• 로봇학회논문지
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• 제17권3호
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권1호
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• 한국태양에너지학회 논문집
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• 제37권1호
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• pp.25-38
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• 2017

Climate change and environmental pollution are becoming serious due to the use of fossil energy. For this reason, renewable energy systems are increasing, especially photovoltaic systems being more popular. The photovoltaic system has characteristics that are affected by ambient weather conditions such as insolation, outside temperature, wind speed. Particularly, it has been confirmed that the performance of the photovoltaic system decreases as the module temperature increases. In order to grasp the influence of the module temperature in advance, several researchers have proposed the prediction models on the module temperature. In this paper, we predicted the module temperature using the aforementioned prediction model on the basis of the weather conditions in Incheon, South Korea during July and August. The influence of weather conditions (i.e. insolation, outside temperature, and wind speed) on the accuracy of the prediction models was also evaluated using the standard statistical metrics such as RMSE, MAD, and MAPE. The results show that the prediction accuracy is reduced by 3.9 times and 1.9 times as the insolation and outside temperature increased respectively. On the other hand, the accuracy increased by 6.3 times as the wind speed increased.

• 한국태양에너지학회 논문집
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• 제39권6호
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• pp.41-54
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• 2019

The purpose of this study is to analyze the performance of solar thermal system according to regional weather conditions and feedwater temperature. The performance analysis of the system was carried out for the annual and winter periods in terms of solar fraction, collector efficiency and it's optimal degree. The system is simulated using TRNSYS program for 6 cities, Seoul, Incheon, Gangneung, Mokpo, Gwangju, and Ulsan. Simulation results prove that the solar fraction of the system varies greatly from region to region, depending on weather conditions and feedwater temperatures. Monthly average solar fraction for winter season from November to February, a time when heat energy is most required, indicated that the highest is 73.6% in Gangnueng and the lowest is 56.9% in Seoul. This is about 30% relative difference between the two cities. On the other hand, the collector efficiency of the system for all six cities was analyzed in the range between 40% and 42%, indicating small difference compare to the solar fraction. The annual average solar fraction is rated the highest at 40 collector degree, while monthly average solar fraction during winter season is rated at 60 degree.

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2008년도 추계학술논문발표회 논문집
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• pp.490-494
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• 2008

We simulated a forest fire which was occurred in Yangyang area on 2005 and compared a results between two different weather conditions(real weather condition and mean weather condition since 1968) using FARSITE, which is a forest fire spread simulator for preventing and predicting fire in USDA. And, we researched a problem in the transition for introducing, so we serve the basic method for prevention and attacking fire. In the result, severe weather condition on 2005 effected a forest fire behavior. The rate of spread under real weather condition was about 4 times faster than mean weather condition. Damaged area was about 10 time than mean weather condition. Therefore, Climate change will make a more sever fire season. As we will encounter to need for accurate prediction in near future, it will be necessary to predict a forest fire linked with future wether and fuel condition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전력기술부문
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• pp.190-193
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• 2001

In order to verify the efficiency or availability and stability of photovoltaic(PV) generation systems, huge system apparatuses are needed, in general, in which an actual size of solar panel, a type of converter system and some amount of load facilities should be installed in a particular location. It is also hardly possible to compare a Maximum Power Point Tracking (MPPT) control scheme with others under the same weather and load conditions in an actual PV generation system. The only and a possible way to bring above-mentioned problem to be solved is to realize a transient simulation scheme for PV generation systems using real weather conditions such as insolation and surface temperature of solar cell. The authors, in this paper, introduces a novel simulation method, which is based on a real-time digital simulator (RTDS), for PV generation systems under the real weather conditions. Firstly, VI characteristic equation of a solar cell is developed as an empirical formula and reconstructed in the RTDS system, then the real data of weather conditions are interfaced to the analogue inputs of the RTDS. The outcomes of the simulation demonstrate the effectiveness of the proposed simulation scheme in this paper. The results shows that the cost effective verifying for the efficiency or availability and stability of PV generation systems and the comparison research of various control schemes like MPPT under the same real weather conditions are possible.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권3호
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• pp.247-256
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• 2015

걸프전 이후 항공력은 전쟁 승리의 핵심 역할을 수행하였다. 하지만 레이저 유도폭탄, 전자광학 장비 같은 첨단무기들은 기상 조건이 맞지 않으면 그 효과가 크게 떨어진다. 따라서 레이저 유도폭탄이 할당된 항공기는 기상 악화 시 무장교체가 이루어져야 한다. 하지만 현재까지 무장교체 시기에 대한 객관적인 기준은 없다. 따라서 본 논문에서는 구름 영상을 처리하여 레이저 유도폭탄의 명중률을 예측하는 알고리즘을 제안한다. 알고리즘의 정확도를 검증하기 위해 레이저 유도폭탄에 영향을 미칠 수 있는 기상 상황을 모의 비행장비에 적용하고 모의 무장투하를 실시하여 데이터를 수집 및 분석하였다. 모의 비행장비에 적용한 기상 조건과 유사한 구름 영상을 제작하여 알고리즘에 적용한 결과 대부분의 기상 조건에서 레이저 유도폭탄의 명중률을 정확하게 예측할 수 있음을 확인하였다.

• 대기
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• 제31권5호
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• pp.539-551
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• 2021

In this paper, the weather records in 'Nanjung Ilgi' were investigated and the weather characteristics of the southern coast of Korea (SC_Korea) was discussed. The Nanjung Ilgi is a personal diary written by admiral Yi Sun-sin from January 1592 to November 1598 during the 7-year war caused by the Japanese invasion. He is a respected great leader in the history of world naval warfare, winning all 23 battles against the Japanese. Of the 1593 days of diaries currently preserved, only 42 days have no weather records. Weather was recorded in detail, including sky conditions, precipitation, wind characteristics and others. Weather records were extracted from the diary, converted to the solar calendar, and compared with the meteorological data of Yeosu. The average annual precipitation day is about 90 days, which is similar to the current 95~100 days. As in the current climate, precipitation frequently occurs for about 30 days in summer, but less than 15 days in other seasons, and the rainy season starts from June 14 to 21 and ends from July 6 to 17. It seems that the abnormal cold and heat phenomena, which deviate significantly from the seasonal average climate, occurred on 6 and 21 days, respectively, over 7 years. This means that the weather records of Nanjung Ilgi can be used as valuable data on the climate of SC_Korea in the late 16th century. The fact that he recorded the weather even in such extreme battle conditions shows that he clearly recognized the importance of weather in warfare.

• The Plant Pathology Journal
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• 제21권2호
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• pp.111-118
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• 2005

Weather data for disease forecasts are usually derived from automated weather stations (AWS) that may be dispersed across a region in an irregular pattern. We have developed an alternative method to simulate local scale, high-resolution weather and plant disease in a grid pattern. The system incorporates a simplified mesoscale boundary layer model, LAWSS, for estimating local conditions such as air temperature and relative humidity. It also integrates special models for estimating of surface wetness duration and disease forecasts, such as the grapevine downy mildew forecast model, DMCast. The system can recreate weather forecasts utilizing the NCEP/NCAR reanalysis database, which contains over 57 years of archived and corrected global upper air conditions. The highest horizontal resolution of 0.150 km was achieved by running 5-step nested child grids inside coarse mother grids. Over the Finger Lakes and Chautauqua Lake regions of New York State, the system simulated three growing seasons for estimating the risk of grape downy mildew with 1 km resolution. Outputs were represented as regional maps or as site-specific graphs. The highest resolutions were achieved over North America, but the system is functional for any global location. The system is expected to be a powerful tool for site selection and reanalysis of historical plant disease epidemics.

• 한국ITS학회 논문지
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• 제18권2호
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• pp.12-28
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• 2019

도로기하구조는 교통사고를 발생시키는 다양한 요인 중 하나이지만, 동일한 도로기하구조 조건하에서도 기상상태에 따라 교통사고에 미치는 영향이 다르게 나타난다. 본 연구에서는 2001년부터 2014년까지 14년간 전국 고속도로 사고자료와 기상자료를 매칭하여 교통사고 심각도에 영향을 미치는 변수들을 분석하였다. 도로기하구조와 기상상태의 상호작용이 사고심각도에 미치는 영향뿐만 아니라, 개별사고 심각도 간의 지역별 상관성을 반영하기 위해 위계적 순서형 모형을 사용하였다. 위계적 모형 중에서도 도로기하구조와 기상상태의 상호작용 변수를 포함한 임의절편모형과 기상상태의 지역별 특성을 상위변수로 포함하는 임의계수모형을 모두 활용하였다. 분석결과 톨게이트 및 램프구간, 내리막 경사 3%이상, 콘크리트 방호벽 등이 기상상태에 따라 사고 심각도에 미치는 영향이 달라지는 것을 확인하였다. 또한 도로기하구조와 기상상태의 복합적인 영향은 강우량 또는 강설량에 선형적이지 않을 수 있음을 보여주었다. 끝으로 본 연구의 분석결과를 기반으로 안전개선 대책을 제시하였으며, 이를 토대로 향후 교통사고 심각도 감소가 가능할 것으로 기대된다.