• Geophysics and Geophysical Exploration
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• v.21 no.3
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• pp.171-182
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• 2018

We performed the seismic field data processing using an open-source software (Madagascar) to verify if it is applicable to processing of field data, which has low signal-to-noise ratio and high uncertainties in velocities. The Madagascar, based on Python, is usually supposed to be better in the development of processing technologies due to its capabilities of multidimensional data analysis and reproducibility. However, this open-source software has not been widely used so far for field data processing because of complicated interfaces and data structure system. To verify the effectiveness of the Madagascar software on field data, we applied it to a typical seismic data processing flow including data loading, geometry build-up, F-K filter, predictive deconvolution, velocity analysis, normal moveout correction, stack, and migration. The field data for the test were acquired in Gunsan Basin, Yellow Sea using a streamer consisting of 480 channels and 4 arrays of air-guns. The results at all processing step are compared with those processed with Landmark's ProMAX (SeisSpace R5000) which is a commercial processing software. Madagascar shows relatively high efficiencies in data IO and management as well as reproducibility. Additionally, it shows quick and exact calculations in some automated procedures such as stacking velocity analysis. There were no remarkable differences in the results after applying the signal enhancement flows of both software. For the deeper part of the substructure image, however, the commercial software shows better results than the open-source software. This is simply because the commercial software has various flows for de-multiple and provides interactive processing environments for delicate processing works compared to Madagascar. Considering that many researchers around the world are developing various data processing algorithms for Madagascar, we can expect that the open-source software such as Madagascar can be widely used for commercial-level processing with the strength of expandability, cost effectiveness and reproducibility.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.919-926
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• 2021

In this study, we wanted to find out the optimal angle as a modified tangential projection of the patella. In the experiment, we used Kyoto Kagaku's PBU-50 phantom. In the supine position, the F-T angle was set to 95°, 105°, 115°, 125°, 135°, 145°, and Patella tangential projection images were obtained by varying the X-ray tube angle by 5° so that the angle between the X-ray centerline and tibia at each angle was 5~20°. Image J was used for image analysis and the congruence angle, lateral patellofemoral angle, patellofemoral index and contrast to noise ratio(CNR) were also measured. SPSS 22 was used for statistical analysis, and the mean values of congruence angle, patellofemoral angle, patellofemoral index, and CNR were compared with Merchant method through one-way batch analysis and corresponding sample t-test. As a result of the study, in the case of congruence angle, the angle of incidence of the knee-angle X-ray centerline was 105°-72.5° (20° tangential irradiation), 115°-72.5°, 77.5° (15, 20° tangential irradiation), 125°-82.5° (20° tangential irradiation), lateral patellofemoral angle is 115°-72.5°, 77.5° (15, 20° tangential irradiation), 125°-72.5° (10° tangential irradiation), patellofemoral index is 115°-72.5° (15° tangential irradiation) and 125°-72.5° (10° tangential irradiation) were not significantly different from Merchant method (p> .05). In case of CNR, it is not different from Merchant method at 105°-67.5°, 72.5° (15, 20° tangential irradiation), 115°-67.5°, 72.5°, 77.5° (10, 15, 20° tangential irradiation). (P> .05). Based on the results of this study, high diagnostic value images can be obtained by setting the knee angle and the angle of incidence of the X-ray tube to 115°-72.5° (15° tangential irradiation) during the modified tangential examination of the knee bone. It was confirmed.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.83-89
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• 2010

Purpose: The Wide Beam Reconstruction (WBR) algorithms that UltraSPECT, Ltd. (U.S) has provides solutions which improved image resolution by eliminating the effect of the line spread function by collimator and suppression of the noise. It controls the resolution and noise level automatically and yields unsurpassed image quality. The aim of this study is WBR of whole body bone scan in usefulness of clinical application. Materials and Methods: The standard line source and single photon emission computed tomography (SPECT) reconstructed spatial resolution measurements were performed on an INFINA (GE, Milwaukee, WI) gamma camera, equipped with low energy high resolution (LEHR) collimators. The total counts of line source measurements with 200 kcps and 300 kcps. The SPECT phantoms analyzed spatial resolution by the changing matrix size. Also a clinical evaluation study was performed with forty three patients, referred for bone scans. First group altered scan speed with 20 and 30 cm/min and dosage of 740 MBq (20 mCi) of $^{99m}Tc$-HDP administered but second group altered dosage of $^{99m}Tc$-HDP with 740 and 1,110 MBq (20 mCi and 30 mCi) in same scan speed. The acquired data was reconstructed using the typical clinical protocol in use and the WBR protocol. The patient's information was removed and a blind reading was done on each reconstruction method. For each reading, a questionnaire was completed in which the reader was asked to evaluate, on a scale of 1-5 point. Results: The result of planar WBR data improved resolution more than 10%. The Full-Width at Half-Maximum (FWHM) of WBR data improved about 16% (Standard: 8.45, WBR: 7.09). SPECT WBR data improved resolution more than about 50% and evaluate FWHM of WBR data (Standard: 3.52, WBR: 1.65). A clinical evaluation study, there was no statistically significant difference between the two method, which includes improvement of the bone to soft tissue ratio and the image resolution (first group p=0.07, second group p=0.458). Conclusion: The WBR method allows to shorten the acquisition time of bone scans while simultaneously providing improved image quality and to reduce the dosage of radiopharmaceuticals reducing radiation dose. Therefore, the WBR method can be applied to a wide range of clinical applications to provide clinical values as well as image quality.

• Journal of the Korean Society of Radiology
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• v.11 no.1
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• pp.27-35
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• 2017

The purpose of this study was to investigate the features of CsI:Tl and $Gd_2O_2S$ detectors with an indirect conversion method using phantom in the DR (digital radiography) system by obtaining images of thick chest phantom, medium thickness thigh phantom, and thin hand phantom and by analyzing the SNR and CNR. As a result of measuring the SNR and CNR according to the thickness change of the subject, the SNR and CNR were higher in CsI:Tl detector than in $Gd_2O_2S$ detector when the medium thickness thigh phantom and thin hand phantom were scanned. However, when the thick chest phantom was used, for the SNR at 80~125 kVp and the CNR at 80~110 kVp in the $Gd_2O_2S$ detector, the values were higher than those of CsI:Tl detector. The SNR and CNR both increased as the tube voltage increased. The SNR and CNR of CsI:Tl detector in the medium thickness thigh phantom increased at 40~50 kVp and decreased as the tube voltage increased. The SNR and CNR of $Gd_2O_2S$ detector increased at 40~60 kVp and decreased as the tube voltage increased. The SNR and CNR of CsI:Tl detctor in the thin hand phantom decreased at the low tube voltage and increased as the tube voltage increased, but they decreased again at 100~110 kVp, while the SNR and CNR of $Gd_2O_2S$ detector were found to decrease as the tube voltage increased. The MTF of CsI:Tl detector was 6.02~90.90% higher than that of $Gd_2O_2S$ detector at 0.5~3 lp/mm. The DQE of CsI:Tl detector was 66.67~233.33% higher than that of $Gd_2O_2S$ detector. In conclusion, although the values of CsI:Tl detector were higher than those of $Gd_2O_2S$ detector in the comparison of MTF and DQE, the cheaper $Gd_2O_2S$ detector had higher SNR and CNR than the expensive CsI:Tl detector at a certain tube voltage range in the thick check phantom. At chest X-ray, if the $Gd_2O_2S$ detector is used rather than the CsI:Tl detector, chest images with excellent quality can be obtained, which will be useful for examination. Moreover, price/performance should be considered when determining the detector type from the viewpoint of the user.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.2
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• pp.36-40
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• 2011

Purpose: $^{18}F$-FDG injected dose to the patient is quite different between the recommended dose from manufacturer and the actual dose applied to each of hospitals. injection of inappropriate $^{18}F$-FDG dose may not only increase the exposed dose to patients but also reduce the image quality. we thus evaluated the proper $^{18}F$-FDG injected dose to decrease the exposed dose to patients considering the image quality. Materials And Methods: NEMA Nu2-1994 phantom was filled with $^{18}F$-FDG increasing hot cylinder radioactivity concentration to 1, 3, 5, 7, 9 MBq/kg based on the ratio of 4:1 between the hot cylinder and background activity. after completing the transmission scan using ct, emission scan was acquired in 3D mode for 2 minutes 30 seconds/bed. ROI was set up on hot cylinder and background radioactivity region. after measuring $SUV_{max}$ those regions, then analyzed SNR at the points. clinical experiment has been conducted the object of patients who have came to smc from november 2009 to august 2010, 97 patients without having a hepatic lesions were selected. ROI was set up in the liver and thigh area. after measuring $SUV_{max}$, the image quality was compared following the injected dose. Results: in phantom study, as the injected radioactivity concentration per unit mass was 1, 3, 5, 7, 9 MBq/kg, $SUV_{max}$ was 23.1, 24.1, 24.3, 22.8, 23.6 and SNR was shown 0.48, 0.54, 0.56, 0.55, 0.55. according to increment of the injected dose, $SUV_{max}$ and SNR was increased under 5 MBq/kg but they were decreased over 7 MBq/kg. in case of clinical experiment, as increased the injected radioactivity concentration per unit mass was 4.72, 5.34, 6.16, 7.41, 8.68 MBq/kg, $SUV_{max}$ was 2.68, 2.67, 2.26, 1.88, 1.95 and SNR was shown 0.52, 0.53, 0.46, 0.46, 0.44. if the injected dose exceeds 5 MBq/kg, showed a decrease pattern as phantom study. Conclusion: increasing $^{18}F$-FDG injected dose considered patient's body weight improve image quality within a certain range. if it exceeds the range, it can be reduced image quality due to random and scatter coincidences. this study indicates that the optimal injected dose was 5 MBq/kg per unit mass the injected radioactivity concentration in 3d wb pet/ct.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Journal of the Korean Vacuum Society
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• v.18 no.3
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• pp.197-202
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• 2009

The long term stability of ion current of QMS has been one of key parameters for monitoring gas process in vacuum. The time dependence of ionic current was monitored while the pressure of nitrogen gas was kept at a fixed pressure by introducing the gas into vacuum chamber. The chamber was evacuated to ${\sim}3{\times}10^{-9}\;Torr$ to reduce background signals before the measurement. Two ion sources were tested; one had brownish or black color due to gas contamination and the other one was new, i.e. cleaner. At a nitrogen pressure of $1{\times}10^{-5}\;Torr$, the ionic currents measured by the contaminated ion source decreased faster with time. The decrease rate was respectively ${\sim}46%$ for cleaner one and ${\sim}84%$ for contaminated one after ${\sim}5.5%$ hours. In order to test the effect of filament material on the ion current decrease, we fabricated a tungsten(W) filament which consisted of two parts; one half was made of W and the other was coated with yttria. The similar decrease of ionic currents were shown for the two types of filaments, indicating that slight change of temperature of filament due to material difference i.e. baking effect could not improve the origin of ionic current decrease. Overall the decreasing rate of ionic current is more closely associated with contaminated ion source of QMS rather than its filament materials.

• Progress in Medical Physics
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• v.21 no.1
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• pp.113-119
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• 2010

Software for GafChromic EBT2 film dosimetry was developed in this study. The software provides film calibration functions based on color channels, which are categorized depending on the colors red, green, blue, and gray. Evaluations of the correction effects for light scattering of a flat-bed scanner and thickness differences of the active layer are available. Dosimetric results from EBT2 films can be compared with those from the treatment planning system ECLIPSE or the two-dimensional ionization chamber array MatriXX. Dose verification using EBT2 films is implemented by carrying out the following procedures: file import, noise filtering, background correction and active layer correction, dose calculation, and evaluation. The relative and absolute background corrections are selectively applied. The calibration results and fitting equation for the sensitometric curve are exported to files. After two different types of dose matrixes are aligned through the interpolation of spatial pixel spacing, interactive translation, and rotation, profiles and isodose curves are compared. In addition, the gamma index and gamma histogram are analyzed according to the determined criteria of distance-to-agreement and dose difference. The performance evaluations were achieved by dose verification in the $60^{\circ}$-enhanced dynamic wedged field and intensity-modulated (IM) beams for prostate cancer. All pass ratios for the two types of tests showed more than 99% in the evaluation, and a gamma histogram with 3 mm and 3% criteria was used. The software was developed for use in routine periodic quality assurance and complex IM beam verification. It can also be used as a dedicated radiochromic film software tool for analyzing dose distribution.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.249-256
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• 2010

To the detect of SPIO-labelled hMSC, in vitro study on various cell concentration and in vivo molecular magnetic resonance imaging(MRI) technique using T2, $T2^*$ and SWI are compared with pathology. Cell concentration was $1.56{\times}10^4$, $3.13{\times}10^4$, $6.25{\times}10^4$, $1.25{\times}10^5$, $2.5{\times}10^5$, $5{\times}10^5\;cells/m{\ell}$ and for control $5{\times}10^5\cells/m{\ell}$. MRI technique using T2, $T^2$ and SWI. Photothrombotic infarction was located 2.5mm from bregma right, posterior. Cell injected through the tail vein of rat for 8 rats. MRI performed pre injection and post injection of 1, 3, 7 and 14days and sacrifice for pathology. MRI analysed on quantitatively. In vitro result, SWI was highest CNR as compared with $T2^*WI$, T2WI and $2.5{\times}10^5\;cells/m{\ell}$ cell concentration. In vivo result among the T2WI, $T2^WI$, SWI, T2WI is highest CNR between normal and infarction. CNR in normal-SPIO and infarction-SPIO is high score in SWI. Therefore, T2WI is good distinguish between normal and infarction, SWI are well detect SPIO-labelled hMSC from normal and infarction. Nowaday, SWI are mostly used on hemorrhage, calcification etc. in clinically, but for the future, stem cell therapy is commonly application at all disease which is good observing tool for SPIO-labelled stem cells.