• Journal of Life Science
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• v.1 no.1
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• pp.15-23
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• 1991

노화현상에 관련된 가설은 프로그램설, 세포손상축적설 등 다양하나 증거와 연구자료가 아직은 불충분하고 미흡하다. 현재 상당한 주목을 받고 있고 또 일견 설득력이 있는 것으로 수용되고 있는 oxygen species에 의한 세포손상축적 가설은 시험관 또는 생체 내에서의 실험과 관측을 통해 연구에 상당한 진전이 있음에도 노화현상을 해석하는 또 다른 실마리에 불과하다. Oxygen radical이 세포내의 거대분자들 중 DNA에 손상과 변이를 일으키거나, 우리기를 수반하지 않는 다른 기작에 의해 조직손상이 일어나면서 세포내의 유리기반응에 이차적 장애가 유도되어 세포내의 분자들이 훼손되거나 변화됨으로서, 이들 손상물이 시간과 더불어 축적하여 신체기능의 퇴행을 수반한 질병과 노화현상이 나타나게 된다는 것이다. 유리기에 대한 효과를 가지는 SOD, catalase, glutathione, peroxidase, metal-chelator와 chain-breaking 효과를 가진 alpha-tocopherol, beta-carotere, urate, ascorbate, ubiquinone, glutathione, protein-thiol 등 항산화물질의 적용과 섭취의 중요성이 인식되었다.

• Magazine of the Korea Concrete Institute
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• v.14 no.3
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• pp.71-76
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• 2002

콘크리트는 거의 반영구적인 재료라고 인식되어져 왔으나, 최근들어 해양환경, 적설한랭지대, 도심과 같은 열악한 환경속에서 예상보다 빨리 조기열화 되는 현상이 널리 알려지면서 콘크리트 구조물의 구조적 성능뿐만 아니라, 장기적인 내구성에 대한 관심이 높아져 가고 있다. 실제로 여러 선진국에서 염해와 같은 열화환경의 피해를 받은 콘크리트 구조물이 예상공용년수 이전에 철거되거나. 개보수 비용이 초기 건설비용보다 더 많이 드는 예가 적지 않게 보고되면서, 적절한 내구성 유지대책 및 구조물의 장수명화를 위해 많은 연구와 노력이 진행되고 있다.(중략)

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.217-229
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• 2020

Recently, weather changes in Korea have intensified due to global warming, and the five major natural disasters that occur mostly include heavy rains, typhoons, storms, heavy snow, and earthquakes. Busan is vulnerable to snow disaster, given that the amount of natural disaster damage in Busan accounts for more than 50% of the total amount in the entire metropolitan cities in Korea, and that the Busan area includes many hilly mountains. In this study, we attempted to identify vulnerable areas for snowfall disasters in Busan areas using the geographic information system (GIS) with the data for both geographical and anthropogenic characteristics. We produced the maps of vulnerable areas for evaluating factors that include altitude, slope, land cover, road networks, and demographics, and overlapped those maps to rank the vulnerability to snowfall disasters as the 5th levels finally. To weight each evaluating factor, we used an entropy method. The riskiest areas are characterized by being located in mountainous areas with roads, including Sansung-ro in Geumjeong-gu, Mandeok tunnel in Buk-gu, Hwangnyeongsan-ro in Suyeong-gu, and others, where road restrictions were actually enforced due to snowfall events in the past. This method is simple and easy to be updated, and thus we think this methodology can be adapted to identify vulnerable areas for other environmental disasters.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.30-38
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• 2015

In cold-climate regions, deicing agents is used for smooth traffic on the road due to freezing and snowdrift in winter. The use of de-icing salts has resulted in the accelerated scaling damage of concrete with salt damage under freezing and thawing condition. Scaling is the deterioration of concrete where in the paste-mortar structure delaminates in flakes from the surface of the concrete. Due to such damage, concrete pavement causes various problems such as early deterioration according to the decrease in the thickness of cover concrete and user's stability issues. Accordingly, various tests and evaluation methods have been suggested in order to evaluate these phenomena in other countries. However, there have been no regulations for the evaluation method in South Korea, and related studies are also very rare. Therefore, in this study, the evaluation methods proposed by each institution and country were investigated and the experiments were performed according to each regulation, followed by the comparison and analysis of the results. Furthermore, this study aims to suggest the optimized experimental method adopted to domestic field through the discussion of such experimental methods and results.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• Journal of the Korean earth science society
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• v.24 no.4
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• pp.250-257
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• 2003

The purpose of this study was to test the hypotheses that student's abductive reasoning ability plays an important role in hypothesis-generating about vapor condensation, and student's hypothesis-generating requires their causal explicans as well as experience. To test the hypotheses, the instruments of hypothesis-generation, prior knowledge, and experience with vapor condensation were developed and administered to 6th grade students. This study found that 72 subjects among 89 students who had prior knowledge about vapor condensation failed to apply their prior knowledge to hypothesis-generating about the vapor condensation. This result showed that the students' failure in hypothesis-generating was related to their deficiency in abductive reasoning ability. In addition, this study showed that 54 subjects among 56 students who had experience with vapor condensation also failed to generate hypotheses. This result supported that student's causal explanations were separated from their experience. Therefore, this study suggests that science education should include the teaching of abductive reasoning skills for developing student's hypothesis-generating skills.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.188-202
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• 2011

Due to emissions of greenhouse gases caused by increased use of fossil fuels, the climate change has been detected and this phenomenon would affect even larger changes in temperature and precipitation of South Korea. Especially, the increase of temperature by climate change can affect the amount and pattern of snowfall. Accordingly, we tried to predict future snowfall and the snowfall pattern changes by using the downscaled GCM (general circulation model) scenarios. Causes of snow varies greatly, but the information provided by GCM are maximum / minimum temperature, rainfall, solar radiation. In this study, the possibility of snow was focused on correlation between minimum temperatures and future precipitation. First, we collected the newest fresh snow depth offered by KMA (Korea meteorological administration), then we estimate the temperature of snow falling conditions. These estimated temperature conditions were distributed spatially and regionally by IDW (Inverse Distance Weight) interpolation. Finally, the distributed temperature conditions (or boundaries) were applied to GCM, and the future snowfall was predicted. The results showed a wide range of variation for each scenario. Our models predict that snowfall will decrease in the study region. This may be caused by global warming. Temperature rise caused by global warming highlights the effectiveness of these mechanisms that concerned with the temporal and spatial changes in snow, and would affect the spring water resources.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.945-958
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• 2014

Soil temperature is one of the most important environmental factors that govern hydrological and biogeochemical processes related to diffuse pollution. In this study, considering the snowmelting and the soil freezing-thawing processes, a set of computer codes to estimate winter soil temperature has been developed for CAMEL (Chemicals, Agricultural Management and Erosion Losses), a distributed watershed model. The model was calibrated and validated against the field measurements for three months at 4 sites across the study catchment in a rural area of Yeoju, Korea. The degree of agreement between the simulated and the observed soil temperature is good for the soil surface ($R^2$ 0.71~0.95, RMSE $0.89{\sim}1.49^{\circ}C$). As for the subsurface soils, however, the simulation results are not as good as for the soil surface ($R^2$ 0.51~0.97, RMSE $0.51{\sim}5.08^{\circ}C$) which is considered resulting from vertically-homogeneous soil textures assumed in the model. The model well simulates the blanket effect of snowpack and the latent heat flux in the soil freezing-thawing processes. Although there is some discrepancy between the simulated and the observed soil temperature due to limitations of the model structure and the lack of data, the model reasonably well simulates the temporal and spatial distributions of the soil temperature and the snow water equivalent in accordance with the land uses and the topography of the study catchment.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.481-491
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• 2014

Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.