• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.264-264
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• 2010

물리증착이나 화학증착으로 제조되는 박막은 공정 조건에 따라 다양한 성장 양태를 보인다. 박막의 성장은 초기에 Seed가 형성되어 그 Seed를 바탕으로 성장하는 것으로 알려져 있으며 기판온도, 이온충돌, 박막의 두께 등에 따라 성장양태나 성장방위 등이 달라진다. 최근 나노에 대한 관심이 높아지면서 진공증착으로 제조한 박막에서도 조직의 나노화에 대한 관심이 높아지고 있으며 특히, Pore-free, Defect-free 박막의 형성을 통해 특성을 향상시키고자 하는 연구도 증가하고 있다. 본 연구에서는 Al과 Cu 같은 금속의 박막을 제조함에 있어서 공정변수가 박막의 조직이나 배향성 등에 미치는 영향을 조사하였다. 특히, 이러한 조직변화와 박막의 특성과의 상관성을 도출하고자 하였다. Al 박막에서는 이온빔의 효과와 함께 공정중에 산소 가스를 주입하거나 플라즈마 처리를 통해 성장조직의 변화를 유도하였고, Cu 박막에서는 고속 증착 조건이 피막의 조직에 미치는 영향을 조사하였다. 한편, TiN 박막의 형성에 미치는 이온빔의 효과를 조사하여 이온빔 조건과 TiN 박막의 형성과의 관계를 규명하였고 이로부터 Normalized Energy가 TiN 박막의 색상에 미치는 영향을 도출하여 Normalized Energy가 Fundamental Parameter가 될 수 있음을 확인하였다.

• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

• Journal of the Korean Geotechnical Society
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• v.15 no.3
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• pp.131-149
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• 1999

It was found that surface roughness has a first-order effect on the interface shear strength and accordingly it should be accurately quantified if its role is to be properly understood. To quantify the surface topography, first of all, a variety of commonly used surface roughness parameters and profiling methods were reviewed in this study. Based on this review, the normalized roughness parameter. $R_n$(Uesugi and Kishida, 1986), the profile roughness parameter, $R_L$, and the surface roughness parameter, $R_n$(Dove and Frost, 1996), were selected to be appropriate candidates of roughness parameters and the digital image analysis based Optical Profile Microscopy(OPM) method(Dove and Frost, 1996) to be an appropriate profiling method for this study. Using a smooth and three textured HDPE geomembranes which encompass the range of textures and texture patterns commonly used, a series of roughness measurements on virgin and previously used geomembranes were performed. The results showed that both $R_L\; and\; R_S$ values appropriately reflect the degree of texturing for the geomembranes used in this study, however, $R_n$ value showed limited ranges of variation which may not be sufficient to permit distinction between roughness values for certain conditions. The results of this study will be extended to the investigation of the influence of surface roughness on interface strength in future study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1078-1086
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• 2009

This paper presents a Fourier based algorithm for estimating the parameters of the low frequency oscillating modes. The proposed methods estimates various parameters(frequency, damping factor, mode magnitude, phase) by fitting Fourier spectrum and phase with a damped exponential cosine function. Dominant frequency is selected by taking frequency corresponding to the peak spectrum, and damping factor is estimated using the left/right spectra of Fourier spectrum. In addition, mode magnitude is calculated by the normalized peak spectrum, and phase is estimated from spectrum phase. Also, we introduce an accuracy index in order to determine the accuracy of the estimated parameters, and the index is calculated using the deviations of the peak spectrum and the left/right spectra. The parameter estimation methods proposed in this paper include very simple arithmetical processes, so the algorithms are simple and the calculation speed is very fast. The proposed methods are applied to test functions with two dominant modes. The results show that the proposed methods are highly applicable to low frequency parameter estimation.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.127-127
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• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• Journal of IKEEE
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• v.24 no.1
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• pp.260-268
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• 2020

This paper presents the high frequency resonant inverter of class D SEPP(Single-Ended Push Pull) type using LS-ZVS-LSTC, which can reduce the switching losses during the turn-on and turn-off switching time. The analysis of high frequency resonant inverter using LS-ZVS-LSTC(Low-loss Turn-off Snubber Capacitor) proposed in this paper is described in general by adopting the normalized parameters. The operating characteristics of the proposed high frequency resonant inverter were also evaluated by using the control parameters such as the normalized control frequency(μ), the normalized load time constant(τ), the coupling factor(κ) and so on. Based on the characteristic values through the characteristics of evaluation, an example of the design method of the 1.8[kW] class D SEPP type high frequency inverter is suggested, and the validity of the theoretical analysis is verified using the experimental data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.433-442
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• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.121-124
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• 2000

We introduce an advanced mountain clustering method which uses a normalized data space, a gaussian type mountain function and a deconstruction method using mountain slope. This is more useful than Yagers mountain method because it needs just one parameter to tune instead of three and finds out more resonable cluster centers. Computational examples are presented to show the validity of the advanced mountain method.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.156-161
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• 1993

Limiting distributions of Studentized test statistics have been shown for testing the slope parameter in a simple linear structural model. Since the limiting distribution of Studentized one appears to yield inaccurate inference, this paper suggests adjustment of critical value and normalization of the Studentized one. As results, we can have procedures for refined inference based on our approximate distrbution instead of the limiting distribution.