Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Design of Gas Classifier Based On Artificial Neural Network

인공신경망 기반 가스 분류기의 설계

  • 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)
  • Received : 2018.09.05
  • Accepted : 2018.09.20
  • Published : 2018.09.30
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Abstract

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.

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.

Keywords

References

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