• International Journal of Automotive Technology
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• v.4 no.2
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.357-358
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• 2008

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.389-402
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• 2023

Multivariate or clustered failure time data often occur in many medical, epidemiological, and socio-economic studies when survival data are collected from several research centers. If the data are periodically observed as in a longitudinal study, survival times are often subject to various types of interval-censoring, creating multivariate interval-censored data. Then, the event times of interest may be correlated among individuals who come from the same cluster. In this article, we propose a unified linear regression method for analyzing multivariate interval-censored data. We consider a semiparametric multivariate accelerated failure time model as a statistical analysis tool and develop a generalized Buckley-James method to make inferences by imputing interval-censored observations with their conditional mean values. Since the study population consists of several heterogeneous clusters, where the subjects in the same cluster may be related, we propose a generalized estimating equations approach to accommodate potential dependence in clusters. Our simulation results confirm that the proposed estimator is robust to misspecification of working covariance matrix and statistical efficiency can increase when the working covariance structure is close to the truth. The proposed method is applied to the dataset from a diabetic retinopathy study.

• Clinics in Orthopedic Surgery
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• v.10 no.4
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• pp.448-454
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• 2018

Background: Traumatic spinal cord injury (SCI) is a tragic event that has a major impact on individuals and society as well as the healthcare system. The purpose of this study was to investigate the strength of association between surgical treatment timing and neurological improvement. Methods: Fifty-six patients with neurological impairment due to traumatic SCI were included in this study. From January 2013 to June 2017, all their medical records were reviewed. Initially, to identify the factors affecting the recovery of neurological deficit after an acute SCI, we performed univariate logistic regression analyses for various variables. Then, we performed a multivariate logistic regression analysis for variables that showed a p-value of < 0.2 in the univariate analyses. The Hosmer-Lemeshow test was used to determine the goodness of fit for the multivariate logistic regression model. Results: In the univariate analysis on the strength of associations between various factors and neurological improvement, the following factors had a p-value of < 0.2: surgical timing (early, < 8 hours; late, 8-24 hours; p = 0.033), completeness of SCI (complete/incomplete; p = 0.033), and smoking (p = 0.095). In the multivariate analysis, only two variables were significant: surgical timing (odds ratio [OR], 0.128; p = 0.004) and completeness of SCI (OR, 9.611; p = 0.009). Conclusions: Early surgical decompression within 8 hours after traumatic SCI appeared to improve neurological recovery. Furthermore, incomplete SCI was more closely related to favorable neurological improvement than complete SCI. Therefore, we recommend early decompression as an effective treatment for traumatic SCI.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.475-482
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• 2011

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.389-401
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• 1998

This paper introduces CybeRClass(Cyber Regression Class). CybeRClass uses the technique of animation arid voice to teach regression analysis. The structure of this system make it possible to extend to multivariate analysis methods such as discriminant analysis and cluster analysis. Tools for multimedia is Multimedia ToolBook, and Xlisp-Stat is used for statistical computation and statistical graphics.

• Geomechanics and Engineering
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• v.16 no.6
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• pp.655-667
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• 2018

To evaluate the stability of a rock slope with one pre-exiting vertical crack, this paper performs corresponding probabilistic stability analysis. The existence of cracks is generally ignored in traditional deterministic stability analysis. However, they are widely found in either cohesive soil or rock slopes. The influence of one pre-exiting vertical crack on a rock slope is considered in this study. The safety factor, which is usually adopted to quantity the stability of slopes, is derived through the deterministic computation based on the strength reduction technique. The generalized Hoek-Brown (HB) failure criterion is adopted to characterize the failure of rock masses. Considering high nonlinearity of the limit state function as using nonlinear HB criterion, the multivariate adaptive regression splines (MARS) is used to accurately approximate the implicit limit state function of a rock slope. Then the MARS is integrated with Monte Carlo simulation to implement reliability analysis, and the influences of distribution types, level of uncertainty, and constants on the probability density functions and failure probability are discussed. It is found that distribution types of random variables have little influence on reliability results. The reliability results are affected by a combination of the uncertainty level and the constants. Finally, a reliability-based design figure is provided to evaluate the safety factor of a slope required for a target failure probability.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.519-526
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• 2019

Response dimension reduction in a sufficient dimension reduction (SDR) context has been widely ignored until Yoo and Cook (Computational Statistics and Data Analysis, 53, 334-343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo (Statistics: A Journal of Theoretical and Applied Statistics, 52, 409-425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo (Journal of the Korean Statistical Society, 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2511-2514
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• 2012

Objective: To evaluate the prognostic factors of gallbladder carcinoma. Methods: Presentation, operative data, complications, and survival outcome were examined for 132 gallbladder carcinoma patients who underwent gallbladder surgery in our unit during 2002-2007, and follow-up results were obtained from every patient for univariate and multivariate survival analysis. Results: The univariate analysis showed that gallbladder lesion history, tumor cell differentiation, Nevin staging, preoperative lymph node metastasis and the surgical approach significantly correlated with the prognosis of the patients (p<0.05). The results of the multivariate analysis (Cox regression) showed that gallbladder lesion history, Nevin staging and the surgical approach were independent predicators with relative risks of 6.9, 4.4, 2.8, respectively (p=0.002, 0.003, 0.008). Conclusion: Gallbladder lesion history, Nevin staging and the surgical approach are independent prognostic factors for gallbladder carcinoma, a rapidly fatal disease. Therefore, early diagnosis, anti-infective therapy and radical surgery are greatly needed to improve the prognosis of gallbladder carcinoma.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.745-754
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• 2022

BACKGROUND/OBJECTIVES: The incidence of cardiovascular diseases (CVDs) has increased worldwide. Although a low serum vitamin D level is known to be associated with the risk of CVD, the mechanism is not well understood yet. The aim of this study was to determine the relationship of serum 25-hydroxyvitamin D₃ (25[OH]D) with homocysteine and apolipoprotein B (ApoB). SUBJECTS/METHODS: Of 777 subjects recruited from one health promotion center for routine heath exam from January 2010 to December 2016, 518 subjects were included in this study. Serum 25(OH)D, serum homocysteine, and other metabolic parameters including ApoB were analyzed. Simple and partial correlations were carried out after adjustments. Simple linear regression analysis was used for precise correlation of parameters. Multivariate regression analysis was done to know which factor (serum homocysteine or ApoB) was more related to serum 25(OH)D after adjustments. Finally, logarithms of homocysteine concentrations according to tertiles of serum 25(OH)D were compared. RESULTS: After sex and age adjustments, serum 25(OH)D showed negative correlations with serum homocysteine (r' = -0.114) and ApoB (r' = -0.098). In simple linear regression analysis, serum 25(OH)D showed a significant negative correlation with ApoB (P = 0.035). However, in multivariate regression analysis, serum 25(OH)D was significantly associated with serum homocysteine after adjustments (P = 0.022). In addition, serum homocysteine concentration was significantly high in the lowest 25(OH)D group (P = 0.046). CONCLUSION: Serum 25(OH)D concentration showed a stronger negative association with serum homocysteine than with ApoB.