• Journal of the Korean Institute of Gas
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• v.5 no.2 s.14
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• pp.43-51
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• 2001

ln this study, a natural gas pipeline network model was established using STONER. First a map of natural gas pipeline network was drawn on STONER and then the length and diameter of the pipe were inputted. And as the specific gravity of gas flowing in the pipeline which is the value of natural gas was inputted. Finally in order to decide the pipeline variables and gas temperature, through the verification with observed real data, the possible error was minimized. For the verification, the pipeline variables and gas temperature were assumed and the pipeline network analysis was accomplished with real demand data. The square deviation of analysed pressure from observed pressure was calculated and the minimum case was selected for the optimum pipeline variables and gas temperature. Thus a proper natural gas pipeline network model for real network was established.

• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1448-1453
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• 2004

The major parameters governing the fluid dynamical and thermo-dynamical behavior in the large pipeline network system are friction loss and the pipeline length. But in local pipeline networks and relatively short distance pipeline system, secondary loss and the considerations of the moving states of the fluid machine are also important. One of the major element in local pressure control system is pressure regulator. It causes the variations of the physical properties in that pipeline system. Especially, as there is not enough information to obtain reliable physical property values such as density, temperature etc. at the downstream of the pressure regulator, It is hard to calculate accurate solution in the pipeline network analysis. In this study, some numerical approaches to investigate the critical-flow-characteristics of the pressure regulator have been done and the detail examinations and considerations of the pressure regulator as a pipeline network elements according to the variations of the inlet-outlet pressure ratio have been carried. Finally the flow-flied distributions, relations and critical-flow-characteristics have been studied. in detail and the 1D analytic method to analyze critical pipe flow have been investigated

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1454-1459
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• 2004

The major parameters governing the fluid dynamical and thermo-dynamical behavior in the large pipeline network system are friction loss and the pipeline length. But in local pipeline networks and relatively short distance pipeline system, secondary loss and the considerations of the moving states of the fluid machine are also important. One of the major element in local pressure control system is pressure regulator. It causes the variations of the physical properties in that pipeline system. When it is under working, the accurate analysis of the flow properties is so difficult. In this study, some numerical approaches to investigate the critical-flow-characteristics of the pressure regulator have been done according to the variations of the opening ratio or cross-sectional area and the detail examinations and considerations of the pressure regulator as a pipeline network elements have been carried. Finally the flow-flied distributions and critical-flow-characteristics have been presented in detail and the critical flow phenomena and the relation to the opening ratio or cross-sectional-area ratio have been studied.

• International Journal of Contents
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• v.13 no.1
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• pp.45-52
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• 2017

Carbon Capture and Storage (CCS) technologies involve three major stages, i.e., capture, transport, and storage. The transportation stage of CCS technologies has received relatively little attention because the requirements for $CO_2$ transport differ based on the industry-related conditions, geological, and demographical characteristics of each country. In this study, we analyzed the properties of $CO_2$ transport using a pipeline. This study has important implications for ensuring the stability of a long-term CCS as well as the large cost savings, as compared to the small cost ratio as a percentage of the entire CCS system. The state of $CO_2$, network topologies, and node distribution are among the major factors that influence $CO_2$ transport via pipelines. For the analysis of the properties of $CO_2$ transport using a pipeline, the $CO_2$ pipeline connections were visualized by the simulator developed by Lee [11] based on the network topologies in $CO_2$ transport. The case of Korean CCS technologies was applied to the simulation.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.251-254
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• 2003

For improving the flow capacity of pipeline system the hydraulic inspection method was developed conducting on-site with survey of pipeline facility such as diversion work, air vent, etc. and pipe network analysis. The pipe network analysis method determine pipe diameter with trial and error. The validity of the hydraulic inspection method proved adapting on S-district pipeline system.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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• pp.1-6
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• 2011

The Underground pipeline facility is a general but most important facility in modern world, but its maintainability has been left behind. An automated and intelligent management technology is needed to prevent the wast of social resource and security. In this paper, we introduce Pipeline Health Monitoring(PHM) with Ubiquitous Sensor Network(USN) for inexpensive structure safety monitoring system, and improve its utility by inventing the advanced impedance converter.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.297-314
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• 2016

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.

• Publications of The Korean Astronomical Society
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• v.28 no.1
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• pp.1-6
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• 2013

Real-time data reduction pipeline for the Korea Microlensing Telescope Network (KMTNet) was developed by Korea Astronomy and Space Science Institute (KASI). The main goal of the data reduction pipeline is to find variable objects and to record their light variation from the large amount of observation data of about 200 GB per night per site. To achieve the goal we adopt three strategic implementations: precision pointing of telescope using the cross correlation correction for target fields, realtime data transferring using kernel-level file handling and high speed network, and segment data processing architecture using the Sun-Grid engine. We tested performance of the pipeline using simulated data which represent the similar circumstance to CTIO (Cerro Tololo Inter-American Observatory), and we have found that it takes about eight hours for whole processing of one-night data. Therefore we conclude that the pipeline works without problem in real-time if the network speed is high enough, e.g., as high as in CTIO.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.693-694
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• 2014

This paper proposes a new pipelined dynamic bandwidth allocation algorithm for XG-PON (10-Gbps-capable passive optical network) passive optical networks) system. The pipelined algorithm is used when a dynamic bandwidth allocation algorithm cannot finished in an unit time. In the proposed mechanism, the request is immediately transferred to each pipeline block to improve performance.