• 식물분류학회지
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• 제40권4호
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• pp.208-217
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• 2010

Crossosomatales is a recently recognized order in the rosid II clade with about 64 species in eight morphologically distinct families that have been previously classified in as many as 15 other orders. Phylogenetic relationships among the families and genera within Crossosomatales were investigated using chloroplast atpB, matK, and rbcL sequences employing maximum parsimony, maximum likelihood, and Bayesian methods. The phylogenetic framework was used to examine the patterns of morphological evolution and synapomorphies for subclades within Crossosomatales. The combined data with representative species from all genera in the order strongly supported monophyly of Crossosomatales. Strong support was found for the families in the Southern Hemisphere, in which Aphloiaceae is sister to the clade of (Geissolomataceae, (Ixerbaceae + Strasburgeriaceae)). The sister relationship between the Southern Hemisphere clade and families distributed primarily in the Northern Hemisphere was also supported. As in the previous studies, following relationships were found within the Northern Hemisphere clade: Staphyleaceae is sister to a clade of (Guamatelaceae, (Stachyuraceae + Crossosomataceae)). The pattern analysis indicates that evolutionary pattern of morphological characters is complex, requiring multiple changes within Crossosomatales. Several reproductive traits, such as inflorescence, aril, stigma, and conspicuous protrusion from pollen aperture, corroborate the molecular phylogeny.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권4호
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• pp.184-188
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• 2016

청소 로봇은 가정에서 사용 가능한 대표적인 지능형 로봇이다. 고가형 청소 로봇은 센서로부터 정보를 제공받아 높은 커버리지 성능을 가진 알고리즘이 존재하지만, 저가형의 청소 로봇엔 적용하기 어렵다. 본 논문은 저가형의 청소 로봇과 같은 환경에서 효율적인 움직임을 구현하기 위해 문법적 진화기법 기반의 청소 로봇의 이동 패턴을 계획하는 알고리즘을 제안한다. 이를 위해 배커스-나우르 표기법을 사용하여 이동 패턴 문법을 정의하고 진화연산을 통해 최적화된 프로그램을 생성하였다. 이와 더불어 프로그램 생성 과정에서 획득한 문법 요소 간 조건부 확률 정보를 활용하였다. 제안 알고리즘의 성능 검증을 위해 청소 로봇 시뮬레이션을 활용하여 기존 알고리즘과 성능을 비교하였으며 실험 결과를 통해 본 논문에서 제안한 기법의 효율성을 확인하였다.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.118-124
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• 2004

This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.

• International Journal of Control, Automation, and Systems
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• 제4권4호
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• pp.466-479
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• 2006

This paper addresses a formation navigation issue for a group of mobile robots passing through an environment with either static or moving obstacles meanwhile keeping a fixed formation shape. Based on Lyapunov function and graph theory, a NN formation control is proposed, which guarantees to maintain a formation if the formation pattern is $C^k,\;k\geq1$. In the process of navigation, the leader can generate a proper trajectory to lead formation and avoid moving obstacles according to the obtained information. An evolutionary computational technique using particle swarm optimization (PSO) is proposed for motion planning so that the formation is kept as $C^1$ function. The simulation results demonstrate that this algorithm is effective and the experimental studies validate the formation ability of the multiple mobile robots system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.277-280
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• 2004

In the research of biomechanical engineering, robotics and neurophysiology, to clarify the mechanism of human bipedal walking is of major interest. It serves as a basis of developing several applications such as rehabilitation tools and humanoid robots Nevertheless, because of complexity of the neuronal system that Interacts with the body dynamics system to make walking movements, much is left unknown about the details of locomotion mechanism. Researchers were looking for the optimal model of the neuronal system by trials and errors. In this paper, we applied Genetic Programming to induce the model of the nervous system automatically and showed its effectiveness by simulating a human bipedal walking with the obtained model.

• 한국공작기계학회논문집
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• 제16권1호
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• pp.69-74
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• 2007

Topology optimization of the inner reinforcement for a vehicle's hood has been performed by evolutionary structural optimization(ESO) using a smoothing scheme. The purpose of this study is to obtain optimal topology of the inner reinforcement for a vehicle's hood considering the static stiffness of bending and torsion simultaneously. To do this, the multiobjective optimization technique was implemented. Optimal topologies were obtained by the ESO method. From several combinations of weighting factors, a Pareto-optimal solution was obtained. Also, a smoothing scheme was implemented to suppress the checkerboard pattern in the procedure of topology optimization. It is concluded that ESO method with a smoothing scheme is effectively applied to topology optimization of the inner reinforcement of a vehicle's hood considering the static stiffness of bending and torsion.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (A)
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• pp.46-48
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• 2006

현재 각 분야에서 다양한 실험 데이터가 산출되면서 이종(heterogeneous) 데이터간의 상관관계 분석에 대한 중요성이 더욱 부각되고 있다. 특히, 대규모 실험에 의해 급속하게 증가하고 있는 대량의 바이오 데이터에서 이런 문제를 해결하기 위한 새로운 데이터 마이닝 방법이 요구된다. 본 논문은 특성이 다른 두 데이터 셋에서 서로 상관관계가 있는 부분 패턴을 파악할 수 있는 새로운 알고리즘을 제안한다. 제안한 알고리즘은 다중 개체 집단을 유지하면서 상호간 공진화하는 확률적 진화컴퓨팅 방법에 기반하고, 전체의 탐색 포인트들을 분해하여 최적해를 찾는 점에서 장점을 가지고 있다. 실험 결과, 본 논문에서는 효모 유전자에 대한 발현 데이터와 모티프 데이터의 이종 데이터에 적용해 보았으며, 이러한 데이터에 있어서 주요 상관관계가 있는 패턴들을 추출한 결과를 제시한다.

• 한국지능시스템학회논문지
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• 제10권4호
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• pp.379-389
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• 2000

A cellular automaton is well-known for self-organizing and dynamic behavions in the filed of artifial life. This paper addresses a new neuronic architecture called an evolvable celluar classifier which evolves with the genetic rules (chromosomes) in the non-uniform cellular automata. An evolvable cellular classifier is primarily based on cellular programming, but its mechanism is simpler becaise it utilizes only mutations for the main genetic operators and resmbles the Hopfield network. Therefore, the desirable bit-patterns could be obtained through evolutionary processes for just one individual agent, As a rusult, an evolvable hardware is derived which is applicable to clessification of bit-string information.

• 천문학회보
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• 제38권1호
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• pp.38.1-38.1
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• 2013

We present observational estimation of the infrared (IR) luminosity function (LF) of local (z < 0.3) star-forming (SF) galaxies derived from the AKARI NEP-Wide samples. We made an analysis of the NEP-Wide data with optical spectroscopic information allowing an accurate determination of luminosity function. Spectroscopic redshifts for about 1650 objects were obtained with MMT/Hectospec and WIYN/Hydra, and the median redshifts is about 0.22. To measure the contribution of SF galaxies to the luminosity function, we excluded AGN sample by comparing their SEDs with various model template. Spectroscopic redshifts and the AKARI's continuous filter coverage in the mid-IR (MIR) wavelength (2 ~ 25 micron) enable us to avoid large uncertainties from the mid-IR SED of galaxies and corresponding k-corrections. The 8-micron luminosity function shows a good agreement with the previous works in the bright-end, whereas it seems not easy to constrain the faint-end slope. The comparison with the results of the NEP-Deep data (Goto et al. 2010) suggests the luminosity evolution to the higher redshifts, which is consistent with the down-sizing evolutionary pattern of galaxies.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.236-240
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• 2000

A cellular automaton is well-known for self-organizing and dynamic behaviors in the field of artificial life. This paper addresses a new neuronic architecture called an evolvable cellular classifier which evolves with the genetic rules (chromosomes) in the non-uniform cellular automata. An evolvable cellular classifier is primarily based on cellular programing, but its mechanism is simpler because it utilizes only mutations for the main genetic operators and resembles the Hopfield network. Therefore, the desirable hi t-patterns could be obtained through evolutionary processes for just one individual agent. As a result, an evolvable hardware is derived which is applicable to classification of bit-string information.