• Journal of Creative Information Culture
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• v.6 no.3
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• pp.209-217
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• 2020

Recently, interest in data analysis has increased, and convergence research through data analysis has been actively conducted in various fields such as engineering, natural science, and social science. In the field of architecture, various studies using data analysis are being conducted, and in particular, efforts are being made to solve the problems in the field of architecture that have been quantitatively expanded through the urbanization process. In this study, data analysis on residential satisfaction of residents in residential environment improvement areas and similar neighborhoods through urban regeneration projects is performed. Through analysis using R for post-residential evaluation elements that are conducted after building construction and occupancy, important evaluation items that affect the satisfaction of the residential environment are identified by analyzing the association rules between each evaluation element and identifying the frequency of major requirements of residents. To grasp. Through this, we intend to conduct convergence research between IT and architecture fields, such as the development of a system that can recommend high-quality residential areas as well as providing data for securing high-quality residential spaces when constructing residential areas in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.173-178
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• 2022

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.145-150
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• 2022

There is a movement to respond to the decline of the city through urban regeneration projects, and in particular, the importance of residents-led is increasing. In addition, the concept of resilience to increase the resilience of the city in response to an uncertain future is emerging around the world. This study aims to diagnose urban regeneration projects from a resilience perspective. The target site of the study was set as a selection area for general neighborhood urban regeneration projects in Seoul, and the factors affecting resilience in response to the recent coronavirus were analyzed. As a result of the analysis, it was found that among the contents of the urban regeneration project revitalization plan, the social regeneration content that can lead to residents had a positive effect on resilience. It is expected that the resilience of the city can be improved if the contents that can enhance the self-sustainability of the residents are included from the stage of the plan.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.470-477
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• 2022

Purpose: As the Act on Punishment of Serious Accidents came into effect in January 2022, it is becoming a big social issue in the labor and management circles. The purpose of this law is to analyze problems and suggest improvement plans so that the purpose of protecting lives and bodies by preventing major accidents can be met. Method: The main contents of the relevant law were identified, and the current application status of the currently enforced law and problems by law were analyzed. Result: Currently, more than 50 accidents have occurred and they have been classified as serious accidents, and no punishment has been imposed under the relevant laws. There are four major problems: 1) the issue of equity in the applied workplace, 2) the lack of clarity in some legal provisions, 3) the issue of entrusting the obligation to secure safety and health to private organizations, and 4) the issue of excessive punishment regulations. was analyzed as. Conclusion: As the law is in the early stages of enforcement, there are trials and errors, but revisions are necessary through the efforts of the labor and management circles to meet the establishment and purpose of the law.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.694-702
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• 2022

This paper is the UX-based technology strategy research which is a solution to how conversational AI can be ethically evolved in the Metaverse environment. Since conversational AI influences people's on-offline decision-making factors through interaction with people, the Metaverse AI ethics must be reflected. In the machine learning process of conversational AI, cultural codes along with user's personal experience data must be included and considered to reduce the error value of user experience. Through this, the super-personalized Metaverse service can evolve ethically with social values. With above hypothesis as a result of the study, a conceptual model of a forward-looking perspective was developed and proposed by adding user experience data to the machine learning (ML) process for context-based interactive AI in the Metaverse service environment.

• Journal of People, Plants, and Environment
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• v.24 no.3
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• pp.285-300
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• 2021

Background and objective: Rural migrants are an important human resource in urban and rural exchange (URE) business, therefore it is necessary to enhance their awareness and intent-to-participate. This study was to analyze the rural migrants' recognition and intent-to-participate of URE business and to propose the enhancement of them both. Methods: The questionnaire was designed to analyze the socio-demographic background, motivations, satisfaction with settlement, intent-to-persist, and intent-to-participate of URE business including tourism. The data of 144 respondents was subject to the statistical analysis. Results: The motives of migration were to enjoy leisure life after retirement, increased tiredness of city life, health problems, etc. The satisfaction with settlement was 3.67 at 5-point Likert scale. The intent-to-recommend and intent-to-publicize were 3.40 and 3.46, respectively. The intent-to-participate was 3.45, which was affected by the necessity of URE business and the support of central/local governments, and also showed a significant correlation with the satisfaction with settlement, intent-to-recommend, intent-to-publicize, tourism resources for green tourism or rural tourism, driving a car in Namhae county, and the service and price of meals. They thought the missions that the Namhae county office must focus on were to establish an internal/external public relations systems, establish a support system of central/local governments, and foster/support local leaders. Conclusion: It is necessary to improve the satisfaction with settlement and intent-to-persist, expand exchanges with local people, improve internal/external public relations systems, foster/support leaders, improve transportation in the county, enhance the service and price of meals, and develop/operate URE programs including tourism.

• The Korean Journal of Franchise Management
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• v.14 no.1
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• pp.1-21
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• 2023

Purpose: During COVID-19, consumers prefer social distancing or contactless activities for safety, and hygienic condition has become one of the most important factors in evaluating restaurants. Therefore, this study aims to investigate whether offline/online word-of-mouth is affected by restaurant quality. Research design, data and methodology: The data were collected from 480 consumers who had experiences of visiting a restaurant in the past 90 days and analyzed with SPSS 28.0 and SmartPLS 4.0 programs. Results: Physical environment and menu had positively significant effects on brand love, while employee service and hygiene had no significance on brand love. Restaurant environment, menu, and hygiene had negatively significant effects on brand hate, but employee service had not significant impact on brand hate. Brand love had positively significant effects on offline and online word-of-mouth, and brand hate had negatively significant effects on offline and online word-of-mouth. Conclusions: First, restaurants need to develop a pleasant space where customers can have emotional experiences. Second, restaurants need to fulfill customers' desire for global food consumption. Third, restaurants should ensure hygiene and safety to prevent customers' brand hate. Lastly, restaurants need to establish offline/online word-of-mouth strategy to identify which restaurant quality attributes influence brand love/hate and offline/online word-of-mouth.

• Journal of Advanced Technology Convergence
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• v.1 no.2
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• pp.37-48
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• 2022

This research is an empirical research of Chinese OTT Audiences on the effects of advertising model attributes on consumers' advertising perception, purchase intention, Flow and addiction. Recently, as the cosmetics market in China has grown, the role of advertising models has been highlighted, and shopping addiction caused by excessive Flow is becoming a social problem. Researchers set up a research model and tried to test which characteristics of the advertising model lead consumers to purchase, Flow and ultimately lead to addiction. Results are as follows. It was confirmed that advertisement model attributes such as recognition and attractiveness had a positive effect on viewers' advertising perception and attitude, and viewers' perceived usefulness had a positive effect on purchase intention and Flow. In addition, the purchase intention of the viewers had a positive effect on the addiction to cosmetics.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.70-77
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• 2022

The total number of Korean restaurants using delivery applications has substantially increased from 7.6% in 2018 to 11.2% in 2019. In 2020, the gross sales in the food delivery service market reached approximately 17 trillion won; this amount is virtually six times that in 2017 (i.e., 2 trillion won). Meanwhile, the annual average death toll of motorcycle riders increased by 3.5%, whereas the number of deaths due to other traffic accidents decreased by 8.2%. Consequently, the foregoing has become a critical social problem. Despite the continuing increase in the number of delivery riders due to the rapid expansion of the delivery industry, no appropriate safety management system has been established. Moreover, the government is experiencing difficulties in assessing the exact situation because of the absence of competent authority. In this study, fundamental data on the characteristics of delivery work and motorcycle accidents were collected through surveys and interviews; then, the influencing factors of traffic accidents were analyzed. Different influencing factors were identified: work experience as a rider; number of deliveries; whether to accept delivery requests in transit; manner of accepting delivery requests; and traffic law violations, such as speeding (for faster delivery) and running a red light. Because the motorcycle delivery industry has a relatively low job-entry barrier (i.e., special qualifications are not required), the riding skills of riders must be improved, and delivery companies must be technically developed to achieve a safe working environment. The results of this study can be utilized as fundamental data for system development or structural improvement of the delivery industry.