• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.119-125
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• 1999

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.228-237
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• 2010

Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances as well as to support index scalability for large scale data. To support these requirements, we propose a distributed high-dimensional indexing structure based on cluster systems, called a Distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and VA-files. We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate the performance of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method contributes to significant performance advantages over existing index structures on difference kinds of datasets.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.154-156
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• 2002

인터넷의 발전으로 우리는 많은 정보와 지식을 인터넷에서 제공받을 수 있으며 HTML, 뉴스그룹 문서, 전자메일 등의 웹 문서로 존재한다. 이러한 웹 문서들은 여러가지 목적으로 분류해야 할 필요가 있으며 이를 적용한 시스템으로는 Personal WebWatcher, InfoFinder, Webby, NewT 등이 있다. 웹 문서 분류 시스템에서는 문서분류 기법을 사용하여 웹 문서의 소속 클래스를 결정하는데 문서분류를 위한 기법 중 대표적인 알고리즘으로 나이브 베이지안(Naive Baysian), k-NN(k-Nearest Neighbor), TFIDF(Term Frequency Inverse Document Frequency)방법을 이용한다. 본 논문에서는 웹 문서를 대상으로 이러한 문서분류 알고리즘 각각의 성능을 비교 및 평가하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.947-949
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• 2015

본 논문은 기존의 시간적으로 이웃한 프레임 사이의 움직임을 추정 보상하여 새로운 프레임을 생성하는 프레임률 향상 기법 (frame rate up conversion)을 제안한다. 움직임 추정(Motion Estimation)을 통하여 계산된 움직임 벡터를 이용하여 프레임을 생성하며, 생성된 프레임에서 발생되는 구명 (hole)과 중첩 (overlap) 영역을 처리하는 기법을 제안한다. 특히 k-NN 보간법(k-nearest neighbor interpolation)[3]과 중간값을 적응적으로 활용하여 향상된 화질의 영상을 생성한다. 실험 결과를 통하여 제안하는 기술의 우수성을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1520-1522
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• 2015

본 논문은 기존의 시간적으로 이웃한 프레임 사이의 움직임을 추정 보상하여 새로운 프레임을 생성하는 프레임률 향상 기법 (frame rate up conversion)을 제안한다. 움직임 추정(Motion Estimation)을 통하여 계산된 움직임 벡터를 이용하여 프레임을 생성하며, 생성된 프레임에서 발생되는 구멍 (hole)과 중첩 (overlap) 영역을 처리하는 기법을 제안한다. 특히 k-NN 보간법(k-nearest neighbor interpolation)[3]과 중간값을 적응적으로 활용하여 향상된 화질의 영상을 생성한다. 실험 결과를 통하여 제안하는 기술의 우수성을 입증하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.181-191
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• 2009

This study suggests the assessment technique for climate change effect on drought in Korea based on the AR4 SRES A2 scenario reported in IPCC fourth assessment report in 2007. IPCC provides monthly outputs of 24 climate models through the DDC. One of the models is BCM2 model which was developed at BCCR in Norway and NCEP data is used for downscaling. The K-NN(K-Nearest Neighbor) and ANN(Artificial Neural Network) are selected as downscaling technique to downscale the temperature and precipitation at Seoul station in Korea. K-NN could downscale both temperature and precipitation well. ANN made a good result for temperature, but it gave a divergence result in precipitation. Finally, SPI of Seoul station is computed to evaluate the effect of climate change on drought. BCM2 predicted that temperature will increase and drought severity will increase because of the increased drought spell at Seoul station.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.7-11
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• 2013

Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.285-292
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• 2020

This paper introduces a new computational efficient Dilution of Precision (DOP)-based landmark exclusion method while ensuring the safety of the LiDAR-based navigation system that uses an innovation-based Nearest-Neighbor (NN) Data Association (DA) process. The NN DA process finds a correct landmark association hypothesis among all potential landmark permutations using Kalman filter innovation vectors. This makes the computational load increases exponentially as the number of landmarks increases. In this paper, we thus exclude landmarks by introducing DOP that quantifies the geometric distribution of landmarks as a way to minimize the loss of integrity performance that can occur by reducing landmarks. The number of landmarks to be excluded is set as the maximum number that can satisfy the integrity risk requirement. For the verification of the method, we developed a simulator that can analyze integrity risk according to the landmark number and its geometric distribution. Based on the simulation, we analyzed the relationship between DOP and integrity risk of the DA process by excluding each landmark. The results showed a tendency to minimize the loss of integrity performance when excluding landmarks with poor DOP. The developed method opens the possibility of assuring the safety risk of the Lidar-based navigation system in real-time applications by reducing a substantial amount of computational load.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1853-1854
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• 2008

본 논문에서는 입력패턴과 그 k 근방 원형상에 잇어서 노름 평균에 기초한 최근접 결정법칙에 의한 패턴식별법을 제안한다. 이 방법은 식별경계 근방의 원형상에 있어서 분산의 차에 의한 가중치를 고려하기 때문에 패턴의 수가 적을 때 입력패턴을 정확하게 분류할 때 사용될 수 있다. 본 방법의 유효성을 평가하기 위해 인공적인 패턴과 실제패턴에 대해 k-NN 등 기존방법과 제안하는 방법을 적용하여 식별률에 의한 평가를 행한 결과, 특히 원형상의 분포가 희박한 경우 제안하는 방법이 기존방법에 비해 높은 식별률을 나타냈다.