• Proceedings of the KSR Conference
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• 2009.05a
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• pp.552-559
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• 2009

Most damage identification methods for structural health monitoring developed to date utilize modal domain responses which require postprocessing and inevitably contain errors in transforming the domain of responses. In this paper, the feasibility of a damage identification method based on dynamics responses from moving loads is experimentally verified. The experiment is performed via applying periodic and non-periodic moving loads to a steel beam and acceleration and displacement responses of the beam is measured. The moving loads is applied using steel balls and the damage of a structure is simulated by saw-cutting the beam. The damage identification results using the measured responses show that the moving load response based damage identification method successfully identify all damages in the beam.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.478-485
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• 2001

A dynamic load simulator(DLS) which can reproduce on-ground the aerodynamic hinge moment of control surface is an essential rig for the performance and stability test of aircraft actuation system. By setting up load actuator as counter acting with the control surface driving actuator and designing an appropriate force control system for load actuator, DLS can be mechanized. Obtaining an accurate mathematical model for the DLS is the first step to successfully design an aerodynamic load replicati on system. Two theoretical models are presented and tested for their validities with the experimental results, which turns out to be not successful. An alternative way of using system identification approaches in investigated to develop a good nominal model for DLS dynamics, and suitable uncertainty bounds for this nominal model are proposed with the consideration of experimental results.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1127-1129
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• 1997

In this paper, we developed to artificial neural network for load characteristic identification of power system. We can acquire active power and reactive power of individual load depending on the variation of voltage and frequency from the experimentation of a dynamic characteristic of load. The data of the experimental results were be used in learning of ANN. A proposed ANN model is applied to analyze the transient stability. To demonstrate the propriety of the power system transient stability with load model using ANN, the simulation of the two-machine five-bus system is carried out.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.365-377
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• 2017

In this paper, we propose a method to identify the parameters of a rotorcraft slung load system using the modal characteristics of a flexible cable. The proposed method estimates the length of the cable and the mass of the payload by means of a frequency analysis. Dynamic equations of the slung load system with the flexible cable are derived using Udwadia-Kalaba equation (UKE) in order to build a simulation program, and the similarity of the simulated slung load movement is verified by comparison with flight test results. Using the computer simulation program, we show that the proposed method works well within various parameter ranges.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.147-153
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• 1990

This paper treats modeling of dynamic load characteristics for power for systems. The dynamic load is represented as 4th order multivariable ARMA model under the assumption that the dynamic load characteristics can be described by the dynamics of only one induction motor. The parameters of the proposed ARMA model are identified using the well-known RLS method. This paper presents two kind of identification results : one is for induction motors and the other is for field data at Donghae station. From these results, the proposed model is quite suitable for the dynamic load characteristics. It has, however, a disadvantage in the viewpoint that the identified parameters are not those of the induction motor.

• 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.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.128-133
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• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.221-225
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• 2008

This paper presents a self-tuning controller for pressurized water reactor (P.W.R.). This self-tuning controller includes two substantial steps, such as parameter identification and control-law building in each cycle. Extended least square algorithm is used for parameter identification, Kalman filter is used for state estimation, and discrete Riccati equation is used for optimal control. Effectiveness of this algorithm is shown through computer simulation and sensitivity analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.907-914
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• 1999

This paper studies on the model identification of electro-hydraulic servo systems, which are composed of servo valves, double-rod cylinder and load mass. The identified plant is described as a discrete-time ARX or ARMAX model which is respectively obtained from the identification algorithms of least square error method, instrumental variable method and prediction error method. where a nominal model and the variation of model parameters are quantitatively evaluated.