• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.852-859
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• 2019

With the development of IT technology and smart devices, various applications utilizing image information are being developed. In order to provide an intuitive interface for pronunciation recognition, there is a growing need for research on pronunciation recognition using mouth feature values. In this paper, we propose a system to distinguish Korean vowel pronunciations by detecting feature points of lips region in images and applying Bayesian based learning model. The proposed system implements the recognition system based on Bayes' theorem, so that it is possible to improve the accuracy of speech recognition by accumulating input data regardless of whether it is speaker independent or dependent on small amount of learning data. Experimental results show that it is possible to effectively distinguish Korean vowels as a result of applying probability based Bayesian classification using only visual information such as mouth shape features.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.19-30
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• 2000

정보통신기술의 비약적인 발전은 온라인으로 생성되는 전자문서의 양을 폭발적으로 증가시키고 있다. 따라서 수동으로 문서를 분류하던 종래의 방법 대신 문서의 자동분유 기술 개발이 특별히 요구되고 있다. 본 논문에서는 베이지안 학습 기법을 이용하여 문서를 자동으로 분류하는 방법을 연구하고, 20개의 유즈넷 뉴스그룹 문서들을 분류하도록 시험하였다. 사용한 알고리즘은 Naive Bayes Classifier이며, 구현한 시스템을 이용해 유즈넷 문서를 대상으로 자동분류를 실험한 결과 분류의 정확률이 약 77%로 나타났다.

• 한국IT서비스학회지
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• 제2권2호
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• pp.87-95
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• 2003

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. in this research we develop e-mail classifiers for e-mail Response Management Systems (ERMS) using naive bayesian learning and centroid-based classification. We analyze which method performs better under which conditions, comparing classification accuracies which may depend on the structure, the size of training data set and number of classes, using the different data set of an on-line shopping mall and a credit card company. The developed e-mail classifiers have been successfully implemented in practice. The experimental results show that naive bayesian learning performs better, while centroid-based classification is more robust in terms of classification accuracy.

• 한국측량학회지
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• 제9권2호
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• pp.81-91
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• 1991

본 연구에서는 원격탐사 수치화상 데이타의 분류단계에 가중치를 고려한 Bayesian MLC를 적용하여 그 분석 정확도를 향상시키고자 하였다. 우선, Bayesian 결정법칙을 원격탐사분야 측면에서 분석해 보고 정규확률 밀도함수를 이용하여 n차원으로 확장시켰다. 이 유도과정에서 정의되는 사천확률 항에, 평행육면체 분류결과를 가중치로 적용하여 분류를 실행하였다 그리고 최종적 분류정확도는 확률함수데이타에$x^2$분포를 가정한 임계치 처리를 하므로써 오분류확률이 높은 화소를 추출하여 그 양을 기준으로 평가하였다. 연구의 전체 처리과정에 사용한 인공위성 데이타는 LANDSAT TM(1985년 10월 21일 ; 116-34)이며 연구 대상지역은 서울시 행정구역 내이다. 가중치를 적용해 본 결과 5.21%의 분석정확도 향상을 이루었으며, 따라서 본 기법은 도시 지역과 같이 복잡한 분포특성을 가지는 지형에 효과적으로 활용될 수 있다고 생각된다.

• 한국공간정보시스템학회 논문지
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• 제10권3호
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• pp.31-43
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• 2008

주행 시간 예측은 첨단 여행정보 시스템 (ATIS) 및 교통관리 시스템 (ITS)에서 필수적이다. 이를 위해 본 연구에서는 대용량의 데이터 분류에서 높은 정확도와 빠른 속도를 보장하는 $Na{\ddot{i}}ve$ Bayesian 분류화 기법을 기반으로 한 주행시간 예측 알고리즘을 제안한다. 제안된 알고리즘은 도로 네트워크 상에서 사용자 지정 주행 경로에 대하여 주행시간 예측이 가능하며, 또한 주어진 경로에 대해 시간대 별 평균 구간 속도를 고려하여 보다 정확한 주행 시간 예측을 수행한다. 제안된 알고리즘을 기존의 링크-기반 예측(link-based prediction)알고리즘[1] 및 Micro T* 알고리즘[2]과 성능 비교를 수행하였다. 성능 비교 결과, 제안된 기법이 타 예측기법에 비해 MARE (mean absolute relative error)가 크게 감소하여 성능이 향상되었음을 보였다.

• Smart Structures and Systems
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• 제29권1호
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• pp.41-51
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• 2022

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

• 대한원격탐사학회지
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• 제20권5호
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• pp.315-327
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• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

• Journal of Biosystems Engineering
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• 제40권4호
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• pp.417-427
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• 2015

Purpose: This study investigated different band selection methods to classify spectrally similar data - obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies - using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

• 응용통계연구
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• 제33권3호
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• pp.309-320
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• 2020

방향성 비순환 그래프(directed acyclic graph; DAG)라고도 하는 베이지안 네트워크(Bayesian network)는 변수 사이의 관계를 확률과 그래프를 통해 모형화할 수 있다는 점에서 최근 의학, 기상학, 유전학 등 여러 분야에서 다양하게 활용되고 있다. 특히 이산형 자료의 예측에 사용되는 베이지안 네트워크 분류분석기(Bayesian network classifier)가 최근 새로운 데이터 마이닝 기법으로 주목받고 있다. 베이지안 네트워크는 그 구조와 학습 방법에 따라 여러 가지 다양한 모형으로 분류할 수 있다. 본 논문에서는 서로 다른 성질을 가진 이산형 자료를 바탕으로 구조 학습 방법에 차이를 두어 베이지안 네트워크 모형을 학습시킨 후, 가장 간단한 방법인 나이브 베이즈 (naïve Bayes) 모형과 비교해 본다. 학습된 모형들을 여러 가지 실제 데이터에 적용하여 그 예측 정확도를 비교함으로써 최적의 분류 분석 결과를 얻을 수 있는지 살펴본다. 또한 각각의 모형에서 나타나는 그래프를 통해 데이터의 변수 사이의 관계를 비교한다.

• 통상정보연구
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• 제9권3호
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• pp.305-320
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• 2007

With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.