• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.871-883
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• 2015

Vector autoregressive (VAR) models in high dimension suffer from noisy estimates, unstable predictions and hard interpretation. Consequently, the sparse vector autoregressive (sVAR) model, which forces many small coefficients in VAR to exactly zero, has been suggested and proven effective for the modeling of high dimensional time series data. This paper studies coupling measures to select non-zero coefficients in sVAR. The basic idea based on the simulation study reveals that removing the effect of other variables greatly improves the performance of coupling measures. sVAR model coefficients are asymmetric; therefore, asymmetric coupling measures such as Granger causality improve computational costs. We propose two asymmetric coupling measures, filtered-cross-correlation and filtered-Granger-causality, based on the filtered residuals series. Our proposed coupling measures are proven adequate for heavy-tailed and high order sVAR models in the simulation study.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.427-439
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• 2017

Quade (1967) proposed RANK ANCOVA, which is a nonparametric method to test differences between treatments when there are covariates. Hwang and Kim (2012) also proposed a joint placement test on covariate-adjusted residuals. In this paper, we proposed a new nonparametric method to control the effect of covariate on a response variable that uses linear statistics on covariate adjusted-residuals. The score function used in the linear statistics was proposed by Jeon and Kim (2016). Monte Carlo simulation is also conducted to compare the empirical powers of the proposed method with previous methods.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.361-369
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• 2015

A general linear mixed-effects model is often used to analyze repeated measurement experiment data of a continuous response variable. However, a general linear mixed-effects model can give improper analysis results when simultaneously detecting heteroscedasticity and the non-normality of population distribution. To achieve a more robust estimation, we used a heavy-tailed linear mixed-effects model for a more exact and reliable analysis conclusion than a general linear mixed-effects model. We also provide reliability analysis results for further research.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.131-140
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• 1994

Box-Jenkins 시계열 분석에서 모형검진을 위한 통계량으로 잔차의 자기상관함수를 이용한 Box와 Pierce(1970)의 포트맨토우 검정과 Ljung과 Box(1978)의 변형된 포트맨토우 검정을 Basawa(1987)가 제안한 예측오차를 이용한 모형 검진 방법과 비교, 분석하였다. 시뮬레이션 연구를 수행하여 경험적 평균, 분산 및 유의 수준을 비교하여 과대적합의 방법을 이용하여 검정력을 비교하였다.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.159-171
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• 1996

The aim of this study is which factors affect the commercial land values and how they act upon them through distribution of commercial land values by multiple regression analysis in Gwangju city. The major findings of this study are as follows: (1) The changes of commercial land values distribution in $1989{\sim}1996$, We see that the commercial area of higher land values extends following the main arterial road. This is related to urbanization in urban fringe while the decline of commercial land values occurs in city center with long history of commercial region. This is due to unsuitableness in rapid changes of commercial environment because of fragmented lots, old buildings. traffic congestion etc. (2) The regions where commercial land values greatly rose are the west in constructed the new planning city center of Sangmu-dong. and the south west in which is related to the extension of high density apartment and the location of big discount stores. (3) Through the changes in commercial land values distribution map. and road map, topographical map, we know that commercial land values is related to various factors; namely, distance from CBD, convenient traffic, reputation of commercial district, condition of a road, size of supplementary, a degree of commercial land use etc. (4) From the above related factor, six variables are extracted by operational definition. That is the spatial distance from the city center, the walking distance to a stopping place, the road width, the amount of bus traffic, the amount of pedestrian, the number of the shop. (5) Data of seven variables are collected on the highest values point of each Dong. We applicate multiple regression analysis with commercial land values as a dependent variable, extracted six variables as independent variables. (6) As a result of multiple regression on the determinants of commercial land values, the variables which is greatly related to commercial land values are the amount of pedestrain, the spatial distance from city center. We identify that two variables explain variance of the commercial land values by 65%. (7) In order to make clear about not explained 35%. we carry out analysis of residual. In consequence, we see small estimate in downtown area and large estimate in urban fringe. This feature is due to simple core structure of Gwangju city and limits of this regression model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.513-522
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• 2021

A smartphone (SMP) includes a MEMS sensor that can record 3-components motions and has a wireless network device to transmit data in live. These features and relatively low maintenance costs are the advantage of using SMPs as an auxiliary seismic observation network. Currently, 279 SMPs are monitoring seismic motions. In this study, we compare the SMP records with the seismic station (SS) records to validate SMP records. The data used for comparison are records for five earthquakes that occurred in 2019, which are 321 SS data recorded by the Korea Meteorological Administration and the Korea Institute of Geoscience and Mineral Resources and 145 recorded by SMPs. The analysis shows that the event-term corrected average residual of the SMP MEMS sensor records is 0.59 which indicating that the peak horizontal acceleration by SMP is 1.8 factor bigger than the peak ground acceleration by SS. In addition, the residuals tend to decrease as the installation floor of the smartphone MEMS sensor increases, which is the similar trend with response spectra from SS.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.320-329
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• 2017

This study aims to evaluate whether the accuracy of LiDAR DEM is affected by the changes of the five input levels ('1','3','5','7' and '9') of median parameter ($F_{md}$), mean parameter ($F_{mn}$) of the Filtering Algorithm (FA) in the GroundFilter module and median parameter ($I_{md}$), mean parameter ($I_{mn}$) of the Interpolation Algorithm (IA) in the GridSurfaceCreate module of the FUSION in order to present the combination of parameter levels producing the most accurate LiDAR DEM. The accuracy is measured by the residuals calculated by difference between the field elevation values and their corresponding DEM elevation values. A multi-way ANOVA is used to statistically examine whether there are effects of parameter level changes on the means of the residuals. The Tukey HSD is conducted as a post-hoc test. The results of the multi- way ANOVA test show that the changes in the levels of $F_{md}$, $F_{mn}$, $I_{mn}$ have significant effects on the DEM accuracy with the significant interaction effect between $F_{md}$ and $F_{mn}$. Therefore, the level of $F_{md}$, $F_{mn}$, and the interaction between two variables are considered to be factors affecting the accuracy of LiDAR DEM as well as the level of $I_{mn}$. As the results of the Tukey HSD test on the combination levels of $F_{md}{\ast}F_{mn}$, the mean of residuals of the '$9{\ast}3$' combination provides the highest accuracy while the '$1{\ast}1$' combination provides the lowest one. Regarding $I_{mn}$ levels, the mean of residuals of the both '3' and '1' provides the highest accuracy. This study can contribute to improve the accuracy of the forest attributes as well as the topographic information extracted from the LiDAR data.

• 한국생물공학회:학술대회논문집
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• 2001.11a
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• pp.303-307
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• 2001

Operation conditions to maximize the hydrolysis of beef tallow was investigated by using the response surface method. In the response surface analysis, reaction temperature, pressure, and ratio of fat to water was considered as independent variables. The concentrations of triglycerides. diglycerides, monoglycerides and free fatty acids were considered as dependent. variables. The optimum conditions for the hydrolysis was as follows' the reaction temperature was $271^{\circ}C$, pressure 86 bar and ratio of fat to water was 106.08g / 133.93 g. The maximum concentration of free fatty acids was 96.49 % at these optimum conditions.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.407-414
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• 2000

Carroll and Ruppert(1988) analyzed the esterase assay data with regression model based on quasi-likelihood. Jung and Lee(1997) introduced a goodness-of-fit test for testing the adequacy of the quasi-likelihood and claimed that there is no gross inadequacy with the model because their test was not rejected. However, Lee and Xelder(199S)'s residual plots revealed that the model did not sufficiently reflect the increase of the variance with that of the mean. In this paper, we re-analyze the esterase assay data with the joint modelling of mean and dispersion in Lee and l\elder(1998) and evaluate the validity of the fitted model by applying the residual plots. And it is illustrated that Lee and Nelder(199S)'s restricted likelihood is more efficient in goodness-of-fit test for the dispersion model.