• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.189-192
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• 1991

This paper presents a modular structure design of analog Hopfield neural network. Each multiplier consists of four MOS transistors which are connected to an op-amp at the front end of a neuron. A pair of MOS transistor is used in order to maintain linear operation of the synapse and can produce positive or negative synaptic weight. This architecture can be expandable to any size neural network by forming tree structure. By altering the connections, other nework paradigms can also be implemented using this basic modules. The stength of this approach is the expandability and the general applicability. The layout design of a four-neuron fully connected feedback neural network is presented and is simulated using SPICE. The network shows correct retrival of distorted patterns.

• The Journal of Information Technology
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• v.4 no.4
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• pp.51-63
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• 2001

Residue Number System has carry free operation and parallelism each modulus, So it is used for special purpose processor such as Digital Signal Processing and Neuron Processor. Magnitude comparison and sign detection are in need of Mixed Radix Conversion, and these operations are impediment to improve the operation speed. So in this Paper, MRC(Mixed Radix Converter) is designed using modified partitioned residue to speed up the operation of MRC, so it has progressed maximum twice operation time but increased the size of converter comparison to other converter.

• The Journal of Information Technology
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• v.4 no.3
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• pp.155-163
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• 2001

In this paper, we implemented sigmoid active function which make it difficult to design of the digital neuron networks. Therefore, we designed of the high speed processing of the sigmoid function in order to digital neural networks. we designed of the MAC(Multiplier and Accumulator) operation unit used residue number system without carry propagation for the high speed operation. we designed of MAC operation unit and sigmoid processing unit are proved that it could run of the high speed. On the simulation, the faster than 4.6ns on the each order, we expected that it adapted to the implementation of the high speed digital neural network.

• 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 KIEE Conference
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• 2005.10b
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• pp.38-40
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• 2005

Microdrive with high precision and light mass enough to install on mouse head was fabricated for recording the reliable signal of neuron cell to understand the brain study. The proposed microdrive has three H-form PZT actuators and its guide structure. The microdrive operation principle is based on the well known inchworm principle. The synchronization of three PZT actuators is able to produce the linear motion along the guide structure. Our proposed microdrive has a precise accuracy of about 100nm and a long stroke of about 5mm. The electrode which is used for the recording of the action potential of the neuron cell was fixed at one of PZT actuators. The proposed microdrive was suited to acquisition of signals from in vivo extra-cellular single-unit recoding. On the condition of the anesthetized mouse, the single-unit signals could be recorded by using the proposed microdrive. In addition, applying the PZT microdrive to an alert mouse, we try to implant it on a mouse brain skull to explore single neuron firing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.101-104
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• 2016

In this study, we propose an algorithm to simulate the function of the coupling structure and having two neurons to find out exactly recover the temporary or permanent position errors that can occur during operation in a digital circuit was separated by function, a 3x3 array. If any particular part in the combined cells are differentiated cells have a problem that function to other cells caused an error and perform the same function are subjected to a step of apoptosis by the surrounding cells. Designed as a function block in the function and the internal structure having a cell structure of this digital circuit proposes an algorithm.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.4
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• pp.311-321
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• 2022

The report reviews recent research efforts in demonstrating a computing system whose operation principle mimics the dynamics of biological neurons. The temporal variation of the membrane potential of neurons is one of the key features that contribute to the information processing in the brain. We first summarize the neuron models that explain the experimentally observed change in the membrane potential. The function of ion channels is briefly introduced to understand such change from the molecular viewpoint. Dedicated circuits that can simulate the neuronal dynamics have been developed to reproduce the charging and discharging dynamics of neurons depending on the input ionic current from presynaptic neurons. Key elements include volatile memristors that can undergo volatile resistance switching depending on the voltage bias. This behavior called the threshold switching has been utilized to reproduce the spikes observed in the biological neurons. Various types of threshold switch have been applied in a different configuration in the hardware demonstration of neurons. Recent studies revealed that the memristor-based circuits could provide energy and space efficient options for the demonstration of neurons using the innate physical properties of materials compared to the options demonstrated with the conventional complementary metal-oxide-semiconductors (CMOS).

• Korean Journal of Veterinary Research
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• v.42 no.4
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• pp.537-543
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• 2002

The purpose of the present study was to investigate structural stability of extensive laminectomy and the effect of subcutaneous fat autograft on restricting formation of postlaminectomy membrane (fibrous tissue). Eighteen healthy dogs of both sexes and of mixed breeding were divided into 6 groups : (1) unilateral hemilaminectomy (group H) on 3rd, 4th and 7th vertebrae ; (2) modified dorsal laminectomy (group D) on 3rd, 4th and 7th vertebrae. Hemilaminectomies were carried out incontinuously at 16 sites in 4 dogs, then subcutaneous fat autografts (group F) were applied to 8 laminectomy sites and no treatment (group C) was assigned to 8 laminectomy sites, too. Operating time of group H ($30.9{\pm}10.4$ minutes) was significantly shorter (p<0.05) than that of group D ($43.1{\pm}12.2$ minutes), but surgical hemorrhage of group H is severer than that of group D. General states, such as standing, gait, defecation and urination, were normal. Upper motor neuron/lower motor neuron signs were not found and superficial/deep pain, proprioception and anal sphincter tone were normal. Gross postmortem findings were similar in all groups. The laminectomy sites of groups H and D were filled with fibous connective tissue at 4 months after operation and histopathological abnormalities of spinal cord were not found. One of eight laminectomy sites in group F was filled with fibrous tissue at 2 months after operation, but all operating sites of group C were covered with fibrous tissue. The present study indicated that extensive laminectomy on 7 vertebrae, using unilateral hemilaminectomy and modified dorsal laminectomy technique, maintained structural stability and subcutaneous fat autograft was effective on reducing the formation of fibrous membrane in laminectomy sites.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.50-55
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• 2011

This research is to apply the adaptive control of neuron networks for the real-time attitude control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of attitude control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. In this research we used the response property curve of the robot instead of the activation function of neural network algorithms, so the adaptive control system of neural networks constructed without the information of modeling can perform a real-time control. The proposed adaptive control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.10
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• pp.89-94
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• 2012

This research is to apply the control of neuron networks for the real-time walking control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of walking control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. This paper proposes a new mode to implement a neural network controller by installing a real object for controlling and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The proposed control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.