• Journal of Environmental Health Sciences
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• v.46 no.4
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• pp.376-387
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• 2020

Objectives: This study is aimed at offering basic data for making plans for offensive odor management after researching offensive odor occurrence and characteristics in Gangwon Province. Methods: The data used in the study is based on offensive odor data analyzed by the Gangwon Institute of Health and Environment from 2012 to 2019. The data were reclassified by year, month, facility, and region to identify characteristics of occurrence. Finally, a distribution map of offensive odors was created using ArcGIS. Results: The highest monthly frequency of offensive odor occurrence falls in June, August, and July, and the summer season and third quarter are the highest. According to the latest eight-year data for Gangwon Province, complaints about offensive odors in county areas are more frequent than those in city areas. There are many offensive odor complaints in Wonju, Cheorwon, and Heongsung. The main offensive odor emission facilities are livestock and waste treatment (recycling) facilities. Complaints about offensive odors are relatively lower the Yeongdong area than Yeongseo area, which is considered to be the result of characteristics of land-sea breezes and geographical factors. Offensive odors from livestock facilities count for an average of 53.9% of the total, and the inadequacy rate of livestock facilities averages 36.9%. Conclusions: To maintain a clean environment in Gangwon Province, it is strongly recommended that an offensive odor reduction plan for livestock facilities be established. Areas with a high density of offensive odor occurrence should be identified and systematically managed with short- and mid-term measures. If offensive odors is managed using GIS, it is possible to identify the characteristics of occurrence by time and space and also by facility. In addition, since systematic data management is possible, it is believed that a rapid response to offensive odors, prediction of their spread, and efficient management are possible.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.1-11
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• 2017

The importance of data curation is increasingly recognized as the need of data reuse drastically grows. Due to recent data explosion, scientists invest almost 90% of their efforts in the retrieval and collection of data needed to their study. In this paper, we deal with the development and application of a collaborative environment for community-driven data curation which is essential to enhance scientific data reusability and citability. The collaborative scientific data curation environment focuses on the cross-linking between data (or data collections) and their associated literatures to capture and organize inter-relations among research results in a specific domain. Also, plenty of contextual information is provided as metadata in order to support users in understanding data. The cross-linking has been realized by using DOI system to guarantee global accessibility to data and their relationships to literatures. The curation environment has been adopted to build a community-driven curated DB by a globally well-known intrinsically-disorderd protein research group. The curated DB will drastically reduce researchers' efforts to retrieve and collect the data required for scientific discovery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2212-2220
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• 2006

Recently enterprises introduce EAI systems and legacy business which already obtained for data integration among legacy systems. EAI systems in cooperative transaction environment can be expected efficient retrieval as sharing and integrating. However existing legacy systems have to introduce particular EAI solution because it is difficult to adjust standard technology to EAI due to be managed independently without considering interoperability. For solving these problems we use metadata registry using data integration. Various types, semantic specification data heterogeneity and heterogeneity of systems, however, are occurred. Therefore retrieval system based on XMDR(extended Meta-Data Registry) for data interoperability in the web environment are proposed in this paper.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.106-111
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• 2020

In the era of the fourth industrial revolution, the application of big data analysis technology is crucial in various industries. In this regard, considerable research is necessary to improve aquafarming productivity, particularly in fish culture, which is one of the primary industries in the world. In this study, a sample experiment using a flop was conducted to improve oyster productivity in fish farms, and a flush was installed in an environment similar to aquaculture farms. Thereafter, the temperature data of the water environment where the formation of burrows considerably improved were collected; the growth rate of burrow seeds was also measured. The gathered experimental data were examined by time series data analysis. Finally, a system that visualizes the analysis results based on big data is proposed. In accord with the results of this study, it is expected that more advanced research on the productivity improvement of oyster aquafarming will be performed.

• Korean Journal of Agricultural Science
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• v.43 no.3
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• pp.299-319
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• 2016

The National Forest Inventory (NFI) is important for providing fundamental data for basic forest planning and the establishment of forest policies for the purpose of implementing sustainable forest management. The purpose of this study is to present the development of Korea's NFI including legal basis, sampling design, and measured variables and to review the usage of NFI data. The survey methods and forestry statistics among the Unites States, Canada, Japan, China, and European countries were briefly compared. Total 140 publications utilizing NFI data between 2008 and 2015 were categorized with 15 subjects. Korea has conducted the NFI 6 times since 1971, but only the $6^{th}$ NFI is comparable with the fifth, the previous NFI, because the permanent sampling plots have been shared between the periods. The Korean Forestry Statistics contains only half as many variables as that of advanced countries in Forestry. More researches were needed to improve consistent measurement of diverse variables through implementation of advanced technologies. Additional data for Forest Health Monitoring since the NFI $6^{th}$ must be under quality control which will be an essential part of the inventories for providing the chronological change of forest health.

• Journal of Soil and Groundwater Environment
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• v.25 no.2_spc
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• pp.28-54
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• 2020

Even though a systematic sampling approach is very crucial in both the general and detailed investigation phases to produce the best conceptual site model for contaminated sites, the concept is not yet established in South Korea. The U.S. Environmental Protection Agency (EPA) issued the 'Strategic Sampling Approaches Technical guide' in 2018 to help environmental professionals choose which sampling approaches may be needed and most effective for given site conditions. The EPA guide broadly defines strategic sampling as the application of focused data collection across targeted areas of the conceptual site model (CSM) to provide the appropriate amount and type of information needed for decision-making. These strategic sampling approaches can prevent the essential data from missing, minimize the uncertainty of projects and secure the data which are necessary for the important site-decisions. Furthermore, these provide collaborative data sets through the life cycle phases of projects, which can generate more positive proofs on the site-decisions. The strategic sampling approaches can be divided by site conditions. This technical guide categorized it into eight conditions; High-resolution site characterization in unconsolidated environments, High-resolution site characterization in fractured sedimentary rock environments, Incremental sampling, Contaminant source definition, Passive groundwater sampling, Passive sampling for surface water and sediment, Groundwater to surface water interaction, and Vapor intrusion. This commentary paper introduces specific sampling methods based on site conditions when the strategic sampling approaches are applied.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.481-491
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• 2008

As the T-DMB (Terrestrial Digital Multimedia Broadcasting) has been actively launched, all the service providers are focusing on finding a new business model using a variety of data services as well as video service. The middleware technology for data broadcasting service, which was presented due to such necessity, known as DMB MATE (Mobile Application Terminal Environment), provides a running environment of the applications and APIs so that the various data applications can support high-level functionalities for the interactive data service. In this paper, in order to effectively provide the data service under restricted channel environment of T-DMB, we introduce a service technology and an interactive data broadcasting system using the DMB MATE, specifically proposing a design method of T-DMB MATE receiver capable of the presented DMB MATE service. Finally, the performance of developed system is confirmed through the T-DMB data broadcasting experiment under a variety of conditions.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.349-356
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• 2022

Currently, the agricultural population in Korea indicates a decreasing and aging orientation. As the population of farm labor continues to decline, so farmers are feeling the pressure to be stable crop production. To solve the problem caused by the decreasing of farm labor, it is necessary to change over to "Digital agriculture". Digital agriculture is tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture, and aims to integrate the several digital technologies into crop and livestock management and other processes in agriculture fields. In addition, digital agriculture can offer the opportunity to increase crop production, save costs for farmer. Therefore, in this study, for data-based Digital Agriculture, a greenhouse environment monitoring system for crop growth monitoring based on Node-RED, which even beginners can use easily, was developed, and the implemented system was verified in a hydroponic greenhouse. Several sensors, such as temperature, humidity, atmospheric pressure, CO₂, solar radiation, were used to obtain the environmental data of the greenhouse. And the environmental data were processed and visualized using Node-RED and MariaDB installed in rule.box digital. The environment monitoring system proposed in this study was installed in a hydroponic greenhouse and obtained the environmental data for almost two weeks. As a result, it was confirmed that all environmental data were obtained without data loss from sensors. In addition, the dashboard provides the names of installed sensors, real time environmental data, and changes in the last three days for each environmental data. Therefore, it is considered that farmers will be able to easily monitor the greenhouse environment using the developed system in this study.

• Journal of People, Plants, and Environment
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• v.22 no.5
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• pp.455-466
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• 2019

This study was carried out to analyze trends in floral products and services through the big data analysis of various social networking services (SNSs) and then to provide objective marketing directions for the floricultural industry. To analyze the big data of SNSs, we used four analytical methods: Cotton Trend (Social Matrix), Naver Big Data Lab, Instagram Big Data Analysis, and YouTube Big Data Analysis. The results of the big data analysis showed that SNS users paid positive attention to flower one-day classes that can satisfy their needs for direct experiences. Consumers of floral products and services had their favorite designs in mind and purchased floral products very actively. The demand for flower items such as bouquets, wreaths, flower baskets, large bouquets, orchids, flower boxes, wedding bouquets, and potted plants was very high, and cut flowers such as roses, tulips, and freesia were most popular as of June 1, 2019. By gender of consumers, females (68%) purchased more flower products through SNSs than males (32%). Consumers preferred mobile devices (90%) for online access compared to personal computers (PCs; 10%) and frequently searched flower-related words from February to May for the past three years from 2016 to 2018. In the aspect of design, they preferred natural style to formal style. In conclusion, future marketing activities in the floricultural industry need to be focused on social networks based on the results of big data analysis of popular SNSs. Florists need to provide consumers with the floricultural products and services that meet the trends and to blend them with their own sensitivity. It is also needed to select SNS media suitable for each gender and age group and to apply effective marketing methods to each target.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.883-888
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• 2023

Since data centers are places that provide IT services 24 hours a day, 365 days a year, data center power consumption is expected to increase to approximately 10% by 2030, and the introduction of high-density IT equipment will gradually increase. In order to ensure the stable operation of IT equipment, various types of research are required to conserve energy in cooling and improve energy management. This study proposes the following process for energy saving in data centers. We conducted CFD modeling of the data center, proposed an artificial intelligence-based thermal environment prediction model, compared actual measured data, the predicted model, and the CFD results, and finally evaluated the data center's thermal management performance. It can be seen that the predicted values of RCI, RTI, and PUE are also similar according to the normalization used in the normalization method. Therefore, it is judged that the algorithm proposed in this study can be applied and provided as a thermal environment prediction model applied to data centers.