• Structural Engineering and Mechanics
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• v.54 no.6
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• pp.1153-1174
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• 2015

Deployable structures have gained more and more applications in space and civil structures, while it takes a large amount of computational resources to analyze this kind of multibody systems using common analysis methods. This paper presents a new approach for dynamic analysis of multibody systems consisting of both rigid bars and arbitrarily shaped rigid bodies. The bars and rigid bodies are connected through their nodes by ideal pin joints, which are usually fundamental components of deployable structures. Utilizing the Moore-Penrose generalized inverse matrix, equations of motion and constraint equations of the bars and rigid bodies are formulated with nodal Cartesian coordinates as unknowns. Based on the constraint equations, the nodal displacements are expressed as linear combination of the independent modes of the rigid body displacements, i.e., the null space orthogonal basis of the constraint matrix. The proposed method has less unknowns and a simple formulation compared with common multibody dynamic methods. An analysis program for the proposed method is developed, and its validity and efficiency are investigated by analyses of several representative numerical examples, where good accuracy and efficiency are demonstrated through comparison with commercial software package ADAMS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Structural Engineering and Mechanics
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• v.84 no.4
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• pp.453-464
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• 2022

Thin plates are the most common spatially stressed members in engineering structures that bear out-of-plane loads. Therefore, it is of great significance to study the deformation performance characteristics of thin plates for structural design. By constructing 12 basic displacement and deformation basis vectors of the four-node square thin plate element, a deformation decomposition method based on the complete orthogonal mechanical basis matrix is proposed in this paper. Based on the deformation decomposition method, the deformation properties of the thin plate can be quantitatively analyzed, and the areas dominated by each basic deformation can be visualized. In addition, the method can not only obtain more deformation information of the structure, but also identify macroscopic basic deformations, such as bending, shear and warping deformations. Finally, the deformation properties of the bidirectional thin plates with different sizes of central holes are analyzed, and the changing rules are obtained.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.153-159
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• 2011

The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.647-666
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• 2009

Multiple-input multiple-output (MIMO) multiplexing is an attractive technology that increases the channel capacity without requiring additional spectral resources. The design of low complexity and high performance detection algorithms capable of accurately demultiplexing the transmitted signals is challenging. In this technical survey, we introduce the state-of-the-art MIMO detection techniques. These techniques are divided into three categories, viz. linear detection (LD), decision-feedback detection (DFD), and tree-search detection (TSD). Also, we introduce the lattice basis reduction techniques that obtain a more orthogonal channel matrix over which the detection is done. Detailed discussions on the advantages and drawbacks of each detection algorithm are also introduced. Furthermore, several recent author contributions related to MIMO detection are revisited throughout this survey.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.915-921
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• 2009

Lenstra-Lenstra-Lovasz (LLL) algorithm, which is one of the lattice reduction (LR) techniques, has been extensively used to obtain better bases of the channel matrix. In this paper, we jointly apply Seysen's lattice reduction Algorithm (SA), instead of LLL, with the conventional linear precoding algorithms. Since SA obtains more orthogonal lattice bases compared to those obtained by LLL, lattice reduction aided (LRA) precoding based on SA algorithm outperforms the LRA precoding with LLL. Simulation results demonstrate that a gain of 0.5dB at target BER of $10^{-5}$ is achieved when SA is used instead of LLL or the LR stage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.125-130
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• 2008

Multivariate analysis is a rapidly expanding approach to data analysis. One specific technique in multivariate analysis is Principal Component Analysis (PCA). PCA is a statistical technique that linearly transform a given set of variables into a new set of composite variables. These new variables are orthogonal to each other and capture most of the information in the original variables. PCA is used to reduce the number of control points to be checked by measurement system. Therefore, the structure of the data set is simplified significantly It is also shown that eigenvectors obtained by conducting principal component analysis on the basis of the covariance matrix can be used to physically interpret the pattern of relative deformation for the points. This case study reveals the twisting deformation pattern of the underbody which is the largest mode of the total variation.