• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.432-435
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• 1990

Neural networks are a class of systems that have many simple processors (neurons) which are highly interconnected. The function of each neuron is simple, and the behavior is determined predominately by the set of interconnections. Thus, a neural network is a special form of parallel computer. Although major impetus for using neural networks is that they may be able to "learn" the solution to the problem that they are to solve, we argue that another, perhaps even stronger, impetus is that they provide a framework for designing massively parallel machines. The highly interconnected architecture of switching networks suggests similarities to neural networks. Here, we present switching applications in which neural networks can solve the problems efficiently. We also show that a computational advantage can be gained by using nonuniform time delays in the network.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2219-2226
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• 2017

This paper deals with artificial neural network approach for automatic detection of severity level of stator winding fault in induction motor. The problem is faced through modelling and simulation of induction motor with inter coil shorting in stator winding. The sum of the absolute values of difference in the peak values of phase currents from each half cycle has been chosen as the main input to the classifier. Sample values from workspace of Simulink model, which are verified with experiment setup practically, have been imported to neural network architecture. Consideration of a single input extracted from time domain simplifies and advances the fault detection technique. The output of the feed forward back propagation neural network classifies the short circuit fault level of the stator winding.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.812-814
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• 1999

In this paper, we propose a new control algorithm based on the neural network(NN) feedback compensation with a desired trajectory modification. The proposed algorithm decreases trajectory errors by a feed-forward desired torque combined with a neural network feedback torque component. And, to robustly control the tracking error, we modified the desired trajectory by variable structure concept smoothed by a fuzzy logic. For the numerical simulation, a 2-link robot manipulator model was assumed. To simulate the disturbance due to the modelling uncertainty. As a result of this simulation, the proposed method shows better trajectory tracking performance compared with the CTM and decreases the chattering in control inputs.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.51-55
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• 1990

In this study, a peripheral auditory model used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean placenames and syllables. As a result of this study, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation. The recurrent network in this study reflects well time information of temporal speech signal.

• Journal of the Korean Mathematical Society
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• v.47 no.6
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• pp.1223-1238
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• 2010

The purpose of this work is to identify the internal structure of the electrical networks with data obtained from only a part of network or the boundary of network. To be precise, it is discussed whether we can identify resistors or electrical conductivities of each link inside networks by the measurement of voltage on the boundary which is induced by a prescribed current on the boundary. As a result, it is shown that the structure of the resistor network can be determined uniquely by only one pair of the data (current, voltage) on the boundary, if the resistors satisfy an appropriate condition. Besides, several useful results about the energy functionals, which means the electrical power, are included.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.815-817
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• 2000

근래 Web Server의 안정된 운용의 중요성이 급증하는 Network Traffic과 더불어 점차 증대되고 있다. 기본적인 Network 구성망은 Ethernet으로 표준적인 프로토콜은 TCP/IP로 기반을 잡고 있다. 날로 증가하는 Network Traffic을 효과적으로 처리하기 위해서는 Network의 bandwidth 및 서버가 처리할 수 있는 데이터 양을 분석하여 병목현상을 줄이고 데이터의 흐름을 원활히 하기 위한 데이터 buffer들의 설치와 적절한 운영 방법에 관한 연구가 필요하다. 본 논문에서는 Web Server로부터 image 데이터를 전송 받아 이를 원활히 display하기 위한 Application Layer에서의 buffer 크기 및 제어방법에 관한 연구를 수행하고 특히 연속적인 이미지의 전송과 재생에 관한 buffer management에 관하여 고찰한다.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.75-78
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• 1995

In this paper it is concerned to develop control method using jar-test results in order to predict the optimum dosage of coaglant, PAC(PoliAluminum Chloride). Considering the relations with the reactions with the reaction of coagulation and flocculation, the five independent variables ( e, g, turbidity of raw water, water turbidity in flocculators, temperature, pH, and alkalynity) are selected out of parameters and they are put into calculation to develop a neural network model for PAC dosing process in water purification system. This model is utilized to predict optimum dosage of PAC. That is, the optimum dosage of PAC is searched in neural network model for PAC dosing process to minimize the water turbidity in flocculators. This searching is implemented by means of expert heuristics. The efficacy of the proposed contorl schemem and feasibility of acquired neural network model for PAC dosing contorl in water purification system is evaluated by means of computer simulation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.05a
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• pp.202-205
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• 1996

In this paper it is researched a relation between network structures and electrical properties - especially dielectric characteristics with changing of network structure. It is resulted that the specimens which have single network structures have smaller dielectric loss than SIN specimens but have relatively larger dependency to variation of temperature and frequency. For that reason formation of structures is attained by introducing of SIN to insulating materials. therefore it is counted that introduction of multiple structure including SIN is necessary to improve heat proof and electrical properties.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.47-50
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• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.