• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.1077-1080
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• 2009

Dense meteorological data are hard to be collected on the limited urban areas because of vest cost which is required to install the corresponding sensors on the areas. Recently, to overcome this problem, the sensor network technique comes to the fore. This paper studies an application to service the meteorological map using mobile phone sensors. A design results for system implementation are introduced in this paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.873-876
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• 2017

최근 딥러닝(Deep-learning)을 활용한 기상 예측, 심리 예측, 교통상황 예측 등 다양한 분야에 걸쳐 여러 모델의 인공신경망이 활용되고 있다. 본 논문에서는 여러 분야 중 스포츠라는 분야에 접근했으며, 딥러닝 모델을 통해 승부를 예측하는 실험을 진행하였다. 야구의 승부는 선수의 능력치, 기상의 변화, 험/어웨이 여부, 교체 여부 등 가늠할 수 없이 수많은 데이터들에 의존하고 있다. 그러나 본 논문에서는 이러한 수많은 데이터 중 경기 외적인 데이터를 제외한 데이터를 활용하여 그 다음 경기의 승부를 예측할 수 있을 지를 연구한다. 날짜 별 경기들이 훈련데이터가 되고 목표는 이전 경기들의 영향으로 예측된 다음 경기의 승/패를 예측한다. 즉 순차적인 데이터의 활용에 적합한 모델, Recurrent Neural-Network을 이용하였다. 이를 위하여 KBreport에서 데이터를 수집하였고, 수집된 데이터를 훈련 데이터 세트로 만들어 Recurrent Neural Network를 통해 훈련시켜 다음 경기의 승패를 예측하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.111-122
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• 2012

There has been a strong demand for providing diverse services to drivers utilizing existing ITS infrastructure. To this end, this study is aiming at improving the accuracy of a variable speed limit system by determining recommended speeds for the system utilizing the information from Urban Traffic Information System(UTIS) and Weather Information System(WIS). In order to determine appropriate speed limits under inclement weather conditions for the variable speed limit system, this study examined three methods: i) the method utilizing the information from WIS, ii) the method utilizing the information from UTIS, and iii) the method which combines the information from WIS and UTIS using different weights for diverse weather conditions. Finally, this study selected the third method which determines an appropriate speed limit using the relationship between the vehicle operating speed and the minimum stopping distance which is estimated using the existing speed limit, surface coefficient of friction and superelevation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.36-44
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• 2006

In this study, we constructed a forest fire danger map in the Yeongdong area of Gangwon-do and Northeastern area of Gyeongsangbuk-do using a forest fire rating model and geographical information system (GIS). We investigated the appropriate positions of the automatic weather station (AWS) and a comprehensive network solution (a system including measurement, communication and data processing) for the establishment of an optimum mountain meteorological observation network system (MMONS). Also, we suggested a possible plan for combining the MMONS with unmanned monitoring camera systems and wireless relay towers operated by local governments and the Korea Forest Service for prevention of forest fire.

• Journal of Sensor Science and Technology
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• v.8 no.3
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• pp.253-258
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• 1999

This paper is written for the purpose of obtaining the information about the weather easily by the development of weather forecast system sensing temperature, humidity, and atmospheric pressure as key information. For this, data is obtained from the Weather Bureau, and analyzed in order to set a standard of weather forecast from the collected data. The pressure sensor and temperature-humidity sensor are fabricated using the piezoresistive effect of semiconductor, which are used to collect data. The weather forecast system is made using microprocessor.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.44-55
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• 2013

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.278-279
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• 2015

최근 해수욕장, 저지대 침식, 해안시설물 노후화 등과 같이 해안지역 구성 및 지형적 요인에 따라 국지적으로 발생하는 피해와 태풍 및 이상너울 등의 대규모 기상현상에 의해 해안재난이 발생가능성이 높아지고 실제 발생하는 실정이다. 본 논문에서는 재난대응을 위한 과학적 재난정보 수집 및 분석을 통해 의사결정에 활용하고 효과적으로 예방 대응하고자 유관기관에서 다양하게 구축된 시스템의 재난관련 자료를 수집하였으며, 태풍 내습시 신속한 대응을 위해 폭풍해일 시뮬레이션을 통해 범람파고를 추정하였다. 기존 상황판단을 위한 정보수집단계에 추가적으로 관측자료 및 시뮬레이션을 통한 정량적 피해추정정보를 신속하게 제공함으로서 재난상황판단을 가능할 수 있도록 방안을 마련하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.444-446
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• 2012

ECMWF(European Centere of Medium-Range Weather Forecasts)에서 정의한 기상 데이터는 254개의 속성으로 구성되어 있다. 기상 데이터는 매 6시간 마다 수집되며 그 양이 방대하다. 하지만 모든 속성을 이용하여 기상을 분석 하는 것은 너무 많은 시간이 소요되고 대부분의 속성들은 기상을 분석하는데 많은 영향을 미치지 않는다. 따라서 적절한 속성을 이용하여 날씨를 정확하게 분석 하는 것은 매우 중요하다. 본 연구에서는 과거의 기상 데이터를 이용하여 6시간 후의 호우 또는 비호우를 예측하는 실험을 진행 하였다. 기상 데이터 속성의 조합과 관찰 영역 변경에 따라 호우상황 예측 정확도의 변화를 살펴본다. 6시간 이후의 호우/비호우 예측을 함에 있어 조합된 속성이 단일 속성보다 더 좋은 결과를 보이는 것을 알수 있었고 관찰 영역이 더 클수록 좋은 예측 결과를 보임을 알 수 있었다. 하지만 일정 범위가 넘어서면 계산 비용이 높아지고 연산 속도가 현저하게 떨어지고 예측 결과도 범위에 비례하여 크게 향상되지 않는것을 알 수 있었다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.