• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.4
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• pp.41-50
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• 2022

Purpose: This study aims to suggest an analysis model finding the relationship between building scale characteristics of Public-use facilities and infectious disease outbreaks around senior leisure welfare facilities and the features and their scopes where quarantine resources are to be concentrated. Methods: Reviewing previous studies found the user characteristics of senior leisure welfare facilities and scale characteristics of urban architectures. The data preprocessing was performed after collecting building data and infectious disease outbreak data in the analysis area. This study derived data for attributes of building size and frequency of infectious disease outbreaks in Public-use facilities around senior leisure welfare facilities. A computing algorithm was implemented to analyze the correlation between the building size characteristics and the infectious disease outbreak frequency as per the change of the spatial scope. Results: The results of this study are as follows: First, the suggested model was to analyze the correlation between the infection frequency and the number of senior leisure welfare facilities, the number of Public-use facilities, building area, total floor area, site area, height, building-to-land ratio, and floor area ratio varied as per the change of spatial scope. Second, correlation results varied between the infection frequency and the number of senior leisure welfare facilities, the number of Public-use facilities, building area, total floor area, site area, height, building-to-land ratio, and floor area ratio. Third, a negative correlation appeared in the analysis between the number of senior leisure welfare facilities and infection frequency. And positive correlations appeared noticeably in the study between the number of Public-use facilities, building area, total floor area, height, building-to-land ratio, and floor area ratio. Implications: This study can be used as primary data on the utilization of limited quarantine resources by analyzing the relationship between the Public-use facilities around the senior leisure welfare facilities and the spread of infectious diseases. In addition, it suggests that infectious disease prevention measures are necessary considering the spatial scope of the analysis area and the size of buildings.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.149-156
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• 2021

The mechanical properties of a woven fabric made of SiC (silicon carbide) fibers were determined in this study using the KES-FB system. The woven fabric is used in high heat settings above 1500℃. Composite spun yarns were used to create SiC fibers. By analyzing the wearing properties, we studied the prospect of using the textiles as fire-retardant work clothes. Mechanical properties determine the wearing attributes. Therefore, the tensile linearity (LT), tensile resilience (RT), and shear stiffness (G) values of the fabric varied according to the yarn type (filament or spun yarn). The thickness, weight per square meter, and density of the fabric were found to have an effect on the shear hysteresis (2HG) and compression resilience (RC) values. In terms of wearable clothing qualities, the fabric qualities of the SiC composite yarn demonstrated the highest ratio of compressive energy to thickness (WC/T), which indicates bulkiness. The fabric manufactured from SiC composite yarns passed the KFI criteria for carbonation length and cumulative flame time in the flame-retardant test. Therefore, we discovered that the material can be used as a fire-resistant work cloth.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.189-196
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• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.287-297
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• 2021

The goal of this study is to investigate and provide information to the Fintech Industry on the main factors responsible for customer loyalty via the mediating effect of customer satisfaction. Secondly, providing traditional banking reasons for customer shifts from banking to Fintech, therefore these factors could be more focused. The consumer choices presented in this study can thus serve as a foundation for further research into post-adoption behaviors associated with Fintech for cross-border payments. This study examines consumer evaluations of how key attributes of fintech using mobile payment services affect their choice by using a conjoint analysis approach, which allows for the approximation of user preferences for specific features. In our study we have used SPSS 26 to test the reliability and mediation effect on the sample size of 348 people who regularly used Fintech for cross border payments. All the questionnaires were prepared if the customers were given fintech as an option instead of traditional banks to send their remittances abroad. The result shows that(Service Quality, Customer's trust and product quality) effecting customer satisfaction significantly would be very helpful for the current fintechs working for home remittances to improve these factors and would serve as a benchmark for the upcoming fintech startups and traditional banks to focus on these factors and catch up the fintech Industry. Finally, it is argued that, in order to be successful, focusing on service quality, customer trust, and product quality triggered customer loyalty for the Fintech in comparison to other traditional options for cross-border payments via the mediation effect of customer satisfaction.

• Smart Media Journal
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• v.11 no.6
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• pp.18-29
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• 2022

This study proposes an evaluation item model for the avatar expression process, satisfaction and self-identification for each expression element in order to investigate the expression attributes on the influence on the user in setting the avatar. The evaluation of avatar expression method was composed of self-identification and satisfaction by ease of expression and expression elements on the avatar production and expression process. To evaluate the proposed evaluation model, 17 people participated in the experiment. As a result, it was found that most of the participants had a high level of understanding of the production process, and the more diverse the expression elements, the higher the satisfaction with the avatar expression process. Hair shape and face shape were the most important expression factors, and clothes and overall harmony was the most concerned facotrs. Most of them tried to express themselves as realistically as possible. However, in self-identification, there was no significant correlation between expression and production process. In this study identify the decision-making factors that appear in the avatar expression process, the direction in which satisfaction can be formed, and the factors that affect self-identification. In addition, it will be a basic study on how avatars have a lasting influence on users in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.

• Korean Journal of Cognitive Science
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• v.33 no.4
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• pp.205-228
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• 2022

This study aimed to confirm the effect of social and relational behavior types of robots on human cognition in human-robot interaction. In the experiment, the participants evaluated trust in robots by watching a video on the robot Nao interacting with a human, in which the robot made an error and then made an effort to restore trust. The trust recovery behavior was set as three conditions: an internal attribution in which the robot acknowledges and apologizes for an error, a condition in which the robot apologizes for an error but attributes it externally, and a non-action condition in which the robot denies the error itself and does not take any action for the error. As the result, in all three cases, the error was perceived as less serious when the robot apologized than when it did not, and the ability of the robot was also highly evaluated. These results provide evidence that human attitudes towards robots can respond sensitively depending on the robot's behavior and how they overcome errors, suggesting that human perception towards robots can change. In particular, the fact that robots are more trustworthy when they acknowledge and apologize for their own errors shows that robots can promote positive human-robot interactions through human-like social and polite behavior.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.229-247
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• 2023

With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.

• Journal of the Daesoon Academy of Sciences
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• v.45
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• pp.187-226
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• 2023

In contemporary society, digital media has become an integral part of daily life that shapes how people interact with the world around them. This phenomenon has also influenced religious activities and practices. Studies on digital religion and religious practices among digital natives in the Western world have indicated that traditional religious practices are on the decline. Instead, more accessible and flexible forms of religious activities and beliefs are emerging. Given this context, it is important to investigate whether similar trends are occurring in Korea. This study aims to explore the religious activities and expressions of Korean individuals in the online environment. Specifically, the study focuses on four main areas: ①the online religious activities of established religions such as Protestantism, Buddhism, and Catholicism; ②the online religious activities related to divination belief systems such as the Four Pillars of Destiny (四柱 saju) and Tarot; ③online holy sites and wish comments or chats; and ④popular religious neologisms such as jileumshin (지름神 a god with the power to justify consumption) and gatsaeng (갓[God]生 one's best life). Through this review, it can be ascertained that religious ideas and practices are restricted by the attributes of digital media. This implies that the emergence of simplistic forms of religious ideas and activities is associated with the features of digital media and the consumption of digital content.