• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.222-232
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• 2021

The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2008년도 추계학술발표대회 논문집
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• pp.202-207
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• 2008

Measures for coping with energy shortage are being sought all over the world. Following such a phenomenon, effort to use less energy in the design of buildings and equipment are being conducted. In particular, a program to evaluate the performance of a building comes into the spotlight. However. indispensable standard wether data to estimate the exact energy consumption of a building is currently unprepared. Thus, after appling standard weather data for four weather factors which were used in previous researches to Visual DOE 4.0, we compared it with the result of the existing data and evaluated them. For the monthly cooling and heating load of our target building, we used revised data for June, July, August, and September during which cooling load is applied. When not the existing data but the revised data was used, the research shows that an average of 14.9% increased in June, August, and September except for July. Also, in a case of heating load, the result by the revised data shows a reduction of an average of 11.9% from October to April during which heating load is applied. In particular, the heating loads of all months for which the revised data was used were more low than those of the existing data. In the maximum cooling and heating load according to load factors, the loads by residents and illumination for which the revised data was used were the same as those of the existing data, but the maximum cooling loads used by the two data have a difference in structures such as walls and roofs. Through the above results, the research cannot clearly grasp which weather data influences the cooling and heating load of a building. However, in the maximum loads by the change of weather data in four factors (dry-bulb temperature, web-bulb temperature, cloud amount, and wind speed) among 14 weather factors, the research shows that 5.95% in cooling load and 27.56% in heating load increased, and these results cannot be ignored. In order to make weather data for Performing energy performance evaluation for future buildings, the flow of weather data for the Present and past should be obviously grasped.

• 한국농림기상학회지
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• 제23권1호
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• pp.34-45
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• 2021

날씨는 밭작물의 가격 측정과 생산량 및 품질에 영향을 미치기 때문에 농산업에서 가장 많이 고려되는 요소이다. 특히, 밭작물의 경우 평지보다 산지에서 재배되는 등 외부 환경에 많이 노출되어 있다. 본 연구는 수치 산림입지토양도를 이용하여 산지를 구성하고 있는 12개의 토양의 특성 자료와 기상정보 간의 연관성을 파악하였다. 공간적 상관관계가 고려된 GAM, 크리깅, RF를 이용하였으며, 연구자료는 2009년 1월부터 2018년 12월까지의 기상청과 농촌진흥청에서 수집한 일 단위 평균기온, 최고기온, 최저기온, 강우량 자료가 사용되었다. 분석결과 지리적 효과만 반영된 GAM이 상대적으로 추정성능이 우수하였고, 산림입지토양도는 밭작물 재배지 기상정보를 추정에 큰 도움이 되지 않았다. 이에 유의수준을 5%로 통계적 가설검정을 수행하여 중요 요인을 선택하였다. 산림입지토양도의 기후대코드(CLZN_CD)와 토양목본코드 B(SIBFLR_LAR)가 기상정보 추정에 상대적 유의미한 요인으로 선정되었다. 기후대코드를 추가한 모형의 경우 일 평균 기온과 일 최저기온의 공간 보간 성능이 향상되었다. 한반도의 국토는 70%가 산지이고 밭작물은 주로 산지에서 재배되고 있다. 따라서 산지의 기상정보를 추가 수집하여 연구를 수행한다면 생육시기별로 밭작물을 관리하는데 도움이 될 것으로 기대한다.

• 한국대기환경학회지
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• 제32권3호
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• pp.237-247
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• 2016

Global climate change is becoming one of the greatest challenges facing humanity. This article proposes a psychological perspective of climate change adaptation. Climate change-related severe adverse weather events may trigger mental health problems, including increased post-traumatic stress disorder (PTSD), depression, anxiety, violence, and even suicide. Forced migration could be considered a coping method for dealing with weather events, but it may also pose a psychological threat. People respond to severe weather events in different ways based on their individual characteristics. Psychological risks from adverse weather events are mediated and moderated by these factors, which are influenced by personal cognition, affect, and motivation. Examinations from a psychological perspective, which have been neglected in the science of climate change thus far, may provide keys to successful adaptation and the prevention of serious psychological problems resulting from the experience of severe weather events. A new prevention strategy has been suggested for coping with climate threats through encouraging attitude change, establishing proactive support systems for vulnerable groups, establishing a PTSD network, and implementing a stress inoculation program.

• 디지털융복합연구
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• 제12권11호
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• pp.1-12
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• 2014

오늘날 각종 재난발생 유형이 다양해지고 다기능화 된 도시 구조로 예측 불허의 재난 발생이 우려되고 있다. 또한 지구 온난화 등으로 재난이 한번 발생했다 하면 대형화되고 있고, 재난 수습을 위하여 가용한 자원을 최대한 동원하여야 하며, 재난현장이 실시간대 언론 등 미디어를 통해 생중계되는 상황이기에 재난현장 지휘자는 그 모든 요소를 고려하여 재난현장을 지휘해야 한다. 그러나 재난현장 지휘에 영향을 미치는 요소는 그 중요성에도 불구하고, 여기에 관련된 사전 연구가 거의 없는 실정이다. 이에 따라 본 연구에서는 국방 분야에서 전투현장 지휘에 적용되고 있는 요소인 METT-TC(Mission, Enemy, Troops, Terrain and weather, Time available, and Civilian considerations)를 기반으로, 일반 재난현장을 효과적으로 지휘하는데 영향을 미치는 요소로 MORT-TEC(Mission, Object, Resources available, Terrain and weather, Time available, Exercise, and Civilian considerations)를 제안한다. 이러한 요소를 세월호 구조 상황에 적용해 봄으로써, 향후 연구 및 재난현장의 지휘자들에게 도움이 될 수 있는 정책적인 의미를 제시한다.

• 한국농공학회지
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• 제16권4호
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• pp.3555-3571
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• 1974

This experiment of which aim contribute to plan irrigation system so as to increase forage crop yields, was conducted to estimate evapotranspiration amount of forage crops and to find out system of consumed water in a pasture-ground. The results obtained by this study are as follows: 1. The general weather conditions which, were closely related to the evapotrannpiration of forage crops were nearly same as those of the average year with the exception that temperature of May and June were slightly low. 2. According to the investigation of potential evapotranspirations (P.E) or forage crops and its changes during growing periods, changes of tenday P.E. were high significant according to the harvesting period. P.E of Alfalfa of which yield was the largest was the biggest. Althrough the correlations between P.E. and meteorological factors were irregular oming to three-time harvesting, correlation between ten-day evapotraspiration amount and copper plated pan evaporation or solar radiation was high positive significant. 4. Predicting formulas of P.E. were led by weather factors, and also relatione between P.E. and weather factors were showed as figure. from the these formulas, P.E. may be calculated by weather factors. 5. Predicting formulas of P.E. were led by mean temperature and copper plated pan evaporation, and by mean temperature and solar radiation. As computed values and measured values showed in figure, these formulas were high signiflent. 6. In the total consumed soil water duration of 10 days which, was non-rain period from 12th to 21th of August, Alfalfa was the largest 48.1mm, second, Orchard grass 40.1mm and Fescue 37.6mm, and Ladino clover was the smallest 37.1mm, also, order of each forage crop yield amound. was same to the abov. Order of soil moisture extraction rate of soil layer of all the for forage crops dulation of ten-day was soil layer 1 which was largest, soil layer 2, 3, and 4 Reviewing the the first five-day and the second five day, in the first five-day, order of that of all the forage crops was same to the above, but in the second five-day, that of soil layer 2 or 3 was more than the of soil layer 1.

• 대기
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• 제27권4호
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• pp.377-384
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• 2017

The effective management of call centers under special circumstances is critical to improve customer satisfaction. In order to effectively respond to call center counseling demand, this paper aims to identify factors having the greatest impact on the number of Korea Meteorological Administration (KMA) call center counseling. To do so, we propose to combine call center data with severe weather announcement data and investigate how the severe weather announcement affects the number of KMA call center counseling. A time lag analysis is conducted and it is found that the severe weather announcement takes about an hour to be reflected in the number of KMA call center counseling. Based on the result of the time lag analysis, we conduct a comparative analysis according to time and season using the data collected from 1 January 2012, to 29 June 2016. The results show that the number of KMA call center counseling increases at lunchtime and decreases during nighttime, and the average rate of change in call center counseling demand tends to be larger under the severe weather announcement. For the comparative analysis according to the season, there are significant differences in the effect of severe weather announcement on the number of KMA call center counseling in spring, fall and winter.

• 대기
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• 제14권3호
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• pp.19-28
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• 2004

In this study, two years Automatic Weather Station (AWS) data observed near the coast and islands are used to evaluate gust factors only when time averaged wind speed is higher than 5 ms. The gust factors are quite different in spatial and temporal domain according to analysis method. As the averaged time is increased, the gust factors are also increased. But the gust factors are decreased when wind speed is increased. It is because each wind speed is averaged one and a maximum wind is the greatest one for each time interval. The result from t-test is shown that all data are included within the 99% significance level. A sample standard deviation of ten minutes and one minute are 0.137~0.197, 0.067~0.142, respectively. Recently, the gust factor provided at the Korea Meteorological Administration (KMA) Homepage is calculated with one-hour averaged method. All though this method is hard to use directly for forecasting the strong wind over sea and coast, the result will be a great help to express Ocean Storm Flash in the Regional Meteorological Offices and the Meteorological Stations.

• 한국환경과학회지
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• 제28권1호
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• pp.85-97
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• 2019

Meteorological factors and air pollutants are associated with respiratory diseases, and appropriate use of weather and air quality information is helpful in the management of patients with such diseases. This study was performed to investigate both the utilization of weather and air quality information by, and the needs of, patients with respiratory diseases. Questionnaires were administered to 112 patients with respiratory diseases, 60.7% of whom were female. The rates of bronchial asthma and chronic obstructive pulmonary disease among patients were 67.0% and 10.7%, respectively. The majority of subjects (90%) responded that prevention was important for respiratory disease management and indicated that they used weather and air quality information either every day or occasionally. However, respondents underestimated the importance of weather and air quality information for disease management and were unaware of some types of weather information. The subjects agreed that respiratory diseases were sensitive to weather and air quality. The most important weather-related factors were diurnal temperature range, minimum temperature, relative humidity, and wind, while those for air quality were particulate matter and Asian dust. Information was gleaned mainly from television programs in patients aged 60 years and older and from smartphone applications for those below 60 years of age. The subjects desired additional information on the management and prevention of respiratory diseases. This study identified problems regarding the utility of weather and air quality information currently available for patients with respiratory diseases, who indicated that they desired disease-related information, including information in the form of action plans, rather than simple health- and air quality-related information. This study highlights the necessity for notification services that can be used to easily obtain information, specifically regarding disease management.