• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2041-2049
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• 1998

This paper suggests a technique for program segmentation and maintenance using PSDG(Post-State Dependency Graph) that improves the quality of a software by identifying and detecting defects in already fixed source code. A program segmentation is performed by utilizing source code analysis which combines the measures of static, dynamic and semantic slicing when we need understandability of defect in programs for corrective maintanence. It provides users with a segmental principle to split a program by tracing state dependency of a source code with the graph, and clustering and highlighting, Through a modeling of the PSDG, elimination of ineffective program deadcode and generalization of related program segments arc possible, Additionally, it can be correlated with other design modeb as STD(State Transition Diagram), also be used as design documents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.532-541
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• 2016

The objective of this paper is to support accurate pavement condition assessment and decision of proper maintenance method and time by conducting visual inspection and calculating the remaining life of pavement from falling weight deflectometer(FWD) data. Each was implemented in the same long-term performance pavement(LTPP) sections. Visual inspection was executed to measure pavement condition indices such as crack, rutting and international roughness index(IRI) and the Seoul Pavement Index(SPI) was calculated based on these results. The dynamic modulus was back-calculated from the FWD data. The remaining pavement lives were determined from equivalent single axle loading(ESAL) and FWD data. Correlation of maintenance priority by each result value was examined. Consequently, the correlation between remaining life to Crack and Rutting was higher than the other factors or indicesbecause IRI is not related to FWD value and SPI value consists with IRI value and other indices. The R-square value of correlation of FWD with Crack and Rutting was 0.65, which indicated an insufficient correlation. Consequently, when decision of maintenance of method, time, etc. is determined, FWD data have to be considered with Crack and Rutting because of those relations.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.31-34
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• 2002

IDC는 기업 및 개인 고객에게 전산 설비나 네트워크 설비를 유치하여 유지·보수 등의 서비스를 제공하는 곳으로, 서버 장비 및 통신장비의 운영과 관리를 대행하여 서버의 안정성, 보안성과 서버가 제공하는 서비스의 품질을 유지해 주는 시설이다. 하지만 학교나 일부기업에서는 자신들의 고유 서비스를 위해 호스트를 자신들의 NOC(Network Operation Center)에 놓아두게 되고, 그러면 IDC에서 지원하는 다양한 관리 서비스를 제공받지 못한다. 본 논문에서는 웹 기반 IDC 관리형 인터넷 서비스 모니터링 및 관리시스템의 구조를 제안하고 시스템을 구현한다. 웹에서 효과적인 모니터링 및 관리를 위하여 JAVA 기반의 설계를 하였으며, 인터넷 서비스에 대한 효율적인 관리를 위하여 ICMP 프로콜을 사용하였다. 제안된 시스템을 이용하여 원격지의 서버의 상태나 인터넷 서비스의 전반적인 상태를 일정하게 유지하며, 웹을 기반으로 하여 모니터링 및 관리를 할 수 있다. 중요 기능은 데이터의 주기적인 수집과, 수집된 정보를 데이터 베이스에 저장함으로써 네트워크의 상태 변화에 대한 지속적인 관찰이 가능하다. 또한 시스템에 어떤 문제가 발생하였다고 판단될 경우 효율적이고 신속한 대처가 이루어지도록 하기 위하여 관리자에게 보고서를 제출하게 된다.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.48-57
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• 2005

A lot of manufacturing knowledge and method have applied to increase manufacturing efficiency in industry field. DES(Discrete Event Simulation) is one of solution to deal with manufacturing problems in factory. Beginning of research, old maintenance system of KNR ( Korea National Railroad) and its technical problems are basically investigated. KNR has maintained railway vehicle with their own solution based on experience. Very advanced railway vehicles such as KTX (Korea Train Express) and TTX(Tilting Train Express) will be difficult to maintain with their old maintenance method. In order to apply knowledge of DES, maintenance field of railway must be considered. Imaginary maintenance machine are selected to variable of DES. Maintenance capability of each machine will be evaluated base on imaginary data from imaginary machine. The machine could be very expensive as well as difficult to replace. Target of research is minimization of number of machine in railway workshop. So basic knowledge of discrete event simulation is introduced. Then five essential stages of discrete event simulation are provided. Each maintenance case defined as event. Each event is discrete and simulated base on different case such as one maintenance line with one machine and one maintenance line with two machines in railway workshop. simple maintenance method, discrete event simulation, will be come out very powerful in complicate maintenance system and will be helpful to reduce maintenance cost as well as maintenance labor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.569-570
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• 2022

Failure situations occur frequently in the industry, and the existing 2D-based manual for this purpose is not intuitive to understand and it is difficult to immediately interact with maintenance experts. In this paper, we propose a technology that enables workers to perform maintenance in real time with the help of experts without restrictions in time and place based on augmented reality when a failure situation occurs at a remote location. A local virtual human-based expert diagnoses a failure situation based on an adapted panoramic image of a remote failure situation while wearing an HMD headset, and gives instructions to a remote operator. In addition, in an augmented reality (AR) environment in a remote location, for the interaction between the operator and the expert, the HMD's microphone is used to create the expert's hand as well as verbal communication. You can use to point or draw a picture. If this technology is used, it is possible to overcome the limitations of the existing 2D-based manual, and to provide assistance in performing maintenance smoothly remotely even if an expert does not directly visit a remote location.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.505-516
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• 2018

In order to establish an efficient bridge maintenance strategy, the future performance of a bridge must be estimated by considering the current performance, which allows more rational way of decision-making in the prediction model with higher accuracy. However, personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. Therefore, in this work, we developed a representative performance prediction model for each bridge element considering uncertainty using domestic bridge inspection data, and proposed a bayesian updating method that can apply the developed model to actual maintenance bridge with higher accuracy. Also, the feasibility analysis based on calculation of maintenance cost for monitoring maintenance scenario case is performed to propose advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6932-6942
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• 2014

The expressway is a key area of road networks for national industry, economics and social development. In addition, the number of lines are increasing continuously. Recently, road management policy have shown a paradigm shift to safe road, low-carbon and green etc. Nevertheless, the road condition has deteriorated dramatically by fatigue cracking, thermal cracking and weather impact. On the other hand, the budgets for highway maintenance have shrunk dramatically. In this study, an effective pavement management and maintenance process was developed to extend the pavement serviceability and pavement life to overcome the rack of budgets.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.9
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• pp.835-842
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• 2016

In Korea, urban railway cars are typically maintained using the strategy of predictive maintenance. In an effort to overcome the limitations of the existing strategy, there is increased interest in adopting the condition-based maintenance strategy. In this study, a novel method is proposed to detect faults in the solenoid valves of the braking system in urban railway vehicles. We determined the key component (i.e., solenoid valve) that leads to braking system faults through the analysis of failure modes, effects, and criticality. Then, an equivalent circuit model was developed with the compensation of the temperature effect on solenoid coils. Finally, we presented how to detect faults with the equivalent circuit model and current signal measurements. To demonstrate the performance of the proposed method, we conducted a case study using real solenoid valves taken from urban railway vehicles. In summary, it was shown that the proposed method can be effective to detect faults in solenoid valves. We anticipate the outcome from this study can help secure the safety and reliability of urban railway vehicles.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.59-60
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• 2009

대다수의 유량계는 특성상 공통구나 맨홀 등 일반적으로 사람의 눈에 잘 보이지 않는 곳에서 동작하는 경우가 많다. 따라서 아날로그 유량계의 경우 상태를 점검하기 위해 많은 불편함을 감수해야 하지만, 디지털 유량계를 도입하면 그러한 불편함을 줄일 수 있다. 디지털 유량계의 도입은 무선 기술을 결합할시 관리를 손쉽게 하는 등 유지보수성을 높일 수 있는데 그 역시 전력을 공급하는데 있어 배선관리 등의 문제점이 다시 발생하게 된다. 본 논문에서는 무선 센서네트워크(이하 USN) 기술을 융합하여 원하는 정보를 손쉽게 얻고 유지보수 비용을 낮추며 향후 다양한 활용을 가능케 하는 배터리 기반의 유량계 관리 시스템과 그 활용 방안을 제시한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.