• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1852-1860
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• 1999

This paper studied performance of the AAL2 for variable rate real time services in ATM network with discrete-time simulation model. In this simulation, input parameters are packet fill delay for AAL2 PDU generation, guard time for ATM cell generation, burstness and number of channels. Though variation of the above mentioned parameters, we obtained end-to end delay variations and throughput, analyzed performance effect of the each parameter for voice packet service.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.9-16
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• 2017

In this paper, we propose to present a model of cloud security that has emerged as the biggest topic of cloud computing, replacing the traditional IT environment. While cloud computing is an extension of existing IT technology, security issues and threats can be applied to traditional security technologies. However, the biggest difference between a typical computing environment and a cloud computing environment is a virtualized environment with a hypervisor. Currently, there are many weaknesses in the virtualized environment, and there are few related security products. In order for a cloud computing environment to function as a reliable IT environment, we expect more research on hypervisor-based security technologies, and we expect to secure safer cloud services through a secure model over the next generation of new-based networks.

• ETRI Journal
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• v.43 no.2
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• pp.288-298
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• 2021

Abstractive text summarization is a process of making a summary of a given text by paraphrasing the facts of the text while keeping the meaning intact. The manmade summary generation process is laborious and time-consuming. We present here a summary generation model that is based on multilayered attentional peephole convolutional long short-term memory (MAPCoL; LSTM) in order to extract abstractive summaries of large text in an automated manner. We added the concept of attention in a peephole convolutional LSTM to improve the overall quality of a summary by giving weights to important parts of the source text during training. We evaluated the performance with regard to semantic coherence of our MAPCoL model over a popular dataset named CNN/Daily Mail, and found that MAPCoL outperformed other traditional LSTM-based models. We found improvements in the performance of MAPCoL in different internal settings when compared to state-of-the-art models of abstractive text summarization.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.105-110
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• 2009

Distributed Generation (DG) is predicted to play a important role in electric power system in the near future. insertion of DG system into existing distribution network has great impact on real-time system operation and planning. It is widely accepted that micro turbine generation (MTG) systems are currently attracting lot of attention to meet customers need in the distributed power generation market In order to investigate the performance of MT generation systems, their efficient modeling is required. This paper presents the modeling and simulation of a MT generation system suitable for grid-connected operation. The system comprises of a permanent magnet synchronous generator driven by a MT. A brief description of the overall system is given, and mathematical models for the MT and permanent magnet synchronous generator are presented. Also, the use of Power electronics in conditioning the power output of the generating system is demonstrated. Simulation studies with MATLAB/Simulink have been carried out in grid-connected operation mode of a DG system. The control strategies for grid connected operation mode of DG system is also presented.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.135-144
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• 2003

Traffic performance in a microcellular system is much more affected by cell dwell time and channel holding time in each cell. Cell dwell time of a call is characterized by its mobility pattern, i.e., stochastic changes of moving speed and direction. Cell dwell time provides important information for other analyses on traffic performance such as channel holding time, handover rate, and the average number of handovers per call. In the next generation mobile communication system, the cell size is expected to be much smaller than that of current one to accommodate the increase of user demand and to achieve high bandwidth utilization. As the cell size gets small, traffic performance is much more affected by variable mobility of users, especially by that of pedestrians. In previous work, analytical models are based on simple probability models. They provide sufficient accuracy in a simple second-generation cellular system. However, the role of them is becoming invalid in a picocellular environment where there are rapid change of network traffic conditions and highly random mobility of pedestrians. Unlike in previous work, we propose an improved probability model evolved from so-called Random walk model in order to mathematically formulate variable mobility of pedestrians and analyze the traffic performance. With our model, we can figure out variable characteristics of pedestrian mobility with stochastic correlation. The above-mentioned traffic performance measures are analyzed using our model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.305-315
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• 2007

In this paper, We generated a wander generation model from really measured clock noise data on the transmission node and DOTS in NG-SDH network. and then, We presented the performance of Synch. clock and maximum node level capable network configuration through the clock characteristics simulation on network having the steady-state clock.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2454-2467
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• 2015

Increasing traffic and bandwidth requirements bring challenges to the next generation wireless networks (5G). As one of the main technology in 5G networks, Ultra-Dense Network (UDN) can be used to improve network coverage. In this paper, a radio over fiber based model is proposed to solve the load balancing problem in ultra-dense network. Stochastic differential game is introduced for the load balancing algorithm, and optimal load allocated to each access point (RAP) are formulated as Nash Equilibrium. It is proved that the optimal load can be achieved and the stochastic differential game based scheme is applicable and acceptable. Numerical results are given to prove the effectiveness of the optimal algorithm.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.272-282
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• 2001

Uncertainties are the main reasons of deterioration of contour control of industrial articulated robot arm. In this paper, a high-precision contour control method was proposed to overcome some main uncertainties, such as torque saturation, system delay dynamics, interference between robot links, friction, and so on. Firstly, each considered factor of uncertainties was introduced briefly. Then proper realizable objective trajectory generation was presented to avoid torque saturation from objective trajectory. According to the model of industrial articulated robot arm, construction of Gaussian neural network controller with considering system delay dynamic, interference between robot links and friction was explained in detail. Finally, through the experiment and simulation, the effectiveness of proposed method was verified. Furthermore, based on the results it was shown that the Gaussian neural network controller can be also adapted for the various kinds of friction and high-speed motion of industrial articulated robot arm.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.651-657
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• 1998

In this study, an intelligent scheduling with hybrid architecture, which integrates expert system and neural network, is proposed. Neural network is trained with the data acquired from simulation model of FMC to obtain the knowledge about the relationship between the state of the FMC and its best dispatching rule. Expert system controls the scheduling of FMC by integrating the output of neural network, the states of FMS, and user input. By applying the hybrid system to a scheduling problem, the human knowledge on scheduling and the generation of non-logical knowledge by machine teaming, can be processed in one scheduler. The computer simulation shows that comparing with MST(Minimum Slack Time), there is a little increment in tardness, 5% growth in flow time. And at breakdown, tardness is not increased by expert system comparing with EDD(Earliest Due Date).