• 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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.1015-1018
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• 1999

This paper describes a vector quantization for speech signal coding using neural networks. We processed speech signal using LPC method that extracts speech signal feature, and speech signal feature is quantized using competitive neural network kohonen self-organization feature map.

• 대한전기학회논문지
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• 제40권4호
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• pp.374-381
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• 1991

As a diagnostic image of hospitl, the utilization of digital image is steadily increasing. Image coding is indispensable for storing and compressing an enormous amount of diagnostic images economically and effectively. In this paper adaptive two stage vector quantization based on Kohonen's neural network for the compression of cardioangiography among typical angiography of radiographic image sequences is presented and the performance of the coding scheme is compare and gone over. In an attempt to exploit the known characteristics of changes in cardioangiography, relatively large blocks of image are quantized in the first stage and in the next stage the bloks subdivided by the threshold of quantization error are vector quantized employing the neural network of frequency sensitive competitive learning. The scheme is employed because the change produced in cardioangiography is due to such two types of motion as a heart itself and body motion, and a contrast dye material injected. Computer simulation shows that the good reproduction of images can be obtained at a bit rate of 0.78 bits/pixel.

• 한국산업정보학회논문지
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• 제7권3호
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• pp.62-67
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• 2002

본 논문에서는 기존의 결정론적 볼츠만 머신의 학습알고리즘을 수정하여 양자화결합을 갖는 결정론적 볼츠만 머신에도 적용할 수 있는 알고리즘을 제안하였다. 제안한 알고리즘을 2-입력 XOR 문제와 3-입력 패리티 문제에 적용하여 성능을 분석하였다. 그 결과 하중이 대폭적으로 양자화된 네트워크에 대해서도 학습이 가능하다는 것과 은닉층 뉴런의 수를 증가시키면 한정된 하중값의 범위로 유지할 수 있는 것을 보여준다. 또한 1회에 갱신하는 하중의 갯수를 제어함으로써 학습계수를 제어하는 효과가 얻어지는 것을 확인하였다.

• 전기전자학회논문지
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• 제22권4호
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• pp.1115-1122
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• 2018

본 논문에서는 기존 1-Bit 합성곱 신경망의 성능 하락에 대한 분석과 이를 완화하기 위한 방안을 제시한다. 기존의 연구는 첫 번째 층과 마지막 층만 32-Bit 연산을 적용하고 나머지 연산은 1-Bit 연산을 적용한 것과 달리 본 논문에서는 두 번째 층도 32-Bit로 연산한다. 또한 입력과 가중치를 이진화하고 1-Bit 연산을 적용한 후에는 비선형 활성화 함수를 제거할 수 있음을 제시한다. 본 논문에서 제시한 방법을 검증하기 위해 차량 번호판 검출을 위한 객체 검출 신경망을 실험하였다. 기존의 방법으로 학습한 결과보다 정확도가 74%에서 96.1%로 상승하였다.

• 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 2003년도 춘계학술대회 논문집 기술교육전문연구회
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• pp.90-95
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• 2003

For the safety driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles 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 vehicles, 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 image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• 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.

• 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.