DOI QR코드

DOI QR Code

Study On the Design of Risk Management Web-Monitoring System using AANN

AANN을 이용한 웹-모니터링 시스템 설계에 관한 연구

  • 김동회 ((주)지피에스코리아 연구소) ;
  • 이영삼 (군산대학교 전자정보공학) ;
  • 김성호 (군산대학교 전자정보공학부)
  • Published : 2004.06.01

Abstract

Recent natural disasters like flooding and slope collapse have shown the need for natural risk management system, as they endanger directly public health and cause severe damages on the national economy. In order to improve the efficiency of risk management systems, this management system based on AANN(Auto-Associative Neural Network)is proposed in this paper. AANN can be effectively used for identification of abnormal data and data compression. The proposed AANN-based risk management system collects and stores measurement data from sensors and transmits them to remote server for web-monitoring. Generally, it is desirable to transmit the compressed data instead of raw data in normal state. However, if dangerous situation happens, rapid tramission of measurement data should be required. These requirements are easily satisfied by using AANN. In order to verify the feasibilities of the proposed system, The AANN-based risk management system is applied to slope collapse monitoring system.

Keywords

References

  1. R. Dunia, 'Identification of faulty sensors using principle component analysis', AIChE J., 42(10), pp. 2797-2812 https://doi.org/10.1002/aic.690421011
  2. M. A. Kramer, 'Autoassociative neural networks,' Computers in Chemical Eng., vol.16, no.4, pp. 313-328, 1992 https://doi.org/10.1016/0098-1354(92)80051-A
  3. J. W. Hines, D. J. Wrest, and R. E. Uhring, 'Plant Wide Sensor Calibration Monitoring', published in the proceedings of The 1996 IEEE International Symposium on Intelligent Control, Sept. 15-18, pp. 378-383, 1996
  4. R. Isermann, 'Process Fault Detection Base on Modelling and Estimation Methodes-A survey', Automatica, vol. 20, no. 4, pp. 387-404, 1984 https://doi.org/10.1016/0005-1098(84)90098-0
  5. K. S. Lee, S. H. Kim and N. Sakawa, 'On-Line Fault Diagnosis by using Fuzzy Cognitive Map,' IEICE Trans. on Fundamentals, vol. E-79-A, no. 6, 1996
  6. J. Shiozaki, and H. Matsuyama, 'An Improved Algorithm for diagnosis of System Failure in Chemical Processes', Comput. Chem. ENG vol. 9, 1985
  7. C. C. Yu and C. Lee, 'Fault diagnosis based on Quailtative/Quantitative Process Knowledge', AICHE Journal, vol. 37, no.4, pp. 617-628, 1991 https://doi.org/10.1002/aic.690370415
  8. 이쌍윤, 김성호, 외 2인, '확장된 퍼지 인식맵을 이용한 고장진단 시스템의 설계', 한국자동제어학술회의, pp. 860-863, 1997
  9. 박순탁, 임준홍, '네트워크 시간지연이 존재하는 분산 제어시스템의 최적 제어기 설계' KACC, vol. 13, pp. 1224-1227, October 1998

Cited by

  1. Implementation of remote monitoring system for prediction of tool wear and failure using ART2 vol.18, pp.1, 2011, https://doi.org/10.1007/s11771-011-0677-7