• 한국산업정보학회논문지
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• 제12권4호
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• pp.25-31
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• 2007

본 연구에서는 문서에 나타나는 용어의 통계값을 이용하여 구술문서자료에 포함되어있는 용어들간의 의미 네트워크를 자동으로 구축하는 시스템을 제안하였다. 본 연구를 위하여 전북 새만금지역에서 채록한 186개의 구술생애사 문서자료를 사용하였으며, 구축된 용어네트워크에서 용어들 사이의 관계는 용어들을 백터화하여 결정하였다. 새만금 구술문서에서 중요단어로 선택된 단어의 수는 약 1700여 개이다. 단어들 사이의 용어네트워크는 구축 시스템을 통해서 실시간 내에 표현할 수 있었다. 이 용어네트워크는 앞으로 전개될 시멘틱 검색시스템 구축에 새로운 장을 열 것이며, 구술문서 분석에 크게 기여할 것으로 기대한다.

• 제어로봇시스템학회논문지
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• 제5권1호
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• 신재생에너지
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• 제4권4호
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• 한국통신학회논문지
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• 제40권3호
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• pp.541-550
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• 2015

급하게 일어나는 인터넷의 활성화는 그 어느 때보다 효율적인 엔터프라이즈 망 운영 방안을 필요로 하고 있다. 효율적인 망 운영을 위해서는 장기간의 트래픽 분석을 통해 망의 특성을 정확히 반영한 운영 정책 적용이 필요하다. 하지만 기존에는 급격하게 증가하는 장기간 트래픽 데이터의 처리가 불가능했고, 다양한 분석 결과를 낼 수 없는 단기간 분석만 이루어졌다. 최근 빅 데이터 분석 플랫폼과 도구의 개발로 인해 장기간 트래픽 분석이 가능하게 되었고, 이를 이용해 망의 특성을 정확히 반영할 수 있는 장기간 트래픽 분석을 통한 엔터프라이즈 망 자원효율화 방안이 요구되고 있다. 본 논문에서는 엔터프라이즈 망에서 발생한 장기간의 트래픽을 수집하고 저장 및 관리하는 방안에 대해 제안한다. 또한 분류기준을 정의하였으며, 수집된 빅 데이터 트래픽을 각 분류 기준으로 분류한 뒤 다각적인 통계 분석을 통해 망 자원을 효율화 하는 방안을 제안한다. 제안하는 방법을 학내 망에 적용하여 실험하였으며, 통계 분석 결과 시간과 공간, 그리고 사용목적에 따라 Quality of Service(QoS)정책을 달리 적용해야 함을 확인하였다.

• The Korean Journal of Ecology
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• 제23권2호
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• pp.75-81
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• 2000

This paper was prepared to have an overview of the Long-Term Ecological Research (LTER ) activities in Korea in order to facilitate further development of Korea LTER Network in the coming 21th century. After the background for the development of the Korea LTER network was reviewed, the network activities of Korea as well as of the world were introduced for sound management and conservation of ecosystems, which can be ultimately carried out by the long-term ecological researches whose results can secure comparability in the dimension of time and space.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.21-23
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup rates when control rod and boron were adjusted in load following operations. Data of UCN 3 were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and seems to be utilized as a handy tool for the use of a plant simulation.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권9호
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• 한국산업융합학회 논문집
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• 제16권3호
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• pp.61-68
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• 2013

Long-term runoff model can be used to establish the effective plan of water reources allocation and the determination of the storage capacity of reservoir. So this study aims at the development of monthly runoff model using artificial neural network technique. For this, it was selected multi-layer neural network(MLN) and radial basis function neural network(RFN) model. In this study, it was applied model to analysis monthly runoff process at the Wi stream basin in Nakdong river which is representative experimental river basin of IHP. For this, multi-layer neural network model tried to construct input 3, hidden 7, and output 1 for each number of layer. As the result of analysis of monthly runoff process using models connected with artificial neural network technique, it showed that these models were effective in the simulation of monthly runoff.

• 한국철도학회논문집
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• 제9권3호
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• pp.319-324
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• 2006

Studied was a strategy of technology development for railway system in terms of railway network. First, The successful launch of the Korean HST system has not only decreased logistics burden but also significantly transformed the Korean trunk-line railway network, revolutionizing the logistics and technology sectors and reinvigorating the Korean railway industry in one century. Korean railway industry sector is now investing to develop many different types of railway system(G7, Post G7, tilting train...) so that these kinds of various railway system development should be integrated with the National Inter-modal Transportation Network Plan. To secure sufficient capacity that is required by the National Railroad Plan, the railway industry needs to establish mid- and long-term train purchase and operation strategies in compliance with railway construction and operation policies. During a railway construction planning, train operators, based on their train operation strategies, should come up with measures to closely cooperate with project operators from the planning stage through to the opening of a railway system. To be more precise, train operators should establish long-term train procurement plans reflecting both long-term national railroad network plans and plans for each railway line in order to suggest appropriate roles and schedules for each line. Also, based on the long-term railway plan, directions should be decided concerning the research and development of trains in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4016-4027
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• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.