• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.86-93
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• 1998

In this paper, we propose an effective on-line identification method for rotor resistance, which is useful in making speed control of induction motors without rotational transducers robust with respect to the variation in rotor resistance. Our identification method for rotor resistance is based on the linearly perturbed equations of the closed-loop system for sensorless speed control about th operating point. Our identification method for rotor resistance uses only the information of stator currents and voltages. In can provide fairly good identification accuracy regardless of load conditions. Some experimental results are presented to demonstrate the practical use of our identification method. For our experimental work, we have built a sensorless control system, in which all algorithms are implemented on a DSP. Our experimental results confirm that our on-line identification method allows for high precision speed control of commercially available induction motors without rotational transducers.

• Smart Structures and Systems
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• v.30 no.3
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Wind and Structures
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• v.24 no.5
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• pp.481-500
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• 2017

A novel three-degree-of-freedom (DOF) forced vibration system has been developed for identification of aeroelastic (self-excited) load parameters used in time-domain response analysis of wind-excited flexible structures. This system is capable of forcing sinusoidal motions on a section model of a structure that is used in wind tunnel aeroelastic studies along all three degrees of freedom - along-wind, cross-wind, and torsional - simultaneously or in any combination thereof. It utilizes three linear actuators to force vibrations at a consistent frequency but varying amplitudes between the three. This system was designed to identify all the parameters, namely, aeroelastic- damping and stiffness that appear in self-excited (motion-dependent) load formulation either in time-domain (rational functions) or frequency-domain (flutter derivatives). Relatively large displacements (at low frequencies) can be generated by the system, if required. Results from three experiments, airfoil, streamlined bridge deck and a bluff-shaped bridge deck, are presented to demonstrate the functionality and robustness of the system and its applicability to multiple cross-section types. The system will allow routine identification of aeroelastic parameters through wind tunnel tests that can be used to predict response of flexible structures in extreme and transient wind conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.199-204
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• 2017

In this study, we present a home appliance load identification algorithm. The algorithm utilizes complex sensory data in order to improve the existing NIALM using total power usage information. We define the influence graph between the appliance status and the measured sensor data. The device identification prediction result is calculated as the weighted sum of the predicted value of the sensor data processing algorithm and the predicted value based on the total power usage. We evaluate proposed algorithm to compare appliance identification accuracy with the existing NIALM algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.2
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• pp.64-73
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• 2014

This study proposes a load identification for the safety monitoring of the steel structure based on measured strain data. Instead of parameterizing the stiffness of structure in the existing system identification researches, the loads on a structure and a matrix (the unit strain matrix) defined by the relationship between strain and load on structure are parameterized in this study. The error function is defined by the difference between measured strain and strain estimated by parameters. In order to minimize this error function, the genetic algorithm which is one of the optimization algorithm is applied and the parameters are found. The loads on the structure can be identified through the founded parameters and measured strain data. When the loads are changed, the unmeasured strains are estimated based on founded parameters and measured strains on changed state of structure. To verify the load identification algorithm in this paper, the static experimental test for 3 dimensional steel frame structure was implemented and the loads were exactly identified through the measured strain data. In case of loading changes, the unmeasured strains which are monitoring targets on the structure were estimated in acceptable error range (0.17~3.13%). It is expected that the identification method in this study is applied to the safety monitoring of steel structures more practically.

• Proceedings of the KSR Conference
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• 2002.10b
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• pp.1065-1073
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• 2002

A new damage identification technique using static displacement data is developed to assess the structural integrity of bridge structures. In the conventional damage assessment techniques using dynamic response, it is usually difficult to obtain a significant natural frequencies variation from the measured data because the natural frequencies variation is intrinsically not sensitive to the damage of a bridge. In this proposed identification method, the stiffness reduction of the bridges can be estimated using the static displacement data measured periodically and a specific loading test is not required. The static displacement data due to the dead load of the bridge structure can be measured by devices such as a laser displacement sensor. In this study, structural damage is represented by the reduction in the elastic modulus of the element. The damage factor of the element is introduced to estimate the stiffness reduction of the bridge under consideration. Finally, the proposed algorithm is verified using various numerical simulation and compared with other damage identification method. Also, the effect of noise and number of damaged elements on the identification are investigated. The results show that the proposed algorithm is efficient for damage identification of the bridges.

• Structural Engineering and Mechanics
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• v.50 no.1
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• pp.105-119
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• 2014

This study presents a steel container crane movement analysis and assessment based on structural health monitoring (SHM). The accelerometers are used to monitor the dynamic crane behavior and a 3-D finite element model (FEM) was designed to express the static displacement of the crane under the different load cases. The multi-input single-output nonlinear autoregressive neural network with external input (NNARX) model is used to identify the crane dynamic displacements. The FEM analysis and the identification model are used to investigate the safety and the vibration state of the crane in both time and frequency domains. Moreover, the SHM system is used based on the FEM analysis to assess the crane behavior. The analysis results indicate that: (1) the mean relative dynamic displacement can reveal the relative static movement of structures under environmental load; (2) the environmental load conditions clearly affect the crane deformations in different load cases; (3) the crane deformations are shown within the safe limits under different loads.

• Smart Structures and Systems
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• v.21 no.1
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• pp.53-64
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• 2018

Nondestructive testing methods are required to assess the condition of civil structures and formulate their maintenance programs. Axial force identification is required for several structural members of truss bridges, pipe racks, and space roof trusses. An accurate evaluation of in situ axial forces supports the safety assessment of the entire truss. A considerable redistribution of internal forces may indicate structural damage. In this paper, a novel compressive force identification method for prismatic members implemented using static deflections is applied to steel beams. The procedure uses the Euler-Bernoulli beam model and estimates the compressive load by using the measured displacement along the beam's length. Knowledge of flexural rigidity of the member under investigation is required. In this study, the deflected shape of a compressed steel beam is subjected to an additional vertical load that was short-term measured in several laboratory tests by using fiber Bragg grating-differential settlement measurement (FBG-DSM) sensors at specific cross sections along the beam's length. The accuracy of midspan deflections offered by the FBG-DSM sensors provided excellent force estimations. Compressive load detection accuracy can be improved if substantial second-order effects are induced in the tests. In conclusion, the proposed method can be successfully applied to steel beams with low slenderness under real conditions.

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.501-507
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• 2003

Medical devices for helping the rehabilitation of the patients such as orthoses and prostheses should be designed to be strong enough. For the strength design, operational load should be identified first. Furthermore. medical devices are susceptible to dynamic load or shock frequently due to its characteristics. These type of the load may be identified by installing the sensors directly. However, it can modify the natural properties of the structures. Therefore, operational load should be identified indirectly from the system characteristics and responses such as vibrations. In this paper, the basic formulation of the indirect load identification is reviewed and the problems of conventional approach are checked. Then, the new approach based upon the principal component analysis is proposed and the validity of the proposed method is demonstrated using experiments.

• International Journal of Contents
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• v.6 no.3
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• pp.5-9
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• 2010

To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.