• East Asian mathematical journal
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• 제30권4호
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• pp.451-474
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• 2014

The purpose of this study is to analyze error patterns third-year middle school students make on quadratic function graph problems and to examine about the possible correct them by providing supplementary tutoring. To exam the error patterns that occur during problem solving processes, to 82 students, We provided 25 quadratic function graph problems in the preliminary-test. The 5 types of errors was conceptual errors, false intuition errors, incorrect use of conditions in problems, technical errors, and errors from slips or carelessness. Statistical analysis of the preliminary-test and post-test shows that achievement level was higher in the post-test, after supplementary tutoring, and the t-test proves this to be meaningful data. According to the per subject analyses, the achievement level in the interest of symmetry, parallel translation, and general graph, respectively, were all higher in the post-test than the preliminary-test and this is meaningful data as well. However, no meaningful relation could be found between the preliminary-test and the post-test on other subjects such as graph remodeling and relations positions of the parabola. For the correction of errors, try the appropriate feedback and various teaching and learning methods.

• 무역학회지
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• 제44권1호
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• pp.75-86
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• 2019

This study performs a factor analysis that affects the bunker oil price using the Co-integration model and Vector Error Correction Model (VECM). For this purpose, we use data from Clarkson and the analysis results show 17.6% decrease in bunker oil price when the amount of crude oil production increases at 1.0%, 10.3% increase in bunker oil price when the seaborne trade volume increases at 1.0%, 1.0% decrease in bunker oil price when total volume of vessels increases at 1.0%, and 0.003% increase in bunker oil price when 1.0% increase in world GDP, respectively. This study is meaningful in that this study estimates the speed of convergence to long-term equilibrium and identifies the price adjust mechanism which naturally exists in bunker oil market. And it is expected that the future study can provide statistically more meaningful econometric results if it can obtain data during more long-periods and use more various kinds of explanatory variables.

• 전자통신동향분석
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• 제38권5호
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.925-936
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• 2008

An association rule mining finds the relation among each items in massive volume database. In generating association rules, the researcher specifies the measurements randomly such as support, confidence and lift, and produces the rules. The rule is not produced if it is not suitable to the one any condition which is given value. For example, in case of a little small one than the value which a confidence value is specified but a support and lift's value is very high, this rule is meaningful rule. But association rule mining can not produce the meaningful rules in this case because it is not suitable to a given condition. Consequently, we creat insignificant error which is not selected to the meaningful rules. In this paper, we suggest clustering technique to association rule measures for finding effective association rules using measure of association.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.959-961
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• 1995

The objective of most image proccessing applications is to extract meaningful information from one or more pictures. It is accomplished efficiently using neural networks, which is used in image classification and image recognition. In neural networks, background and meaningful information are processed with same weight in input layer. In this paper, we propose the image classification method using neural networks, especially EBP(Error Back Propagation). Preprocessing is needed. In preprocessing, background is compressed and meaningful information is emphasized. We use the quadtree approach, which is a hierarchical data structure based on a regular decomposition of space.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.149-154
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• 1993

In heavy grinding that is on of the high efficient grinding method, meaningful deformation is generated by high temperature. So, after machining, geomeric error generated od the workpiece. The most important factor on the geometric error is temperature difference between upper layer and lower layer (T $_{d}$) . Relations between Td and grinding condition and maximum geometric error and grinding condition are obtained by experiment. This relations are used in fuzzy algorithm for improvement geometric accuracy. The main results are follows : (1) The linear relation between maximum geometric error and grinding condition is ovtained by experiment. (2) The linear relation between maximum temperature difference between upper layer and lower layer and grinding condition is ovtained by experiment. (3) Control peth of wheel for improvement geometric accuracy is obtained by using the fuzzy algorithm.m.

• 품질경영학회지
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• 제47권4호
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• 한국건축시공학회지
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• 제22권4호
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• pp.415-423
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• 2022

건설 사고재해의 가장 큰 원인은 인적요인이므로, 건설공사에서 휴먼에러를 감소시켜 사고재해를 감소시키는 것이 중요하다. 그러나, 휴먼에러는 조직적 상황의 연속적인 흐름이 배후 요인으로 작용한다. 따라서, 휴먼에러의 배후 요인을 파악할 수 있는 m-SHEL 모델을 사용하여 건설 중대 사고재해를 분석하였다. 분석 결과, 건설 중대 사고재해 유형에 따라 휴먼에러를 일으키는 배후 세부 요인이 차이가 있다는 것을 파악하였으며, 휴먼에러 배후 요인 중 L-m 요인, L-H 요인, L 요인 순으로 많이 차지하고 있는 것을 알 수 있었다. 본연구는 건설공사에서 휴먼에러를 줄이기 위해서는 조직적 상황을 파악하고 대응하는 것이 중요하다는 것을 사례를 통해서 확인하였다는 점에 의의가 있다.

• 컴퓨터교육학회논문지
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• 제17권5호
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• pp.43-51
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• 2014

본 연구는 중 고등학생들의 온라인 프로그래밍 과제 수행결과에서 발생한 오류를 파악하여, 오류유형과 오류내용을 분류하고, 프로그래밍 개념학습 성취수준에 따른 오류 빈도의 차이와 성취수준과 오류유형과의 상관관계를 분석하여 향후 프로그래밍 교육에 대한 방향과 인지수준에 따른 교육방법을 제시하였다. 연구를 위해 88명의 학생들의 프로그래밍 과제 수행 결과를 가지고 문법오류, 논리오류, 코딩오류로 오류유형과 오류내용을 분류하고 분석하였다. 분석결과, 세 오류유형 중 논리오류의 발생비율이 69.3%로 가장 높았으며, 성취수준에 따른 오류 빈도의 차이에서는 성취수준 상, 중, 하 세 집단 간에 유의한 차이가 있었다. 성취수준과 오류유형과의 상관관계 분석 결과에서는 논리오류와 코딩오류에서 부적 상관관계를 보여, 성취수준이 높을수록 논리오류와 코딩오류를 적게 범함을 알 수 있었다. 오류유형간의 상관관계에서는 문법오류와 코딩오류간의 정적상관관계를 보였다.