• 한국수자원학회논문집
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• 제54권9호
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• pp.643-656
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• 2021

최근 국내 상수도 관리 기술은 고도로 발달하고 있으며, 이 과정에서 상수관망 내 용수공급 현황을 파악하고 예측하기 위한 컴퓨터 수리·수질 해석 모형은 핵심적인 역할을 수행하고 있다. 그러나 대규모 네트워크의 경우 컴퓨터 해석모형의 부담을 가중하고, 특히 짧은 계산시간 간격과 긴 모의 시간이 요구되는 수질해석의 경우, 막대한 계산시간이 소요되어 다양한 수질모의 및 분석이 어려운 경우가 발생한다. 본 연구에서는 대규모 상수관망시스템의 수질해석의 계산효율을 개선하기 위해 상수도 공급계통을 2단계로 계층화한 후, 계층화된 네트워크를 대상으로 수질모의를 수행하는 계층적 수질모의 기법을 제안하였다. 제안된 모의기법은 국내 대규모 상수도 네트워크에 적용하였으며, 다양한 염소투입농도 시나리오에 따른 잔류염소농도의 시공간적 분포를 모의하고 분석한 결과를 제시하였다.

• 한국통신학회논문지
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• 제32권5B호
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• pp.295-303
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• 2007

An algorithm is proposed to seek a local optimal solution of the network utility maximization problem in a wireless mesh network, where the architecture being considered is an infrastructure/backbone wireless mesh network. The objective is to achieve proportional fairness amongst the end-to-end flows in wireless mesh networks. In order to establish the communication constraints of the flow rates in the network utility maximization problem, we have presented necessary and sufficient conditions for the achievability of the flow rates. Since wireless mesh networks are generally considered as a type of ad hoc networks, similarly as in wireless multi-hop network, the network utility maximization problem in wireless mesh network is a nonlinear nonconvex programming problem. Besides, the gateway/bridge functionalities in mesh routers enable the integration of wireless mesh networks with various existing wireless networks. Thus, the rate optimization problem in wireless mesh networks is more complex than in wireless multi-hop networks.

• 한국도서관정보학회지
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• 제13권
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• pp.113-130
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• 1986

The rapidly growing at which information is produced and used in our complex society has presented us with major problems in information transfer. Resource sharing is almost universally accepted by librarians as the only realistic means for meeting future demands and no doubt the future will see continued growth in computer-based library networks for resource sharing. The resort to networking by many library and information institutions may be symptomatic of the difficulties they face in dealing with their rapidly changing environment. In this article, the library network is examined in relationship to resource sharing. Included is a discussion of the definitions of library network and other related terms, the main factors in the emergence of library network concept, the history of the concept of library networks, resource sharing through the library networks, the problems by which the development of networks is confronted, and prospects.

• 통상정보연구
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• 제9권4호
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• pp.3-19
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• 2007

The purpose of this paper is to study the pattern of interactive learning in strategic networks. Interactive learning is defined as the exchange and sharing of knowledge resources conducive to innovation between an innovator firm, its suppliers, and/or its customers. The strength of internal knowledge resources can either hamper or facilitate levels of interactive learning. We assume that more complex innovative activities urge firms to co-ordinate and exchange information between users and producers, which implies a higher level of interactive learning. To test our theoretical claims, we estimated the level of interactive learning of firms in strategic networks with: (1) their customers, (2) their suppliers. Theses analyses allow a comparison of the antecedents of interactive learning of firms participating in strategic networks. Our findings suggest that interactive learning with customers is positively affected by company's capabilities and value-created activities, and with supplies is positively affected by value-created activities and technology innovation centers.

• 정보학연구
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• 제5권2호
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• pp.17-26
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• 2002

본 연구에서는 자기조직화 교사학습 신경망인 SOSL(Self-Organized Superised Learning)과 이 신경망의 구조를 제안한다. SOSL신경망은 하이브리드 형태의 신경망으로써 다수 개의 컴포넌트 에러 역전파 신경망들과 수정된 PCA신경망으로 구성된다. CBP신경망은 군집화되고 복잡한 입력패턴에 대하여 교사학습을 병렬적으로 수행한다. 수정된 PCA신경망은 군집화 및 지역투영에 의하여 원 입력패턴을 보다 작은 차원으로 변환시키기 위하여 사용된다. 제안된 SOSL은 많은 입력패턴을 가짐으로써 큰 네트워크 크기를 가지게 되는 신경망에 효과적으로 적용이 가능하다.

• 한국통신학회논문지
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• 제22권4호
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• pp.823-832
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• 1997

As enterprises use ATM networks for their private networks and as these private networks use public ATM networks for wide area communication, the need for the customers to be able to manage both private and public networks. Currently, some standardization work is being done towards providing this capability to customers. In this paper, we propose a new customer network management (CNM) system architecture for the management of both ATM a private network and a public network in a uniform way. The particular features of the proposed architecture lies in the efficient support of the complex hierarchial TMN manager-agent relationships at M3 and M4 interfaces, and the support of SNMP and CMIP integration which is necessary for the implementation of a CNM system. The TMN hierarchical many-to-many manager-agent relationships are realized by the utilization of CORBA-Based SMK (Shared Management Knowledge) implementation. We have also implemented the prototype of a ATM CNM system, and measures the performance for the demonstration of the suitability of the proposed architecture.

• 대한전기학회논문지
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• 제45권4호
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• pp.574-581
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.

• 한국재난정보학회 논문집
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• 제18권4호
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• pp.908-921
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• 2022

연구목적: 도시의 물리적·비물리적 구조간 연결이 확대되고 복잡해짐에 따라 재난으로 인한 피해가 복합적으로 발생하는 복합재난의 위험성이 증가하고 있다. 이러한 복합재난에 대비하기 위해서는 복합재난으로 전개될 수 있는 재난들을 선제적으로 식별하여 관리하는 것이 중요하다. 이러한 배경에서 본 연구는 국내외 복합재난 관련 연구의 동향을 분석함으로써 복합재난으로 연구된 재난 유형을 분석하고, 이를 통해 향후 복합재난 관리의 방향성을 제시하고자 한다. 연구방법: 본 연구는 재난과 관련된 국내외 학술지에 최근 20년(2002-2021년)간 등재된 복합재난 관련 993편의 서지정보에 기반하여 동시출현 빈도분석을 수행하여 재난 유형 간 네트워크를 구축하였으며, 네트워크 분석을 통해 복합재난 연구 동향에 관한 국내외 및 시기별 비교분석을 수행하였다. 연구결과: 국내에서는 풍수해(집중호우, 태풍등), 기반시설 붕괴, 화재와 관련한 복합재난 연구의 비중이 높았으며, 최근 들어 지진과 산사태와 관련된 복합재난 연구가 증가하는 것으로 분석되었다. 반면, 국외에서는 풍수해 및 지진과 더불어 기반시설 붕괴에 관한 연구의 비중이 높았으며, 지진해일과 정전 등 재난 연계 유형이 다양하게 나타났다. 결론:본 연구는 복합재난 연구 동향에 대한 이해도를 높이고, 앞으로 국내 복합재난 연구가 가져야 할 방향성을 제안하는 데 활용할 수 있을 것으로 기대된다.

• Computers and Concrete
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• 제25권5호
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• pp.401-409
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• 2020

Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.

• 반도체디스플레이기술학회지
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• 제23권1호
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• pp.1-6
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• 2024

Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.