• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1635-1640
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• 2017

In this paper, a hierarchical image tree model for weather classification is defined in a single outdoor image, and a weather classification algorithm using image intensity and k-mean segmentation image is proposed. In the first level of the hierarchical image tree model, the indoor and outdoor images are distinguished. Whether the outdoor image is daytime, night, or sunrise/sunset image is judged using the intensity and the k-means segmentation image at the second level. In the last level, if it is classified as daytime image at the second level, it is finally estimated whether it is sunny or foggy image based on edge map and fog rate. Some experiments are conducted so as to verify the weather classification, and as a result, the proposed method shows that weather features are effectively detected in a given image.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.249-254
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• 2019

As an alternative to the growing problem of weather anomalies, users are using mobile weather apps to predict the weather. Even though it provides clear information, the user makes mistakes in forecasting the weather. For this reason, The purpose of this study is to find elements that can be prepared for the volatile weather through mobile application. Jakob Nielsen's "heuristic" evaluation found weaknesses in the application. Then I proceeded to analyze it from a behavioral economics standpoint. As a result, the two applications had various functions and accurate information. However, user accessibility was low and focused on 'information delivery'.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.78-83
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• 2022

Many domestic construction companies are continuously trying to utilize weather information for construction management. The effect of the weather is greatly reflected in the construction industry because there are many outdoor work. Therefore, weather information is clearly needed to predict the exact construction period. And the calculation of the number of non-working days considering the weather information is very important. However, many construction companies have difficulty calculating the exact construction period because it is difficult to predict the exact long-term weather. In this study, it is analyzed the past long-term weather information. Then the weather information by region and season is applied to the construction management system. Finally, it is confirmed the workable date, the field information and the weather information.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.155-160
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• 2024

Weather indexes such as temperature, humidity, wind speed and air pressure have been studied for diverse life-related factors: Food poisoning, discomfort, and others. In that, the Korea Meteorological Administration(KMA) has been released indexes such as 'Life industrial weather information', 'Safety weather information', and even 'picnic weather information' that shows how an weather like to enjoy picnic. Those weather-life effects also reveal on shopping preference such as an weather affects offline shopping purchase behaviors especially big-marts because they have outside leisure activity attribute However, since online shopping has not physical attribute, weather factors may not affect on same way to offline. Although previous researches have focused on psychological factors that have been utilized in marketing criteria, this research utilize KMA weather dataset that affects psychological factors. This research utilize 1,033 online survey for SEM analysis to clarify relationships between weather factors and online shopping purchase behaviors. As a result, online purchase intention is affected by temperature and humidity.

• 한국지구과학회:학술대회논문집
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• 2006.02a
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• pp.167-168
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• 2006

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.603-623
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• 2013

This paper proposes analyzing monthly rainfall patterns using copula and pricing related rainfall insurance using it. We analyze 30-year monthly precipitation data for 9 Korean cities between June and September using copula showing so that it can effectively generate realistic monthly rainfall patterns. In addition, we show that our copula rainfall models can be used in pricing various kinds of rainfall insurances effectively.

• Journal of Digital Contents Society
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• v.12 no.4
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• pp.611-618
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• 2011

This paper presents a design and implementation of Android-base Total Weather Information using XML(eXtensible Markup Language) techniques. Weather Information are changed using GPS location provider and XML parsing techniques according to the current location, This application alarms the weather information as the English voices when some alarm events appear regardless of the application access or finish. We design the user interface simple because the first impression is important in all applications.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.277-278
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• 2015

태양광발전소를 설치하기 위한 경제적 타당성을 분석하는 경우 기상청에서 제공하는 해당지역의 날씨정보를 기반으로 하는 PV Cell의 연간 발전량 예측 및 분석이 중요한 변수가 된다. 또한 날씨 조건에 대한 PV 발전의 예측은 기 설치되어 운전중에 있는 태양광발전소의 고장진단 및 성능평가에도 사용될 수 있다. 본 논문은 다양한 날씨 조건 중 주변온도, 풍속, 일사량에 따른 PV Cell의 특성을 분석하고, 실시간으로 변화하는 날씨환경에 대하여 순시적으로 PV Cell의 출력특성을 정확히 예측할 수 있는 모델을 수립한다.