• 전자공학회논문지A
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• 제31A권6호
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• pp.149-158
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• 1994

Through high level synthesis, designers can obtain the precious information on the area and speed trade-offs as well as synthesized datapaths from behavioral design descriptions. While previous researches were concentrated on the synthesis of pipelined, datapaths with fixed DII (Data Initiation Interval) by inserting delay elements where needed, we propose a novel methodology of synthesizing pipeline structures with variable DIIs. Determining the time-overlapping of pipeline stages with variable DIIs, the proosed algorithm performs scheduling and module allocation using the time-overlapping information. Experimental results show that significant improvement can be achieved both in speed and in area.

• 한국가스학회지
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• 제14권6호
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• pp.51-56
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• 2010

해외 하천통과 매설배관 사고사례를 수집하여 분석하였다. 이는 국내 하천통과 도시가스매설배관의 합리적인 매설심도를 결정하기 위한 기본 자료로 활용하기 위함이다. 사고사례 조사결과 하천을 통과하는 천연가스매설배관 사고의 주요원인은 홍수로 분석되었다. 홍수에 의한 배관 노출 및 과도 유량에 의한 하중이 파손 원인인 경우가 많다. 파손발생 위치에서 부식이 발생한 것도 원인이 되었다. 따라서 국내 하천통과 매설배관의 합리적이고 효율적인 매설심도 결정을 위해서는 국내 하천특성에 맞는 하천의 수리학적 특성평가와 배관의 구조해석이 요구된다. 일반 천연가스배관의 사고사례 조사 결과도 주요 원인이 외부간섭과 부식임도 요약하였다. 이들 두 주요원인은 매설환경에 따라 전체사고에서 차지하는 비율이 차이가 있었다.

• Water Engineering Research
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• 제4권2호
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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.

• 전자공학회논문지C
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• 제35C권3호
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• pp.24-31
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• 1998

This paper presents a pipeline high-level synthesis tool for designing trace-back systolic array viterbi decoder. It consists of a dta flow graph(DFG) generator and a pipeline data path synthesis tool. First, the DFG of the vitrebi decoder is generated in the from of VHDL netlist. The inputs to the DFG generator are parameters of the convolution encoder. Next, the pipeline scheduling and allocationare performed. The synthesis tool explores the design space efficiently, synthesizes various designs which meet the given constraints, and choose the best one.

• Journal of Mechanical Science and Technology
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• 제15권4호
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• pp.500-509
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• 2001

Offshore subsea pipelines must be stable against external loadings, which are mostly due to waves and currents. To determine the stability of a subsea pipeline on the seabed, the Morrison equation has been applied with prediction of inertia and drag forces. When the pipeline is placed in a trench, the force acting on it is reduced considerably. Therefore, to consider the stability of a pipeline in a trench, one must employ reduction factors. To investigate the stability of various trenches, we numerically simulated flows over various trenches and compared them with experimental data from PIV (Particle Image Velocimetry) measurements. The present results were produced ar Reynolds numbers ranging from 6$\times$10$^3$to 3$\times$10(sub)5 based on the diameter of the cylinder. Quasi-periodic flow patterns computed by large-eddy simulation were compared with experimental data in terms of mean flow characteristics fro typical trench configurations (W/H=1 and H/D=3, 4). The stability for various trench conditions was addressed in terms of mean amplitudes of oscillating lift and drag, and the reduction factor for each case was suggested for pipeline design.

• Ocean Systems Engineering
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• 제12권3호
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• pp.267-284
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• 2022

A subsea pipeline designed across active shipping lane prones to failure against external interferences such as anchorage activities, hence risk assessment is essential. It requires quantifying the geometric probability derived from ship traffic distribution based on Automatic Identification System (AIS) data. The actual probability density function from historical vessel traffic data is ideal, as for rapid assessment, conceptual study, when the AIS data is scarce or when the local vessels traffic are not utilised with AIS. Recommended practices suggest the probability distribution is assumed as a single peak Gaussian. This study compares several fitted Gaussian distributions and Monte Carlo simulation based on actual ship traffic data in main ship direction in an active shipping lane across a subsea pipeline. The results shows that a Gaussian distribution with five peaks is required to represent the ship traffic data, providing an error of 0.23%, while a single peak Gaussian distribution and the Monte Carlo simulation with one hundred million realisation provide an error of 1.32% and 0.79% respectively. Thus, it can be concluded that the multi-peak Gaussian distribution can represent the actual ship traffic distribution in the main direction, but it is less representative for ship traffic distribution in other direction. The geometric probability is utilised in a quantitative risk assessment (QRA) for subsea pipeline against vessel anchor dropping and dragging and vessel sinking.

• 한국농공학회논문집
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• 제66권1호
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 춘계학술발표대회논문집
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• pp.14-18
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• 2003

This paper presents the pipeline architecture for the low and column address generator of 2D DCT/IDCT(Discrete Cosine Transform/Inverse Discrete Cosine Transform). For the real time process of image data, it is required that high speed operation and small size hardware In the proposed architecture, the area of hardware is reduced by using the DA(distributed arithmetic) method and applying the concepts of pipeline on the parallel architecture. As a results, the designed pipeline of the low and column address generator for 2D DCT/IDCT architecture is implemented with an efficiency and high speed compared as the non-pipeline architecture. And the operation speed is improved about 50％ up. The design for the proposed pipeline architecture of DCT/IDCT is coded using VHDL.

• Steel and Composite Structures
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• 제51권4호
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• pp.351-361
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• 2024

Accurate measurement of K_IC values for gas pipeline steels is important for assessing pipe safety using failure assessment diagrams. As direct measurement of K_IC was impossible for the API X70 pipeline steel, multi-specimen fracture tests were conducted to measure J_IC using three-point bend geometry. The J values were calculated from load-displacement (F-δ) plots, and the associated crack extensions were measured from the fracture surface of test specimens. Valid data points were found for the constructed J-Δa plot resulting in J_IC=356kN/m. More data points were added analytically to the J-Δa plot to increase the number of data points without performing additional experiments for different J-Δa zones where test data was unavailable. Consequently, displacement (δ) and crack-growth (Δa) from multi-specimen tests (with small displacements) were used simultaneously, resulting in the variation of Δa-δ (crack growth law) and δ-Δa obtained for this steel. For new Δa values, corresponding δ values were first calculated from δ-Δa. Then, corresponding J values for the obtained δ values were calculated from the area under the F-δ record of a full-fractured specimen (with large displacement). Given Δa and J values for new data points, the developed J-Δa plot with extra data points yielded a satisfactory estimation of J_IC=345kN/m with only a -3.1% error. This is promising and showed that the developed technique could ease the estimation of J_IC significantly and reduce the time and cost of expensive extra fracture toughness tests.