• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.141-146
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• 2002

In this article we consider the problem of constructing confidence intervals for a linear regression model with nested error structure. A popular approach is the likelihood-based method employed by PROC MIXED of SAS. In this paper, we examine the ability of MIXED to produce confidence intervals that maintain the stated confidence coefficient. Our results suggest the intervals for the regression coefficients work well, but the intervals for the variance component associated with the primary level cannot be recommended. Accordingly, we propose alternative methods for constructing confidence intervals on the primary level variance component. Computer simulation is used to compare the proposed methods. A numerical example and SAS code are provided to demonstrate the methods.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2189-2196
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• 2015

Stereo matching algorithm is usually used to obtain a disparity map from a pair of images. However, the disparity map obtained by using stereo matching contains lots of noise and error regions. In this paper, we propose a virtual-view synthesis algorithm using disparity refinement in order to improve the quality of the synthesized image. First, the error region is detected by examining the consistency of the disparity maps. Then, motion information is acquired by applying optical flow to texture component of the image in order to improve the performance. Then, the occlusion region is found using optical flow on the texture component of the image in order to improve the performance of the optical flow. The refined disparity map is finally used for the synthesis of the virtual view image. The experimental results show that the proposed algorithm improves the quality of the generated virtual-view.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.589-595
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• 2010

In discriminant analysis, we consider an intraclass correlation pattern by principal component analysis. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider two procedures, i.e., the test and proportion procedures, for selecting the principal components in classifica-tion. We compare the regular classification method and the proposed two procedures. We consider two methods for estimating error rate, i.e., the leave-one-out method and the bootstrap method.

• Journal of the Korean Mathematical Society
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• v.57 no.2
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• pp.401-414
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• 2020

It is computationally expensive to compute principal components from scratch at every update or downdate when new data arrive and existing data are truncated from the data matrix frequently. To overcome this limitations, incremental principal component analysis is considered. Specifically, we present a sliding window based efficient incremental principal component computation from a covariance matrix which comprises of two procedures; simultaneous update and downdate of principal components, followed by the rank-one matrix update. Additionally we track the accurate decomposition error and the adaptive numerical rank. Experiments show that the proposed algorithm enables a faster execution speed and no-meaningful decomposition error differences compared to typical incremental principal component analysis algorithms, thereby maintaining a good approximation for the principal components.

• East Asian mathematical journal
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• v.5 no.1
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.12
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• pp.949-954
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• 2011

In this study, the characteristics and error ranges of the mechanical bonding strength were analyzed according to before and after thermal shock test for various chips of automotive application component using Sn-3.0Ag-0.5Cu solder. In the after thermal shock test, the mechanical bonding strengths tend to decrease, meanwhile decreasing rates of mechanical strengths were less then 12% at specimen's bonding area below 3.5$mm^2$, and were from 17 to 21% at specimen's bonding area above 12 $mm^2$. On the other hand, Specimen's mean deviation rates were about 5% at specimen's bonding area more than 12 $mm^2$. Inversely, at specimen's bonding area is less then 3.5 $mm^2$, mean deviation rates were increased to about 8%. It means that the smaller device size is, the larger mean deviation rate. In addition, error ranges and deviation rates of the mechanical bonding strengths may differ slightly depending on their bonding area. Furthermore, process conditions as well as method of mechanical reliability evaluation should be established to reduce the error ranges of bonding strength.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.24-32
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• 1996

A rigorous theory for modeling, analysis, optimum nonseparable filter bank in multidimensional M-band quantized subband codec are developed in this paper. Each pdf-optimized quantizer is modeled by a nonlinear gain-plus-additive uncorrelated noise and embedded into the subband structure. We then decompose the analysis/synthesis filter banks into their polyphase components and shift the down-and up-samplers to the right and left of the analysis/synthesis polyphase matrices respectively. Focusing on the slow clock rate signal between the samplers, we derive the exact expression for the output mean square quantization error by using spatial-invariant analysis. We show that this error can be represented by two uncorrelated components : a distortion component due to the quantizer gain, and a random noise component due to fictitious uncorrelated noise at the uantizer. This mean square error is then minimized subject to perfect reconstruction (PR) constraints and the total bit allocation for the entire filter bank. The algorithm gives filter coefficients and subband bit allocations. Numerical design example for the optimum nonseparable orthonormal filter bank is given with a quincunx subsampling lattice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1954-1971
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• 2017

In this work, first, a multiuser detection (MUD) algorithm based on component-level soft interference cancellation and linear minimum mean square error (CLSIC-LMMSE) is proposed, which can enhance the bit error ratio (BER) performance of the traditional SIC-LMMSE-based MUD by mitigating error propagation. Second, for non-binary low density parity check (NB-LDPC) coded high-order modulation systems, when the proposed algorithm is integrated with partial mapping, the receiver with iterative detection and decoding (IDD) achieves not only better BER performance but also significantly computational complexity reduction over the traditional SIC-LMMSE-based IDD scheme. Extrinsic information transfer chart (EXIT) analysis and numerical simulations are both used to support the conclusions.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.764-769
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• 1988

In this paper the failure possibility and the error possibility are used to represent reliability of a technical component and that of a human operator, respectively. The failure possibility and the error possibility are fuzzy sets on the interval [0,1]. In a man-machine system, reliability of the technical component and that of the human operator are usually affected by many factors, e.g., the environment in which a machine is operated, psychological stress of the human operator, etc. The possibility is derived from not only the failure or the error rate but also estimates of these factors. The fuzzy reasoning plays an important role in the derivation. The reliability analysis is performed by the use of the possibility obtained by the present method. Moreover this paper discusses the sensitivity analysis which evaluates what extent the change of the estimation of each factor has an influence on reliability of a man-machine system. The important factors to be ameliorated are shown through the sensitivity analysis.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1089-1092
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• 1999

Applications using 3D models are increasing recently. Since 3D polygonal models are structured by a triangular mesh, the coding of polygonal models in strips of triangles is an efficient way of representing the data. These strips may be very long, and may take a long time to render or transmit. If the triangle strips are partitioned, it may be possible to perform more efficient data transmission in an error-prone environment and to display the 3D model progressively. In this paper, we devised the Component Based Data Partitioning (CODAP) which is based on Topological Surgery (TS). In order to support the error resilience and the progressively build-up rendering, we partition the connectivity, geometry, and properties of a 3D polygonal model. Each partitioned component is independently encoded and resynchronization between partitioned components is done.