• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.639-646
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• 1995

The objective of this study is to minimize the weight of a damped anisotropic roto-bearing system considering whirl natural frequency and stability. The system is modeled as an assemblage of rigid disks, flexible shafts and discrete bearings. The system design variables are the crosssectional areas of shaft elements and the properties of bearings. To analyze the system, the polynomial method which is derived by rearranging the calculations performed by a transfer matrix method is adopted. For the optimization, the optimization software IDOL (Integrated Design Optimization Library) which is based on the Augmented Lagrange Multiplier (ALM) method is employed. Also, an analytical design sensitivity analysis of the system is used for high accuracy and efficiency. To demonstrate the usefulness of the proposed optimal design program incorporating analysis, design sensitivity analysis, and optimization modules, a damped anisotropic rotor-bearing system is optimized to obtain 34$ weight reduction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2303-2312
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• 1994

A high resolution algorithm is presented for resolving multiple narrowband plane waves that are incident on an equispaced linear array. To overcome the deleterious effects due to coherent sources, a number of noise-eigenvector-based approaches have been proposed for narrowband signal processing. For differing reasons, each f these methods provide a less than satisfactory resolution of the coherency problem. The proposed algorithm makes use of fundamental property possessed by those eigenvectors of the spatial covariance matrix that are associated with eigenvalues that are larger than the sensor noise level. This property is then used to solve the incoherent and coherent sources incident on an equispaced linear array. Simulation results are shown to illustrate the high resolution performance achieved with this new approach relative to that obtained with MUSIC and spatial smoothed MUSIC.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.419-429
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• 2005

This paper considers eigenstructure assignment in high-order linear systems via proportional plus derivative feedback. It is shown that the problem is closely related with a type of so-called high-order Sylvester matrix equations. Through establishing two general parametric solutions to this type of matrix equations, two complete parametric methods for the proposed eigenstructure assignment problem are presented. Both methods give simple complete parametric expressions for the feedback gains and the closed-loop eigenvector matrices. The first one mainly depends on a series of singular value decompositions, and is thus numerically very simple and reliable; the second one utilizes the right factorization of the system, and allows the closed-loop eigenvalues to be set undetermined and sought via certain optimization procedures. An example shows the effect of the proposed approaches.

• Journal of KSNVE
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• v.4 no.1
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• pp.71-82
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• 1994

We propose a systematic method to select the master states, which are retained in the reduced model after the order reduction process. The proposed method is based on the fact that the range space of right eigenvector matrix is spanned by orthogonal base vectors, and tries to keep the orthogonality of the submatrix of the base vector matrix as much as possible during the reduction process. To quentify the skewness of that submatrix, we define "Absolute Singularity Factor(ASF)" based on its singular values. While the degree of observability is concerned with estimation error of state vector and up to n'th order derivatives, ASF is related only to the minimum state estimation error. We can use ASF to evaluate the estimation performance of specific partial measurements compared with the best case in which all the state variables are identified based on the full measurements. A heuristic procedure to find suboptimal master states with reduced computational burden is also proposed. proposed.

• Bulletin of the Korean Mathematical Society
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• v.55 no.6
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• pp.1691-1701
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• 2018

New inclusion sets are obtained for the eigenvalues of real matrices for which the all 1's vector is an eigenvector, i.e., the constant row-sum real matrices. A number of examples are provided. This paper builds upon the work of the authors in [7]. The results of this paper are in terms of $Ger{\check{s}}gorin$ discs of the second type. An application of the main theorem to bounding the algebraic connectivity of connected simple graphs is obtained.

• MALSORI
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• no.66
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.112-115
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• 2001

Eigenvalue perturbation theory of augmented system matrix(AMEP) is a useful tool in the analysis and design of large scale power systems. This paper describes the application results of AMEP algorithm with respect to all controller parameter of KEPCO systems. AMEP for interarea and local mode can be used for turning controller parameter, and verifying system data and linear model of controller.

• The Journal of the Acoustical Society of Korea
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• v.17 no.4
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• pp.82-87
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• 1998

여러 음원들에 의해 형성된 파동장내에서 각 신호음의 주파수 특성과 시간 지연 (time delay)을 추정할 수 있는 알고리즘을 개발하였다. 이 알고리즘의 관련 수식은 두 개의 상호 간섭하는 신호가 입사하고 여기에 주변 환경에 의한 랜덤 잡음이 첨가된다고 가정하여 유도되었으며 두 개 이상의 신호음이 있는 상황에 대해 확장이 가능하다. 본 논문에서 시간 지연이 일정한 수신 신호 영역에 등간격으로 놓여진 수신기로부터 각 센서에 수신된 신호의 스펙트럼은 M개의 센서에 대해 K개의 음원 스펙트럼과 K개의 조정 벡터(steering vector) 의 선형 조합(linear combination)으로 주파수에서 모델된다. 각 음원의 주파수 특성과 음원 으로 들어오는 신호의 입사각을 결정하기 위하여 본 알고리즘은 전달 행렬(transfer matrix) 을 계산하고 그 전달 행렬의 고유값(eigenvalue)과 고유벡터(eigenvector)를 분석한다. 이 고 유값들은 복소수이며 그 크기는 진폭 변환 계수를 결정한다. 위상은 수신기의 간격으로부터 시간 지연을 결정하는 기울기를 갖는 주파수의 선형 함수이다. 전달 행렬에의 입력 자료들 은 동일 간격 소자간의 cross-power spectra이다.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.293-301
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• 2022

Direction of arrival (DOA) estimation of space signals is a basic problem in array signal processing. DOA estimation based on the multiple signal classification (MUSIC) algorithm can theoretically overcome the Rayleigh limit and achieve super resolution. However, owing to its inadequate real-time performance and accuracy in practical engineering applications, its applications are limited. To address this problem, in this study, a DOA estimation algorithm with high parallelism and precision based on an analysis of the characteristics of complex matrix eigenvalue decomposition and the coordinate rotation digital computer (CORDIC) algorithm is proposed. For parallel and single precision, floating-point numbers are used to construct an orthogonal identity matrix. Thus, the efficiency and accuracy of the algorithm are guaranteed. Furthermore, the accuracy and computation of the fixed-point algorithm, double-precision floating-point algorithm, and proposed algorithm are compared. Without increasing complexity, the proposed algorithm can achieve remarkably higher accuracy and efficiency than the fixed-point algorithm and double-precision floating-point calculations, respectively.