• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.47 no.9
• /
• pp.1-6
• /
• 2010

H.264 video coding standard is widely used due to the high compression rate and quality. H.264 decoders usually have pipeline architecture by a macroblock or a $4{\times}4$ sub-block. The period of the pipeline is usually fixed to guarantee the operation in the worst case which results in many idle cycles and the requirement of high data bandwidth and high performance processing units. We propose adaptive pipeline architecture for H.264 decoders for efficient decoding and lower the requirement of the bandwidth for the memory bus. Parameters and coefficients are delivered using hand-shaking communication through dedicated interconnections and frame pixel data are transferred using AMBA AHB network. The processing time of each block is variable depending on the characteristics of images, and the processing units start to work whenever they are ready. An H.264 decoder is designed and implemented using the proposed architecture to verify the operation using an FPGA.

• Journal of the Korean Institute of Gas
• /
• v.4 no.3 s.11
• /
• pp.59-64
• /
• 2000

The real time monitoring system is developed to detect third party damage imposed on natural gas pipeline and to estimate a damage position in section of pipeline in need of monitoring the third party damage. The monitoring system uses wireless data communication in order to build up data communication network. The availability of monitoring system was evaluated through full scale field damage test at Masan's submarine gas pipeline. It was turned out that the estimation error was one percentage of the propagation speed of damage sound in the gas pipeline.

• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.5
• /
• pp.335-349
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.171-174
• /
• 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.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.12
• /
• pp.75-82
• /
• 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
• /
• v.53 no.12
• /
• pp.4060-4066
• /
• 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.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.14 no.6
• /
• pp.171-178
• /
• 2010

Pipeline structure is one of core underground infrastructure which transports primary sources. Since the almost pipeline structures are placed underground and connected each other complexly, it is difficult to monitor their structural health condition continuously. In order to overcome this limitation of recent monitoring technique, recently, a Ubiquitous Sensor Network (USN) system based on on-line and real-time monitoring system is being developed by the authors' research group. In this study, real-time pipeline health monitoring (PHM) methodology is presented based on electromechanical impedance methods using USN. Two types of damages including loosened bolts and notches are artificially inflicted on the pipeline structures, PZT and MFC sensors that have piezoelectric characteristics are employed to detect these damages. For objective evaluation of pipeline conditions, Damage metric such as Root Mean Square Deviation (RMSD) value was computed from the impedance signals to quantify the level of the damage. Optimal threshold levels for decision making are estimated by generalized extreme value(GEV) based statistical method. Throughout a series of experimental studies, it was reviewed the effectiveness and robustness of proposed PHM system.

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.702-708
• /
• 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
• /
• v.12 no.2
• /
• pp.79-84
• /
• 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.

• Journal of Power System Engineering
• /
• v.2 no.1
• /
• pp.45-51
• /
• 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.