• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2003년도 추계종합학술대회 논문집
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• pp.132-139
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• 2003

본 논문은 공개키 기반구조(PKI - Public Key Infrastructure)에서 중추적인 역할을 담당하는 공개키 인증시스템을 설계하고 구현한다. 본 논문에서는 인증기관의 역할을 수행할 수 있는 공개키 인증시스템의 요구사항 및 특징을 분석하여, 실제 보안서비스가 제공 가능한 인증과정은 HA 및 FA한테 위임하고 설계한다. 보안구조는 Root CA와 Home Network 및 Foreign Network로 구성되어 있고, 필요할 때 CA은 HA 및 FA한테 인증을 위탁한다.

• 한국산업정보학회논문지
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• 제10권4호
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• pp.110-119
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• 2005

본 논문은 공급사슬구조를 크게 공급네트워크(supply network), 전환네트워크(conversion network), 그리고 유통네트워크(distribution network)라는 세 유형의 구조로 구분하였으며 각 공급사슬구조별 경영성과에 어떠한 영향을 미치는가에 대하여 국내 103개 상장기업을 대상으로 실증분석을 하였다. 또한 경영성과에 대하여서는 균형성과표(Balanced Scorecard; BSC) 모형을 이용하였다. 연구결과, 학습 및 성장성과, 고객성과, 재무성과의 경우 공급사슬구조에 따른 경영성과간 차이가 있음이 검증되었으나, 내부프로세스성과의 경우 경영성과간 차이가 통계적으로 유의하지 않았다. 또한, Scheff의 다중비교 결과에 따라 유통네트워크 중심기업의 경우 다른 공급사슬구조보다 상대적으로 경영성과가 높은 것으로 나타났다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.122-125
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• 2003

By advent of NNC(Neural Network Chip), it is possible that process in parallel and discern the importance of signal with learning oneself by experience in external signal. So, the design of general purpose operation unit using VHDL(VHSIC Hardware Description Language) on the existing FPGA(Field Programmable Gate Array) can replaced EN(Expert Network) and learning algorithm. Also, neural network operation unit is possible various operation using learning of NN(Neural Network). This paper present general purpose operation unit using hierarchical structure of EN EN of presented structure learn from logical gate which constitute a operation unit, it relocated several layer The overall structure is hierarchical using a module, it has generality more than FPGA operation unit.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.247-250
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• 2001

In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology that a network designer can constructs wavelet neural network according to one's intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• 정보관리학회지
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• 제6권1호
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• pp.37-56
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• 1989

This study is a structural analysis of a multi-type and multi-level library network within the framework of a regional interlibrary loan (ILL) system. The study monitored to information network structure for resource sharing of academic and research library materials transmitted through the ILL. The local flow of academic and research information was measured by a survey of the filled ILL transactions by individual libraries in the Western 3Rs region. The major findings were as follows: 1) the regional ILL network showed less than half of participation of the total subject libraries, 2) existing structure surveyed was identified as a composite centralized network with three communication groups, 3) depending on the types of materials transacted, the structure were changed, 4) statewide and multi-state library cooperatives had direct interactions with some of the local libraries, 5) individual libraries participated in the ILL network more for periodicals than book materials, 6) academic libraries throughout the total six structure analyzed showed the highest percentage of participation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권3호
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• pp.135-142
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• 2011

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (D)
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• pp.105-108
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• 2007

Bayesian 학습 네트워크는 여러 가지의 다양한 응용 분야에 적용된다. 본 논문은 다양한 무선 센서 네트워크 환경에 적용될 수 있는 온라인 Bayesian 학습 네트워크의 추론 알고리즘 구조에 대하여 논의한다. 첫째, 논문은 Bayesian 파라메타 학습과 Bayesian DAG 구조 학습을 논의하고, 다음에 무선 센서 네트워크의 특징과 무선 환경에서의 데이터 수집에 대하여 논의한다. 둘째, 논문은 온라인 Bayesian 학습 네트워크에서의 중요한 고려 사항과 네트워크 학습 알고리즘의 개념적 구조에 대하여 논의한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권12호
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• pp.804-810
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• 2004

There are two important things for the general purpose neural network processor. The first is a capability to build various structures of neural network, and the second is to be able to support suitable learning method for that neural network. Some way to process various learning algorithms is required for on-chip learning, because the more neural network types are to be handled, the more learning methods need to be built into. In this paper, an improved hardware structure is proposed to compute various kinds of learning algorithms flexibly. The hardware structure is based on the existing modular neural network structure. It doesn't need to add a new circuit or a new program for the learning process. It is shown that rearrangements of the existing processing elements can produce several neural network learning modules. The performance and utilization of this module are analyzed by comparing with other neural network chips.