• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Trans-
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• v.12
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• pp.141-171
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• 2022

This study was conducted to understand media and cultivate personality by using media as data for personality education. To achieve this purpose, the Personality Education Promotion Act and the Korea Educational Development Institute's personality virtues were selected as educational elements, and a personality education program using media was developed in combination with the middle school curriculum. For this study, first, in order to extract personality virtues, 13 personality virtues were finally selected as educational elements by comparing and synthesizing the personality virtues of the Personality Education Promotion Act and the Korea Education Development Institute. The final personality virtues selected are self-esteem, courage, sincerity, self-regulation, wisdom, consideration, communication, courtesy, social responsibility, cooperation, citizenship, justice, and respect for human rights. Second, in order to select media and set the direction of development of personality education programs, the process of collecting media data was confirmed, and the direction and goal of the program were set by analyzing the middle school curriculum. Third, in order to propose a method of applying a personality education program using media, the personality grafting unit was selected by referring to the commentary on all subjects of the 2015 revised curriculum.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.318-329
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• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.4
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• pp.357-367
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• 2022

Sleep disordered breathing (SDB) is a disease characterized by repeated hypopnea and apnea during sleep due to complete or partial obstruction of upper airway. The prevalence of pediatric SDB is approximately 12 - 15%, and the most common age group is preschool children aged 3 - 5 years. Children show more varied presentations, from snoring and frequent arousals to enuresis and hyperactivity. The main cause of pediatric SDB is obstruction of the upper airway related to enlarged tonsils and adenoids. If SDB is left untreated, it can cause complications such as learning difficulties, cognitive impairment, behavioral problems, cardiovascular disease, metabolic syndrome, and poor growth. Pediatric dentists are in a special position to identify children at risk for SDB. Pediatric dentists recognize clinical features related to SDB, and they should screen for SDB by using the pediatric sleep questionnaire (PSQ), lateral cephalometry radiograph, and portable sleep monitoring test and refer to sleep specialists. As a therapeutic approach, maxillary arch expansion treatment, mandible advancement device, and lingual frenectomy can be performed. Pediatric dentists should recognize that prolonged mouth breathing, lower tongue posture, and ankyloglossia can cause abnormal facial skeletal growth patterns and sleep problems. Pediatric dentists should be able to prevent these problems through early intervention.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.185-191
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• 2021

The purpose of this study is to develop middle school English classes using Problem-Based Learning(PBL) based on flipped learning and to examine its effects. Recently, various attempts to combine flipped learning and PBL have been made; however, many studies have not been applied to middle and high school curriculums yet. The attempt of this study is expected to have theoretical and practical significance. The instructional model was derived from the review of previous studies, and the development of instructional program followed the general design procedure(analysis-design-development-implement-evaluation), and its validity was secured with the advice of related experts. To verify the effectiveness of the program, the English academic achievement test and the English key competency test were conducted before and after the program. Changes in English academic achievement were analyzed by the paired-sample t-test, and the effect of key competency and the level of achievement test performance (high vs, low) on the pre-post score change was analyzed by the mixed effects repeated measures ANOVA. As a result of the analysis, both academic achievement and key competencies increased, and the low-level students in the pre-academic achievement test showed more improvements. In conclusion, the PBL class based on flipped learning is effective in improving the English academic achievement and key competencies of middle school students, and in particular, it is shown to be an effective teaching method for students with low academic achievement.

• Journal of The Korean Association For Science Education
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• v.42 no.1
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• pp.1-18
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• 2022

The two most influential rationales for the 21^st century science curriculum reform can be said to be core competence and scientific literacy. However, the relationship between the two has not been scrutinized but remained speculative - and this has made the harmonization of the general guideline and subject-matter curriculum difficult in Korean national curriculum system. This study compares the two discourses to derive implications for future science curriculum development. This study took a literature research approach. In chapter II, national curriculum or standards, position papers, and research articles were reviewed to delineate the historical development of the discourses. In chapter III and IV, the intersections of those two discourses are delineated. In chapter III, the commonalities of the two discourses are explicated with regard to crisis rhetoric, multi-faceted meanings (individual, community, and global aspects), organization of subject-matter content and teaching and learning method, and the role of high-stake exams. In chapter IV, their respective strengths and weaknesses are juxtaposed. In chapter V, it is suggested that understanding scientific literacy and core competence discourses to have a family resemblance as 21^st century science curriculum reform rationale, after Wittgenstein and Kuhn. Finally, the ways to resolve the conflict between the two ideas from the general guideline and subject-matter curriculum over crisis rhetoric were explored.

• Journal of the Korean Applied Science and Technology
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• v.40 no.1
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• pp.59-70
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• 2023

This study is a meta-analysis study to comprehensively investigate the effects of flipped learning teaching applied to nursing students in Korea through systematic review. Data collection was conducted by a team of two researchers from November 20 to December 20, 2022. A total of 129 papers were searched through the domestic database, and duplicate papers were removed and the final 9 studies were selected. Flipped learning improved critical thinking disposition of nursing students 0.91(Z=8.36, p<.001), learning self-efficacy 0.35 (Z=2.62, p=.009), self-directed learning ability 0.81(Z=6.53, p<.001), academic achievement 0.60(Z=5.18, p<.001), and self-efficacy 0.66(Z=4.79, p<.001). Based on the results of this study, it was confirmed that flipped learning is an effective teaching method applicable to the domestic nursing education field, and an objective basis was presented for the direction of flipped learning class design. In the future, we suggest repeated studies that comprehensively analyze the effects of various outcome variables that have a positive effect on flipped learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.444-444
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• 2021

하천의 오염부하량 관리 계획은 지속적인 모니터링을 통한 자료 구축과 모형을 이용한 예측결과를 기반으로 수립된다. 하천의 모니터링과 예측 분석은 많은 예산과 인력 등이 필요하나, 정부의 담당 공무원 수는 극히 부족한 상황이 일반적이다. 이에 정부는 전문가에게 관련 용역을 의뢰하지만, 한국과 같이 지형이 복잡한 지역에서의 오염부하량 배출 특성은 각각 다르게 나타나기 때문에 많은 예산 소모가 발생 된다. 이를 개선하고자, 본 연구는 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 BOD 및 총인의 부하량 예측 모형을 개발하였다. 합성곱 신경망의 입력자료는 일반적으로 RGB (red, green, bule) 사진을 이용하는데, 이를 그래도 오염부하량 예측에 활용하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이에, 본 연구에서는 오염부하량이 수문학적 조건과 토지이용 등의 변수에 의해 결정된다는 인과관계를 만족시키고자 수문학적 속성이 내재된 수문학적 이미지를 합성곱 신경망의 훈련자료로 사용하였다. 수문학적 이미지는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는데, 여기서 각 grid의 수문학적 속성은 SCS 토양보존국(soil conservation service, SCS)에서 발표한 수문학적 토양피복형수 (curve number, CN)를 이용하여 산출한다. 합성곱 신경망의 구조는 2개의 Convolution Layer와 1개의 Pulling Layer가 5회 반복하는 구조로 설정하고, 1개의 Flatten Layer, 3개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 마지막으로 1개의 Dense Layer가 연결되는 구조로 설계하였다. 이와 함께, 각 층의 활성화 함수는 정규화 선형함수 (ReLu)로, 마지막 Dense Layer의 활성화 함수는 연속변수가 도출될 수 있도록 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 연구의 대상지역은 경기도 가평군 조종천 유역으로 선정하였고, 연구기간은 2010년 1월 1일부터 2019년 12월 31일까지로, 2010년부터 2016년까지의 자료는 모형의 학습에, 2017년부터 2019년까지의 자료는 모형의 성능평가에 활용하였다. 모형의 예측 성능은 모형효율계수 (NSE), 평균제곱근오차(RMSE) 및 평균절대백분율오차(MAPE)를 이용하여 평가하였다. 그 결과, BOD 부하량에 대한 NSE는 0.9, RMSE는 1031.1 kg/day, MAPE는 11.5%로 나타났으며, 총인 부하량에 대한 NSE는 0.9, RMSE는 53.6 kg/day, MAPE는 17.9%로 나타나 본 연구의 모형은 우수(good)한 것으로 판단하였다. 이에, 본 연구의 모형은 일반 ANN 모형을 이용한 선행연구와는 달리 2차원 공간정보를 반영하여 오염부하량 모의가 가능했으며, 제한적인 입력자료를 이용하여 간편한 모델링이 가능하다는 장점을 나타냈다. 이를 통해 정부의 물관리 정책을 위한 의사결정 및 부족한 물관리 분야의 행정력에 도움이 될 것으로 생각된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.