• Korean Journal of Cognitive Science
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• v.6 no.3
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• pp.25-40
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• 1995

This paper proposes a generating method of fuzzy rules by genetic and descent method (GAGDM),and its applied to classification problems.The number of inference rules and the shapes of membership function in the antecedent part are detemined by applying the genetic algorithm,and the real numbers of the consequent parts are derived by using the descent method.The aim of the proposed method is to generation a minmun set of fuzzy rules that can correctly classify all training patterns,and fiteness function of GA defined by the aim of th proposed method.Finally,in order to demonstrate the effectiveness of the present method,simulation results are shown.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.37-49
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• 2008

Services are reusable units in business level. Ubiquitous computing provides computing services anytime and anywhere. The combination of both is becoming an important paradigm of computing environment. Fundamentals of services require flexibility and interoperability, and key elements of ubiquitous modeling require interoperability and context-awareness. There are two kinds of methods to identify services. The top-down approach is based on business process, and the bottom-up approach is based on components. The first approach depends on experts' intuitions, while the second approach suffers the incapability of expressing non-functional expression through components. Although a feature-based approach is capable of expressing non-functional expression and identifying services in ubiquitous environment, the research on this issue is not adequately addressed by far. To promote this research, this paper proposes a feature-based method to identify services in ubiquitous computing. The method extracts initial-candidate-services from a feature model. Then, the ultimate services are identified through optimizing and analyzing the candidate-services. The proposed method is expected to enhance the service reusability by effectively analyzing ubiquitous domain based on feature, and varying reusable service units.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2001.11a
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• pp.15-19
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• 2001

암호 알고리즘 출력문에 대한 난수성 검정들은 평문과 암호문 식별에 중요한 역할을 하고 있다. 실제로, 난수열의 생성자는 비밀키의 생성자와 같은 많은 암호체계에서 사용되고 있으며, 이때 사용되고 있는 난수열은 모의 난수라고 한다. 따라서, 이진수열에 대한 난수성을 검정하는 통계적 검정방법이나 다른 이론적 기준이 필요하다. 본 논문에서는 모의난수열이 갖고 있는 난수성 판정에 관하여 universal 엔트로피 검정방법과 근사 엔트로피 검정방법을 이용하며, 위의 두 방법에 대한 각각의 이론적인 배경과 모의실험을 통한 판정기준을 제공한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.325-328
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• 2002

본 논문에서는 사람의 식별과 감정을 인식하기 위한 하나의 방법을 제안한다. 제안된 방법은 색차 정보에 의한 형판의 위치 인식과 형판 벡터 추출에 기반한다. 단일 색차 공간만을 이용할 경우 살색 영역을 정확히 추출하기 힘들다. 이를 보완하기 위해서 여러 가지 색차 공간을 병행하여 살색 영역을 추출하며, 이를 응용하여 각각의 형판을 추출하는 방법을 제안한다. 그리고, 사람의 식별과 감정 인식을 위해서 추출된 형판에 대한 각각의 특징 벡터 추출 방법을 제시하며, 마지막으로 추출된 형판 벡터를 이용하여 신경망을 통한 학습과 인식을 수행하는 방법을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1162-1169
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• 2007

In this paper, we proposed a container identifier recognition method for containers used in harbors. After converting a real container image to a gray image, edges are detected from the gray image applying Prewitt mask and candidate identifier area is extracted using morphological features of individual identifier for identifying containers. Because noises are included in the extracted candidate identifier area, noises are eliminated and each identifier is separated using 4-directional edge tracking algorithm and Grassfire algorithm. Each identifier in the noise-free candidate identifier area is recognized using FCM-based row RBF network for discriminating containers. We used 300 real container images for experiment to evaluate the performance of the proposed method, and we could verify the proposed method is better than a conventional method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.169-177
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• 2020

Malware analysis is one of the important concerns of computer security, and advances in analysis techniques have become important for computer security. In the past, the signature-based method was used to detect malware. However, as the percentage of packed malware increased, it became more difficult to detect using the conventional method. In this paper, we propose a method for identifying packers of packed programs using machine learning. The proposed method parses the packed program to extract specific PE information that can identify the packer and identifies the packer using the Adaptive Boosting algorithm among the machine learning models. To verify the accuracy of the proposed method, we collected and tested 391 programs packed with 12 types of packers and found that the packers were identified with an accuracy of about 99.2%. In addition, we presented the results of identification using PEiD, a signature-based PE identification tool, and existing machine learning method. The proposed method shows better performance in terms of accuracy and speed in identifying packers than existing methods.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.3
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• pp.5-17
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• 2012

In citation analysis, author names are often used as the unit of analysis and some authors are indexed under the same name in bibliographic databases where the citation counts are obtained from. There are many techniques for author name disambiguation, using supervised, unsupervised, or semisupervised learning algorithms. Unsupervised approach uses machine learning algorithms to extract necessary bibliographic information from large-scale databases and digital libraries, while supervised approaches use manually built training datasets for clustering author groups for combining them with learning algorithms for author name disambiguation. The study examines various techniques for author name disambiguation in the hope for finding an aid to improve the precision of citation counts in citation analysis, as well as for better results in information retrieval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1380-1386
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• 2007

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is blacker white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm. Experiments using real images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.187-189
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• 2015

해양사고를 야기하는 위기(risk) 또는 해저드(hazard)는 세계 경제 및 물류, 기상환경, 금융, 선종개발, 기술개발 등 다양한 요소에 의해서 수시로 변한다. 변한 위기에 대해서 사전에 대응방안을 수립하지 못한 경우 대규모 해양사고로 연계될 가능성이 크다. 그 이유는 대응방안을 사전에 고려하지 못했거나 준비하지 못한 해양사고는 사전대응책이 마련된 사고와 비교하여 대응하는 방법과 대응속도 등이 다르기 때문이다. 본 연구에서는 현재까지 새로 창출된 다양한 위기를 각종 보고서, 논문 등을 통해서 식별하고 분류하여 현재까지 식별하지 못한 위기를 구분하고, 식별하였더라도 새로운 사고를 유발한 위기를 식별하여 사전에 예방책을 강구하는 것이 목적이다. 연구 대상이 방대하기 때문에 본 연구에서는 일단, 알리안츠 보험회사 자료에서 획득한 위기분석 결과를 토대로 위기를 식별하고, 이에 대한 시나리오를 전개하였다. 이를 통해서 추후 연구방법을 검토하고자 한다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2001.11a
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• pp.432-436
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• 2001

본 논문에서는 XTR을 이용해 Schnorr 개인식별 프로토콜을 구성하여 블랙메일링 공격이 있을 경우에 은행에게 블랙메일링 공격에 대한 정보를 개인식별 과정에서 알려주는 방법을 제안한다. 본 논문에서 제안한 XTR 버전의 Schnorr 개인식별 프로토콜을 사용하면 기존의 방법들이 블랙메일링 공격을 막기 위해 필요로 하는 가정들을 사용하지 않고도 효과적으로 블랙메일링 공격을 막을 수 있는 새로운 방법이 된다.