• Korean Journal of Metals and Materials
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• v.47 no.1
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• pp.7-12
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• 2009

The detailed quantitative microstructural data on the cracking of coarse constituent particles in 7075 (T651) series wrought Al-alloys have been studied using the utility of a novel digital image processing technique, where the particle cracks are generated due to monotonic loading. The microstructural parameters such as number density, volume fraction, size distribution, first nearest neighbor distribution, and two-point correlation function have been quantitatively characterized using the developed technique and such data are very useful to verify and study the theoretical models for the damage evolution and fracture of Al-alloys. The data suggests useful relationships for damage modeling such as a linear relationship between particle cracking and strain exists for the uniaxial tensile loading condition, where the larger particles crack preferentially.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.121-127
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• 2011

Spatial distribution of void space in concrete materials strongly affects mechanical and physical behaviors. Therefore, the identification of characteristic void distribution helps understand material properties and is essential to estimate the integrity of material performance. The 3D micro CT (X-ray microtomography) is implemented to examine and to quantify the void distribution of a lightweight aggregate concrete using an image analysis technique and probabilistic approach in this study. The binarization and subsequent stacking of 2D cross-sectional images virtually create 3D images of targeting void space. Then, probability distribution functions such as two-point correlation and lineal-path functions are applied for void characterization. The lightweight aggregates embedded within the concrete are individually analyzed to construct the intra-void space. Results shows that the low-order probability functions and the density distribution based on the 3D micro CT images are applicable and useful methodology to characterize spatial distribution of void space and constituents in concrete.

• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1229-1238
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• 2002

In this paper, a method to obtain the sensitivity of eigenvalues and the random responses of the structure with uncertain parameters is proposed. The concept of the proposed method is that the perturbed equation of each uncertain substructure is obtained using the finite element method, and the perturbed equation of the overall structure is obtained using the mode synthesis method. By this way, the reduced order perturbed equation of the uncertain system can be obtained. And the response of the uncertain system is obtained using probability method. As a numerical example, a simple piping system is considered as an example structure. The damping and spring constants of the support are considered as the uncertain parameters. Then the variations of the eigenvalues, the correlation function and the power spectral density function of the responses are calculated. As a result, the proposed method is considered to be useful technique to analyze the sensitivities of eigenvalues and random response against random excitation in terms of the accuracy and the calculation time.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.114-121
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• 2008

Within 2 to 5 months after stroke, patients recover variable degrees of function, depending on the initial deficit. An impaired hand function is one of the most serious disability in chronic stroke patients. Therefore, to evaluate the extent of motor dysfunction in the hemiplegic hand is important in stroke rehabilitation. In this paper, motor recoveries in 8 chronic stroke patients with Fugl-Meyer (FM) and white matter changes before and after the training program with a designed bilateral symmetrical arm trainer (BSAT) system were examined. The training was performed at 1 hr/day, 5 days/week during 6weeks. In all patients, FM was significantly improved after the 6-week training. Diffusion tensor imaging (DTI) results showed that tractional anisotropy ratio (FAR) and fiber tracking ratio (FTR) in the posterior internal capsule were significantly increased after the training. It seemed that the cortical reorganization was induced by the 6 week training with the BSAT. In all parameters proposed this study, a significant correlation was found between these parameters (FAR and FTR) and motor recoveries. This study demonstrated that DTI technique could be useful in predicting motor recovery in chronic hemiparetic patients.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.732-737
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• 2016

A pulse radar signal exhibits periodic appearance of pulses in time. So it leads to a high correlation between two samples separated in time by multiples of its period. In this paper, we present a spectrum sensing technique for a radar signal which exploits the periodicity of its autocorrelation function and a radar pulse interval estimation scheme in order to address the case that the radar pulse interval is not known a priori. Finally, we evaluate the sensing performance of the proposed scheme through computer simulation and compare its performances with those of energy detection.

• 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.

• Wind and Structures
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• v.7 no.2
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• pp.107-130
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• 2004

This paper presents a time-domain approach for analyzing nonlinear random vibrations of long-span suspended cables under transversal wind. A consistent continuous model of the cable, fully accounting for geometrical nonlinearities inherent in cable behavior, is adopted. The effects of spatial correlation are properly included by modeling wind velocity fluctuation as a random function of time and of a single spatial variable ranging over cable span, namely as a one-variate bi-dimensional (1V-2D) random field. Within the context of a Galerkin's discretization of the equations governing cable motion, a very efficient Monte Carlo-based technique for second-order analysis of the response is proposed. This procedure starts by generating sample functions of the generalized aerodynamic loads by using the spectral decomposition of the cross-power spectral density function of wind turbulence field. Relying on the physical meaning of both the spectral properties of wind velocity fluctuation and the mode shapes of the vibrating cable, the computational efficiency is greatly enhanced by applying a truncation procedure according to which just the first few significant loading and structural modal contributions are retained.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.149-152
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• 1996

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of Volterra kernel of the nonlinear system. The authors have recently obtained a new method for measuring Volterra kernels of nonlinear control systems by use of a pseudo-random M-sequence and correlation technique. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssection of Volterra kernels up to 3rd order. Once we can get Volterra kernels of nonlinear system, we can construct a linearization method of the nonlinear system. Simulation results show good agreement between the observed results and the theoretical considerations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.161-164
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• 1996

The authors have recently proposed a new method for identifying Volterra kernels of nonlinear control systems by use of M-sequence and correlation technique. A specially chosen M-sequence is added to the nonlinear system to be identified, and the crosscorrelation function between the input and output is calculated. Then every crosssection of Volterra kernels up to 3rd order appears at a specified delay time point in the crosscorrelation. This method is applied to a saturation-type nonlinear feedback control system of mechanical-electrical servo system having torque saturation nonlinearity. Simulation experiments show that we can obtain Volterra kernels of saturation-type nonlinear system, and a good agreement is observed between the observed output and the calculated one from the measured Volterra kernels.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.558-570
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.