• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• ETRI Journal
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• v.30 no.6
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• pp.783-789
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• 2008

A bio-inspired vision chip for edge detection was fabricated using 0.35 ${\mu}m$ double-poly four-metal complementary metal-oxide-semiconductor technology. It mimics the edge detection mechanism of a biological retina. This type of vision chip offer several advantages including compact size, high speed, and dense system integration. Low resolution and relatively high power consumption are common limitations of these chips because of their complex circuit structure. We have tried to overcome these problems by rearranging and simplifying their circuits. A vision chip of $160{\times}120$ pixels has been fabricated in $5{\times}5\;mm^2$ silicon die. It shows less than 10 mW of power consumption.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.280-280
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• 2000

In this paper, we propose a method of constructing equation using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is. we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.224-227
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• 2000

In this Paper, we prepose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series, Sun spot data and KOSPI data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.7
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• pp.571-577
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• 2016

In this paper, we propose a novel propulsion method for a Biomimetic underwater robot, which is a bio-inspired approach. The proposed propulsion method mimics the pectoral fins of a real fish. Pectoral fins of real fish are able to propel and change direction. We designed the propulsion mechanism of 1 D.O.F. that has two functions (propel and change direction). We named this propulsion system 'Flipper'. The proposed propulsion method can control forward, pitch and yaw motion using the Flipper. We made an experimental underwater robot system and verified the proposed propulsion method. We measured its maximum speed and turning motion using an experimental underwater robot system. We also analyzed the thrust force from the maximum speed, using the thrust equation. Experimental results showed that our propulsion method enabled the thrust system of the biomimetic robot.

• Smart Structures and Systems
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• v.8 no.1
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• pp.93-105
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• 2011

This paper presents a flexible low-profile antenna sensor for fatigue crack detection and monitoring. The sensor was inspired by the sense of pain in bio-systems as a protection mechanism. Because the antenna sensor does not need wiring for power supply or data transmission, it is an ideal candidate as sensing elements for the implementation of engineering sensor skins with a dense sensor distribution. Based on the principle of microstrip patch antenna, the antenna sensor is essentially an electromagnetic cavity that radiates at certain resonant frequencies. By implementing a metallic structure as the ground plane of the antenna sensor, crack development in the metallic structure due to fatigue loading can be detected from the resonant frequency shift of the antenna sensor. A monostatic microwave radar system was developed to interrogate the antenna sensor remotely. Fabrication and characterization of the antenna sensor for crack monitoring as well as the implementation of the remote interrogation system are presented.

• The KIPS Transactions:PartD
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• v.17D no.3
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• pp.175-184
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• 2010

Experiments and research on genes have become very convenient by using DNA chips, which provide large amounts of data from various experiments. The data provided by the DNA chips could be represented as a two dimensional matrix, in which one axis represents genes and the other represents samples. By performing an efficient and good quality clustering on such data, the classification work which follows could be more efficient and accurate. In this paper, we use a bio-inspired algorithm called the Particle Swarm Optimization algorithm to propose an efficient clustering mechanism for large amounts of DNA chip data, and show through experimental results that the clustering technique using the PSO algorithm provides a faster yet good quality result compared with other existing clustering solutions.

• Journal of Ginseng Research
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• v.46 no.3
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• pp.489-495
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• 2022

Background: Although ginsenosides and saponins in Korea red ginseng (KRG) shows various pharmacological roles, their roles in the inflammatory response are little known. This study investigated the anti-inflammatory role of ginsenosides identified from KRG saponin fraction (RGSF) and the potential mechanism in macrophages. Methods: The ginsenoside composition of RGSF was identified by high-performance liquid chromatography (HPLC) analysis. An anti-inflammatory effect of RGSF and its mechanisms were studied using nitric oxide (NO) and prostaglandin E₂ (PGE₂) production assays, mRNA expression analyses of inflammatory genes and cytokines, luciferase reporter gene assays of transcription factors, and Western blot analyses of inflammatory signaling pathways using the lipopolysaccharide (LPS)-treated RAW264.7 cells. Results: HPLC analysis identified the types and amounts of various panaxadiol ginsenosides in RGSF. RGSF reduced the generation of inflammatory molecules and mRNA levels of inflammatory enzymes and cytokines in LPS-treated RAW264.7 cells. Additionally, RGSF inhibited the signaling pathways of NF-κB and AP-1 by suppressing both transcriptional factors and signaling molecules in LPS-treated RAW264.7 cells. Conclusion: RGSF contains ginsenosides that have anti-inflammatory action via restraining the NF-κB and AP-1 signaling pathways in macrophages during inflammatory responses.