• Journal of Ginseng Research
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• v.15 no.1
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• pp.31-35
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• 1991

In order to know the optimum planting density under shading structures at different light intensity, We investigated the growth status, distribution of ginseng leaf area, correlation between planting density and root weight per plant and yield, correlation between leaf area index and root weight per plant and yield. According to the increase of planting density the leaf area per plant was decreased, but leaf area index (L.A.I) was increased. Ginseng leaf population at different lines under common straw shading were distributed mainly in frost lines but polyethylene net shading at 10fo light intensity were distributed equally in all lines. Optimum planting density in common straw shading at 5% light intensity was 55 plant per tan (90 cmX180 cm) and polyethylene net shading 81 10% light intensity was 60 plant per tan, in consideration of root weight and yield. Optimum leaf area index was 2.4 under common straw shading at 5% light intensity but was 2.7 under polyethylene net shading at 10% light intensity.

• Journal of the Korea Organic Resources Recycling Association
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• v.1 no.2
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• pp.259-266
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• 1993

This experiment was carried out to the effect of population densities on the growth reproduction, cast production of the earthworm and chemical composition of worm casts. The population densities of 50, 100, 150, 200 and 250 individuals of the earthworm fed with cow manure was studied in rearing box($6,400cm^3$) and fertility stage during a period of 60 days. The results were summarized as follows 1. Survival rates(SR) of the earthworms had significantly positive correlated with increasing rate(IR). But, survival rates(SR) and increasing rate(IR) had significantly negative correlated with reproductive efficiency(RE). 2. The ranges of optimum population densities estimated were $42.7cm^3-128.0cm^3/worm$. 3. The cast production estimated were 36.5mg-80.9mg/day/worm grown in optimum population densities(50-150 worms/$6,400cm^3$). 4. The earthworm castings are an excellent soil conditioning material with a high chemical composition and their physical properties.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1059-1070
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• 2005

For genetic algorithms, the population may get stuck in a local optimum. The population can escape from this after a long duration. This phenomenon is called punctuated equilibrium. The punctuated equilibria observed in nature and computational ecosystems are known to be well described by diffusion equations. In this paper, simple genetic algorithms are theoretically analyzed to show that they can also be described by a diffusion equation. When fitness is the function of unitation, this analysis can be further refined to make the parameters of genetic algorithms appear in this equation. Using theoretical results on the diffusion equation, the duration of equilibrium is shown to be exponential of such parameters as population size, 1/(mutation probability), and potential barrier. This is corroborated by simulation results for bistable potential landscapes with one local optimum and one global optimum.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.52 no.3
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• pp.334-340
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• 2007

A super sweet corn hybrid, "Cambella 90", was grown at 5 plant populations (35,000, 45,000, 55,000, 65,000, 75,000 plants/ha) on 1 April, 1 May, and 1 June in 2004 under black polyethylene (P. E.) film mulch to find out the optimum plant populations at different planting dates. Emergence rate and percent stand increased as planting dates delayed. Culm length and ear height were highest at the planting on 1 May and increased with increased plant population. Soluble solids content was highest at the planting on 1 May and decreased as plant population increased. Ear length, seed set ear length, ear diameter, and ear weight decreased as plant population increased. Seed set ear length could not substitute for ear weight to evaluate marketable ears. The optimum plant population of "Cambella 90" was $65,000{\sim}75,000$ plants/ha at the planting on 1 April when emergence rate was low due to low temperature, $55,000{\sim}65,000$ plants/ha at the planting on 1 May, and 65,000 plants/ ha after the planting on 1 June.

• Journal of the Korea Organic Resources Recycling Association
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• v.20 no.1
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• pp.23-30
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• 2012

In the state that re-evaluation of calculating optimum amount of incineration in the future is needed, as considering the amount of waste, increase of heat value and change in floating population in each area in city B, the purpose of this research was to predict optimum available capacity in incineration plant and to study alternatives for the amount of disposal in each incineration plant based on the available capacity that was predicted. As a result of predicting the change in population based on progress of population in city B in the past, it is expected that an overall population is decreasing, but in some areas, population is concentrated due to increased apartment complexes, showing similar figures as the present. Moreover, when predicting the amount of waste through forecasting population, it is considered that the amount of waste by decreased population is also decreasing. However, the amount of combustible component among a total amount of waste is expected to increase, so it is predicted that the amount of incineration and combustible component will be reasonable except D incineration plant, Therefore, D incinerating plant showed 72.7% of rate of utilization of incineration facility compared to 59.1% of national rate. However, if shortfall of waste in the future can be used wisely in other areas, the use of renewable energy using burner useless heat can be maximized.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.116-124
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• 1999

In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.106.4-106
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• 2001

The current research field to find near-optimum solutions explores in a small population, which is coined as Micro-Genetic Algorithms (${\mu}$GAs), with some genetic operators. Just as in the Simple-Genetic Algorithms (SGAs), the ${\mu}$GAs work with encoding population and are implemented serially. The major difference between SGAs and ${\mu}$GAs is how to make reproductive plan for more better searching strategy due to the population choice. This paper is conducted to implement ${\mu}$GAs in order to achieve fast searching for more better evolution and associated cost evaluation in global solution space. To achieve this implementation, the Air-Borne Selection (ABS) for a new reproductive plan is developed as new strategic conception for ${\mu}$GAs. In this paper, it is shown that the ${\mu}$GAs implementation reaches a near-optimal region much earlier than the SGAs implementation. The superior ...

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.681-695
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• 2012

In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1401-1403
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• 1996

A new method is proposed for tracking the optimum points in nonstationary problem via genetic search. Cache Pool to save the past genetic informations is added to population in search using Sexual Reproduction Genetic Algorithm(SRGA). In Cache Pool, elite chromosomes from population are accumulated. A best Individual is made up from these chromosomes in varying environment and inserted into the newly reproduced population every generation. Experimental results indicate changing global optima are accurately identified and followed.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.165-181
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• 2010

This paper presents some chain ratio-type estimators for estimating finite population variance using two auxiliary variables in two phase sampling set up. The expressions for biases and mean squared errors of the suggested c1asses of estimators are given. Asymptotic optimum estimators(AOE's) in each class are identified with their approximate mean squared error formulae. The theoretical and empirical properties of the suggested classes of estimators are investigated. In the simulation study, we took a real dataset related to pulmonary disease available on the CD with the book by Rosner, (2005).