• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.273-285
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• 2007

Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.308-318
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• 2022

Noise is a serious issue. While sending images via electronic communication, Impulse noise, which is created by unsteady voltage, is one of the most common noises in digital communication. During the acquisition process, pictures were collected. It is possible to obtain accurate diagnosis images by removing these noises without affecting the edges and tiny features. The New Average High Noise Density Median Filter. (HNDMF) was proposed in this paper, and it operates in two steps for each pixel. Filter can decide whether the test pixels is degraded by SPN. In the first stage, a detector identifies corrupted pixels, in the second stage, an algorithm replaced by noise free processed pixel, the New average suggested Filter produced for this window. The paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. In this paper the comparison of known image denoising is discussed and a new decision based weighted median filter used to remove impulse noise. Using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Method (SSIM) metrics, the paper examines the performance of Gaussian Filter (GF), Adaptive Median Filter (AMF), and PHDNF. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the HNDMF model has reached to a better performance with the maximum picture quality. images affected by various amounts of pretend salt and paper noise, as well as speckle noise, are calculated and provided as experimental results. According to quality metrics, the HNDMF Method produces a superior result than the existing filter method. Accurately detect and replace salt and pepper noise pixel values with mean and median value in images. The proposed method is to improve the median filter with a significant change.

• Journal of the Korean Magnetics Society
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• v.3 no.3
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• pp.179-184
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• 1993

The X-ray diffraction pattern of amorphous $Fe_{78}Si_{9}B_{13}$ alloy was analyzed to obtain the radial distribution function (RDF) where the first peak was in the form of Gaussian function. The calculated coordination number of the form of Gaussian functiono The calculated coordination number of the sample is 13.5, the mean distance betweeon near-neighbor atoms $r_{0}$ is $2.595{\AA}$ and a Gaussian parametet ${\delta}r$ indicating near-neighbor atomic distri-bution is $0.27{\AA}$. The temperature dependence of saturated magnetization at low temperature could be explained by spin wave excitations theory yielding the spin wave stiffness constant as $117.8\;meV\;{\AA}^2$. Also, we tried to fit the observed temperature dependence of saturated magnetization with the Handrich's equation of the modified molecular field theory for the amorphous ferromagnet. Nice fittings are obtained when we used the parameters ${\Delta}=0.32$(S=1/2) and ${\Delta}=0.23$(S=1), respectively. Finally, the calculated spin wave stiffness constant using the parameters and the structural data are $149\;meV\;{\AA}^2$ for S=1/2 and $138\;meV\;{\AA}^2$ for S=1, respectively. The mean exchange coupling integral between near-neighbor atoms was estimated to be 17.9 meV for S=1/2 and 6.7 meV for S=1.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.3
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• pp.37-45
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• 1982

This paper is a sequel of the Part I paper[1] on the modified digital Costas loop. In this Part II we analyze the performance of the system in the presence of noise. It is shown that, when the input signal is corrupted by additive white Gaussian noise, the noise process in the loop becomes Rician as a result of the tan-1 (.) function of the phase error detector. Steady state probability density functions of phase errors of the first-and second-order loops have been obtained by solving the Chapman-Kolmogorov equation numerically. Also, the mean and variance of phase error in the steady state have been obtained analytically, and are compared with the results obtained by computer simulation.

• Journal of The Korean Astronomical Society
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• v.47 no.6
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• pp.255-258
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• 2014

Using sunspot number data from 270 historical stations for the period 1981-2013, we investigate their personal reduction coefficients (k) statistically. Chang & Oh (2012) perform a simulation showing that the k varies with the solar cycle. We try to verify their results using observational data. For this, a weighted mean and weighted standard deviation of monthly sunspot number are used to estimate the error from observed data. We find that the observed error (noise) is much smaller than that used in the simulation. Thus no distinct k-variation with the solar cycle is observed contrary to the simulation. In addition, the probability distribution of k is determined to be non-Gaussian with a fat-tail on the right side. This result implies that the relative sunspot number after 1981 might be overestimated since the mean value of k is less than that of the Gaussian distribution.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.48-53
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• 1999

This paper presents an analytic derivation of the erroneous effect when the sliding-DFT is implemented in a recursive way with the finite-bit approximation of the twiddle factors. The analysis result is obtained in a closed form equation of the noise-to-signal power ratio(NSR) employing the zero-mean white Gaussian signal as the target input of the DFT. The parameters of the wordlength used in representing the twiddle factors and the blocklength of the DFT appear in the NSR explicitly as its function variables. The derivation is based on the error dynamic equation which is derived from the recursive SDFT, and on the analytic exploration of the statistical characteristics of the approximation coefficients treating them as random variables of having spatial distributions. The analytically derived results are verified through the comparison with the data actually measured from the computer simulation experiment.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1227-1232
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• 2013

Recently, the expectation of quality about images over the increasing demand of digital devices is increasing with the development of the technology of the digital. But the images are degraded by a variety of causes, and the main reason is the noises. Therefore, the necessity of denoising comes to the fore, and the research for denoising is progressing dynamically. The images are mainly degraded by AWGN(additive white Gaussian noise), and the characteristics of denoising of existing methods such as mean filter are insufficient. In this paper, an algorithm combined by the spatial weighted filter and the modified adaptive weighted filter is proposed in order to effectively remove the AWGN. In the simulation result, the proposed algorithm showed excellent denoising capabilities.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.9-23
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• 2021

In the following paper we introduce a variational Bayes method that approximates posterior distributions with mean-field method. In particular, we introduce automatic differentiation variation inference (ADVI), which approximates joint posterior distributions using the product of Gaussian distributions after transforming parameters into real coordinate space, and then apply it to pharmacokinetic models that are models for the study of the time course of drug absorption, distribution, metabolism and excretion. We analyze real data sets using ADVI and compare the results with those based on Markov chain Monte Carlo. We implement the algorithms using Stan.