• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.45-49
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• 2003

The velocity structure beneath the CHNB broadband station is determined by receiver function analysis using by from teleseismic P waveforms. The detailed broadband receiver functions are obtained by stacking method for source-equalized vertical, radial and tangential components of teleseismic P waveforms. A time domain inversion uses the stacked radial receiver function to determine vertical P wave velocity structure beneath the station. The crustal velocity structures beneath the stations are estimated using the receiver function inversion method in the case at the crustal model parameterized by many thin, flat-tying, homogeneous layers. The result of crust at model inversion shows the crustal velocity structure beneath the CHNB station varies smoothly with increasing depth, and there are six discontinuity around 2.5km, 6.25km, 12.5km, 22.5km and 27.5km depth, with Moho discontinuity at about 32.5km depth.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.627-630
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• 1999

For depth extraction from the focus and recovery the shape, determination of criterion function for focus measure and size of the criterion window are very important. However, Texture, illumination, and magnification have an effect on focus measure. For that reason, depth map has a partial high and low peak. In this paper, we propose a depth extraction method from focused images using the error interpolation. This method is modified the error depth into mean value between two normal depth in order to improve the depth map.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.1
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• pp.88-93
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• 1994

This study is concerned with an analytic derivation of the probability density function applicable for wave heights in finite water depth using two different methods. As the first method of the study, a probability density function is developed by applying a series of polynomials which is orthogonal with respect to Rayleigh probability density function. The newly derived probability density function is compared with the histogram constructed from wave data obtained in finite water depth which indicate strong non-Gaussian characteristics. Although the probability density represents the histogram very well. it has negative density at large values. Although the magnitude of the negative density is small. it negates the use of the distribution function fer estimating extreme values. As the second method of the study, a probability density function of wave height is developed by applying the maximum entropy method. The probability density function thusly derived agrees very well with the wave height distribution in shallow water, and appears to be useful in estimating extreme values and statistical properties of wave heights in finite water depth. However, a functional relationship between the probability distribution and the non-Gaussian characteristics of the data cannot be obtained by applying the maximum entropy method.

• Journal of Navigation and Port Research
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• v.28 no.7
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• pp.629-634
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• 2004

Continuous depth data are often required in applications of both onboard systems and maritime simulation. But data available are usually discrete and irregularly distributed. Based on the neuron network technique, methods of interpolation to the charted depth are suggested in this paper. Two algorithms based on Levenberg-Marquardt back-propaganda and radial-basis function networks are investigated respectively. A dynamic neuron network system is developed which satisfies both real time and mass processing applications. Using hyperbolic paraboloid and typical chart area, effectiveness of the algorithms is tested and error analysis presented. Special process in practical applications such as partition of lager areas, normalization and selection of depth contour data are also illustrated.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.37-44
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• 2004

Continuous depth data are often required in applications of both onboard systems and maritime simulation. But data available are usually discrete and irregularly distributed. Based on the neuron network technique, methods of interpolation to the charted depth are suggested in this paper. Two algorithms based on Levenberg-Marquardt back-propaganda and radial-basis function networks are investigated respectively. A dynamic neuron network system is developed which satisfies both real time and mass processing applications. Using hyperbolic paraboloid and typical chart area, effectiveness of the algorithms is tested and error analysis presented. Special process in practical applications such as partition of lager areas, normalization and selection of depth contour data are also illustrated.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.27-32
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• 2007

Determination of crack depth in filed using the self-calibrating surface wave transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural networks (NNs) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1003-1007
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• 2015

Depth calibration is a procedure for finding the conversion function that maps disparity data from a depth-sensing camera to actual distance information. In this paper, we present an optimal depth calibration method for Kinect$^{TM}$ sensors based on an experimental design and convex optimization. The proposed method, which utilizes multiple measurements from only two points, suggests a simplified calibration procedure. The confidence ellipsoids obtained from a series of simulations confirm that a simpler procedure produces a more reliable calibration function.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.97-104
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• 2020

In this paper, we propose a method to derive the kernel function directly from the measured apparent earth resistivity. At this time, the kernel function is obtained through the process of solving a nonlinear system. Nonlinear systems with many variables are difficult to solve. This paper also introduces a method for converting nonlinear derived systems to linear systems. The kernel function is a function of the depth and resistance of the Earth's layer. Being able to derive an accurate kernel function means that we can estimate the earth parameters i.e. layer depth and resistivity. We also use various Earth models as simulation examples to validate the proposed method.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.400-410
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• 2012

In recent years, 3D displays are used in many media including 3D movies, TV, mobile phones, and PC games. Although 3D displays provide realistic viewing experience as compared with 2D displays, they also carry issues such as visual fatigue or size distortion. Focusing on the latter, we developed prediction function of object size perception as a function of object depth in 3D display. In Experiment 1, subjects observed 3D square of a fixed size of varying depth, and manipulated 2D square to make it as large as the 3D square. Conversely, in Experiment 2, subjects observed 2D square of a fixed size, and manipulated 3D square of varying depth to make it as large as the 2D square. In both Experiments 1 and 2, we found that size perception of 3D square linearly changed depending on depth of the square, and the linear relationship between depth and size was identical in both experiments. The predictive regression function, which predicts object size perception based on object depth, obtained in this research will be very useful in the creation of 3D media contents.

• Journal of Korea Water Resources Association
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• v.47 no.8
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• pp.717-728
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• 2014

In this study, we developed a depth-damage function based on flood damage survey with focusing on building damage in urban area. We designed items for the questionnaire survey to develop a depth-damage function which estimates the amount of damage based on inundation depth targeting Dongducheon, Korea, which has experienced severe inundation damage due to significant flooding in July 2011. Based on the survey of the area, we developed a depth-damage function and used this to estimate the real amount of damage on buildings in the inundation area. To assess the damage on buildings, we categorized buildings into two groups; namely residential buildings and commercial buildings. Also, in order to calculate the real amount of damage caused by flooding, properties and detailed damaged items were sub-divided into two groups for the survey; facilities loss (wall paper, floor paper, painting, electrical facilities, and boilers) and furnishing loss (furniture, electronic products, and daily necessities. We expect this study on the process for developing depth-damage function and on the investigation research for flooded area to help in the efficient implementation of all kinds of disaster management policies and the attainment of a society safe from disaster.