• Tunnel and Underground Space
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• v.16 no.3 s.62
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• pp.232-240
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• 2006

In this paper, the blasting load has been calculated using Input Identification method and measured data in borehole blasting to reflect the exact blast behavior and soil vibration. The fitness of calculated blasting load is examined by comparing measured data and results of numerical analysis. According to the results, blasting load estimated by Input Identification method was more adequate than proposed blasting pressure equation in the reflection of blast behavior and soil vibration. In addition, it showed more reasonable results at the evaluation of structure's vibration in the 3D finite element method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.495-513
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• 2020

The correlation identification of the subsurface is a novel electrical prospecting method which could suppress stochastic noise. This method is increasingly being utilized by geophysicists. It achieves the frequency response of the underground media through division of the cross spectrum of the input & output signal and the auto spectrum of the input signal. This is subject to the spectral leakage when the cross spectrum and the auto spectrum are computed from cross correlation and autocorrelation function by Discrete Fourier Transformation (DFT, "To obtain an accurate frequency response of the earth system, we propose an improved correlation identification method which uses all phase Fast Fourier Transform (APFFT) to acquire the cross spectrum and the auto spectrum. Simulation and engineering application results show that compared to existing correlation identification algorithm the new approach demonstrates more precise frequency response, especially the phase response of the system under identification.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.119-126
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• 1996

In this paper, a method of substructural identification is presented for the estimation of localized structural parameters. for this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for the substructure to process the measurement data impaired by noises. The sequential prediction error method is used fer the estimation of unknown localized parameters. Using the substructural method, the number of unknown parameters can be reduced and the convergence and accuracy of estimation can be improved. For some substructures, the effect of the input excitation is expressed in terms of the responses at the inferences with the main structure, and substructural identification may be carried out without measuring the actual input excitation to the whole structure. Example analysis is carried out for idealized structural models of a multistory building and a truss bridge. The results indicate that the present method is effective and efficient for local damage estimation of complex structures.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.101-108
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• 2002

This paper presents an input and system identification technique for a free-field system using forced vibration data. Identification is carried out on geotechnical experiment site at Yong-jong Island where Inchon International Airport being constructed. The identified quantities are the input load as well as the shear moduli of the free-field soil regions. The dynamic response analysis on the free-field system is carried out using the finite element method incorporating the infinite element formulation fur the unbounded layered soil medium. The criterion function for the parameter estimation is constructed using the frequency response amplitude ratios of the dynamic responses measured at several points of the free-field, so that the information on the input loading may be excluded. The constrained steepest descent method is employed to obtain the revised parameters. The simulated dynamic responses using the identified parameters and input load show excellent agreements with the measured responses.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.127-139
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• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.644-646
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• 2004

The closed loop identification(CLID) is a very useful method for on-line applications since it can identify the plant in the closed-loop system composed of the plant and the controller. There are some literatures on CLID, but they and mainly focused on SISO(Single-Input/Single-Output) problem. In this paper, a CLID method is proposed for MIMO(Multi-Input/Multi-Output) systems. The CLID method is applied to a MIMO benchmark plant, TRMS(Twin-Rotor MIMO System). To illustrate the performance of the closed-loop system identification., unit step responses in the TRMS are represented and compared with the open-loop identification via some simulation.

• Smart Structures and Systems
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• v.29 no.5
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• pp.715-728
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• 2022

Recently, numbers of long span pedestrian suspension bridges have been constructed worldwide. While recent tragedies regarding pedestrian suspension bridges have shown how these bridges can wreak havoc on the society, there are no specific guidelines for construction standards nor safety inspections yet. Therefore, a structural health monitoring system that could help ensure the safety of pedestrian suspension bridges are needed. System identification is one of the popular applications for structural health monitoring method, which estimates the dynamic system. Most of the system identification methods for bridges are currently adapting output-only system identification method, which assumes the dynamic load to be a white noise due to the difficulty of measuring the dynamic load. In the case of pedestrian suspension bridges, the pedestrian load is within specific frequency range, resulting in large errors when using the output-only system identification method. Therefore, this study aims to develop a system identification method for pedestrian suspension bridges considering both input and output of the dynamic system. This study estimates the location and the magnitude of the pedestrian load, as well as the dynamic response of the pedestrian bridges by utilizing artificial intelligence and computer vision techniques. A simulation-based validation test was conducted to verify the performance of the proposed system. The proposed method is expected to improve the accuracy and the efficiency of the current inspection and monitoring systems for pedestrian suspension bridges.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1769-1776
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• 2000

In this paper a new method is proposed to identify a modulation method in the case of unknown digitally modulated input signals. The proposed identification method is implemented with an artificial neural network which is based on characteristic feature extracted from the instantaneous amplitude the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 9 type signals (ASK2, FSK2, FSK4, PSK2, PSK4, PSK8, QAM8, QAM16) in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.145-148
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• 1999

It is known that Volterra kernel models can represent a wide variety of nonlinear chemical processes. Also, it is necessary for Volterra model identification to excite the process to be identified with a signal having wide range of frequency spectrum and high enough amplitude of input signals. Kashiwagi[4 ∼ 7] has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. However, in practice, since it is not always possible to apply such input sequences to the actual chemical plants. Even when we can apply such a pseudorandom signal to the process, it takes much time to obtain higher order Volterra kernels. Considering these problems, the authors propose here a new method for practical identification of Volterra kernels by use of approximate open differential equation (ODE) model and simple plant test. Simulation results are shown for verifying the usefulness of our method of identification of nonlinear chemical processes.