• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1339-1346
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• 2015

The traffic identification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of identification methods have been introduced in literature, the payload signature-based identification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method's processing speed is much slower than other identification method such as header-based and statistical methods. In this paper, we first classifies signatures by matching type based on range, order, and direction of packet in a flow which was automatically extracted. By using this classification, we suggest a novel method to improve processing speed of payload signature-based identification by reducing searching space.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.764-776
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• 2008

Image identification is the process of checking whether the query image is the transformed version of the specific original image or not. In this paper, image identification method based on feature composition is proposed. Used features include color distance, texture information and average pixel intensity. We extract color characteristics using color distance and texture information by Modified Generalized Symmetry Transform as well as average intensity of each pixel as features. Individual feature is quantized adaptively to be used as bins of histogram. The histogram is normalized according to data type and it is used as the signature in comparing the query image with database images. In matching part, Manhattan distance is used for measuring distance between two signatures. To evaluate the performance of the proposed method, independent test and accuracy test are achieved. In independent test, 60,433 images are used to evaluate the ability of discrimination between different images. And 4,002 original images and its 29 transformed versions are used in accuracy test, which evaluate the ability that the proposed algorithm can find the original image correctly when some transforms was applied in original image. Experiment results show that the proposed identification method has good performance in accuracy test. And the proposed method is very useful in real environment because of its high accuracy and fast matching capacity.

• Advances in Computational Design
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• v.4 no.2
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• pp.155-177
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• 2019

Operation and Maintenance (O&M) phase is the main contributor to the total lifecycle cost of a building. Previous studies have described that Building Information Models (BIM), if available with detailed asset information and their properties, can enable rapid troubleshooting and execution of O&M tasks by providing the required information of the facility. Despite the potential benefits, there is still rarely BIM with Mechanical, Electrical and Plumbing (MEP) assets and properties that are available for O&M. BIM is usually not in possession for existing buildings and generating BIM manually is a time-consuming process. Hence, there is a need for an automated approach that can reconstruct the MEP systems in BIM. Previous studies investigated automatic reconstruction of BIM using architectural drawings, structural drawings, or the combination with photos. But most of the previous studies are limited to reconstruct the architectural and structural components. Note that mechanical components in the building typically require more frequent maintenance than architectural or structural components. However, the building mechanical drawings are relatively more complex due to various type of symbols that are used to represent the mechanical systems. In order to address this challenge, this paper proposed a symbol recognition framework that can automatically recognize the different type of symbols in the building mechanical drawings. This study applied vector-based computer vision techniques to recognize the symbols and their properties (e.g., location, type, etc.) in two vector-based input documents: 2D drawings and the symbol description document. The framework not only enables recognizing and locating the mechanical component of interest for BIM reconstruction purpose but opens the possibility of merging the updated information into the current BIM in the future reducing the time of repeated manual creation of BIM after every renovation project.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1234-1244
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• 2009

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services and applications on Internet, which makes the need of application-level traffic classification important for the efficient management and control of network resources. Although lots of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in terms of accuracy and completeness. In this paper we propose an application traffic classification method using statistic signatures, defined as a directional sequence of packet size in a flow, which is unique for each application. The statistic signatures of each application are collected by our automatic grouping and extracting mechanism which is mainly described in this paper. By matching to the statistic signatures we can easily and quickly identify the application name of traffic flows with high accuracy, which is also shown by comprehensive excrement with our campus traffic data.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.455-460
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• 2004

There have been many speculations about the presence of cosmic ray protons (CRps) in galaxy clusters over the past two decades. However, no direct evidence such as the characteristic $\gamma$-ray signature of decaying pions has been found so far. These pions would be a direct tracer of hadronic CRp interactions with the ambient thermal gas also yielding observable synchrotron and inverse Compton emission by additionally produced secondary electrons. The obvious question concerns the type of galaxy clusters most likely to yield a signal: Particularly suited sites should be cluster cooling cores due to their high gas and magnetic energy densities. We studied a nearby sample of clusters evincing cooling cores in order to place stringent limits on the cluster CRp population by using non-detections of EGRET. In this context, we examined the possibility of a hadronic origin of Coma-sized radio halos as well as radio mini-halos. Especially for mini-halos, strong clues are provided by the very plausible small amount of required CRp energy density and a matching radio profile. Introducing the hadronic minimum energy criterion, we show that the energetically favored CRp energy density is constrained to $2\%{\pm}1\%$ of the thermal energy density in Perseus. We also studied the CRp population within the cooling core region of Virgo using the TeV $\gamma$-ray detection of M 87 by HEGRA. Both the expected radial $\gamma$-ray profile and the required amount of CRp support this hadronic scenario.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.59-64
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• 2007

In these days, malicious codes have become reality and evolved significantly to become one of the greatest threats to the modern society where important information is stored, processed, and accessed through the internet and the computers. Computer virus is a common type of malicious codes. The standard techniques in anti-virus industry is still based on signatures matching. The detection mechanism searches for a signature pattern that identifies a particular virus or stain of viruses. Though more accurate in detecting known viruses, the technique falls short for detecting new or unknown viruses for which no identifying patterns present. To cope with this problem, anti-virus software has to incorporate the learning mechanism and heuristic. In this paper, we propose a fuzzy diagnosis system(FDS) using fuzzy c-means algorithm(FCM) for the cluster analysis and a decision status measure for giving a diagnosis. We compare proposed system FDS to three well known classifiers-KNN, RF, SVM. Experimental results show that the proposed approach can detect unknown viruses effectively.