• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.407-416
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• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.275-278
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• 2002

Sharpness enhancement, which strengthen the edge(high frequency) of image, is widely studied for image processing research area. In this paper, psychophysical experiment is conducted by the 20 observers with simple linear unsharp masking for sharpness enhancement. The experimental results extracted using z-score analysis and linear regression suggests observer preferable sharpness enhancement method for digital television.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.781-790
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• 2009

This paper deals with a rank transformation method and a Theil's method based on an ${\alpha}$-level set of a fuzzy number to construct a fuzzy linear regression model. The rank transformation method is a simple procedure where the data are merely replaced with their corresponding ranks, and the Theil's method uses the median of all estimates of the parameter calculated from selected pairs of observations. We also consider two numerical examples to evaluate effectiveness of the fuzzy regression model using the proposed method and of another fuzzy regression model using the least square method.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1009-1018
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• 2013

Regression analysis is useful to understand the relationship of variables; however, we need to test if the slope of each regression lines is the same when comparing several populations. This paper suggests a new parallelism test for several linear regression lines. We use F-test of ANOVA and Kruskal-Wallis (1952) tests after obtaining slope estimator from a sequential slope. In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of Park and Kim (2009).

• Journal of Chest Surgery
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• v.54 no.6
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• pp.466-472
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• 2021

Background: Major intraoperative hemorrhage reportedly predicts unfavorable survival outcomes following surgical resection for esophageal carcinoma (EC). However, the factors predicting the amount of blood lost during thoracoscopic esophagectomy have yet to be sufficiently studied. We sought to identify risk factors for excessive blood loss during video-assisted thoracoscopic surgery (VATS) for EC. Methods: Using simple and multiple linear regression models, we performed retrospective analyses of the associations between clinicopathological/surgical factors and estimated hemorrhagic volume in 168 consecutive patients who underwent VATS-type esophagectomy for EC. Results: The median blood loss amount was 225 mL (interquartile range, 126-380 mL). Abdominal laparotomy (p<0.001), thoracic duct resection (p=0.014), and division of the azygos arch (p<0.001) were significantly related to high volumes of blood loss. Body mass index and operative duration, as continuous variables, were also correlated positively with blood loss volume in simple linear regression. The multiple linear regression analysis identified prolonged operative duration (p<0.001), open laparotomy approach (p=0.003), azygos arch division (p=0.005), and high body mass index (p=0.014) as independent predictors of higher hemorrhage amounts during VATS esophagectomy. Conclusion: As well as body mass index, operation-related factors such as operative duration, open laparotomy, and division of the azygos arch were independently predictive of estimated blood loss during VATS esophagectomy for EC. Laparoscopic abdominal procedures and azygos arch preservation might be minimally invasive options that would potentially reduce intraoperative hemorrhage, although oncological radicality remains an important consideration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.59-65
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• 2003

Four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigante the effect of sintering time on the microstructure, mechanical properties, grain sizes and the cutting performance. An endeavor was also made to determine the relation among mechanical property, Brain size and tool life. $Si_3N_4$ home made cutting tool sintered for 1 hour under $1760^{\circ}$ temperature and 25MPa pressure showed the best cutting performance among selected ceramic tools during machining both Bray cast iron and heat treated SCM440. Multiple linear regression model was used to estimate the tool lift from mechanical property, grain size and showed good result. It was also shown that hardness imposed the biggest offect on tool life.

• Journal of Environmental Science International
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• v.32 no.3
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• pp.161-171
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• 2023

The present study evaluated growth factors affecting oak community plantations through literature review and a field survey. Specifically, 41 related literature sources were analyzed and field surveys were conducted to collect growth data. Previous studies were analyzed to identify variables with high frequency of use. The frequency of use was in the order of tree size > environment > planting density > forest age. Analysis of factors impacting height and diameter growth revealed that the growth rate of species other than Quercus variabilis was negative in the field survey. This may be because of differences between the actual trees planted and specifications in the construction drawings, which may be attributed to the site conditions and decisions made by the project subject during construction. Furthermore, simple linear regression analysis was conducted with time, height at planting, density, and species code as the independent variables and growth rate as the dependent variable. A strong positive linear correlation was noted between height and diameter. This work builds a foundation for developing a forest restoration model and simulation program based on a regression model derived from the four variables tested.

• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.54-60
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• 2004

Face recognition is one of the problems to be solved by appearance based matching technique. However, the appearance of face image is very sensitive to variation in illumination. One of the easiest ways for better performance is to collect more training samples acquired under variable lightings but it is not practical in real world. ]:n object recognition, it is desirable to focus on feature extraction or normalization technique rather than focus on classifier. This paper presents a simple approach to normalization of faces subject to directional illumination. This is one of the significant issues that cause error in the face recognition process. The proposed method, ICR(illumination Compensation based on Multiple Linear Regression), is to find the plane that best fits the intensity distribution of the face image using the multiple linear regression, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experimental results show a significant improvement of the recognition accuracy.