• 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 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.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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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.

• Atmosphere
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• v.29 no.4
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• pp.451-461
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• 2019

Medium-range forecast is highly dependent on ensemble forecast data. However, operational weather forecasters have not enough time to digest all of detailed features revealed in ensemble forecast data. To utilize the ensemble data effectively in medium-range forecasting, representative weather patterns in East Asia in this study are defined. The k-means clustering analysis is applied for the objectivity of weather patterns. Input data used daily Mean Sea Level Pressure (MSLP) anomaly of the ECMWF ReAnalysis-Interim (ERA-Interim) during 1981~2010 (30 years) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the Explained Variance (EV), the optimal study area is defined by 20~60°N, 100~150°E. The number of clusters defined by Explained Cluster Variance (ECV) is thirty (k = 30). 30 representative weather patterns with their frequencies are summarized. Weather pattern #1 occurred all seasons, but it was about 56% in summer (June~September). The relatively rare occurrence of weather pattern (#30) occurred mainly in winter. Additionally, we investigate the relationship between weather patterns and extreme weather events such as heat wave, cold wave, and heavy rainfall as well as snowfall. The weather patterns associated with heavy rainfall exceeding 110 mm day^-1 were #1, #4, and #9 with days (%) of more than 10%. Heavy snowfall events exceeding 24 cm day^-1 mainly occurred in weather pattern #28 (4%) and #29 (6%). High and low temperature events (> 34℃ and < -14℃) were associated with weather pattern #1~4 (14~18%) and #28~29 (27~29%), respectively. These results suggest that the classification of various weather patterns will be used as a reference for grouping all ensemble forecast data, which will be useful for the scenario-based medium-range ensemble forecast in the future.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.41-49
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• 2021

The causes of climate change are natural and artificial. Natural causes include changes in temperature and sunspot activities caused by changes in solar radiation due to large-scale volcanic activities, while artificial causes include increased greenhouse gas concentrations and land use changes. Studies have shown that excessive carbon use among artificial causes has accelerated global warming. Climate change is rapidly under way because of this. Due to climate change, the frequency and cycle of infectious disease viruses are greater and faster than before. Currently, the world is suffering greatly from coronavirus infection-19 (COVID-19). Korea is no exception. The first confirmed case occurred on January 20, 2020, and the number of infected people has steadily increased due to several waves since then, and many confirmed cases are occurring in 2021. In this study, we conduct a study on climate change before and after COVID-19 using weather data from Korea to determine whether climate change affects infectious disease viruses through logistic regression analysis. Based on this, we want to classify before and after COVID-19 through a logistic regression model to see how much classification rate we have. In addition, we compare monthly classification rates to see if there are seasonal classification differences.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.11a
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• pp.214-215
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• 2014

Recently, Russia decided to export an annual 7.5 million tons of natural gas to Korea over 30 years from 2015, as also deal with China, planed to build a pipeline connecting Siberia to Shandong Peninsula about 4000km. Risk management is required depending on the project in extreme cold weather, because it is concerned about the behavior of the seasonal changes in soil temperature and the strain of pipe according to the long-distance pipeline construction. The plan of data management shall be prepared in parallel for a sophisticated risk management, because a data is massive scale and it is generated/accumulated in real time. Therefore, this research is aimed to classify a data items in engineering stage of pipeline by previous studies for managing a generated data depending on the detail works in extreme cold weather. We expect to be provided the foundation of an efficient classification system of a generated data from the pipeline project life cycle.

• Korean Journal Plant Pathology
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• v.12 no.4
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• pp.445-452
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• 1996

• Korean Journal of Remote Sensing
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• v.6 no.1
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• pp.11-24
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• 1990

This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.685-691
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• 2009

The purpose of this paper is to classify the traffic condition in freeway by the impacts of road weather. The factor analysis showed that weather factors, which are considered as influential, are identified as weather condition (rain or clear), temperature and sight distance with RWIS and VDS data in Seohae bridge used. The result of ANOVA shows that weather is dividedinto clear and rainy; temperature into below and equal or above $5^{\circ}C$ and sight distance into below or equal or above 10km. Based on those factors, the freeway traffic condition has been classified as five different types. The flow-speed model for each traffic conditions was proposed, which was not significant due to the lack of smaple data. Although not sufficient, the methodology to categorize traffic situation model presented in this paper may shed light on the idea for the future and can be used for proper traffic management for each weather condition.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.65-68
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• 2005

In order for effective disaster prevention, it is necessary to have some idea of when, where, why and what kind of weather disasters may occur, and how large they may be. But the regional characteristics of Shizuoka Prefecture from the viewpoint of weather disasters have not been studied before. In this study, the authors gathered the data which represent how many times weather disasters occurred in Shizuoka Prefecture in the last fourteen years, and then divided it into some regions using a multivariate analysis. The authors adopted principal component analysis on this data, and then adopted cluster analysis with principal component scores which must be significant in the previous analysis. Finally the authors set the regional division based on these clusters and described the regional characteristics. This study could contribute to the weather disaster prevention in Shizuoka Prefecture.