• Information Systems Review
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• v.25 no.4
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• pp.89-112
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• 2023

In March 2020, as it was declared a COVID-19 pandemic, various quarantine measures were taken. Accordingly, many changes have occurred in the tourism and hospitality industries. In particular, quarantine guidelines, such as the introduction of non-face-to-face services and social distancing, were implemented in the restaurant industry. For decades, research on restaurant attributes has emphasized the importance of three attributes: atmosphere, service quality, and food quality. Nevertheless, to the best of our knowledge, research on restaurant attributes considering the COVID-19 situation is insufficient. To respond to this call, this study attempted an exploratory approach to classify new restaurant attributes based on understanding environmental changes. This study considered 31,115 online reviews registered in Naverplace as an analysis unit, with 475 general restaurants located in Euljiro, Seoul. Further, we attempted to classify restaurant attributes by clustering words within online reviews through TF-IDF and LDA topic modeling techniques. As a result of the analysis, the factors of "prevention of infectious diseases" were derived as new attributes of restaurants in the context of COVID-19 situations, along with the atmosphere, service quality, and food quality. This study is of academic significance by expanding the literature of existing restaurant attributes in that it categorized the three attributes presented by existing restaurant attributes and further presented new attributes. Moreover, the analysis results have led to the formulation of practical recommendations, considering both the operational aspects of restaurants and policy implications.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.337-343
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• 2020

This study proposed piezoelectric ceramic bender actuators for application to small walking robots. As the space where human access has recently become increasingly restricted (e.g., highly concentrated radioactive storage areas, viral contaminated areas, terrorist zones, etc.), the scope of using robots is becoming more diverse, and many actions that were possible only in the past have been attempted to be replaced by small robots. This robotic concept has the advantage of being simple in structure, making it compact and producing a large size work force. The dynamic modeling, using finite element analysis, maximized the robot's mobility performance by optimizing the shape of the actuator, and the results were verified through fabrication and experimentation. The actuator moved at a maximum speed of 236 mm/s under no load conditions, and it could move at a speed of 156 mm/s under load conditions of 5g. The proposed actuator has the advantage of modular additions depending on the mission and required performance, which ensured that they are competitive against similar drive sources previously created.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.399-404
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• 2005

In this paper, An immunized PID(I-PID) controller based on cell mediated immune response is proposed to improve the control performance of the controller with PID scheme. And it is applied to the vibration of the building structure in the port with active damper systems. The immune system of organism in the real body regulates the antibody and T-cells to protect the attack from the foreign materials which are virus, germ cell, and other antigens. It has similar characteristics that are the adaptation and robustness to overcome disturbances and to control the plant of engineering application. At firstly, we build a model of the T-cell regulated immune response mechanism. We have also designed an I-PID controller focusing on the T-cell regulated immune response of biological immune system. Finally, we show that some computer simulations of the vibraton control for the building structure system with wind force excitation. These results for the proposed method also show that is has performance than other conventional controller design method.

• Convergence Security Journal
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• v.23 no.5
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• pp.209-221
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• 2023

Due to the rapid spread of the COVID-19 virus in December 2019, the working environment was rapidly converted to telecommuting. However, since the defense industry is an organization that handles technology related to the military, the network separation policy is applied, so there are many restrictions on the application of telecommuting. Telecommuting is a global change and an urgent task considering the rapidly changing environment in the future. Currently, in order for defense companies to implement telecommuting, VPN, VDI, and network interlocking systems must be applied as essential elements. Eventually, some contact points will inevitably occur, which will increase security vulnerabilities, and strong security management is important. Therefore, in this paper, attack types are selected and threats are analyzed based on the attack tactics of the MITER ATT&CK Framework, which is periodically announced by MITER in the US to systematically detect and respond to cyber attacks. Then, by applying STRIDE threat modeling, security threats are classified and specific security requirements are presented.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (