• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.61-67
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• 2023

Heat pipes located underground in urban areas and operated under high temperature and pressure conditions can cause large-scale human and economic damage if damaged. In order to predict damage in advance, damage and construction information of heat pipe are analyzed to derive independent variables that have a correlation with frequency of damage, and a simple regression analysis modified model using each variable is applied to the field. However, as the correlation between independent variables applied to the model increases, the independence between variables is harmed and the reliability of the model decreases. In this study, the independence of the pipe diameter, burial depth, insulation level of monitoring system, and disconnection or short circuit of the detection line, which are judged to be interrelated, was tested to derive a method for combining variables and setting categories necessary to apply to the frequency of damage estimation model. For the test of independence, the continuous variables pipe diameter and burial depth were each converted into three categories, insulation level of monitoring system was converted into two categories, and the categorical variable disconnection or short circuit of the detection line status was kept as two categories. As a result of the test of independence, p-value between pipe diameter and burial depth, level of monitoring system and disconnection or short circuit of the detection line was lower than the significance level (α = 0.05), indicating a large correlation between them. Therefore, the pipe diameter and burial depth were combined into one variable, and the categories of the combined variable were set to 9 considering the previously set categories. The insulation level of monitoring system and the disconnection or short circuit of the detection line were also combined into one variable. Since the insulation level is unreliable when the detection line status is disconnection or short circuit, the categories of the combined variable were set to 3.

• The Journal of Engineering Geology
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• v.4 no.3
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• pp.297-309
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• 1994

For a total of 210 city and Kun areas in Korea, a model was developed to predict the amount of groundwater use at each area. At first, the total areas were classified into 3 groups by the characteristics of groundwater use: residential(87), industrial(27) and agricultural (96) areas. Among them, type areas, represented by the dominant groundwater usage for typical purposes, were selected: residential(22), industrial(8) and agricultural(32) areas. Data for the various factors possibly related to the groundwater use were statistically analyzed. The factors include, 1) agricultural area, 2) industrial area, 3) adininistrative unit area(city or Kun), 4) population, 5) groundwater capadty for community water supply, 6) average water supply for a person per day, 7) agricultural water-use, 8) industrial water-use, 9) residential wateruse, 10) rates of community water supply. The data were correlated to the total amount of groundwater use, and the correlations tested at the 95% and 99% significance levels. Influential, significantly related, factors were identified from the tests. Using the multiple regression method with the influential factors, predictive equations were drawn to calculate the amount of groundwater use for residential-industrial and agricultural areas, respectively. The equations were calibrated to minimize the RMS(root mean square) of the differences between predicted and observed groundwater use. After the validation with future data, the model can be utilized in the regional development plans to predict the maximum groundwater demand at each area.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.2
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• pp.133-141
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• 2020

A geological repository comprises a natural barrier and an engineered barrier system. Its design components consist of canisters, buffers, backfill, and near-field rock. Among the engineered barrier system components, bentonite buffers minimize the groundwater flow from near-field rock and prevent the release of nuclide. Investigation of the hydraulic conductivity of the buffer to groundwater flow is an important factor in the performance evaluation of the stability and integrity of the engineered barrier of the repository. In this study, saturated hydraulic conductivity tests were performed using Gyeongju bentonite at various dry densities and temperatures, and a hydraulic conductivity prediction model was developed through multiple regression analysis using the 120 result sets of hydraulic conductivity. The test results showed that the hydraulic conductivity tends to decrease as the dry density increases. In addition, the hydraulic conductivity increased with increasing temperature. The multiple regression analysis results showed that the coefficient of determination (R²) of the hydraulic conductivity prediction equation was as high as 0.93. The hydraulic conductivity prediction equation presented in this study could be used for the design of engineered barrier systems.

• Journal of Animal Science and Technology
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• v.44 no.1
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• pp.31-38
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• 2002

Interval mapping using microsatellite markers was employed to detect quantitative trait loci (QTL) in the experimental cross between Berkshire and Yorkshire pigs. In order to derive critical values (CV) for test statistics for declaring significance of QTL, permutation test (PT) of Churchill and Doerge method(1994) and the analytical method(LK) of Lander and Kruglyak(1995) were used by each trait and chromosome. 525 $F_2$ progeny phenotypes of five traits(carcass weight, loin eye area, marbling score, cholesterol content, last back fat thickness) and genotypes of 125 markers covering the genome were used. Data were analyzed by line cross regression interval mapping with an F-test every by 1cM. PT CV were based on 10,000 permutations. CV at genome-wise test were 10.5 for LK and ranged from 8.1 to 8.3 for PT, depending on the trait. CV, differed substantially between methods, led to different numbers of quantitative trait loci (QTL) to be detected. PT results in the least stringent CV compared at the same % level.

• Spatial Information Research
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• v.22 no.3
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• pp.89-99
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• 2014

The aim of this study is to represent the residential population distribution in Seoul, Korea more precisely through the dasymetric mapping method. Dasymetric mapping can be defined as a mapping method to calculate details from truncated spatial distribution of main statistical data by using ancillary data which is spatial data related to the main data. In this research, there are two types of data used for dasymetric mapping: the population data (2010) based on a output area survey in Seoul as the main data and the building footprint data including register information as ancillary spatial data. Using the binary method, it extracts residential buildings as actual areas where residents do live in. After that, the regression method is used for calculating the weights on population density by considering the building types and their gross floor areas. Finally, it can be reproduced three-dimensional density of residential population and drew a detailed dasymetric map. As a result, this allows to extract a more realistic calculating model of population distribution and draw a more accurate map of population distribution in Seoul. Therefore, this study has an important meaning as a source which can be applied in various researches concerning regional population in the future.

• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.237-245
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• 2018

The purpose of this study is to suggest the improvement plan for an effective benefit sharing support policy through empirical analysis on the benefit sharing operation of government as well as the benefit sharing support policy of government affecting firm performance. Therefore, this study analyzed current problems on benefit sharing operation of government as well as performed a survey for 225 participating and cooperation firms regarding firm performance. This study found some improvements such as insufficient diverse incentive measures leading to the difficulty of participating firm's increase and spread, insufficient substantial benefit sharing such a cash allocation between consignment firms, the increase of biz management system model failing to induce innovation of new technology or product, and difficult spread into 2nd, 3rd cooperative firms besides 1st ones, meanwhile, verifying the positive influence of government benefit sharing support policy on both participating and cooperation firms, especially on the performance of cooperation firms. As a further study, it is necessary to increase the objectiveness and accuracy of a research through verification of the interrelationships between firm statue and performance on the basis of more objective and quantitative data such a sales increase or R&D capability of cooperation firms.

• 한국사회정책
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• v.24 no.4
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• pp.63-87
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• 2017

Self-employed women are highly vulnerable to social risks like unemployment and poverty as job instability has increased in recent decades. Despite this, the Korean public policy focus has been on employees, not the self-employed. This may be closely linked to the legacy of the East Asian welfare model policy logic. Therefore, this study explores social risk levels by gender and employment status and examines the relation between social risks of self-employed women and the East Asian welfare model policy logic, through comparing-means analysis and ordered logit regression analysis using the 9th wave data of the Korea Welfare Panel Study Korea. The study yields evidence of divisions in social risk levels according to gender and employment status: that is, a gender difference, and a substantial gap between self-employed workers and regular employees. Furthermore, the findings of the study indicate that self-employed women — especially in small businesses — are more vulnerable to social risks than are self-employed men. This strongly supports the conclusion that the higher social risks of self-employed women in Korea are closely linked to the legacy of East Asian welfare model policy logic, which focuses on social protection for core workers and largely neglects women.

• Journal of Technology Innovation
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• v.29 no.1
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• pp.1-38
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• 2021

In this paper, an exploratory analysis study was conducted on establishing a strategy to utilize living labs to enhance the innovation of the energy sector. Through the previous research literature, it was possible to confirm the concept, essential components, innovation characteristics of living labs, and types of innovation issues in the energy sector as the theoretical background. Based on this, the case studies of energy living lab (8 overseas, 1 domestic) were analyzed focusing on the possibility of utilizing living lab as an approach to innovation issues in the energy sector, establishing a customized strategy for essential components of living lab and enhancing innovation. It was confirmed that the establishment of a customized strategy for the essential components of the living lab could be a driving force in enhancing innovation, and the Living Lab is effectively used as an approach method for innovation issues(demand management, supply technology, enhance R&D acceptance and promote commercialization, technology policies) in the energy sector. As a result of the case studies, the driving force of each living lab was derived from the viewpoint of contributing to innovation, and strategies for using the living labs for each energy innovation problem were established. This study is an exploratory and descriptive analytical study of the utilization strategy and value of the living lab model as an approach to innovation issues in the energy field, which can provide a living lab strategy framework that has not been tried in the past and enables living lab activation and network formation. It can also be considered to have academic, practical, and policy implications in that it can also contribute.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.43-54
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• 2024

This study employs text mining techniques to compare user responses across various Over-The-Top (OTT) service platforms. The primary objective of the research is to understand user satisfaction with OTT service platforms and contribute to the formulation of more effective review strategies. The key questions addressed in this study involve identifying prominent topics and keywords in user reviews of different OTT services and comprehending platform-specific user reactions. TF-IDF is utilized to extract significant words from positive and negative reviews, while BERTopic, an advanced topic modeling technique, is employed for a more nuanced and comprehensive analysis of intricate user reviews. The results from TF-IDF analysis reveal that positive app reviews exhibit a high frequency of content-related words, whereas negative reviews display a high frequency of words associated with potential issues during app usage. Through the utilization of BERTopic, we were able to extract keywords related to content diversity, app performance components, payment, and compatibility, by associating them with content attributes. This enabled us to verify that the distinguishing attributes of the platforms vary among themselves. The findings of this study offer significant insights into user behavior and preferences, which OTT service providers can leverage to improve user experience and satisfaction. We also anticipate that researchers exploring deep learning models will find our study results valuable for conducting analyses on user review text data.

• Journal of Platform Technology
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• v.12 no.1
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• pp.55-66
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• 2024

The K-POP market has become influential not only in culture but also in society as a whole, including diplomacy and environmental movements. As a result, various papers have been conducted based on machine learning to identify the success factors of idols by utilizing traditional data such as music and recordings. However, there is a limitation that previous studies have not reflected the influence of new media platforms such as Instagram releases, YouTube shorts, TikTok, Twitter, etc. on the popularity of idols. Therefore, it is difficult to clarify the causal relationship of recent idol success factors because the existing studies do not consider the daily changing media trends. To solve these problems, this paper proposes a data collection system and analysis methodology for idol-related data. By developing a container-based real-time data collection automation system that reflects the specificity of idol data, we secure the stability and scalability of idol data collection and compare and analyze the clusters of successful idols through a K-Means clustering-based outlier detection model. As a result, we were able to identify commonalities among successful idols such as gender, time of success after album release, and association with new media. Through this, it is expected that we can finally plan optimal comeback promotions for each idol, album type, and comeback period to improve the chances of idol success.