The purpose of this study is to establish a foundation for IoT-related industries to secure financial soundness and to dominate the global market after COVID-19. Through this study, the quantitative management status of IoT-related companies was checked. It also was attempted to preemptively prepare for corporate insolvency by examining the relationship between financial ratios in accordance with stock price fluctuations and designation of management items. This study selected 502 companies that were listed on the KOSPI and KOSDAQ in the stock market from 2019 to 2020. For statistical analysis, multiple regression analysis, difference analysis and logistic regression analysis were performed. The research results are as follows. First, it was found that the impact of IoT company accounting information on stock prices differs depending on before and after COVID-19. Second, it was found that there is a difference in the closing stock prices of IoT companies before and after COVID-19. Third, it was found that financial ratios according to stock price fluctuations exist differently after COVID-19. Fourth, it was found that the financial ratios according to the designation of management items after COVID-19 exist differently. Through these studies, some suggestions were made to secure the financial soundness of IoT companies and to lay the groundwork for leaping into the global market after COVID-19. Through the results of this study, it is expected that it will lead the growth of IoT companies and contribute to growth as a decacorn company of the future that can guarantee financial soundness in the changing financial market.
블록체인은 암호화폐의 거래가 활발해짐에 따라 다양한 분야에서 접목시키려는 노력이 계속해서 이어지고 있다. 블록체인은 한번 기록된 사실에 대해서는 수정 및 삭제가 불가능하다는 특징이 있다. 이러한 특징으로 인해 특히 투표나 소유권 증명과 같은 어떠한 사실을 기록하고 증명하는 분야에서의 활용이 주목받고 있다. 본 논문에서는 블록체인의유형 중 하나인 프라이빗 블록체인을 활용하여 거래 과정에 참여하고자 하는 이용자들을 부동산 중개인, 건물 소유주, 매입인(임대인)으로 구분하여 이용자별 역할을 부여한다. 또한, 기관이 참여하여 신뢰성을 높이는 시스템을 제안하고자한다. 이를 통해 허위 매물, 사기 계약 등과 관련된 부동산 사기 피해를 방지하고 신뢰성 높이는 부동산 거래 시스템을제시할 뿐만 아니라 향후 블록체인 활용 방안 모색에 있어 기여하고자 한다.
본 논문은 사물인터넷 기반의 자가 전력을 이용한 무선 버스 정보 및 재난 정보 시스템에 관한 연구이다. 기존의버스 정보 시스템은 유선으로 전력과 통신을 제공함에 따라 유선 매설작업으로 인한 설치비용 증가와 설치 장소 제약의문제가 있다. 이러한 문제를 해결하기 위해 자가 전력을 이용한 무선 버스 정보 및 재난 정보 시스템을 제안하였다. 제안된 시스템은 버스 도착 정보를 제공하며, 자연재해 등의 재난 발생 시, 시스템의 스피커를 통해 재난 정보도 알림으로써 혼란과 피해를 줄일 수 있다. 본 연구에서는 태양광 모듈을 이용한 자가 전력 시스템을 제안하였고, 무선 WiFi 또는 LTE를 통해 데이터를 송·수신하기 때문에 설치비용을 줄일 수 있고 설치 장소 제약의 문제를 해결할 수 있다
The product to be developed in this study is a heat recovery device which generates steam or hot water at high temperature and high pressure by heating water using exhaust gas from diesel engine, gas engine, gas turbine, etc. as an exhaust gas boiler off heat boiler(EGB) type for ship and power generation. The steam vapor or the created warm water is used as the power source required for the steerage heating and hot water facility or the HFO heating of the ship, and the turbine drive. The principle of waste heat boilers serves to heat water as high temperature exhaust gas with heat pass through the tube of the boiler. The heated water is a structure that is sent to a cabin or turbine device in the form of steam. In this study, the objective of this study is to maximize the efficiency by increasing the heat transfer surface by replacing the tube which is the heat transfer part of EGB with the plate tube.
로컬에 국한되지 않고 글로벌한 지식경제사회가 진행됨에 따라 사회 많은 분야에 글로벌 수요가 급증하고 있다. 이에 대학을 졸업한 학생들은 국내 취업을 위해 노력을 하고 있지만, 국내 기업의 높은 스펙과 자격요건에 대한 취업진입 장벽이 높아 정부와 지자체, 대학에서는 글로벌 해외 취업에 많은 노력을 하고 있다. 본 연구는 일본의 IT 기업 취업을 목표로 한 K-MOVE 해외취업연수 사업의 교육과정 설계이다. 이 교육과정을 설계하기 위해 구인업체와 학생들의 수요조사를 진행하고, 이 결과를 바탕으로 교과를 도출한다. 이후, 정규교과와 K-MOVE 교육과의 효율적인 연계에 관하여 기술한다. 본 연구는 일본 IT분야 해외 취업을 기획하는 교육 전문가에게 유익한 자료가 될 수 있다.
The fundamental basis of AI technology is learningable data. Recently, the types and amounts of data collected and produced by the government or private companies are increasing exponentially, however, verified data that can be used for actual machine learning has not yet led to it. This study discusses the conditions that data actually can be used for machine learning should meet, and identifies factors that degrade data quality through case studies. To this end, two representative cases of developing a prediction model using public big data was selected, and data for actual problem solving was collected from the public data portal. Through this, there is a difference from the results of applying valid data screening criteria and post-processing. The ultimate purpose of this study is to argue the importance of data quality management that must be most fundamentally preceded before the development of machine learning technology, which is the core of artificial intelligence, and accumulating valid data.
This study was aimed at providing how to improve the service quality of home care workers, particularly through investigating their job stress and job satisfaction. Participants were randomly sampled persons who were living in P city and simultaneously serving as home care workers at the time of the study. This research conducted a self-reported questionnaire survey of them over one month of May 2021. Of collected data, responses from 130 of the participants were finally analyzing here using SPSS and PROCESS macro model 4. As a result, it was found that home care workers’ job stress had no direct effect on their service quality, but had an indirect influence on it via job satisfaction. This means that the higher those workers are in job stress, the less they are in job satisfaction, ultimately having a negative impact on their service quality. Based on these findings, this study suggested some ways to raise home care workers’ service quality.
Smoothing is a transmission plan that converts video data stored at a variable bit rate into a constant bit rate. In the study of [6-7], when a data rate increase is required, the frame with the smallest increase is set as the start frame of the next transmission rate section, when a data tate decrease is required. the frame with the largest decrease is set as the start frame of the next transmission rate section, And the smoothing algorithm was proposed and performance was evaluated in an environment where network traffic is not considered. In this paper, the smoothing algorithm of [6-7] evaluates the adaptive CBA algorithm and performance with minimum frame rate, average frame rate, and frame rate variation from 512KB to 32MB with E.T 90 video data in an environment that considers network traffic. As a result of comparison, the smoothing algorithm of [6-7] showed superiority in the comparison of the minimum refresh rate.
Existing edge discovery techniques only found edges of defined shapes based on precise definitions of edges. Therefore, there are many limitations in finding edges for images of complex and diverse shapes that exist in the real world. A method for solving these problems and discovering various types of edges is a cost minimization method. In this method, the cost function and cost factor are defined and used. This cost function calculates the cost of the candidate edge model generated according to the candidate edge generation strategy. If a satisfactory result is obtained, the corresponding candidate edge model becomes the edge for the image. In this study, a new candidate edge generation strategy was proposed to discover edges for images of more diverse shapes in order to improve the disadvantage of only finding edges of a defined shape, which is a problem of the cost minimization method. In addition, the contents of improvement were confirmed through a simple simulation that reflected these points.
The Internet of Things (IoT) provides data convergence and sharing functions, and IoT technology is the most fundamental core technology in creating new services by convergence of various cutting-edge technologies. However, there are different classification systems for the Internet of Things, and when it is limited to the domestic public sector, it is difficult to properly grasp the current status of which devices are installed and operated with what share, and systematic data or research The results are very difficult to find. Therefore, in this study, the relevance of the classification system for IoT devices was analyzed according to reality based on sales, shipments, and growth rate, and based on this, the actual share of IoT devices among domestic public institutions was analyzed in detail. The derived detailed analysis results are expected to be efficiently utilized in the process of selecting IoT devices for research and analysis to advance information protection technology such as responding to malicious code attacks on IoT devices, analyzing incidents, and strengthening security vulnerabilities.
Due to COVID-19, the digital transformation centered on unmanned and non-face-to-face is accelerating. Like a digital civilization, it is changing into a digital age, but for those who cannot use digital, the inconveniences in daily life are falling to the level of digital illiteracy. There is a social gap between digital users and non-users, and digital non-users are separated from society. For the digital socialization of the digitally underprivileged, the transition to the digital world is accelerating, and the ability to use digital in everyday life is becoming more important. As the transition to the digital world accelerates, individual social and economic differences are occurring. These problems are hindering the sustainable growth of society. In this study, in order to receive digital education conveniently, we intend to design an education model with excellent accessibility as an education that can be received by all citizens. It is intended to propose a digital education model that can be used universally and solve the inconvenience of life through digital comprehensive competency education that can be received step by step and can be easily received.
Internet of Things (IoT) connects devices with various platforms, computing power, and functions. Due to the diversity of networks and the ubiquity of IoT devices, demands for security and privacy are increasing. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, while at the same time effective enough to be implemented in devices with long-range specifications. In this paper, we present the performance and memory limitations of modern cryptographic primitives and schemes for different types of devices that can be used in IoT. In addition, detailed performance evaluation of the performance of the most commonly used encryption algorithms in low-spec devices frequently used in IoT networks is performed. To provide data protection, the binary ring uses encryption asymmetric fully homomorphic encryption and symmetric encryption AES 128-bit. As a result of the experiment, it can be seen that the IoT device had sufficient performance to implement a symmetric encryption, but the performance deteriorated in the asymmetric encryption implementation.