• 대한기계학회논문집A
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• 제32권11호
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• pp.1022-1027
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• 2008

TPE is used as alternative for rubber, the best example is the weather strip for automobile. The nonlinear material properties of weather strip were important to predict the behaviors of weather strip. Uniaxial tension and equi-biaxial tension tests were performed to achieve the nonlinear material constant and stress-strain curves. The nonlinear material constant of weather strip is evaluated by using the nonlinear finite element analysis. In this paper, the prediction for weather strip is analyzed by using commercial finite element program, ANSYS. The nonlinear finite element analysis of weather strip is executed to predict the behavior of weather strip for automobile.

• Agribusiness and Information Management
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• 제6권1호
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• pp.1-11
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• 2014

The purpose of this study is to develop the growth prediction model that can predict growth and development information influencing on the production of citrus fruits. The growth model was developed to predict the floral leaf ratio, number of fruit sets, fruit width, and overweight fruits depending on the main period of growth and development by considering the weather factors because the fruit production is influenced by weather depending on the growth and development period. To predict the outdoor-grown citrus fruit production, the investigation result for the standard farms is used as the basic data; in this study, we also understood that the influence of weather factors on the citrus fruit production based on the data from 2004 to 2013 of the outdoor-grown citrus fruit observation report in which the standard farms were targeted by the Agricultural Research Service and suggested the growth and development information prediction model with the weather information as an independent variable to build the observation model. The growth and development model for outdoor-grown citrus fruits was assumed by using the Ordinary Least Square method (OLS), and the developed growth prediction model can make a prediction in advance with the weather factors prior to the observation investigation for the citrus fruit production. To predict the growth and development information of the production of citrus fruits having a great ripple effect as a representative crop in Jeju agriculture, the prediction result regarding the production applying the weather factors depending on growth and development period could be applied usefully.

• 통합자연과학논문집
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• 제10권4호
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• pp.216-224
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• 2017

In the terrain of the Korean peninsula, mountainous and flat lands are complexly distributed in small areas. Therefore, local severe weather develops and disappears in a short time due to the influence of the terrain. Particularly in the case of local severe weather with heavy wind that has the greatest influence on aviation meteorology, the scale is very small, and it occurs and disappears in a short time, so it is impossible to predict with fragmentary data alone. So, we use weather radar to detect and predict local severe weather. However, due to the development of wireless communication services and the rapid increase of wireless devices, radio wave jamming and interference problems occur. In this research, we confirmed through the cases that when the radio interference echo which is one of the non-precipitation echoes that occur during the operation of the weather radar is displayed in the image, its form and shape are shown in a long bar shape, and have a strong dBZ. We also found the cause of the interference through the radio tracking process, and solved through the frequency channel negotiation and AP output minimizing. The more wireless devices increase as information communication technology develops in the future, the more emphasized the problem of radio wave interference will be, and we must make the radio interference eliminated through the development of the radio interference cancellation algorithm.

• 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.

• 한국융합학회논문지
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• 제5권1호
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• pp.9-15
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• 2014

교통사고는 다양한 요인으로 인해 발생한다. 그 중에는 교통사고가 발생할 당시의 기상상태가 있다. 기상상태에 따라 교통사고로 인해 발생하는 사망자의 비율은 차이가 있다. 교통사고로 인한 사망자의 수를 줄이려면 기상 상태에 따라 발생될 교통사고 발생 수를 예측 하는 것이 필요하다. 본 논문은 기상 상태에 따른 교통사고 발생 빈도수를 예측하는 모델링을 제안한다. 예측 모델링의 이론으로는 마코프 프로세스를 적용하였다. 제안된 모델링에 실제 데이터를 적용하여 교통사고 발생 수를 예측 하였고, 실제 발생 수와 비교하였다. 본 논문은 기상 변화에 따른 교통사고 정책수립에 도움을 줄 것이다.

• 한국IT서비스학회지
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• 제18권1호
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• 한국건설관리학회논문집
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• 제23권4호
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• pp.78-83
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• 2022

많은 국내 건설업계들은 건설관리에 날씨정보를 활용하기 위해 지속적으로 노력하고 있다. 건설업은 옥외작업이 많기 때문에 날씨의 영향이 크게 반영된다. 그러므로 정확한 공사기간을 예측하기 위해서는 분명히 날씨 정보가 필요하며, 이를 고려한 작업 불가능 일수 산정은 매우 중요하다. 하지만 정확한 장기 날씨 예측이 힘들기 때문에 많은 건설 회사들이 정확한 공사기간 산정에 어려움을 갖고 있다. 이에 본 연구에서는 과거 장기 날씨 정보를 분석한 후 현장 위치 및 일자에 따라 지역별, 계절별 기상정보를 건설관리 시스템에 적용하여 작업가능일과 현장정보, 기상정보를 확인하고자 한다.

• 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.

• 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.

• 한국농공학회논문집
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• 제66권4호
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• pp.17-26
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• 2024

This study aimed to design and build a web-based decision support system for wheat cultivation management. The system is designed to collect and measure the weather environment at the growth stage on a daily basis and predict the soil moisture content. Based on this, APSIM, one of the process-based crop models, was used to predict the potential yield of wheat cultivation in real time by making decisions at each stage. The decision-making system for wheat crop management was designed to provide information through a web-based dashboard in consideration of user convenience and to comprehensively evaluate wheat yield potential according to past, present, and future weather conditions. Based on the APSIM model, the system estimates the current yield using past and present weather data and predicts future weather using the past 40 years of weather data to estimate the potential yield at harvest. This system is expected to be developed into a decision support system for farmers to prescribe irrigation and fertilizer in order to increase domestic wheat production and quality by enhancing the yield estimation model by adding influence factors that can contribute to improving wheat yield.