원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.6 No.6
2013.12
pp.129-140
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
For natural gas pipeline, it has a leak or not is critical. The most commonly problems in the pipeline leak detection methods are the difficulties to identify, inaccuracy to locate, thus, the natural gas pipeline detection is difficult to be applied, therefore, the use of neural network multi-sensor data fusion of the natural gas pipeline leak detection is particularly important. In this paper, the method is proposed based on RBF neural network and the data fusion of D-S evidence theory for detecting the pipeline leak. Extracting neural network's input parameters through wavelet denoising, then substitute them into neural network and calculate them by multi-sensor data fusion algorithm so as to acquire leaking information.
목차
Abstract
1. Introduction
2. Signal Preprocessing based on Wavelet Transform
3. Leakage Signal Identification based on RBF Neural Network
3.1. Artificial Neural Network
3.2. Radial-Basis Function Network
3.3. The Signal Identifying Type based on RBF Neural Network
4. Leak Detection based on multi-sensor data fusion
4.1. The Combination Rule of Evidence Theory
4.2. The Recursive Target Identification Fusion of Incompatible Data Structure
4.3 Experimental Simulation
5. Conclusion
References
1. Introduction
2. Signal Preprocessing based on Wavelet Transform
3. Leakage Signal Identification based on RBF Neural Network
3.1. Artificial Neural Network
3.2. Radial-Basis Function Network
3.3. The Signal Identifying Type based on RBF Neural Network
4. Leak Detection based on multi-sensor data fusion
4.1. The Combination Rule of Evidence Theory
4.2. The Recursive Target Identification Fusion of Incompatible Data Structure
4.3 Experimental Simulation
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보