• Journal of information and communication convergence engineering
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• 제10권1호
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• pp.45-52
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• 2012

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2011년도 제40회 동계학술대회 초록집
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• pp.14-14
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• 2011

Graphene is known to possess excellent thermal properties, including high thermal conductivity, that make it a prime candidate material for heat management in ultra large scale integrated circuits. For device applications, the key parameters are the thermal expansion coefficient and the thermal conductivity. There has been no reliable experimental determination on the thermal expansion coefficient of graphene whereas the estimates of the thermal conductivity vary widely. In this work, we estimate the thermal expansion coefficient of graphene on silicon dioxide by measuring the temperature dependence of the Raman spectrum. The shift of the Raman peaks due to heating or cooling results from both the intrinsic temperature dependence of the Raman spectrum of graphene and the strain on the graphene film due to the thermal expansion mismatch with silicon dioxide. By carefully comparing the experimental data against theoretical calculations, it is possible to determine the thermal expansion coefficient. The thermal conductivity is measured by estimating the thermal profile of a graphene film suspended over a circular hole of the substrate.

• Asian-Australasian Journal of Animal Sciences
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• 제31권10호
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• 대한수학회보
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• 제38권3호
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• pp.449-461
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• 2001

The purpose of this paper is to study several geometric properties for the new class $G_{\kappa}(\beta)$ including geometric interpretation, coefficient estimates, radius of convexity, distortion property and covering theorem.

• 대한수학회논문집
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• 제14권1호
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• pp.147-156
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• 1999

This paper deals with a class H(A,B,$\alpha$) of analytic functions and we obtain coefficient estimates and related results.

• Korean Journal of Mathematics
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• 제28권1호
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• pp.137-147
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• 2020

In this paper, we introduce two subclasses of analytic and Spiral-like functions and investigate convolution properties, the necessary and sufficient condition, coefficient estimates and inclusion properties for these classes.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.1-10
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• 1999

When the component proportions in mixture experiments are restricted by lower and upper bounds multicollinearity appears all too frequently. The ridge regression can be used to stabilize the coefficient estimates in the fitted model. I propose a graphical method for evaluating the mixture component effects of ridge regression estimator with respect to the prediction variance and the prediction bias.

• Communications for Statistical Applications and Methods
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• 제5권1호
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• pp.19-24
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• 1998

When multicollinearity appears in linear regression model, we can use ridge regression for stabilizing the regression coefficient estimates. We propose the screen diagram as a graphical method for detecting multicollinearity and estimating ridge constant in linear regression model.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.667-676
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• 1999

We investigate the effects of omission of initial observations in each individuals in the panel data regression model when the disturbances follow a serially correlated one way error components. We show that the first transformed observation can have a relative large hat matrix diagonal component and a large influence on parameter estimates when the correlation coefficient is large in absolute value.

• Asian-Australasian Journal of Animal Sciences
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• 제29권12호
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• pp.1756-1760
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• 2016

Estimation of feed intake (FI) for individual animals within a pen is needed in situations where more than one animal share a feeder during feeding trials. A partitioning method (PM) was previously published as a model to estimate the individual FI (IFI). Briefly, the IFI of a pig within the pen was calculated by partitioning IFI into IFI for maintenance ($IFI_m$) and IFI for growth. In the PM, $IFI_m$ is determined based on the metabolic body weight (BW), which is calculated using the coefficient of 106 and exponent of 0.75. Two simulation studies were conducted to test the hypothesis that the use of different coefficients and exponents for metabolic BW to calculate $IFI_m$ improves the accuracy of the estimates of IFI for pigs, and that PM is applied to pigs fed in group-housing systems. The accuracy of prediction represented by difference between actual and estimated IFI was compared using PM, ratio (RM), or averaging method (AM). In simulation studies 1 and 2, the PM estimated IFI better than the AM and RM during most of the periods (p<0.05). The use of 0.60 as the exponent and the coefficient of 197 to calculate metabolic BW did not improve the accuracy of the IFI estimates in both simulation studies 1 and 2. The results imply that the use of $197kcal{\times}kg\;BW^{0.60}$ as metabolizable energy for maintenance in PM does not improve the accuracy of IFI estimations compared with the use of $106kcal{\times}kg\;BW^{0.75}$ and that the PM estimates the IFI of pigs with greater accuracy compared with the averaging or ratio methods in group-housing systems.