Earticle

초록

영어

Aiming at the problem of extracting steady-state data automatically and rapidly from the process data which contains outliers, a rapid steady-state data extracting method fusing outliers detection based on automatic piecewise curve fitting is proposed. Firstly, the method carries out outliers detection according to local deviation and replaces it with grey theory. Then the noise is minimized through sliding mean filter and the quasi-steady-state data is extracted rapidly by involved rules. Lastly, the quasi-steady-state data is further judged by automatic piecewise curve fitting. The simulation test shows that the proposed method can not only eliminate the effect of outliers and noise, but also extract the steady-state data conforming to human’s experience quickly and efficiently.

목차

Abstract
 1. Introduction
 2. Outliers Detection and Substation
  2.1. Outliers Detection Based on the Improved Local Deviation
  2.2. Outliers Substitution Based on Grey Prediction Theory
 3. Quasi-Steady-State Data Screening and Steady-State Data Extraction
  3.1. The Quasi-Steady-State Data Screening Rules
  3.2. The Steady-State Extraction Rules
 4. Simulation Experiment
 5. Conclusion
 References

키워드

저자

• Hanming Fang [ Department of Logistics Information & Logistics Engineering, LEU, Chongqing, China ] Corresponding Author
• Aishe Shui [ Department of Logistics Information & Logistics Engineering, LEU, Chongqing, China ]
• Fuxing Zong [ Department of Logistics Information & Logistics Engineering, LEU, Chongqing, China ]
• Yuheng Wu [ Department of Logistics Information & Logistics Engineering, LEU, Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605