• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.912-919
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• 2005

When we apply binary classification to multi-class classification for text categorization, we use the One-Against-All method generally, However, this One-Against-All method has a problem. That is, documents of a negative set are not labeled by human. Thus, they can include many noisy documents in the training data. In this paper, we propose that the Sliding Window technique and the EM algorithm are applied to binary text classification for solving this problem. We here improve binary text classification through extracting noise documents from the training data by the Sliding Window technique and re-assigning categories of these documents using the EM algorithm.

• Communications of Mathematical Education
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• v.18 no.1 s.18
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• pp.223-237
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• 2004

본 연구는 중학교 1학년을 대상으로 일차방정식의 풀이 과정에서 나타나는 오류를 분석하고 그래핑 계산기를 활용하여 오류의 교정 과정을 제시하였다. 오류의 유형을 개념적 이해 미흡 오류, 등식의 성질에 대한 오류, 이항에 대한 오류, 계산 착오로 인한 오류, 기호화에 의한 오류로 분류하였으며, 이 중에서 등식의 성질에 대한 오류와 개념적 이해 미흡으로 인한 오류를 많이 범하고 있었다. 학생들이 TI-92를 활용하여 일차방정식의 해를 구할 때, Home Mode에서 Solve 기능을 이용하여 단순히 결과만을 보는 것 보다 Symbolic Math Guide를 이용하여 풀이 과정을 선택하여 대수적 알고리즘을 형성하면서 해를 구하는 것을 선호하였다. 그리고 학생들의 정의적 및 기능적 측면을 고려해야 할 필요성을 느끼게 되었다.

• Journal of Elementary Mathematics Education in Korea
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• v.24 no.1
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• pp.1-30
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• 2020

This study was carried out in order to identify the error types of statistical graphs for statistical literacy education. We analyze the meaning of using graphs in statistical problem solving, and identify categories, frequencies, and contexts as the components of statistical graphs. Error types of representing categories and frequencies make statistics consumers see incorrect distributions of data by subjective point of view of statistics producers and visual illusion. Error types of providing contexts hinder the interpretation of statistical information by concealing or twisting the contexts of data. Moreover, the findings show that tasks provide standardized frame already for drawing graphs in order to avoid errors and pay attention to the process of drawing the graph rather than statistical literacy for analyzing data. We suggest some implications about statistical literacy education, ethical problems, and knowledge for teaching to be considered when teaching the statistical graph in elementary mathematics classes.

• Journal of the Korea Institute of Building Construction
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• v.17 no.6
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• pp.535-544
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• 2017

This paper analyzes the relations between design errors, prevented by building information modeling (BIM)-based design validation, and their impacts in order to identify critical consideration factors for implementing BIM-based design validation in architecture, engineering, and construction (AEC) projects. More than 800 design errors detected by BIM-based design validation in two BIM-based projects in South Korea are categorized according to their causes (illogical error, discrepancy, and missing item) and work types (structure, architecture, and mechanical, electrical, and plumbing (MEP)). The probabilistic relations among the independent variables, including the causes and work types of design errors, and the dependent variables, including the project delays, cost overruns, low quality, and rework generation that can be caused by these errors, are analyzed using logistic regression. The characteristics of each design error are analyzed by means of face-to-face interviews with practitioners. According to the results, the impacts of design error causes in predicting the probability values of project delays, cost overruns, low quality, and rework generation were statistically meaningful.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2021-2028
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• 2010

Rail human factors research has grown rapidly in both quantity and quality of output over the past few years. Human factors, also, still plays a significant part in many railway accidents. In this paper we review categorized performance shaping factors of human errors associated with railway accidents within and out of the country. This paper deals with the selection of the important performance shaping factors under accident management situations in railway for use in the assessment of human errors. The purpose of this study is to classify which human error would be selected for accident analysis. Therefore, the classification of human errors suggested in this study may be useful to enhance the Korean railway system safety.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.239-246
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• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.102-102
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• 2017

• Journal of Navigation and Port Research
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• v.42 no.3
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• pp.159-166
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• 2018

The identification of behavioral errors by seafarers that have caused marine accidents may provide important clues for the reduction or prevention of marine accidents. The purpose of this study is to identify the behavioral errors of seafarers by the type of marine accident using the theory of Skill-, Rule-, and Knowledge-Based Behavior (SRKBB). In order to identify behavioral errors, we collected the information related to 1,744 cases of maritime accidents over a 9 year period (2008 ~ 2016). The behavior errors of the seafarers who caused the marine accidents were classified as SBBE (Skill-Based Behavioral Error), RBBE (Rule-Based Behavioral Error), and KBBE (Knowledge-Based Behavioral Error). After analyzing the frequency of behavioral errors according to the type of marine accident, results showed SBBE had the highest frequency of errors, followed by RBBE. Additionally, the frequency of occurrence of accidents such as stranding, overturning, and sinking was high in KBBE. This study showed it is possible to identify behavioral errors of seafarers according to the type of marine accidents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.9B
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• pp.1292-1298
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• 2001

본 논문에서는 공간 영역에서의 블록 분류 (block classification)와 순방향 신경망 필터(feedforward neural network filter)를 이용한 블록 기반 부호화에서의 적응적 블록화 현상 제거 알고리듬을 제안하였다. 제안한 방법에서는 각 블록 경계를 인접 블록간의 통계적 특성을 이용하여 평탄 영역과 에지 영역으로 분류한 후, 각 영역에 대하여 블록화 현상이 발생하였다고 분류된 클래스에 대하여 적응적인 블록간 필터링을 수행한다. 즉, 평탄 영역으로 분류된 영역 중 블록화 현상이 발생한 영역은 오류 역전파 학습 알고리듬 (error backpropagation learning algorithm)에 의하여 학습된 2계층 (2-layer) 신경망 필터를 이용하여 블록화 현상을 제거하고, 복잡한 영역으로 분류된 영역 중 블록화 현상이 발생한 영역은 에지 성분을 보존하기 위하여 선형 내삽을 이용하여 블록간 인접 화소의 밝기 값만을 조정함으로써 블록화 현상을 제거한다. 모의 실험 결과를 통하여 제안한 방법이 객관적 화질 및 주관적 화질 측면에서 기존의 방법보다 그 성능이 우수함을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.790-792
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• 2004

문서 범주화에서 이진분류를 다중 분류에 적용할 때, 일반적으로 One-Against-All 방법을 사용한다. 하지만, 이 One-Against-All 방법은 한가지 문제점을 가진다. 즉, positive 집합의 문서들은 사람이 직접 범주를 할당한 것이지만, negative 집합의 문서들은 사람이 직접 범주를 할당한 것이 아니기 때문에 오류 문서들이 포함될 수 있다는 것이다. 본 논문에서는 이러한 문제점을 해결하기 위해 Sliding Window기법과 EM 알고리즘을 이진 분류 기반의 문서 범주화에 적용할 것을 제안한다. 먼저 Sliding Window 기법을 이용하여 학습 데이터로부터 오류 문서들을 추출하고 이 문서들을 EM 알고리즘을 사용해서 다시 범주를 할당함으로써 이진 분류 기반의 문서 범주화 기법의 성능을 향상시킨다.