• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.11
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• pp.3589-3597
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• 1996

The Reynolds-averaged Navier-Stokes equations with the equations of the k-.epsilon. turbulence model are solved numerically in a general curvilinear system for a three-dimensional turbulent flow around an Ahmed body. The simulation is especially aimed at the evaluation of three finite differencing schemes for the convection term, which include the upwind differencing scheme(UDS), the second order upwind differencing scheme(SOU scheme) and the QUICK scheme. The drag coefficient, the velocity and pressure fields are found to be changed considerably with the adopted finite differencing schemes. It is clearly demonstrated that the large difference between computation and experiment in the drag coefficient is due to relatively high predicted values of pressure drag from both front part and vertical rear end base. The results also show that the simulation with the QUICK or SOU scheme predicts fairly well the flow field and gives more accurate drag coefficient than other finite differencing scheme.

• Journal of the korean Society of Automotive Engineers
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• v.11 no.3
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• pp.29-36
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• 1989

A multidimensional numerical simulation for flows in an engine with axisymmetric geometry was performed. Three kinds of differencing schemes, namely, skew upwind differencing scheme (SUDS), interpolated upwind differencing scheme (IUDS), upwind differencing scheme (UDS), are used in a comparative study. Simultaneously, the effects of the artificial dampings and the grids on numerical results are estimated. Compared with the measurements, the calculations with SUDS and proper artificial damping show very similar qualitative tendency with observed results. But there are some discrepancies due to numerical errors and unclear boundary conditions.

• The KSFM Journal of Fluid Machinery
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• v.6 no.1 s.18
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• pp.23-29
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• 2003

Three-dimensional flow through a tubular centrifugal fan with airfoil type blades is analyzed, and the effects of turbulence model and numerical scheme on the results are investigated. Standard $k-{\epsilon}$ model and k - w model are tested as turbulence closures. The numerical schemes for convection terms, i.e., Upwind Differencing Scheme (UDS), Mass Weighted Skewed upstream differencing scheme (MWS), Linear Profile Skewed upstream differencing scheme (LPS), and Modified Linear Profile Skewed upstream differencing scheme (MLPS) are also tested, and the performances of these schemes coupled with two turbulence models are evaluated. The static pressure distributions are compared with experimental data obtained in this work, which shows that the $k-{\epsilon}$ model gives better results than the k-w model.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.1
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• pp.104-113
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• 1999

A hybrid spatial differencing scheme for the discrete ordinates method is proposed to predict radiative heat transfer in two-dimensional rectangular enclosures. Since this scheme takes the advantages of the diamond scheme and step scheme and includes the characteristics of medium, more accurate and stable results can be obtained. In its development several spatial differencing schemes are examined to address the effect of numerical smearing (or false scattering). Predictions from the proposed hybrid scheme are compared to those of other schemes for transparent, purely absorbing, purely scattering, or absorbing-emitting-isotropically scattering media. It is found that the proposed scheme predicts stable and less smeared results than others.

• 유체기계공업학회:학술대회논문집
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• 2001.11a
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• pp.23-29
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• 2001

Three-dimensional flow through a tubular centrifugal fan with airfoil type blades are analyzed using CFX-TASCflow. Standard k - $\epsilon$ model and k - $\omega$ model are used as turbulence closures. The numerical schemes for convetion terms, i.e., Upwind Differencing Scheme(UDS), Mass Weighted Skewed Upstream Differencing Scheme(MWS), Linear Profile Skewed Upstream Differencing Scheme(LPS), and Modified Linear Profile Skewed Upstream Differencing Scheme(MLPS) are also tested. And, the performance of these schemes coupled with two turbulence models are evaluated. Computational static pressure distributions are compared with experimental data obtained in this work.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.1
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• pp.73-80
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• 1999

This paper describes a numerical study of three-dimensional buoyant turbulent flow in a stairwell model with three convective differencing schemes, which include the upwind differencing scheme, the hybrid scheme and QUICK scheme. The Reynolds-averaged Navier-Stokes and energy equations are solved with a two-equation turbulence model. The Boussinesq approximation is used to model buoyancy terms in the governing equations. Three-dimensional predictions of the velocity and temperature fields are presented and are compared with experimental data. Three-dimensional simulations with each scheme have predicted the overall features of the flow fairly satisfactorily. A better agreement with experimental is achieved with QUICK scheme.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• Smart Structures and Systems
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• v.28 no.6
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• pp.811-825
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• 2021

High-voltage isolating switches play a paramount role in ensuring the safety of power supply systems. However, their exposure to outdoor environmental conditions may cause serious physical defects, which may result in great risk to power supply systems and society. Image processing-based methods have been used for anomaly detection. However, their accuracy is affected by numerous uncertainties due to manually extracted features, which makes the anomaly detection of isolating switches still challenging. In this paper, a vision-based anomaly detection method for isolating switches, which uses the rotational angle of the switch system for more accurate and direct anomaly detection with the help of deep learning (DL) and image processing methods (Single Shot Multibox Detector (SSD), improved frame differencing method, and Hough transform), is proposed. The SSD is a deep learning method for object classification and localization. In addition, an improved frame differencing method is introduced for better feature extraction and a hough transform method is adopted for rotational angle calculation. A number of experiments are conducted for anomaly detection of single and multiple switches using video frames. The results of the experiments demonstrate that the SSD outperforms the You-Only-Look-Once network. The effectiveness and robustness of the proposed method have been proven under various conditions, such as different illumination and camera locations using 96 videos from the experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.95-104
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• 2016

Subspace-based direction-of-arrival (DOA) estimation algorithms have a difficulty in dealing with coherent signals caused by multi-path environment. As one of attempts to solve this problem, a spatial differencing method is known to be useful for not only estimating DOAs of the coherent signals but also improving the number of resolvable wavefronts even more than the number of antenna elements. However, since the conventional spatial differencing method uses only the partial statistics of the observed data, this method suffers from the performance degradation in estimation accuracy caused by the residual correlation between the uncorrelated signals. To cope with this problem, in this paper, a generalized spatial differencing method is proposed. Unlike the conventional method, the proposed method utilizes the entire statistics of the received signals. Therefore, the additional performance enhancement in both estimation accuracy and the number of resolvable wavefronts can be achieved. The performance analyses with computer simulations show that the proposed method outperforms the conventional method in terms of the estimation accuracy and the number of resolvable wavefronts.

• Journal of the military operations research society of Korea
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• v.8 no.1
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• pp.99-107
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• 1982

This paper is concerned with the optimality of exponential smoothing applied to the general IMA process with different moving average and differencing orders. Numerical experiments were performed for IMA(m,n) process with various combinations of m and n, and the corresponding forecast errors were compared. Results show that the higher differencing order is more critical to the optimality of exponential smoothing, i.e., the IMA process with the higher moving average order, forecasted by exponential smoothing, has comparatively smaller forecast error. If the difference between the differencing order and the moving average order becomes larger, the accuracy of forecast by exponential smoothing declines gradually.