• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• Journal of Korean Society of Disaster and Security
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• v.13 no.2
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• pp.27-38
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• 2020

Climate variations have become worse and diversified recently, which caused catastrophic disasters for our communities and ecosystem including economic property damages in Korea. Heat wave of summer season is one of causes for such damages of which outbreak tends to increase recently. Related short-term forecasting information has been provided by the Korea Meteorological Administration based on results from numerical forecasting model. As the study area, the ◯◯ province was selected because of the highest mortality rate in Korea for the past 15 years (1998~2012). When comparing the forecasted temperatures with field measurements, it showed RMSE of 1.57℃ and RMSE of 1.96℃ was calculated when only comparing the data corresponding to the observed value of 33℃ or higher. The forecasting process would take at least about 3~4 hours to provide the 4 hours advanced forecasting information. Therefore, this study proposes a methodology for temperature prediction using LSTM considering the short prediction time and the adequate accuracy. As a result of 4 hour temperature prediction using this approach, RMSE of 1.71℃ was occurred. When comparing only the observed value of 33℃ or higher, RMSE of 1.39℃ was obtained. Even the numerical prediction model of the whole range of errors is relatively smaller, but the accuracy of prediction of the machine learning model is higher for above 33℃. In addition, it took an average of 9 minutes and 26 seconds to provide temperature information using this approach. It would be necessary to study for wider spatial range or different province with proper data set in near future.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.205-215
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• 2015

Common mode (CM) conducted interference are predicted and compared with experiments in a motor drive system of Electric vehicles in this study. The prediction model considers each part as an equivalent circuit model which is represented by lumped parameters and proposes the parameter extraction method. For the modeling of the inverter, a concentrated and equivalent method is used to process synthetically the CM interference source and the stray capacitance. For the parameter extraction in the power line model, a computation method that combines analytical method and finite element method is used. The modeling of the motor is based on measured date of the impedance and vector fitting technique. It is shown that the parasitic currents and interference voltage in the system can be simulated in the different parts of the prediction model in the conducted frequency range (150 kHz-30 MHz). Experiments have successfully confirmed that the approach is effective.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.124-127
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• 2006

In this paper, noise and vibration reduction of double-resiliently mounted pump-like machinery is studied. SBN(Structure-borne noise) reduction through upper and lower mount is analyzed by assuming that the system is modeled as a mass-spring system. In addition, the impedance of the floor is included in the prediction. The comparison of the SBN difference through upper mount show that the effect of impedance is negligible, while the measurement differs significantly from the prediction for high frequency range. It is found that the assumption of point mass-spring system leads to the disagreement between prediction and measurements.

• 유체기계공업학회:학술대회논문집
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• 1998.12a
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• pp.91-96
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• 1998

The broadband noise generated aerodynamically from a two-bladed axial flow fan has been measured and compared to the result of a self-noise prediction method. The prediction scheme is based on the experimental data set acquired from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections. For low blade loading case the comparison showed a reasonably good agreement, but as the loading becomes larger the empirical formula overpredict the sound pressure level at high frequency range. This is probably due to the use of stationary wing data for the prediction of rotating blade case, which will be quite different in their vortex strength at the blade tip.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.319-322
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• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• International Journal of Fluid Machinery and Systems
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• v.4 no.2
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• pp.243-254
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• 2011

In the present paper, we focus on the flow computation of a low head Propeller turbine at a wide range of design and off-design operating conditions. First, we will present the results on the efficiency hill chart prediction of the Propeller turbine and discuss the consequences of using non-homologous blade geometries for the CFD simulation. The flow characteristics of the entire turbine will be also investigated and compared with experimental data at different measurement planes. Two operating conditions are selected, the first one at the best efficiency point and the second one at part load condition. At the same time, for the same selected operating points, the numerical results for the entire turbine simulation will be compared with flow simulation with our standard stage calculation approach which includes only guide vane, runner and draft tube geometries.

• Journal of Mechanical Science and Technology
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• v.15 no.9
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• pp.1292-1301
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• 2001

The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more resonable empirical models are reeded.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.2 s.17
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• pp.88-99
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• 2004

A development of a software on radar target signature analysis is presented in this paper The target signature includes Radar Cross Section(RCS) prediction, Range Profile(RP) processing and Inverse Synthetic Aperture Radar(ISAR) processing. Physical Optics(PO) is the basic calculation method for RCS prediction and Geometrical Optics(GO) is used for ray tracing in the field calculation of multiple reflection. For RP and ISAR, Fast Fourier Transform(FFT) and Matrix Pencil(MP) method were implemented for post-processing. Those results are integrated into two separate softwares named as Radar Target Signature Generator(RTSG) and Radar Target Signature Analyser(RTSA). Several test results show good performances in radar signature prediction and analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.4
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• pp.410-418
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• 2004

The multiaxial fatigue test under in-phase and out-of$.$phase load were performed to study what degradation phenomenon affects fatigue life with virgin and 3600 hrs degraded materials. The various kind of fatigue data fur fatigue life prediction were acquired under pure axial and pure torsional load of fully reversal condition. The models which was investigated are: 1) the von Mises equivalent strain range, 2) the critical shear plane approach method of Fatemi-Socie(FS) parameter, 3) the modified Smith-Watson-Topper(SWT) parameter. The result showed that, fatigue life by material degradation are decreased and life prediction which was used the FS parameter is not conservative but the best result.