Investigations for generalization capability of the pattern recognition based leak localization method

1997 ◽  
Vol 30 (5) ◽  
pp. 321
Keyword(s):  
Pattern Recognition ◽  
Generalization Capability ◽  
Localization Method ◽  
Leak Localization

A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

2013 ◽  
Vol 2 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Onsy A. Abdel Alim ◽  
Amin Shoukry ◽  
Neamat A. Elboughdadly ◽  
Gehan Abouelseoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Object Recognition ◽  
Probabilistic Neural Network ◽  
Training Procedure ◽  
Generalization Capability ◽  
Mine Detection ◽  
Recognition Of Objects

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.

Pipeline Leak Localization Using Pattern Recognition and a Bayes Detector

2006 ◽  
Author(s):  
Marti´n Di Blasi ◽  
Renan Martins Baptista ◽  
Carlos Muravchik
Keyword(s):  
Pattern Recognition ◽  
Real Data ◽  
Estimation Algorithm ◽  
Normal Operation ◽  
Successful Implementation ◽  
Bayesian Decision ◽  
Statistical Decision ◽  
Oil Pipeline ◽  
Gravitational Flow ◽  
Leak Localization

A novel leak localization method for multi section pipelines is presented. Based on normal operation flowing thermodynamic pressure drop patterns along the pipeline, the system continuously compares with the measured pressure drops, and makes a decision based on the best fit finding the section where the leak occurs. A statistical approach is used accounting for noisy measured signals. The method uses steady state fluid equations, a recursive parameter estimation algorithm, and statistical decision and pattern recognition techniques. A modification is introduced to consider the cost of making a wrong leaky section choice in terms of the excess volume spilled due to gravitational flow after pipeline shut down. This leads to a Bayesian decision scheme minimizing a risk functional. The costs are the spill volumes, obtained from dynamical simulation of the pipeline, under the various possible decision scenarios. Finally, details are given of the successful implementation of the system on a 500km long oil pipeline, and real data from a simulated leak experiment are shown.

Acoustic leak localization method based on signal segmentation and statistical analysis

2021 ◽  
Author(s):  
Georgios-Panagiotis Kousiopoulos ◽  
Nikolaos Karagiorgos ◽  
Dimitrios Kampelopoulos ◽  
Vasileios Konstantakos ◽  
Spyridon Nikolaidis
Keyword(s):  
Statistical Analysis ◽  
Localization Method ◽  
Signal Segmentation ◽  
Leak Localization

A dead reckoning localization method for in-pipe detector of water supply pipeline: An application to leak localization

Measurement ◽  
2021 ◽  
Vol 171 ◽  
pp. 108835
Author(s):  
Wenming Wang ◽  
Dashan Yang ◽  
Jifeng Zhang ◽  
Liyun Lao ◽  
Yuanfang Yin ◽  
...  
Keyword(s):  
Water Supply ◽  
Dead Reckoning ◽  
Localization Method ◽  
Leak Localization ◽  
Water Supply Pipeline

The Intelligent Analysis Method of Mine Equipment Lubrication and Wear Condition

2012 ◽  
Vol 184-185 ◽  
pp. 553-556
Author(s):  
Wu Xin Huang
Keyword(s):  
Pattern Recognition ◽  
Bp Algorithm ◽  
Analysis Method ◽  
Generalization Capability ◽  
Intelligent Analysis ◽  
Lubrication Condition ◽  
Improved Bp Algorithm ◽  
Forecasting Theory ◽  
Wear Condition ◽  
Condition Recognition

Aimed at the actuality of the Mine-Concentrator, Dexing Copper-mine, the paper comprehensively apply such technology as the statistics analysis, the forecasting theory, the pattern recognition, the neutral networks and the expert system etc, to study the intelligent method of wear condition and lubrication condition. An improved BP algorithm was presented, based on this an automate wear condition recognition model and its system was designed. The example shows that this system can promote the accuracy of wear condition recognition and improve the generalization capability. After all the problems were solved properly, wear and lubrication condition of the equipment were guaranteed as well. Therefore, the monitored equipment was running very well that year.

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