• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.27-38
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• 1999

This paper proposes a high speed digital neuron processor which uses the residue number system, making the high speed operation possible without carry propagation,. Consisting of the MAC(Multiplier and with Accumulator) operation unit, quotient operation unit and sigmoid function operation unit, the neuron processor is designed through 0.8$\mu$m CMOS fabrication. The result shows that the new implemented neuron processor can run at the speed of 19.2 nSec and the size can be reduced to 1/2 compared to the neuron processor implemented by the real number operation unit.

• 전자공학회논문지 IE
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• v.44 no.3
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• pp.12-22
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• 2007

In this paper, we designed of the high speed digital neuron processor 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. and we implemented sigmoid active function which make it difficult to design neuron processor. The Designed circuits are descripted by VHDL and synthesized by Compass tools. we designed of MAC operation unit and sigmoid processing unit are proved that it could run time 19.6 nsec on the simulation and decreased to hardware size about 50%, each order. Designed digital neuron processor can be implementation in parallel distributed processing system with desired real time processing, In this paper.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.546-549
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• 2006

In this paper the neuron operation based on Brillouin-active fiber in optical fiber is described. The inherent optical feedback by the backscattered stokes wave in optical fiber leads to instabilities in the form of optical chaos. Controlling of chaos induced transient instability in Brillouin-active fiber is implemented with Kerr nonlinearity having a non-instantaneous response in network systems. The controlling chaotic instabilities can lead to multistable periodic states; create optical logic 'on' or high level '1' or 'off', or low level '0'. It is theoretically possible to apply the multi-stability regimes as an optical memory device for encoding and decoding series and complex data transmission in optical systems.

• Journal of information and communication convergence engineering
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• v.1 no.3
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• pp.104-108
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• 2003

A biologically inspired fuzzy multilayer perceptron is proposed in this paper. The proposed algorithm is established under consideration of biological neuronal structure as well as fuzzy logic operation. We applied this suggested learning algorithm to benchmark problem in neural network such as exclusive OR and 3-bit parity, and to digit image recognition problems. For the comparison between the existing and proposed neural networks, the convergence speed is measured. The result of our simulation indicates that the convergence speed of the proposed learning algorithm is much faster than that of conventional backpropagation algorithm. Furthermore, in the image recognition task, the recognition rate of our learning algorithm is higher than of conventional backpropagation algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1998.06a
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• pp.55-58
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• 1998

Recently, neural networks with self-adaptability like human brain have attracted much attention. It is desirable for the neuron-function to be implemented by exclusive hardware system on account of huge quantity in calculation. We have proposed a novel neuro-device composed of a MFSFET(ferroelectric gate FET) and oscillation circuit with CUJT(complimentary unijuction transistor) and PUT(programmable unijuction transistor). However, it is difficult to preserve ferroelectricity on Si due to existence of interfacial traps and/or interdiffusion of the constitutent elements, although there are a few reports on good MFS devices. In this paper, we have simulated CUJT and PUT devices instead of fabricating them and composed oscillation circuit. Finally, we have resented, as an approach to the MFSFET neuron circuit, adaptive learning function and characterized the elementary operation properties of the pulse oscillation circuit.

• The Journal of Information Technology
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• v.6 no.4
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• pp.11-23
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• 2003

We proposes that Design of the Digital Neuron Processor and Development of the Algorithm for the real time object recognition in the making Automatic system which uses the residue number system making the high speed operation possible without carry propagation, in this paper. Consisting of MAC(Multiplication and Accumulation) operator unit using Residue number system and sigmoid function operator unit using Mixed Residue Conversion is designed. The Designed circuits are descripted by C language and VHDL and synthesized by Compass tools. Finally, the designed processor is fabricated in 0.8${\mu}m$ CMOS process. Result of simulations shows that critical path delay time is about 19nsec and operation speed is 0.6nsec and the size can be reduced to 1/2 times co pared to the neural networks implemented by the real number operation unit. The proposed design the digital neuron processor can be implemented of the object recognition in the making Automatic system with desired real time processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.457-461
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• 2014

Using $0.18{\mu}m$ CMOS process silicon neuron circuit of the pulse type for modeling biological neurons, were designed in the semiconductor integrated circuit. Neuron circuiSt providing is formed by MOS switch for initializing the input terminal of the capacitor to the input current signal, a pulse signal and an amplifier stage for generating an output voltage signal. Synapse circuit that can convert the current signal output of the input voltage signal, using a bump circuit consisting of NMOS transistors and PMOS few. Configure a chain of neurons for verification of the neuron model that provides synaptic neurons and two are connected in series, were performed SPICE simulation. Result of simulation, it was confirmed the normal operation of the synaptic transmission characteristics of the signal generation of nerve cells.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.204-213
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• 1997

In this paper, an application of fuzzy-neuron reasoning to the control of an activated sludge plant is presented. The activated sludge process is widely used in modern wastewater treatment plants. The operation control of the activated sludge process, however, is difficult due to the following reasons : 1)The complexity of the wastewater components, 2)the change of the wastewater influent, and 3)the adjustment errors in the control process. Because of these reasons, it is difficult to obtain mathematical model that really reflect the relationship between the variables and parameters in the process of wastewater treatment correctively and effectively. In this paper, the activated sludge process(A.S.P.) is modeled by a new fuzzy-neuron network representing nonlinear characteristics. These fuzzy-neurons have fuzzy rules with complementary membership function. Based on the constructed model, graphic simulator on X-window system as a graphic integrated environment is implemented. The efficacy of the proposed control scheme was evaluated and demonstrated by means of the field test.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.37-50
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• 2007

In this paper, we designed and implementation of the high speed neuron processor for real time object recognition in the making automatic system. and we designed of the PE(Processing Element) used residue number system without carry propagation for the high speed operation. Consisting of MAC(Multiplication and Accumulation) operator using residue number system and sigmoid function operator unit using MAC(Mixed Radix conversion) is designed. The designed circuits are descript by C language and VHDL(Very High Speed Integrated Circuit Hardware Description Language) and synthesized by compass tools and finally, the designed processor is fabricated in $0.8{\mu}m$ CMOS process. we designed of MAC operation unit and sigmoid proceeding unit are proved that it could run time 0.6nsec on the simulation and improved to the speed of the three times and decreased to hardware size about 50%, each order. The designed neuron processor can be implemented of the object recognition in making automatic system with desired real time processing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1654-1660
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• 2004

In this paper, we describes the design and implementation of LonWorks communication module for distributed intelligent control using LonWorks technology of Echelon. LonWorks communication module can be divided hardware and firmware. First, hardwares is divided into microcontroller attaching sensors and LonWorks components for working together control network and data network. Hardwares are consisted of neuron chip, microcontroller, transceiver, LONCard. Second, operating firmware is realized with neuron C using NodeBulider 3.0 development tool. Produced and implemented LonWorks communication module is pretested using LTM-10A, Gizmo 4 I/O board, parallel I/O Interface. For field test, microcontroller module part is tested by HyperTerminal, communication procedure in data network is certified by transmitting and receiving short message using LonMaker for Windows tool. Herewith, LON technology is based on network communication technique using LonWorks.