• Journal of Sensor Science and Technology
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• v.21 no.4
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• pp.303-310
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• 2012

We carried out the power performance evaluation for 1.5 MW${\times}$2 by using anemometer installed on WTGS(Wind Turbine Generator System) in the wind farm at Shi-hwa bang-a-mu-ri. In this paper, we compared and analyzed the performance of guaranteed output and measured output of WTGS which includes output curve, output coefficient, AEP(Annual Energy Product) and availability, etc.. The power performance of WTGS was optimized in the low wind speed sections(3 m/s ~ 10 m/s) and the measured output was more produced by AEP 109 % and availability 112 % than the guaranteed output. In addition, we could also cut the high cost of testing WTGS performance by using anemometer as a substitute for weather mast.

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.535-544
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• 2003

Integrated services networks should guarantee end-to-end delay bounds for real-time applications to provide high quality services. A real-time scheduler is installed on all the output ports to provide such guaranteed service. However, scheduling algorithms studied so far have problems with either network utilization or scalability. Here, network utilization indicates how many real-time sessions can be admitted. In this paper, we propose service curve allocation schemes that result in both high network utilization and scalability in a service curve algorithm. In service curve algorithm, an adopted service curve allocation scheme determines both network utilization and scalability. Contrary to the common belief, we have proved that only a part of a service curve is used to compute deadlines, not the entire curve. From this fact, we propose service curve allocation schemes that result in a constant time for computing deadlines. We through a simulation study that our proposed schemes can achieve better network utilizations than Generalized processor Sharing (GPS) algorithms including the multirate algorithm. To our knowledge, the service curve algorithm adopting our schemes can achieve the widest network utilization among existing scheduling algorithms that have the same scalability.