• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.180-185
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• 2000

In this paper, we designed 2303.15MHz Sequency synthesizer for the purpose of the phase noise prediction. For the modeling of phase noise Oersted in the designed system through inooducing the noise-modeling method suggested by Lascari we analyzied a variation of phase noise as according as that of offest frequency. Especially, for the third-order system of the PLL among some kinds of phase noise generated from VCO we analyzed the aspect of 1/f-noise appearing troubles in the low frequency band. Since it is difficult to analyze mathematically 1/f-noise in the third-order system of the PLL, introducing the concept of pseudo-damping factor has made an ease of the access of the 1/f-noise variance. we showed a numerical formula of 1/f-noise variance in the third-order system of the PLL which is compared with that of 1/f-noise variance in the second-order system of the PLL

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.653-660
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• 2001

In this paper, we designed 2303.15MHz frequency synthesizer for the purpose of the phase noise prediction. For the modeling of phase noise generated in the designed system through introducing the noise-modeling method suggested by Lascari we analyzed a variation of phase noise as according as that of offset frequency. Especially, for the third-order system of the PLL among some kinds of phase noise generated from VCO we analyzed the aspect of 1/f-noise appearing troubles in the low frequency band. Since it is difficult to analyze mathematically 1/f-noise in the third-order system of the PLL, introducing the concept of pseudo-damping factor has made an ease of the access of the 1/f-noise variance. we showed a numerical formula of 1/f-noise variance in the third-order system of the PLL which is compared with that of 1/f-noise variance in the second-order system of the PLL. As a result, In case of txco we found the reduce rapidly along the offset frequency after passed through that phase-noise was -160dBc/Hz before passed through a loop at 10kHz offset frequency and -162.6705dBc/kHz after passed through the loop, -180dBc/Hz at 100kHz offset frequency and -560dBc/kHz after passed through the loop. We can notice that the variance of third-order system more occurs (or the variance of second-order system in connection with noise bandwidth and variance factor of second-order and third-order system.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.33-45
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• 2016

Variance components should be estimated based on mean change when the mean of the observations drift gradually over time. Consistent estimators for the variance components are studied for a particular modeling situation with some underlying functions or drift. We propose a new variance estimator with Fourier estimation of variations. The consistency of the proposed estimator is proved asymptotically. The proposed procedures are studied and compared empirically with the variance estimators removing trends. The result shows that our variance estimator has a smaller mean square error and depends on drift patterns. We estimate and apply the variance to Nile River flow data and resting state fMRI data.

• The Mathematical Education
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• v.58 no.3
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• pp.443-458
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• 2019

Mathematical modeling has been a crucial topic in mathematics education as students' problem solving competency are regarded as a core skill for future society. Despite of the importance of mathematical modeling in school mathematics, there have been very limited studies relating pre-service teachers' knowledge and perceptions on mathematical modeling. In this vein, this study aimed to investigate pe-service mathematics teachers' perceptions on mathematical model, mathematical modeling and educational use of mathematical modeling, and their relationships. The current study utilized a survey consisted of 18 items. The responses of 210 pre-service mathematics teachers to the survey items were quantitatively analyzed using descriptive statistics, analysis of variance, exploratory and confirmatory factor analysis, the structural equation model, and multi group analysis. The results of analysis of variance revealed that pre-service teachers in difference groups (majors, grades, and experiences with mathematical modeling) showed statistically significant differences in mean values. Moreover, according to the results from the structural equation modeling analysis, pre-service mathematics teachers' perceptions on mathematical model and modeling affected their perceptions on educational use of mathematical modeling. In addition, depending on their pre-experiences with mathematical modeling, pre-service teachers represented a different relationship between perceptions on mathematical modeling and educational use of mathematical modeling. Implications for future studies and mathematics classrooms were discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.483-487
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• 1986

The LQG/LTR method is applied to the real servomechanism with the unknown modeling error and system noise variance Q$_{2}$. The equivalent discretized LQG controller is implemented on the 16-bit microcomputer and the experimental results show the improved stability and the satisfactory performance when the noise variance Q$_{2}$ is increased infinitly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• Journal of the Korean Mathematical Society
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• v.57 no.5
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• pp.1167-1186
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• 2020

This paper considers the case of pricing discretely-sampled variance swaps under the class of equity-interest rate hybridization. Our modeling framework consists of the equity which follows the dynamics of the Heston stochastic volatility model, and the stochastic interest rate is driven by the Cox-Ingersoll-Ross (CIR) process with full correlation structure imposed among the state variables. This full correlation structure possesses the limitation to have fully analytical pricing formula for hybrid models of variance swaps, due to the non-affinity property embedded in the model itself. We address this issue by obtaining an efficient semi-closed form pricing formula of variance swaps for an approximation of the hybrid model via the derivation of characteristic functions. Subsequently, we implement numerical experiments to evaluate the accuracy of our pricing formula. Our findings confirm that the impact of the correlation between the underlying and the interest rate is significant for pricing discretely-sampled variance swaps.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.788-794
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.575-589
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• 2010

Exponential L$\acute{e}$evy models have become popular in modeling price processes recently in mathematical finance. Although it is a relatively simple extension of the geometric Brownian motion, it makes the market incomplete so that the option price is not uniquely determined. As a trial to find an appropriate price for an option, we suppose a situation where a hedger wants to initially invest as little as possible, but wants to have the expected squared loss at the end not exceeding a certain constant. For this, we assume that the underlying price process follows a variance-gamma model and it converges to a geometric Brownian motion as its quadratic variation converges to a constant. In the limit, we use the mean-variance approach to find the asymptotic minimum investment with the expected squared loss bounded. Some numerical results are also provided.