• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.801-807
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• 2023

This study aims to examine the cases of overseas experience programs centered around middle school students in South Korea and to derive implications for future overseas experience programs. To achieve this, data were systematically collected through search engines based on keywords, followed by comparative analysis. Frequency analysis, independent sample t-tests, and cross-analysis were conducted using SPSS 23. The research findings are as follows: First, the programs are operated nationwide, with a focus on smaller schools in various regions, and are particularly active in the Jeolla provinces. Diverse public funding, such as from the board of education and local governments, has been invested, categorizing operational costs into full financial coverage among others. The programs primarily took place in Southeast Asian countries close to South Korea. Second, the purposes of these middle school overseas experience programs largely encompass career exploration, cultural experiences, tourism, and sister school visits. We hope that school-based overseas career exploration programs are actively operated to provide opportunities for enhancing global competence and global citizenship, as well as exploring career paths.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.856-864
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• 2023

There are a variety of microorganisms in the animal intestine, and it has been known that they play important roles in the host such as suppression of potentially pathogenic microorganisms, modulation of the gut immunity. In addition, the gut microbiota and the livestock growth performance have long been known to be related. Therefore, we evaluated the interrelation between the growth performance and the gut microbiome of the pigs from 3 different farms, with pigs of varied ages ready to be supplied to the market. When pigs reached average market weight of 118 kg, the average age of pigs in three different farms were < 180 days, about 190 days, and > 200 days, respectively. Fecal samples were collected from pigs of age of 70 days, 100 days, 130 days, and 160 days. The output data of the 16S rRNA gene sequencing by the Illumina Miseq platform was filtered and analyzed using Quantitative Insights into Microbial Ecology (QIIME)2, and the statistical analysis was performed using Statistical Analysis of Metagenomic Profiles (STAMP). The results of this study showed that the gut microbial communities shifted as pigs aged along with significant difference in the relative abundance of different phyla and genera in different age groups of pigs from each farm. Even though, there was no statistical differences among groups in terms of Chao1, the number of observed operational taxonomic units (OTUs), and the Shannon index, our results showed higher abundances of Bifidobacterium, Clostridium and Lactobacillus in the feces of pigs with rapid growth rate. These results will help us to elucidate important gut microbiota that can affect the growth performance of pigs.

• Journal of Korean Society of Steel Construction
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• v.29 no.6
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• pp.467-477
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• 2017

The live load effect of KL-510 of the current Korean bridge design code is examined by comparing with that of the multiple trucks of which the weights are statistically estimated from measured traffic data as well as with those of the related live load models. The truck weight data measured on the expressway before and after overweight enforcement are used to obtain the truck weights following the same procedures in deciding the live load model of the design codes and the results are compared with the load effect of KL-510. KL-510 yields a very uniform loading effect compared with the multiple truck effects when the weights are estimated from the data which contains some of the heavy trucks over the operational weight limit. KL-510 yields consistent results with the live load of AASHTO LRFD and shows less variation than the past load model DB-24 over the span lengths considered in this study. As a result of this research, the actual truck combinations equivalent to the notional KL-510 load model are constructed and it can be applied to the evaluation of the existing bridge and the calibration of the load factor of the permit vehicle.

• Journal of Biosystems Engineering
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• v.43 no.1
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• pp.72-80
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• 2018

Purpose: A modern greenhouse consists of various Information and Communications Technology (ICT) components e.g., sensor nodes, actuator nodes, gateways, controllers, and operating softwarethat communicate with each other. The interoperability between these components is an essential characteristic for any greenhouse control system. A greenhouse control system could not work unless the components communicate via common interfaces. The TTAK.KO-06.0288 is an interface standard consisting of four parts. Notably, TTAK.KO-06.0288-Part3, which describes the interface between a greenhouse operating system (GOS) and a greenhouse control gateway (GCG), is the core standard of TTAK.KO-06.0288. The objectives of this study were to analyze the TTAK.KO-06.0288-Part3 standard, to suggest alternative solutions for identified issues, and to develop a library as a proof of the alternative solutions. Methods: The "data field" was analyzed using a comparative analysis method, since it is a data transmission unit of TTAK.KO-06.0288-Part3. It was compared with other parts of TTAK.KO-06.0288 in terms of definition, format, size, and possible values. Although TTAK.KO-06.0288-Part1 and TTAK.KO-06.0288-Part2 do not use a "data field," they have a similar data structure. That structure was compared with the "data field" of TTAK.KO-06.0288-Part3. Results: Twenty-one issues were identified across four categories: inter-standard issues, intra-standard issues, operational issues, and misprint issues. Since some of the issues can raise interoperability problems, 16 alternative solutions were suggested. In order to prove the alternative solutions, an open-source communication library called libtp3 was developed. The library passed 14 unit tests and was adapted to two research. Conclusions: Although TTAK.KO-06.0288-Part3 is an interface standard for communication between a GOS and a GCG, it might not communicate between different implementations because of the identified issues in the standard. These issues could be solved by the alternative solutions, which could be used to revise TTAK.KO-06.0288. In addition, a relevant organization should develop a program for compatibility testing and should pursue test products for smart greenhouses.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.19-37
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• 2017

Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS-DNB) data provides a much higher capability for observing and quantifying nighttime light (NTL) brightness in comparison with Defense Meteorological Satellite-Operational Linescan System (DMSP-OLS) data. In South Korea, there is little research on the detection of NTL brightness change using VIIRS-DNB data. This study analyzed the spatial distribution and change of NTL brightness between 2013 and 2016 using VIIRS-DNB data, and detected its spatial relation with possible influencing factors using regression models. The intra-year seasonality of NTL brightness in 2016 was also studied by analyzing the deviation and change clusters, as well as the influencing factors. Results are as follows: 1) The higher value of NTL brightness in 2013 and 2016 is concentrated in Seoul and its surrounding cities, which positively correlated with population density and residential areas, economic land use, and other factors; 2) There is a decreasing trend of NTL brightness from 2013 to 2016, which is obvious in Seoul, with the change of population density and area of industrial buildings as the main influencing factors; 3) Areas in Seoul, and some surrounding areas have high deviation of the intra-year NTL brightness, and 71% of the total areas have their highest NTL brightness in January, February, October, November and December; and 4) Change of NTL brightness between summer and winter demonstrated a significantly positive relation with snow cover area change, and a slightly and significantly negative relation with albedo change.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.182-199
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• 2023

Since 2021, government departments have been promoting Automated Driving Technology Development and Innovation Project as national research and development(R&D) project. The automated vehicles and service technologies developed as part of these projects are planned to be subsequently provided to the public at the selected Living Lab City. Therefore, it is important to determine a spatial area and operation section that enables safe and stable automated driving, depending on the purpose and characteristics of the target service. In this study, the static Operational Design Domain(ODD) elements for Level 4 automated driving services were reclassified by reviewing previously published papers and related literature surveys and investigating field data. Spatial analysis techniques were used to consider the reclassified ODD elements for level 4 in the real area of level 3 automated driving services because it is important to reflect the spatial factors affecting safety related to real automated driving technologies and services. Consequently, a total of six driving mode changes(disengagement) were derived through spatial information analysis techniques, and the factors affecting the safety of automated driving were crosswalk, traffic light, intersection, bicycle road, pocket lane, caution sign, and median strip. This spatial factor analysis method is expected to be useful for determining special areas for the automated driving service.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1275-1288
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• 2015

A microscale weather analysis module (about 1km or less) is a microscale numerical weather prediction model designed for operational forecasting and atmospheric research needs such as radiant energy, thermal energy, and humidity. The accuracy of the module is directly related to the usefulness and quality of real-time microscale weather information service in the metropolitan area. This paper suggests an object based verification method useful for spatio-temporal evaluation of the accuracy of the microscale weather analysis module. The method is a graphical method comprised of three steps that constructs a lattice field of evaluation statistics, merges and identifies objects, and evaluates the accuracy of the module. We develop lattice fields using various evaluation spatio-temporal statistics as well as an efficient object identification algorithm that conducts convolution, masking, and merging operations to the lattice fields. A real data application demonstrates the utility of the verification method.