• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.19-30
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• 2013

We study sub-Peres functions that are defined recursively as Peres function for random number generation. Instead of using two parameter functions as in Peres function, the sub-Peres functions uses only one parameter function. Naturally, these functions produce less random bits, hence are not asymptotically optimal. However, the sub-Peres functions runs in linear time, i.e., in O(n) time rather than O(n logn) as in Peres's case. Moreover, the implementation is even simpler than Peres function not only because they use only one parameter function but because they are tail recursive, hence run in a simple iterative manner rather than by a recursion, eliminating the usage of stack and thus further reducing the memory requirement of Peres's method. And yet, the output rate of the sub-Peres function is more than twice as much as that of von Neumann's method which is widely known linear-time method. So, these methods can be used, instead of von Neumann's method, in an environment with limited computational resources like mobile devices. We report the analyses of the sub-Peres functions regarding their running time and the exact output rates in comparison with Peres function and other known methods for random number generation. Also, we discuss how these sub-Peres function can be implemented.

• Journal of Korean Society of Forest Science
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• v.86 no.2
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• pp.135-145
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• 1997

This study aimed at evaluating one curvilinear equation and nine non-linear equations for estimating stand growth characteristics(mean dbh, mean height and volume per ha) for the plantations of Pinus koraiensis and the natural stands of Quercus mongolica. The data were collected from 92 plots in Pines koraiensis stands and 83 plots in Quercus mongolica stands, and the site index of all the stands is 14. The curvilinear equation, $Y=at^be^{-c/t}$, used in preparing the yield tables was well fitted within the range of data, but was likely to give overestimates when extrapolating in old stage due to the tendency of linear increase. Among the non-linear equations, logistic equation and Sloboda equation gave overestimates in young stands and reached the asymptotic status early which means underestimates in old stage. Extrapolating in old stage, Hossfeld equation generally gave larger values than others due to its large estimates of parameter a, the maximum value. On the other hand, Bertalanffy equation gave underestimates in young and old stands and overestimates in middle-aged stands. The estimates with Korf equation was relatively low for Pinus koraiensis stands, and this tendency was more obvious in dbh growth of Quercus mongolica stands. Ueno-Ohzaki equation was liable to give over or underestimates depending on the value of parameter b when extrapolating in old stands. Considering the accuracy of estimates and the biological base of the growth equations, Gompertz equation, Chapman-Richards equation and Weibull equation were generally applicable for estimating the stand growth characteristics of both species in the whole range of stand ages including extrapolated range. To get more accurate and precise parameter estimates, more data, especially in old stands, should be required in further study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.3
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• pp.190-198
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• 2006

Radiation use efficiency (RUE), the amount of biomass produced per unit intercepted photosynthetically active radiation (PAR), constitutes a main part of crop growth simulation models. The objective of the present study was to evaluate the variation of RUE of rice plants under various nitrogen nutritive conditions. from 1998 to 2000, shoot dry weight (DW), intercepted PAR of rice canopies, and nitrogen nutritive status were measured in various nitrogen fertilization regimes using japonica and Tongil-type varieties. These data were used for estimating the average RUEs before heading and the relationship between RUE and the nitrogen nutritive status. The canopy extinction coefficient (K) increased with the growth of rice until maximum tillering stage and maintained constant at about 0.4 from maximum tillering to heading stage, rapidly increasing again after heading stage. The DW growth revealed significant linear correlation with the cumulative PAR interception of the canopy, enabling the estimation of the average RUE before heading with the slopes of the regression lines. Average RUE tended to increase with the increased level of nitrogen fertilization. RUE increased approaching maximum as the nitrogen nutrition index (NNI) calculated by the ratio of actual shoot N concentration to the critical N concentration for the maximum growth at any growth stage and the specific leaf nitrogen $(SLN;\;g/m^2\;leaf\;area)$ increased. This relationship between RUE (g/MJ of PAR) and N nutritive status was expressed well by the following exponential functions: $$RUE=3.13\{1-exp(-4.33NNNI+1.26)\}$$ $$RUE=3.17\{1-exp(-1.33SLN+0.04)\}$$ The above equations explained, respectively, about 80% and 75% of the average RUE variation due to varying nitrogen nutritive status of rice plants. However, these equations would have some limitations if incorporated as a component model to simulate the rice growth as they are based on relationships averaged over the entire growth period before heading.