• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.619-625
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• 2012

This study was conducted to investigate the variation of fruit, seed and germination characteristics among populations of Serrateleaf Pearlbush (Exochorda serratifolia S. Moore) distributed in Korea. Fruits collected from 96 trees in five natural populations and their five fruit, seven seed and three germination characteristics were analyzed. In 14 characteristics except for mean germination time, there were significant differences among populations and among individuals within population. Generally, fruit and seed characteristics showed higher values among population in total variance component. Coefficients of variations in weight of fruit (27.0%), length/thickness of seed (28.1%) and germination rate (52.5%) were relatively high compared to other traits. In seed germination behaviors, germination percentage, mean germination time and germination rate showed 62.9%, 64.0 days and 0.40 ea./day, respectively. As a result of simple correlation analysis, mean germination time showed a significant positive correlation with seed thickness, germination rate showed a significant positive correlation with height of parent tree and latitude, respectively. Also, latitude showed a positive correlation with fruit weight. The populations close geographically did not show the tendency of clustering into the same group. The results of principal component analysis showed that the first for principal components (PC's) explained 63.0% of the total variation. Primary 3 principal components appeared to be major variables because of the loading contribution of 97.0%.

• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.135-144
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• 2012

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.