• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.464-467
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• 1999

Rapid progress in the modeling of biological structures and simulation of their development has occurred over the last few years. Cellular automata (CA) and Lindenmayer-system(L-system) are the representative models of development/morphogenesis of multicellular organism. L-system is applied to the visualization of biological plant. Also, CA are applied to the study of artificial life and to the construction of an artificial brain. To design the L-system and CA automatically, we make this model evolve. It is necessary to code the developmental rules for evolution. In this paper, we propose a DNA coding method for evolution the models of development/morphogenesis of biological multicellular organisms. DNA coding has the redundancy and overlapping of gene and is apt for the representation of the rule. In this paper, we propose the DNA coding method of CA and L-system.

• Journal of The Korean Digital Architecture Interior Association
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• v.6 no.2
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• pp.17-23
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• 2006

The purpose of this study was to investigate how student felt the strengths and shortness of presentation methods for formation of interior spaces. For this study, the process of the interior architecture design class was divided into three stages: the programming. the design development, and the design completion. In the design development stage, students used presentation methods: hand sketch, scale model, computer modeling, and virtual realty. The strengths of hand sketch was that quick expression. Models provided three-dimensional feelings. Computer modelling provide realistic color and texture. Virtual reality provided three-dimensional immersion and real scale. It is effective that students collect brain storm images using quick hand sketch in the beginning of design development stage. After that, they compose interior spaces in study models with small scale. Watching the models, they design details of spaces by using hand sketch and computer modelling. Using virtual reality, they can check the scale and circulation. Finally, they complete computer modelling by texture mapping and check the final design in virtual reality.

• Journal of The Korean Digital Architecture Interior Association
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• v.6 no.2
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• pp.25-30
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• 2006

The purpose of this study was to investigate how student felt the strengths and shortness of presentation methods for formation of interior spaces. For this study, the process of the interior architecture design class was divided into three stages: the programming. the design development, and the design completion. In the design development stage, students used presentation methods: hand sketch, scale model, computer modeling, and virtual realty. The strengths of hand sketch was that quick expression. Models provided three-dimensional feelings. Computer modelling provide realistic color and texture. Virtual reality provided three-dimensional immersion and real scale. It is effective that students collect brain storm images using quick hand sketch in the beginning of design development stage. After that, they compose interior spaces in study models with small scale. Watching the models, they design details of spaces by using hand sketch and computer modelling. Using virtual reality, they can check the scale and circulation. Finally, they complete computer modelling by texture mapping and check the final design in virtual reality.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.963-966
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• 2003

IPv6-IPv4 변환시스템은 기존의 IPv4 네트워크와 신규 구축되는 IPv6 네트워크 간의 통신을 가능하게 하는 게이트웨이 기반 기술이다. 이러한 IPv6-IPv4 변환시스템에서는 네트워크 간의 모든 트래픽을 변환해야 하므로 높은 성능을 요구된다. 이에 본 연구에서는 이전 연구에서 구현된 게이트웨이 기반 IPv6-IPv4 변환시스템과 변환기의 성능분석에 적용될 수 있는 큐잉 모델을 제시하고 부과되는 트래픽에 따른 처리 성능을 산출하는 분석적인 방법을 제시하였다. IPv6-IPv4 변환시스템의 분석 모델의 경우, 도착간격은 지수분포를 따르고, 서비스시간은 M/M/l/K 모델 기반의 일반분포를 따른다. 또한 IPv6-IPv4 변환시스템의 변환기는 트래픽에 대한 변환 처리를 담당하는 핵심 모듈로서, 순차적인 단계로 이루어진다. 즉, 변환시스템의 변환기 자체의 분석 모델의 도착간격은 지수분포를 따르고, 서비스시간은 M/G/l/K 모델 기반의 일반분포를 따른다. 이렇게 제안된 모델에 대해 상세하게 설명하였으며, 이를 검증하기 위해서 모델을 적용하여 근사한 결과와 실제 측정 결과를 비교하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.701-708
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• 2001

Recently, the research on intelligence of computer has actively been under way in various areas and gradually extended to adapt to uncertain and complex environments. In this paper, we propose the MVL-Neural Valued Logic. Also, we verify that the MVL-Automata can be implemented to Neural Network and the MVL-Neural Network Model can be a simulator by MVL-Automata. Therefore, we propose that the MVL-Neural Network Model can be widely used in such area, as intelligent system or modeling of brain. In particular, the MVL-Neural Network is expected to be used as core technology of next generation computer.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1428-1429
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• 2007

The paper used to the Neral Netwok for a forecasting conservation system. A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. The true power and advantage of neural network lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Form results of this study, the Neral Netwok is will play an important role for insulation diagnosis system of real site GIS and power equipment using Dry-Air gas.

• Molecules and Cells
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• v.26 no.2
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• pp.121-130
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• 2008

Methamphetamine, a commonly used addictive drug, is a powerful addictive stimulant that dramatically affects the CNS. Repeated METH administration leads to a rewarding effect in a state of addiction that includes sensitization, dependence, and other phenomena. It is well known that susceptibility to the development of addiction is influenced by sources of reinforcement, variable neuroadaptive mechanisms, and neurochemical changes that together lead to altered homeostasis of the brain reward system. These behavioral abnormalities reflect neuroadaptive changes in signal transduction function and cellular gene expression produced by repeated drug exposure. To provide a better understanding of addiction and the mechanism of the rewarding effect, it is important to identify related genes. In the present study, we performed gene expression profiling using microarray analysis in a reward effect animal model. We also investigated gene expression in four important regions of the brain, the nucleus accumbens, striatum, hippocampus, and cingulated cortex, and analyzed the data by two clustering methods. Genes related to signaling pathways including G-protein-coupled receptor-related pathways predominated among the identified genes. The genes identified in our study may contribute to the development of a gene modeling network for methamphetamine addiction.

• International Journal of Vascular Biomedical Engineering
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• v.2 no.1
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• pp.17-24
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• 2004

Tumor angiogenesis was simulated using a two-dimensional computational model. The equation that governed angiogenesis comprised a tumor angiogenesis factor (TAF) conservation equation in time and space, which was solved numerically using the Galerkin finite element method. The time derivative in the equation was approximated by a forward Euler scheme. A stochastic process model was used to simulate vessel formation and vessel elongation towards a paracrine site, i.e., tumor-secreted basic fibroblast growth factor (bFGF). In this study, we assumed a two-dimensional model that represented a thin (1.0 mm) slice of the tumor. The growth of the tumor over time was modeled according to the dynamic value of bFGF secreted within the tumor. The data used for the model were based on a previously reported model of a brain tumor in which four distinct stages (namely multicellular spherical, first detectable lesion, diagnosis, and death of the virtual patient) were modeled. In our study, computation was not continued beyond the 'diagnosis' time point to avoid the computational complexity of analyzing numerous vascular branches. The numerical solutions revealed that no bFGF remained within the region in which vessels developed, owing to the uptake of bFGF by endothelial cells. Consequently, a sharp, declining gradient of bFGF existed near the surface of the tumor. The vascular architecture developed numerous branches close to the tumor surface (the brush-border effect). Asymmetrical tumor growth was associated with a greater degree of branching at the tumor surface.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.299-308
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• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.132-137
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• 2019

The amount of data generated from medical images is increasingly exceeding the limits of professional visual analysis, and the need for automated medical image analysis is increasing. For this reason, this study evaluated the classification and accuracy according to the presence or absence of tumor using Inception V3 deep learning model, using MRI medical images showing normal and tumor findings. As a result, the accuracy of the deep learning model was 90% for the training data set and 86% for the validation data set. The loss rate was 0.56 for the training data set and 1.28 for the validation data set. In future studies, it is necessary to secure the data of publicly available medical images to improve the performance of the deep learning model and to ensure the reliability of the evaluation, and to implement modeling by improving the accuracy of labeling through labeling classification.