• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.653-662
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• 1989

Cerebellar Model Arithmetic Controller (CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d.o.f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process. A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.10
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• pp.1702-1711
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• 1997

A numerical study for the number of terms of a power series stress function and the effect of hybrid element size on stress analysis around a hole in loaded orthotropic composites is presented. The hybrid method coupling experimental and/or theoretical inputs and complex variable formulations involving conformal mappings and analytical continuity is used to calculate tangential stress on the boundary of the hole in uniaxially loaded, finite width glass epoxy tensile plate. The tests are done by rarying the number of terms, element size and nodal locations on the external boundary of the hybrid region. The numerical results indicate that the hybrid method is accurate and powerful in both experimental and numerical stress analysis.

• ETRI Journal
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• v.34 no.5
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• pp.696-705
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• 2012

Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.

• Geotechnical Engineering
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• v.12 no.2
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• pp.95-106
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• 1996

In this study, a constitutive model which can describe the anisotropic and plastic behaviors of natural cohesive soils, was developed based on anisotropic bounding surface theory. The model was fomulated by the concepts of the improved anisotropic bounding surface function, nonassociated flow rule with new plastic potential function, anisotropic hardening rule, and new mapping rule governing the plastic behavior inside bounding sutraface. Comparing with the results of Ku consolidation and triaxial shearing tests, the predictions by the proposed model agree quite well with real soil responses.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1177-1194
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• 2009

In this paper we establish the general solution of the functional equation f(2x+y)+f(x-2y)=2f(x+y)+2f(x-y)+f(-x)+f(-y) and investigate the Hyers-Ulam-Rassias stability of this equation in quasi-Banach spaces. The concept of Hyers-Ulam-Rassias stability originated from Th. M. Rassias' stability theorem that appeared in his paper: On the stability of the linear mapping in Banach spaces, Proc. Amer. Math. Soc. 72 (1978), 297-300.

• Communications of the Korean Mathematical Society
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• v.35 no.4
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• pp.1107-1121
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• 2020

The purpose of this paper is to introduce the notion of generalized multivalued Ƶ-contraction and generalized multivalued Suzuki type Ƶ-contraction for pair of mappings and establish common fixed point theorems for such mappings in complete metric spaces. Results obtained in this paper extend and generalize some well known fixed point results of the literature. We deduce some corollaries from our main result and provide examples in support of our results.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.740-745
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• 2001

The plastic deformation behavior of formed CICC fur the superconducting Tokamac fusion device was examined and appropriate manufacturing information was provided. A relation between travel of the bending roller and spring back displacement was obtained via virtual manufacturing. The radius of CICC after forming was expressed as a function of the bend-roll travel. The maximum von Mises stress after spring back was also monitored fur the SAGBO prediction. Next, the variation of the CICC cross-sectional area was examined during the first turn and during conduit bending with the largest curvature. Finally, the coil radius was measured and compared with the data generated from the virtual manufacturing. The measured data showed similar pattern as predicted one. Using the mapping function found to match with the real data, the data from the virtual manufacturing may facilitate accurate manufacturing.

• Journal of Biomedical Engineering Research
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• v.5 no.2
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• pp.161-166
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• 1984

In this paper, we describe an effective technique for computing the steady-state motion in a two-dimensional cochlear model. With the cochlear fluid assumed incompressible and invisid, the problem reduces to solving an integral equation for a region with yielding boundary. Using the conformal mapping, Jacobian elliptic function and hyperbolic function, a pair of second-order differential equation is derived. What we will show in this paper is that by appropriately transforming integral equation, the same computation can be performed with comparable accuracy in a short time.

• Applied Microscopy
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• v.46 no.2
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• pp.100-104
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• 2016

Electron microscopy is currently the only available technique with a spatial resolution sufficient to identify fine neuronal processes and synaptic structures in densely packed neuropil. For large-scale volume reconstruction of neuronal connectivity, serial block-face scanning electron microscopy allows us to acquire thousands of serial images in an automated fashion and reconstruct neural circuits faster by reducing the alignment task. Here we introduce the whole reconstruction procedure of synaptic network in the rat hippocampal CA1 area and discuss technical issues to be resolved for improving image quality and segmentation. Compared to the serial section transmission electron microscopy, serial block-face scanning electron microscopy produced much reliable three-dimensional data sets and accelerated reconstruction by reducing the need of alignment and distortion adjustment. This approach will generate invaluable information on organizational features of our connectomes as well as diverse neurological disorders caused by synaptic impairments.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.91-98
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• 2011

In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.