Earticle

초록

영어

In order to solve the data missing problem caused by sensor faults during the waste gas monitoring in animal building, a method for missing data recovery was presented based on support vector machine (SVM) combined with genetic algorithm (GA). Multiple factors that influence monitoring values of the waste gas in animal building such as temporal, spatial and environmental, were considered to established a SVM regression prediction model to estimate the missing data of the waste gas monitoring. Meanwhile, to obtain better prediction accuracy, model parameters were optimized by the GA. The data processing of the ammonia (NH3) concentration was taken as an example; monitoring data of 3 days were randomly selected in a farm to test the presented model in this paper. It is shown that there was a very little error between the estimated data and the monitoring data, the maximal relative error was 6.99 % (percent), and the average relative error was 2.15 % (percent). It is an effective method for missing data recovery and a practical way of data processing for waste gas monitoring in animal building.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Theoretical Basis
  2.2. Input-output Parameters of SVM
  2.3. Selection of SVM Kernel Function
  2.4. Optimization of SVM Parameters based on GA
  2.5. Prediction of Missing Data by SVM
 3. Results and Analysis
  3.1. Data Sources
  3.2. Setting of Related Parameters
  3.3. Analysis of Simulation Results
 4. Discussion
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Jinming Liu [ College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang 163319, China ]
• Qiuju Xie [ College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang 163319, China ]
• Yuanyuan Zhang [ College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing Heilongjiang 163319, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605