• 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.

• 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.

• 한국철도학회논문집
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• 제14권1호
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• pp.73-79
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• 2011

본 연구는 기상현상에 의해 발생하는 노선별 철도기상사고의 통계적 특성을 밝혀 철도기상사고를 저감하고 향후 기후변화에 대비하고자 한다. 이를 위해 사용한 자료는 KROIS 자료와 철도사고분석보고서를 주로 이용하였으며 그 결과는 다음과 같다. 철도기상사고에 가장 취약한 노선은 경부선이며, 영동선과 중앙선, 태백선도 취약한 지역이라 할 수 있다. 철도기상사고의 종류는 선로장애, 신호장애, 급전장애 순으로 나타났으며, 선로 장애의 2차 원인은 노선별로 약간 차이는 있었으나 노반유실이 대부분이었고, 한랭전선을 동반한 기상패턴은 단시간의 집중호우로 철도기상사고를 일으키는 주요 기상패턴임을 알 수 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.43-45
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• 2021

초단기 강수예측 시스템은 단시간 발생하는 집중호우와 같은 위험기상에 대응하기 위해 사회·경제적으로 중요하다. 최근 국내·외에서 심층신경망을 활용한 초단기 강수예측 연구가 활발히 진행되고 있다. 심층신경망을 이용한 강수예측 모델은 훈련 데이터를 만들 때 기상데이터의 구조와 종류가 복잡하고 방대하므로 기상학적 이해를 바탕으로 복잡한 전처리 과정이 필요하다. 또한, 비선형적인 패턴의 강수 현상을 예측하기 위하여 기상의 상호작용에 대한 이해를 바탕으로 입력 데이터를 구성해야 한다. 따라서 본 연구에서는 다음과 같은 접근법을 제안하고자 한다. i) 기상레이더 합성 강수장과 강수발달에 영향을 줄 수 있는 주요 인자(레이더, 지형, 온도, 등)를 훈련 데이터 구축을 위해 패턴 분석에 적합한 형태로 정제하고 이를 구조화하여 통합한다. ii) 합성곱 신경망과 합성곱 장단기 기억 신경망을 접목하여 초단기 예측 강수장을 산출한다. 2020년 강수 사례를 이용하여 제안한 모델의 정확성을 검증하였다. 제안한 모델은 비선형적인 패턴의 강수 현상을 잘 모의하였고, 강수의 규모 및 강도에 대한 예측성능이 향상되었다. 이는 강수를 동반한 초단기 위험기상의 방재에 활용할 수 있을 것으로 기대된다.

• 한국기후변화학회지
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• 제9권3호
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• pp.209-221
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• 2018

The characteristics of heat wave events in Seoul are analyzed using weather station data from Korea Meteorological Administration (KMA) and European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data from 1979 to 2016. Heat waves are defined as events in the upper 10th percentile of the daily maximum temperatures. The associated synoptic weather patterns are then classified into six clusters through Self-Organizing Map (SOM) analysis for sea-level pressure anomalies in East Asia. Cluster 1 shows an anti-cyclonic circulation and weak troughs in southeast and west of Korea, respectively. This synoptic pattern leads to southeasterly winds that advect warm and moist air to the Korean Peninsula. Both clusters 2 and 3 are associated with southerly winds formed by an anti-cyclonic circulation over the east of Korea and cyclonic circulation over the west of Korea. Cluster 4 shows a stagnant weather pattern with weak winds and strong insolation. Clusters 5 and 6 are associated with F?hn wind resulting from an anti-cyclonic circulation in the north of the Korean Peninsula. In terms of long-term variations, event frequencies of clusters 4 and 5 show increasing and decreasing trends, respectively. However, other clusters do not show any long-term trends, indicating that the mechanisms that drive heat wave events in Seoul have remained constant over the last four decades.

• 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.

• 대한토목학회논문집
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• 제32권1D호
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• pp.23-31
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• 2012

부산광역시는 2007년부터 시내버스 준공영제를 시행하고 있으며 급격히 증가하고 있는 재정지원금이 큰 문제로 대두되었다. 현재 부산시의 재정 상태를 감안한 경우 시내버스 준공영제의 성공 여부는 매년 투입되어야 하는 재정지원금을 연차적으로 감소시켜 나가야 하는 부분이다. 부산시 재정부담을 완화하기 위해서는 시내버스 이용을 보다 편리하게 하여 버스 이용객의 수요를 지속적으로 증가시켜야 할 것이다. 교통수요에 영향을 미치는 기상조건을 살펴보면 눈, 안개, 비 등이 있으며 이러한 기상조건은 통행자들의 수단선택에 영향을 미쳐 결과적으로 버스 이용의 감소를 가져온다. 그리하여 기상 악화시 이용률 저하는 수익금 감소로 이어지게 되어 부산시의 막대한 공공재정 부담으로 돌아오게 된다. 본 연구에서는 기상 상태에 따른 교통수단간 수송 분담률을 파악하여 기상 악화시 버스 승객이 타 교통수단으로 전환하는 패턴을 분석하고 설문조사를 통해 타 수단으로 전환하는 이유를 분석함으로써 향후 대중교통시설 및 환승체계 구축 등 버스정책 수립에 필요한 정책적 시사점을 도출하는데 목적이 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• 설비공학논문집
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• 제14권11호
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• pp.897-906
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• 2002

Standard temperature and absolute humidity weather correlations of Seoul for dynamic energy simulation have been developed regressing the measured data compiled by the Korea Meteorological Adminstration during a 10-year period from 1991 to 2000. The mathematical equations can generate the daily and yearly variations of outdoor weather data with consistency unlike the measured data which may show abnormal behavior, Considering that each hour of the day follows a certain yearly pattern, the correlations are developed for each hour. The derived 24 simple mathematical equations can be used for estimating outdoor temperature and humidity conditions for any arbitrary time of the year.

• International Journal of Air-Conditioning and Refrigeration
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• 제11권4호
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• pp.199-208
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• 2003

Standard temperature and absolute humidity weather data correlations of Seoul for dynamic energy simulation have been developed regressing the measured data compiled by the Korea Meteorological Adminstration during a l0-year period from 1991 to 2000. The mathematical equations can generate consistent daily and yearly variations of outdoor weather data unlike the measured data which may show abnormal behavior. Considering that each hour of the day follows a certain yearly pattern, 24 correlations are developed for each hour of the day. The derived simple mathematical equations can be used for estimating outdoor temperature and humidity conditions for any arbitrary time of the year.