• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• 대한전기학회논문지:전력기술부문A
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• 제50권1호
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• pp.6-12
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• 2001

This paper deals with the development of dynamic equivalents of the Korea Electric Corporation (KEPCO) systems. The weak coupling method is chosen for the most suitable coherency identification to represent the characteristics of the KEPCO system by the dynamic characteristic comparison of several coherency identification methods. Also, this paper shows the detail aggregation of generator groups makes a better representation of the dynamic and static characteristics of the original system than the classical aggregation. The simulation results of the developed KEPCO equivalent system are presented in comparison with the original system to illustrate the validity of the equivalent system are presented in comparison with the original system to illustrate the validity of the equivalent system.

• International Journal of Aeronautical and Space Sciences
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• 제6권1호
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• pp.52-60
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• 2005

The CFD coupled aeroelastic analyses have significant advantages over linear panel methods in their accuracy and usefulness for the simulation of actual aeroelastic motion after specific initial disturbance. However, in spite of their advantages, a heavy computation time is required. In this paper, a method is discussed to save a computational cost in the time domain aeroelastic analysis based on the system identification technique. The coefficients of system identification model are fit to the computed time response obtained from a previously developed aeroelastic analysis code. Because the non-dimensionalized data is only used to construct the model structure, the resulting model of the unsteady CFD solution is independent of dynamic pressure and this independency makes it possible to find the flutter dynamic pressure without the unsteady aerodynamic computation. To confirm the accuracy of the system identification methodology, the system model responses are compared with those of the CFD coupled aeroelastic analysis at the same dynamic pressure.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2143-2145
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• 2003

In the linear system identification using the discrete time constant coefficients, there is a subspace method based on 4SID recently much suggested instead of the parametric method like as the maximum likelihood method. The subspace method is not related with the impulse response and difference equation in its input-output equation, but with the system matrix of the direct state space model from the input-output data. The subspace method is a very useful tool to adopt in the multivariable system identification, but it has a shortage unable to adopt in the closed-loop system identification. In this paper, we are suggested the methods to get rid of the shortage of the subspace method in the closed-loop system identification. The subspace method is used in the estimate of the output prediction values from the estimating of the state space vector. And we have compared the results with the outputs of the recursive least square method in the numerical simulation.

• Steel and Composite Structures
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• 제32권6호
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• pp.731-741
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• 2019

Shear deformation effects are neglected in most structural system identification methods. This assumption might lead to important errors in some structures like built up steel or composite deep beams. Recently, the observability techniques were presented as one of the first methods for the inverse analysis of structures including the shear effects. In this way, the mechanical properties of the structures could be obtained from the nodal movements measured on static tests. One of the main controversial features of this procedure is the fact that the measurement set must include rotations. This characteristic might be especially problematic in those structures where rotations cannot be measured. To solve this problem and to increase its applicability, this paper proposes an update of the observability method to enable the structural identification including shear effects by measuring only vertical deflections. This modification is based on the introduction of a numerical optimization method. With this aim, the inverse analysis of several examples of growing complexity are presented to illustrate the validity and potential of the updated method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1458-1463
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• 2003

This paper presents an improved identification algorithm of active magnetic bearing rotor systems considering sensor and actuator dynamics. An AMB rotor system has both real and complex poles so that it is very hard to identify them together. In previous research, a linear transformation through a fictitious proportional feedback was used in order to shift the real poles close to the imaginary axis. However, the identification result highly depends on the fictitious feedback gain, and it is not easy to identify the additional dynamics including sensor and actuators at the same time. First, this paper discusses the necessity and a selection criterion of the fictitious feedback gain. An appropriate feedback gain minimizes dominant SVD(Singular Value Decomposition) error through maximizing rank deficiency. Second, more improvement in the identification is achieved through separating the common additional dynamics in all elements of frequency response matrix. The feasibility of the proposed identification algorithm is proved with two theoretical AMB rotor models. Finally, the proposed scheme is compared with previous identification methods using experimental data, and a great improvement in model quality and large amount of time saving can be achieved with the proposed method.

• KIEE International Transactions on Power Engineering
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• 제12A권1호
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• pp.20-25
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• 2002

This paper deals with the development of dynamic equivalents of the Korea Electric Corporation (KEPCO) systems. Several coherency identification methods are studied in order to find a proper method to well represent the dynamic characteristics of the KEPCO system. Also, this paper presents the comparison of the detail and classical aggregation methods in terms of the dynamic and static characteristics of the system. The nonlinear time simulation of the developed KEPCO equivalent system and the original system is performed to illustrate the validity of the equivalent system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1997년도 춘계학술대회논문집; 경주코오롱호텔; 22-23 May 1997
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• pp.102-109
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• 1997

A study to determine a proper sensor placement was developed to improve force identification. Improper selection of response position cause erroneous result in force identification problem. This paper presents two methods to improve the conditioning of the system's FRM(Frequency Response Matrix) which affects the accuracy of result. The basic strategy of the two methods in selecting the response position is to let the smallest singular value be as large as possible by maximizing the orthogonality of FRM. The suggested methods are tested numerically with a fixed-fixed beam model. The test results show that the proposed methods are very effective in dealing with the force identification problem.

• 대한안전경영과학회지
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• 제15권3호
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• pp.105-112
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• 2013

To meet the growing needs from a variety of stakeholders, the development of modern systems is getting more complex and thus, the systems failure in the actual operations can potentially become more serious. This is why several international or military standards on systems safety have been published. In spite of the importance of meeting those standards such as IEC 61508 and ISO 26262 in the systems development, the associated practical methods seem deficient since those standards do not provide them. The objective of this paper is to present a method to identify potential hazards in fulfilling the requirements of the safety standards. In particular, the approach taken here is based on applying the functional analysis that covers several levels of the system under development. Note, however, that in the most of the conventional methods for hazards identification, the analysis has been focused on the failure at or underneath the component level of the system. The hazards identification method in this paper would cover the level up to the system by utilizing the functions-oriented approach. The case study of the safety enhancement for locomotive cabs is also discussed.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.