한국기계가공학회지 (Journal of the Korean Society of Manufacturing Process Engineers)

  • 제17권2호
  • /
  • Pages.1-7
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  • 2018
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  • 1598-6721(pISSN)
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  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
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오픈소스 기반 빅데이터 플랫폼의 에너지 하베스터 최적설계 적용 연구

Application of Open Source, Big Data Platform to Optimal Energy Harvester Design

  • 유은섭 (경북대학교 정밀기계공학과) ;
  • 김석찬 (한국기계연구원 기계시스템안전연구본부) ;
  • 이한민 (한국기계연구원 기계시스템안전연구본부) ;
  • 문두환 (경북대학교 정밀기계공학과)
  • Yu, Eun-seop (Department of Precision Mechanical Engineering, Kyungpook National University) ;
  • Kim, Seok-Chan (Mechanical Systems Safety Research Division, Korea Institute of Machinery & Materials) ;
  • Lee, Hanmin (Mechanical Systems Safety Research Division, Korea Institute of Machinery & Materials) ;
  • Mun, Duhwan (Department of Precision Mechanical Engineering, Kyungpook National University)
  • 투고 : 2018.02.19
  • 심사 : 2018.03.04
  • 발행 : 2018.04.30
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초록

Recently, as interest in the internet of things has increased, a vibration energy harvester has attracted attention as a power supply method for a wireless sensor. The vibration energy harvester can be divided into piezoelectric types, electromagnetic type and electrostatic type, according to the energy conversion type. The electromagnetic vibration energy harvester has advantages, in terms of output density and design flexibility, compared to other methods. The efficiency of an electromagnetic vibration energy harvester is determined by the shape, size, and spacing of coils and magnets. Generating all the experimental cases is expensive, in terms of time and money. This study proposes a method to perform design optimization of an electromagnetic vibration energy harvester using an open source, big data platform.

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참고문헌

  1. Laney, D., "3D Data Management: Controlling Data Volume, Velocity and Variety", Gartner, 2001.
  2. Beyer, M., "Gartner Says Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data", Gartner, 2012.
  3. Volvo and Teradata, "A Car Company Powered by Data", Teradata, 2012.
  4. Kerber, "Demystifying Big Data", Tech America Foundation, 2012.
  5. Hessman, "Putting Big Data to Work", Industryweek, pp. 14-18, 2013.
  6. Oh, "Prediction of Machining Performance using ANN and Training using ACO", Journal of the Korean Society of Manufacturing Process Engineers, Vol. 16, No. 6, pp. 125-132, 2017.
  7. Punuhsingon, C. S., & Oh, S. C., "Prediction of Surface Roughness and Electric Current Consumption in Turning Operation using Neural Network with Back Propagation and Particle Swarm Optimization", Journal of the Korean Society of Manufacturing Process Engineers Vol. 14, No. 3, pp. 65-73, 2015. https://doi.org/10.14775/ksmpe.2015.14.3.065
  8. Jeong, Y. H., "Tool Breakage Detection Using Feed Motor Current" ,Journal of the Korean Society of Manufacturing Process Engineers Vol. 14, No. 6, pp. 1-6, 2015. https://doi.org/10.14775/ksmpe.2015.14.6.001
  9. Kim S. C., Kim Y. C, Seo J. H and Lee H. M, "Design Optimization of Electromagnetic Vibration Energy Harvesters Considering Aspect Ratio" Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 27, No. 3, pp. 360-371, 2017. https://doi.org/10.5050/KSNVE.2017.27.3.360
  10. nnet, https://cran.r-project.org/web/packages/nnet/index.html, 2018.
  11. visreg, https://cran.r-project.org/web/packages/visreg/index.html, 2018.
  12. Breheny P. and Burchett W., "Visualization of regression models using visreg", R package, 1-15. 2013.