• Atmosphere
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• v.23 no.1
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• pp.103-112
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• 2013

In this study, we developed a fog classification algorithm to classify fog type based on fog generation mechanism. For the analysis period of 1986-2005, 15,748 fog events had been reported from the 40 observational sites in South Korea. Thus, practically, it is almost impossible to individually classify the fog type of the whole fog events occurred in South Korea manually. In this study, the characteristics of fog during the research period were investigated and the fog classification flowchart were developed base on the analysis, and the fog classification algorithm was applied for the classification of fogs occurred at the observational sites. Finally, the classified fog-type and hindcasted fog occurance results obtained from the flowchart were evaluated for verification.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.120-129
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• 1996

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.283-284
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• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.984-987
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• 2003

In order to rapidly catch up urban region’s detailed land-use or land-cover information; this research used the post-classification algorithm (Spatial Reclassification Kernel: SPARK) to create a land-use map of Taichung City. We discussed the urban land-use classification model with the IKONOS images. The conclusions may be distinguished as follows:(a) Using the Maximum-Likelihood algorithm to classify seven broad land-cover categories. The overall accuracy in this stage achieves 92.72% and Kappa coefficient will be obtained 0.91; and (b) Using the SPARK method to classify images for detect the land-use, the overall accuracy achieves higher 89.64% and Kappa coefficient will be 0.86. To conclude, the research process in this study can fully and carefully describe local land-use pattern and assist the demand of land management and resources planning reference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6145-6158
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• 2019

It is a challenge for the current security industry to respond to a large number of malicious codes distributed indiscriminately as well as intelligent APT attacks. As a result, studies using machine learning algorithms are being conducted as proactive prevention rather than post processing. The k-NN algorithm is widely used because it is intuitive and suitable for handling malicious code as unstructured data. In addition, in the malicious code analysis domain, the k-NN algorithm is easy to classify malicious codes based on previously analyzed malicious codes. For example, it is possible to classify malicious code families or analyze malicious code variants through similarity analysis with existing malicious codes. However, the main disadvantage of the k-NN algorithm is that the search time increases as the learning data increases. We propose a fast k-NN algorithm which improves the computation speed problem while taking the value of the k-NN algorithm. In the test environment, the k-NN algorithm was able to perform with only the comparison of the average of similarity of 19.71 times for 6.25 million malicious codes. Considering the way the algorithm works, Fast k-NN algorithm can also be used to search all data that can be vectorized as well as malware and SSDEEP. In the future, it is expected that if the k-NN approach is needed, and the central node can be effectively selected for clustering of large amount of data in various environments, it will be possible to design a sophisticated machine learning based system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.63-66
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• 1996

In this paper, we propose a new content-based color image retrieval algorithm. The algorithm makes use of two features; colors as global features and block classification results as local features. More specifically, we obtain R, G, B color histograms and classify nonoverlapping small image blocks into texture, monotone, and various edges, then using these histograms and classification results were make a similarity measure. Experimental results show that retrieval rate of the proposed algorithm is higher than the previous method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.33-36
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• 1998

Most image restoration problems are ill-posed and need to e regularized. A difficult task in image regularization is to avoid smoothing of image edges. In this paper, were proposed an edge-preserving image restoration algorithm using block-based edge classification. In order to exploit the local image characteristics, we classify image blocks into edge and no-edge blocks. We then apply an adaptive constrained least squares (CLS) algorithm to eliminate noise around the edges. Experimental results demonstrate that the proposed algorithm can preserve image edges during the regularization process.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.8
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• pp.766-771
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• 2005

In this study, PD(partial discharge) signals which occur at stator coil of traction Motor are acquired these data are used for classifying the PD sources. NN(neural network) has recently applied to classify the PD pattern. The PD data are used for the learning process to classify PD sources. The PD data come from normal specimen and defective specimens such as internal void discharges, slot discharges and surface discharges. PD distribution parameters are calculated from a set of the data, which is used to realize diagnostic algorithm. NN which applies distribution parameters is useful to classify the PD patterns of defective sources generating in stator coil of traction motor.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.997-1004
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• 2009

A standard approach is to classify all of future observations. In some cases, however, it would be desirable to defer a decision in particular for observations which are hard to classify. That is, it would be better to take more advanced tests rather than to make a decision right away. This motivates a classifier with a reject option that reports a warning for those observations that are hard to classify. In this paper, we present the method which gives efficient computation with a reject option. Some numerical results show strong potential of the propose method.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.113-121
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• 2005

In this paper, we design the hybrid learning algorithm of LVQ which is to perform EMG pattern recognition. The proposed hybrid LVQ learning algorithm is the modified Counter Propagation Networks(C.p Net. ) which is use SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of LVa. The weights of the proposed C.p. Net. which is between input layer and subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVd algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights which is between subclass layer and class layer of C.p. Net. is learned to classify the classified subclass. which is enclosed a class . To classify the pattern vectors of EMG. the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.