• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.24-29
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• 2006

A new oscillatory neural circuit with computational function has been designed and been designed and fabricated in an $0.5{\mu}m$ double poly CMOS technology. The proposed oscillatory circuit consists of 3 neural oscillators with excitatory synapses and a neural oscillator with inhibitory synapse. The oscillator block which is a basic element of the neural circuit is designed with a variable negative resistor and 2 transconductors. The variable negative resistor which is used as a input stage of the oscillator consist of a bump circuit with Gaussian-like I-V curve. SPICE simulations of a designed neural circuit demonstrate cooperative computation. Measurements of the fabricated neural chip in condition of ${\pm}$ 2.5 V power supply are shown and compared with the simulated results.

• Applied Microscopy
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• v.46 no.4
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• pp.176-178
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• 2016

Efficient neural circuit reconstruction requires sufficient lateral and axial resolution to resolve individual synapses and map a large enough volume of brain tissue to reveal the molecular identity and origin of these synapses. Sparse circuit reconstruction using array tomography meets many of these requirements but also has some limitations. In this minireview, the advantages and disadvantages of applicable imaging techniques will be discussed.

• Applied Microscopy
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• v.51
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• pp.9.1-9.10
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• 2021

Brain disorders seriously affect life quality. Therefore, non-invasive neuroimaging has received attention to monitoring and early diagnosing neural disorders to prevent their progress to a severe level. This short review briefly describes the current MRI and PET/CT techniques developed for non-invasive neuroimaging and the future direction of optical imaging techniques to achieve higher resolution and specificity using the second near-infrared (NIR-II) region of wavelength with organic molecules.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.569-572
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• 1992

This paper describes a new method of pseudorandom testing of a digital circuit by use of correlation method and a neural network. The authors have recently proposed a new method of fault diagnosis of logical circuit by applying a pseudorandom M-sequence to the circuit under test, calculating the crosscorrelation function between the input and the output, and comparing the crosscorrelation functions with the references. This method, called MSEC method, is further extended by using a neural network in order to not only detect the existence of faults but also find the place or location of the faults. An experiment by using a simple digital circuit shows enough applicability of this method to industrial testing of circuit board.

• Journal of Sensor Science and Technology
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• v.12 no.1
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• pp.51-56
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• 2003

We describe a charge pump circuit using VCO (voltage controlled oscillator) for storing information into local memories in neural networks. The VCO is used for adjusting the output voltage of the charge pump to the reference voltage and for reducing the fluctuation generated by the clocking scheme. The charge pump circuit is simulated by using Hynix 0.35um CMOS process parameters. The proposed charge pump operates properly regardless to the temperature and the supply voltage variation.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.103-106
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• 2003

An oscillatory neural network circuit has been designed and fabricated in an 0.5 ${\mu}{\textrm}{m}$ double poly CMOS technology. The proposed oscillatory neural network consists of 3 neural oscillator cells with excitatory synapses and a neural oscillator cell with inhibitory synapse. Simulations of a network of oscillators demonstrate cooperative computation. Measurements of the fabricated chip in condition of $\pm$ 2.5 V power supply is shown.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.11B no.2
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• pp.15-21
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• 2001

This paper presents an improved characteristic analysis methodology for a 5-phase hybrid stepping motor. The basic approach is based on the use of equivalent magnetic circuit taking into account the localized saturation throughout the hybrid stepping motor. The finite element method(FEM) is used to generate the magnetic circuit parameters for the complex stator and rotor teeth and airgap considering the saturation effects in tooth and poles. In addition, the neural network is used to map a change of parameters and predicts their approximation. Therefore, the proposed method efficiently improves the accuracy of analysis by using the parameter characterizing localized saturation effects and reduces the computational time by using the neural network. An improved circuit model of 5-phase hybrid stepping motor is presented and its application is provided to demonstrate the effectiveness of the proposed method.

• Applied Microscopy
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• v.46 no.4
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• pp.171-175
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• 2016

A distinctive neuronal network in the brain is believed to make us unique individuals. Electron microscopy is a valuable tool for examining ultrastructural characteristics of neurons, synapses, and subcellular organelles. A recent technological breakthrough in volume electron microscopy allows large-scale circuit reconstruction of the nervous system with unprecedented detail. Serial-section electron microscopy-previously the domain of specialists-became automated with the advent of innovative systems such as the focused ion beam and serial block-face scanning electron microscopes and the automated tape-collecting ultramicrotome. Further advances in microscopic design and instrumentation are also available, which allow the reconstruction of unprecedentedly large volumes of brain tissue at high speed. The recent introduction of correlative light and electron microscopy will help to identify specific neural circuits associated with behavioral characteristics and revolutionize our understanding of how the brain works.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.3-16
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• 1992

We present a modified Tank and Hopfield's neural network model for solving Linear Programming problems. We have found the fact that the Tank and Hopfield's neural circuit for solving Linear Programming problems has some difficulties in guaranteeing convergence, and obtaining both the primal and dual optimum solutions from the output of the circuit. We have identified the exact conditions in which the circuit stops at an interior point of the feasible region, and therefore fails to converge. Also, proper scaling of the problem parameters is required, in order to obtain a feasible solution from the circuit. Even after one was successful in getting a primal optimum solution, the output of the circuit must be processed further to obtain a dual optimum solution. The modified model being proposed in the paper is designed to overcome such difficulties. We describe the modified model and summarize our computational experiment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.275-281
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• 2003

A new neural oscillator has been designed and fabricated in an 0.5 ${\mu}{\textrm}{m}$ double poly CMOS technology. The proposed neural oscillator consists of a nonlinear variable resistor with negative resistance as well as simple transconductors and capacitors. The variable negative resistor which is used as a input stage of the oscillator consists of a positive feedback transconductors and a bump circuit with Gaussian-like I-V curve. The proposed neural oscillator has designed in integrated circuit with SPICE simulations. Simulations of a network of 4 oscillators which are connected with excitatory and inhibitory synapses demonstrate cooperative computation. Measurements of the fabricated oscillator chip with a $\pm$ 2.5 V power supply is shown and compared with the simulated results.