• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.10
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• pp.901-906
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• 2007

In this study, we measured the partial discharge phenomenon of inside the closed switchgear, using ultra wide band antenna. The characteristics of $\Phi-q-n$ in the normal state are stable, and confirmed at less than 0.01, but in proceeding states, about 2 times larger. And in the abnormal state, it grew hundreds of times larger compared with normal state. According to K-means analysis, if slant of discharge characteristics is a straight line close to "0" and standard deviation is small, it is in a normal state. However if we can find a peak from K-means clusters and standard deviation to be large, it is in an abnormal state.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.700-705
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• 2004

Clustering of incomplete data using the Autoencoder and the Fuzzy c-Means(PCM) is proposed in this paper. The Proposed algorithm, called Optimal Completion Autoencoder Fuzzy c-Means(OCAEFCM), utilizes the Autoencoder Neural Network (AENN) and the Gradiant-based FCM (GBFCM) for optimal completion of missing data and clustering of the reconstructed data. The proposed OCAEFCM is applied to the IRIS data and a data set from a financial institution to evaluate the performance. When compared with the existing Optimal Completion Strategy FCM (OCSFCM), the OCAEFCM shows 18％-20％ improvement of performance over OCSFCM.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.281-288
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• 2014

A method for edge detection using K-means clustering is proposed in this paper. The method is performed through there steps. Histogram equalizing is applied to the image for the uniformed intensity distribution. Pixels are clustered by K-means clustering technique. Then Sobel mask is applied to detect edges. Experiments showed that this method detected edges better than conventional method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1783-1793
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• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.249-258
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using the grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.229-238
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis, data analysis, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters, because it is more primitive and explorative. In this paper we propose a new method of k-means clustering using the grid-based representative value(arithmetic and trimmed mean) for sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.3
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• pp.73-83
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• 2003

Methods of fuzzy C-means have been used to characterize geotechnical information from static cone penetration data. As contrary with traditional classification methods such as Robertson classification chart, the FCM expresses classes not conclusiveness but fuzzy. The results show that the FCM is useful to characterize ground information that can not be easily found by using normal classification chart. But optimal number of classes may not be easily defined. So, the optimal number of classes should be determined considering not only technical measures but engineering aspects.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1413-1425
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• 2006

When X and Y have independent gamma distributions, we develop a Bayesian procedure for testing the equality of two gamma means. The reference prior is derived. Using the derived reference prior, we propose a Bayesian test procedure for the equality of two gamma means using fractional Bayes factor and intrinsic Bayes factor. Simulation study and a real data example are provided.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.9-14
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• 2015

In this paper, we present an color quantization method by complementing the disadvantage of K-means color quantization that is one of the well-known color quantization. We named the proposed method "octree-means" color quantization. K-means color quantization does not use all of the clusters because it initializes the centroid of clusters with random value. The proposed method complements this disadvantage by using the octree color quantization which is fast and uses the distribution of colors in image. We compare the proposed method to six well-known color quantization methods on ten test images to evaluate the performance. The experimental results show 68.29 percent of mean square error(MSE) and processing time increased by 14.34 percent compared with K-means color quantization. Therefore, the proposed method improved the K-means color quantization and perform an effective color quantization.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.65-74
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• 2012

A barcode is a representative means of cognition. It is either printed on the package of a product or attached to it as a sticker. It is used for the fast cognition of a product at a store. It is considerably cheap to make a barcode. Also, it is possible to read it fast by using a barcode reader. Because of such convenience provided by the barcode, a new system using the barcode as a means of settling payments like a currency or a credit card has been developed. However, due to its characteristics, it is easy to reduplicate, forge or falsify a barcode easily. Therefore, this study focuses on the case of applying the system using barcodes as a means of settling payments without providing solutions for the potential weaknesses. Also, this study suggests various points to consider regarding the creation of safe barcodes as one of the related measures, while providing various methods using additional means of certification other than the one of using barcodes in addition to the way of applying complexity with barcode numbers. Throughout this study, it will be possible to safely establish and operate the payment-settlement system using barcodes.