• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.191-196
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• 2005

Genetic networks are a key to unraveling dynamic properties of biological processes and regulation of genes plays an essential role in dynamic behavior of the genetic networks. A popular characterization of regulation of the gene is a kinetic model. However, many kinetic parameters in the genetic regulation have not been available. To overcome this difficulty, in this report, state-space approach to modeling gene regulation is presented. Second-order systems are used to characterize gene regulation. Interpretation of coefficients in the second order systems as resistance, capacitance and inductance is studied. The mathematical methods for transient response analysis of gene regulation to external perturbation are investigated. Criterion for classifying gene into three categories: underdamped, overdamped and critical damped is discussed. The proposed models are applied to yeast cell cycle gene expression data.

• BMB Reports
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• v.50 no.9
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• pp.437-444
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• 2017

Different types of eukaryotic cells may adopt seemingly distinct modes of directional cell migration. However, several core aspects are regarded common whether the movement is either ameoboidal or mesenchymal. The region of cells facing the attractive signal is often termed leading edge where lamellipodial structures dominates and the other end of the cell called rear end is often mediating cytoskeletal F-actin contraction involving Myosin-II. Dynamic remodeling of cell-to-matrix adhesion involving integrin is also evident in many types of migrating cells. All these three aspects of cell migration are significantly affected by signaling networks of TorC2, TorC1, and PP2A/B56. Here we review the current views of the mechanistic understanding of these regulatory signaling networks and how these networks affect eukaryotic cell migration.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.1-6
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• 1990

Neural network is a computational model of ft biological nervous system developed ID exploit its intelligence and parallelism. Applying neural networks so robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 am shows that it can move a high speed as well as adapt to unforseen load changes and sensor noise. The results are compared with the conventional PD control scheme.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.500-503
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• 1990

Neural network is a computational model of the biological nervous system developed to exploit its intelligence and parallelism. Applying neural networks to robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 arm shows that it can move at high speed as well as adapt to unforseen load changes. The results are compared with the conventional PD control scheme.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.83-88
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• 1990

A New speech processing algorithm using neural networks is proposed. We transform input data into frequency domain and process them by neural networks of 22 output neurons which have Bark scale on the ground that the Bark scale is similiar with that of the characteristics of human cochlea. An utilized neural network is multilayer perceptron, and the characteristics of cochlea have it trained by error back propagation learning algorithm. The trained neural networks suffices functions of human cochlea including the effects of automatic gain control, compression and equalization. Simulation results show that the proposed speech processing algorithm has good performance in automatic gain control, compression and equalization.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.291-296
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• 1996

Poly(ethylene glycol)(PEG) macromers terminated with diacrylate Iyoups and interpenetrating poly- mer networks(IPN) composed of poly(hydroxyethyl methacrylate)(PHEMA) or poly(hydroxyethyl methacrylate-co-hydronypropyl methacrylate-co- N-vinyl pyrrolidone ) [ P( HEM A-co- HPM A-co- NVP) ] and PEG macromer were synthesized with the aim of obtaining protein adsorption resistant soft contact lens. Polymerization of PEC macromer resulted in the formation of cross-linked gels due to the multifunctionality of macromer. Crosslinked P(HEMA) or P(HEMA-co-HPMA-co-WVP) chains were interpenetrated into the cross-linked three-dimensional networks of PEG. It was found that albumin adsorption onto the contact lens prepared by P(HEMA-co-HPMA-co-NVP) /PEG IPW decreases with an increase of molecular weight of PEG. Also, it was found that albumin adsorption onto the both contact lens decreases with an increase of concentration of PEC macromer in the IPN preparation. There are also more adequate in the bioinertnen for the contact lens by P(HEMA)/PEG IPN or P (HEMA-co-HPMA-co-NVP)/PEG IPN than that by P(HEMA) or P(HEMA-co-HPMA-co-NVP)

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.471-481
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• 2003

The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.21-40
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• 2019

This paper presents a comprehensive survey of various artificial intelligence (AI) techniques implemented in cognitive radio engine to improve cognition capability in cognitive radio networks (CRNs). AI enables systems to solve problems by emulating human biological processes such as learning, reasoning, decision making, self-adaptation, self-organization, and self-stability. The use of AI techniques is studied in applications related to the major tasks of cognitive radio including spectrum sensing, spectrum sharing, spectrum mobility, and decision making regarding dynamic spectrum access, resource allocation, parameter adaptation, and optimization problem. The aim is to provide a single source as a survey paper to help researchers better understand the various implementations of AI approaches to different cognitive radio designs, as well as to refer interested readers to the recent AI research works done in CRNs.

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2001.11a
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• pp.69-70
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• 2001

• Molecules and Cells
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• v.40 no.5
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• pp.315-321
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• 2017

The inositol polyphosphates are a group of multifunctional signaling metabolites whose synthesis is catalyzed by a family of inositol kinases that are evolutionarily conserved from yeast to humans. Inositol polyphosphate multikinase (IPMK) was first identified as a subunit of the arginine-responsive transcription complex in budding yeast. In addition to its role in the production of inositol tetrakis- and pentakisphosphates ($IP_4$ and $IP_5$), IPMK also exhibits phosphatidylinositol 3-kinase (PI3-kinase) activity. Through its PI3-kinase activity, IPMK activates Akt/PKB and its downstream signaling pathways. IPMK also regulates several protein targets non-catalytically via protein-protein interactions. These non-catalytic targets include cytosolic signaling factors and transcription factors in the nucleus. In this review, we highlight the many known functions of mammalian IPMK in controlling cellular signaling networks and discuss future challenges related to clarifying the unknown roles IPMK plays in physiology and disease.