• The Journal of Korea Robotics Society
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• v.17 no.3
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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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• v.16 no.1
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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.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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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.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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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.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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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.

• Proceedings of the KIEE Conference
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• 2001.05a
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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.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.247-256
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• 2015

Since the Gulf War, air power has played a key role. However, the effect of high-tech weapons, such as laser-guided bombs and electronic optical equipment, drops significantly if they do not match the weather conditions. So, aircraft that are assigned to carry laser-guided bombs must replace these munitions during bad weather conditions. But, there are no objective criteria for when weapons should be replaced. Therefore, in this paper, we propose an algorithm to predict the hit rate of laser-guided bombs using cloud image processing. In order to verify the accuracy of the algorithm, we applied the weather conditions that may affect laser-guided bombs to simulated flight equipment and executed simulated weapon release, then collected and analyzed data. Cloud images appropriate to the weather conditions were developed, and applied to the algorithm. We confirmed that the algorithm can accurately predict the hit rate of laser-guided bombs in most weather conditions.

• Atmosphere
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• v.31 no.5
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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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• v.21 no.2
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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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.12-28
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• 2019

Road geometry is one of the many factors that cause crashes, but the effect on traffic accident depends on weather conditions even under the same road geometry. This study identifies the variables affecting the crash severity by matching the highway accident data and weather data for 14 years from 2001 to 2014. A hierarchical ordered Logit model is used to reflect the effects of road geometry and weather condition interactions on crash severity, as well as the correlation between individual crashes in a region. Among the hierarchical models, we apply a random intercept model including interaction variables between road geometry and weather condition and a random coefficient model including regional weather characteristics as upper-level variables. As a result, it is confirmed that the effects of toll, ramp, downhill slope of 3% or more, and concrete barrier on the crash severity vary depending on weather conditions. It also shows that the combined effects of road geometry and weather conditions may not be linear depending on rainfall or snowfall levels. Finally, we suggest safety improvement measures based on the results of this study, which are expected to reduce the severity of traffic accidents in the future.