• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.167-186
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• 2008

This study is to investigate, from the perspective of implicit theory, what elements influence children's motivation to learn and how their configurations are different according to different sexes. One analysis was based on answers to a motivation questionnaire by fourth to sixth graders from four different cities in South Korea. The subjects children were most highly motivated to learn were math and science for boys, and math and English for girls, respectively. Factors influencing the motivation were near 30 in number, including later happier life, joy of learning, parental rewards, pleasure of being informed, and meeting parental expectations, among others. Another analysis was an exploratory and confirmative factor analysis on motivation to learn among 856 fourth to sixth graders randomly sampled from 7 different cities all over South Korea. Factors revealed to contribute to the motivated learning here were five factors of utility, interest, recognition, knowledge acquisition(being informed), and expectancy sufficiency. There were some differences in the structure of factors between sexes; importance was given to five factors of utility, interest, recognition, knowledge acquisition, and expectancy sufficiency in descending order for boys, and six factors of interest, utility, rewards, recognition, expectancy sufficiency, and competition for girls.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.26-41
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• 2024

To ensure safer road conditions, understanding the human factors influencing driving behavior is crucial. However, there are many difficulties in deriving the characteristics of individual human factors that affect actual driving behaviors. Therefore, this study analyzes self-reported dangerous-driving propensities in order to explore potential correlations with drivers' behaviors. The goal is to propose a method for assessing driving tendencies based on varying traffic scenarios. The study employed a questionnaire to gauge participants' propensity to drive dangerously, utilizing a simulator to analyze their driving behaviors. The aim is to determine any notable connections between dangerous-driving propensity and specific driving behaviors. Results indicate that individuals exhibiting a high propensity for reckless driving, as identified by the Korean DBQ, tend to drive at higher speeds and display more aggressive acceleration patterns. These findings contribute to a potential method for assessing reckless driving drivers.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.1-8
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• 2024

The traveling salesman problem(TSP) is one of the well known combinatorial optimization problems. Local search has been used as a method to solve TSP. Greedy Random Insertion(GRI) is known as an effective neighbor generation method for local search. GRI selects some cities from the current solution randomly and inserts them one by one into the best position of the current partial solution considering only one city at a time. We first propose another greedy neighbor generation method which is named Full Greedy Insertion(FGI). FGI determines insertion location one by one like GRI, but considers all remaining cities at once. And then we propose a method to combine GRI with FGI, in which GRI or FGI is randomly selected and executed at each iteration in simulated annealing. According to the experimental results, FGI alone does not necessarily perform very well. However, we confirmed that the combined method outperforms the existing local search methods including GRI.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.284-298
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• 2023

With the rapid advances in automated driving technology, opportunities to experience automated driving directly or indirectly are being provided to the public. On the other hand, research on the preferred automated driving patterns from the user's perspective has not been conducted in Korea. This study used a driving simulator and an experimental vehicle capable of automated driving to evaluate the user satisfaction regarding longitudinal and lateral accelerations. Automated driving patterns were implemented in a virtual environment simulation using five values of longitudinal and lateral accelerations derived from driving experiments. Among these values, three were implemented through experimental vehicle-based automated driving to evaluate satisfaction and anxiety. The participants evaluated lateral acceleration more sensitively than longitudinal acceleration and showed higher levels of anxiety. Based on these results, the necessity of user-oriented evaluation research for automated driving patterns and the suitability of simulator-based evaluation methods were presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.29-47
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• 2024

Many domestic and foreign studies derive factors that significantly affect the use of shared E-scooters based on performance data, but few studies have been conducted with comparative analysis models using predictive power, applying them to other regions. Therefore, by clearly establishing detailed influencing factors and scope in Gwangjin-gu and Gangnam-gu by using domestic shared E-scooter performance data, this study enhances predictive power, and the Geographically Weighted Regression model is derived through spatial autocorrelation verification. Based on the results, the direction of a construction model created from regional differences was presented, and major implications from the user's perspective are derived based on the difference between actual use and the model's prediction.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.319-326
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• 2024

Construction projects account for a significant proportion of workplace hazards globally. While construction cost reports typically emphasize direct accident costs such as treatment expenses, nursing care costs, or disability benefits, indirect factors like work interruption loss costs or consolation costs are frequently overlooked, because it is relatively difficult to estimate those factors in advance. Recognizing and accurately estimating the indirect costs factors associated with construction accidents would not only shed light on the monetary impact these incidents have on overall project costs but also would enable to estimate the total accident cost in advance. The current study seeks to identify factors influencing indirect costs, which ultimately govern the total accident cost, through a data mining approach. A survey was conducted in domestic construction companies, resulting in a dataset of 1038 accident records collected from construction sites. First, statistical analysis was performed to uncover characteristics and patterns of factors affecting construction accident costs from both direct and indirect perspectives. Later, this study proposes four distinct machine learning (ML) models, comparing their performances in predicting the total accident cost (including indirect costs) in advance. Additionally, this research sheds light on an important issue in construction data analysis, which is the scarcity of data in a particular class, by applying random oversampling and random undersampling techniques. The suggested framework can assist practitioners and management in estimating construction accident costs and identifying the relevant attributes that impact accidents at the construction site for future practices.

• Tunnel and Underground Space
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• v.34 no.5
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• pp.543-553
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• 2024

There has been increased interest, media coverage, and debate over constructing new underground structures to replace existing at-grade rail tracks. This new scheme aims to free up space to provide cities with room for more housing with green amenities. Due to urbanisation, tunnel engineers have encountered greenfield ground conditions in cities on only a few occasions. However, the new scheme provides opportunities to investigate a unique scenario where new tunnels are driven parallel to the existing rail tracks on the surface with little ground cover in soft ground. This paper presents findings obtained from a semi-empirical approach that aims to investigate the likely track irregularity associated with tunnelling-induced ground movements. This paper presents contour maps that show track rotation according to the relative position of the new tunnel and existing rail tracks. Tunnel engineers would consult these maps for their tunnel route design for the scheme.

• Wind and Structures
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• v.39 no.4
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• pp.271-285
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• 2024

The multiple-fan wind tunnel is an important facility for reproducing target wind field. Existing control methods for the multiple-fan wind tunnel can generate wind speeds that satisfy the target statistical characteristics of a wind field (e.g., power spectrum). However, the frequency-domain features cannot well represent the nonstationary winds of extreme storms (e.g., downburst). Therefore, this study proposes a multiple-fan wind tunnel control scheme based on Deep Reinforcement Learning (DRL), which will completely transform into a data-driven closed-loop control problem, to reproduce the target wind field in the time domain. Specifically, the control scheme adopts the Deep Deterministic Policy Gradient (DDPG) paradigm in which the strong fitting ability of Deep Neural Networks (DNN) is utilized. It can establish the complex relationship between the target wind speed time series and the current control strategy in the DRL-agent. To address the fluid memory effect of the wind field, this study innovatively designs the system state and control reward to improve the reproduction performance based on historical data. To validate the performance of the model, we established a simplified flow field based on Navier Stokes equations to simulate a two-dimensional numerical multiple-fan wind tunnel environment. Using the strategy of DRL decision maker, we generated a wind speed time series with minor error from the target under low Reynolds number conditions. This is the first time that the AI methods have been used to generate target wind speed time series in a multiple-fan wind tunnel environment. The hyperparameters in the DDPG paradigm are analyzed to identify a set of optimal parameters. With these efforts, the trained DRL-agent can simultaneously reproduce the wind speed time series in multiple positions.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.321-335
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• 2020

Given that with the development of the 4th industry, interest and desire for smart cities are gradually increasing and related technologies are developed as a way to strengthen urban competitiveness by utilizing big data, information and communication technology, IoT, M2M, and AI, the purpose of this study is to find out how to achieve this goal on the premise of the idea of smart well fair city. In other words, the purpose is to devise a smart well-fair city in the care area, such as health care, medical care, and welfare, and see if it is feasible. With this recognition, the paper aimed to review the concept and scope of smart city, the discussions that have been made so far and the issues or limitations on its connection to social security and social welfare, and based on it, come up with the concept of welfare city. As a method of realizing the smart welfare city, the paper reviewed characteristics and features of a social security platform as well as the applicability of smart city, especially care services. Furthermore, the paper developed discussions on the standardization of the city in terms of political and institutional improvements, utilization of personal information and public data as well as ways of institutional improvement centering on social security information system. This paper highlights the importance of implementing the digitally based community care and smart welfare city that our society is seeking to achieve. With regard to the social security platform based on behavioral design and the 7 principles(6W1H method), the present paper has the limitation of dealing only with smart cities in the fields of healthcare, medicine, and welfare. Therefore, further studies are needed to investigate the effects of smart cities in other fields and to consider the application and utilization of technologies in various aspects and the corresponding impact on our society. It is expected that this paper will suggest the future course and vision not only for smart cities but also for the social security and welfare system and thereby make some contribution to improving the quality of people's lives through the requisite adjustments made in each relevant field.

• Proceedings of The Korean Society of Health Promotion Conference
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• 2004.10a
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• pp.111-129
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• 2004

This paper develops the argument that the 'Healthy Cities Approach' extends beyond the boundaries of officially designated Healthy Cities and suggests that signs of it are evident much more widely in efforts to promote health in the United Kingdom and in national policy. It draws on examples from Leeds, a major city in the north of England. In particular, it suggests that efforts to improve population health need to focus on the wider determinants and that this requires a collaborative response involving a range of different sectors and the participation of the community. Inequality is recognised as a major issue and the need to identify areas of deprivation and direct resources towards these is emphasised. Childhood poverty is referred to and the importance of breaking cycles of deprivation. The role of the school is seen as important in contributing to health generally and the compatibility between Healthy Cities and Health Promoting Schools is noted. Not only can Health Promoting Schools improve the health of young people themselves they can also develop the skills, awareness and motivation to improve the health of the community. Using child pedestrian injury as an example, the paper argues that problems and their cause should not be conceived narrowly. The Healthy Cities movement has taught us that the response, if it is to be effective, should focus on the wider determinants and be adapted to local circumstances. Instead of simply attempting to change behaviour through traditional health education we need to ensure that the environment is healthy in itself and supports healthy behaviour. To achieve this we need to develop awareness, skills and motivation among policy makers, professionals and the community. The 'New Health' education is proposed as a term to distinguish the type of health education which addresses these issues from more traditional forms.