• The Journal of the Acoustical Society of Korea
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• v.28 no.2E
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• pp.35-42
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• 2009

In this paper, we present criteria of objective measurement of speech quality to provide the mobile-VoIP services efficiently over wireless mobile internet. The mobile-VoIP service, which is based on mobility and is error-prone compared to conventional VoIP over wired network, is about to be launched, but there have not been adequate quality indexes and the Quality of Service (QoS) standards for evaluating speech quality of Mobile-VoIP. In addition, there are many factors influencing on the speech quality in packet network of which packet loss contribute directly to the overall voice communication quality. For this reason, we adopt the Gilbert-Elliot Channel Model for modeling packet network based on IP and assess the voice quality through the objective speech method of ITU-T P. 862 PESQ and ITU-T P. 862.1 MOS-LQO under various packet loss rates in the transmission channel environments. Our simulation results address the specific criteria and QoS for the mobile-VoIP services in terms of the various packet loss environments.

• Geomechanics and Engineering
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• v.9 no.1
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• pp.115-124
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• 2015

Determination of mobilized shear strength parameters (that identify stresses on the failure plane) is required for analyzing the stability by limit equilibrium method. Generalized Hoek-Brown (GHB) and Mohr-Coulomb (MC) failure criteria are usually used for obtaining stresses on the plane of failure. In the present paper, the applicability of these criteria for determining the stresses on failure plane is investigated. The comparison is based on stresses on the real failure plane which are obtained from the Mohr stress circle. To do so, 18 sets of data (consist of principal stresses and angle of failure plane) presented in the literature are used. In addition, the values account for (VAF) and the root mean square error (RMSE) indices were calculated to check the determination performance of the obtained results. Values of VAF and RMSE for the normal stresses on the failure plane evaluated from MC are 49% and 31.5 where for GHB are 55% and 30.5, respectively. Also, for the shear stresses on failure plane, they are 74% and 36 for MC, 76% and 34.5 for GHB. Results show that the obtained stresses and angles of failure plane for each criterion differ from real ones, but GHB results are closer to the empirical results. Also, it is inferred that results are affected by the failure envelope not real failure plane. Therefore, obtained shear strength parameters are not mobilized. Finally, a multivariable regressed relation is presented for determining the stresses on the failure plane.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.133-141
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• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.671-675
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• 2011

DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based matching criteria are evaluated and the proposed model can be effectively used for estimating the MSE noise.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.253-260
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• 1997

Various algorithms are investigated with respect to finding the best model-based samples according to criteria such as D-optimality and minimum mean square error. These two criteria are slightly different, but related to each other. Therefore, it is not surprising that these two are producing the almost identical samples. Some simple examples follow and critiques are provided along with directions for further research.

• Journal of Environmental Science International
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• v.12 no.7
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• pp.809-816
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• 2003

The short-term version of Industrial Source Complex Model(ISCST3) was evaluated for estimating short-term concentrations using criteria pollutant(SO$_2$, NO$_2$, CO, PM10) data from emission inventory of Young Tong area in Suwon for the year 2002. The contribution of pollutant concentration from point, line, area sources was found 21.8, 76.5 and 1.6%. Statistical parameters, such as correlation coefficient, index of agreement(IA), normalized mean square error(NMSE) and fractional bias(FB) were calculated for each pollutants. The model performance were found good for PM10(82%) and NO$_2$(69%), but poor for SO$_2$(34%) and CO(13%).

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.273-286
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• 2017

We develop an inspection method for bearing faults with a rapid change in the rotation speed and present indexes for the pass/fail inspection. At the end of line, impulse noises generated by the operation of machines and conveyors may distort the inspection results. In this paper, we present robust inspection indexes for bearing faults under impulse noises, by taking into account fault signals having pulse train. Using logistic regression, we optimize the pass/fail criterion for each index and evaluate the performance of the inspection indexes based on the total error rate.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.933-935
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• 1998

The HMM-Net is an architecture for a neural network that implements a hidden markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria used for learning HMM-Net classifiers are maximum likelihood(ML) and minimization of mean squared error(MMSE). In this paper we propose an efficient learning method of HMM_Net classifiers using a ML-MMSE hybrid criterion and report the results of an experimental study comparing the performance of HMM_Net classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numeric digits from /0/ to /9/ show that the performance of the proposed method is better than the others in the repects of learning and recognition rates.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1E
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• pp.8-13
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• 2006

In this paper, an Optimized Ambisonic Panning Algorithm (OAPA) which reduces sound localization error, is proposed. In the conventional Ambisonic Panning Algorithm (APA), sound localization is usually different from the panning angle, especially when listeners are not in an ideal listening position, because of low signal separation among other channels. To overcome this problem, an OAPA using window functions is proposed. A proper window function can be verified, comprising of higher harmonic components than 2M+1 and improved DPC and channel separation. Analysis results demonstrate that the proposed method results in higher signal separation among other channels and lower sound localization errors than the conventional APA.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.61-67
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• 1997

The HMM-Net is an architecture for a neural network that implements a hidden markov model(HMM). The architecture is developed for the purpose of combining the classification power of neural networks with the time-domain modeling capability of HMMs. Criteria which are used for learning HMM_Net classifiers are maximum likelihood(ML), maximum mutual information (MMI), and minimization of mean squared error(MMSE). In this classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numbers from /young/to/koo/ show that in the binary inputs the performance of MMSE is better than the others, while in the fuzzy inputs the performance of MMI is better than the others.