• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.425.1-425.1
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• 2016

고온의 플라즈마를 긴 펄스 및 장시간 연속운전 유지기술 개발 및 연구를 위해서는 플라즈마는 더욱 가열되어야 하고, 고온 고밀도의 플즈마 상태를 유지시켜야 한다. 이러한 고성능 플라즈마 개발은 향후 핵융합 에너지의 상용화를 위한 절대필수적 기반기술이다. 현재 KSTAR 토카막에서는 플라즈마를 가열하기 위한 장치들 중 하나로서, 출력 6 MW 급의 중성입자빔을 입사하는 NBI (Neutral Beam Injection) 가열장치가 설치 운영 중에 있다. 이 NBI 가열장치는 진공환경에서 고온, 고압, 고전압 방전 및 수냉 등이 작동 및 운전되고 있기 때문에, 구성 부품 들의 미세한 구조적 결함에도 장치의 치명적 failed로 이어질 수 있다. 이번 연구에서는 NBI 가열장치의 특성상 극한 운전 환경에 있는 진공용기 부품 중 하나 인 빔인출을 위한 가속 그리드 (accelerating grid)의 구조적 손상및 결함 여부를 고속중성자 이미지 기법을 적용하여 내부를 투시 진단하였다. 가속 그리드는 copper로 제작되었고, 빔인출을 위한 원형의 구멍과 냉각관을 가진 평면판 형태로 되었다. 본 연구에서 내부투시 및 진단할 수 있는 고속중성자 이미징 기법의 적용으로 진공용기 부품 및 장치의 구조적 결함 및 손상 여부를 판단 가능하다는 연구 결과를 얻었다.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.235-235
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• 2012

반도체/디스플레이 공정의 설치 layout의 단순화와 전체 공정 cost의 저감을 위하여 대용량, 고진공 성능의 진공시스템 수요가 증대되고 있다. 이로써 고진공 펌프 국산화의 일환으로 터보분자 펌프와 크라이오 펌프 개발이 진행 중이다. 개발 시제품의 최종 상용화 단계에 진입하기 위한 목적으로 핵심부품의 진공, 기계적 특성평가, 운전상태 및 진공 특성평가, 신뢰성 확보/공정대응성 평가를 위한 관련 기술 및 장치가 개발되었다. 본 연구에서는 터보분자펌프의 공정별 gas load 다변화에 따른 기계적 안정성과 작동 신뢰성의 확인을 위해 내구성 평가 장치를 설계/제작하였다. 상용화 제품의 성능 테스트를 통한 RPM, 소비전력, 압력 그리고 온도 등의 패턴을 확인하여 내구성 평가시스템의 신뢰성을 확인하고 터보분자펌프의 성능 특성 변화를 고찰하고자 한다. 본 연구는 지경부 산업원천기술개발사업 중 "초고진공펌프 개발" 사업의 제 3 세부과제인 "고진공펌프 종합특성평가시스템 개발(과제번호: 11201044)" 및 "스마트형 진공 배기 진단 제어 시스템 개발(과제번호: 11201038)" 과제에서 수행된 연구결과의 일부임.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.283-287
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• 2020

In this paper, we implemented a control for ventilation system based on IoT. It can on/off of system and monitoring current status through the smartphone app. We applied linear regression, one of machine learning algorithm. It autonomously collects data about temperature, humidity in home and works diagnosing system status. Using this proposed control method, the energy efficiency can be improved. It is expected to be used in energy efficiency and convenience.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.11
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• pp.735-742
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• 2017

In this study, an effective model-based sensor fault detection methodology that can detect and isolate PEM temperature sensors fault is introduced. In fuel cell vehicle operation process, the stack temperature affects durability of a fuel cell. Thus, it is important for fault algorithm to detect the fault signals. The major objective of sensor fault detection is to guarantee the healthy operations of the fuel cell system and to prevent the stack from high temperature and low temperature. For the residual implementation, parity equation based on the state space is used to detect the sensors fault as stack temperature and coolant inlet temperature, and residual is compared with the healthy temperature signals. Then the residuals are evaluated by various fault scenarios that detect the presence of the sensor fault. In the result, the designed in this study fault algorithm can detect the fault signal.

• Journal of Conservation Science
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• v.35 no.3
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• pp.237-244
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• 2019

In this study, we applied terahertz imaging technology to three Korean modern oil paintings ('Boy,' 'Girl,' and 'Hyehwadong Landscape'); investigated the types of inner layer information in the pictures that can be extracted with terahertz imaging technology; and analyzed the conditions for extracting them. The biggest problem in the terahertz imaging analysis we encountered was the image distortion caused by the change of the distance between the target surface and the terahertz detector, depending on the surface curvature of the target paintings. We then developed a 'working distance maintaining device' to solve this problem. As a result, the terahertz imaging technique was used to identify the base material characteristics and any patterns of damage inside, and presented the optimal conditions for identifying each characteristic. In addition, it was useful to employ the terahertz frequency-division image to check the characteristics of the background materials. To confirm coloring techniques such as brush strokes, it is effective to compare the maximum reflection peak image with the cross-section image; and to detect damage information inside the paintings that cannot be observed on the surface, to compare the cross-section image with the frequency-division image. On the other hand, according to the terahertz imaging analysis of the oil paintings, the internal structural damage marks of 'Boy' and 'Girl' were confirmed, and the artist's painting style was confirmed in 'Hyehwadong Landscape.' The above results are expected to be useful for the analysis and diagnosis of Korean modern oil paintings for their preservation.

• Journal of Practical Engineering Education
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• v.8 no.2
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• pp.91-94
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• 2016

This study is focused on the development and application of automotive lighting system simulator device to help understanding of the repair and overhaul, electrical instrumentation and automotive circuit checks the contents of the automotive electrical system. The purpose of this study is to define the circuit numeracy, circuit repair preparation skills, detachable power, circuit analysis capabilities, inspection and measurement capability, and repair (problem solving) skills, through the cultivation of clean ability to increase the understanding of electrical equipment maintenance circuitry to verify the improvement of the repair. Automotive electrical device requires understanding of the invisible parts, and understanding of the various symbols and complex circuitry to measure the basic checks and repair are indispensable. This paper would likely contribute to help students to gain more interest in the fields that they feel difficult such as basic skills which necessary to cultivate a variety of electrical equipment fault diagnosis of the basic knowledge needed for electric cars practical.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.66-73
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• 2024

Stay-cable is one of the most important load carrying members in cable-stayed bridges. Monitoring structural integrity of stay-cables is crucial for evaluating the structural condition of the cable-stayed bridge. For stay-cables, tension and damping ratio are estimated based on modal properties as a measure of structural integrity. Since the monitoring system continuously measures the vibration for the long-term period, data acquisition systems should be stable and power-efficiency as the hardware system. In addition, massive signals from the data acquisition systems are continuously generated, so that automated analysis system should be indispensable. In order to fulfill these purpose simultaneously, this study presents an autonomous cable monitoring system based on domain-knowledge using IoT for continuous cable monitoring systems of cable-stayed bridges. An IoT system was developed to provide effective and power-efficient data acquisition and on-board processing capability for Edge-computing. Automated peak-picking algorithm using domain knowledge was embedded to the IoT system in order to analyze massive data from continuous monitoring automatically and reliably. To evaluate its operational performance in real fields, the developed autonomous monitoring system has been installed on a cable-stayed bridge in Korea. The operational performance are confirmed and validated by comparing with the existing system in terms of data transmission rates, accuracy and efficiency of tension estimation.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.141-151
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• 2011

This paper suggests the Facility Management System for plant factory promising to be a core technology of the agriculture in the future. This system makes diagnoses that status from sensors or facilities in the factory for exact operation and monitors the internal environment with the control status in real-time. It is expected that we could operate a plant factory safely and effectively by using the system. The system consists of the data management module, the context provider module, the context interpreter module, the service provider module, the data storage and user interface. The system provide with the failure diagnosis service, the facility control service, and the high-reliability monitoring service via the interactions between above modules. The failure diagnosis service determines whether the sensors or facility devices are in failure or not, and informs the administrator of their conditions. The facility control service is activated in case if the facilities need to be managed during the diagnosis for failure or malfunction processes. The high-reliability monitoring service provides the administrator with verified data through the failure diagnosis service. Then we confirmed that the suggested system operates correctly through the system simulation.