• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.790-793
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• 2002

In this paper, dynamic path planning of two mobile robots using a modified Hopfield neural network is studied. An area which excludes obstacles and allows gradually changing of activation level of neurons is derived in each step. Next moving step can be determined by searching the next highest activated neuron. By learning repeatedly, the steps will be generated from starting to goal points. A path will be constructed from these steps. Simulation showed the constructed paths of two mobile robots, which are moving across each other to their goals.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.613-619
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• 2006

This paper introduces a new structural health monitoring using a nano sensor. The sensor is made of nano smart composite material based on carbon nanotubes. The nano sensor is fabricated as a thin and narrow polymer film sensor that is bonded or deposited onto a structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensorcan form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1714-1728
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• 2016

This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLAB-Simulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

• Korean Journal of Acupuncture
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• v.26 no.3
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• pp.27-42
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• 2009

Objectives : The vestibular system detects head movement and serve to regulate and maintain the equilibrium and orientation of the body. It is known that the vestibular imbalance leads to vestibular symptoms such as nausea, vomiting, vertigo and postural disturbance. The objectives of the present study were to examine a modification of the dynamic activities of medial vestibular nucleus (MVN) neurons following electroacupuncture (EA) of GB43 (Hyepgye). Methods : In Sprague-Dawley rats weighing $250{\sim}300g$, dynamic responses induced by sinusoidal whole body rotation about vertical axis at 0.2 Hz were observed in MVN of rats during EA of GB43 (Hyepgye) with 0.2 ms, 40 Hz and $600{\pm}200{\mu}A$. Also, expression of cFos protein was observed 2 hours after EA for 30 mins. Results : In dynamic response of vestibular neuron, the excitatory or inhibitory responses of gain were predominant in the ipsilateral MVN neurons during EA but not predominant in the contralateral MVN. Most neurons showing decreased gain were classified to inhibitory responses of spontaneous firing discharge during EA and ones showing increased gain were classified to excitatory response of spontaneous firing discharge during EA. Also, EA of the left GB43 (Hyepgye) for 30 mins produced the expression of cFos protein in MVN, inferior olive (IO) and solitary tract nuclei (SOL). Spatial expressions of cFos protein were predominant in the contralateral MVN, ipsilateral IO and bilateral SOL. Conclusion : These results suggest that the excitability of MVN neurons was influenced by EA of GB43 (Hyepgye) and EA may be related to the convergence on MVN.

• Progress in Medical Physics
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• v.9 no.2
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• pp.95-104
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• 1998

The dynamic properties of the 3rd-order neuron of the retina was investigated by using conventional intracellular recording techniques. Experiments were performed in the superfused retina-eyecup preparation of the channel catfish, Ictalurus punctatus. The cornea, iris, lens, and vitreous were removed by absorption with Kimwipe tissue under the dissection microscope thereby exposing the retina in a hemi -eyecup. The electrical signal was amplified by electrometer, viewed on oscilloscope. Regular signals from the cells were recorded on a penwriter and stored by data recorder and computer. Full-field, spot or annular light stimuli were generated on a computer monitor and focused onto the retina. Baclofen hyperpolarized the dark membrane potential, suppressed sustained component and enhanced transient component of the ON-sustained cell with a large transient component, but did not affect the surround antagonism of the cell. Baclofen selectively suppressed responses evoked by moving bar light stimuli on the ON-OFF transient cell. The results suggest that transient cells have directional selectivity in the inner retina. These dynamic properties of amacrine and ganglion cells were modulated by baclofen. Therefore, it is presumed that there is baclofen-induced directional selectivity in ON-OFF transient cells in the catfish retina.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.179-184
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• 2006

An intuitive understanding of the dynamic pattern generation in asymmetric networks may be considered an essential component in developing models for the dynamic information processing. It has been reported that the neural network with cyclic connections generates multiple limit cycles. The dynamics of discrete time network with cyclic connections has been investigated intensively. However, the dynamics of a cyclic connection neural network in continuous-time has not been well-known due to the considerable complexity involved in its calculation. In this paper, the dynamic behavior of a continuous-time cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$, has been investigated. Furthermore, the dynamics and stability of the network have been analyzed using a piece-wise linear approximation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.60-65
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• 2012

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.396-401
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• 2006

It is well-known that a neural network with cyclic connections generates plural limit cycles, thus, being used as a memory system for storing large number of dynamic information. In this paper, a continuous-time cyclic connection neural network was built so that each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$. The type and the number of limit cycles generated by such network has also been demonstrated through simulation. In particular, the effect of chaos signal for transition between limit cycles has been tested. Furthermore, it is evaluated whether the chaotic noise is more effective than random noise in the process of the dynamical neural networks.

• Journal of Life Science
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• v.16 no.6
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• pp.955-959
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• 2006

A dynamic cycle of addition and removal of O-linked N-acetylglucosamine (O-GlcNAc) at serine and threonine residues is emerging as a key regulator of nuclear and cytoplasmic protein activity. In this work, immunocytochemistry was carried out to investigate the subcellular expression of GlcNAc kinase (NAGK, EC 2.7.1.59) that catalyzes the phosphorylation of GlcNAc to GlcNAc 6-phosphate. Immunostainings of cultured rat hippocampal neurons revealed patchy or punctate distribution of NAGK. When NAGK is doublestained with caveolin-1 or flotillin, markers for caveolae and lipid rafts, respectively, NAGK was co-localized with these markers. These results indicate that most, if not all, of the NAGK immunopunctae represent caveolae and lipid rafts, and suggest NAGK's role in these membrane microdomains.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.238-244
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• 2008

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing. This paper presents gamma neural network(GAM) to improve the dynamics of multilayer network. The GAM network uses the gamma memory kernel in the hidden layer of feedforword multilayer network. The GAM network is evaluated in linear and nonlinear system identification, and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of its performance. Experimental results show that the GAM network performs better with respect to the convergence and accuracy, indicating that it can be a more effective network than conventional multilayer networks in system identification.