• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.57-60
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• 2001

본 논문에서는 유연 패턴(elastic pattern)을 갖는 규칙(rule)을 탐사하고 매칭하는 기법에 대해 논의한다. 유연 패턴은 시간 축으로 확장 및 수축할 수 있는 요소들의 순서화된 리스트이다. 유연 패턴은 서로 다른 샘플링 비율을 갖는 데이터 시퀀스들로부터 규칙들을 찾아내는데 유용하게 사용된다. 본 연구에서는 헤드(head: 규칙의 왼쪽 부분)와 바디(body: 규칙의 오른쪽 부분)가 모두 유연 패턴으로 구성된 규칙들을 신속하게 찾도록 하기 위하여 데이터 시퀀스로부터 서픽스 트리(suffix tree)를 구성한다. 이 서픽스 트리는 유연 규칙들의 압축된 표현이며, 타깃 헤드 시퀀스와 매치되는 규칙을 찾기 위한 인덱스 구조로서 사용된다. 만일, 매치되는 규칙을 찾을 수 없는 경우에는 규칙 완화(rule relaxation)의 개념을 이용한다. 클러스터 계층(cluster hierarchy)과 완화 오차(relaxation error)를 사용하여 타깃 헤드 시퀀스의 고유한 정보를 대부분 포함하고 있는 최소한으로 완화된 규칙을 찾는다. 다양한 실험을 통한 성능 평가를 통하여 제안한 기법의 우수성을 검증한다.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.304-309
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• 1999

In this paper, we presents a new disparity map refinement algorithm using statistical characteristics of disparity map and edge information. The proposed algorithm generate a refined disparity map using disparity maps which are obtained from area and feature-based Stereo Matching by selecting a disparity value of edge point based on the statistics of both disparity maps. Experimental results on aerial stereo image show the better results than conventional fusion algorithms in the disparity error. This algorithm can be applied to the reconstruction of building image from the high resolution remote sensing data.

• Korean Journal of Computational Design and Engineering
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• v.6 no.2
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• pp.133-139
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• 2001

Since a great number of 2D engineering drawings are being used in industry and at the same time 3D CAD becomes popular in recent years, we need to reconstruct 3D CAD models from 2D legacy drawings. In this thesis, a combination of a feature recognition method and an expert system is suggested for the 3D solid model reconstruction. Modeling primitives of 3D CAD systems are recognized and constructed by using the pattern matching technique of the features modeling. Additional information for the 3D model reconstruction can be generated by extracting symbols or text entities which are related to form entities. For complex and indefinite cases which cannot be solved by the process of feature recognition, an expert system with a rule base has been used for decision-making. A 3D reconstruction system which recognizes 2D DXF drawing files has been implemented where models composed with protrusions, holes, and cutouts can be handled.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.291-303
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• 2006

Data mining is the work to extract information from existing data file. So, the one of best important thing in data mining process is the quality of data to be used. In this thesis, we propose the data fusion technique using decision rule for data enrichment that one phase to improve data quality in KDD process. Simulations were performed to compare the proposed data fusion technique with the existing techniques. As a result, our data fusion technique using decision rule is characterized with low MSE or misclassification rate in fusion variables.

• International Journal of Safety
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• v.4 no.2
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.11-18
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• 2012

Deep packet inspection which perform pattern matching to search for malicious patterns in the packet is most computationally intensive task. Hardware-based pattern matching is required for real-time packet inspection in high-speed network. In this paper, we have designed and implemented network intrusion detection hardware as a Microblaze-based SoC using Virtex-6 FPGA, which capture the network input packet, perform hardware-based pattern matching for patterns in the Snort rule, and provide the matching result to the software. We verify the operation of the implemented system using traffic generator and real network traffic. The implemented hardware can be used in network intrusion detection system operated in wire-speed.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.429-436
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• 2005

A simple stereo matching algorithm using population-based incremental learning(PBIL) is proposed in this paper to decrease the general problem of genetic algorithms, such as memory consumption and inefficiency of search. PBIL is a variation of genetic algorithms using stochastic search and competitive teaming based on a probability vector. The structure of PBIL is simpler than that of other genetic algorithm families, such as serial and parallel ones, due to the use of a probability vector. The PBIL strategy is simplified and adapted for stereo matching circumstances. Thus, gene pool, chromosome crossover, and gene mutation we removed, while the evolution rule, that fitter chromosomes should have higher survival probabilities, is preserved. As a result, memory space is decreased, matching rules are simplified and computation cost is reduced. In addition, a scheme controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities, like a result of coarse-to-fine matchers. Because of this scheme, the proposed algorithm can produce a stable disparity map with a small fixed-size window. Finally, an alterative version of the proposed algorithm without using probability vector is also presented for simpler set-ups.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.121-126
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• 1995

A new method for Chinese word segmentation named Conditional F'||'&'||'BMM (Forward and Backward Maximal Matching) which incorporates both bigram statistics (ie., mutual infonllation and difference of t-test between Chinese characters) and linguistic rules for ambiguity resolution is proposed in this paper The key characteristics of this model are the use of: (i) statistics which can be automatically derived from any raw corpus, (ii) a rule base for disambiguation with consistency and controlled size to be built up in a systematic way.