• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.389-395
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• 1999

최근 몇 년간 생물학적 발생에 대한 구조 및 둥작원리의 모델링에 대한 빠른 진전이 일어나고 있다. 세포자동자(cellular automata CA)와 린드마이어-시스템(L-system)은 다세포의 대표적인 발생/발달 모델이다. L-시스템은 식물의 그래픽 표현에 적용되어 오고 있으며 CA는 인고생명의 연구모델과 인공두뇌의 건축 등의 분야에 적용되어 오고 있다, 현재까지 CA와 L-시스템의 발생규칙은 설계자의 설계에 의존하고 있다. 그러나 진화연사방법을 도입하면 CA와 L-시스템을 자동으로 설계할수 있다. 발생규칙의 진화를 위해서는염색체의 코트화가 필요하다. DNA 코딩방법은 유전자의 중복과 여분을 가지고 있으며 규칙의 표현에 적합한 코딩방법이다. 본 논문에서는 CA와 L-시스템의 규칙을 진화시키기 위한 DNA 코딩 방법을 제안한다.

• Journal of the military operations research society of Korea
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• v.32 no.2
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• pp.114-130
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• 2006

This paper deals with complexity theory to describe amphibious warfare situation using EINSTein (Enhanced ISAAC Neural Simulation Tool) simulation model. EINSTein model is an agent-based artificial "laboratory" for exploring self-organized emergent behavior in land combat. Many studies have shown that existing Lanchester equations used in most war simulation models does not describe changes of combat. Future warfare will be information warfare with various weapon system and complex combat units. We have compared and tested combat results with Lanchester models and EINSTein model. Furthermore, the EINSTein model has been applied and analyzed to amphibious warfare model such as amphibious assault and amphibious sudden attack. The results show that the EINSTein model has a possibility to apply and analyze amphibious warfare more properly than Lanchester models.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.17-18
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• 2024

악성 흉수의 진단은 세포학적 검사로 암세포를 확인하는 것이 필수적이며 진단율은 50~80%로 나타난다. 양성자 단층 촬영은 비침습적으로 암 병기를 평가하는 유용한 방법이다. 하지만 암이 아닌 다른 원인으로 인한 포도당 대사로 인하여 양전자 단층 촬영만으로 악성 흉수를 진단하는 데 어려움이 있다. 악성 흉수 자동 진단 모델은 암세포를 진단하는데 있어서 보조적인 역할이 가능하다. 이에 따라 본 연구는 컨볼루션 신경망 기반의 딥러닝 모델을 개발하여 악성 흉수 진단 성능을 확인하고 진단의 보조적 목적으로써 딥러닝의 사용 가능성을 확인하고자 하였다. 결과적으로 모델 전반적으로 accuracy 0.7~0.86의 높은 성능을 보였다. 본 연구의 결과를 통해 실제 의료 환경에서 악성 흉수를 진단하는데 딥러닝 모델이 보조적인 역할을 할 수 있을 것으로 기대된다.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.97-105
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• 2011

In rivers and streams, biofilms are thin layers of greenish-brown slime attached to rocks, plants, and other surfaces. Biofilms play key roles in primary production and cycling of nutrients, water quality remediation, suspended sediment removal, and energy flow to higher trophic levels. In the present study, we developed a two-dimensional cellular automata model to simulate mixed biofilms of toxin-sensitive and toxin-producing species in hydrodynamic flow. The flow was generated by a stochastic process for uniform flow and by using the Navier-Stokes equation for non-uniform flow. Minimized local rules governing reproduction and mortality of the species were executed in the self-organizing processes to elucidate interactions between toxin-producing and toxin-sensitive species in competition over nutrients. We briefly discuss the morphology of the simulated biofilm under different flow conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.2
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• pp.122-131
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• 2010

This paper introduces the concept of a virtual ecosystem and reports the following three mathematical approaches that could be widely used to construct such an ecosystem, along with examples: (1) a molecular dynamics simulation approach for animal flocking behavior, (2) a stochastic lattice model approach for termite colony behavior, and (3) a rule-based cellular automata approach for biofilm growth. The ecosystem considered in this study consists of artificial organisms and their environment. Each organism in the ecosystem is an agent that interacts autonomously with the dynamic environment, including the other organisms within it. The three types of model were successful to account for each corresponding ecosystem. In order to accurately mimic a natural ecosystem, a virtual ecosystem needs to take many ecological variables into account. However, doing so is likely to introduce excess complexity and nonlinearity in the analysis of the virtual ecosystem's dynamics. Nonetheless, the development of a virtual ecosystem is important, because it can provide possible explanations for various phenomena such as environmental disturbances and disasters, and can also give insights into ecological functions from an individual to a community level from a synthetic viewpoint. As an example of how lower and higher levels in an ecosystem can be connected, this paper also briefly discusses the application of the second model to the simulation of a termite ecosystem and the influence of climate change on the termite ecosystem.