전기전자학회논문지 (Journal of IKEEE)

  • 제22권3호
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  • Pages.700-705
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  • 2018
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  • 1226-7244(pISSN)
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  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
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인공신경망 기반 가스 분류기의 설계

Design of Gas Classifier Based On Artificial Neural Network

  • Jeong, Woojae (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Kim, Minwoo (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Cho, Jaechan (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Jung, Yunho (School of Electronics and Information Engineering, Korea Aerospace University)
  • 투고 : 2018.09.05
  • 심사 : 2018.09.20
  • 발행 : 2018.09.30
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초록

본 논문에서는 restricted coulomb energy(RCE) 신경망 기반 가스 분류기를 제안하고, 이의 실시간 학습 및 분류를 위한 하드웨어 구현 결과를 제시한다. RCE 신경망은 네트워크 구조가 학습에 따라 유동적이며, 실시간 학습 및 분류가 가능하므로, 가스 분류 응용에 적합한 특징을 갖는다. 설계된 가스 분류기는 UCI gas dataset에 대해 99.2%의 분류 정확도를 보였으며, Intel-Altera cyclone IV FPGA 기반 구현 결과, 26,702개의 logic elements로 구현 가능함을 확인하였다. 또한, FPGA test system을 구성하여 63MHz의 동작 주파수로 실시간 검증을 수행하였다.

In this paper, we propose the gas classifier based on restricted column energy neural network (RCE-NN) and present its hardware implementation results for real-time learning and classification. Since RCE-NN has a flexible network architecture with real-time learning process, it is suitable for gas classification applications. The proposed gas classifier showed 99.2% classification accuracy for the UCI gas dataset and was implemented with 26,702 logic elements with Intel-Altera cyclone IV FPGA. In addition, it was verified with FPGA test system at an operating frequency of 63MHz.

키워드

참고문헌

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피인용 문헌

  1. 자가학습 가능한 SVM 기반 가스 분류기의 설계 vol.23, pp.4, 2018, https://doi.org/10.7471/ikeee.2019.23.4.1400
  2. 보안 감시용 레이다 시스템을 위한 면적-효율적인 특징점 추출기 설계 vol.24, pp.1, 2018, https://doi.org/10.7471/ikeee.2020.24.1.200