• Water for future
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• v.20 no.4
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• pp.257-266
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• 1987

The existing stochastic models for the data with hydrologic persistence can be classified into two categories; the short-term and long-term models.For the present study, the Hurst coefficients which are the dominant parameter in the Fast Fractional Gaussian Noise(FFGN)model, one of the long-term models. are estimated with historical annual and monthly streamflows. In order to verify the applicability of these estimators the statistical properties of the generated annual streamflows by FFGN model are compared with those of the historical annual streamflows. Then the generated annual streamflows by FFGN model are disaggregated into the monthly streamflows by disaggregation model at two sites, i.e. Waekman and Jindong, in the Nakdong River Basin. On the other hand, the monthly stream flows at the two sites were also generated by the two-site Matalas model which is one of the short-term models. To evaluate the applicability of the above models and to select the better model the statistical properties of the generated monthly streamflows by two models were compared with those of the historicals, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.344-355
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• 2008

In this paper, we are dealing with the problems of performance evaluation of Differentiated Services(DiffServ) networks. For successful performance evaluation, the ability to accurately represent "real" traffic on the network by suitable traffic models is an essential ingredient. Many research results on the nature of real traffic measurements demonstrated LRD(long-range dependence) property for the Internet traffic including Web, TELNET, and P2P traffic. The LRD can be effectively represented by self-similarity. In this paper, we design and implement self-similar traffic generator using the aggregated On/Off source model, based on the analysis of the On-Off source model, FFT-FGN(Fast Fourier Transform-Fractional Gaussian Noise) model, and RMD(Random Midpoint Displacement) model. We confirmed the self-similarity of our generated traffic by checking the packet inter-arrival time of TCPdump data. Further we applied the implemented traffic generator to the performance evaluation of DiffServ networks and observed the effect of performance to the a value of the On/Off model, and performance of EF/BE class traffic by CBQ.