Earticle

초록

영어

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1 Time series analysis
  2.2 Confidence interval
  2.3 Research for anomaly detection
 3. Efficient Anomaly Detection Algorithm
  3.1 Anomaly detection algorithm
  3.2 Confidence interval
 4. Performance Evaluation
  4.1 Sensor data cleaning
  4.2 Estimation by the moving average method
  4.3 Estimation by the exponential smoothing method
  4.4 Result of time series analysis
  4.5 Implementation
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Yeong-Ju Kim [ Department of Computer Engineering, Mokpo National University, Muan-gun, Korea ]
• Min-A Jeong [ Department of Computer Engineering, Mokpo National University, Muan-gun, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380