• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4189-4200
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• 2021

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• New & Renewable Energy
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• v.20 no.1
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• pp.95-101
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• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.

• International Journal of Contents
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• v.18 no.1
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• pp.17-26
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• 2022

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants.

• New & Renewable Energy
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• v.20 no.1
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• pp.102-109
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• 2024

The Typical Meteorological Year (TMY) dataset compiles 12 months of data that best represent long-term climate patterns, focusing on global horizontal irradiance and other weather-related variables. However, the irradiance measured on the plane of the array (POA) shows certain distinct distribution characteristics compared with the irradiance in the TMY dataset, and this may introduce some biases. Our research recalculated POA irradiance using both the Isotropic and DIRINT models, generating an updated dataset that was tailored to POA characteristics. Our analysis showed a 28% change in the selection of typical meteorological months, an 8% increase in average irradiance, and a 40% reduction in the range of irradiance values, thus indicating a significant shift in irradiance distribution patterns. This research aims to inform stakeholders about accurate use of TMY datasets in potential decision-making. These findings underscore the necessity of creating a typical dataset by using the time series of POA irradiance, which represents the orientation in which PV panels will be deployed.

• Atmosphere
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• v.29 no.1
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• pp.75-86
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• 2019

In this paper, we introduce a novel Climate & Environment Database System (CEDS). The CEDS is developed by the National Institute of Meteorological Sciences (NIMS) to provide easy and efficient user interfaces and storage management of climate model data, so improves work efficiency. In uploading the data/files, the CEDS provides an option to automatically operate the international standard data conversion (CMORization) and the quality assurance (QA) processes for submission of CMIP6 variable data. This option increases the system performance, removes the user mistakes, and increases the level of reliability as it eliminates user operation for the CMORization and QA processes. The uploaded raw files are saved in a NAS storage and the Cassandra database stores the metadata that will be used for efficient data access and storage management. The Metadata is automatically generated when uploading a file, or by the user inputs. With the Metadata, the CEDS supports effective storage management by categorizing data/files. This effective storage management allows easy and fast data access with a higher level of data reliability when requesting with the simple search words by a novice. Moreover, the CEDS supports parallel and distributed computing for increasing overall system performance and balancing the load. This supports the high level of availability as multiple users can use it at the same time with fast system-response. Additionally, it deduplicates redundant data and reduces storage space.

• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.925-934
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• 2015

The frequency of natural disasters because of global warming is gradually increasing, risks of flooding due to typhoon and torrential rain have also increased. Among these causes, the roads are flooded by suddenly torrential rain, and then vehicle and personal injury are happening. In this respect, because of the possibility that immersion of a road may occur in a second, it is necessary to study the rapid data collection and quick response system. Our research proposes a big data analysis system based on the collected information and a variety of system information collection methods for searching flooded road areas by torrential rains. The data related flooded roads are utilized the SNS data, meteorological data and the road link data, etc. And the big data analysis system is implemented the distributed processing system based on the Hadoop platform.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.64-79
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• 2016

The purpose of this study is to find the relationship between precipitation deficit, SPI(standardized precipitation index)-12 month, agricultural reservoir water storage deficit and agricultural drought-related big data, and to evaluate the usefulness of agricultural risk management through big data. For the long term drought (from January 2014 to September 2015), each data was collected and analysed with monthly and Provincial base. The minimum SPI-12 and maximum reservoir water storage deficit compared to normal year were occurred at the same time of July 2014, and August and September 2015. The maximum frequency of big data was occurred at June and July of 2014, and March and June to September of 2015. The maximum big data was occurred 1 month advanced in 2014 and 2 months advanced in 2015 than the maximum reservoir water storage deficit. The occurrence of big data was sensitive to spring drought from March, late Jangma of June, dry Jangma of July and the rainfall deficit of September 2015. The big data was closely related with the meteorological drought and agricultural drought. Because the big data is the in situ feeling drought, it is proved as a useful indicator for agricultural risk management.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.113-116
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• 2020

본 연구의 목적은 최근 비중이 급증하고 있는 국내 전자상거래 시장에서 농산물 판매 현황이 지역 날씨와 생육 환경 등 농산물 생산과 연관성이 높은 데이터와 어떤 관계가 있는지를 분석하는 것이다. 이를 위해 전라남도 농산물의 온라인 판매 현황을 분석하고, 전남 지역 날씨와 생육 환경에 관한 표준화된 데이터를 안정적으로 확보할 수 있도록 빅데이터 시스템을 구축하고자 한다. 본 연구의 결과는 지역 농업인의 농산물 생산과 유통 의사결정에 시사점을 제공하고 궁극적으로는 생산성과 수익성 향성에 기여할 것으로 기대된다.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.91-101
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• 2018

This study has analyzed applicable characteristics on big data of the meteorology and climate depending on press releases in the media. As a result, more than half of them were conducted by governmental departments and institutions (26.9%) and meteorological administration (25.0%). Most articles were written by journalists, especially the highest portion stems from straight articles focusing on delivering simple information. For each field, the number of cases had listed in order of rank to be exposed to the media; information service, business management, farming, livestock, and fishing industries, and disaster management, but others did rank far behind; insurance, construction, hydrology and energy. Application of big data about meteorology and climate differed depending on the seasonal change, it was directly related to temperature information during spring, to weather phenomenon such as monsoon and heat wave during summer, to meteorology and climate information during fall, and to weather phenomenon such as cold wave and heavy snow during winter.