• 대한전자공학회논문지SD
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• 제47권9호
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• pp.1-6
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• 2010

H.264 영상 압축 표준은 높은 압축률과 화질로 널리 이용되고 있다. H.264 복호기는 일반적으로 마크로블록 또는 $4{\times}4$ 하위 블록 단위로 파이프라인을 적용하여 동작한다. 이러한 파이프라인 한 단의 주기는 보통 최악의 상황에서도 동작을 보장하도록 결정되어 높은 전송 대역폭과 고성능 연산기를 요구하고 연산기가 일을 하지 않고 쉬는 사이클이 많아지는 결과를 초래한다. 본 논문에서는 이러한 연산기의 쉬는 사이클을 줄이고 데이터 전송 대역폭과 연산기 성능 요구 조건을 완화시킬 수 있는 적응적 파이프라인 구조를 채택한 효율적인 영상 복호기 구조를 제안한다. 제안한 구조에서는 파라미터와 계수는 핸드셰이킹 방식으로 전용 신호선을 통해 전달되고 복호된 영상 데이터는 AMBA AHB 네트워크를 통해 메모리에 저장하거나 읽어 온다. 각 블록의 복호 처리 시간은 영상의 특성에 따라 가변적으로 변하고 각 연산기는 데이터가 준비되면 언제든지 동작을 할 수 있다. 제안한 구조에 따라 H.264 복호기를 설계하였고 FPGA를 이용하여 동작을 검증하였다.

• 한국가스학회지
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• 제4권3호
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• pp.59-64
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• 2000

배관에 가해지는 충격을 실시간으로 모니터링하고, 충격위치를 산출 할 수 있는 모니터링 시스템을 무선데이타 통신을 이용하여 개발하였고, 마산만 해저배관에 현장적용 후 충격실험을 통하여 유용성을 검증하였다. 실험에 사용된 배관은 총 길이 1365m, 운용압력 8.6kgf/cm^2$$, 직경 12inch인 실제 운용중인 해저가스배관을 사용하였다. 충격실험에서 충격위치 계산오차는 배관내에서 충격음파 속도의 $1\%$정도의 거리였다.

• 상하수도학회지
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• 제35권5호
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• pp.335-349
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• 2021

As the frequency of seismic disasters in Korea has increased rapidly since 2016, interest in systematic maintenance and crisis response technologies for structures has been increasing. A data-based leading management system of Lifeline facilities is important for rapid disaster response. In particular, the water supply network, one of the major Lifeline facilities, must be operated by a systematic maintenance and emergency response system for stable water supply. As one of the methods for this, the importance of the structural health monitoring(SHM) technology has emerged as the recent continuous development of sensor and signal processing technology. Among the various types of SHM, because all machines generate vibration, research and application on the efficiency of a vibration-based SHM are expanding. This paper reviews a vibration-based pipeline SHM system for seismic disaster response of water supply pipelines including types of vibration sensors, the current status of vibration signal processing technology and domestic major research on structural pipeline health monitoring, additionally with application plan for existing pipeline operation system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.171-174
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• 2003

In this paper, a Learning module for a reconfigurable neural network processor(ERNIE) was proposed for an On-chip learning. The existing reconfigurable neural network processor(ERNIE) has a much better performance than the software program but it doesn't support On-chip learning function. A learning module which is based on Back Propagation algorithm was designed for a help of this weak point. A pipeline structure let the learning module be able to update the weights rapidly and continuously. It was tested with five types of alphabet font to evaluate learning module. It compared with C programed neural network model on PC in calculation speed and correctness of recognition. As a result of this experiment, it can be found that the neural network processor(ERNIE) with learning module decrease the neural network training time efficiently at the same recognition rate compared with software computing based neural network model. This On-chip learning module showed that the reconfigurable neural network processor(ERNIE) could be a evolvable neural network processor which can fine the optimal configuration of network by itself.

• 전자공학회논문지B
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• 제28B권12호
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• pp.75-82
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• 1991

We proposed an optimal parallel implementation of an optimization neural network with linear increase of speedup by using multicomputer system and presented performance analysis model of the system. We extracted the temporal-and the spatial-parallelism from the optimization neural network and constructed a parallel pipeline processing model using the parallelism in order to achieve the maximum speedup and efficiency on the CSP architecture. The results of the experiments for the TSP using the Transputer system, show that the proposed system gives linear increase of speedup proportional to the size of the optimization neural network for more than 140 neurons, and we can have more than 98% of effeciency upto 16-node system.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• 한국구조물진단유지관리공학회 논문집
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• 제14권6호
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• pp.171-178
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• 2010

가스관, 송유관 등의 배관구조물은 주요자원의 수송을 책임지는 핵심 지하시설물 중 하나이다. 이들은 사고 및 자연적인 노후화로 인해 국부적인 손상이 발생할 위험에 노출 되어있다. 하지만 대부분의 배관구조물은 지하의 좁은 공간에 복잡하게 연결되어있기 때문에 구조물의 건전성을 지속적으로 모니터링 하는데 어려움이 있었다. 이러한 지금까지 관리방식의 한계점을 극복하기 위해 최근 유비쿼터스 센서 네트워크 기반의 온라인 방식의 상시적 구조물 건전성 평가방법에 대한 연구가 활발히 이뤄지고 있다. 본 논문에서는 전기-역학적 임피던스 기반의 실시간 배관 구조물 건전성 평가방법에 대하여 연구하였다. 배관 구조물에 발생하기 쉬운 볼트 풀림과 균열의 두 가지 국부손상을 가정하였고 압전효과를 가진 PZT와 MFC 센서를 이용하여 구조물의 상태에 따른 임피던스를 계측하여 손상탐색 실험을 수행하였다. 하나의 센서로 가진과 센싱을 동시에 수행할 수 있는 저비용 셀프센싱 기법을 사용하였고 배관 상태에 대한 객관적인 판단을 위해 손상지수인 RMSD 값을 사용하여 계측된 신호를 이용하여 손상의 정도를 정량화 시켰다. 손상여부의 판단을 위해 일반 극치 분포를 이용하여 최적화된 통계적인 정상상태의 임계값을 설정하였다. 위와 같은 실험적 연구과정을 통해 제안된 실시간 배관 구조물 건전성 평가 방법의 타당성과 효율성을 확인해 보았다.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Corrosion Science and Technology
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• 제12권2호
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• pp.79-84
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• 2013

In general, pipelines at nuclear power plants are affected by various types of degradation mechanisms and may be ruptured after gradually thinning. FAC (Flow-Accelerated Corrosion) is typical aging mechanism affecting the secondary side piping system. In Korea nuclear power plants, CHECWORKS program have been used for management of wall thinning damages. However, sometimes, CHECWORKS program shows wrong results at the stage of NFA (Network Flow Analysis) in case of complex pipelines. This paper describes the calculation results of pressure drop in a complex pipeline and single line by using the CHECWORKS program and the analysis results are compared with those of engineering calculation results including errors between them.

• 동력기계공학회지
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• 제2권1호
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• pp.45-51
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• 1998

An analyzing method for pressure fluctuations in oil hydraulic pipe network was developed in this study. The object pipe network has multi-branch configuration, and the pipelines of it are composed of metal tubes and flexible hoses. Transfer matrix method, in other words impedance method, was used for the analysis. Values of physical parameters describing the characteristics of flexible hose were measured by experiments and reflected to the analysing procedure. The reliability and usefulness of the analyzing method were confirmed by investigating computed results and experimental results got in this study.