• Journal of Magnetics
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• v.19 no.4
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• pp.385-392
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• 2014

Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.407-417
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• 1995

The h-plot is a graphical technique for displaying the structure of one population's variance-covariance matrix. This follows the mathematical algorithem of the principle component biplot based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, since the mathematical algorithm of the h-plot is equivalent to that of principal component biplot of Choi and Huh (1994), we derive the resistant h-plot.

• Environmental Engineering Research
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• v.22 no.2
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• pp.193-202
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• 2017

The study estimated the relationship between carbon dioxide, crop and livestock production index in Ghana: Estimating the long-run elasticities and variance decomposition by employing a time series data spanning from 1960-2013 using both fit regression and ARDL models. There was evidence of a long-run equilibrium relationship between carbon dioxide emissions, crop production index and livestock production index. Evidence from the study shows that a 1% increase in crop production index will increase carbon dioxide emissions by 0.52%, while a 1% increase in livestock production index will increase carbon dioxide emissions by 0.81% in the long-run. There was evidence of a bidirectional causality between a crop production index and carbon dioxide emissions and a unidirectional causality exists from livestock production index to carbon dioxide emissions. Evidence from the variance decomposition shows that 37% of future fluctuations in carbon dioxide emissions are due to shocks in the crop production index while 18% of future fluctuations in carbon dioxide emissions are due to shocks in the livestock production index. Efforts towards reducing pre-production, production, transportation, processing and post-harvest losses are essential to reducing food wastage which affects Ghana's carbon footprint.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2307-2310
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• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.77-85
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• 2015

In this paper, we present a new method that can detect forged color region using color pattern decomposition and hypothesis testing approach. On the basis of the fact that the variance of the interpolated pixel is smaller than that of the original pixel, we use a statistical test method to judge the statistical inconsistency of variance. For this, we calculate the variance adopting a color pattern decomposition according to the demosaicking pattern. In addition, we apply high-pass filtering to enlarge the difference between the variances of original and interpolated pixel. Through experimental simulations, we can see that our proposed method can effectively detect forged color regions and shows superior detection performance compared to the conventional method.

• Journal of Korean Society for Quality Management
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• v.41 no.2
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• pp.233-248
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• 2013

Purpose: This study develops heuristic process capability indices (PCIs) using distribution-decomposition methods and evaluates the performances. The heuristic methods decompose the variation of a quality characteristic into upper and lower deviations and adjust the value of the PCIs using decomposed deviations in accordance with the skewness. The weighted variance(WV), new WV(NWV), scaled WV(SWV), and weighted standard deviation(WSD) methods are considered. Methods: The performances of the heuristic PCIs are investigated under the varied situations such as various skewed distributions, sample sizes, and specifications. Results: WV PCI is the best under the normal populations, WSD and SWV PCIs are the best under the low skewed populations, NWV PCI is the best under the moderate and high skewed populations. Conclusion: Comprehensive analysis shows that the NWV method is most adequate for a practical use.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.35-48
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• 2015

We analyze the systemic risk based on the information flows using the variance decomposition, DebtRank methods, and the Industry Sector Indices during 2001. 01 to 2015. 08. Using the KOSPI stock market as our setting, we find that (i) the systemic risk calculated by information flows of variance decompositions method shows strong positive relations with the market volatility, (ii) the magnitude of systemic risk measured from the information flows network by DebtRank method increases after the subprime financial crisis.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Journal of ILASS-Korea
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• v.25 no.3
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• pp.132-138
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• 2020

N₂O is hazardous atmosphere pollution matter which can damage the ozone layer and cause green house effect. There are many other nitrogen oxide emission control but N₂O has no its particular method. Preventing further environmental pollution and global warming, it is essential to control N₂O emission from industrial machines. In this study, the thermal decomposition experiment of N₂O gas mixture is conducted by using cylindrical reactor to figure out N₂O reduction and NO formation. And CHEMKIN calculation is conducted to figure out reaction rate and mechanism. Residence time of the N₂O gas in the reactor is set as experimental variable to imitate real SNCR system. As a result, most of the nitrogen components are converted into N2. Reaction rate of the N₂O gas decreases with N₂O emitted concentration. At 800℃ and 900℃, N₂O reduction variance and NO concentration are increased with residence time and temperature. However, at 1000℃, N₂O reduction variance and NO concentration are deceased in 40s due to forward reaction rate diminished and reverse reaction rate appeared.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.1
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• pp.11-23
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• 2003

This study Investigates the short and long-run dynamic relationships between the domestic and U.S. stock markets for the period of declining stock prices. It Is well known that the domestic stock market variations are largely caused by the U.S. stock market movements. Multivariate causal tty test Is utilized to examine the lead-lag relationships among four stock prices of KOSPI and KOSDAQ In the domestic part and DOWJONES and NASDAQ In the U.S. part. When the stock prices tend to decrease In the long run, It Is found that both KOSPI and KOSDAQ have closer relations with NASDAQ than DOWJONES. When both of domestic stock markets are severely fluctuate, bidirectional causal relationships appear to exist between NASDAQ and each of KOSPI and KOSDAQ. On the other hand. when the domestic stock markets are relatively stable, unidirectional causality Is found to exist between NASDAQ and each of KOSPI and KOSDAQ. which is explicitly validated by the analysis of variance decomposition.