• Journal of Sensor Science and Technology
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• v.13 no.6
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• pp.446-453
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• 2004

A portable electronic tongue (E-Tongue) system using an array of ion-selective electrode (ISE) and personal digital assistants (PDA) for recognizing and analyzing food and drink have been designed. By the employment of PDA, the complex algorithm such as fuzzy c-means algorithm (FCMA) could be used in E-Tongue, PUMA could iteratively solve the cluster centers of pre-determined standard patterns. And the membership between the standard patterns and unknown pattern could be analyzed easily by the present E-Tongue combined with PDA.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.625-630
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• 2014

The amount of information is increasing rapidly with the development of the internet and the computer. Since these enormous information is managed by the document forms, it is necessary to search and process them efficiently. The document clustering technique which clusters the related documents through the similarity between the documents help to classify, search, and process the large amount of documents automatically. This paper proposes a method to find the initial seed points through principal component analysis when the documents represented by vectors in the feature vector space are clustered by K-means algorithm in order to increase clustering performance. The experiment shows that our method has a better performance than the traditional K-means algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1463-1471
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• 2016

The indoor localization algorithm using the Difference Means based on Fingerprint (DMFPA) to improve the performance of indoor localization in moving Wi-Fi environment is proposed in this paper. In addition to this, the performance of the proposed algorithm is also compared with the Original Fingerprint Algorithm (OFPA) and the Gaussian Distribution Fingerprint Algorithm (GDFPA) by our developed indoor localization simulator. The performance metrics are defined as the accuracy of the average localization accuracy; the average/maximum cumulative distance of the occurred errors and the average measurement time in each reference point.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.97-100
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• 2005

A new model-based clustering algorithm is proposed. The idea starts from the assumption that observations are realizations of Gaussian processes and so are correlated. With a special covariance structure, the posterior probability that an observation belongs to each cluster is computed using the ECM algorithm. A preliminary result of small-scale simulation study is given to compare with the k-means clustering algorithms.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.609-619
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• 2016

RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. In this paper, we implement K-Means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. The main idea introduces a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. We showed that our K-Means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. We also implemented Elbow method with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset. Comparison with our MapReduce implementation of Elbow method and classical kmeans() in R with small data showed similar results.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.1-16
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• 1998

The zone clustering problem arising from several area such as deciding the optimal location of ambient measuring stations is to devide the 2-dimensional area into several sub areas in which included individual zone shows simimlar properties. In general, the optimal solution of this problem is very hard to obtain. Therefore, instead of finding an optimal solution, the generation of near optimal solution within the limited time is more meaningful. In this study, the combination of a genetic algorithm and the modified k-means method is used to obtain the near optimal solution. To exploit the genetic algorithm effectively, a representation of chromsomes and appropriate genetic operators are proposed. The k-means method which is originally devised to solve the object clustering problem is modified to improve the solutions obtained from the genetic algorithm. The experiment shows that the proposed method generates the near optimal solution efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1411-1416
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• 2020

In this paper, we propose a method of extraction analysis of blood flow area on color doppler ultrasonography by using 4-directional contour tracking and K-Means algorithm. In the proposed method, ROI is extracted and a binarization method with maximum contrast as a threshold is applied to the extracted ROI. 4-directional contour algorithm is applied to extract the trapezoid shaped region which has blood flow area of brachial artery from the binarized ROI. K-Means based quantization is then applied to accurately extract the blood flow area of brachial artery from the trapezoid shaped region. In experiment, the proposed method successfully extracts the target area in 28 out of 30 cases (93.3%) with field expert's verification. And comparison analysis of proposed K-Means based blood flow area extraction on 30 color doppler ultrasonography and brachial artery blood flow ultrasonography provided by a specialist yielded a result of 94.27% accuracy on average.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.17-26
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• 2018

Recently, through the emergence and convergence of Internet services, the unified Internet of thing(IoT) service platform have been researched. Currently, the IoT service is constructed as an independent system according to the purpose of the service provider, so information exchange and module reuse are impossible among similar services. In this paper, we propose a operation based service classification algorithm for various services in order to provide an environment of unfied Internet platform. In implementation, we classify and cluster more than 100 commercial IoT services. Based on this, we evaluated the performance of the proposed algorithm compared with the K-means algorithm. In order to prevent a single clustering due to the lack of sample groups, we re-cluster them using K-means algorithm. In future study, we will expand existing service sample groups and use the currently implemented classification system on Apache Spark for faster and more massive data processing.