• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.537-538
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• 2015

전기혜택을 받지 못하는 개발도상국의 도서지역이나 산간지역 등에 전기를 공급하기 위한 방법으로 마이크로그리드를 구축하고 있다. 그러나 24시간 전기 공급을 위해서는 많은 투자 재원이 필요하다. 따라서 본 논문에서는 첨두 부하를 삭감하여 공급지장을 감수하더라도 투자 재원을 줄여 전기 공급의 확대를 목표로 한다. 또한 HOMER 프로그램을 이용하여 첨두 부하 삭감을 고려하는 가상발전기 개념을 도입하고 발전기의 고장정지율을 고려한 감도 분석을 통하여 마이크로그리드의 각 설비별 기대 적정 규모를 구하는 방법론을 제안한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.351-362
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• 1999

버퍼는 생산시스템에서 시스템내의 기계고장, 프로세스시간의 변화, 그리고 부품들의 이동경로의 복잡성 등과 같은 요인들로 인해 발생 가능한 blocking현상과 starving현상을 감소시키는데 사용되고 있다. 유연 생산시스템(Flexible Manufacturing System)에서는 버퍼의 단위당 비용이 높아 시스템내의 총 버퍼 크기는 제한적이어서, 적절한 버퍼 관리를 통해 시스템 효율성을 향상시킬 수 있다. 본 연구는 이러한 버퍼의 특성을 분석하고 AGVS(Automated Guided Vehicle System)을 사용하는 다중셀방식의 FMS(multi-cell Flexible Manufacturing Systems) 환경에서의 버퍼관리와 관련된 기존 연구들을 검토하고, 새로운 개념의 가상시스템버퍼를 소개한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.5-8
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• 2015

소프트웨어의 작동 중에 중대한 고장 없이 유연하게 대처할 수 있는 성질은 견고성이라 불리며 임베디드 소프트웨어에서 중요하게 여겨진다. 본 논문에서는 개발 중인 임베디드 소프트웨어의 견고성을 조기에 검증하기 위하여, 실물 시스템을 이용한 테스트가 갖는 물리적 한계를 극복할 수 있는 Software-in-the-Loop 시뮬레이션을 이용하여 개발 중인 PC에 견고성 테스팅 환경을 갖추는 방법을 제시한다. 제시한 방법은 소프트웨어의 견고성을 표현할 수 있게끔 만드는 테스트 케이스를 생성하고, 가상 시뮬레이션 환경을 구성하여 테스트 케이스를 실행함으로써 소프트웨어의 견고성을 객관적인 수치의 형태로 나타내는 방법을 보인다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.12
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• pp.54-60
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• 2007

This paper proposes a built-in-self-repair (BISR) structure using 2-dimensional spare memory to effectively self-repair faults of an embedded memory. In case of multiple faults in the same row (column) of an embedded memory, the previous method using 1-D spare column (row) memory needs the same number of spare memory columns (rows) as the number of faults to self-repair them. while the new method using 2-D spare memory needs only one spare row (column) to self-repair them. Also, the virtual divided memory is adopted to be able to self-repair using not a full spare column memory but the only partial spare column memory corresponding to the faults. A self-repair circuit with $64\times1-bit$ core memory and $2\times8$ 2-D spare memory is designed. And the circuit includes a built-in-self-test block using the 13N March algorithm. The circuit has been implemented using the $0.25{\mu}m$ MagnaChip CMOS process and has $1.1\times0.7mm^2$ chip area with 10,658 transistors.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.205-216
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• 2024

Recently, seismic Probabilistic Safety Assessment (PSA) methods have been developed for process plants, such as gas plants, oil refineries, and chemical plants. The framework originated from the PSA of nuclear power plants, which aims to assess the risk of reactor core damage. The original PSA method was modified to adopt the characteristics of a process plant whose purpose is continuous operation without shutdown. Therefore, a fault tree, whose top event is shut down, was constructed and transformed into a Bayesian Network (BN), a probabilistic graph model, for efficient risk-informed decision-making. In this research, the fault tree-based BN from the previous research is further developed to consider the multi-hazard of earthquake-induced fire and explosion (EQ-induced F&E). For this purpose, an event tree describing the occurrence of fire and explosion from a release is first constructed and transformed into a BN. And then, this BN is connected to the previous BN model developed for seismic PSA. A virtual plot plan of a gas plant is introduced as a basis for the construction of the specific EQ-induced F&E BN to test the proposed BN framework. The paper demonstrates the method through two examples of risk-informed decision-making. In particular, the second example verifies how the proposed method can establish a repair and retrofit strategy when a shutdown occurs in a process plant.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.172-183
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• 1994

In the multidatabase systems(MDBS), local autonomy and global consistency are important issues. Global consistency could be maintained by a global concurrency control algorithm and a global recovery algorithm. In this thesis, we propose a global concurrency control algorithm to ensure local autonomy and to guarantee global serializability, and a global recovery algorithm which is possible to recover the multudatabase from any failures. The proposed global concurrency control algorithm uses bottom-up approach, based on three-level transaction processing model. It can produce a local history that the execution order of subtransactions is identical to their serialization order by using dummy-operations in the server when an indirect conflict is caused between subtransactions due to local transactions. At the global module, it can efficiently validate global serializability of global transactions by checking global serializability only for the global transactions which conflict with each other.

• Journal of IKEEE
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• v.24 no.2
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• pp.385-391
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• 2020

Overcurrent and abnormal voltages in the distribution system can cause not only burden of power plant but also damage to customers. As a result, researches related to the distribution automation have been widely conducted by utilizing a real time digital simulation to improve the reliability of power supply through rapid failure handing, reduction of power failure intervals and failure recovery. However, the distribution automation systems using the real time digital simulator are expensive and limited to verify actual hardwares. Therefore, in this paper, an external hardware devices was developed based on the distribution system analysis results of the digital simulator. And real-time simulation and functional verification are implemented by the real feeder remote terminal units used in distribution automation. The effectiveness of the proposed system is verified through several experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.274-283
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• 2016

The study of maintenance planning is important in military weapon systems because it can improve their availability and reduce the operational and maintenance cost during the total life cycle. In maintenance planning, it is important to determine the preventive maintenance task and its optimal interval. This paper focuses on the hard time task, which is one of the preventive maintenance tasks. A hard time task removes an item or restorative action before some specified maximum age limit to prevent functional failure. The Monte-Carlo simulation model was proposed to help understand the cost effectiveness of a hard time task. In the simulation, various shape parameters of the Weibull distribution and cost ratio of corrective maintenance to preventive maintenance were assumed. Using a Monte-Carlo simulation, a quantified cost saving effect and optimal preventive maintenance interval were suggested.

• Journal of the Society of Naval Architects of Korea
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• v.41 no.6
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• pp.126-133
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• 2004

As the automation of ship production system proliferates, it is unavoidable to move the real machinery, facility, application software, and virtual environment from the heterogeneous data environment to the integrated one. However, it is a prevailing situation that each component operates under the different environment, due to the inability of interchanging the data in a structured manner between the components. The issue is obstacles to improvement of the application system. To facilitate seamless exchange of data among the manufacturing systems, STEP, PDML, and XML has been tried by other studies. This study has investigated how XML data can be applied to exchange the data among the ship production system components. XML data is applied to the forming shop in shipyard to overcome the present difficulties on sharing and exchanging of the information. An example of forming information is represented by XML to demonstrate the feasibility of the proposed approach. In addition, an application of XML information applicable to virtual factory and electronic work order sheet is included.

• 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.