• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.10
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• pp.367-375
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• 1983

An extended Kalman filter (EKF) algorithm for estimating aerodynamic parameters from missile flight data is evaluated using simulated test data. The algorithm includes a general purpose 6-DOF missile airframe suitable for representing a variety of missile configurations. The EKF is demonstrated to be well suited as a postflight analysis tool for extracting large numbers of airframe parameters from flight test measurements. A structure identification algorithm is evaluated using synthetic measurement data. This algorithm used in conjunction with the parameter identification algorithm, can select that model from a family of candidate models which most likely produced the synthetic measurement data.

• Tunnel and Underground Space
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• v.13 no.4
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• pp.294-303
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• 2003

The structural behaviour of rock mass structure, such as tunnel or slope is critically dependent on the various characteristics of discontinuities. Therefore, it is important to survey and analyze discontinuities correctly for the design and construction of rock mass structure. One inevitable Procedure of discontinuity survey and analysis is joint set identification from a lot of raw directional joint data. The identification procedure is generally done by a graphical method. This type of analysis has some shortcomings such as subjective identification results, inability to use extra information on discontinuity, and so on. In this study, a computer program for joint set identification based on the fuzzy clustering algorithm was implemented and tested using two kinds of joint data. It was confirmed that fuzzy clustering method is effective and valid for joint set identification and estimation of mean direction and degree of clustering of huge joint data through the applications.

• Bulletin of the Korean Chemical Society
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• v.30 no.1
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• pp.261-263
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• 2009

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.1-14
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• 1990

This paper proposes an approach to the identification of trnasfer function models. A strategy for the identification of the model structure is based on R and S arrays constructed by the impulse response function of the model. Theoretical patterns of the arrays associated with the model are investigated, and the practical implementation method of the suggested approach is also discussed. Finally two published samples are employed to demonstrate the practicability of the approach.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.8-15
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• 1992

This paper analyzes the properties of such algorithm that corresponds to the LMS algorithm with additional update terms, parameterized by the scalar factors $\alpha$ and $\beta$, and presents its structure. The analysis of convergence leads to complex eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The computational cmomplexities of MLMS algorithms are compared with those of MADF, sign and the conventional LMS algorithms. In application of the system identification the second order momentum MLMS algorithm has faster convergence speed than LMS and the first order MLMS algorithms.

• Journal of The Korean Astronomical Society
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• v.34 no.1
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• pp.1-8
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• 2001

We present a fast reduction method of survey data obtained using a single-dish radio telescope. Along with a brief review of classical method, a new method of identification and elimination of negative and positive bad channels are introduced using cloud identification code and several IRAF (Image Reduction and Analysis Facility) tasks relating statistics. Removing of several ripple patterns using Fourier Transform is also discussed. It is found that BACKGROUND task within IRAF is very efficient for fitting and subtraction of base-line with varying functions. Cloud identification method along with the possibility of its application for analysis of cloud structure is described, and future data reduction method is discussed.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.301-304
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• 2003

This raper propose a matching algorithm using voronoi diagram for rotation and translation invariant fingerprint identification. The proposed algorithm extracts geometrical structures that ate derived from voronoi diagram of a fingerprint image. Then distances and angles are extracted from the geometrical structure and saved indexing form for fingerprint matching. Experimental results show that the proposed algorithm invariant to fingerprint rotation and translation requirements and matching time.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.221-225
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• 2004

This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.527-533
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• 2013

This paper presents damage detection and estimation of stiffness parameter on a concrete scale model and a real structure of concrete pontoon using dynamic properties such as mode shapes and natural frequencies. In case of damage detection, dynamic impact test on a concrete scale model is accomplished to extract mode shapes and the practicality is verified by utilizing a damage detection technique. And the stiffness parameter of a real structure of concrete pontoon was estimated via system identification technique using the natural frequencies of the structure. The results indicate that the damaged elements of the scale model are found exactly using damage detection technique and the effective stiffness property of the real structure of concrete pontoon can be estimated by system identification technique.