• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.265-279
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• 2008

The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.354-362
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• 2010

This paper proposes a new method for a hierarchical analysis of human gait by dividing the motion into gait direction and gait posture using the tool of dynamic Bayesian network. Based on Factorial HMM (FHMM), which is a type of DBN, we design the Gait Motion Decoder (GMD) in a circular architecture of state space, which fits nicely to human walking behavior. Most previous studies focused on human identification and were limited in certain viewing angles and forwent modeling of the walking action. But this work makes an explicit and separate modeling of pedestrian pose and posture to recognize gait direction and detect orientation change. Experimental results showed 96.5% in pose identification. The work is among the first efforts to analyze gait motions into gait pose and gait posture, and it could be applied to a broad class of human activities in a number of situations.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.230-231
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• 2007

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.44-52
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• 2001

This paper describes an off-line method for recognizing totally unconstrained handwritten digits using modified chaotic neural networks(MCNN). The chaotic neural networks(CNN) is modified to be a useful network for solving complex pattern problems by enforcing dynamic characteristics and learning process. Since the MCNN has the characteristics of highly nonlinear dynamics in structure and neuron itself, it can be an appropriate network for the robust classification of complex handwritten digits. Digit identification starts with extraction of features from the raw digit images and then recognizes digits using the MCNN based classifier. The performance of the MCNN classifier is evaluated on the numeral database of Concordia University, Montreal, Canada. For the relative comparison of recognition performance, the MCNN classifier is compared with the recurrent neural networks(RNN) classifier. Experimental results show that the classification rate is 98.0%. It indicates that the MCNN classifier outperforms the RNN classifier as well as other classifiers that have been reported on the same database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.691-699
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• 2011

Recently, diverse approaches retrieval services based on social network are suggested. Although existing recommendation systems can retrieve experts of specific fields, profiles and evaluations about experts that users want to be recommended are in a system. The proposed expert recommendation system can automatize collection of evaluation to evaluate experts and experts' profiles in separate systems by using the Knowledge Base and XMDR. We also attempt to construct system which can recommend a number of experts by dynamically constructing Social Network by using diverse resources distributed 로컬ly and composed of heterogeneous data sources. To resolve these problems efficiently, there is a need to provide constructed resources between heterogeneous systems with transparency and independence and provide users with a singular interface. Therefore, the proposed system in this paper uses Knowledge Base and XMDR for extracting distributed experts' profiles and designs expert recommendation system connecting Knowledge Base with Social Network.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.52-55
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• 2008

The paper aims at showing the subsumption relations of the Wordnet can be captured automatically from a dynamic encyclopedia such as Wikipedia with a meaningful precision. The idea behind the proposal is that a knowledge base in the form of Wordnet can be dynamically obtained and maintained accordingly to the online dictionaries so that the scalability of knowledge base construction may be achieved to some degree. To show the plausibility of dynamic ISA construction, we have tested how well the ISA relations among the 100 technology terms selected from the Wordnet can be saved from the ISA construction by the wiki definitions of the selected terms. As a result the wiki definition led to the ISA relations of the Wordnet with the precision of 80%.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.11-26
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• 1996

본 논문에서는 화학공정의 이상 진단을 위한 지식 기반 조업 지원 시스템의 개발에 관하여 살펴보고자 한다. 조업지원 시스템에서 가장 핵심적인 부분은 공정에 비정상 상황이 발생한 경우 이를 감지하고, 공정에 발생한 증상들을 분석하여 이상의 근본 원인을 찾아내는 작업-이상 진단이다. 이상 진단을 효과적으로 수행하기 위해서는 적절한 데이터의 해석이 매우 중요한데, 기존의 데이터 해석법들은 정상상태에 기반한 방법들을 동적거동을 효과적으로 표현하기에는 어려움이 많다. 본 연구에서는 RBF에 기반한 신경망을 사용하여 동적을 효과적으로 표현할 수 있는 정성적인 데이터 해석 모듈을 구축하였으며, 이 모듈에서는 공정에서 측정된 정략적인 센서값들을 정성적인 정보로 가공하여 이상진단 모듈에 제공한다. 본 연구에서는 효과적인 이상진단을 위하여 기존의 인과관계 그래프 모델(Cause Effect DiGraph)에 기반한 두가지 그래프 모델을 개발하였다. RCED(Reduced Caue Effect Digraph)는 공정의 측정 변수만으로 공정의 인과관계를 표현하는 오프라인으로 구축된 지식베이스 모델이며, PGTT(Pattern Graph Through Time)는 공정에서 발생한 증상간의 인과관계를 실시간으로 나타내는 동적인 모델이다. 이상, 신경망에 기반한 정성적인 데이터 해석 모듈과 이상진단 모듈을 전문가 시스템 도구인 G2를 DEC AlphaStation 상에서 폴리프로필렌 공정에 대한 조업지원전문가 시스템을 구축하고 이를 적용하여보았다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8S
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• pp.2688-2699
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• 2000

본 논문에서는 시간속성과 능동특성을 지원하는 관리객체의 동적특성 표현언어 BDL(Behaviour Description Language)을 이용하여 시스템 망관리 모델의 관리기능을 정형적으로 표현하였다. 그리고 BDL로 표현된 관리기능을 CORBA IDL로 변환하는 BDL＿to＿IDL 컴파일러를 설계·구현하였다. 특히, 망 관리 정보베이스에 저장되어있는 관리객체를 안전하게 보호하기 위해 ITU-T 권고안에 정의된 강제적 접근제어 모델과 역할기반 접근제어 모델을 상호연동한 접근제어모델을 정의하였다. 또한, 관리속성값을 제어하는 관리연산을 연관된 유형별로 묶어 역할로 정의하고 관리자와 관리객체에 인가등급과 보안등급을 부여하여 역할배정규칙과 제약조건에 따라 관리정보의 접근을 제어함으로써 보다 무결성을 보장받도록 하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.1
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• pp.87-98
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• 2001

ATM-LAN 스위칭 망에서 논리적인 브로드캐스트 도메인 서비스를 제공하는 가상랜을 정의하는 방법으로 LANE(LAN Emulation)을 사용할 수 있다. 이 때 LE 구성서버는 각 클라이언트에게 가상랜을 할당하고 관리한다. 본 논문에서는 서버 내에 정의된 정책과 구성 데이타베이스를 기반으로 하여 가상랜을 결정하는 기능을 설계하고 구현하였다. 또한 운영자가 쉽게 가상랜의 추가, 변경, 삭제 등의 기능을 수행할 수 있는 관리 모듈도 구현하였다. 이로서, LANE에서의 ELAN(Emulated LAN)을 가상랜으로 간주할 때 부족했던 동적인 가상랜을 지원할 수 있게 하였고, 운영자로 하여금 효율적오르 가상랜을 구성하고 관리할 수 있게 하였다.

• Journal of Internet Computing and Services
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• v.4 no.1
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• pp.75-86
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• 2003

In this paper, we extended hierarchial structure of managed object class to support Role-Based Access Control, and described constraint conditions that have support dynamic temporal function as well as statical temporal function established by management process. And we defined about violation notifications should report to manager when rules violate constraint conditions. Also we presented system architecture that support RBAC with MIB(Management Information Base) of ITU-T recommendation. By access control enforcement and decision function, constraint conditions and activated translation procedure of each roles are described, our system presents dynamic temporal property systematically.