• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.1 s.114
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• pp.69-80
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• 2006

The purposes of this study are to analyze the meanings of urban spaces which are the background of the Korean films, and to capture the interactions of ordinary culture and urban spaces. By reading urban spaces through films, it is possible to understand the variety of experiences that are hardly captured with direct eyes, specific and vivid urban images, and various events formed by the interactions of spaces and people. The scope of the study is the urban spaces revealed in Korean films portraying cities after the 1960's as their settings, and the total of 18 films was selected with every $4{\sim}5$ films for each time stage. With the selected films, analytical meanings were developed with considering three aspects; 1) phylogenetic meaning that simply reflects social-cultural and historical background, 2) the outer strum meaning that concerns the situation of special background and film scene, and 3) metaphorical and metanymic meaning on films. According to the appearance frequency, spatial backgrounds of film scenes are mainly alleys, main streets, railroad, loft, and riverside. And then the connection between spaces and meaning clusters was grasped, and reflected meanings were derived for every spaces. And the meaning of urban space in films was analyzed based on the meaning of developments and outer stratum. The fundamental characteristics and feelings of people in media such as films are more emphasized than in real world. Urban space is not considered as a simple visible shell, but is recognized as 'a real situation' created by people. The intension of this study was to open the possibility of the various views of urban spaces. The construction of the urban space should be approached from a perspective of creating new places at where the space and human beings interact with considerations of stories of various human lives. I hope new vistas can be opened up for the research subjects and methodologies about the hereafter study of urban spaces through the mutual communications with various adjacent regions including films.

• Journal of Fashion Business
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• v.23 no.1
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• pp.14-24
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• 2019

Various types of functional bedding for inducing and maintaining sleep, are developed and launched with the importance of improving health through sleep emphasized currently. The purpose of this study is to examine development status and direction of functional bedding in the $4^{th}$ Industrial Revolution era, through systematic classification of elements of IoT-based smart bedding cases actively developed as functional bedding at home and abroad. Through previous research, literature and Internet data, characteristics and functional extension of smart bedding and the background of smart bed development was analyzed. And it was analyzed that smart bedding pursues recent functionalism and convergence of physical and digital concept such as IoT or AI, and also mental value to improve sleep quality. As bedroom where smart bedding place in has the private and limited characteristics and users are in sleep-conscious, that hard to ensure power and discomfort in carrying are moderated and the aesthetic elements are not very important, and that the smart bedding performance while sleeping were affected on developmental background. Based on CES case study and analysis on how smart beds are functionally expanded from conventional bedding, smart beds have gained information through digital sensing, and common properties that can be controlled anytime, anywhere, using a smart phone. Some set up the right environment and pose, while others stimulate nerves directly as active intervention. It is expected that smart bedding will be developed to cure user's body and mind, through active intervention when sleeping.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• Journal of the Korea Fashion and Costume Design Association
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• v.23 no.1
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• pp.87-102
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• 2021

The purpose of this study is to consider effective digital marketing strategies through analysis of luxury fashion brands. This study conducted both quantitative analysis and case studies of the brands Louis Vuitton, Gucci, Burberry, and Chanel. To measure the brand image of the luxury fashion brands, the survey was distributed to Millennials, and total of 277 responses were used for the final analysis by using SPSS 25.0 statistical program. Other than survey, this paper analyzed digital marketing strategies of luxury fashion brands through brand-related papers, website and social media of each brand, Samsung Designnet's database, and news posted on search engines. The results of this study are as follows: First, according to the result of examining brand image of luxury fashion brands, there was no significant difference between brands, except Gucci. Second, this study analyzed each luxury fashion brand to understand the characteristics of digital marketing, and common characteristics were identified. Third, by analyzing the brand image and digital marketing strategies of luxury fashion brands, it was confirmed that Gucci's brand image and digital marketing strategies were consistent, while there was a difference between Burberry's brand image and digital marketing strategy. Therefore, this article proposes the following digital marketing strategies that are suitable for luxury fashion brands. First, is the connection of on/offline channels. Second, is the use of AI technology. Third, is a blockchain-based platform.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.1-9
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• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.659-665
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• 2023

The exciter of a ship generator adjusts the magnetic flux through excitation current control to maintain the output terminal voltage constant. The voltage controller inside the exciter typically uses a proportional integral control method. however, the response characteristics determined by the gain and time constant produce unwanted output owing to an inappropriate setting value that can reduce the quality and stability of power within the ship. In this study, a neural network circuit is learned using stable input/output data that can be obtained through the AC4A type exciter model provided by IEEE, and the simulation is performed by replacing the existing proportional integral control type voltage controller with the learned neural network circuit controller. Consequently, overshooting was improved by up to 9.63% compared with that of the previous model, and excellence in stable response characteristics was confirmed.

• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of Distribution Science
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• v.22 no.4
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• pp.11-22
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• 2024

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants' characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.93-100
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• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.