• Journal of the Korean Magnetics Society
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• v.20 no.2
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• pp.45-51
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• 2010

We suggest the algorithm that it detects volume and shape according with a variation of magnetic field in non-contact electromagnetic measurement system. It is possible to assess an object shape through a variation of magnetic field. The basic idea is compared a length difference with a variation of magnetic field in a detected object and a circle which modeled equivalent area. And the shape is detected to many calibration process that it is similar to signal pattern between a length difference and a variation of magnetic field in object and equivalent circle. This is the shape detection algorithm that use only the variation of magnetic field. In this paper, it has application to the shape detection algorithm about the object as hexagon, pentagon, rectangle, trigon. we can detect the object shape easily because the shape detection algorithm is only used to the variation of magnetic field.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.3
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• pp.202-209
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• 2000

In order to estimate demersal fishes using acoustic echo sounders and echo integrators, we consider several problems that are accurate bottom detection, optimum bottom offset and dead zone. The dead zone where no fish detection are summed distance resolution by the half pulse length of transmitted pulse and beam angle above the seabed. This paper has considered the dead-zone correction method to be technically correct for survey of demersal fishes. A comparison between near-bottom SV profiles acquired in Funka Bay, Hokkaido, of Japan, the East China Sea and the Yellow Sea, of Korea, with before and after the bottom correction, shows that the SV obtained with after the bottom correction is 2∼3dB higher than before the bottom correction in Funka Bay, and 17dB higher in East China Sea, too.

• Journal of Korean Society of Steel Construction
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• v.24 no.1
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• pp.119-128
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• 2012

In this study, experimental vibration tests are performed on a real full-scale railway steel plate girder bridge, which resides in open-space environments. Using experimental modal analysis techniques, the modal parameters of the railway steel plate girder bridge yielded by the modal testing of the impact hammer are compared and investigated with the natural frequencies and mode shapes obtained by finite element analysis. This work focuses on the application of model updating techniques to measured experimental data and output-only data from an analytical vibration study that takes into account various geometric and material properties of the bridge members. A finite element model of the railway bridge structure is used to verify the modal experimental results. It is subsequently updated using the corresponding modal identification technique. The basic database is provided to evaluate damage, which can be determined based on the changes in the element properties, resulting from the process of updating the finite element model benchmark and experimental data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.1-11
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• 2016

The information about the deformations of high-aspect-ratio wings is needed for the real-time monitoring of structural responses. Wing deformation in flight can be predicted by using relationship between the curvatures and the strains on the wing skin. It is also necessary to consider geometric nonlinearity when the large deformation of wing is occurred. The strain distribution on fixed-end is complex in the chordwise direction because of the geometric shape of fixed-wings on fuselages. Hence, the wing displacement can be diversely predicted by the location of the strain sensing lines in the chordwise direction. We conducted a study about prediction method of displacements regardless of the chordwise strain sensing locations. To correct spanwise strains, the ratio of spanwise strain to chordwise strain, Poisson's ratio, and the ratio of the plate strain to the beam strain were used. The predicted displacements using the strain correction were consistent with those calculated by the FEA and verified through the bending testing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.2
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• pp.85-93
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• 2021

In this paper, the aerodynamic characteristics of general tail controlled missile were predicted and corrected the result using semi-empirical analysis tool. The cause of the error was confirmed by comparing the aerodynamic characteristics prediction result of the semi-empirical analysis tool with the wind tunnel test result, and the main error factor of the semi-empirical analysis tool was the interference component between the main wing and the tail wing. The semi-empirical analysis results were corrected using the wind tunnel test results and the computational analysis results, and it was confirmed that the corrected data agrees well with the wind tunnel test results. Through this study, it was confirmed that the wing-tail interference component correction is needed when predicting the aerodynamic characteristics of a general tail controlled missile using a semi-empirical analysis tool.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2844-2851
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• 2015

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.1
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• pp.1-8
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• 2008

The object of this study is to develop the examination technique for measuring the O-ring deformation behavior under the pressure and the squeezed condition simultaneously. The O-ring deformation measuring device in which two grooves were dug to insert the two O-rings was manufactured to be not deformed under the high pressure and the 1 mm and 0.1 mm gap were designed to measure the extrusion lengths under the internal pressure. The beam hardening correction, the histogram analysis and the dead zone correction were executed to exactly measure the shape of O-ring deformation and the lengths of the O-ring deformation were measured by the LSF and the ERF. The computed tomography applied the pressure of 0, 1.378, 4.902, 9.804, 15.692 MPa at 22.3% squeezed condition and the expanded diameter, contact length and extrusion depth were measured in each pressure. The shape of O-ring deformation was evaluated by the FEM to verify the results of measuring by the computed tomography and the area of O-ring was mutually compared to the area measured by the computed tomography.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.663-670
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• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.4
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• pp.107-114
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• 2004

In this paper, a simple improved 8-node finite element for the finite element analysis of fibrous composite plates is presented by using the direct modification. We drive explicit expressions of shape functions for the 8-node element with bilinear element geometry, which is modified so that it can represent any quadratic fields in Cartesian coordinates. The refined first-order shear deformation theory is proposed, which results in parabolic through-thickness distribution of the transverse shear strains and stresses from the formulation based on the third-order shear deformation theory. It eliminates the need for shear correction factors in the first-order theory. This finite element is further improved by combined use of assumed strain, modified shape function, and refined first-order theory. To show the effectiveness of our simple modification on the 8-node finite elements, numerical studies are carried out the static, buckling and free vibration analysis of fibrous composite plates.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.141-148
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• 2011

This study presents the optimal grouping technique of columns which groups together columns of similar shortening trends to improve the efficiency of column shortening compensation. Here, Kohonen's self-organizing feature map which can classify patterns of input data by itself with unsupervised learning was used as the optimal grouping algorithm. The Kohonen network applied in this study is composed of two input neurons and variable output neurons, here the number of output neuron is equal to the column groups to be classified. In input neurons the normalized mean and standard deviation of shortening of each columns are inputted and in the output neurons the classified column groups are presented. The applicability of the proposed algorithm was evaluated by applying it to the two buildings where column shortening analyses had already been performed. The proposed algorithm was able to classify columns with similar shortening trends as one group, and from this we were able to ascertain the field-applicability of the proposed algorithm as the optimal grouping of column shortening.