• 식물병연구
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• 제8권4호
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• pp.199-206
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• 2002

벼 도열병 발병 모의 실황 포장을 발병 상습지(안동대 실험포장; 산간 협곡 위치한 천수답)에 설정하여 벼 식물생육군락(일품벼 공시)의 실황 기상자료를 무인기상관측 장치를 통해 수집, 가공하여 도열병 발병에 미치는 기상요인의 가변값을 추정, 분석하였다. 기상요소 측정은 시험포장에 무인기상관측장치를 설치하여 매시단위로 기온, 상대습도, 일사량, 강우량, 풍향, 풍속, 지온, 잎습전지속 시간 등을 측정하였다. 각 기상요인중 도열병 발병에 가장 많은 영향을 미친 것은 발병 전 10일간 평균최고기온으로 결정계수 0.95＊을 나타냈으며, 도열병 발병에 영향을 가장 미치지 않은 요인은 풍속으로 결정계수 0.24$^{ns}$ 로 나타났다. 도열병 발병과 병 진전에 가장 높은 유의성을 보인 기상요인은 평균온도(T-ave), 최고온도(T-max), 상대습도(RH), 상대습도 90%이상인 누적시간(RD) 등이었으며, 이들을 이용한 통계적 모형은 아래와 같다. Y = -3410.91 - 23.91 $\times$ T-ave ＋ 28.56 $\times$ T-max ＋ 41.0 $\times$ RH - 3.75 $\times$ RD, ($R^2$= 0.99＊), (T-ave >= 19$^{\circ}C$ and T-max - T-ave >= 5.2$^{\circ}C$ and RH% >= 90.4%. 발병모형과 발병심각도의 적합도 검정($\chi$$^2$)은 유의도 0.001로 발병모형이 실제 발병 심각도와 유사함을 나타내었다.

• 한국기후변화학회지
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• 제4권4호
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• pp.317-330
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• 2013

홍수와 가뭄, 고온 등 이상기상의 영향으로 쌀 단수가 감소할 수 있다. 본 연구의 목적은 등숙기 이상기상이 쌀 단수에 미치는 영향을 파악하는 것이며, 이를 위하여 횡단면 자료와 시계열 자료를 모두 이용할 수 있는 패널모형을 이용하였다. 본 연구에서는 기상요소의 평균값을 기준으로 ${\pm}2{\sigma}$의 범위를 벗어날 때를 이상기상으로 정의하였다. 분석결과를 보면, 이상고온이 발생하였을 때 쌀 단수가 5.8~16.3% 감소, 이상고온과 폭우가 동시에 발생하였을 때 8.8~20.8% 감소하는 것으로 나타났다. 이상기상으로 인한 쌀 생산량 감소를 최소화하고, 농가의 소득안정을 위하여 고온과 폭우에 강한 신품종 개발, 농업용 수리시설의 현대화, 농작물보험 채택 등의 적응전략이 필요하다.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.969-980
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• 2017

Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

• 한국경영과학회지
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• 제38권1호
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• pp.153-181
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• 2013

Weather is an influential factor to sales of companies. There have been growing attempts with which companies apply weather to developing their strategic marketing plans. By executing weather marketing activities, companies minimize risks (or negative impacts) of weather to their business and increase sales revenues. In spite of managerial importance of weather management, there are scarce empirical studies that comprehensively investigate its impact and present an efficient method that optimally allocates marketing budget. Our research was conducted in two parts. In the first part, we investigated influences of weather on sales based on real-world daily sales data. We specifically focused on the contextual factors that were less focused in the weather related research. In the second part, we propose an optimization model that can be utilized to efficiently allocate weather marketing budget across various regions (or branches) and show how it can be applied to real industry cases. The results of our study are as follow. Study 1 investigated the impact of weather on sales using store sales data of a family restaurant company and an outdoor fashion company. Results represented that the impacts of weather are context-dependent. The impact of weather on store sales varies across their regional and location characteristics when it rains. Based on the results derived from Study 1, Study 2 proposes a method on how optimally companies allocate their weather marketing budgets across each region.

• International Journal of Concrete Structures and Materials
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• 제7권2호
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• pp.165-174
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• 2013

Portland cement concrete exposed to high temperatures during mixing, transporting, casting, finishing, and curing can develop undesirable characteristics. Applicable requirements for such the hot weather concrete differ from country to country and government agencies. The current study is an attempt at evaluating the hardened properties of the concrete exposed to hot weather in fresh state. First of all, this study reviews the current state of understanding and practice for hot weather concrete placement in US and then roadway sites with suspected hot weather concrete problems were investigated. Core samples were obtained from the field locations and were analyzed by standard resonance frequency analysis and the boil test. Based on the results, there does not appear to be systematic evidence of frequent cracking problems related to high temperature placement. Thus, the suspicious deteriorations which are referable to hot weather concreting would be due to other factors.

• 농촌계획
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• 제24권3호
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• pp.25-31
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• 2018

This study analyzed factors influencing cultivation area of two major apple cultivars, Fuji and Hongro, applying the panel SUR model to survey data from farms. Characteristics of farms, distribution factors, and weather factors were the independent variables of the model. The analysis indicated that characteristics of farms, distribution factors, and weather factors influence the cultivation area of Hongro and Fuji. The independent variables were also found to have different levels of influence on increase and decrease of the cultivated area. Helping predict changes in cultivation area of Hongro and Fuji, the research results can be used as primary data to support efforts to prevent price fluctuations due to changes in supply.

• Preventive Nutrition and Food Science
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• 제7권1호
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• pp.88-94
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• 2002

The purpose of this study was to identify factors influencing the consumption of calcium-rich foods among adolescents. A total of 96 adolescents divided into twelve focus groups were investigated during April to May 2000 in Busan. Focus group discussions were audio-taped, transcribed and analyzed using a grounded theory approach. Key factors influencing the consumption of calcium-rich foods were taste, food type, body image, and family. Motivators among the factors were taste, flood type, body image, health, family and perception, and barriers were taste, flood type, location, friends, price, weather and lactose intolerance. Taste, flood type, location and weather were found to be both motivators and barriers of consumption of calcium-rich floods according to circumstances. Some of these factors varied in importance by gender and age. Younger adolescents were more strongly influenced by taste and family than older ones. Older adolescents were strongly influenced by body image, convenience and perception. These findings could be used as a guideline for adolescents to consume calcium-rich foods.

• 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.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• 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.