• 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 KIISE:Databases
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• v.33 no.4
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• pp.423-436
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• 2006

In recent years, publish-subscribe (pub-sub) systems based on XML document filtering have received much attention. In a typical pub-sub system, subscribing users specify their interest in profiles expressed in the XPath language, and each new content is matched against the user profiles so that the content is delivered only to the interested subscribers. As the number of subscribed users and their profiles can grow very large, the scalability of the system is critical to the success of pub-sub services. In this paper, we propose a novel scalable filtering system called FiST(Filtering by Sequencing Twigs) that transforms twig patterns expressed in XPath and XML documents into sequences using Prufer's method. As a consequence, instead of matching linear paths of twig patterns individually and merging the matches during post-processing, FiST performs holistic matching of twig patterns with incoming documents. FiST organizes the sequences into a dynamic hash based index for efficient filtering. We demonstrate that our holistic matching approach yields lower filtering cost and good scalability under various situations.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.611-615
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• 2009

Due to the popular use of digital camera, a great number of photos are taken at every usage of camera. It is essential to reveal relationship between photos to manage digital photos efficiently. We propose a method that tessellates image into unit blocks and applies 2D alignment to extend content-based similar region from seed block pair having high similarity. Through an alignment, we can get a block region scoring best matching value on whole image. The method can distinguish whether photos are sharing the same object or background. Our result is less sensitive to transition or pause change of objects. In experiment, we show how our alignment method is applied to real photo and necessities for further research like photo clustering and massive photo management.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.1-17
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• 2021

Identifying the exact train and the type of train boarded by passengers is practically cumbersome. Previous studies identified the trains boarded by each passenger by matching the Automated Fare Collection (AFC) data and the train schedule diagram. However, this approach has been shown to be inefficient as the exact train boarded by a considerable number of passengers cannot be accurately determined. In this study, we demonstrate that the AFC data - diagram matching technique could not estimate 28% of the train type selected by passengers using the Seoul Metro line no.9. To obtain more accurate results, this paper developed a two-step method for estimating the train type boarded by passengers by applying the AFC data - diagram matching method followed by a mixture distribution analysis. As a result of the analysis, we derived reasonable express train use/non-use passenger classification points based on 298 origin-destination pairs that satisfied the verification criteria of this study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.1
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• pp.115-122
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• 2006

The most methods for intrusion detection are based on the misuse detection which accumulates hewn intrusion information and makes a decision of an attack against any behavior data. However it is very difficult to detect a new or modified aoack with only the collected patterns of attack behaviors. Therefore, if considering that the method of anomaly behavior detection actually has a high false detection rate, a new approach is required for very huge intrusion patterns based on sequence. The approach can improve a possibility for intrusion detection of known attacks as well as modified and unknown attacks in addition to the similarity measurement of intrusion patterns. This paper proposes a method which applies the multiple sequence alignments technique to the similarity matching of the sequence based intrusion patterns. It enables the statistical analysis of sequence patterns and can be implemented easily. Also, the method reduces the number of detection alerts and false detection for attacks according to the changes of a sequence size.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.29-36
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• 2009

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, is an important data mining problem with broad applications. Since a sequential pattern in DNA sequences can be a motif, we studied to find sequential patterns in DNA sequences. Most previously proposed mining algorithms follow the exact matching with a sequential pattern definition. They are not able to work in noisy environments and inaccurate data in practice. Theses problems occurs frequently in DNA sequences which is a biological data. We investigated approximate matching method to deal with those cases. Our idea is based on the observation that all occurrences of a frequent pattern can be classified into groups, which we call approximated pattern. The existing PrefixSpan algorithm can successfully find sequential patterns in a long sequence. We improved the PrefixSpan algorithm to find approximate sequential patterns. The experimental results showed that the number of repeats from the proposed method was 5 times more than that of PrefixSpan when the pattern length is 4.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.349-354
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• 2014

PIN(Personal Identification Number)-based identification method has been used to identify the user of smart cards. However, this type of identification method has several problems. Firstly, PIN can be forgotten by owners of the card. Secondly, PIN can be used by others illegally. Furthermore, the possibility of hacking PIN can be high because this PIN type matching process is performed on terminal. Thus, in this paper we suggest a new identification method which is performed on smart card using face feature information. The proposed identification method uses low-sized face feature vectors and simple matching algorithm in order to get around the limits in computing capability and memory size of smart card.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• Korean Journal of Social Welfare
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• v.60 no.4
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• pp.55-77
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• 2008

After the economic crisis of Korea, the character of Korean Poverty has changed. Most notably, many people are working but poor. Therefore it is important to understand the characteristics of the working poor, especially the unstable work experiences of the working poor since one of the causes of poverty is that. Prior research about the working poor has not fully proven this issue. This study is to examine the job sequences of the working poor. Thus I utilized the KLIPS(Korea Labor and Income Study), and analyzed it by event sequence analysis and optimal matching methods. The job sequences are divided as follows: total years of working in the labor market, the number of gaps and the length of gaps in their careers, and the characteristics of experienced jobs since they have entered the labor market from age 15. As a result, there are no statistically significant in the total years of working in the labor market. And the number of gaps and the length of gaps in their careers, and the characteristics of experienced jobs show that working poor have been experiencing more unstable than non-poor. Thus, almost all of the male working poor has unstable jobs their whole lives, and the female working poor's job sequences show distinct features according to women's life course. These results can give political implications to the anti-poverty policy in Korea.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.327-332
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• 2016

This study is a leading research project to develop an automatic grade decision making algorithm of a 6-years-old fresh ginseng. For this work, we developed a Ginseng image acquiring instrument which can take 4-direction's images of a Ginseng at the same time and obtained 245 jingen images using the instrument. The 12 parameters were extracted for each image by a manual way. Lastly, 4 parameters were selected depending on a Ginseng grade classification criteria of KGC Ginseng research institute and a survey result which a distribution of averaging 12 parameters. A pattern recognition classifier was used as a support vector machine, designed to "k-class classifier" using the OpenCV library which is a open-source platform. We had been surveyed the algorithm performance(Correct Matching Ratio, False Acceptance Ratio, False Reject Ratio) when the training data number was controlled 10 to 20. The result of the correct matching ratio is 94% of the $1^{st}$ ginseng grade, 98% of the $2^{nd}$ ginseng grade, 90% of the $3^{rd}$ ginseng grade, overall, showed high recognition performance with all grades when the number of training data are 10.