• 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 the Optical Society of Korea
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• v.18 no.2
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• pp.174-179
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• 2014

Since in vitro neural recording and imaging applications based on a surface plasmon resonance (SPR) technique have expanded dramatically in recent years, cytotoxicity assessment to ensure the biosafety and biocompatibility for those applications is crucial. Here, we report the cytotoxicity of the SPR substrate incorporating a flint glass whose refractive index is larger than that of a conventional crown glass. A high refractive index glass substrate is essential in neural signal detection due to the advantages such as high sensitivity and wide dynamic range. From experimental data using primary hippocampal neurons, it is found that a lead-based flint glass is not appropriate as a neural recording template although the neuron cells are not directly attached to the toxic glass. We also demonstrate that the adhesion layer between the glass substrate and the gold film plays an important role in achieving the substrate stability and the cell viability.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Steel and Composite Structures
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• v.47 no.6
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.361-380
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• 2023

Magnetoencephalography (MEG) is a technique to record oscillatory magnetic fields coming from ongoing neuronal activity. Functional brain activities performing cognitive or physiological tasks are performed on structural connections between neurons or brain regions. MEG data can be characterized as highly correlated, spatio-temporal, multidimensional, multilayered dynamic networks. Due to its complex structure, many studies on MEG network have not yet been conducted. In this study, we will explain the concept, necessity, and possible approaches of MEG network analysis. We reviewed the characteristics of MEG data. Network measures and potential network models in MEG and clinical studies are also reviewed.

• Journal of Acupuncture Research
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• v.19 no.4
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• pp.167-182
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• 2002

This experiment was designed to investigate the effects of electroacupuncture (EA) on chronic pains and factors that affected EA effects. The responses of wide dynamic range (WDR) cells to electrical stimulation of $A{\delta}$ & C afferent fibers were used as an index of pain in rats with chronic pains induced by intraplantar injection of complete Freund's adjuvant or peripheral nerve injury. In rats with chronic pains, low (2Hz) and high (100Hz) frequency EA stimulation applied to zusanli caused the inhibition of WDR cell responses in about 60% of rats and the inhibitory actions were dependent on the stimulus strength. EA stimulation also induced an excitation of WDR cell responses in 23.9% of rats and no effect in 15.8% of rats. However, it seemed that in normal rats compared to the rat with chronic pains, the incidence of which EA stimulation caused the excitation or no effect was high. Reversible spinalization almost completely blocked EA-induced inhibitory or excitatory effects. EA stimulation more frequently induced the excitation of WDR cell responses in lightly anesthetized (0.6%) rats and the enhanced responses of WDR cells were inhibited by EA stimulation in the rat anesthetized with 1.5% enflurane. These experimental findings suggest that in rats with chronic pain, EA stimulation inhibited WDR cell responses to slow $A{\delta}$ and C fiber stimulation and EA-induced inhibitory action was under the control of descending inhibitory system and degree of anesthesia.

• Journal of Biomedical Engineering Research
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• v.32 no.1
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• pp.55-60
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• 2011

Although sound intensity is considered as one of important factors in auditory processing, its neural mechanism in auditory neurons with limited dynamic range of firing rates is still unclear. In this study, we examined the effect of sound intensity adaptation on the change of glucose metabolism in a rat brain using [F-18] micro positron emission tomography (PET) neuroimaging technique. In the experiment, broadband white noise sound was given for 30 minutes after the [F-18]FDG injection in order to explore the functional adaptation of rat brain into the sound intensity levels. Nine rats were scanned with four different sound intensity levels: 40 dB, 60 dB, 80 dB, 100 dB sound pressure level (SPL) for four weeks. When glucose uptake during the adaptation of a high intensity sound level (100 dB SPL) was compared with that during adaptation to a low intensity level (40 dB SPL) in the experiment, the former induced a greater uptake at bilateral cochlear nucleus, superior olivary complexes and inferior colliculi in the auditory pathway. Expectedly, the metabolic activity in those areas linearly increased as the sound intensity level increased. In contrast, significant decrease interestingly occurred in the bilateral auditory cortices: The activities of auditory cortex proportionally decreased with higher sound intensities. It may reflect that the auditory cortex actively down-regulates neural activities when the sound gets louder.

• The Korean Journal of Physiology and Pharmacology
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• v.3 no.3
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• pp.237-244
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• 1999

Magnesium ion is known to selectively block the N-methyl-D-aspartate (NMDA)-induced responses and to have anticonvulsive action, neuroprotective effect and antinociceptive action in the behavioral test. In this study, we investigated the effect of $Mg^{2+}$ on the responses of dorsal horn neurons to cutaneous thermal stimulation and graded electrical stimulation of afferent nerves as well as to excitatory amino acids and also elucidated whether the actions of $Ca^{2+}$ and $Mg^{2+}$ are additive or antagonistic. $Mg^{2+}$ suppressed the thermal and C-fiber responses of wide dynamic range (WDR) cell without any effect on the A-fiber responses. When $Mg^{2+}$ was directly applied onto the spinal cord, its inhibitory effect was dependent on the concentration of $Mg^{2+}$ and duration of application. The NMDA- and kainate-induced responses of WDR cell were suppressed by $Mg^{2+}$, the NMDA-induced responses being inhibited more strongly. $Ca^{2+}$ also inhibited the NMDA-induced responses current-dependently. Both inhibitory actions of $Mg^{2+}$ and $Ca^{2+}$ were additive, while $Mg^{2+}$ suppressed the EGTA-induced augmentation of WDR cell responses to NMDA and C-fiber stimulation. Magnesium had dual effects on the spontaneous activities of WDR cell. These experimental findings suggest that $Mg^{2+}$ is implicated in the modulation of pain in the rat spinal cord by inhibiting the responses of WDR cell to noxious stimuli more strongly than innocuous stimuli.

• The Korean Journal of Physiology and Pharmacology
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• v.3 no.1
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• pp.35-45
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• 1999

Excitatory amino acid (EAA) and substance P (SP) have been known to be primary candidates for nociceptive neurotransmitter in the spinal cord, and calcium ions are implicated in processing of the sensory informations mediated by EAA and SP in the spinal cord. In this study, we examined how $Ca^{2+}$ modified the responses of dorsal horn neurons to single or combined iontophoretical application of EAA and SP in the rat. All the LT cells tested responded to kainate, whereas about 55% of low threshold (LT) cells responded to iontophoretically applied NMDA. NMDA and kainate excited almost all wide dynamic range (WDR) cells. These NMDA- and kainate-induced WDR cell responses were augmented by iontophoretically applied EGTA, but suppressed by $Ca^{2+},\;Mn^{2+},$ verapamil and ${\omega}-conotoxin$ EVTA, effect of verapamil being more prominent and well sustained. $Ca^{2+}$ and $Mn^{2+}$ antagonized the augmenting effect of EGTA. On the other hand, prolonged spinal application of EGTA suppressed the response of WDR cell to NMDA. SP had triple effects on the spontaneous activity as well as NMDA-induced responses of WDR cells: excitation, inhibition and no change. EGTA augmented, but $Ca^{2+},\;Mn^{2+}$ and verapamil suppressed the increase in the NMDA-induced responses and spontaneous activities of WDR cells following iontophoretical application of SP. These results suggest that in the spinal cord, sensory informations mediated by single or combined action of EAA and SP can be modified by the change in calcium ion concentration.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).