• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.64-71
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• 2013

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: $10^{\circ}-25^{\circ}$), and 14 severe scoliosis patients (Cobb angle: $25^{\circ}-50^{\circ}$) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement ($R^2$ = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately $3.2{\pm}1.8$. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.

• Journal of the Korean Statistical Society
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• v.26 no.1
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• pp.1-22
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• 1997

In the linear regression model $y_{i}$ = .alpha. $x_{i}$ $^{T}$ .beta. + .epsilon.$_{i}$ , i = 1,2,...,n, the weighted pairwise absolute deviation (WPAD) estimator was defined by minimizing the dispersion function D (.beta.) = .sum..sum.$_{{i $w_{{ij}}$$\mid$ $r_{j}$ (.beta.) $r_{i}$ (.beta.)$\mid$, where $r_{i}$ (.beta.)'s are residuals and $w_{{ij}}$'s are weights. This estimator can achive bounded total influence with positive breakdown by choice of weights $w_{{ij}}$. In this paper, we consider a more general type of dispersion function than that of D(.beta.) and propose a pairwise GM-estimator based on the dispersion function. Under some regularity conditions, the proposed estimator has a bounded influence function, a high breakdown point, and asymptotically a normal distribution. Results of a small-sample Monte Carlo study are also presented. presented.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.475-485
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• 1995

In this study, statistical analysis of fatigue data which had obtained from respective 24 fatigue crack, was examined for SiC whisker reinforced aluminium 6061 composite alloy (SiC$_{w}$/A16061) and aluminium 6061 alloy. SiC volume fraction in composite alloy is 25%. The analysis results stress intensity factor range and 0.1 mm fatigue crack initiation life for SiC$_{w}$/A16061 composite & A16061 matrix are the log-normal distribution. And regression analysis by linear model, exponential model and multiplicative model were performed to find out the relationship between fatigue crack growth rate(da/dN) and stress intensity for find out the relationship between fatigue crack growth rate(da/dN) and stress intensity factor range(.DELTA.K) in the SiC$_{w}$/A16061 composite and examine the applicability of Paris' equation to SiC$_{w}$A16061 composite. Also computer simulation was performed for fatigue life prediction of SiC$_{w}$/A16061 composite using the statistical results of this study.udy.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1021-1033
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• 2021

Objective: To assess the expression of vascular normalization genes in different molecular subtypes of breast cancer and to determine whether molecular subtypes with a higher vascular normalization gene expression can be identified using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). Materials and Methods: This prospective study evaluated 306 female (mean age ± standard deviation, 50 ± 10 years), recruited between January 2014 and August 2017, who had de novo breast cancer larger than 1 cm in diameter (308 tumors). DCE MRI followed by IVIM DWI studies using 11 different b-values (0 to 1200 s/mm²) were performed on a 1.5T MRI system. The Tofts model and segmented biexponential IVIM analysis were used. For each tumor, the molecular subtype (according to six [I-VI] subtypes and PAM50 subtypes), expression profile of genes for vascular normalization, pericytes, and normal vascular signatures were determined using freshly frozen tissue. Statistical associations between imaging parameters and molecular subtypes were examined using logistic regression or linear regression with a significance level of p = 0.05. Results: Breast cancer subtypes III and VI and PAM50 subtypes luminal A and normal-like exhibited a higher expression of genes for vascular normalization, pericyte markers, and normal vessel function signature (p < 0.001 for all) compared to other subtypes. Subtypes III and VI and PAM50 subtypes luminal A and normal-like, versus the remaining subtypes, showed significant associations with K^trans, k_ep, v_p, and IAUGC_BN90 on DEC MRI, with relatively smaller values in the former. The subtype grouping was significantly associated with D, with relatively less restricted diffusion in subtypes III and VI and PAM50 subtypes luminal A and normal-like. Conclusion: DCE MRI and IVIM parameters may identify molecular subtypes of breast cancers with a different vascular normalization gene expression.

• Journal of the Korean earth science society
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• v.23 no.7
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• pp.587-596
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• 2002

A super ensemble model was developed for the seasonal prediction of regional precipitation in Korea using the lag correlated large scale predictors, based on the empirical orthogonal function (EOF) analysis and multiple linear regression model. The predictability of this model was also evaluated by cross-validation. Correlation between the predicted and the observed value obtained from the super ensemble model showed 0.73 in spring, 0.61 in summer, 0.69 in autumn and 0.75 in winter. The predictability of categorical forecasting was also evaluated based on the three classes such as above normal, near normal and below normal that are clearly defined in terms of a priori specified by threshold values. Categorical forecasting by the super ensemble model has a hit rate with a range from 0.42 to 0.74 in seasonal precipitation.

• Clinical and Experimental Pediatrics
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• v.51 no.6
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• pp.629-633
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• 2008

Purpose : This study was designed to investigate normal domestic values for the diameter of the left main coronary artery (LCA), the left anterior descending coronary artery (LAD) and the right coronary artery (RCA). These data are necessary to define dilatation of coronary arteries in Kawasaki disease cases. Methods : Study subjects were 43 normal healthy children whose ages ranged from 3 months to 6 years. They children visited Konkuk University hospital for echocardiograph examination between March 2005 and November 2007. Measurements of coronary arterial diameters at each branch were done by off-line analyses of recorded images. Simple regression analysis of each the measurements were performed using the body size (body surface area, etc.) as the independent variable. Results : Body surface area was significantly related to the diameters of LCA ($r^2=0.20$, P=0.0038), of LAD ($r^2=0.41$, P<0.0001), and of RCA ($r^2=0.30$, P=0.0002). In the regression model, the estimates of the y-intercept were 1.703, 1.058, and 1.007; the estimates of the regression coefficient were 0.971, 1.175, and 1.177; and the estimates of the standard deviation were 0.315, 0.221, and 0.282 with respect to the three coronary arteries. Conclusion : A the linear regression model of the diameters of three coronary arteries adjusted for body surface area was produced. With these results, the Z-score calculation of the diameter of three coronary arteries, based on normal domestic data, will be possible.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1127-1137
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• 2021

Given the different effects of agricultural mechanization on various stages of wheat planting in Henan, this article selects 78 observation samples from Henan, a major wheat-growing province. It uses different research methods (multiple linear regression, social network analysis model, multi-layer sensory nerves network) to conduct a comparative study, and the calculation results of the model show that the experimental results have a strong convergence and consistency. Agricultural mechanization services have significant effects on the three stages of wheat planting: harvesting, plowing and sowing. A higher degree of mechanized service in several stages can reduce the cost of growing wheat on family farms.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.19-34
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• 2018

The purpose of this study is to estimate monthly agricultural reservoir storage with multiple linear regression model(MLRM) based on reservoir storage and meteorological data. The regression model was developed using 15 years(2002 to 2016) of 3,067 reservoirs by KRC(Korea Rural Community) and 63 meteorological stations by KMA (Korean Meteorological Administration), and the MLRM showed the determination coefficient($R^2$) of 0.51~0.95. The MLRM was applied to 9 selected reservoirs among the whole reservoirs and validated with $R^2$ of 0.44~0.81. The ROC(Receiver Operating Characteristics) analysis of Reservoir Drought Index(RDI) classified by comparing the present reservoir storage with normal year(1976~2005 average) reservoir storage showed average value of 0.64 for 2 years(2015~2016) with the highest value of 0.70 for winter period, lowest value of 0.58 for summer period. If 1 to 3 months weather forecasting data such as Glosea5 produced by KMA are applied, the predicted monthly reservoir storage from the MLRM can be a useful information for agricultural drought pre-preparation.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• Journal of Preventive Medicine and Public Health
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• v.43 no.2
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• pp.125-130
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• 2010

Objectives: The current study was performed to assess the distribution of intraocular pressure (IOP) and its association with metabolic syndrome (MS) in a community. Methods: We measured IOP and MS components from 446 adults, age 20 or more years old, who reside in a community in Kyunggi Province, South Korea. We compared the level of IOP according to the number of metabolic abnormalities and between normal and abnormal metabolic components. Linear regression analyses were used to determine the relationship between IOP and metabolic components. Results: No significant difference in IOP (mean${\pm}$SE) was found between men ($12.24{\pm}2.42$) and women ($12.55{\pm}2.41$ mmHg, p > 0.1), while IOP of men tended to decrease as age increased (p for trend < 0.01). After adjusting for age, IOP of subjects with abdominal obesity in men and high blood pressure in women were significantly higher than those without abdominal obesity or high blood pressure (p < 0.05). Female subjects with MS showed significantly higher IOP than those without MS. Participants with more metabolic disturbances tended to have a greater IOP elevation with a linear trend after adjusting for age and sex. In the univariate regression analysis, age and waist circumference were significantly associated with IOP in men, but systolic and diastolic blood pressure were associated with IOP in women. In final multiple regression model, age, systolic blood pressure, and triglyceride were associated with IOP in women, and age in men. Conclusions: These findings suggest that MS and its components may be important determinants of elevated IOP.