• 수산해양기술연구
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• 제27권1호
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• pp.56-63
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• 1991

This paper aims to describe the relation between the weather condition, especially typhoon and a shipwreck in Korean waters. For this study, it was investigated the statistical characteristics of a shipwreck due to the weather, pressure patterns governing the shipwreck in Korean waters. and the relation between the intensity of typhoon and the amount of a disaster. The results are summarized as follows: 1) The monthly occurrence frequency of a shipwreck was the heighest in July followed by February, March in descending order. 2) The pressure patterns governing the shipwreck were classified broadly into six types and pressure pattern which had most occurrence frequency of a shipwreck was Type V and then cames Type I, Type III and type IV in that order. 3) Occurence frequency of a shipwreck and the amount of a kinetic energy of typhoon have nothing to do with each other. In case of Wind-Typhoon that brought more a strong wind than a heavy rainfall, there were seriously affected ships and buildings by the wind.

• 한국대기환경학회지
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• 제25권6호
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• pp.539-549
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• 2009

The purpose of this study is to estimate hourly Asian dust emission flux in springtime by using the optimized Weather Research Forecasting model (WRF) in order to accurately predict the horizontal flux of Asian dusts. Asian dust emission flux using 5 empirical formulas such as US EPA, Park and Inn, Wang, The Goddard Chemistry Aerosol Radiation and Transport (GOCART) and Dust Entrainment and Deposition (DEAD) were calculated and compared by using classified land-use types and size distribution at various locations in China and Mongolia together with the hourly meteorological elements of the WRF model. As a result, the empirical formula in US EPA among them, which was considered the various conditions such as vegetation, soil type and terrain, was better than the other 4 empirical formulas. However, these formulas were adjusted hourly and vertically in time and space because there was different order and time resolution of dust emissions from original empirical formulas.

• 전기학회논문지
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• 제64권9호
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• 한국도로학회논문집
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• 제15권2호
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• pp.139-147
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• 2013

PURPOSES : This study aims to draw differences between primary and secondary crashes by comparing crash characteristics and to identify the unique characteristics of secondary crashes for making better effective countermeasures to reduce secondary crashes. METHODS : The characteristics of secondary crashes were compared to those of primary crashes through a two sample proportional test (z-test). RESULTS : The results showed that vehicle-to-vehicle crashes and vehicle-to-person crashes are dominant crash types in secondary crashes. Compared to primary crashes, secondary crashes were likely to occur during nighttime. With respect to season and weather, the proportion of secondary crashes occurred during winter and in snowy weather is relatively higher than that of primary crashes. The main causes of primary crashes were found to be drowsiness, speeding, and exaggerated steering control, whereas main factors affecting the occurrence of secondary crashes were negligence of keeping eyes forward and no keeping a safe distance as expected. CONCLUSIONS : The characteristics affecting the occurrence of secondary crashes are different from those of primary crashes, indicating that proper countermeasures should be established to prevent the occurrence of secondary crashes on highways.

• KIEAE Journal
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• 제6권3호
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• pp.49-56
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• 2006

The roof among the outer surfaces of buildings is an optimum place to install PV since it is the best favorable part in the building to be exposed to day light. Especially, in case of module of BIPV for Roof, it should have essentially the functions of both electricity generation and roof-finish as a construction material. The followings are the results of the study which has analyzed the architectural conditions and applications thereof at the job site. -The aesthetic function of BIPV module is very important because the roof, mostly located at the top of the buildings, is easily recognized and affects outer interior design of the building a lot. -The heat proof of BIPV for Roof could affect the energy consumption through the roof having a wide area. -For architectural condition to the weather, the roof has to ensure the stability of the weather, humidity proof, and airtightness to the wind respectively. -For architectural condition of the structure, endurance by physical power such as stability of both combining and fixing and transfer of load should be ensured. -For residents protection, it has also architectural functions to secure for the space and shield ozone, UV and noxious substances. -Through its practical applications, It is already confirmed that there are various types of BIPV modules overseas and its application has been proved successfully.

• 한국농림기상학회지
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• 제8권1호
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• pp.22-27
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• 2006

최근 수십년간 관측자료에 의하면 기후는 여러 측면에서 눈에 띄게 달라졌다. 이제는 관측이래 최고기온 혹은 최대강수량이란 단어가 그렇게 낯설지않은 시대가 되었고 앞으로의 변화와 그 여파에 더욱 긴장하며 살고 있다. 하지만 기후변화와 그 영향을 조금만 잘 이해하면 생태, 사회, 경제적 영향 가운데 우리가 충분히 받아들일 수 있는 부분도 상당하다. 식물과 자연생태계는 기후변화에 적응할 수 있는 다양한 방법을 이미 우리에게 보여주었다. 지구온난화에 의해 우리의 기후는 더욱 예측불허의 혼란에 빠질 것으로 보인다. 이 논문을 통해 역사적인 기후이변사례와 식물의 적응전략을 찾아보며, 인류가 기후변화를 극복하고 생태계를 유지하기 위해 보여주었거나 혹은 그렇지 못했던 사례에 대해 설명한다.

• 한국환경과학회지
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• 제28권11호
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• pp.1047-1059
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• 2019

To address the increase of weather hazards and the emergence of new types of such hazards, an optimization technique for three-dimensional (3D) representation of meteorological facts and atmospheric information was examined in this study as a novel method for weather analysis. The proposed system is termed as "meteorological and air quality information visualization engine" (MAIVE), and it can support several file formats and can implement high-resolution 3D terrain by employing a 30 m resolution digital elevation model. In this study, latest 3D representation techniques such as wind vector fields, contour maps, stream vector, stream line flow along the wind field and 3D volume rendering were applied. Implementation of the examples demonstrates that the results of numerical modeling are well reflected, and new representation techniques can facilitate the observation of meteorological factors and atmospheric information from different perspectives.

• 대한임베디드공학회논문지
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• 제6권5호
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• pp.311-317
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• 2011

The blind spot is an area where drivers visibility does not reach. When drivers change a lane to adjacent lane, they need to give an attention because of the blind spot. If drivers try to change lane without notice of vehicle approaching in the blind spot, it causes a reason to have a car accident. This paper proposes a night-time blind spot vehicle detection using cameras. At nighttime, head-lights are used as characteristics to detect vehicles. Candidates of headlight are selected by high luminance feature and then shape filter and kalman filter are employed to remove other noisy blobs having similar luminance to head-lights. In addition, vehicle position is estimated from detected head-light, using virtual center line represented by approximated the first order linear equation. Experiments show that proposed method has relatively high detection porformance in clear weather independent to the road types, but has not sufficient performance in rainy weather because of various ground reflectors.

• 산업경영시스템학회지
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• 제45권2호
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제26권1호
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• pp.23-48
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• 2022

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.