• 대한예방의학회:학술대회논문집
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• 1996.10a
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• pp.135-136
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• 1996

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.243-245
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• 2016

광학 문자 인식(OCR)을 통해 문서의 글자를 인식할 때 띄어쓰기 오류가 발생한다. 본 논문에서는 이를 해결하기 위해 OCR의 후처리 과정으로 동적 프로그래밍을 이용한 분절(Segmentation) 방식의 띄어쓰기 오류 교정 시스템을 제안한다. 제안하는 시스템의 띄어쓰기 오류 교정 과정은 다음과 같다. 첫째, 띄어쓰기 오류가 있다고 분류된 어절 내의 공백을 모두 제거한다. 둘째, 공백이 제거된 문자열을 동적 프로그래밍을 이용한 분절로 입력 문자열에 대하여 가능한 모든 띄어쓰기 후보들을 찾는다. 셋째, 뉴스 기사 말뭉치와 그 말뭉치에 기반을 둔 띄어쓰기 확률 모델을 참조하여 각 후보의 띄어쓰기 확률을 계산한다. 마지막으로 띄어쓰기 후보들 중 확률이 가장 높은 후보를 교정 결과로 제시한다. 본 논문에서 제안하는 시스템을 이용하여 OCR의 띄어쓰기 오류를 해결할 수 있었다. 향후 띄어쓰기 오류 교정에 필요한 언어 규칙 등을 시스템에 추가한 띄어쓰기 교정시스템을 통하여 OCR의 최종적인 인식률을 향상에 대해 연구할 예정이다.

• Journal of the Korean School Mathematics Society
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• v.15 no.3
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• pp.429-451
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• 2012

This paper examined eight novice elementary teachers' knowledge in terms of the types and sources of students' errors and teaching strategies on plane figures through a questionnaire and teachers' discussion. The teachers tended to predict students' diverse error types, but they attributed the sources of such errors mainly to their characteristics. The analysis of teachers' responses of teaching strategies revealed that they recognized the importance of the teacher's clear explanation and students' own problem-solving, while they were somewhat negative in presenting diverse examples and classifying, drawing, or constructing figures. Building on these results, this paper provides the implications for novice teachers' professional development programs.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.79-88
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• 2000

It is essential to build a high quality database in developing a UIS to enhance the administrative effectiveness of municipal governments. To secure such a high quality DB, a proper methodology of quality control should be established. It is imperative to have such a method fit UIS DB considering that the conventional method has mainly been focusing on the quality control of the digital layers itself. Therefore, this study have analyzed the city of Inchon's UIS DB to devise a proper method to categorize the types of errors and to identify major relevant items. Also, the magnitude and frequency of each error along with its major cause have been analyzed to propose a quality control procedure to minimize the errors

• Journal of the Korean School Mathematics Society
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• v.9 no.2
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• pp.195-208
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• 2006

Among different geometrical representations of a mathematical concept, learners are likely to form their geometrical concept image of the given concept based on a specific one. A learner's image is not always in accord with the definition of a concept. This can induce his or her errors in mathematical problem solving. We need to analyse types of such errors and the cause of the errors. In this study, we analyse learners' geometrical concept images for geometrical concepts and errors related to such images. Furthermore we propose a theoretical framework for error analysis related to a learner's concept image for a general mathematical concept in mathematical problem solving.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.243-245
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• 2016

광학 문자 인식(OCR)을 통해 문서의 글자를 인식할 때 띄어쓰기 오류가 발생한다. 본 논문에서는 이를 해결하기 위해 OCR의 후처리 과정으로 동적 프로그래밍을 이용한 분절(Segmentation) 방식의 띄어쓰기 오류 교정 시스템을 제안한다. 제안하는 시스템의 띄어쓰기 오류 교정 과정은 다음과 같다. 첫째, 띄어쓰기 오류가 있다고 분류된 어절 내의 공백을 모두 제거한다. 둘째, 공백이 제거된 문자열을 동적 프로그래밍을 이용한 분절로 입력 문자열에 대하여 가능한 모든 띄어쓰기 후보들을 찾는다. 셋째, 뉴스 기사 말뭉치와 그 말뭉치에 기반을 둔 띄어쓰기 확률 모델을 참조하여 각 후보의 띄어쓰기 확률을 계산한다. 마지막으로 띄어쓰기 후보들 중 확률이 가장 높은 후보를 교정 결과로 제시한다. 본 논문에서 제안하는 시스템을 이용하여 OCR의 띄어쓰기 오류를 해결할 수 있었다. 향후 띄어쓰기 오류 교정에 필요한 언어 규칙 등을 시스템에 추가한 띄어쓰기 교정시스템을 통하여 OCR의 최종적인 인식률을 향상에 대해 연구할 예정이다.

• Journal of the Korean School Mathematics Society
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• v.14 no.3
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• pp.277-293
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• 2011

The purpose of mathematics education is to develop the ability of transforming various problems in general situations into mathematics problems and then solving the problem mathematically. Various teaching-learning methods for improving the ability of the mathematics problem-solving can be tried. However, it is necessary to choose an appropriate teaching-learning method after figuring out students' level of understanding the mathematics learning or their problem-solving strategies. The error analysis is helpful for mathematics learning by providing teachers more efficient teaching strategies and by letting students know the cause of failure and then find a correct way. The following subjects were set up and analyzed. First, the error classification pattern was set up. Second, the errors in the solving process of the function problems were analyzed according to the error classification pattern. For this study, the survey was conducted to 90 first grade students of ${\bigcirc}{\bigcirc}$high school in Chung-nam. They were asked to solve 8 problems in the function part. The following error classification patterns were set up by referring to the preceding studies about the error and the error patterns shown in the survey. (1)Misused Data, (2)Misinterpreted Language, (3)Logically Invalid Inference, (4)Distorted Theorem or Definition, (5)Unverified Solution, (6)Technical Errors, (7)Discontinuance of solving process The results of the analysis of errors due to the above error classification pattern were given below First, students don't understand the concept of the function completely. Even if they do, they lack in the application ability. Second, students make many mistakes when they interpret the mathematics problem into different types of languages such as equations, signals, graphs, and figures. Third, students misuse or ignore the data given in the problem. Fourth, students often give up or never try the solving process. The research on the error analysis should be done further because it provides the useful information for the teaching-learning process.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.445-455
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• 2011

Although misclassified binary data occur frequently in practice, the statistical methodology available for the data is rather limited. In particular, the interval estimation of population proportion has relied on the classical Wald method. Recently, Lee and Choi (2009) developed a new confidence interval by applying the Agresti-Coull's approach and showed the efficiency of their proposed confidence interval numerically, but a theoretical justification has not been explored yet. Therefore, a Bayesian model for the misclassified binary data is developed to consider the Agresti-Coull confidence interval from a theoretical point of view. It is shown that the Agresti-Coull confidence interval is essentially a Bayesian confidence interval.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.57-63
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• 2010

The damping parameter of Levenberg-Marquardt algorithm switches between error backpropagation and Gauss-Newton learning and affects learning speed. Fixing the damping parameter induces some oscillation of error and decreases learning speed. Therefore, we propose the way of a variable damping parameter with referring to the alternation of error. The proposed method makes the damping parameter increase if error rate is large and makes it decrease if error rate is small. This method so plays the role of momentum that it can improve learning speed. We tested both iris recognition and wine recognition for this paper. We found out that this method improved learning speed in 67% cases on iris recognition and in 78% cases on wine recognition. It was also showed that the oscillation of error by the proposed way was less than those of other algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2562-2570
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• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.