• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.641-647
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• 2008

This paper introduces a new technique that can extract natural frequencies and damping ratios from output-only vibration data. Firstly, the free vibration data is obtained from the cross correlations of the output-only response data using a singular value decomposition process. Secondly, the well-known system identification algorithm is applied to extract the natural frequencies and damping ratios from the extracted free vibration data. Comparing to ERADC technique, the accuracy of the proposed modal parameter identification algorithm has been numerically examined. Furthermore, the practicability of the proposed algorithm has been examined through the output-only acceleration data collected from the Seohae cable-stayed bridge. Using the proposed technique, total 24 modes have been identified for the deck plate motions of the bridge.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.70-78
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• 1995

The identification of a fuzzy model using input-output data consists of two parts :Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures is suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• Structural Engineering and Mechanics
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• v.31 no.5
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• pp.527-544
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• 2009

In this study, the periodic seismic performance evaluation scheme is proposed using a structural health monitoring system in terms of seismic fragility. An instrumented highway bridge is used to demonstrate the evaluation procedure involving (1) measuring ambient vibration of a bridge under general vehicle loadings, (2) identifying modal parameters from the measured acceleration data by applying output-only modal identification method, (3) updating a preliminary finite element model (obtained from structural design drawings) with the identified modal parameters using real-coded genetic algorithm, (4) analyzing nonlinear response time histories of the structure under earthquake excitations, and finally (5) developing fragility curves represented by a log-normal distribution function using maximum likelihood estimation. It is found that the seismic fragility of a highway bridge can be updated using extracted modal parameters and can also be monitored further by utilizing the instrumented structural health monitoring system.

• Structural Engineering and Mechanics
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• v.56 no.3
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• pp.423-442
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• 2015

This study examines the wavelet transform for output-only system identification of ambient excited engineering structures with emphasis on its utilization for modal parameter estimation of high-order and closely-spaced modes. Sophisticated time-frequency resolution analysis has been carried out by employing the modified complex Morlet wavelet function for better adaption and flexibility of the time-frequency resolution to extract two closely-spaced frequencies. Furthermore, bandwidth refinement techniques such as a bandwidth resolution adaptation, a broadband filtering technique and a narrowband filtering one have been proposed in the study for the special treatments of high-order and closely-spaced modal parameter estimation. Ambient responses of a 5-story steel frame building have been used in the numerical example, using the proposed bandwidth refinement techniques, for estimating the modal parameters of the high-order and closely-spaced modes. The first five natural frequencies and damping ratios of the structure have been estimated; furthermore, the comparison among the various proposed bandwidth refinement techniques has also been examined.

• Journal of Korean Port Research
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• v.14 no.2
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• pp.155-163
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• 2000

A discrete system has interpreted by using the network model, and PERT network is one of these methods. For the purpose of analysing the real system, it is necessary to measure the parameter of the real system. And system identification problem is to assume the parameter of a real system when we get to know the system model, the input data and output data. System identification method has been only developed to a system of which a structure has expressed a differential equation or a polynomial expression. But it has been scarcely developed yet in that case of network model. The aim of this paper is to examine a changes when new system is introduced to the present system. The changes are as follows : how the present system will be changed, when the changes will be happened. In this paper, genetic algorithm is used to assume the parameter.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.101-108
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• 1999

A discrete system has interpreted by using the network model, and PERT network is one of these methods. For the purpose of analysing the real system. it is necessary to measure the parameter of the real system. And system identification problem is to assume the parameter of a real system when we get to know the system model, the input data and output data. System identification method has been only developed to a system of which a structure has expressed a differential equation or a polynomial expression. But it has been scarcely developed yet in that case of network model. The aim of this paper is to examine a changes when new system isn introduced to the present system, The changes are as follows: how the present system will be changed, when the changes will be happened. In this paper, genetic algorithm is used to assume the parameter.