• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.75.1-75.1
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• 2009

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.201-208
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• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.370-370
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• 2017

최근 기후변화와 이상기후의 영향으로 인하여 홍수재해의 시 공간적 패턴은 보다 복잡해지고, 예측이 어려워지고 있다. 이러한 기상이변에 따른 홍수피해를 예방하기 위한 비구조적 대책으로 홍수위험등급 및 범람범위 등의 정보를 포함하고 있는 홍수위험지도의 작성이 필요하다. 실제로 고정밀도 홍수위험지도를 작성하기 위해서는 지형, 지질, 기상 등의 디지털 정보 및 사회 경제와 관련된 다양한 DB를 필요로 하며, 강우-유출-범람해석 모델링을 통해 범람면적 및 침수깊이 등의 정보를 획득하게 된다. 하지만 일부지역, 특히 개발도상국에서는 이러한 계측 홍수 데이터가 부족하거나 획득할 수가 없어 홍수위험지도 제작이 불가능하거나 그 정확도가 매우 낮은 실정이다. 따라서 본 연구에서는 ASTER 또는 SRTM과 같은 범용 DEM 등 지형자료만을 기반으로 한 선형이진분류법(Liner binary classifiers)과 ROC분석(Receiver Operation Characteristics)을 이용하여 미계측 유역 (DB부재 또는 부족으로 강우-유출-범람해석 모델링이 불가능한 북한지역)의 홍수위험지역을 탐지하고, 적용성을 평가하고자 한다. 5개의 단일 지형학적 지수와 6개의 복합 지형학적 지수를 이용하여 Area Under the Curve (AUC)를 계산하고, Sensitivity (민감도)와 Specificity (특이도)가 가장 높은 지수를 선별하여 홍수위험지도를 작성하고, 실제 홍수범람 영상(2007년 북한 함경남도지역 용흥강 홍수)과 비교 분석하였다. 본 연구에서 제시하는 선형이진분류법과 ROC분석 방법은 홍수범람해석을 위한 다양한 기초정보를 필요로 하지 않고, 지형정보만을 사용하기 때문에 관측 데이터가 없거나 부족한 지역에 대해서 우선적으로 홍수위험지역을 탐지하고, 선별하는데 유용할 것으로 판단된다.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.291-300
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• 2019

This study investigates the characteristics of evaluation measures for classification models on a binary response variable in order to evaluate their suitability for use. Six measures are considered: Accuracy, Sensitivity, Specificity, Precision, F-measure, and the Heidke's skill score (HSS). Evaluation measures are reformulated using x(ratio of actually 1), y(ratio predicted by 1), z(ratio of both actual and predicted by 1) from the confusion matrix. We suggest two necessary conditions to assess the suitability of the evaluation measures. The first condition is that the measure function is constant for x and y in the case of a random model. The second condition is that the measure function is increasing for z and decreasing for x and y. Since only HSS satisfies the two conditions, that is always appropriate as an evaluation measure for the classification model on the binary response variable, and the other measures should be used within a limited range.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.19-30
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• 2012

Based on the internet-based infrastructures such as various information devices, social network systems and cloud computing environments, distributed and sharable data are growing explosively. Recently, as a data analysis and mining technique for extracting, analyzing and classifying the inherent and useful knowledge and information, Formal Concept Analysis on binary or many-valued data has been successfully applied in many diverse fields. However, in formal concept analysis, there has been little research conducted on analyzing interval data whose attributes have some interval values. In this paper, we propose a new approach for classification of interval data based on the formal concept analysis. We present the development of a supporting tool(iFCA) that provides the proposed approach for the binarization of interval data table, concept extraction and construction of concept hierarchies. Finally, with some experiments over real-world data sets, we demonstrate that our approach provides some useful and effective ways for analyzing and mining interval data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.514-522
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• 2003

Contrary to the previous methods, in this paper, we propose an enhanced shot change detection method using multiple features and binary decision tree. The previous methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using multiple features, which are supplementary each other, rather than using single feature. In order to classify the shot changes, we use binary classification tree. According to this classification result, we extract important features among the multiple features and obtain threshold value for each feature. We also perform the cross-validation and droop-case to verify the performance of our method. From an experimental result, it was revealed that the EI of our method performed average of 2% better than that of the conventional shot change detection methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.536-539
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• 2017

본 논문에서는 스캔된 악보 영상을 ART2 알고리즘을 이용하여 음자리표를 인식하고 자동으로 연주하는 방법을 제안한다. 제안된 자동 악보 인식 방법은 스캔된 영상에서 이진 영상으로 변환하기 위해서 평균 이진화 기법을 적용한다. 평균 이진화 기법이 적용된 영상에서 수평 히스토그램을 이용하여 악보 영상의 오선을 제거한다. 제거된 악보 영상에서 8방향 윤곽선 추적 알고리즘을 이용하여 음표를 탐색하여 추출하고 추출된 음표를 ART2 알고리즘에 적용하여 쉼표와 음표를 분류한다. 분류된 음표를 이용하여 악보를 인식하고 인식된 악보를 이용하여 연주한다. 실제 악보를 스캐너로 획득한 악보 영상을 대상으로 실험한 결과, 단순한 악보 영상에서 효과적으로 악보가 인식되고 연주할 수 있는 것을 확인하였다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.801-814
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• 2004

In this paper, we propose a block adaptive binarization (BAB) using a modified quadratic filter (MQF) to binarize business card images acquired by personal digital assistant (PDA) cameras effectively. In the proposed method, a business card image is first partitioned into blocks of 8${\times}$8 and the blocks are then classified into character Hocks (CBs) and background blocks (BBs). Each classified CB is windowed with a 24${\times}$24 rectangular window centering around the CB and the windowed blocks are improved by the pre-processing filter MQF, in which the scheme of threshold selection in QF is modified. The 8${\times}$8 center block of the improved block is barbarized with the threshold selected in the MQF. A binary image is obtained tiling each binarized block in its original position. Experimental results show that the MQF and the BAB have much better effects on the performance of binarization compared to the QF and the global binarization (GB), respectively, for the test business card images acquired in a PDA. Also the proposed BAB using MQF gives binary images of much better quality, in which the characters appear much better clearly, over the conventional GB using QF. In addition, the binary images by the proposed BAB using MQF yields about 87.7% of character recognition rate so that about 32.0% performance improvement over those by the GB using QF yielding about 55.7% of character recognition rate using a commercial character recognition software.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.455-456
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• 2022

본 연구에서는 항공편 결항 시, 이용객이 겪게 되는 시간적 / 비용적 피해를 최소화하기 위해 머신러닝·딥러닝 기법을 이용하여 항공편 결항 예측 모델을 제안한다. 이 모델은 5가지 이진 분류기법을 사용하여 과거 2017년~2021년 제주공항 기상 데이터와 항공편 스케줄 데이터를 병합하여 결항, 출발을 분류한다. 본 연구는 기상으로 인한 항공편 결항의 피해 최소화를 목적으로 한다.