• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.1
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• pp.124-132
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• 2018

This paper presents a shelf life assessment for K-1 military gas masks in the Republic of Korea using test data of Chemical Materiels Stockpile Reliability Program(CSRP). For the shelf life assessment, over 2,500 samples between 2006 and 2015 were collected from field tests and analyzed to estimate a probability of proper and improper functionality using Bayesian estimation. For this, three stages were considered; a pre-processing, a processing and an assessment. In the pre-processing, major components which directly influence the shelf life of the mask were statistically analyzed and selected by applying principal component analysis from all test components. In the processing, with the major components chosen in the previous stage, both proper and improper probability of gas masks were computed by applying Bayesian estimation. In the assessment, the probability model of the mask shelf life was analyzed with respect to storage periods between 0 and 29 years resulting in between 66.1 % and 100 % performances in accuracy, sensitivity, positive predictive value, and negative predictive value.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.100-102
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• 2005

인터넷 환경에서의 B2B Market Place의 출현은 판매자와 구매자와의 다자간 거래를 가능하게 하였다. 이러한 기반에서 상품정보를 포함하는 전자 카탈로그의 활용은 나날이 증가하고 있다. 그러나 동일한 상품에 대한 분류체계와 기준이 다르므로 전자카탈로그에 대한 재분류는 고비용을 초래하는 필수 불가결한 문제로 남게 되었다. 본 연구에서는 이러한 문제를 해결하기 위해 기계학습 기법을 이용한 $Na\ddot{i}ve$ Bayesian classifier 모델을 사용하였다 학습 데이터를 생성해야 하는 $Na\ddot{i}ve$ Bayesian 알고리즘 적용 시 전자 카탈로그는 일반 문서보다 상대적으로 학습 정보가 적으므로 데이터 모델의 확장을 통해 학습 정보를 생성하여 이러한 단점을 보완하였다. 전자 카탈로그 자동분류에 있어서 효과적이고 풍부한 양의 학습 데이터를 생성하는 것이 분류 정확도 향상에 중요한 영향을 미침을 실험을 통해 확인하였다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.407-410
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• 2001

최근 영상 검색(retrieval)과 분류(classification)에서 질감 특징(texture feature)을 이용한 연구들이 활발하게 진행되고 있다. 본 논문에서는 효율적인 질감 특징 추출을 위해 명암도 상호발생 행렬법(gray level co-occurrence matrix)과 웨이블릿 변환(wavelet transform)을 이용하여 질감의 특징을 추출한 후 특징의 중요도에 따라서 가중치를 부여하는 방법을 제안한다. 이렇게 추출된 가중치 대표 벡터들을 기반으로 베이시안 분류기(Bayesian classifier)를 통해 임의의 질감을 인식하였다.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.183-193
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• 1999

In the problem of estimating estimable functions in classification linear models, we propose a method of obtaining least squares estimators of estimable functions. This method is based on the hierarchical Bayesian approach for estimating a vector of unknown parameters. Also, we verify that estimators obtained by our method are identical to least squares estimators of estimable functions obtained by using either generalized inverses or full rank reparametrization of the models. Some examples are given which illustrate our results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.333-340
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• 2008

This paper shows that the use of prior probabilities of the involved classes improve the accuracy of classification in case of poor separability between classes. Three cases of experiments are designed with two LiDAR datasets while considering three different classes (building, tree, and flat grass area). Moreover, random sampling method with human interpretation is used to achieve the approximate prior probabilities in this research. Based on the experimental results, Bayesian classification with the appropriate prior probability makes the improved classification results comparing with the case of non-prior probability when the ratio of prior probability of one class to that of the other is significantly different to 1.0.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.210-219
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• 2015

A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.325-332
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• 2014

It is generally accepted that classification accuracy is affected by the number of learning documents, but there are few studies that show how this influences automatic text classification. This study is focused on evaluating the deviation-based classification model which is developed recently for genre-based classification and comparing it to other classification algorithms with the changing number of training documents. Experiment results show that the deviation-based classification model performs with a superior accuracy of 0.8 from categorizing 7 genres with only 21 training documents. This exceeds the accuracy of Bayesian and SVM. The Deviation-based classification model obtains strong feature selection capability even with small number of training documents because it learns subject information within genre while other methods use different learning process.

• Proceedings of the Korean Society of Toxicology Conference
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• 2005.05a
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• pp.193-193
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• 2005

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.331-337
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• 2014

Paradigm shift to cloud computing has increased the importance of security. Even though public cloud computing providers such as Amazon, already provides security related service like firewall and identity management services, it is not suitable to protect data in cloud environments. Because in public cloud computing environments do not allow to use client's own security solution nor equipments. In this environments, user are supposed to do something to enhance security by their hands, so the needs of visualized security management arises. To implement visualized security management, developing near realtime data handling & packet classification mechanisms are crucial. The key technical challenges in packet classification is how to classify packet in the manner of unsupervised way without human interactions. To achieve the goal, this paper presents automated packet classification mechanism based on naive-bayesian and packet Chunking techniques, which can identify signature and does machine learning by itself without human intervention.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.