• Korean Journal of Clinical Laboratory Science
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• v.49 no.4
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• pp.366-373
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• 2017

Urinalysis is considered to be easier and simpler than other tests. It has been known to cause no burden to patients, while offering important information on diagnosing, treating, and determining the prognoses of kidney and urinary tract diseases. Urinary sediments are usually performed by microscopic examination of centrifuged urine by technologist. The guidelines proposed by the Korean Association of External Quality Assessment Service are actually different from those actually practiced by medical institutions and taught to biomedical students in textbooks. Therefore, we verified whether different sediment preparation methods lead different test results. Specimens that tested positive from the occult blood and leukocyte esterase in the urine dipstick test were randomly selected for a microscopic examination. The differences in the urine sediment preparation affected the sediment concentrations, which influenced the cell grade and cell number per HPF. The first factor in determining the sediment concentration is the centrifugal force. Many medical institutions use 1,500 rpm as the centrifugal speed without considering the radius of the centrifuge; such a value may not be accurate for 400 G. Consequently, there were differences in urine concentrations, which influenced the results. The second factor is the amount of sediment in urine. Different amounts of the remaining supernatant led to different sediment concentration factors, again, causing different results. Furthermore, not only by using a pipette to obtain an accurate amount as stipulated, but also by roughly obtaining a drop, the microscopic examination using such a volume of sediment examined affected the results. Therefore, this study highlights the importance of standardization of urine sediment preparation procedures to promote consistency and accuracy across institutions.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.468-475
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• 2018

The current system of making course evaluations mandatory in universities in Korea in order to supplement low participation levels is a major hindering factor for the validity of the evaluation results due to students' insincere responses. This study points out the problems in course evaluations that are in the form of surveys and instead proposes a sample course evaluation system that utilizes a sample mean to deduce a population mean, attempting to prove through simulation that this is statistically significant. Thus, small, medium, and large scale courses as well as cyber courses that were available in S University in the fall 2016 semester were set as the population, and each course's evaluation average grade was set as the population mean. Furthermore, we used the average grade and standard deviation of the same courses from the previous year in order to decide the sample number, the reliability level was set as 95%, and the margin of error was set as ${\pm}0.25$. As a result of carrying out a simulation using the data analysis tool R, all the courses (small scale, medium scale, large scale, and cyber) showed to have a reliability level close to 95% including the population mean, and consequently practicality of the representative sample course evaluation system was proven.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.181-193
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• 1996

A new technique for land cover classification which applies digital image pre-classified by unsupervised classification technique, clustering, to Canonical Correlation Analysis(CCA) was proposed in this paper. Compared with maximum likelihood classification, the proposed technique had a good flexibility in selecting training areas. This implies that any selected position of training areas has few effects on classification results. Land cover of each cluster designated by CCA after clustering is able to be used as prior information for maximum likelihood classification. In case that the same training areas are used, accuracy of classification using Canonical Correlation Analysis after cluster analysis is better than that of maximum likelihood classification. Therefore, a new technique proposed in this study will be able to be put to practical use. Moreover this will play an important role in the construction of GIS database

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.444-453
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• 2018

The study addresses the analysis on the financial determinants of corporate research and development (R&D) expenditure in finance. Overall level of R&D spending was estimated as one of the top-tier on a global basis and a majority of the expenditure was invested by large domestic firms in private sector. Consequently, financial factors that influence R&D intensity were empirically tested in the first hypothesis by using conditional quantile regression model for firms listed in KOSPI stock market in the post-era of the global financial turmoil. Firms in the groups of high- and low-R&D intensity were statistically compared to detect financial differences in the second hypothesis which was accompanied by the test of multi-logit model that included firms without R&D outlay. Concerning the results of the hypothesis tests, R&D spending of the prior fiscal year, firm size, business risk and advertising expense overall showed statistically significant impacts to determine the level. As an extended study of [1] that had examined financial factors of R&D intensity at the macro-level, the results of the present study are anticipated to contribute to maximizing shareholders' wealth in advance or emerging capital markets, when applied to find an optimal level of R&D expenditure.

• Journal of Technology Innovation
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• v.20 no.3
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• pp.57-91
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• 2012

This paper investigates determinants of litigation success in the two distinctive types of patent litigations, ex-parte and inter-parte cases, which are brought in the process where a filed application becomes a valid patent right. We regress winning rates of patent applicants on the characteristics of firms, trials, patent lawyer, and patent itself, using a probit model with sample selections. The paper finds that the relative suit rate of a firm, time to be sued, changes of patent agents by applicants, and multiple agents among explanatory variables affect ex-parte reexamination and in-parte post-grant patent trials differently in the point of average marginal effects. These variables lower the probability of applicant's victory in the ex-parte cases, while they raise the probability in the inter-parte trials. However, the experience that agents represent applicants is a winning rate-increasing factor both in inter-parte and ex-parte reexamination, unexpectedly. This result cannot be applied to the entire domain of the variable, since sample selection effects are reflected in the result. The number of claim increases the winning probability of the applicant in the both types of patent litigations. This study has some limitations because it ignores the information on the legal person to which a patent agent belongs, and confined agent's experience to patent filing. We leave it future studies to investigate the effects of lawsuit experience of patent agent, and those of characteristics of the law firm to which individual patent lawyer is affiliated.

• Journal of IKEEE
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• v.22 no.2
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• pp.491-494
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• 2018

This paper presents a similarity search method based on mixed norm for enhancing self-example-based single image super-resolution. In order to incorporate the local statistical characteristics of the patches into the super-resolution image reconstruction, we propose a method to determine the order of the norm according to the patch inclination and use it as a similarity search between patches. Experimental results demonstrate that the proposed similarity search method has the capability to improve the performance of existing search method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.621-636
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• 2018

Research on the application of informative sampling technique has been conducted in order to reduce the influence of non-response. Chung and Shin (Korean Journal of Applied Statistics, 30, 993-1004, 2017) showed that the estimation accuracy improved when using exponential response rate information for the parameter estimation if the distribution of errors included in the super population model follows normal distribution. However this method divides the stratum into equally spaced substrata to obtain the sample weight of the informative sampling technique and shows that the accuracy of the estimation improves as the number of substrata increases. In this study, with the given number of total sample size, the optimal substratum boundary points are calculated using equal space, quantile, and LH algorithm; consequently, the results using those methods are compared through simulation. We also studied the criteria to determine the number of substrata and substratum boundaries that can be used in practice with various types of auxiliary variable distributions.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.393-403
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• 2021

The purpose of this study is to propose a method of designing a systematic panel survey at the institutional level to lay the foundation for data-based decision-making using vocational training teachers and instructors as the population. In this study, the target population and sampling frame, which are the main elements necessary for planning a panel survey, are proposed. Also based on expert advice and empirical data analysis, the sampling unit and sampling method taking into account the outer and inner variables are presented, comprehensively considering the representativeness of data, the efficiency and sustainability of data collection. As a result of the study, with the unit of the panel as a vocational training institution, a two-stage stratified proportional sampling plan is proposed so that the institution selected as the panel and the vocational training teachers and instructors belonging to the institution can participate in the survey. Based on this, implications for the panel survey sample design are presented.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1017-1028
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• 2011

When it is not easy to decide the credit scoring for some loan applicants, credit evaluation is postponded and reserve to ask a specialist for further evaluation of undecided applicants. This undecided inference is one of problems that happen to most statistical models including the biostatistics and sportal statistics as well as credit evaluation area. In this work, the undecided inference is regarded as a missing data mechanism under the assumption of MNAR, and use the bivariate probit model which is one of sample selection models. Two undecided inference methods are proposed: one is to make use of characteristic variables to represent the state for decided applicants, and the other is that more accurate and additional informations are collected and apply these new variables. With an illustrated example, misclassification error rates for undecided and overall applicants are obtainded and compared according to various characteristic variables, undecided intervals, and thresholds. It is found that misclassification error rates could be reduced when the undecided interval is increased and more accurate information is put to model, since more accurate situation of decided applications are reflected in the bivariate probit model.