• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.51-56
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• 2016

High brittleness is a characteristic of glass, and in many cases it is broken during the process of machining due to processing problems, such as scratches, chipping, and notches. Machining defects occur due to the vibration of the equipment. Therefore, design techniques are needed that can control the vibration generated in the equipment to increase the strength of tempered glass. The natural frequency of the machine tool via vibration analysis (computer simulation) must be accurately understood to improve the design to ensure the stability of the machine. To accurately understand the natural frequency, 3D modeling, which is the same as actual apparatus, was used and a constraint condition was also applied that was the same as that of the actual apparatus. The maximum speeds of ultrasonic and high frequency, which are 15,000 rpm and 60,000 rpm, respectively, are considerably faster than those of typical machine tools. Therefore, an improved design is needed so that the natural frequency is formed at a lower region and the natural frequency does not increase through general design reinforcement. By restructuring the top frame of the glass processing, the natural frequency was not formed in the operating speed area with the improved design. The lower-order natural frequency is dominant for the effects that the natural frequency has on the vibration. Therefore, the design improvement in which the lower-order natural frequency is not formed in the operating speed area is an optimum design improvement. It is possible to effectively control the vibrations by avoiding resonance with simple design improvements.

• Smart Structures and Systems
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• v.1 no.4
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• pp.325-338
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• 2005

Utilizing robotic based reconfigurable nodal structural health monitoring systems has many advantages over static or human positioned sensor systems. However, creating a robot capable of traversing a variety of civil infrastructures is a difficult task, as these structures each have unique features and characteristics posing a variety of challenges to the robot design. This paper outlines the design and implementation of a novel robotic platform for deployment on ferromagnetic structures as an enabling structural health monitoring technology. The key feature of this design is the utilization of an attachment device which is an advancement of the common magnetic base found in the machine tool industry. By mechanizing this switchable magnetic circuit and redesigning it for light weight and compactness, it becomes an extremely efficient and robust means of attachment for use in various robotic and structural health monitoring applications. The ability to engage and disengage the magnet as needed, the very low power required to do so, the variety of applicable geometric configurations, and the ability to hold indefinitely once engaged make this device ideally suited for numerous robotic and distributed sensor network applications. Presented here are examples of the mechanized variable force magnets, as well as a prototype robot which has been successfully deployed on a large construction site. Also presented are other applications and future directions of this technology.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.701-707
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• 2024

Industry 4.0 has brought about significant changes in the roles of workers through the introduction of innovative technologies. In smart factory environments, workers are required to interact seamlessly with robots and automated systems, often utilizing equipment enhanced by Virtual Reality (VR) and Augmented Reality (AR) technologies. This study aims to systematically analyze recent research literature on monitoring the physical and mental states of workers in Industry 4.0 environments. Relevant literature was collected using the Web of Science database, employing a comprehensive keyword search strategy involving terms related to Industry 4.0 and health monitoring. The initial search yielded 1,708 documents, which were refined to 923 journal articles. The analysis was conducted using VOSviewer, a tool for visualizing bibliometric data. The study identified general trends in the publication years, countries of authors, and research fields. Keywords were clustered into four main areas: 'Industry 4.0', 'Internet of Things', 'Machine Learning', and 'Monitoring'. The findings highlight that research on health monitoring of workers in Industry 4.0 is still emerging, with most studies focusing on using wearable devices to monitor mental and physical stress and risks. This study provides a foundational overview of the current state of research on health monitoring in Industry 4.0, emphasizing the need for continued exploration in this critical area to enhance worker well-being and productivity.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.19-33
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• 2024

CCTV for crime prevention is expanding; however, due to the absence of guidelines for determining installation locations, CCTV is being installed in locations unrelated to areas with frequent crime occurrences. In this study, we developed a CCTV Priority Installation Index and applied it in a case study area. The index consists of crime vulnerability and surveillance vulnerability indexes, calculated using machine learning algorithms to predict crime incident counts per grid and the proportion of unmonitored area per grid. We tested the index in a pilot area and found that utilizing the Viewshed function in CCTV visibility analysis resolved the problem of overestimating surveillance area. Furthermore, applying the index to determine CCTV installation locations effectively improved surveillance coverage. Therefore, the CCTV Priority Installation Index can be utilized as an effective decision-making tool for establishing smart and safe cities.

• Informatization Policy
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• v.25 no.3
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• pp.3-28
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• 2018

The purpose of this study is to analyze the research trends and present future research directions in the field of Cyber Physical System (CPS), a key element in the 4th Industrial Revolution, Industry 4.0, and Smart Manufacturing that are currently promoted as important innovation agenda both at home and abroad. In this study, (1) the concepts of industry 4.0, smart manufacturing and CPS are summarized; (2) analysis criteria of these fields are established; and 3) analysis results are presented and future research direction is proposed. 74 overseas and 8 domestic literature on manufacturing CPS from 2013 to 2017 are identified through 'Google Scholar Search'. Major results of the analysis are summarized as follows: (1) research on a common methodology and framework for the manufacturing CPS needs to be done based on the analysis of the existing methodologies and frameworks of various perspectives; (2) in order to improve the maturity of the manufacturing CPS, it is necessary to study actual deployment and operations of CPS, including the existing systems; (3) it is necessary to study the diagnostic methodology that can evaluate manufacturing CPS and suggest improvement strategy; and (4) as for the detailed model and tool, it is necessary to reinforce research on SCM production planning and human-machine collaboration while considering the characteristics of CPS.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.367-378
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• 2006

A mobile phone has became as a payment tool in e-commerce and on-line banking areas. This trend of a payment system using various types of mobile devices is rapidly growing, especially in the Internet transaction and small-money payment. Hence, there will be a need to define its standard for secure and safe payment technology. In this thesis, we consider the service types of the current mobile payments and the authentication method, investigate the disadvantages, problems and their solutions for smart and secure payment. Also, we propose a novel authentication method which is easily adopted without modification and addition of the existed mobile hardware platform. Also, we present a simple implementation as a demonstration version. Based on virtual machine (VM) approach, the proposed model is to use a pseudo-random number which is confirmed by the VM in a user's mobile phone and then is sent to the authentication site. This is more secure and safe rather than use of a random number received by the previous SMS. For this payment operation, a user should register the serial number at the first step after downloading the VM software, by which can prevent the illegal payment use by a mobile copy-phone. Compared with the previous SMS approach, the proposed method can reduce the amount of packet size to 30% as well as the time. Therefore, the VM-based method is superior to the previous approaches in the viewpoint of security, packet size and transaction time.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.153-161
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• 2020

Building information modeling (BIM) is a smart construction technique that is recognized as essential for current construction facility projects. The Public Procurement Service (a construction project-ordering agency) announced a plan to introduce BIM and has required changing the operation of projects by using BIM design information. LCC analysis is essential for items, quantity, and cost information of the construction, and it is expected that efficient work will be achieved by using BIM design information. In this study, a BIM-LCC analysis system was established for selecting optimal design alternatives by actively using open KBIMS libraries. The BIM-LCC analysis system consists of a single alternative and an optimal alternative LCC analysis, but it has a limitation in that only the architecture and machine libraries have been applied. However, by applying BIM, practical use and work efficiency can be expected. In order to use the method as an LCC analysis support tool with BIM design information in the future, it will be necessary to collect user opinions and improve the UI.

• Journal of Nutrition and Health
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• v.53 no.3
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• pp.319-328
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• 2020

Purpose: This study was conducted to evaluate the validity of the Gene-Health application in terms of estimating energy and macronutrients. Methods: The subjects were 98 health adults participating in a weight-control intervention study. They recorded their diets in the Gene-Health application, took photographs before and after every meal on the same day, and uploaded them to the Gene-Health application. The amounts of foods and drinks consumed were estimated based on the photographs by trained experts, and the nutrient intakes were calculated using the CAN-Pro 5.0 program, which was named 'Photo Estimation'. The energy and macronutrients estimated from the Gene-Health application were compared with those from a Photo Estimation. The mean differences in energy and macronutrient intakes between the two methods were compared using paired t-test. Results: The mean energy intakes of Gene-Health and Photo Estimation were 1,937.0 kcal and 1,928.3 kcal, respectively. There were no significant differences in intakes of energy, carbohydrate, fat, and energy from fat (%) between two methods. The protein intake and energy from protein (%) of the Gene-Health were higher than those from the Photo Estimation. The energy from carbohydrate (%) for the Photo Estimation was higher than that of the Gene-Health. The Pearson correlation coefficients, weighted Kappa coefficients, and adjacent agreements for energy and macronutrient intakes between the two methods ranged from 0.382 to 0.607, 0.588 to 0.649, and 79.6% to 86.7%, respectively. Conclusion: The Gene-Health application shows acceptable validity as a dietary intake assessment tool for energy and macronutrients. Further studies with female subjects and various age groups will be needed.