• Atmosphere
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• v.27 no.2
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• pp.119-131
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• 2017

We present and discuss the Tropopause Folding Turbulence Detection (TFTD) algorithm for the Korean Communication, Ocean, Meteorological Satellite (COMS) which is originally developed for the Tropopause Folding Turbulence Product (TFTP) from the Geostationary Operational Environmental Satellite (GOES)-R. The TFTD algorithm assumes that the tropopause folding is linked to the Clear Air Turbulence (CAT), and thereby the tropopause folding areas are detected from the rapid spatial gradients of the upper tropospheric specific humidity. The Layer Averaged Specific Humidity (LASH) is used to represent the upper tropospheric specific humidity calculated using COMS $6.7{\mu}m$ water vapor channel and ERA-interim reanalysis temperature at 300, 400, and 500 hPa. The comparison of LASH with the numerical model specific humidity shows a strong negative correlation of 80% or more. We apply the single threshold, which is determined from sensitivity analysis, for cloud-clearing to overcome strong gradient of LASH at the edge of clouds. The tropopause break lines are detected from the location of strong LASH-gradient using the Canny edge detection based on the image processing technique. The tropopause folding area is defined by expanding the break lines by 2-degree positive gradient direction. The validations of COMS TFTD is performed with Pilot Reports (PIREPs) filtered out Convective Induced Turbulence (CIT) from Dec 2013 to Nov 2014 over the South Korea. The score test shows 0.49 PODy (Probability of Detection 'Yes') and 0.64 PODn (Probability of Detection 'No'). Low POD results from various kinds of CAT reported from PIREPs and the characteristics of high sensitivity in edge detection algorithm.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.314-321
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• 2007

Recently, multi-dimensional joint inversion of geophysical data based on fundamentally different physical properties is being actively studied. Joint inversion can provide a way to obtaining much more accurate image of the subsurface structure. Through the joint inversion, furthermore, it is possible to directly estimate non-geophysical material properties from geophysical measurements. In this study, we developed a new algorithm for jointly inverting dc resistivity and seismic traveltime data based on the multiple constraints: (1) structural similarity based on cross-gradient, (2) correlation between two different material properties, and (3) a priori information on the material property distribution. Through the numerical experiments of surface dc resistivity and seismic refraction surveys, the performance of the proposed algorithm was demonstrated and the effects of different regularizations were analyzed. In particular, we showed that the hidden layer problem in the seismic refraction method due to an inter-bedded low velocity layer can be solved by the joint inversion when appropriate constraints are applied.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.6
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• pp.59-66
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• 2013

Spray characteristics of single round jet with liquid nitrogen and coaxial spray with liquid nitrogen and gaseous argon were observed. Shadowgraph method was used for spray visualization, and density gradient magnitude image was used to analyse the result. In subcritical condition, irregularity of the jet surface was harder in the coaxial spray. In supercritical condition, diffusion of nitrogen injected from shear coaxial injector was faster than single jet. Jet diameter was induced by averaging images, in supercritical condition, difference of diameter of coaxial jet was rapidly decreased than that of single jet.

• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.57-63
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• 2019

Motion errors which are caused by several reasons, non-ideal path, errors of navigation systems, and radar system errors, have to be corrected. Motion compensation methods can compensate the motion error, but not exactly. To correct these residual errors, several autofocus methods are invented. A popular method is phase gradient autofocus (PGA). PGA does not assume specific circumstances, such as isolated point targets and shapes of errors. PGA is an iterative and adaptive method, so that the processing time is the main problem for the real time processing. In this paper, the improved method to select targets for PGA is proposed to reduce processing time. The variances of image pixels are used to select targets with high SNR. The processing of PGA with these targets diminishes the processing time and iterations effectively. The processed results with real radar data, obtained by flight tests, show that the proposed method compensates errors well, and reduce working time.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.141-147
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• 2024

Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons progressively generated from lights. Its hyperparameter, which controls the reduction rate of the density estimation, highly affects the quality of its rendering image due to the bias-variance tradeoff of pixel estimates in photon-mapped results. We can minimize the errors of rendered pixel estimates in progressive photon mapping by estimating the optimal parameters based on gradient-based optimization techniques. To this end, we derived the gradients of pixel estimates with respect to the parameters when performing progressive photon mapping and compared our estimated gradients with finite differences to verify estimated gradients. The gradient estimated in this paper can be applied in an online learning algorithm that simultaneously performs progressive photon mapping and parameter optimization in future work.

• Journal of the Korean Society of Radiology
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• v.15 no.7
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• pp.999-1005
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• 2021

The object of this research is CS, which increases resolution while shortening inspection time, is applied to MRA to compare the quality of images for SENSE and CS techniques and to evaluate SNR and CNR to find out the optimal techniques and to provide them as clinical basic data based on this information. Data were analyzed on 32 patients who performed TOF MRA tests at a university hospital in Chung cheong-do (15 males, 17 females), ICA stenosis:10, M1 Aneurysm:10, and average age 53 ± 4.15). In the inspection, the inspection equipment was Ingenia CX 3.0T, Archieva 3.0T, and 32 channel head coil and 3D gradient echo as a method for equipment data. SNR and CNR of each image were measured by quantitative analysis, and the quality of the image was evaluated by dividing the observer's observation into 5 grades for qualitative evaluation. Imaging evaluation is described as being significant when the p-value is 0.05 or less when the paired T-test and Wilcoxon test are performed. Quantitative analysis of SNR and CNR in TOF MRA images Compared to the SENSE method, the CS method is a method measurement method (p <0.05). As an observer's evaluation, the sharpness of blood vessels: CS (4.45 ± 0.41), overall image quality: CS (4.77 ± 0.18), background suppression of images: CS (4.57 ± 0.18) all resulted in high CS technique (p = 0.000). In conclusion, the Compressed SENSE TOF MRA technique shows superior results when comparing and evaluating the SENSE and Compressed SENSE techniques in increased flow rate magnetic resonance angiography. The results are thought to be the clinical basis material in the 3D TOF MRA examination for brain disease.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• Journal of Korean Neurosurgical Society
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• v.30 no.2
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• pp.168-172
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• 2001

Objective : The authors have studied the clinical outcome of patients with diffuse axonal injuries(DAI) to evaluate the prognostic value of gradient-echo MR imaging findings. Materials and Methods : From March 1995 to March 1998, there were nineteen patients with DAI whose initial Glasgow coma scales were eight or less. Authors divided them into two groups according to Glasgow outcome scales ; those patients with GOS 3 or less(group A ; 9) and those with 4 or more(group B ; 10). We subdivided the lesions as superficial and deep lesion, and analyzed the numbers, anatomical loci of the lesions on the gradient echo images of each group. Results : Mean numbers of the lesions were 15 per case in group A(135/9) and 10 in group B(100/10). The common loci involved in DAI were cerebral cortex, brain stem, and corpus callosum. Cortical lesions were 31.1% in group A(42/135) and 47% in group B(47/100). Brain stem lesions were 25.9%(35/135) and 15%(15/100) each. Callosal lesions were 31.1%(26/135) and 13%(13/100) each. The frequency of callosal and brain stem lesions was significantly different between two groups(p<0.05). We divided callosal lesions as genu, body, and splenium and body lesions as anterior, middle, posterior, but no significant topographical difference of lesions was observed between two groups. Deep lesions were observed more frequently in group A(58.5%, 79/135) than group B(36%, 36/100). Conclusion : The poor outcome group showed more numbers of lesion and more frequent involvement of brain stem and corpus callosum than favorable outcome group. Gradient-echo MR imaging seems to have predictive value for clinical outcome in patients with DAI.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.142-147
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• 1997

Purpose: To compare the effectiveness of the in-phase (IP) sequence and the opposed-phase (Op) sequence in the detection of focal hepatic lesions in the single breath-hold hepatic MR imaging with fast gradient T1-weighted pulse sequences. Materials and Methods: IP and OP T1-weighted breath-hold imaging was performed using fast gradient echo sequences in 45 patients referred for known focal hepatic lesions, in which 78 lesions were detected. Three blind readers independently reviewed the images for lesion detectability. The signal-to-noise ratio (SNR) of the liver, the lesion-to-liver contrast-to-noise ratio (CNR) and the liver-to-spleen CNR were also compared. A consensus was reached by three readers to determine which sequence is better in image quality. Results: On OP images, 61(78%), 61(78%), and 63(89%) lesions were correctly identified for reader 1, 2 and 3, respectively. On IP images, 66(85%), 65(83%), and 65(93%) lesions were detected for each reader, respectively. When two image sets were combined, 71(91 %), 69(88 %), and 76(97%) lesions respectively were detected for each reader. In cases of hepatocellular carcinoma, liver-to-Iesion CNR was greater on the OP images(p (0.05), but in other lesions significant difference was not demonstrated. Liver-to-spleen CNR was higher on OP images(p ( 0.1), but the SNR of the liver was higher on the IP images. Conclusion: Use of both IP and OP imaging can be helpful to avoid erroneous missing of some focal hepatic lesions.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.169-179
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• 2013

Change detection algorithms take two image frames and return the locations of newly introduced objects which cause differences between the images. This paper presents a new change detection method, which classifies intensity changes due to introduced objects, reflected light and shadow from the objects to their neighborhood, and the noise, and exactly localizes the introduced objects. For classification and localization, first we analyze the histogram of the intensity difference between two images, and estimate multiple threshold values. Second we estimate candidate object boundaries using the gradient difference between two images. Using those threshold values and candidate object boundaries, we segment the frame difference image into multiple regions. Finally we classify whether each region belongs to the introduced objects or not using textures in the region. Experiments show that the proposed method exactly localizes the objects in various scenes with different lighting.