• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.171-178
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• 2010

The surveillance system in general, has been sufficiently studied in the field of wireless semiconductor using basic sensors and its study of image surveillance system mainly using camera as a sensor has especially been fully implemented. In this paper, we propose 'Intelligent Image Detection System' used by image object identification technique based on the result analysis of various researches. This 'Intelligent Image Detection System' can easily trace and judge before and after a particular incident and ensure affirmative evidence and numerous relative information. Therefore, the 'Intelligent Image Detection System' proposed in this paper can be effectively used in the lived society such as traffic management, disaster alarm system and etc.

• Structural Engineering and Mechanics
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• v.67 no.4
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• pp.359-368
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• 2018

In this paper, a method is presented to identify the physical and modal parameters of multistory shear building based on substructural technique using block pulse generalized operational matrix and genetic algorithm. The substructure approach divides a complete structure into several substructures in order to significantly reduce the number of unknown parameters for each substructure so that identification processes can be independently conducted on each substructure. Block pulse functions are set of orthogonal functions that have been used in recent years as useful tools in signal characterization. Assuming that the input-outputs data of the system are known, their original BP coefficients can be calculated using numerical method. By using generalized BP operational matrices, substructural dynamic vibration equations can be converted into algebraic equations and based on BP coefficient for each story can be estimated. A cost function can be defined for each story based on original and estimated BP coefficients and physical parameters such as mass, stiffness and damping can be obtained by minimizing cost functions with genetic algorithm. Then, the modal parameters can be computed based on physical parameters. This method does not require that all floors are equipped with sensor simultaneously. To prove the validity, numerical simulation of a shear building excited by two different normally distributed random signals is presented. To evaluate the noise effect, measurement random white noise is added to the noise-free structural responses. The results reveal the proposed method can be beneficial in structural identification with less computational expenses and high accuracy.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.808-815
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• 2002

The nonlinear characteristics for hinge of a deployable missile control fin are investigated experimentally. The nonlinearity is caused by a worn or loose hinge and manufacturing tolerance and cannot be eliminated completely. The structural nonlinearity has an effect on the static and dynamic characteristics of the control fin. Therefore, it is necessary to establish the accurate nonlinear model for the hinge of the control fin. In the present study the existence of nonlinearities in the hinge is confirmed from the frequency response experiments such as tip random excitation and base sine sweep. Using the system identification method. especially, ″Force-state Mapping Technique″, the types of nonlinearities are identified and the nonlinear hinge model of the control fin is established.

• Smart Structures and Systems
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• v.12 no.6
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• pp.695-705
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• 2013

The purpose of this paper is to identify through experiments the finite element (FE) model of a building structure using a magnetorheological (MR) fluid damper. The FE model based system identification (FEBSI) technique evaluates the control performance of an MR damper that has nonlinear characteristics as equivalent linear properties such as mass, stiffness, and damping. The Bingham and Bouc-Wen models were used for modeling the MR damper and the equivalent damping increased by the MR damper was predicted by applying an equivalent linearization technique. Experimental results indicate that the predicted equivalent damping matches well with the experimentally obtained damping.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.848-854
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• 2002

The nonlinear characteristics for the hinge of a deployable missile control fin are investigated experimentally. The nonlinearity is caused by a worn or loose hinge and manufacturing tolerance and cannot be eliminated completely. The structural nonlinearity has an effect on the static and dynamic characteristics of the control fin. Therefore, it is necessary to establish the accurate nonlinear model for the hinge of the control fin. In the present study the existence of nonlinearities in the hinge is confirmed from the frequency response experiments such as tip random excitation and base sine sweep. Using the system identification method, especially, “Force-State Mapping Technique”, the types of nonlinearities are identified and the nonlinear hinge model of the control fin is established.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.809-813
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• 2010

For the purpose of developing a modeling technique for reduced-order dynamics of thermoacoustic instability, we constructed an electrically heated Rijke tube and measured the pressure oscillation inside the tube. Analysing the measured pressure data showed that the instability generated three major oscillation modes, among which the first mode frequency quite well matches the result from a rough acoustic analysis. As a continuing research, a data-based modeling technique for the thermoacoustic instability will be developed in the near future.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.898-902
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• 2003

In this paper we proposes a new technique for identification of breast cancer by classification of proteome pattern generated from 2-D polyacrylamide gel electrophoresis (2-D PAGE) and development of cancer diagnosis system : HABIT. Proteome patterns reflect the underlying pathological state of a human organ and it is believed that the anomalies or diseases of human organs are identified by the analysis or classification of the patterns. Proteome patterns consist of quantitative information of the spots such as their size, position, and density in the proteome image produced from 2-D PAGE, for the Image mining of proteome pattern, SVM(support vector machine) and GA(genetic algorithm) are used to generate a decision model for the identification of breast cancer The decision model was then used to classify an independent set of test proteome patterns into the affecter and unaffecter classes. The proposed technique was tested by actual clinical test samples and showed a good performance of a hit ratio of 90%.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.141-144
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• 1999

The authors have recently developed a new method for identification of Volterra kernels of nonlinear systems by use of pseudorandom M-sequence and correlation technique. And it is shown that nonlinear systems which can be expressed by Volterra series expansion are well identified by use of this method. However, there exist many nonlinear systems which can not be expressed by Volterra series mathematically. A nonlinear system having backlash type nonliear element is one of those systems, since backlash type nonlinear element has multi-valued function between its input and output. Since Volterra kernel expression of nonlinear system is one of the most useful representations of non-linear dynamical systems, it is of interest how the method of Volterra kernel identification can be ar plied to such backlash type nonlinear system. The authors have investigated the effect of application of Volterra kernel identification to those non-linear systems which, accurately speaking, is difficult to express by use of Volterra kernel expression. A pseudorandom M-sequence is applied to a nonlinear backlash-type system, and the crosscorrelation function is measured and Volterra kernels are obtained. The comparison of actual output and the estimated output by use of measured Volterra kernels show that we can still use Volterra kernel representation for those backlash-type nonlinear systems.

• Structural Engineering and Mechanics
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• v.14 no.4
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• pp.417-434
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• 2002

This paper explores the possibility of using a combination of the empirical mode decomposition (EMD) and the Hilbert transform (HT), termed the Hilbert-Huang transform (HHT) method, to identify the modal damping ratios of the structure with closely spaced modal frequencies. The principle of the HHT method and the procedure of using the HHT method for modal damping ratio identification are briefly introduced first. The dynamic response of a two-degrees-of-freedom (2DOF) system under an impact load is then computed for a wide range of dynamic properties from well-separated modal frequencies to very closely spaced modal frequencies. The natural frequencies and modal damping ratios identified by the HHT method are compared with the theoretical values and those identified using the fast Fourier transform (FFT) method. The results show that the HHT method is superior to the FFT method in the identification of modal damping ratios of the structure with closely spaced modes of vibration. Finally, a 36-storey shear building with a 4-storey light appendage, having closely spaced modal frequencies and subjected to an ambient ground motion, is analyzed. The modal damping ratios identified by the HHT method in conjunction with the random decrement technique (RDT) are much better than those obtained by the FFT method. The HHT method performing in the frequency-time domain seems to be a promising tool for system identification of civil engineering structures.

• Smart Structures and Systems
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• v.6 no.1
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• pp.39-56
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• 2010

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.