• Journal of Conservation Science
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• v.37 no.5
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• pp.411-425
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• 2021

The Stupa of State Preceptor Jigwang from Beopcheonsa Temple Site in Wonju (National Treasure) is a representative stupa of the Goryeo Dynasty, with outstanding Buddhist carvings and splendid patterns, clearly indicating its honoree and year of construction. However, it was destroyed by bombing during the Korean War (1950-1953) and repaired and restored with cement and reinforcing bars in 1957. The surface condition of the original stone shows long-term deterioration due to the m ortar used in past restorations. In order to identify the exact causes of deterioration, the m ortar and surface contaminants on the original stone were analyzed. Portlandite, calcite, ettringite, and gypsum from the mortar were identified, and its ongoing deterioration was observed through pH measurements and the neutralization reaction test. Analysis of surface contaminants identified calcite and gypsum, both poorly water-soluble substances, and their growth in volume among rock-forming minerals was observed by microscopy. Based on those results, semi-quantitative analysis of Ca and S contents significantly influencing the formation of salt crystals was conducted using P-XRF to analyze the basis of surface deterioration, and cross-validation was performed by comparing the body stone affected by the mortar and the upper stylobate stone unaffected by the mortar. Results indicate that the elements are directly involved in the surface deterioration of the body stone.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• Land and Housing Review
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• v.12 no.4
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• pp.93-101
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• 2021

As the number of small households (1 to 2 persons per household) in Korea gradually increases, so does the importance of housing supply policies for small households. In response to the increase in small households, the government has been continuously supplying urban housing for these households. Since housing for small households is a sales and rental business similar to apartments and general business facilities, it is important for the building owner to calculate the project's estimated construction duration during the planning stage. Review of literature found a model for estimating the duration of construction of large-scale buildings but not for small-scale buildings such as urban housing for small households. Therefore this study aimed to develop and verify a model for estimating construction duration for urban housing at the planning stage based on multiple regression analysis. Independent variables inputted into the estimation model were building site area, building gross floor area, number of below ground floors, number of above ground floors, number of buildings, and location. The modified coefficient of determination (R_a²) of the model was 0.547. The developed model resulted in a Root Mean Square Error (RMSE) of 171.26 days and a Mean Absolute Percentage Error (MAPE) of 26.53%. The developed estimation model is expected to provide reliable construction duration calculations for small-scale urban residential buildings during the planning stage of a project.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.71-85
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• 2021

A variety of methods have been proposed through a number of studies on sophisticated demand forecasting models that can reduce logistics costs. These studies mainly determine the applicable demand forecasting model based on the pattern of demand quantity and try to judge the accuracy of the model through statistical verification. Demand patterns can be broadly divided into regularity and irregularity. A regular pattern means that the order is regular and the order quantity is constant. In this case, predicting demand mainly through regression model or time series model was used. However, this demand is called "intermittent demand" when irregular and fluctuating amount of order quantity is large, and there is a high possibility of error in demand prediction with existing regression model or time series model. For items that show intermittent demand, predicting demand is mainly done using Croston or HOLTS. In this study, we analyze the demand patterns of various items of air cargo with intermittent patterns and apply the most appropriate model to predict and verify the demand. In this process, intermittent optimal demand forecasting model of air cargo is proposed by analyzing the fit of various models of air cargo by item and region.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.18-23
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• 2018

FPSO consists of topside modularized plants for production of crude oil, and hullside structures that serve as support for the topside and storage of produced crude oil. The structural behavior of the FPSO topside module and its supporting hull depends on the interface structure that connects them, and the interface structure consists of a combination of individual unit support structures called Module Support Seat (MSS). Types of interface structures are various and, accordingly, the basic design of the FPSO topside module structure is greatly influenced, so various design methods should be considered from the initial design phase. Structural design of FPSO topside module requires consideration of the number of MSSs, connection type, and structural analysis options such as the range of finite element models, load conditions, and boundary conditions for verification of structural strength. In this study, the comparison combination cases for the above considerations were derived and the strength evaluation was performed, and the structural behavior characteristics of the topside module were compared and analyzed through a detailed review of the analysis results. The results of this study are considered to be a good reference for designing a more reliable topside module structure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.441-453
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• 2018

This study was conducted to understand and describe Korean nurses' perception of person-centered care for elderly patients in long-term care hospitals. Qualitative data were collected through four focus group interviews consisting of 21 nurses working in four long-term care hospitals in Jeolla and Gyeongsang provinces. Participants completed interviews from July 19 to 30, 2018. All interviews were recorded, transcribed, and analyzed by employing the thematic analysis method. Six main themes for the attributes of person-centered care for elderly patients in the long-term care hospitals were conceptualized: respecting individual needs, walking to the end, supporting hidden dreams, becoming a family partner, helping patients live like they are at home, and changing culture. Person-centered care perceived by nurses was conducted to provide individualized nursing according to elderly's preferences and help them discover the value and meaning of life through various activity programs. Nurses also recognized person-centered care to maintain cooperative relationships with their family members and share their decision-making process, as well as to form a physical environment and organizational culture that respects the rights and autonomy of the elderly. Based on the results of this study, it is necessary to identify the diverse needs of the elderly and develop nursing intervention programs based on person-centered care.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.448-459
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• 2019

The purpose of this study is to investigate the differences between children's internal variables (gender, temperament, development) and environmental variables (teaching efficacy, teaching - child interaction, classroom environment) The data collected for this study were analyzed using descriptive statistics, reliability analysis, correlation analysis, cluster analysis, and t-test using the SPSS 22.0 program. The results are as follows. First, the peer interaction of the infant showed a difference in sex between the types. Second, children's peer interaction showed differences in interstitial temperament, language development, and cognitive development. Third, the peer interaction of young children was different between the types of environment variables such as teaching efficacy, teacher - infant interaction, and classroom environment. As a result, it is suggested that the children's social temperament will lead to healthy peer interaction, and that language development and cognitive development will lead to a positive developmental process. In this study, the meaning and meaning of children's intergenerational behaviors in children's gender and temperament, language and cognitive development, and environmental variables such as teaching efficacy, teacher - infant interaction, I checked. In addition, it is meaningful that the positive and negative peer interactions are segmented and analyzed in detail to examine the peer interaction of infants. However, the limitation of this study is that it is not possible to investigate all the fields belonging to the infant's personal variables and environmental variables.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.457-467
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• 2018

Currently, the regulation and rules for public surveying and the UCPs (Unified Control Points) adapts those of the triangulated traverse surveying. In addition, such regulations do not take account of the unique characteristics of GNSS (Global Navigation Satellite System) surveying, thus there are difficulties in field work and data processing afterwards. A detailed procesure of GNSS processing has not yet been described either, and the verification of accuracy does not follow the generic standards. In order to propose an appropriate procedure for field surveys, we processed a short session (30 minutes) based on the scenarios similar to actual situations. The reference network in Seoul was used to process the same data span for 3 days. The temporal variation during the day was evaluated as well. We analyzed the accuracy of the estimated coordinates depending on the parameterization of tropospheric delay, which was compared with the 24-hr static processing results. Estimating the tropospheric delay is advantageous for the accuracy and stability of the coordinates, resulting in about 5 mm and 10 mm of RMSE (Root Mean Squared Error) for horizontal and vertical components, respectively. Based on the test results, we propose a procedure to estimate the daily solution and then combine them to estimate the final solution by applying the minimum constraints (no-net-translation condition). It is necessary to develop a web-based processing system using a high-end softwares. Additionally, it is also required to standardize the ID of the public control points and the UCPs for the automatic GNSS processing.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.673-686
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• 2018

The physical properties of rocks constituting the rock mass were analyzed by using various methods such as 7 kinds of physical properties of about 2,400 data. The correlation equation was derived from the correlation equation with the dependent variables by screening independent variables through the significance level using multiple regression analysis. In order to verify the reliability of this equation, verification was performed through comparison with actual data using artificial neural network learning. The analysis results by petrogenesis and strength confirmed that the elastic wave velocity (compressional wave) and elastic modulus as the main influence factors for the independent variables affecting the dependent variables. This proves that most of the correlation equations using the above items are found in existing studies. And through this study, it is confirmed whether the rock classification is based on the above items in various standards. In addition, the analysis results of representative rocks showed a high correlation as the equation for estimating unconfined compressive strength and elastic modulus exceeds the coefficient of determination 0.8.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.105-112
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• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.