous class and the students positively perceived on PBL model. Moreover, this study showed that using blog as a online learning tool was a good way to enhance cooperative learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.126-137
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• 2020

This study provided implications for applying learner-centered teaching methods in the field of management education by adopting flipped learning and team-based learning to Business Communication classes and operating the classes according to its characteristics. Business communication capability, especially related to drawing up a document or a written report, is considered one of the crucial factors for making individuals valuable in an organization. For the identification of the subject outputs, a total of 64 students from the first and second semesters in 2018 were sampled, and the Wilcoxon signed rank test and paired t-tests were carried out. The results show that all types of communication capabilities have significantly increased at the end of each of the semesters. Also, the overall satisfaction level proved to be higher at the end rather than at the beginning of each semester. This study is especially meaningful because the results suggest concrete ways to apply learner-centered teaching methods for business education.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.