• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.237-244
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• 2002

Televiewer is a logging tool capable of scanning the borehole wall. The tool uses a rotating acoustic beam generator that acts as both a transmitter and receiver. The beams are sent toward the wall. The amplitude of a returning signal from the wall has nearly a linear relationship with the reflection coefficient R of the borehole wall, when the wall is smooth. As R depends only on rock impedance for fixed water impedance, the amplitude is directly associated with mass density and seismic velocity of rock. Meanwhile, the amplitude can be further reduced by wall roughness that may be caused by drilling procedures, differences in rock hardness, because the rough surface can easily scatter the acoustic energy and sometimes the hole becomes elongated in all directions according to the degree of weathering. In this sense, the amplitude is related to the hardness of rocks. For convenience of analysis, the measured amplitude image(2-D data(azimuth ${\times}$ depth)) is converted, with an appropriate algorithm, to the 1-D data(depth), where the amplitude image values along a predetermined fracture signature(sinusoid) are summed up and averaged. The resulting values are subsequently scaled simply by a scalar factor that is possibly consistent with a known strength. This scaled Televiewer reflectivity is named, as a matter of convenience,“Televiewer rock strength”. This paper shows, based on abundant representative case studies from about 8 years of Televiewer surveys, that Televiewer rock strength might be regarded, on a continuous basis with depth, as a quitely robust indicator of rock classification and in most cases as an approximate uniaxial strength that is comparable to the rebound value from Schmidt hammer test.

• Journal of the Korea Computer Graphics Society
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• v.3 no.2
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• pp.27-34
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• 1997

3-D image modeling is in high demand for automated visual inspection and non-destructive testing. It also can be useful in biomedical research, medical therapy, surgery planning, and simulation of critical surgery (i.e. cranio-facial). Image processing and image analysis are used to enhance and classify medical volumetric data. Analyzing medical volumetric data is very difficult In this paper, we propose a new image modeling method based on tetrahedrization to improve the visualization of three-dimensional medical volumetric data. In this method, the trivariate piecewise linear interpolation is applied through the constructed tetrahedral domain. Also, visualization methods including iso-surface, color contouring, and slicing are discussed. This method can be useful to the correct and speedy analysis of medical volumetric data, because it doesn't have the ambiguity problem of Marching Cubes algorithm and achieves the data reduction. We expect to compensate the degradation of an accuracy by using an adaptive sub-division of tetrahedrization based on least squares fitting.

• Journal of Korea Game Society
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• v.7 no.4
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• pp.63-70
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• 2007

Since the fisheye lens has a wide field of view, it can capture the scene and illumination from all directions from far less number of omnidirectional images. Due to these advantages of the omnidirectional camera, it is widely used in surveillance and reconstruction of 3D structure of the scene In this paper, we present a new self-calibration algorithm of omnidirectional camera from uncalibrated images by considering the inlier distribution. First, one parametric non-linear projection model of omnidirectional camera is estimated with the known rotation and translation parameters. After deriving projection model, we can compute an essential matrix of the camera with unknown motions, and then determine the camera information: rotation and translations. The standard deviations are used as a quantitative measure to select a proper inlier set. The experimental results showed that we can achieve a precise estimation of the omnidirectional camera model and extrinsic parameters including rotation and translation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.475-485
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• 2017

This paper develops a conversion function and method transforming from daily peak load curve used $LOLE_D$ [days/year] to hourly load curve used $LOLE_H$[hours/year]and describes relationship between $LOLE_D$ [days/year] and $LOLE_H$ [hours/year]. The indices can not only be transformed just arithmetically but also have different characteristics physically because of using their different load curves. The conversion function is formulated as variables of capacity and forced outage rate of generator, hourly load daily load factor and daily peak load yearly load factor, etc. Therefore, the conversion function (${\gamma}={\varphi}$(.)) can not be simple. In this study, therefore, the function is formulated as linear times of separated two functions. One is an exponential formed conversion function of daily load factor. Another is formulated with an exponential typed conversion function of daily peak load yearly load factor. Futhermore, this paper presents algorithm and flow chart for transforming from $LOLE_D$[days/year] to $LOLE_H$[hours/year]. The proposed conversion function is applied to sample system and actual KPS(Korea Power System) in 2015. The exponent coefficients of the conversion functions are assessed using proposed method. Finally, assessment errors using conversion function for case studies of sample system and actual system are evaluated to certify the firstly proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1131-1140
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• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.1
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• pp.7-12
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• 2003

The purpose of testability analysis is to estimate the difficulty of testing a stuck-at fault in logic circuits. A good testability measurement can give an early warning about the testing problem so as to provide guidance in improving the testability of a circuit. There have been researches attempting to efficiently compute the testability analysis. Conventional testability measurements, such as COP and SCOAP, can calculate the testability value of a stuck-at fault efficiently in a tree-structured circuit but may be very inaccurate for a general circuit. The inaccuracy is due to the ignorance of signal correlations for making the testability analysis linear to a circuit size. This paper proposes an efficient method for computing testability analysis, which takes into account signal correlation to obtain more accurate testability. The proposed method includes the algorithm for identifying all reconvergent fanouts in a given n circuit and the gates reachable from them, by which information related to signal correlation is gathered.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.43-48
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• 2017

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.

• Journal of Biosystems Engineering
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• v.34 no.2
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• pp.95-105
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• 2009

A method was proposed which employed control of the drop location of fertilizer particles on a spinner disc to optimize the spread pattern uniformity. The system contained an optical sensor as a feedback mechanism, which measured discharge velocity and location, as well as particle diameters to predict a spread pattern of a single disc. Simulations showed that the feed gate adaptation algorithm produced high quality patterns for any given application rate in the dual disc spreader. The performance of the feed gate control method was assessed using data collected from a Sulky spinner disc spreader. The results showed that it was always possible to find a spread pattern with an acceptable CV lower than 15%, even though the spread pattern was obtained from a rudimentary flat disc with straight radial vanes. A mathematical optimization method was used to find the initial parameter settings for a specially designed experimental spreading arrangement, which included the feed gate control system, for a given flow rate and swath width. Several experiments were carried out to investigate the relationship between the gate opening and flow rate, disc speed and particle velocity, as well as disc speed and predicted landing location of fertilizer particles. All relationships found were highly linear ($r^2$ > 0.96), which showed that the time-of-flight sensor was well suited as a feedback sensor in the rate and uniformity controlled spreading system.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.26-36
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• 2013

Purpose of this study is to develop virtual components and environment for developing a controller of an Active Air Suspension System in laboratory that slough off existing development environment using real vehicle test. This paper presents an air spring modeling and analysis of air suspension system for a commercial vehicle. Preferentially, It was performed vehicle test for pneumatic system and an air spring for characteristic analysis of system. Each component of an air spring suspension system was developed through emulations and modeling of system for pressure and height sensors in the basis on test results in SILS environment. Non-linear characteristics of air spring are accounted for using the measured data. Also, pressure and volume relations for vehicle hight control is considered. After performance verification of virtual model was performed, we developed virtual environment based on HILS for an Active Air Suspension System. We studied estimation and verification technology for control algorithm that developed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.18-24
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• 2017

The process of transmitting messages through a medium with a limited bandwidth or channel dispersion inevitably involves signal distortion and noise influxes, resulting in the degradation of transmission quality due to the inter-symbol interference and additional noise, which increases the error rate of the received symbols. The main role of the equalizer is to remove the channel distortion and noise from the received signal to recover the transmitted messages. A number of studies on the equalizer composed of a combination of linear filter and error control coding have shown that they played a key role in enhancing the transmission efficiency, which is essential for digital communication. This paper proposes a new algorithm to correct the residual symbol errors in the message signal. In general, equalizer performance improvement algorithms were developed to improve the initial convergence speed or steady-state error. In this paper, however, the equalizer input signal was reconstructed using the equalizer decision symbols and the channel estimates to directly correct the decision errors by analyzing the statistical characteristics of the difference signal between the actual received signal and the reconstructed signal.