• Advances in Computational Design
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• v.4 no.1
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• pp.43-52
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• 2019

Variation Analysis (VA) is used to simulate final product variation, taking into consideration part manufacturing and assembly variations. In VA, all the manufacturing and assembly processes are defined at the product design stage. Process Capability Data Bases (PCDB) provide information about measured variation from previous products and processes and allow the designer to apply this to the new product. A new challenge to this traditional approach is posed by the Industry 4.0 (I4.0) revolution, where Smart Manufacturing (SM) is applied. The manufacturing intelligence and adaptability characteristics of SM make present PCDBs obsolete. Current tolerance analysis methods, which are made for discrete assembly products, are also challenged. This paper discusses the differences expected in future factories relevant to VA, and the approaches required to meet this challenge. Current processes are mapped using I4.0 philosophy and gaps are analysed for potential approaches for tolerance analysis tools. Matching points of simulation capability and I4.0 intents are identified as opportunities. Applying conditional variations, incorporating levels of adjustability, and the un-suitability of present Monte Carlo simulation due to changed mass production characteristics, are considered as major challenges. Opportunities including predicting residual stresses in the final product and linking them to product deterioration, calculating non-dimensional performances and extending simulations for process manufactured products, such as drugs, food products etc. are additional winning aspects for next generation VA tools.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.1-11
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• 2010

The conditional covering problem is an important variation of well studied set covering problem. In the set covering problem, the problem is to find a minimum cardinality vertex set which will cover all the given demand points. The conditional covering problem asks to find a minimum cardinality vertex set that will cover not only the given demand points but also one another. This problem is NP-complete for general graphs. In this paper, we present an efficient algorithm to solve the conditional covering problem on interval graphs with n vertices which runs in O(n)time.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.71-81
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• 2011

In this paper, we systematically analyzed the variation of KOSPI returns using a GARCH-ARJI(auto regressive jump intensity) model. This model is possibly to capture time varying volatility as well as time varying conditional jump intensity. Thus, we can decompose return volatility into usual variation explained by the GARCH model and unusual variation that resulted from external news or shocks. We found that the jump intensity implied on KOSPI return series clearly shows time varying. We also found that conditional volatility due to jump is generally smaller than that resulted from usual variation. We also analyzed the effect of 9.11 and the 2008 financial crisis on the volatility of KOSPI returns and conclude that there is strong and persistent impact on the KOSPI from the 2008 financial crisis.

• Journal of the Korean Mathematical Society
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• v.44 no.4
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• pp.1025-1050
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• 2007

Let $X_k(x)=({\int}^T_o{\alpha}_1(s)dx(s),...,{\int}^T_o{\alpha}_k(s)dx(s))\;and\;X_{\tau}(x)=(x(t_1),...,x(t_k))$ on the classical Wiener space, where ${{\alpha}_1,...,{\alpha}_k}$ is an orthonormal subset of $L_2$ [0, T] and ${\tau}:0 is a partition of [0, T]. In this paper, we establish a change of scale formula for conditional Wiener integrals $E[G_{\gamma}|X_k]$ of functions on classical Wiener space having the form $$G_{\gamma}(x)=F(x){\Psi}({\int}^T_ov_1(s)dx(s),...,{\int}^T_o\;v_{\gamma}(s)dx(s))$$, for $F{\in}S\;and\;{\Psi}={\psi}+{\phi}({\psi}{\in}L_p(\mathbb{R}^{\gamma}),\;{\phi}{\in}\hat{M}(\mathbb{R}^{\gamma}))$, which need not be bounded or continuous. Here S is a Banach algebra on classical Wiener space and $\hat{M}(\mathbb{R}^{\gamma})$ is the space of Fourier transforms of measures of bounded variation over $\mathbb{R}^{\gamma}$. As results of the formula, we derive a change of scale formula for the conditional Wiener integrals $E[G_{\gamma}|X_{\tau}]\;and\;E[F|X_{\tau}]$. Finally, we show that the analytic Feynman integral of F can be expressed as a limit of a change of scale transformation of the conditional Wiener integral of F using an inversion formula which changes the conditional Wiener integral of F to an ordinary Wiener integral of F, and then we obtain another type of change of scale formula for Wiener integrals of F.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.345-351
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• 1997

In this paper, controller for PWM converter considering unsymetrical input voltage is designed and current controller using LQ controller with conditional integrator is proposed. And the proposed current controller is compared with other current controller-predictive controller, decoupling PI controller. As simulation results, LQ controller with Conditional Integrator shows the improved performance for DC link voltage regulation through transient test of load variation. And when unsymeritrical input voltage is applied to converter with conventional current controller considering only symetrical input voltage, input current is distorted but it is showed that current controller considering unsymetrical input has robust control characteristics under phase voltage unbalance.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1531-1548
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• 2016

Let C[0, t] denote the space of real-valued continuous functions on [0, t] and define a random vector $Z_n:C[0,t]{\rightarrow}\mathbb{R}^n$ by $Z_n(x)=(\int_{0}^{t_1}h(s)dx(s),{\ldots},\int_{0}^{t_n}h(s)dx(s))$, where 0 < $t_1$ < ${\cdots}$ < $ t_n=t$ is a partition of [0, t] and $h{\in}L_2[0,t]$ with $h{\neq}0$ a.e. Using a simple formula for a conditional expectation on C[0, t] with $Z_n$, we evaluate a generalized analytic conditional Wiener integral of the function $G_r(x)=F(x){\Psi}(\int_{0}^{t}v_1(s)dx(s),{\ldots},\int_{0}^{t}v_r(s)dx(s))$ for F in a Banach algebra and for ${\Psi}=f+{\phi}$ which need not be bounded or continuous, where $f{\in}L_p(\mathbb{R}^r)(1{\leq}p{\leq}{\infty})$, {$v_1,{\ldots},v_r$} is an orthonormal subset of $L_2[0,t]$ and ${\phi}$ is the Fourier transform of a measure of bounded variation over $\mathbb{R}^r$. Finally we establish various change of scale transformations for the generalized analytic conditional Wiener integrals of $G_r$ with the conditioning function $Z_n$.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Economic and Environmental Geology
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• v.30 no.5
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• pp.493-498
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• 1997

The purpose of this study is to develop geostatistical model for evaluating the abundance of deep-sea manganese nodule. The abundance data used in this study were obtained from the KODOS (Korea Deep Ocean Study) area. The variation of nodule abundance was very high within short distance, while sampling methods was very limited. As the distribution of nodule abundance showed non-gaussian, indicator simulation method was used instead of conditional simulation method and/or ordinary kriging. The abundance data were encoded into a series of indicators with 6 cutoff values. They were used to estimate the conditional probability distribution function (cpdf) of the nodule abundance at any unsampled location. The standardized indicator variogram models were obtained according to variogram analysis. This SIS method had the advantage over other traditional techniques such as the turning bands method and ordinary kriging. The estimating values by indicator conditional simulation near high abundance area were more detailed than by ordinary kriging and indicator kriging. They also showed better spatial characteristics of distribution of nodule abundance.

• Journal of the Korean Society of Combustion
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• v.14 no.3
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• pp.29-36
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• 2009

Simulation is performed to analyze the characteristics of turbulent spray combustion in a diesel engine condition. An extended Conditional Moment Closure (CMC) model is employed to resolve coupling between chemistry and turbulence. Relevant time and length scales and dimensionless numbers are estimated at the tip and the mid spray region during spray development and combustion. The liquid volume fractions are small enough to support validity of droplets assumed as point sources in two-phase flow. The mean scalar dissipation rates (SDR) are lower than the extinction limit to show flame stability throughout the combustion period. The Kolmogorov scales remain relatively constant, while the integral scales increase with decay of turbulence. The chemical time scale decreases abruptly to a small value as ignition occurs with subsequent heat release. The Da and Ka show opposite trends due to variation in the chemical time scale. More work is in progress to identify the spray combustion regimes.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.673-683
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• 2012

Cabral et al. (2012) defined a mixture model of multivariate skew t-distributions(STMM), and proposed the use of an ECME algorithm (a variation of a standard EM algorithm) to fit the model. Their estimation by the ECME algorithm is closely related to the estimation of the degree of freedoms in the STMM. With the ECME, their purpose is to escape from the calculation of a conditional expectation that is not provided by a closed form; however, their estimates are quite unstable during the procedure of the ECME algorithm. In this paper, we provide a conditional expectation as a closed form so that it can be easily calculated; in addition, we propose to use the ECM algorithm in order to stably fit the STMM.