• IE interfaces
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• v.18 no.1
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• pp.10-21
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• 2005

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.4
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• pp.191-199
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• 2014

Currently, HTTP traffic has been developed rapidly due to appearance of various applications and services based web. Accordingly, HTTP Traffic classification is necessary to effective network management. Among the various signature-based method, Payload signature-based classification method is effective to analyze various aspects of HTTP traffic. However, the payload signature-based method has a significant drawback in high-speed network environment due to the slow processing speed than other classification methods such as header, statistic signature-based. Therefore, we proposed various classification method of HTTP Traffic based HTTP signatures of hierarchical structure and to improve pattern matching speed reflect the hierarchical structure features. The proposed method achieved more performance than aho-corasick to applying real campus network traffic.

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.202-209
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• 2002

This paper deals with simplification of classification rules for data mining and rule bases for control systems. Datamining that extracts useful information from such a large amount of data is one of important issues. There are various ways in classification methodologies for data mining such as the decision trees and neural networks, but the result should be explicit and understandable and the classification rules be short and clear. The rough sets theory is an effective technique in extracting knowledge from incomplete and inconsistent data and provides a good solution for classification and approximation by using various attributes effectively This paper investigates granularity of knowledge for reasoning of uncertain concopts by using rough set approximations and uses a hierarchical classification structure that is more effective technique for classification by applying core to upper level. The proposed classification methodology makes analysis of an information system eary and generates minimal classification rules.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.1
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• pp.101-126
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• 2023

Photographic archives of universities are one of the most valuable types of records that establish the university's identity and provide historical evidence. Unlike text records, however, they are weak in conveying meanings. Therefore, it is difficult to support users' search and utilization unless the information of photo records is comprehensively described. In this study, for the university photo archives, we tried to structure the classification system of photo archives and develop a metadata set that reflects the category characteristics in the classification. To this end, the photo archives classification system and metadata elements of domestic and American university archives were analyzed and based on this, the model of information structure was proposed. The information structure model presented in this study can help university archives improve the data quality of their photo archives and support users with the abundant discovery of photo archives.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.448-453
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• 2003

Most urban transit companies recognize the necessity of classification for facility management Classification for urban transit facility is necessary for standardization of maintenance management. The practical application. however. is not easy because of the absence of standardization of classification for urban transit facility and the difficulty in objectification of breakdown structure. This study suggests a proposal of classification for maintenance management in urban transit facility. This study defines standardization of classification as facility, work, maintenance and attribute to manage urban transit facility. And attribute classification consist of material, equipment and document. The suggested classification can be used as a useful maintenance management tool that enables evaluation of urban transit facility by standardization. The results of this study could be used as references for related urban transit companies.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.365-370
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip using tree structure. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment, co-occurrence matrix, histogram-ratio features are calculated. The histogram-ratio feature is taken from the gray-level image. For the defect classification, we suggest a tree structure of which nodes are multilayer neural network clasifiers. The proposed algorithm reduced error rate comparing to one stage structure.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.787-800
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• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

• Journal of Korean Library and Information Science Society
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• v.33 no.1
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• pp.1-13
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• 2002

This article introduces the general structure and characteristics of ICC(International Centesimal Classification) including main classes and auxiliary tables - form divisions; chronological divisions; geographical divisions; language divisions; racial, ethnic, national groups; divisions of individual religion; divisions of individual language; subdivisions for individual literatures; and groups of persons.