• Journal of Korea Society of Industrial Information Systems
• /
• v.12 no.4
• /
• pp.25-31
• /
• 2007

An automatic term-network construction system is proposed in this paper. This system uses the statistical values of the terms appeared in a document corpus. The 186 oral history documents collected from the Saemangeum area of Chollapuk-do, Korea, are used for the research. The term relationships presented in the term-network are decided by the cosine similarities of the term vectors. The number of the terms extracted from the documents is about 1700. The system is able to show the term relationships from the term-network as quickly as like a real-time system. The way of this term-network construction is expected as one of the methods to construct the ontology system and to support the semantic retrieval system in the near future.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.1
• /
• pp.95-104
• /
• 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.

• New & Renewable Energy
• /
• v.4 no.4
• /
• pp.23-29
• /
• 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.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.3
• /
• pp.541-550
• /
• 2015

The importance of efficient enterprise network management has been emphasized continuously because of the rapid utilization of Internet in a limited resource environment. For the efficient network management, the management policy that reflects the characteristics of a specific network extracted from long-term traffic analysis is essential. However, the long-term traffic data could not be handled in the past and there was only simple analysis with the shot-term traffic data. However, as the big data analytics platforms are developed, the long-term traffic data can be analyzed easily. Recently, enterprise network resource efficiency through the long-term traffic analysis is required. In this paper, we propose the methods of collecting, storing and managing the long-term enterprise traffic data. We define several classification categories, and propose a novel network resource efficiency through the multidirectional statistical analysis of classified long-term traffic. The proposed method adopted to the campus network for the evaluation. The analysis results shows that, for the efficient enterprise network management, the QoS policy must be adopted in different rules that is tuned by time, space, and the purpose.

• The Korean Journal of Ecology
• /
• v.23 no.2
• /
• pp.75-81
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.21-23
• /
• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.9
• /
• pp.567-569
• /
• 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.

• Journal of the Korean Society of Industry Convergence
• /
• v.16 no.3
• /
• pp.61-68
• /
• 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.

• Journal of the Korean Society for Railway
• /
• v.9 no.3 s.34
• /
• pp.319-324
• /
• 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)
• /
• v.8 no.11
• /
• pp.4016-4027
• /
• 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.