• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.27-40
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• 2022

In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model. The observation and the latent variable are the degraded image and the original image, respectively. The posterior distribution is built using the Markov random field and the Ising model. Estimating the posterior distribution is the same as reconstructing a degraded image. MCMC and variational Bayesian inference are two methods for estimating the posterior distribution. However, for the sake of computing efficiency, we adapt the variational technique. When the image is restored, the iterative method is used to solve the recursive problem. Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state. Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.134-141
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• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1241-1241
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• 2001

We propose a new calibration method, which uses the linearization method for spectral responses and the repetitive adoptions of the linearization weight matrices to construct a frature. Weight matrices are estimated through multiple linear regression (or principal component regression or partial least squares) with forward variable selection. The proposed method is applied to three data sets. The first is FTIR spectral data set for FeO content from sinter process and the second is NIR spectra from trans-alkylation process having two constituent variables. The third is NIR spectra of crude oil with three physical property variables. To see the calibration performance, we compare the new method with the PLS. It is found that the new method gives a little better performance than the PLS and the calibration result is stable in spite of the collinearity among each selected spectral responses. Furthermore, doing the repetitive adoptions of linearization matrices in the proposed methods, uninformative variables are disregarded. That is, the new methods include the effect of variables subset selection, simultaneously.

• Structural Engineering and Mechanics
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• v.47 no.3
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• pp.361-381
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• 2013

A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4E
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• pp.154-158
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• 2006

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.244-253
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• 2017

Reversible DNA watermarking is capable of continuous DNA storage and forgery prevention, and has the advantage of being able to analyze biological mutation processes by external watermarking by iterative process of concealment and restoration. In this paper, we propose a reversible DNA watermarking method based on histogram multiple shifting of noncoding DNA sequence that can prevent false start codon, maintain original sequence length, maintain high watermark capacity without biologic mutation. The proposed method transforms the non-coding region DNA sequence to the n-th code coefficients and embeds the multiple bits of the n-th code coefficients by the non-recursive histogram multiple shifting method. The multi-bit embedding process prevents the false start codon generation through comparison search between adjacent concealed nucleotide sequences. From the experimental results, it was confirmed that the proposed method has higher watermark capacity of 0.004-0.382 bpn than the conventional method and has higher watermark capacity than the additional data. Also, it was confirmed that false start codon was not generated unlike the conventional method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.12
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• pp.1195-1202
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• 1991

This paper describes a newly developed method of production simulation by using the Mixture of Cumulant Approximation (MOCA). In this method, the load is modelled as random variable (r.v.) which can be interpreted in terms of partitioning the load into various categories. We can consider the load shape of multi-modal characteristics. The number of load category and demarcation points of each load category are calculated automatically by using interpolation and least square method. Each generating unit of a supply system is modelled as r.v. of unit outage capacity according to the number of unit outage subset. Since the computation burden of each subset's moments increases exponentially as units are convolved to the system, we further derive the specific recursive formulae. In simulating the energy limited units, hydro unit simulation is performed using Energy Invariance Property and the simulation of pumped storage unit is modelled as compulsory and economic operations. The proposed MOCA method is applide to the test systems and the results are compared with those of cumulant and Booth Baleriaux method. It is verified that the MOCA method is considerably reliable and stable both pathological and well behaved system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.9
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• pp.1719-1729
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• 1994

In this paper, an adaptive CAC(Connection Admission Control) method is proposed. The adaptive CAC uses traffic estimates derived from both traffic parameter specified by user and cell flow measurements. Traffic estimation using user-specified parameters is performed at every moment of connection request or connection release by recursive formula which makes real-time calculation possible. Traffic estimation using cell flow measurement is carried out when the number of connected calls does not change during a measurement reflection period-renewal period. The most import ant thing for the traffic estimation using cell flow measurement is the determination of the length of a renewal period to trace a real traffic flow with an allowable time lag and the measurement reflection ratio(MRR) both to reduce the portion of overestimation and to avoid underestimation of real traffic flow. To solve these problems, the adaptive CAC updates renewal period and MRR adaptively according to the number of connections and the elapsed time after last connection or release respectively. Performance analysis for the proposed method is evaluated in several aspects for the cases of both homogeneous and heterogeneous bursty traffic. Numerical examples show the adaptive CAC method has the better performance compared with conventional CAC method based on burst model from the both utilization and QOS point of view.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Computational Structural Engineering
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• v.1 no.1
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• pp.87-94
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• 1988

The linerization method as one of the recursive quadratic programming method is applied for the optimal design of a large-scale structure supported by Pshenichny's proof of global convergence of the algorithm and convergence rate estimates. The linearization method transforms all constants of the design problem into an equivalent linearized constraint and employs the active-set strategy. This results in substantial computational savings by reducing the number of sate and adjoint to be solved at every design iteration. The illustrative example of plates with beams supported by columns is the typical one of a large-scale structure to give successful optimum solutions with satisfactory convergence criteria. Hopefully, the method may be applicable to all classes of optimization problems.