• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.10-20
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• 2024

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder's status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

• Journal of the Korean Institute of Gas
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• v.9 no.4 s.29
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• pp.57-61
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• 2005

The objective of paper is to carry out a comparative Quantitative Risk Assessment (QRA) of two KOGAS tank designs using a fault tree methodology, a standard 'Full Containment' tank and a 'Membrane' tank. For the membrane tank, both the initial KOGAS design and 4 modified KOGAS designs have been assessed, giving six separate cases. In this paper, the frequencies of releases are quantified using a fault tree approach. For clarity in the analysis, and to ensure consistency, all cases have been quantified using the same fault tree. Logic within the fault tree is used to select each of the cases. Full quantification of risks is often difficult, owing to a lack of relevant failure data, but the aim of this study has been to be as quantitative as possible, with full transparency of failure information. The most significant general cause of external LNG leaks is predicted to be a seismic event, which has been quantified nominally. 4modified KOGAS desiens to Prevent damage of bottom membrane panels that was shown in preparatory estimation could quantitively confirm safety improvement. According to result, the predicted frequencies of an external LNG leak for the full containment and modified membrane tanks are very similar, failures due to dropped pumps are predicted to be significantly greater for the membrane tank with thickened plate than for the full containment tank.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.672-682
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• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• Journal of Energy Engineering
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• v.21 no.4
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• pp.339-345
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• 2012

RMembrane LNG storage tanks have been recently investigated to replace full-containment LNG storage tanks because of safety and cost aspects. Quantitative Risk Analysis (QRA) and Finite Element Method (FEM) were used to evaluate safety of membrane LNG storage tanks. In this study, structural safety evaluation results via FEM analysis showed that both membrane type and full-containment type cryogenic LNG storage tanks with 140,000 $m^3$ capacity were equivalently safe in terms of strength safety and leakage safety of a storage tank system. Also, Fault Tree Analysis (FTA) was used to improve the safety of membrane LNG storage tanks and membrane LNG tanks were modified by adding three safety equipments: impact absorber structure for the low part of the membrane, the secondary barrier to diminish the thermal stress of the corner part of the outer tank, and a pump catcher in case of falling of a pump. Consequently, the safety of the modified membrane LNG storage tanks were proved to be equivalent to that of full-containment LNG storage tanks.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.2383-2386
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• 2002

많은 ISP업체와 서버 관리자들은 자신의 시스템 향상을 위하여 많을 비용을 쏟아 붇고 있지만 그 결과는 비관적이다. 지금까지는 하드웨어적인 성능만을 고려하였으나, 최근에는 리눅스를 탑재하여 낮은 가격과 높은 가용성을 가지면서, 증가하는 네트워크 요청에 효과적으로 대응하기 위한 해결책으로 여러 대의 호스트로 구성되는 클러스터링 기술이 각광을 받고 있다. 또한 클러스터는 저렴한 호스트들로 구성되므로 구축 비용이 절감되는것 뿐만 아니라, 일부 호스트가 고장 나더라도 다른 호스트는 네트워크 작업 분담을 재설정하여 정상적으로 동작함으로 가용성이 항상 유지될 수 있다는 것을 보여준다. 최근 군에서도 정보화 과학화의 열풍에 힘입어 그 어느때보다도 업무의 전산화, 정보화에 박차를 가하고 있는 실정이다. 이로 인한 군업무에 적용되는 중대형 서버의 증가와 1인 1PC정책에 의한 클라이언트의 증가, 네트웍 인프라 구축에 심혈을 기울이고 있다. 그러나 사용자의 요구만큼 군환 경하에서의 정보화 업무를 감당하는 서버의 역량은 제한이 있다. 군업무의 특수성에 비추어 볼때에도 사회의 금융업무와 마찬가지로 중단없는 서비스의 수행과 서버의 안정화는 군전산의 가장 중요한 요소중의 하나임에 불구하고 아직까지도 현실성은 많이 미비한 상태이다. 본 연구는 이와 같은 문제를 해결하기 위하여 NAS와 SAN개념(네트웍기반)을 도입하여 군 서버 구축 새로운 패러다임을 제공하여 업무의 통폐합과 함께 서버의 통폐합의 전초단계인 무정지 클러스터링 서버의 구축방안을 제시하여 군정보화, 과학화의 초석을 다지는 계기로 삼고자 한다.위상변화에 대한 적응성을 높일 수 있도록 한다. SQL Server 2000 그리고 LSF를 이용하였다. 그리고 구현 환경과 구성요소에 대한 수행 화면을 보였다.ool)을 사용하더라도 단순 다중 쓰레드 모델보다 더 많은 수의 클라이언트를 수용할 수 있는 장점이 있다. 이러한 결과를 바탕으로 본 연구팀에서 수행중인 MoIM-Messge서버의 네트워크 모듈로 다중 쓰레드 소켓폴링 모델을 적용하였다.n rate compared with conventional face recognition algorithms. 아니라 실내에서도 발생하고 있었다. 정량한 8개 화합물 각각과 총 휘발성 유기화합물의 스피어만 상관계수는 벤젠을 제외하고는 모두 유의하였다. 이중 톨루엔과 크실렌은 총 휘발성 유기화합물과 좋은 상관성 (톨루엔 0.76, 크실렌, 0.87)을 나타내었다. 이 연구는 톨루엔과 크실렌이 총 휘발성 유기화합물의 좋은 지표를 사용될 있고, 톨루엔, 에틸벤젠, 크실렌 등 많은 휘발성 유기화합물의 발생원은 실외뿐 아니라 실내에도 있음을 나타내고 있다.>10)의 $[^{18}F]F_2$를 얻었다. 결론: $^{18}O(p,n)^{18}F$ 핵반응을 이용하여 친전자성 방사성동위원소 $[^{18}F]F_2$를 생산하였다. 표적 챔버는 알루미늄으로 제작하였으며 본 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.7
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• pp.663-670
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• 2016

This paper introduces the concept of the computer-aided engineering(CAE) design method for a bypass valve in a system that is used for the safe lifting of mineral resources in deep-seabed mining. Although the bypass valve has a simple mechanism, its design is very difficult because of various influencing factors. This equipment, which has a complex design process, should be developed by CAE-based design method. The method can perform the design, design verification, and virtual experiment at the same time. In this study, the CAE-based method for the design of the bypass valve has been developed using fluid dynamics, multi-body dynamics, and optimization method.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.395-404
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• 2005

There have been large concerns about survivability defined as the capability of a system to perform a mission-critical role, in a timely manner, in the presence of attacks, failures. In particular, One of the most important core technologies required for the design of the ITS(Intrusion Tolerance System) that performs continuously minimal essential services even when the computer system is partially compromised because of intrusions is the survivability one of In included the dependability analysis of a reliability and availability etc. quantitative dependability analysis of the In. In this Paper, we applied self-healing mechanism utilizing two factors of self-healing mechanism (fault model and system response), the core technology of autonomic computing to secure the protection power of the ITS and consisted of a state transition diagram of the ITS composed of a primary server and a backup server. We also defined the survivability, availability, and downtime cost of the ITS, and then performed studies on simulation experiments and two cases of vulnerability attack. Simulation results show that intrusion tolerance capability at the initial state is more important than coping capability at the attack state in terms of the dependability enhancement.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.6
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• pp.531-537
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• 2016

The damage and fracture of machine or structure are caused by the crack happened from the defect existed at the inside of material. The properties of crack propagation and growth characteristic must be considered because there are many cases at which these cracks are densely existed. Therefore, this study investigates the fracture property due to the position of crack and hole inside the standard compact tension (C. T.) specimen. When the concentrated load is applied eccentrically at the standard C. T. specimen, the fracture mechanical behavior due to the existence or non-existence and the position of hole near crack is investigated. As the result of analysis study, model 3 (in case of the distance of 2mm on the horizontal direction between the end part and hole as the specimen model existed with one hole near the crack) has the maximum deformation, stress and deformation energy of the most values among three models. As the distance between the crack and hole inside the specimen becomes nearer, the maximum stress becomes higher in cases of three models. Apart from the number of holes, it is seen that the maximum stress becomes higher near the crack when the hole exists near the crack inside the specimen. If the hole inside the machine or the mechanical structure is punctured by using the result of this study, it is thought that the occurred breakage or breakdown can be prevented by reducing the fracture stress happened at the specimen.