• 제어로봇시스템학회논문지
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• 제12권4호
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• pp.358-363
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• 2006

This paper demonstrates a new non-contact electronic joystick using single hall sensor which detects a horizontal vector of the magnetic field. Furthermore, in this paper, it is mathematically modeled that nonlinear characteristics between the output of hall sensor and the movement of joystick bar. The dynamic horizontal vector of magnetic flux is detected by the hall sensor while a permanent magnet is rotated with the joystick bar, which has two dimension detecting area. Using the nonlinear adjustment equations, the output signals of hall sensor have been linearized to give higher accuracy in the two dimension movement. Finally, through the real experiments, it is showed that the single hall sensor structure mechanism is superior to the dual sensor structure in sensing the two-dimensional motion without offset.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.533-541
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• 2017

Sufficient dimension reduction (SDR) replaces original p-dimensional predictors to a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) has the longest and most popular history of SDR methodologies. The critical weakness of SIR is its known sensitive to the numbers of slices. Recently, a fused sliced inverse regression is developed to overcome this deficit, which combines SIR kernel matrices constructed from various choices of the number of slices. In this paper, the fused sliced inverse regression and SIR are compared to show that the former has a practical advantage in survival regression over the latter. Numerical studies confirm this and real data example is presented.

• 한국멀티미디어학회논문지
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• 제12권6호
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• pp.794-803
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• 2009

In this paper, we will present a container auto-landing system, the system use the stereo camera to measure the container depth information. And the container region can be detected by using its hough line feature. In the line feature detection algorithm, we will detect the parallel lines and perpendicular lines which compose the rectangle region. Among all the candidate regions, we can select the region with the same aspect-ratio to the container. The region will be the detected container region. After having the object on both left and right images, we can estimate the distance from camera to object and container dimension. Then all the detect dimension information and depth inform will be applied to reconstruct the virtual environment of crane which will be introduce in this paper. Through the simulation result, we can know that, the container detection rate achieve to 97% with simple background. And the estimation algorithm can get a more accuracy result with a far distance than the near distance.

• 한국실내디자인학회논문집
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• 제19호
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• pp.30-37
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• 1999

This study presents a methods on the construction of integrated system for the purpose of automation of design plans, calculation of quantity of materials and estimation by abstracting information on building materials which is produced on the course of three dimension modeling by using computer. Therefore, an object oriented methodology is introduced to compose design informations in three dimension, space for unifying building informations, and expressing properties of building factors and materials, and to construct a database for computers to recognize architecture informations. An object indicates a conceptual individual existing in real world or existence of individual and necessity in composing a building could be called as objects such as column, wall, beam, slab, door and window and these contain materiality and immateriality. It is systemized to which properties of these building's objects are installed by the user of computer and by API(Application Programming Interface), chosen informations automatically converse to each unit work such as design plan structure plan, calculation of amount of materials, etc.

• 대한수학회보
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• 제42권4호
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• pp.777-787
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• 2005

In this paper we study (n + 1)-dimensional compact contact CR-submanifolds of (n - 1) contact CR-dimension immersed in an odd-dimensional unit sphere $S^{2m+1}$. Especially we provide necessary conditions in order for such a sub manifold to be the generalized Clifford surface $$S^{2n_1+1}(((2n_1+1)/(n+1))^{\frac{1}{2}})\;{\times}\;S^{2n_2+1}(((2n_2+1)/(n+1)^{\frac{1}{2}})$$ for some portion (n1, n2) of (n - 1)/2 in terms with sectional curvature.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.9-14
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• 2003

A Bayesian model-based clustering method is proposed for clustering objects on the basis of dissimilarites. This combines two basic ideas. The first is that tile objects have latent positions in a Euclidean space, and that the observed dissimilarities are measurements of the Euclidean distances with error. The second idea is that the latent positions are generated from a mixture of multivariate normal distributions, each one corresponding to a cluster. We estimate the resulting model in a Bayesian way using Markov chain Monte Carlo. The method carries out multidimensional scaling and model-based clustering simultaneously, and yields good object configurations and good clustering results with reasonable measures of clustering uncertainties. In the examples we studied, the clustering results based on low-dimensional configurations were almost as good as those based on high-dimensional ones. Thus tile method can be used as a tool for dimension reduction when clustering high-dimensional objects, which may be useful especially for visual inspection of clusters. We also propose a Bayesian criterion for choosing the dimension of the object configuration and the number of clusters simultaneously. This is easy to compute and works reasonably well in simulations and real examples.

• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.97-113
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• 2017

This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.312-317
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• 2015

The dynamic system approach in time series has been used in many real problems. Based on Taken's embedding theorem, we can build the predictive function where input is the time delay coordinates vector which consists of the lagged values of the observed series and output is the future values of the observed series. Although the time delay coordinates vector from multivariate time series brings more information than the one from univariate time series, it can exhibit statistical redundancy which disturbs the performance of the prediction function. We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction. Our experiment uses delayed Lorenz series; least squares support vector regression approximates the predictive function. The result shows that linearly preserving projection improves the prediction performance.

• 한국전기전자재료학회논문지
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• 제16권6호
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• pp.515-521
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• 2003

There are now many theoretical investigations and real manufactures for numerous applications of photonic crystals (PCs) associated with photonic band gap and photonic integrated circuits. However, there are some difficulties to design and fabricate the desired pattern quality. It is not easy to satisfy accurate critical dimension (CD) for patterns with arbitrary shapes and pitch sizes aligned in various directions. In this work, we report the optimum conditions to better fabricate and design, and greatly improve pattern quality in delineating two-dimensional (2D) PCs in the nanometer range using single- step e-beam lithography system with conventional exposure mode.