• 천문학회보
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• 제44권1호
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• pp.52.1-52.1
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• 2019

The Helioseismic and Magnetic Imager (HMI) is the instrument of Solar Dynamics Observatory (SDO) to study the magnetic field and oscillation at the solar surface. The HMI image is not enough to analyze very small magnetic features on solar surface since it has a spatial resolution of one arcsec. Super resolution is a technique that enhances the resolution of a low resolution image. In this study, we use a method for enhancing the solar image resolution using a Deep-learning model which generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained a model based on a very deep residual channel attention networks (RCAN) with HMI images in 2014 and test it with HMI images in 2015. We find that the model achieves high quality results in view of both visual and measures: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is much better than the conventional bi-cubic interpolation. We will apply this model to full-resolution SDO/HMI and GST magnetograms.

• 천문학회보
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• 제46권1호
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• pp.42.2-43
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• 2021

We address a question of what are three main channels that can best translate other channels in ultraviolet (UV) and extreme UV (EUV) observations. For this, we compare the image translations among the nine channels of the Atmospheric Imaging Assembly on the Solar Dynamics Observatory using a deep learning model based on conditional generative adversarial networks. In this study, we develop 170 deep learning models: 72 models for single-channel input, 56 models for double-channel input, and 42 models for triple-channel input. All models have a single-channel output. Then we evaluate the model results by pixel-to-pixel correlation coefficients (CCs) within the solar disk. Major results from this study are as follows. First, the model with 131 Å shows the best performance (average CC = 0.84) among single-channel models. Second, the model with 131 and 1600 Å shows the best translation (average CC = 0.95) among double-channel models. Third, among the triple-channel models with the highest average CC (0.97), the model with 131, 1600, and 304 Å is suggested in that the minimum CC (0.96) is the highest. Interestingly they are representative coronal, photospheric, and chromospheric lines, respectively. Our results may be used as a secondary perspective in addition to primary scientific purposes in selecting a few channels of an UV/EUV imaging instrument for future solar satellite missions.

• Educational Technology International
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• 제6권2호
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• pp.113-130
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• 2005

The purpose of this study was to determine the teaching styles of professors who teach adult students in selected higher institutions. It also identified whether professors' teaching styles were teacher-centered or learner-centered and examined the relationship between instructors' teaching styles and such instructor demographic variables as gender, years of teaching experience, and taught level of courses. This study used The Principles of Adult Learning Scale(PALS) (Conti,1983) to measure instructional preferences. Demographic characteristics were collected through a personal data inventory. The analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) tests were used to analyze the data. The data were examined for significance at the .05 level of confidence by means of analysis of variance. The dependent variables in this study were teaching styles of full-time professor, as represented by the seven subscores from the standardized instrument on the PALS. The seven subscores were: (1) learner-centered activities, (2) personalizing instruction, (3) relating to experience, (4) assessing student needs, (5) climate building, (6) participation in the learning process, and (7) flexibility for personal development. The study established that there was a significant difference in mean scores on the PALS between participants when examined by the number of years of teaching experiences.

• 방사선산업학회지
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• 제17권4호
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• 한국과학교육학회지
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• 제23권1호
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• pp.35-46
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• 2003

문제 해결 능력의 신장은 과학 교육의 중요한 목표로 간주되어 왔고 현 사회의 교육이 담당해야할 중요한 과제이다. 그러나 오래 동안 과학 수업을 받은 후에도 학생들의 문제 해결 수준은 향상되지 않았고 이에 효과적인 교수 방법으로 학생들의 능동적인 참여와 토론을 기반으로 긍정적 상호 의존성을 기르면서 문제 해결 능력의 향상을 가져올 수 있는 협동학습이 제안되었다. 그러나 고등학교 생물 분야에서는 타 과학 과목에 비해 협동학습을 적용한 충분한 연구가 수행되지 못하였다. 따라서 본 연구에서는 인문계 고등학교 자연계 생물II의 'II. 물질대사' 단원에 대하여 협동학습을 실시한 후. 학업 성취도 및 과학 학습 태도에 대한 효과를 전통적 수업과 비교하여 분석하였다. 연구 대상은 서울시 소재 여자 고등학교 2학년 자연계 학생들 100명이며, 2개 학급 중 한 학급의 협동학습을 실시하고 나머지 한 학급은 전통적 수업을 하였다. 본 연구의 검사도구는 학업 성취도 검사지의 경우 단원의 내용 및 수업 목표를 근거로 객관식 24문항으로 구성되었고 과학 학습 태도 검사 도구는 김인희(1994) 연구에서 개발된 총 40개의 리커어트 척도 문항으로 구성되었다. 본 연구는 사전-사후 검사 통제 집단(pretest-posttest control group) 설계에 기초하여 협동학습의 모형 중 STAD모형을 적용하였고 5주 동안 총 13차시에 걸쳐 실시되었다. 협동학습은 학업성취도를 향상시키는데 전통적 수업보다 더 효과적이지 않았다(p> .05). 협동학습은 상위 및 중위 수준의 학생들에게 효과가 없었으나 학위 수준의 학생들에게는 전통적 수업보다 더 효과적이었다.(p< .05). 협동학습은 전통적 수업에 비해 학생들의 과학 학습 태도 변화에 있어서 더 효과적이었다.(p< .05). 과학 학습 태도에서 4개의 하위 영역 중 '과학에 대한 태도', '과학의 사회적 의미', '과학 교과에 대한 태도'의 영역에서는 협동학습이 전통적 수업 보다 효과가 없었으나(p> .05) '과학 교과에 대한 태도' 영역에서는 협동학습이 효과적이었다(p< .05). 협동학습은 상위 및 중위 수준의 학생들의 과학 학습태도를 변화시키는데 효과가 없었으나 하위 수준의 학생들에는 효과적이었다(p< .05).

• 한국초등과학교육학회지:초등과학교육
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• 제27권1호
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• pp.42-48
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• 2008

The purpose of this study was to develop an instrument for assessing teachers' awareness of science teaching through analogy (ASTA) and to establish its validity and reliability. Based on the literatures on science teaching with analogies, we constructed 23 survey items. Face validity of the items was established using three science education experts. Through exploratory factor analysis with responses of 35 pre- and 26 inservice elementary school teachers, the instruments were constructed on four subcategories: awareness of analogies in science, use of analogy in teaching and learning, self-efficacy in science knowledge, and knowledge of analogy. The data collected from pre- and in-service elementary teachers demonstrated that overall the teachers' awareness of analogy in science was neutral, which indicated they did not have clear standpoints of science teaching through analogy. Further examination demonstrated that there was no significant difference between pre- and in-service teachers and between two genders. Moreover, there was no significant difference among teachers who preferred either didactic or discovery teaching approaches. We conclude that ASTA test would contribute assessment of teachers' awareness of analogy in science teaching while further examination of the instrument will warrant for its broader use.

• 한국초등과학교육학회지:초등과학교육
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• 제27권2호
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• pp.144-157
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• 2008

The purpose of this study was to analyse the influence of mother's characteristics on elementary school students' attitudes toward science. Elementary school students (N=667) and their mothers (N=681) were selected from three other regions, big city, small city, and country. Attitudes toward science and supports for scientific activities were measured by two kinds of instruments. The instrument for the measurement of attitudes toward science includes three scales: cognition about value of science, affection toward science & science learning, and cognitive participation in scientific activities. And the instrument to measure parents' support for scientific activities includes two scales: indirect support and direct support. This research showed that mothers' various characteristics resulted in a difference in students' attitudes toward science. And there were positive correlations between students' attitudes toward science and their mothers' attitudes toward science and support for scientific activities. Also mothers' attitudes toward science and support for scientific activities affected students' attitudes. Especially, mothers' personal interest in science and her mental and physical supports for children's scientific activities had a close relation with students' attitudes toward science.

• 한국과학교육학회지
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• 제24권6호
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• pp.1143-1157
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• 2004

이 연구의 목적은 STS에 대해서 우리 나라 고등학생들이 가지는 견해를 알아보기 위한 평가 도구인 HS-VOSTS(Korean Students' Views on STS)를 개발하는 것이다. HS-VOSTS 문항을 개발하기 위해서 다음과 같은 연구과정을 수행하였다. 우선, 문헌과 선행 연구 고찰을 통해 4가지 범주의 10가지 하위 범주와 관련하여 23개의 논제를 포함하는 STS 범주 체계를 설정하였다. STS 범주 체계는 정의 과학의 외적 사회학 과학의 내적 사회학 인식론의 네 가지의 커다란 범주로 구성되어 있다. 다음은 STS 범주 체계를 기초로 4단계에 걸쳐 문항을 개발하였다. 1단계에서는 각 논제에 대한 짝진술문을 기초로 학생진술 문항지를 작성하여 772명의 고등학생들(16.3세)에게 투입하였다. 2단계에서는 짝진술문 중 하나를 제거하여 문항의 진술문으로하고, 분류된 공통견해를 답지로 하여 1차 다지선다형 문항지를 구성하였다. 3단계에서는 고등학생 28명(16.5세)을 대상으로 반구조화 면담을 실시하여 그 결과 분석을 통해 2차 다지선다형 문항지를 구성하였다. 4단계에서는 2차 다지선다형 문항지를 고등학생 306명에게 적용하여 낮은 반응비율을 보인 답지들을 제거하여 최종 검사도구를 완성하였다. HS-VOSTS는 학생들의 STS에 대한 신념 및 견해를 알아볼 수 있는 유용한 도구로서, 그 결과는 교사뿐만 아니라 교육과정 개발자, 교과서 저자, 교육정책 입안자들에게 많은 시사점을 줄 수 있을 것이라 생각한다.

• 한국과학교육학회지
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• 제35권2호
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• pp.247-258
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• 2015

학생은 대화뿐만 아니라 비언어적인 행동을 통해서도 수업에 참여한다. 특히 과학교실에서는 다른 교과수업과 다르게 과학기구의 사용, 관찰, 측정 등의 비언어적인 행동들이 나타난다. 그런데 학생의 행동적 참여를 분석하는 기존의 도구들은 과학교과에서 나타나는 이러한 특징적인 활동을 반영하지 못하고 있다. 본 연구는 과학교과의 학습 활동을 고려한 행동적 참여 영상 분석 도구를 개발하였다. 분석 도구를 개발하기 위하여, 첫째, 문헌연구와 초등학교 과학수업 관찰을 바탕으로 수업 중에 나타나는 학생의 행동을 14가지로 범주화하였다(자유발화, 음독, 경청하기, 묵독, 쓰기, 주의 집중하기, 손들기, 이동하기, 비참여적 움직임, 과제 움직임, 관찰하기, 측정하기, 분류하기, 실험도구 다루기). 둘째, 이를 바탕으로 '과학수업 중 행동적 참여 상태 분석틀'을 개발하였다. 셋째, Microsoft Excel Visual Basic을 이용하여 분석틀에 따라 학생들의 발화 여부, 시선, 몸의 움직임 등을 기록하고 분석할 수 있는 분석 도구를 개발하였다. 개발된 도구를 이용하면 수업 중학생의 각 행동이 수행된 시간과 학생의 네 가지 수업참여 상태(즉, 참여적 발화, 참여적 침묵, 비참여적 발화, 비참여적 침묵)를 파악할 수 있다. 개발된 분석 도구를 실제 과학시간의 초등학생 두 명을 대상으로 예시적으로 적용해 본 결과, 교사는 수업활동(일반, 시범실험, 실험활동)에 따라 발화의 양을 달리하였으며, 분석대상인 두 학생은 전체 수업시간 동안 참여적 침묵 상태에 있는 시간이 가장 길었다(학생 A: 63%, 학생 B: 72%). 참여적 침묵 상태에 있는 두 학생은 '경청하기'를 하는 시간이 가장 길었는데(학생 A: 51%, 학생 B: 42%), 교사의 발화가 상대적으로 적었던 실험활동 시간에는 오히려 '경청하기'를 거의 하지 않은 대신에 '관찰하기'를 가장 많이 하였다(학생 A: 47%, 학생 B: 53%). 개발된 분석 도구가 비언어적인 행동을 통하여 과학수업에 참여하고 있는 학생의 행동을 이해하는 데 도움을 줄 수 있을 것이라 기대한다.

• PNF and Movement
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• 제18권2호
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• pp.287-296
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• 2020

Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters. Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system. Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99~0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31~1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98~0.99), and CV error values were very small for all gait parameters (0.33~1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86~0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17~5.58%). Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.