화학교사들의 메타 모델링 지식 수준 향상을 위한 교육 프로그램의 개발 및 효과

남윤지(Nam, Yunji),백성혜(Paik, Seounghey) 한국교원대학교 융합교육연구소 2022 융합교육연구 Vol.8 No.1

본 연구는 화학교사들의 메타 모델링 지식 수준을 향상할 수 있는 교육 프로그램을 개발하고 교육적 효과를 알아보는 연구이다. 이를 위해 여러 가지 차원의 메타 모델링 지식 수준 중 모델에 관련된 ‘모델의 다중성’과 모델링에 관련된 ‘모델의 변화성’ 및 ‘모델의 평가’를 선정하여 메타 모델링 지식 수준을 재구성하였다. 연구자가 개발한 총 15차시 교육프로그램은 재구성한 메타 모델링 지식 수준과 GEM cycle을 기준으로 개발하였으며, 화학교육의 맥락적 상황 구성을 위해 물질의 세 가지 상태를 소재로 선정하였다. 본 연구를 통해 얻은 결과는 다음과 같다. 교육프로그램을 투입하기 전에는 모델의 다중성, 모델의 변화성, 모델의 평가에 대한 화학교사들의 수준이 모두 0 혹은 1 단계였으나, 교육프로그램을 통해 점차 수준이 향상됨을 확인할 수 있었다. 특히 모델의 다중성과 모델의 변화성에서는 교육프로그램이 마무리될 때 모든 교사들이 3 단계까지 향상되었다. 그러나 모델의 평가는 절반 정도의 교사들이 교육프로그램 마무리 단계에서도 2 단계에 머물렀다. 이는 모델의 평가 2 단계는 모델의 정교성에서 멈추지만, 3 단계는 모델로 현상을 예측할 수 있어야하기 때문이다. 따라서 많은 화학교사들이 모델로 현상을 예측하는 활동으로의 전환을 어려워함을 확인할 수 있다. 이에 따른 화학교사들을 대상으로 모델의 평가에 대한 교수 역량을 기를 수 있는 연구가 지속적으로 이루어질 필요가 있다. The purpose of this study is to develop educational programs that can improve the level of meta-modeling knowledge of chemistry teachers and to find out the educational effects. For this study, we reconstruct the level of meta-modeling knowledge based by ‘model multiplicity’ which is related to model, and ‘model changeavility’ and ‘evaluation of models’ which is related th the modeling. Educational program is consisted of 15 sessions and its based on reconstructed meta-modeling knolede level and GEM cycle. Also three states of materials were selected as materials for the Non-contextual situation of chemical education. The results obtained through this study are as follows. The level of chemistry teachers about ‘model multiplicity’, ‘model changeavility’ and ‘evaluation of models’ were all 0 or 1 at beginning the program, but after the program, the lever gradually improved. In particular, in terms of model multiplicity and model changeavility, all teachers improved to level 3 at the end of the educational program. However, about half of the teachers’ evaluation of models stayed at the level 2 at the end of the educational program. This is because level 2 of ‘evaluation of models’ stops at the sophistication of the model, but level 3 must be able to predict the phenomenon with the model. Therefore, it can be confirmed that it is difficult for many chemistry teachers to switch to activities that predict phenomena with models. Accordingly, it is necessary to continuously conduct research on chemical teachers to develop teaching capabilities for model evaluation.

Development of Tissue Equivalent Materials for a Multi-modality (CT&MRI) Phantom in MRI-guided Radiation Treatment

Yunji Seol,Jina Kim,Aeran Kim,Jinho Hwang,Taegeon Oh,Jin-sol Shin,Hong Seok Jang,Byung Ock Choi,Young-nam Kang 한국물리학회 2018 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.73 No.7

This study proposed to develop a phantom material that can represent the various contrasts of both MRI and CT images and is available to use in MRI-guided radiation treatment. Materials used for making a phantom that can be used for both MRI and CT image were agarose (T2 modifier), gadolinium-based contrast agent (T1 modifier), sodium uoride (CT number modifier), and distilled water. They were mixed at various composition ratios and stirred until transparent. For the relationship between the ingredients and values, 48 samples were manufactured at various composition ratios. The relationship was expressed as equations, to be able to get the composition ratios of organs that we wanted to make. MR relaxation times were measured using 1.5 T MRI equipment. CT scans were performed at 120 kVp and extracted CT numbers from images. Based on the fitted equations derived from the relationship between ingredients and values, materials were manufactured using the composition ratio of human organs; brain (white and gray matter), liver, spleen, kidney, and prostate. The all values were within the reference range, but some exceeded the range due to the image noise. A phantom composed of substitutes made from the derived equations added other substances of different density like bone or lung can be used as an inhomogeneity dose calculation phantom for both CT and MRI. Furthermore, it can be applied to MRI-only based RTP systems and MRI-guided radiation treatment QA in the future.

Prediction of Tumor Temperature in Regional Hyperthermia by Using LED Luminance

이재현,Yunji Seol,Taegeon Oh,Na young An,Kyumin Han,Jinho Hwang,Hong Seok Jang,Byung Ock Choi,Young-nam Kang 한국물리학회 2020 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.77 No.6

Hyperthermia is used to destroy tumors by generating heat in the body (40-45 °C). In particular, regional hyperthermia entails intense heating of tumors rather than raising the temperature of the body. In regional hyperthermia, the prediction of the tumor temperature before treatment is essential to ensure treatment efficiency and patient safety. The goal of this study is to predict the temperature of tumors in regional hyperthermia by using a light emitting diode (LED). LED luminance shows a linear relationship with current above a certain voltage. Thus, the temperature may be predicted via LED luminance based on these electrical characteristics, Special Absorption Rate (SAR), and Pennes' Bio-heat Transfer Equation. LED was located in the agar phantom at the same intervals to measure the luminance, and measure temperature at same spot using thermometer and well verified using a commercialized simulation program (Sim4Life). Within the range of electrode size, the difference in luminance between the predicted and the measured temperatures was within 2.5%. In addition, the difference between the predicted temperature and the result of the simulation program was within 1.5%. In this study, the tumor temperature in regional hyperthermia was predicted using LED luminance to ensure treatment accuracy and patient safety. This study showed the possibility of temperature prediction based on LED luminance.

한국, 중국, 대만인의 전통의학 이용 의향에 미치는 요인 비교

남민호(Nam Minho),김윤지(Kim Yunji),김호선(Kim Hosun),박준형(Park Junhyeong),葉杏伊(Yeh Hsin-Yi),이예슬(Lee Ye-seul),이순호(Lee Soonho),정원모(Jung Won-mo),홍윤기(Hong Yun-Ki),이혜정(Lee Hyejung),박히준(Park Hi-Joon),채윤병(Chae Younbyo 대한한의학회 2011 대한한의학회지 Vol.32 No.1

유기성 폐수로부터 바이오전기화학적 과산화수소생산을 위한 탄소기반 전극성능개선 연구

엄현지(Yunji Eom),남주연(Joo-Youn Nam),좌은진(Eunjin Jwa),목영선(Young-Sun Mok) 대한환경공학회 2021 대한환경공학회 학술발표논문집 Vol.2021 No.11

Evaluation of Developed Thermal Distribution Prediction Algorithm Using Mass Density Distribution with CT Image

Jinho Hwang,Yunji Seol,Taegeon Oh,Na young An,Jaehyeon Lee,Chul-Seung Kay,Hong Seok Jang,Byung Ock Choi,Young-nam Kang 한국물리학회 2020 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.76 No.1

Prior to the hyperthermia, the amount of heat energy delivered to the tumors must be confirmed. If it cannot be confirmed before hyperthermia, normal tissues may also be heated, leading to possible necrosis. In a previous study, the thermal distribution was calculated using mass density distribution with CT image. The previous study was not performed various evaluations of accuracy for the developed thermal distribution prediction algorithm. In this study, the developed thermal distribution prediction algorithm was evaluated by comparing the phantom with the measured temperature and a commercial simulation software (Sim4Life) has been used as a reference data for hyperthermia studies. The difference between the measured temperature and the commercial simulation software (Sim4Life) was within 3%, and the difference between the measured temperature and the developed thermal distribution algorithm was also within 2%. The difference between the developed thermal distribution algorithm and the commercial simulation software was also within 3%. The thermal distribution algorithm developed in this study could predict the internal temperature of the patient before hyperthermia and increase the treatment accuracy by preventing necrosis from occurring in normal organs. In addition, it could easily predict the temperatures for hyperthermia without modeling CT images taken for the diagnosis of lesions.

자동 위성영상 수집을 통한 다종 위성영상의 시계열 데이터 생성

남윤지,정성우,김태정,이수암,Yunji Nam,Sungwoo Jung,Taejung Kim,Sooahm Rhee 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

Using deep learning to predict radiation pneumonitis in patients treated with stereotactic body radiotherapy (SBRT) for pulmonary nodules: preliminary results

Choi Kyu Hye,Seol Yunji,Kang Young-nam,Lee Young Kyu,Ahn Sang Hee,Song Jin Ho,Choi Byung-Ock,Kim Yeon-Sil,Jang Hong Seok 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.81 No.5

This study aimed to develop a predictive model using clinical, dosimetric, and radiomic features for radiation-induced pneumonitis (RP) after lung stereotactic body radiation therapy (SBRT). We retrospectively analyzed the clinical data of 153 patients who underwent SBRT for lung nodules between 2010 and 2019. A total of 3,350 radiomic computed tomography (CT) features of radiotherapy simulation (shape, intensity, texture, and log flters) were extracted. Among them, 30 factors were selected through Pearson’s correlation analysis and subjected to analysis. A proposed lung toxicity prediction model was developed using a deep neural network algorithm. The programming language used was Python. The data were divided into a training set (70% of data) and a test/validation set (30% of data). We adjusted the original data by oversampling to correct the uneven sample distribution to balance the data set. The Talos library was used in this study for hyperparameter determination and the model with the highest accuracy was selected. The Talos library provided the RP prediction model with the highest accuracy of 94.8%. The area under the curve of the receiver operating characteristics curve was 0.90, which was relatively fair. It showed relatively high accuracy in the RP prediction model based on the clinical, dosimetric, and radiomic factors of patients who received SBRT for lung nodules. A further study using more cases from other medical centers is being planned for external validation.

효과적인 Fur 렌더링을 위한 적응적 시스템 : 혼합 렌더링을 이용한 빠른 Fur 렌더링 방법

김혜선(Hyesun Kim),반윤지(Yunji Ban),이충환(Chunghwan Lee),남승우(Seungwoo Nam),최진성(Jinsung Choi),오준규(Junkyu Oh) 한국HCI학회 2009 한국HCI학회 학술대회 Vol.2009 No.2

Fur rendering is difficult in that there are huge numbers of objects and it takes so much time. The previous method considers fur as cylinder, transforms it into 2D ribbon, triangulates and commits rendering. But this method has problem like under sampling and takes rendering time so long. To resolve these shortcuts we proposed new algorithm. We divide fur into thick and thin fur and we applied adaptive rendering methods for each type of fur. Also we can perform an effective rendering according to the proposed rendering framework. 털 렌더링은 사실적인 렌더링을 위해 많은 수의 털 데이터를 처리해야 하는 어려움이 있다. 대량의 털 데이터를 렌더링함에 있어서 가장 어려운 점은 렌더링 시간이 많이 걸린다는 점이다. 기존의 털 렌더링 방법은 털을 원통형의 실린더로 간주하고 2D 형태의 리본으로 변환하고 삼각화하여 렌더링하는 방법이다. 하지만 이 방법은 언더 샘플링(under sampling)문제가 있고 렌더링 시간이 오래 걸린다는 단점이 있다. 이런 단점을 개선하기 위해서 이 논문에서는 새로운 알고리즘을 제안하였다. 털을 굵기에 따라 나누고 굵은 털과 가는 털에 각각의 렌더링 방법을 사용함으로써 렌더링 속도를 개선하였다. 또한 전체 렌더링 프레임워크에 대한 제안을 함으로써 보다 효과적인 렌더링을 수행할 수 있다.

Comparison of Proportional Mortality Between Korean Atomic Bomb Survivors and the General Population During 1992–2019

Jeong Ansun,Moon Seong-geun,Han Yunji,Nam Jin-Wu,김미경,김인아,Kim Yu-Mi,박보영 대한의학회 2023 Journal of Korean medical science Vol.38 No.13

Background: Atomic bombs dropped on Hiroshima and Nagasaki in Japan in August 1945 were estimated to have killed approximately 70,000 Koreans. In Japan, studies on the health status and mortality of atomic bomb survivors compared with the non-exposed population have been conducted. However, there have been no studies related to the mortality of Korean atomic bomb survivors. Therefore, we aimed to study the cause of death of atomic bomb survivors compared to that of the general population. Methods: Of 2,299 atomic bomb survivors registered with the Korean Red Cross, 2,176 were included in the study. In the general population, the number of deaths by age group was calculated from 1992 to 2019, and 6,377,781 individuals were assessed. Causes of death were categorized according to the Korean Standard Classification of Diseases. To compare the proportional mortality between the two groups, the P value for the ratio test was confirmed, and the Cochran-Armitage trend test and χ2 test were performed to determine the cause of death according to the distance from the hypocenter. Results: Diseases of the circulatory system were the most common cause of death (25.4%), followed by neoplasms (25.1%) and diseases of the respiratory system (10.6%) in atomic bomb survivors who died between 1992 and 2019. The proportional mortality associated with respiratory diseases, nervous system diseases, and other diseases among atomic bomb survivors was higher than that of the general population. Of the dead people between 1992 and 2019, the age at death of survivors who were exposed at a close distance was younger than those who were exposed at a greater distance. Conclusion: Overall, proportional mortality of respiratory diseases and nervous system diseases was high in atomic bomb survivors, compared with the general population. Further studies on the health status of Korean atomic bomb survivors are needed.