• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.89-94
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• 1999

In vector quantization (VQ), mean squared error (MSE) is widely used as a distance measure between vectors. But the distance between averages appears as a dominant quantity in MSE. In the case of image vectors, the coincidence of edge pattern is also important considering human visual system (HVS). Therefore, this paper presents a new distance measure using the variance of difference (VD) as a criterion for the coincidence of edge pattern. By using this in the VQ encoding, we can reduce the degradation of edge region in the reconstructed image. And applying this to the codebook design, we can obtain the final codebook that has a lot of various edge codevectors instead of redundant shade ones.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.901-903
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• 2010

본 연구에서는 2007-2009년에 간호학과 및 스포츠과학부 해부학 강의에 있어 가상강의를 실시하였고 이에 따른 학생들의 가상강의 컨텐츠의 만족도와 가상강의실 활용빈도에 따른 학업성취도와의 상관관계를 분석하였다. 2007-2009학년도 1학기 해부학 강의를 가상강의 방식으로 수강한 2개 학과 231명을 대상으로 강의 종료 후 가상강의실 게시판 및 컨텐츠 활용 빈도, 개별 설문조사, 학기말 성적을 확보하여 상호간의 상관관계를 분석하였다. 각 학과별 일반 특성을 보기 위하여 연속 변수의 경우 평균과 표준오차를, 범주형 변수의 경우 그 분포 퍼센트를 이용하여 제시하였다. 학과별로 모든 학생들의 총점을 3분위수로 구분하여 낮음, 중간, 높음으로 분류하였으며, 조사된 모든 변수들의 일반 선형성을 GLM 모델을 이용하여 검증하였다. 사후 검증은 최소자승법을 이용하여 실시하였으며, 이를 이용하여 각각의 세부 집단별 점수 차이에 대한 유의성을 평가하였다. 관련 항목들 간 상관성 분석을 위하여 스피어만 상관계수를 이용하여 p 값 0.05를 기준으로 유의성 검증을 실시하였다. 모든 통계분석은 SAS 9.12 버전을 이용하여 분석하였다. 설문 대상자들은 학과와 해부학 성적에 상관없이 가상강의 전반에 대해 높은 만족도를 가졌다. 해부학 성적이 좋을수록 가상강의실 접속횟수가 유의하게 높았다. 아울러 해부학 성적이 좋을수록 난이도가 낮고 흥미도는 높게 나타났다. 또한 평가 요인들 간의 상관관계를 분석한 결과, 가상강의의 만족도는 흥미도와 전공과의 연계성과 밀접한 관련이 있었다. 가상강의 게시판을 통한 과제물 관리, 질의응답에 대한 적절성에 대해서는 성적에 따라 일부 유의한 차이가 나타났으나 높은 만족도를 나타내었다. 결론적으로 건강 및 의료 전공자들을 위한 해부학 강의에 있어 가상강의의 도입과 적용은 성공적이었으며, 이는 해부학 전공 교수진이 매우 부족한 현실에서 해부학 강의의 질적 저하를 막고 효율적인 교육을 위한 대안이 될 것으로 판단된다. 단, 해부학 가상강의 컨텐츠의 개선, 자료 보강 및 가상강의의 접근성 확보는 시급히 개선해야할 과제로 남아 있다.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• Radiation Oncology Journal
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• v.8 no.1
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• pp.115-124
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• 1990

The characteristics of 23 MV photon beam have been presented with respect to clinical parameters of central axis depth dose, tissue-maxi mum ratios, scatter-maximum ratios, surface dose and scatter correction factors. The nominal accelerating potential was found to be $18.5\pm0.5$ MV on the central axis. The half-value layer (HVL) of this photon beam was measured with narrow beam geometry from central axis, and it has been showed the thickness of $24.5\;g/cm^2$. The tissue-maximum ratio values have been determined from measured percentage depth dose data. In our experimental dosimetry, the surface dose of maximum showed only $9.6\%$ of maximum dose at $10\times10\;cm^2$, 100 cm SSD, without blocking tray in. The TMR'S of $0\times0$ field size have been determined to get average $2.3\%$ uncertainties from three different methodis; are zero effective attenuation coefficient, non-ilnear least square fit of TMR's data and effective linear attenuation coefficient from the HVL of 23 MV photon beams of dual energy linear accelerator.

• Geophysics and Geophysical Exploration
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• v.17 no.1
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• pp.11-20
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• 2014

To reveal shear-wave velocities ($v_s$) and site characterization of Wonju, Korea, Rayleigh waves were recorded at 78 sites of lower altitude using 12 to 24 4.5-Hz vertical geophones for 20 days during the period of February to September 2013. Dispersion curves of the Rayleigh waves obtained by the extended spatial autocorrelation method were inverted using the damped least-squares method to derive $v_s$ models. From these 1-D models, the average $v_s$ to a depth of 30 m ($v_s30$), $v_s$ of weathered rocks, depths to these basement rocks, and average $v_s$ of the overburden layer were derived to be $16.3{\pm}0.7m$, $576{\pm}8m/s$, $290{\pm}7m/s$, and $418{\pm}13m/s$, respectively, in the 95% confidence range. To determine adequate proxies for $v_s30$, we computed correlation coefficients of $v_s30$ with topographic slope (r = 0.46) and elevation (r = 0.43). An empirical linear relationship is presented as a combination of individually estimated $v_s30$ with weighting factors of 0.45, 0.45, and 0.1 for topographic slope, elevation, and mapped lithology, respectively. Due to a weak correlation between $v_s30$ obtained from inversion of dispersion curves and the proxy-based estimation (r = 0.50), however, the relatively large error range should be considered for applications of this relationship.

• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.717-726
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• 2009

Multivariate models were developed for the simultaneous spectrophotometric determination of copper (II), nickel (II) and zinc (II) in water with 1-(2-thiazolylazo)-2-naphthol as chromogenic reagent in the presence of Triton X-100. To overcome the drawback of spectral interferences, principal component regression (PCR) and partial least square (PLS) multivariate calibration approaches were applied. Performances were validated with several test sets, and their results were then compared. In general, no significant difference in analytical performance between PLS and PCR models. The root mean square error of prediction (RMSEP) using three components for $Cu^{2+}$, $Ni^{2+}$ and $Zn^{2+}$ were 0.018, 0.010, 0.011 ppm, respectively. Figures of merit such as sensitivity, analytical sensitivity, limit of detection (LOD) were also estimated. High reliability was achieved when the proposed procedure was applied to simultaneous determination of $Cu^{2+}$, $Ni^{2+}$ and $Zn^{2+}$ in synthetic mixture and tap water.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.5
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• pp.278-284
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• 2014

Water temperature due to climate change can be estimated using the air temperature because the air and water temperatures are closely related and the water temperatures have been widely used as the indicators of the environmental and ecological changes. It is highly necessary to estimate the frequency distribution of the air and water temperatures, for the climate change derives the change of the coastal water temperatures. In this study, the distribution function of the air temperatures is estimated by using the long-term coastal air temperature data sets in Korea. The candidate distribution function is the bi-modal distribution function used in the previous studies, such as Cho et al.(2003) on tidal elevation data and Jeong et al.(2013) on the coastal water temperature data. The parameters of the function are optimally estimated based on the least square method. It shows that the optimal parameters are highly correlated to the basic statistical informations, such as mean, standard deviation, and skewness coefficient. The RMS error of the parameter estimation using statistical information ranges is about 5 %. In addition, the bimodal distribution fits good to the overall frequency pattern of the air temperature. However, it can be regarded as the limitations that the distribution shows some mismatch with the rapid decreasing pattern in the high-temperature region and the some small peaks.

• 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.