• Title/Summary/Keyword: neural network.

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A SoC Based on a Neural Network for Embedded Smart Applications (임베디드 스마트 응용을 위한 신경망기반 SoC)

  • Lee, Bong-Kyu
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.10
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    • pp.2059-2063
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    • 2009
  • This paper presents a programmable System-On-a-chip (SoC) for various embedded smart applications that need Neural Network computations. The system is fully implemented into a prototyping platform based on Field Programmable Gate Array (FPGA). The SoC consists of an embedded processor core and a reconfigurable hardware accelerator for neural computations. The performance of the SoC is evaluated using a real image processing application, an optical character recognition (OCR) system.

Realtime Hardware Neural Networks using Interpolation Techniques of Information Data (정보데이터의 복원기법 응용한 실시간 하드웨어 신경망)

  • Kim, Jong-Man;Kim, Won-Sop
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2007.11a
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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.

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Artificial Neural Network: Understanding the Basic Concepts without Mathematics

  • Han, Su-Hyun;Kim, Ko Woon;Kim, SangYun;Youn, Young Chul
    • Dementia and Neurocognitive Disorders
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    • v.17 no.3
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    • pp.83-89
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    • 2018
  • Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.

Neural Networks which Approximate One-to-Many Mapping

  • Lee, Choon-Young;Lee, Ju-Jang
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.41.5-41
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    • 2001
  • A novel method is introduced for determining the weights of a regularization network which approximates one-to-many mapping. A conventional neural network will converges to the average value when outputs are multiple for one input. The capability of proposed network is demonstrated by an example of learning inverse mapping.

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A Study on Automatic Learning of Weight Decay Neural Network (가중치감소 신경망의 자동학습에 관한 연구)

  • Hwang, Chang-Ha;Na, Eun-Young;Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.12 no.2
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    • pp.1-10
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    • 2001
  • Neural networks we increasingly being seen as an addition to the statistics toolkit which should be considered alongside both classical and modern statistical methods. Neural networks are usually useful for classification and function estimation. In this paper we concentrate on function estimation using neural networks with weight decay factor The use of weight decay seems both to help the optimization process and to avoid overfitting. In this type of neural networks, the problem to decide the number of hidden nodes, weight decay parameter and iteration number of learning is very important. It is called the optimization of weight decay neural networks. In this paper we propose a automatic optimization based on genetic algorithms. Moreover, we compare the weight decay neural network automatically learned according to automatic optimization with ordinary neural network, projection pursuit regression and support vector machines.

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The application of convolutional neural networks for automatic detection of underwater object in side scan sonar images (사이드 스캔 소나 영상에서 수중물체 자동 탐지를 위한 컨볼루션 신경망 기법 적용)

  • Kim, Jungmoon;Choi, Jee Woong;Kwon, Hyuckjong;Oh, Raegeun;Son, Su-Uk
    • The Journal of the Acoustical Society of Korea
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    • v.37 no.2
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    • pp.118-128
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    • 2018
  • In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.