• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.331-339
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• 2013

LiDAR has been one of the widely used and important technologies for 3D modeling of ground surface and objects because of its ability to provide dense and accurate range measurement. The objective of this research is to develop a method for automatic detection and modeling of railroad power lines using high density LiDAR data and RANSAC algorithms. For detecting railroad power lines, multi-echoes properties of laser data and shape knowledge of railroad power lines were employed. Cuboid analysis for detecting seed line segments, tracking lines, connecting and labeling are the main processes. For modeling railroad power lines, iterative RANSAC and least square adjustment were carried out to estimate the lines parameters. The validation of the result is very challenging due to the difficulties in determining the actual references on the ground surface. Standard deviations of 8cm and 5cm for x-y and z coordinates, respectively are satisfactory outcomes. In case of completeness, the result of visual inspection shows that all the lines are detected and modeled well as compare with the original point clouds. The overall processes are fully automated and the methods manage any state of railroad wires efficiently.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.4 s.27
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• pp.21-27
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• 2003

The rational polynomial coefficients(RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS-2 and QuickBird. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective camera the linear pushbroom sensor and the SAR This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects $510{\sim}730nm$ panchromatic images with a ground sample distance (GSD) of 6.6m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.35-44
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• 2021

As the execution speed of Python is slower than those of other programming languages (e.g., C, C++, and FORTRAN), Python is not considered to be efficient for writing numerical geodynamic code that requires numerous iterations. Recently, many computational techniques, such as the Just-In-Time (JIT) compiler, have been developed to enhance the calculation speed of Python. Here, we developed two-dimensional (2D) numerical geodynamic code that was optimized for the JIT compiler, based on Python. Our code simulates mantle convection by combining the Particle-In-Cell (PIC) scheme and the finite element method (FEM), which are both commonly used in geodynamic modeling. We benchmarked well-known mantle convection problems to evaluate the reliability of our code, which confirmed that the root mean square velocity and Nusselt number obtained from our numerical modeling were consistent with those of the mantle convection problems. The matrix assembly and PIC processes in our code, when run with the JIT compiler, successfully achieved a speed-up 30× and 258× faster than without the JIT compiler, respectively. Our Python-based FEM-PIC code shows the high potential of Python for geodynamic modeling cases that require complex computations.

• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.1
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• pp.45-58
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• 2013

The purpose of this study was to analyze the area of occlusal contact points using visual method. One subject was selected who had Angle Class I, normal dentition, without dental caries, periodontal disease and temporomandibular disorders. Forty times PVS impressions were taken and 10 pairs casts were fabricated using dental super hard stone. After mounting the casts with customized loading apparatus, 78.9kg/f force was loaded as a maximum biting force. In T-Scan method, occlusal contact points measurement was repeated twice. Then, using Photoshop program (Adobe photoshop CS3, Adobe. San Jose, USA), the pixels which indicated occlusal contact points by color was recognized, and the distribution of recognized pixels were calculated to area. In Add picture method, polyether bite material applied to the occlusal surface of the casts. Then, the image of the translucent areas was recorded and classified $0{\sim}10{\mu}m$, $0{\sim}30{\mu}m$, $0{\sim}60{\mu}m$ area by the amount of transmitted light. To acquire occlusal surface, the numbers of pixels from the photograph of the contact area indicated cast converted to $mm^2$. The mean occlusal contact area by two methods was statistically analyzed (paired t-test). Part of the red and pink area in T-Scan image were almost equivalent to the $0{\sim}10{\mu}m$, $0{\sim}30{\mu}m$, $0{\sim}60{\mu}m$ area in Add picture image. The distribution of occlusal contact points were similar, but the average area of occlusal contact points was wider in T-scan image (P<.05). Pink and red area in T-scan image was wider than $0{\sim}10{\mu}m$, $0{\sim}30{\mu}m$ area in Add picture image (P<.05), but similar to $0{\sim}60{\mu}m$area in Add picture image (P>.05). Occlusal contact points in T-scan image did not indicate real occlusal contact points. Occlusal contact areas in T-scan method were enlarged results comparing with those in Add picture method.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.6
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• pp.642-652
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• 2008

The aim of our research was to apply experimental design methodology in the optimization condition of Photo-Fenton oxidation of the residual livestock wastewater after the coagulation process. The reactions of Photo-Fenton oxidation were mathematically described as a function of parameters amount of Fe(II)($x_1$), $H_2O_2(x_2)$ and pH($x_3$) being modeled by the use of the Box-Behnken method, which was used for fitting 2nd order response surface models and was alternative to central composite designs. The application of RSM using the Box-Behnken method yielded the following regression equation, which is an empirical relationship between the removal(%) of livestock wastewater and test variables in coded unit: Y = 79.3 + 15.61x$_1$ - 7.31x$_2$ - 4.26x$_3$ - 18x$_1{^2}$ - 10x$_2{^2}$ - 11.9x$_3{^2}$ + 2.49x$_1$x$_2$ - 4.4x$_2$x$_3$ - 1.65x$_1$x$_3$. The model predicted also agreed with the experimentally observed result(R$^2$ = 0.96) The results show that the response of treatment removal(%) in Photo-Fenton oxidation of livestock wastewater were significantly affected by the synergistic effect of linear terms(Fe(II)($x_1$), $H_2O_2(x_2)$, pH(x$_3$)), whereas Fe(II) $\times$ Fe(II)(x$_1{^2}$), $H_2O_2$ $\times$ $H_2O_2$(x$_2{^2}$) and pH $\times$ pH(x$_3{^2}$) on the quadratic terms were significantly affected by the antagonistic effect. $H_2O_2$ $\times$ pH(x$_2$x$_3$) had also a antagonistic effect in the cross-product term. The estimated ridge of the expected maximum response and optimal conditions for Y using canonical analysis were 84 $\pm$ 0.95% and (Fe(II)(X$_1$) = 0.0146 mM, $H_2O_2$(X$_2$) = 0.0867 mM and pH(X$_3$) = 4.704, respectively. The optimal ratio of Fe/H$_2O_2$ was also 0.17 at the pH 4.7.

• Sleep Medicine and Psychophysiology
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• v.24 no.2
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• pp.106-117
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• 2017

Objectives: Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is characterized by repetitive collapse or partial collapse of the upper airway during sleep in spite of ongoing effort to breathe. It is believed that OSA is usually worsened in REM sleep, because muscle tone is suppressed during REM sleep. However, many cases showed a higher apnea-hypopnea index (AHI) during NREM sleep than during REM sleep. We aimed here to determine the characteristics of REM sleep-dependent OSA (REM-OSA) and NREM sleep-dependent OSA (NREM-OSA). Methods: Five hundred sixty polysomnographically confirmed adult OSA subjects were studied retrospectively. All patients were classified into 3 groups based on the ratio between REM-AHI and NREM-AHI. REM-OSA was defined as REM-AHI/NREM-AHI > 2, NREM-OSA as NREM-AHI/REM-AHI > 2, and the rest as sleep stage-independent OSA (IND-OSA). In addition to polysomnography, questionnaires related to subjective sleep quality, daytime sleepiness, and emotion were completed. Chi-square test, ANOVA, and ANCOVA were performed. Results: There was no age difference among subgroups. The REM-OSA group was comprised of large proportions of mild OSA and female OSA patients. These patients experienced poor sleep and more negative emotions than other two groups. The AHI and oxygen desaturation index (ODI) were lowest in REM-OSA. Sleep efficiency and N3 percentage of REM-OSA were higher than in NREM-OSA. The percentage of patients who slept in a supine position was higher in REM-OSA than other subgroups. IND-OSA showed higher BMI and larger neck circumference and abdominal circumference than REM-OSA. The patients with IND-OSA experienced more sleepiness than the other groups. AHI and ODI were highest in IND-OSA. NREM-OSA presented the shortest total sleep time and the lowest sleep efficiency. NREM-OSA showed shorter sleep latency and REM latency and higher percentage of N1 than those of REM-OSA and the highest proportion of those who slept in a lateral position than other subgroups. NREM-OSA revealed the highest composite score on the Horne and ${\ddot{O}}stberg$ questionnaire. With increased AHI severity, the numbers of apnea and hypopnea events during REM sleep decreased, and the numbers of apnea and hypopnea events during NREM sleep increased. The results of ANCOVA after controlling age, sex, BMI, NC, AC, and AHI showed the lowest sleep efficiency, the highest AHI in the supine position, and the highest percentage of waking after sleep onset in NREM-OSA. Conclusion: REM-OSA was associated with the mild form of OSA, female sex, and negative emotions. IND-OSA was associated with the severe form of OSA. NREM-OSA was most closely related to position and showed the lowest sleep efficiency. Sleep stage-dependent characteristics could provide better understanding of OSA.