• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.139-144
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• 2011

This paper presents a boundary element application to determine the optimal impressed current densities at defect position on the pipe line. In this protection paint, enough current must be impressed to lower the potential distribution on the metal surface to the critical values. The optimal impressed current densities are determined in order to minimize the power supply for protection. This inverse problem was formulated by employing the boundary element method. Since the system of linear equations obtained was ill-conditioned, including singular value decomposition, conjugate gradient method were applied and the accuracies of these estimation. Several numerical examples are presented to demonstrate the practical applicability of the proposed method.

• Journal of Ocean Engineering and Technology
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• v.20 no.6 s.73
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• pp.24-34
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• 2006

Hydrodynamic coefficients strongly affect the dynamic performance of an AUV. Thus, it is important to know the true values of these coefficients, in order to accurately simulate the AUV's dynamic performance. Although these coefficients are generally obtained experimentally, such as through the PMM test, the measured values are not completely reliable because of experimental difficulties and errors. Another approach, by which these coefficients can be obtained, is the observer method, in which a model-based estimation algorithm estimates the coefficients. In this paper, the hydrodynamic coefficients are estimated using two nonlinear observers: a sliding mode observer and an extended Kalman filter. Their performances are evaluated in Matlab simulations, by comparing the estimated coefficients obtained from the two observer methods, with the experimental values as determined from the PMM test. A sliding mode controller is constructed for the diving and steering maneuver by using the estimated coefficients. It is demonstrated that the controller, applied with the estimated values, maintains the desired depth and path with sufficient accuracy.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.653-658
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• 2012

Load modeling has a significant influence on power system analysis and control. In recent years, measurement-based load modeling has been widely practiced. In the load modeling algorithm, the model structure is determined and the parameters of the established model are estimated. For parameter estimation, least-squares optimization method is applied. The model parameters are estimated so that the error between the measured values and the predicted values is to be minimized. By introducing sliding window concept, on-line load modeling method can be performed which reflects the dynamic behaviors of loads in real-time. For the purpose of data acquisition, the measurement system including PMU is implemented in university level. In this paper, case studies are performed using real PMU data from Korea Univ. and Seoul National University of Science and Technology. The performances of modeling real and reactive power behaviors using exponential and ZIP load model are evaluated.

• Journal of Environmental Science International
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• v.23 no.4
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• pp.605-613
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• 2014

In this work, the delay time for groundwater recharge was estimated by comparing simulated recharges by means of SWAT(Soil and Water Assessment Tool) model and WTF(Water Table Fluctuation) method. The delay time for groundwater recharge means that the time when the water from rainfall travelled through vadose zone just after getting out of soil zone bottom. As measuring this delay time is almost impossible, we used to compare the estimated values from modeling(SWAT) and analytic method(WTF). The test site is Hancheon watershed which has 8 groundwater measurement stations. The results show that the altitude has a linear relationship with the estimated delay time values. To validate these results, we conducted corelation analysis between transformed groundwater levels and observed ones. The results showed that computed groundwater levels have good corelation($R^2$=0.97, 0.87, respectively). The estimated delay time would be used for the groundwater behaviour characteristics in vadose zone. As recharge rates vary according to the height, the delay time is thought to be an import variable for the proper groundwater recharge estimation.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.3
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• pp.223-236
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• 2016

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

• Journal of Industrial Technology
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• v.44 no.1
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• pp.1-7
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• 2024

Since 2015, the PM_2.5 measurement data has been publicly available nationwide in South Korea, but its use is restricted to after 2015, unlike other air pollutants. To overcome this limitation, multiple linear regression and artificial neural network models were developed to predict the daily average PM_2.5 values in South Korea before 2015. The daily data of air pollution measurement(SO₂, CO, O₃, NO₂, PM₁₀) and meteorological observation data (temperature, humidity, wind speed, atmospheric pressure, precipitation, snowfall) were used as input variables to develop regional prediction models for five regions(Seoul, Incheon, Gwangju, Daejeon, Ulsan) and a national prediction model. The models were developed and validated using the air pollution measurement data after 2015, and applied to predict PM_2.5 values before 2015. The multiple linear regression model showed R² values of 0.80 nationwide, 0.73 in Seoul, and 0.67 in Incheon, which enabled estimation of daily average PM_2.5 values before 2015. The artificial neural network model showed good prediction power with R2 values of 0.79 in Gwangju, 0.81 in Daejeon, and 0.72 in Ulsan. The regional prediction models showed good prediction power in most regions, and both the multiple linear regression and artificial neural network models showed good prediction power.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.451-459
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• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.237-244
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• 2007

Estimation of target position is one of the main functions of surveillance UAVs, and is being used to various purposes but generally noisy target position is estimated due to the existence of random measurement errors. In this report, a method of diminishing target position estimation error by calculating target position using Kalman Filtered optimum values such as position, attitude of UAV and sight vector of optical instrument, is proposed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.46 no.2
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• pp.157-163
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• 2010

The indirect target strength (TS) estimation method which uses the Kirchhoff ray mode model (KRM model) was discussed to apply for a biomass estimation in the water of mixed species. TS of 25 live scorpion fishes for 120kHz were measured by a tethered method and of others dominant 5 species in the marine ranching ground of Jeju coastal water including a scorpion fish were also estimated by KRM model. The measurement TS of scorpion fish well agreed with the theoretical values and the standard formula of scorpion fish was estimated as $TS_{120kHz}=20Log\;(L)-72.9$ ($r^2=0.67$). TScm values estimated on trial to each sample of dominant 5 species were from -69.3dB to -75.1dB at 120kHz and they were in the general range of swimbladdered fish. It was clarified that TS by KRM model can be used to estimate fish biomass estimation by increasing a sample number and is more effective under the condition that there is rare TS information for inhabiting species in mixed-species area.