• International Journal of Contents
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• v.18 no.1
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• pp.1-16
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• 2022

With the advent of the 5G, networks and information and communication technologies have been continuously developed. In the fields of art galleries, virtual reality (VR) exhibitions that can be visited online have emerged, innovating the way of human-computer interaction and creating new artistic experiences for users. This study explores the three-dimensionality, clarity, and innovative interactions that users experience when viewing a VR exhibit, which affects the exhibit's presence. Besides, in terms of research method, the research sets spatial participation and immersion as dependent variables, with three-dimensionality (high versus low), clarity (high versus low), and innovation (high versus low) in a 2×2×2 design as the base, and explores their interaction effects. The results show that three-dimensionality and innovative interactions affect spatial participation. First of all, in groups with high innovation and low three-dimensionality, spatial participation presents a higher positive factor. Secondly, with regard to immersion, three-dimensionality, clarity and innovation present a tripartite interaction. Groups with low three-dimensionality and high clarity have a higher positive effect on immersion when the level of innovation is low. When the degree of innovation is high, the positive effect on immersion is higher in groups with high three-dimensionality and low clarity. The above results show that in the production of VR exhibitions, it is necessary to increase the three-dimensionality and clarity of exhibited image contents, while taking into account the user's perception and innovativeness. On the other hand, this study puts forward suggestions for the design, content and future development of VR exhibitions, which has important reference significance for the improvement and innovation of future VR exhibitions.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.219-228
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• 2022

One of the most pressing and demanding issues today in the conditions of widespread transformation and digitalization of spheres of human activity is information security and ensuring the integrity of data. The main research and development in the field of information security is aimed at improving efficiency and rationalization. One of the main means of data transmission and operation of information complexes are fiber-optic systems. To date, there have been incidents of illegal intrusion and theft of information, passing through this type of communication. Thus, today there is a problem associated with insufficient information security in fiber-optic data transmission systems. One of the most effective tools to counter acts of illegal interference in systems are artificial intelligence and cryptographic algorithms of information protection. It is the symbiosis of these two tools that can qualitatively improve the level of information security in fiber-optic data transmission systems. Thus, the authors of this article pursue the goal associated with the description of an innovative system for protecting information from violations in fiber-optic data transmission systems based on the integration of intelligent cryptographic algorithms.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.1
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• pp.287-295
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• 2017

Recently, MTC (Machine Type Communication) is known as an important part to support IoT (Internet of Things) applications. MTC provides network connectivities between MTC devices without human intervention. In MTC, a large number of devices try to access over communication resource with a short period of time. Due to the limited communication resource, resource contention becomes severe and it brings about access failures of devices. To solve the problem, it needs to regulate device accesses. In this paper, we present an efficient access management scheme. We measure the number of devices which try to access in a certain time period and predict the change of the number of devices in the next time period. Using the predicted change, we control the number of devices which try to access. To verify our scheme, we conduct experiments in terms of success probability, failure probability, collision probability and access delay.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.11
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• pp.519-528
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• 2016

Most cultural contents currently we face are not suitable for associating with state of arts and high technology as simply providing one-sided learning. Pictures and movies of cultural contents also sees to utilize for efficacy of cultural education. There are still some limitations to draw interest from users when providing one-sided learning for cultural study, which aims to only deliver knowledge itself. In this paper, we propose interactive HMD VR cultural contents that can support more experience to get rid of aforementioned limitations. To this end, we first select quite interesting and wellknown cultural contents from world wide to draw more attention and effect. To increase immersion, presence and interactivity we have used HMD VR and Leapmotion, which intentionally draws more attention to increase interest. The cultural contents also facilitate augmented information as well as puzzle gaming components. To verify, we have carried out a user study as well.

• Korean Medical Education Review
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• v.25 no.2
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• pp.109-113
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• 2023

An educational outcome cohort has been established at Keimyung University School of Medicine to help make educational policy decisions and improve educational programs based on data. The purpose of the educational outcome cohort is to support educational policy decisions for achieving graduation outcomes smoothly and to accomplish the intended human resources development of the university through objective analyses and regular monitoring, providing continuous feedback. The data collected for the educational outcome cohort include the student identifications of freshmen, entrance exam scores, premedical and medical school grades, titles and forms of student academic research, the results of psychological testing, scholarship recipient lists, volunteer clubs, and so forth. The data are collected using an information utilization agreement approved by the Institutional Review Board, and the collected data are encrypted and stored on a dedicated computer for enhanced personal information security. Proposals to access and utilize the educational outcome cohort data must be discussed and approved by the Educational Outcome Cohort Committee, which decides on the scope and method of utilization. The collected and managed educational outcome cohort data have been used to develop comparative programs to improve students' competency and to support admission policy decisions through an analysis of the characteristics and performance of medical school students. The establishment and utilization of the educational outcome cohort will play an important role in determining the School of Medicine's educational policies and suggesting new directions for educational policies in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.165-166
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• 2020

Internationally, manufacturers attempted respectable portion of in-house logistics to satisfy end users and decrease manpower to compete for manufacturing price and quality optimization. Mostly, manufacturers operate variety of facilities such as collaborative robots, conveyor, etc. based on PLC. To achieve it, manufactures shall operate the optimized number of manufacturing processes with logic controlled by computer to reduce human errors. In prior to it, manufacturing industry still own plenty of fields which have not yet been adjusted with automation. For example, we shall put in-house logistics on the issue. This study focuses on manufacturing industry, evaluate efficiency, costs, etc. in all aspects and suggest alternatives by analysis SWAT and OEE, let alone reason of weakness.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.475-481
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• 2023

Softwares, especially like machine learning applications, affect human's life style tremendously. Accordingly, the importance of the cost model for softwares increases rapidly. As cost models, LOC(Line of Code) and M/M(Man-Month) estimates the quantitative aspects of the software. Differently from them, FP(Function Point) focuses on estimating the functional characteristics of software. FP is efficient in the aspect that it estimates qualitative characteristics. FP, however, has a limit for evaluating machine learning softwares because FP does not evaluate the critical factors of machine learning software. In this paper, we propose an extended function point(ExFP) that extends FP to adopt hyper parameter and the complexity of its optimization as the characteristics of the machine learning applications. In the evaluation reflecting the characteristics of machine learning applications. we reveals the effectiveness of the proposed ExFP.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.37-46
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• 2022

The purpose of this study is to provide basic data onto preparing soft landing plan of future education policy by exploring direction of future education for the common good using big data and keyword network analysis. Based on the big data provided by Textom, data was collected under the keyword 'future education + common Good' and then keyword network analysis was performed. As a result of the research, it was found that 'common good', 'social', 'KAIST future warning', 'measures', 'research', 'future education', 'politics' were common keywords in the social awareness of future education for the common good. The results of this study suggest that the social awareness of future education for the common good is related to factors related to human, physical environment, social response, academic interest, education policy, education plan, and related variables, It was closely related. Based on these results, we suggested implications for the support for the preparation of a soft landing plan of future education for the common good.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.2-5
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• 2022

Human organs in the body have a complex structure, and in particular, the small intestine is about 7m long, so endoscopy is not easy and the risk of endoscopy is high. Currently, the test is performed with a capsule endoscope, and the test time is very long. The doctor connects the removed storage device to the computer to store the patient's capsule endoscope image and reads it using a program, but the capsule endoscope test results in a long image length, which takes a lot of time to read. In addition, in the case of the small intestine, there are many curves due to villi, so the occlusion area or light and shade of the image are clearly visible during the examination, and there may be cases where lesions and abnormal signs are missed during the examination. In this paper, we provide a method of assisting small intestine capsule endoscopic lesion examination using artificial neural networks to shorten the doctor's image reading time and improve diagnostic reliability.