• 한국체육학회지인문사회과학편
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• v.54 no.5
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• pp.705-716
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• 2015

The purpose of this research is to explore the role of Taekwondo in foreign migrant workers' adaptation process in Korean society, based on segmented assimilation concept by Portes & Zhou(1993) as a theoretical framework. In order to achieve this, an in-depth interview of 13 foreign migrant workers in Gyeonggi-region was carried out by using purpose sampling and theoretical sampling together, and the interview was analyzed using grounded theory method. The analysis result demonstrated that foreign migrant workers understand Korean culture, learn Korean, and interact with Koreans through Taekwondo, which facilitates their acculturation into the mainstream society. Also, they were assimilated into their own subculture of being fascinated by the charms of Taekwondo and trying to become Taekwondo instructors by returning to their home country. Lastly, they built a social network and overcame tough labor and the difficulties of living in a foreign country through Taekwondo. This research is significant as it examined the role of Taekwondo that preserves Korean culture and checked its value, in terms of the adaptation of foreign migrant workers who take a part in the current rearrangement of Korean society into a multicultural society.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.511-517
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• 2006

Flood damage is one of the most important and influential natural disaster which has an effect on human beings. Local concentrated heavy rainfall in urban area yields flood damage increase due to insufficient capacity of drainage system. When the excessive flood occurs in urban area, it yields huge property losses of public facilities involving roadway inundation to paralyze industrial and transportation system of the city. To prevent such flood damages in urban area, it is necessary to develop adequate inundation analysis model which can consider complicated geometry of urban area and artificial drainage system simultaneously. In this study, an urban flood analysis model adopting the unstructured computational grid was developed to simulate the urban flood characteristics such as inundation area, depth and integrated with subsurface drainage network systems. By the result, we can make use of these presented method to find a flood hazard area and to make a flodd evacuation map. The model can also establish flood-mitigation measures as a part of the decision support system for flood control authority.

• Korean Journal of Social Welfare Studies
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• v.44 no.2
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• pp.59-87
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• 2013

The goal of this study is to understand the experiences of emigration to Vietnam for Korean older males through a qualitative case study. The specific research questions are following. Firstly, what do they experience through emigration to Vietnam? Secondly, what are the meanings of emigration to Vietnam for them? Thirdly, what are the contextual meanings of it? To explore these questions, the data were collected through diverse data collection methods including in-depth interviews with seven research participants for eleven months. Each case was carefully examined and summarized in the within-case analysis and major issues appeared in each case were described in the cross-case analysis before the reconstitution of story-telling considering a holistic context on the older males' experiences of emigration to Vietnam. The six integrated themes are 'Motivation and background of immigration', 'Acculturation', 'Social network', 'Meaning of work', 'Family' and 'Spirituality and attitude to the life', 'Perceptions on death'. Finally, the critical results were summarized before indicating limits and implications of this study and then some suggestions for following studies are summarized on the conclusion.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• Korean Journal of Culture and Social Issue
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• v.19 no.3
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• pp.457-485
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• 2013

This study attempted to discover the dimensions of Korean culture, with the presumption that the cross-cultural studies(Hofstede, 1980, 1997; Schwartz, 1992, 1994; Trompenaars and Hampden-Turner, 1997; House et al., 2004) have limitation to explain non-western culture including Korean culture. Even though there are some Korean cultural studies, they used heuristic approaches applying the authors' experiences and intuitions. This study applied the Cultural Consensus Theory to overcome the previous studies' shortcomings and to discover the dimensions that can be empirically proved by data. In specific this study conducted in-depth interview, used content analysis, did frequency analysis, and applied pilesort technique, multidimensional scaling and network analysis. As a result, this study obtained five categories: public self-consciousness, group-focused orientation, affective human relations, hierarchical culture, and result-orientation. It is expected that these dimensions can be used as important variables that may explain Korean social phenomena.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• Journal of Fashion Business
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• v.28 no.1
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• pp.98-112
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• 2024

The purpose of this study was to provide basic information on the development of local governments and upcycling industries that want to establish centers in the future. The study investigated the current situation and programs of domestic upcycling centers for regional sustainable growth. As a result of comparing and analyzing the programs operated by the upcycling centers by region, they could be classified into culture and arts experience programs, resource circulation experience programs, and environmental culture education programs according to the nature of the operation programs that are more focused on in addition to the experience and education programs reflected by each center. Among the upcycling materials and items used in the operation program, fashion-related education was being operated in a more diverse manner in the area of culture and arts experience programs. As a result of the analysis, it was found that it was necessary to establish a smooth material supply network, develop an in-depth step-by-step upcycling fashion education program, and strengthen the upcycling center program using regional characteristics. The results of this study are significant in that they provide the local governments with basic information for the establishment of upcycling centers in areas where the upcycling centers have not been established. In addition, this study presents the types and directions of programs necessary for establishing upcycling centers in the future.

• Journal of Digital Policy
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• v.3 no.3
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• pp.9-14
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• 2024

In this paper, proposes a method using a multi block structure composed of residual blocks with adaptive weights to improve the quality of results in single image super resolution. In the process of generating super resolution images using deep learning, the most critical factor for enhancing quality is feature extraction and application. While extracting various features is essential for restoring fine details that have been lost due to low resolution, issues such as increased network depth and complexity pose challenges in practical implementation. Therefore, the feature extraction process was structured efficiently, and the application process was improved to enhance quality. To achieve this, a multi block structure was designed after the initial feature extraction, with nested residual blocks inside each block, where adaptive weights were applied. Additionally, for final high resolution reconstruction, a multi kernel image reconstruction process was employed, further improving the quality of the results. The performance of the proposed method was evaluated by calculating PSNR and SSIM values compared to the original image, and its superiority was demonstrated through comparisons with existing algorithms.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.