• Journal of the Korean Data and Information Science Society
• /
• v.9 no.1
• /
• pp.63-70
• /
• 1998

The singular value decomposition is a tool which is used to find a linear structure of reduced dimension and to give interpretation of the lower dimensional structure about multivariate data. In this paper the singular value decomposition is reviewed from both algebraic and geometric point of view and, is illustrated the way which the tool is used in the multivariate techniques finding a simpler geometric structure for the data.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.4
• /
• pp.1135-1143
• /
• 2007

The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.

• Journal of Industrial Technology
• /
• v.28 no.B
• /
• pp.135-141
• /
• 2008

In this paper, the Proper Orthogonal Decomposition (POD) method, which is a statistical analysis technique to find the modal characteristics of a structure, is adapted to identify the modal parameters of a tall chimney structure. A wind force time history, which is applied to the structure, is obtained by a wind tunnel test of a scale down model. The POD method is applied on the wind force induced responses of the structure, and the true normal modes of the structure can be obtained. The modal parameters including, natural frequency, mode shape, damping ratio and kinetic energy of the structure can be estimated accurately. With these results, it may be concluded that the POD method can be applied to obtain accurate modal parameters from the wind-induced building responses.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.2
• /
• pp.89-96
• /
• 2015

In this paper, time-frequency analysis algorithms, empirical mode decomposition and local mean decomposition, are reviewed and their applications to nonlinear and nonstationary real-world data are discussed. In addition, their generic extensions to complex domain are addressed for the analysis of multichannel data. Simulations of these algorithms on synthetic data illustrate the fundamental structure of the algorithms and how they are designed for the analysis of nonlinear and nonstationary data. Applications of the complex version of the algorithms to the synthetic data also demonstrate the benefit of the algorithms for the accurate frequency decomposition of multichannel data.

• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.818-823
• /
• 2000

In practical design studies, most of designers solve multidisciplinary problems with complex design structure. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder original design processes to minimize total cost and time. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.5
• /
• pp.1049-1054
• /
• 2013

In this paper, two-dimensional(2-D) Finite Difference Time Domain(FDTD) method using the domain decomposition method is proposed. We calculated the electromagnetic scattering field of a two dimensional rectangular Perfect Electric Conductor(PEC) structure using the 2-D FDTD method with Schur complement method as a domain decomposition method. Four domain decomposition and eight domain decomposition are applied for the analysis of the proposed structure. To validate the simulation results, the general 2-D FDTD algorithm for the total domain are applied to the same structure and the results show good agreement with the 2-D FDTD using the domain decomposition method.

• Journal of Mechanical Science and Technology
• /
• v.18 no.5
• /
• pp.814-819
• /
• 2004

The conceptual design of a rotorcraft system involves many different analysis disciplines. The decomposition of such a system into several subsystems can make analysis and design more efficient in terms of the total computation time. Adaptive parallel decomposition makes the structure of the overall design problem suitable to apply the multidisciplinary design optimization methodologies and it can exploit parallel computing. This study proposes a decomposition method which adaptively determines the number and sequence of analyses in each sub-problem corresponding to the available number of processors in parallel. A rotorcraft design problem is solved and as a result, the adaptive parallel decomposition method shows better performance than other previous methods for the selected design problem.

• Interaction and multiscale mechanics
• /
• v.1 no.1
• /
• pp.33-51
• /
• 2008

This paper investigates the modelling of coupled soil-structure interaction problems by domain decomposition techniques. It is assumed that the soil-structure system is physically partitioned into soil and structure subdomains, which are independently modelled. Coupling of the separately modelled partitioned subdomains is undertaken with various algorithms based on the sequential iterative Dirichlet-Neumann sub-structuring method, which ensures compatibility and equilibrium at the interface boundaries of the subdomains. A number of mathematical and computational characteristics of the coupling algorithms, including the convergence conditions and choice of algorithmic parameters leading to enhanced convergence of the iterative method, are discussed. Based on the presented coupling algorithms a simulation environment, utilizing discipline-oriented solvers for nonlinear structural and geotechnical analysis, is developed which is used here to demonstrate the performance characteristics and benefits of various algorithms. Finally, the developed tool is used in a case study involving nonlinear soil-structure interaction analysis between a plane frame and soil subjected to ground excavation. This study highlights the relative performance of the various considered coupling algorithms in modelling real soil-structure interaction problems, in which nonlinearity arises in both the structure and the soil, and leads to important conclusions regarding their adequacy for such problems as well as the prospects for further enhancements.

• ETRI Journal
• /
• v.36 no.1
• /
• pp.167-170
• /
• 2014

In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.

• Environmental and Resource Economics Review
• /
• v.31 no.4
• /
• pp.825-848
• /
• 2022

This paper analyzed the factors for increasing energy consumption in the domestic manufacturing sector using the LMDI (Log mean division index) decomposition method for the period from 1999 to 2019. Among the LMDI decomposition analysis methods, both additive and multiplicative factor decomposition methods were used. in this analysis. According to the result of the analysis, the factor that increased energy consumption in the domestic manufacturing industry was the production effect, and the structure effect and intensity effect were found to be the factors that decreased energy consumption. In particular, the reduction of energy consumption due to the structure effect was greater than that of energy consumption effect due to the intensity effect. By period, it can be seen that energy consumption increased rapidly due to the production effect until 2011, but after that, the increase in energy consumption due to the production effect slowed down. On the other hand, after that, the energy reduction effect due to the structure effect and the intensity effect became prominent. In order to save energy in the manufacturing sector in the future, energy diagnosis and management through EMS (Energy management system) and FEMS (Factory energy management system) are more necessary. In addition, restructuring into a low-energy consumption industry seems more necessary.