• 전기학회논문지P
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• 제58권4호
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• pp.511-515
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• 2009

For detecting of the faulty insulator, Correlation Propagation Neural Networks(CPNN) has been proposed. Faulty insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to detect the faulty insulator and exchange the new one. And thus, we have designed the CPNN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. 1-D CPNN hardware has been implemented with general purpose. Experiments with static and dynamic signals have been done upon the CPNN hardware. Through the results of simulation experiments, we define the ability of real-time detecting the faulty insulators.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 추계학술대회 논문집 Vol.14 No.1
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• pp.46-51
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• 2001

For Fast Real-time Recognition of Nonlinear Error Data, a new Neural Network algorithm which recognized the map in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion, In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of map, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear map information is processed,

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 추계학술대회 논문집
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• pp.46-51
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• 2001

For Fast Real-time Recognition of Nonlinear Error Data, a new Neural Network algorithm which recognized the map in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of map, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear map information is processed.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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• pp.506-507
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• 2007

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 하계학술대회 논문집 Vol.3 No.2
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• pp.1097-1102
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• 2002

For detecting of Faulty Insulator, a new Lateral Information Propagation Networks (LIPN) has been proposed. Energized insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the faulty insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments,real time reconstruction of the nonlinear image information is processed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2546-2549
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• 2004

For Reduction of Power-Loss, a new Lateral Information Propagation Networks(LIPN) has been proposed. Damaged insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the damaged insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interrelation is achieved. Through the results of simulation experiments, we difine the ability of real-time detecting the damaged insulators.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2276-2279
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• 2002

For detecting of Energized Insulator, a new Lateral Information Propagation Networks(LIPN) has been proposed. Faulty insulator is reduced the rate of insolation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the faulty insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through the results of simulation experiments, we define the ability of real-time detecting the faulty insulators.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 춘계학술대회 논문집
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• pp.25-29
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• 2004

For detecting of Energized Insulator, a new Lateral Information Propagation Networks(LIPN) has been proposed. Faulty insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the faulty insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. 1n the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through the results of simulation experiments, we difine the ability of real-time detecting the faulty insulators.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 학술대회 논문집 전문대학교육위원
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• pp.83-87
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• 2007

Correlation Propagation Neural Networks is proposed for On-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D CPNN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the CPNN hardware.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.461-464
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• 1998

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the LIPN hardware.