In an apartment or private building, it is difficult for the user to operate the elevator button in a similar situation with luggage in both hands. In an environment where human contact must be minimized due to a highly infectious virus such as COVID-19, it is inevitable to operate an elevator based on untact. This paper proposes an operating system capable of operating the elevator by using the user's voice and image processing through the user's face without pressing the elevator button. The elevator can be operated to a designated floor without pressing a button by detecting the face of a person entering the elevator by detecting the person's face from the camera installed in the elevator, matching the information registered in advance. When it is difficult to recognize a person's face, it is intended to enhance the convenience of elevator use in an untouched environment by controlling the floor of the elevator using the user's voice through a microphone and automatically recording access information.
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.
Purpose: To identify the epidemiological characteristics and risk factors of coronavirus disease 19 (COVID-19) outbreaks depending on the type of educational facility by analyzing the COVID-19 cluster associated with educational facilities. Methods: This study is based on epidemiological investigation of COVID-19 cluster in Gangwon-do, Korea from December 10, 2020 to September 23, 2021 reported to the Korea Disease Control and Prevention Agency's Integrated Disease and Health Management System. Four hundred seven patients in 19 facilities, classified as cluster related to educational facilities, were the study population. The result of preliminary epidemiology survey report, in-depth epidemiological survey by phone and the result of risk assessment derived from the field epidemiology investigation were retrospectively analyzed to evaluate infectivity and the characteristics of the risk factors. Results: There were total of 407 confirmed patients related to 19 educational facilities, with 204 students under the age of 19 (50.1%). One hundred fifty-five preceding spreaders were from families (38.1%) and 125 were the teachers (30.7%). The place exposed to confirmed patients was the highest with 139 people (34.2%) at home. Conclusions: It was confirmed that the cause of the occurrence of clusters related to educational facilities was higher due to family transmission than the risk of facilities in schools. Nevertheless, continuous efforts should be made to control infection in educational facilities, and that teachers' implementation of principles for prevention of COVID-19 personal hygiene in their daily lives should be strengthened.
The number of public transportation users has dropped drastically due to COVID-19. In this work, my survey was conducted to uncover the factors that influence citizens' travel patterns. Data were collected and logistic regression analysis on the shifts in transportation was undertaken. Additionally, an importance-performance analysis was carried out to investigate how to effectively operate public transportation systems and improve facilities. The main research findings were as follows: First, the more individuals were concerned about COVID-19 (+) and being infected when using public transportation (+), the greater the tendency to switch to private transportation modes. Secondly, when it came to personal traits, respondents who could drive a car (+) or owned a car (+)or did more online shopping (+) or used public transportation for trips (+) tended to switch over, compared with respondents who could not drive or did not own a caror used public transportation to commute. In addition, respondents who were vaccinated (-) or had more household members tended not to switch transportation modes, compared with those who were not vaccinated or had fewer household members. Third, it is important to continue the following efforts to safeguardhygiene linked to public transportation: wearing masks, disinfecting hands, controlling diseases, and general cleaning. The conclusion was that it is important to put traffic congestion and ventilation issues first, especially in regards public transportation, which was not rated as satisfactory enough compared to its importance. The research findings can provide useful basic data when establishing countermeasures to the current COVID-19 circumstances in the areas of public transportation operation and management and in the event of an infectious disease outbreak in the future.
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 (