• 한국자원식물학회지
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• 제24권1호
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• pp.69-75
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• 2011

우리나라 가문비나무 천연집단의 구과, 종자 및 발아특성에 대한 집단간 및 집단내 개체간 변이를 조사하였다. 지리산과 덕유산 집단의 25개 개체목에서 구과를 채취하여 13가지 구과 및 종자특성과 3가지 발아특성에 대하여 분석하였다. 분산분석 결과 종자의 폭 및 무게, 종자날개지수 및 평균발아일수 특성을 제외한 9개 형질들에서 집단간 및 집단내 개체간에 유의적인 차이가 인정되었다. 변이계수 값은 구과와 종자의 무게, 충실율, TTC, 발아율 및 발아속도 형질들에서 29.7%~57.1% 범위의 높은 값을 보였으며, 나머지 형질들에서는 10% 내외의 비교적 변이가 작은 것으로 나타났다. 대체적으로 형태적 특성은 지리산집단의 경우 구과 및 종자날개가 작으며 긴 경향을 보인반면 덕유산 집단이 종자가 크고 긴 형태를 나타냈다. 종자충실율과 TTC 활력은 지리산 집단이 덕유산 집단에 비해 각각 1.79배, 1.87배 정도 우수한 집단간 차이를 나타냈다. 가문비나무의 종자발아에 미치는 온도조건의 영향을 보면 발아적온은 $20^{\circ}C$였으며, 이때의 평균발아일수는 7.5일, 발아속도는 2.9개/일로 나타났다. 지리산 집단의 경우 평균발아율이 40.7%, 발아속도가 0.90개/일로서 덕유산 집단(17.7%, 0.37개/일)보다 발아특성이 우수한 것으로 나타났다. 그러나 구과, 종자 및 발아특성에 대한 상관분석 결과 국내 가문비나무 천연집단은 종자활력이 한정된 분포에 따른 근친교배 및 특정 개체의 편중개화 현상에 많은 영향을 받는 것으로 추정되어 소멸을 초래할 가능성이 매우 높은 것으로 나타났다.

• 응용통계연구
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• 제28권6호
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• pp.1113-1120
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• 2015

본 논문에서는 사회적으로나 개인적으로 매우 민감한 조사에서 세대별, 연령별 또는 계층별에 따라 조사하고자 하는 모집단이 여러 개의 층으로 구성되어 있고, 각 층이 다지속성으로 되어 있는 경우에, Abul-Ela 등의 다지모형과 Eriksson의 다지무관모형에서 사용한 단순임의추출법 대신에 층화추출법을 적용하여 각 층의 다지속성에 대한 모비율의 추정뿐만 아니라 모집단 전체 모비율에 대한 추정을 할 수 있는 층화 다지 확률화응답모형을 제안하였다. 그리고 층화 다지모형에 있어서 각 층의 표본배분에 대하여 비례배분과 최적배분을 고려하여 다루었다. 또한 층화 다지 확률화응답모형들간의 효율성을 비교해 본 결과 Eriksson의 다지무관모형이 Abul-Ela 등의 다지모형보다 효율적임을 알 수 있었다.

• 한국연초학회지
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• 제14권1호
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• pp.12-23
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• 1992

This experiment was conducted to determine the optimum dosage of gamma rays for inducing artificial mutation of several mutant characters in the flue-cured tobacco. 1) In Hicks and BY 104, the gammarays irradiation has no significantly different effect on seed germination from the control. However, the average dosage for 50% growth inhibition was 25-30kr for all the varieties tested, which inhibition 46-52% and 43-57% of the seedling growths for Hicks and BY 104, respectively. 2) A mutant line 83H-5 was selected from Hicks by irradiation gamma ray at the level of 30kr. It has white flower, more resistance to bacterial wilt, Pssudomonas solanacearum, lower plant and stalk height, narrower leaf width, larger leaf shape index(lento width) and later days to flower when compared with the original variety Hicks. 3) White flower was recessive to pink flower in F, and Br (F1 X Hicks) progenies. F2 population of the cross gave segregation ratio of 3 pink flower:1 white flower, and B, (F1 X 83H-5) Population gave 1:1 ratio. Results showed that the white flower character is governed by a single recessive gene.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제2권1호
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• pp.95-110
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• 1998

Evolutionary Algorithms (EAs) are population-based optimization methods based on the principle of Darwinian natural selection. The representative methodology in EAs is genetic algorithm (GA) proposed by J. H. Holland, and the theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the meaning of these foundational concepts, simple genetic algorithm (SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithm. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. And predator-prey co-evolution and symbiotic co-evolution, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. And the experimental results show a co-evolutionary algorithm works well in optimization problems even though in deceptive functions.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.123-126
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• 1998

The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the Meaning of these foundational concepts, simple genetic algorithm(SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithms. In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. So we propose a co-evolutionary method finding optimal fuzzy rules. Our algorithm is that after constructing two population groups m de up of rule vase and its schema, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the proposed method to a path planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• Advances in Computational Design
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• 제6권1호
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• pp.31-42
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• 2021

This research aims to demonstrate the optimal geometrical design of splayed multiple cross-sectional pin fin heat sinks (SMCSPFHS), which are a type of side-inlet-side-outlet heat sink (SISOHS). The optimiser strength Pareto evolutionary algorithm2 (SPEA2)is employed to explore a set of Pareto optimalsolutions. Objective functions are the fan pumping power and junction temperature. Function evaluations can be accomplished using computational fluid dynamics(CFD) analysis. Design variablesinclude pin cross-sectional areas, the number of fins, fin pitch, thickness of heatsink base, inlet air speed, fin heights, and fin orientations with respect to the base. Design constraints are defined in such a way as to make a heat sink usable and easy to manufacture. The optimum results obtained from SPEA2 are compared with the straight pin fin design results obtained from hybrid population-based incremental learning and differential evolution (PBIL-DE), SPEA2, and an unrestricted population size evolutionary multiobjective optimisation algorithm (UPSEMOA). The results indicate that the splayed pin-fin design using SPEA2 issuperiorto those reported in the literature.

• 대한설비공학회지:설비저널
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• 제5권3호
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• pp.169-177
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• 1976

The importance of surface conditions of nucleate boiling is well recognized and it has been known that the heat transfer to boiling liquid is closely related to the bubble population density. The bubble population density should depend on various factors such as heat flux, surface roughness, surface contamination, properties of liquid, etc. In this paper the effect of surface conditions on heat transfer in nucleate boiling is treated. The experiments were carried out with distilled water boiler, on the horizontal heating surfaces, sintered with various bronze particle, under atmospheric pressure. In addition, experimental investigation for the polished bronze surface was performed. By studing a coefficient Xb defined by eq. (9), which represents the bubble foaming ability of heating surface, generalized fomula on the heat transfer in the nucleate toiling were expressed. The coefficient $X_b$, determined empirically, is not constant and indicates a major influence of the sintered metal surfaces on the $\Delta$, necessary to sustain nucleate boiling at any given heat flux. In this study, the main results are obtained as follows; (1) At low temperature difference, the coefficient $X_b$ of sintered metal surface was found to he higher than the polished surface throughout the full range of experiments. (2) The optimum sintered structure showing the maximum coefficient $X_b$ has been confirmed to exist and it is encountered when particle diameter is $256{\mu}$.

• 한국소음진동공학회논문집
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• 제19권5호
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• pp.482-490
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• 2009

In this paper, the author proposes a new method for acoustic radiation optimum design to minimize noise from a vibrating panel-like structure using a collaborative population-based search method called the particle swarm optimization algorithm(PSOA). The PSOA is a parallel evolutionary computation technique initially developed by Kennedy and Eberhart. The acoustic radiation optimization method based on the PSOA consists of two processes. In the first process, the acoustic radiation analysis by an integrated p-version FEM/BEM, which was developed by using MATLAB, is performed to evaluate the exterior acoustic radiation field of the panel. The second process is to search the optimum design variables: 1) Shape of Bezier curves and 2) Shape and position of ribs, to minimize noise from the panel using the PSOA. The optimization method based on the PSOA is compared to that based on the steady state genetic algorithm(SSGA) in order to verify the effectiveness and validity of the optimal solution by PSOA. Finally, it is shown that the optimal designs of the panel obtained by using the PSOA can achieve effective reductions in radiated sound power.

• 한국토양비료학회지
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• 제49권1호
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• pp.87-92
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• 2016

With the population growth and industrialization, the characteristics of discharged waste water and sewage have become more diverse. The removal of phosphorus (P) in the wastewater is essential for the prevention of eutrophication in the river and stream. This study was performed in order to estimate the field application of the Bacillus sp. 3434 BRRJ. Bacillus sp. 3434 BRRJ was cultured in the raw wastewater and synthetic medium at the 5 L reactor. The best optimum conditions for P removal by Bacillus sp. 3434BRRJ in the synthetic medium at the 5 L reactor were as follows: temperature, $30^{\circ}C$; P concentration, 20 mg/L; carbon sources, glucose + acetate (1:1); oxygen concentration, alternatively anaerobic and aerobic conditions. P removal efficiency under the optimum condition was 89.4%. In case of wastewater, P removal efficiency was 95.5% under controlled at $30^{\circ}C$. Through this study we confirmed that P removal by Bacillus sp. 3434BRRJ in case of wastewater was as effective as the synthetic medium. It is considered that Bacillus sp. 3434 BRRJ can be applied to the treatment of wastewater in order to biologically remove P from the wastewater on a large scale.

• Structural Engineering and Mechanics
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• 제45권1호
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• pp.111-127
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• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.