• Journal of the Korean Society for Library and Information Science
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• v.36 no.4
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• pp.5-19
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• 2002

The KERIS Union Catalog is the largest bibliographic database in Korea. It has over 5.7 million bibliographic records and at present 330 university libraries are participating in shared cataloging services. The database, however, includes various errors and a large number of duplicate data because separate databases from many university libraries were merged without proper quality control in a short period. This study intends to find error data types by analyzing dissertation bibliographic data. The results show that error data are mainly caused by input errors, and the mistakes in using MARC formats and cataloging rules.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.131-142
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• 2015

The aim of study is to present a better teaching strategy to reduce writing errors of Chinese learners of Korean language after finding out what reasons of errors were after analyzing of their writing errors in Korean language. Analyzed contents were writhing in Korean language of 'how I think Korean', 'about Chinese and Korean culture', 'friends' and analyzed what errors were occurred. The vocabulary errors frequencies were counted by the criteria which was set by a researcher. The results were as follows. The frequency of substitute error was the most and were followed by spelling error, wrong type error, omission error and adding error. It is suggested when we teach Korean Language to Chinese learners and develop text for them, the vocabularies should be presented with examples of how to be used in context instead of presenting only vocabulary on the text. It would be a better way to reduce writing errors of Chinese learners of Korean language.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.619-630
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• 2011

In order to estimate an appropriate threshold and evaluate its performance for the data mixed with two different distributions, nine kinds of well-known classification accuracy measures such as MVD, Youden's index, the closest-to- (0,1) criterion, the amended closest-to- (0,1) criterion, SSS, symmetry point, accuracy area, TA, TR are clustered into five categories on the basis of their characters. In credit evaluation study, it is assumed that the score random variable follows normal mixture distributions of the default and non-default states. For various normal mixtures, optimal cut-off points for classification measures belong to each category are obtained and type I and II error rates corresponding to these cut-off points are calculated. Then we explore the cases when these error rates are minimized. If normal mixtures might be estimated for these kinds of real data, we could make use of results of this study to select the best classification accuracy measure which has the minimum error rate.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.599-624
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• 2009

The purpose of this study was to investigate the relationship between error types and Polya's problem solving process. For doing this, we selected 106 sophomore students in a middle school and gave them algebra word problem test. With this test, we analyzed the students' error types in solving algebra word problems. First, We analyzed students' errors in solving algebra word problems into the following six error types. The result showed that the rate of student's errors in each type is as follows: "misinterpreted language"(39.7%), "distorted theorem or solution"(38.2%), "technical error"(11.8%), "unverified solution"(7.4%), "misused data"(2.9%) and "logically invalid inference"(0%). Therefore, we found that the most of student's errors occur in "misinterpreted language" and "distorted theorem or solution" types. According to the analysis of the relationship between students' error types and Polya's problem-solving process, we found that students who made errors of "misinterpreted language" and "distorted theorem or solution" types had some problems in the stage of "understanding", "planning" and "looking back". Also those who made errors of "unverified solution" type showed some problems in "planing" and "looking back" steps. Finally, errors of "misused data" and "technical error" types were related in "carrying out" and "looking back" steps, respectively.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.522-532
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• 2016

Purpose: This study was conducted to develop case-based self-learning multimedia contents for preventing malpractice that frequently occurred among nurses working in the operating room. Methods: Based on the learning needs of operating room nurses, real case reports, and literature reviews, the case-based multimedia learning contents were developed according to the instructional design procedure. The assessment of learning needs was performed by the combination of surveys using structured questionnaire and of interviews for 40 operating room nurses. Results: The learning contents included four learning modules with real malpractice cases from the areas of operating preparation, nursing skills during operation, environmental management of operating room, and patient safety and observation-related. The 80 minute long case-based multimedia learning contents were finally developed after content validity tests from clinical experts. Each module contained photos, sounds and flash animation with voice recording on the contents of nursing error cases and standardized protocols. Conclusion: The developed multimedia learning contents based on real error cases in this study can be utilized as an educational hands-on training materials for nurses to prevent malpractice in the operating room.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.786-791
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• 2005

경향성을 분석하기 위한 여러 기법 가운데 비모수적 방법인 Mann-Kendall 검정(MK 검정)은 수문시계열의 분석에서 널리 사용되어지고 있다. 이 검정 방법은 분석 대상 자료가 독립이라는 가정 하에 수행되며, 자료가 계열상관되어 있는 경우에는 그 상관성의 영향으로 경향성 존재의 유무를 정확하게 판단할 수 없게 된다. 따라서 MK 검정을 이용할 때 자료의 상관성으로 인해 받게되는 영향을 소거시키기 위해 효과적인 자료의 크기(Effective Sample Size, ESS)를 이용하는 수정된 MK 검정 방법을 이용하고자 한다. 본 연구에서는 ESS가 얼마만큼 계열상관성을 제거할 수 있는지 Monte Carlo모의를 통해 검토하였다. MK 검정 결과 계열상관계수의 증가에 따라 자료가 경향성을 나타내는 비율이 높아 겼으나, ESS에 의해 수정된 MK 검정을 수행한 결과 계열상관계수의 영향이 제거되었음을 알 수 있었다. 그러나 ESS에 치한 방법은 상관성의 영향을 과대하게 제거하게 되고, 이에 의해 실제 경향성이 존재하는 자료마저도 비 경향성이라고 판단하게 되는 오류를 가지고 있었다. 이러한 문제점을 해결하기 위해 MK 검정에서 경향성으로 판단된 자료에 대해 경향정도를 분석하여 제거한 후 다시 ESS에 의해 수정된 MK 검정을 실시하였다. 본 연구를 통하여 상관성이 존재하는 자료에 대한 MK 검정 방법의 오류를 제거 할 수 있었으며, 또한 실측 자료의 적용을 통해 수정 MK 검정이 경향성 분석에 매우 유용함을 확인 하였다.

• Water for future
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• v.29 no.3
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• pp.129-142
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• 1996

This study investigates the magnitude of errors that can be expected in integrating sediment concentration in a vertical, basede on a single-point measurement, because of errors in input data. Potential error sources, including sampler location, water surface elevation, bed elevation, fall velocity, $\beta$ value, and $\kappa$ value were comparatively examined using data from a special study on the Rio Grande Conveyance channel in New Mexico. It is concluded that simple forms of equations for the vertical distribution of velocity and sediment concentration based on a single-point field sample of suspended sediment. The most uncertain point in the computation is related to the Rouse number z in the equation for the vertical concentration distribution of suspended sediment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.7-8
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• 2019

자료경합은 두 개 이상의 스레드가 같은 공유메모리에 적절한 동기화 없이 접근하고, 적어도 한 개의 접근사건이 쓰기일 때 발생할 수 있는 동시성 오류이다. 자료경합은 프로그래머가 의도하지 않은 비결정적인 수행결과를 초래하여, 항공기 소프트웨어와 같은 고신뢰성이 요구되는 프로그램에서 치명적인 오류를 발생시켜 인적 물적 손해로 이어질 수 있다. 자료경합 탐지기법은 이러한 문제를 사전에 탐지하여 수정하는데 사용되어진다. 하지만 GPGPU 프로그램에서의 자료경합은 CPU 병행프로그램에서보다 복잡한 실행구조를 가지고 있어 스레드 및 메모리 계층, 스케줄링, 동기화 기법 등의 많은 변수가 존재한다. 이로 인해 실세계 프로그램에 자료경합 탐지기법을 적용하여 검증 시 이러한 변수들을 반영하여 실험하는데 많은 노력이 소요된다. 본 논문은 실세계 프로그램에서의 자료경합을 대표하는 4가지 패턴의 합성프로그램으로 이루어지고 실행 시 스레드 및 메모리 계층, 스레드 구조, 메모리 사용량 및 동기화 방안을 지정할 수 있는 벤치마크 모음을 제시한다.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.3-8
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• 2010

본 연구에서는 일련의 연구에서 수집된 영작문 오류 유형의 정제된 자료를 토대로 연관 규칙을 생성하고, 학습을 통해서 효용성이 검증된 연관 규칙을 활용해서 영작문 데이터의 형태 통사 오류를 자동으로 탐지한다. 영작문 데이터에서 형태 통사 오류를 찾아내는 작업은 많은 시간과 자원이 소요되는 작업이므로 자동화가 필수적이다. 기존의 연구들이 통계적 모델을 활용한 어휘적 오류에 치중하거나 언어 이론적 틀에 근거한 통사 처리에 집중하는 반면에, 본 연구는 데이터 마이닝을 통해서 정제된 데이터에서 연관 규칙을 생성하고 이를 검증한 후 형태 통사 오류를 감지한다. 이전 연구들에서는 이론적 틀에 맞추어진 규칙 생성이나 언어 모델 생성을 위한 대량의 코퍼스 데이터와 같은 다량의 지식 베이스 생성이 필수적인데, 본 연구는 적은 양의 정제된 데이터를 활용한다. 영작문 오류 유형의 형태 통사 연관 규칙을 생성하기 위해서 Apriori 알고리즘을 활용하였다. 알고리즘을 통해서 생성된 연관 규칙 중 잘못된 규칙이 생성될 가능성이 있으므로, 상관성 검정, 코사인 유사도와 같은 규칙 효용성의 통계적 검증을 활용해서 타당한 규칙만을 학습하였다. 이를 통해서 축적된 연관 규칙들을 영작문 오류를 자동으로 탐지하는 실험에 활용하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.131-146
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• 2023

Smart card data is widely used in the public transportation field. Despite the inevitability of various errors occur during the data collection and storage; however, smart card data errors have not been extensively studied. This paper investigates inherent errors in boarding and alighting station information in smart card data. A comparison smart card data and bus boarding and alighting survey data for the same time frame shows that boarding station names differ by 6.2% between the two data sets. This indicates that the error rate of smart card data is 6.2% in terms of boarding station information, given that bus boarding and alighting survey data can be considered as ground truth. This paper propose 6-step algorithm for correcting errors in smart card boarding station information, linking them to corresponding information in Bus Information System(BIS) Data. Comparing BIS data and bus boarding and alighting survey data for the same time frame reveals that boarding station names correspond by 98.3% between the two data sets, indicating that BIS data can be used as reliable reference for ground truth. To evaluate its performance, applying the 6-step algorithm proposed in this paper to smart card data set shows that the error rate of boarding station information is reduced from 6.2% to 1.0%, resulting in a 5.2%p improvement in the accuracy of smart card data. It is expected that the proposed algorithm will enhance the process of adjusting bus routes and making decisions related to public transportation infrastructure investments.