• Journal of the Korean Society of Marine Environment & Safety
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• 제27권1호
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

• Land and Housing Review
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• 제13권1호
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• pp.55-65
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• 2022

This paper investigates the distinct characteristics of freehold and leasehold households living in the seven largest cities and the other areas. We employ the two-stage logit regression analysis to identify the marginal effects of wealth and income after controlling for the other one. We document the following results. First, households with more net wealth are more likely to reside in their own houses, regardless of living areas. Second, the pure income effect after controlling for wealth and other variables lowers the tendency of freeholders to live in the seven largest cities while increasing the tendency to live in the other areas. Furthermore, the income effects reduce the tendency to live in the former regions. Our results suggest that the pure income effects enhance preferences for a better living environment (e.g., larger spaces, better school districts, etc.), whereas the wealth effect increases the likelihood of freeholds.

• Journal of Korea Water Resources Association
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• 제54권spc1호
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• KIPS Transactions on Software and Data Engineering
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• 제12권2호
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of the Society of Disaster Information
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• 제19권3호
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• pp.710-718
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• 2023

Purpose: The purpose of this study is to objectify the analysis results using the disaster safety awareness survey for high school students, which has been systematically and continuously educated since the Ferry Sewol disaster, and to promote educational effects by identifying the educational status. Method: The 12 questions of the disaster safety awareness survey were answered using the direct entry method on a Likert 5-point scale, and the SPSS 24 and varimax (orthogonal rotation) methods were used to establish and test research hypotheses. Result: As a result of the verification, it was found that the independent variables, knowledge competency and attitude competency, had a positive effect on the dependent variable, behavioral competency, and there was no multicollinearity, so it was verified that it was meaningful. Conclusion: As a result of the survey analysis, domestic disaster safety education showed a significant impact on the level of disaster safety awareness as an education that meets its goals. Various aspects of disasters show that the educational effect can be improved only when education is established as education by life cycle.

• The Journal of the Convergence on Culture Technology
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• 제9권1호
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• pp.61-68
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• 2023

As global growth has gradually declined, the Customer to Customer (C2C) market has expanded. And the growth potential of the C2C market is getting higher than in the past. Therefore, in this study, we examined what factors affect the price of used products within the C2C market. In order to examine the factors, we used data provided by Kaggle, which is a data science platform, and Mercari, Japan's largest C2C community marketplace platform. In research methods, the characteristics of the products were selected such as product categories, product status, shipping costs, product brands, and the data were analyzed using a linear mixing model to predict the price of C2C used goods. As a result, the variable that most affected the price was the shipping cost. When the seller paid for the shipping cost, the price would drop more than if the buyer had to pay. This study has been shown that the shipping costs is also an important factor in the used market, which can provide practical implications for customers of real transactions.

• Journal of the Korea Society of Computer and Information
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• 제28권4호
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• pp.133-142
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• 2023

National parks are one of the important types of protected area management systems established by IUCN and a management model for implementing effective conservation and sustainable use of natural and cultural heritage in countries around the world, and they assume important roles in conservation, scientific research, education, recreation and driving community development. In the context of big data, this study takes China's Liaohe National Park, a typical representative of global coastal wetlands, as a case study, and using Python technology to collect tourists' travelogues and reviews from major OTA websites in China as a source. The text spans from 2015 to 2022 and contains 2998 reviews with 166,588 words in total. The results show that wildlife resources, natural landscape, wetland ecology and the fishing and hunting culture of northern China are fully reflected in the perceptions of visitors to Liaohe National Park; visitors have strong positive feelings toward Liaohe National Park, but there is still much room for improvement in supporting services and facilities, public education and visitor experience and participation.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제26권5D호
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• pp.797-804
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• 2006

A development of regression model for asphalt concrete pavements using Falling Weight Deflectometer deflections is presented in this paper. A backcalculation program based on layered elastic theory was used to generate the synthetic modulus database, which was used to generate 95% confidence intervals of modulus in each layer. Using deflection basins of FWD data used in developing this procedure were collected from Pavement Management System in flexible pavements. Assumptions of back-calculation are that one is 3 layered flexible pavement structure and another is depth to bedrock is finite. It is found that difference of between 95% confidence intervals and modulus ranges of other papers does not exist. So, the data of 95% confidence intervals in each layer was used to develop multiple regression models. Multiple regression equations of each layer were established by SPSS, package of Statics analysis. These models were proved by regression diagnostics, which include case analysis, multi-collinearity analysis, influence diagnostics and analysis of variance. And these models have higher degree of coefficient of determination than 0.75. So this models were applied to predict modulus of domestic asphalt concrete pavement at FWD field test.

• 한국노년학
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• 제31권3호
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• pp.653-672
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• 2011

The present study empirically confirmed Korean elder's four major pains consisted of poverty, disease, role loss, loneliness and investigated the mediating role of depression between the four major pains and the elder's suicidal thought. To investigate the cause and effect of factors, we conveniently collected 309 samples from 16 Gyungrodangs evenly located in Jeonju and 291 samples, survived the data cleaning such as missing values, outliers, normality and covariance conditions, were analyzed by frequency, factor analysis, reliability, confirmatory factor analysis and structural model analysis. Followed were the selected contributions of the present study. First, the constructs of four major pains such as poverty, disease, role loss, loneliness were predictors of suicidal thought mediated by depression. Second, the elder's poverty, that was the heaviest factor of the four major pain constructs, was a predictor of role loss leading to loneliness. Third, four major pains were predictors of the elder's depression. Note that poverty were not direct but indirect predictor of depression. The present study confirmed the concept of four major pains. Also those who practice in the area of the elderly care should consider the four major pains as well as depression while intervening in the elderly's suicidal thought.

• 한국노년학
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• 제32권2호
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• pp.359-376
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• 2012

The present study empirically confirmed knowledge of ageing and quality of contact were predictors affecting middle-aged Korean's ageism against the elderly and verified mediating role of intergroup anxiety between not only knowledge of ageing but also quality of contact and ageism. To investigate causalities of factors, we purposively collected 400 samples from 20 Dongs evenly located in Jeonju and 393 samples, survived the data cleaning such as missing values, outliers, normality and covariance conditions, were analyzed by frequency, factor analysis, reliability, confirmatory factor analysis and structural model analysis. Followed were the selected contributions of the present study. First, the knowledge of ageing and quality of contact were predictors of ageism mediated by intergroup anxiety. Second, the knowledge of ageing and quality of contact did not directly affect middle-aged Korean's ageism against the elderly. Third, intergroup anxiety had strong effect on ageism. The contributions suggested increasing knowledge of ageing and providing contact experience to middle-aged Korean as combating strategy against ageism.