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ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling
- Source :
- Advances in Civil Engineering, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- The process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect different flow patterns after two-dimensional section reconstruction in closed filling pipeline based on ECT (electrical capacitance tomography) visualization method. Slurry flow in pipeline is regarded as a two-phase flow, and the multishape distribution was reconstructed into images by ECT and intelligently recognized by broad learning system (BLS) algorithm. BLS is a feedforward neural network with few optimization parameters and fast training speed. In this paper, three features of two-phase sample images, the number of regional blocks, the roundness of regional blocks, and barycenter of regional blocks, are combined with network structure of BLS to recognize different flow patterns. Through the simulation, the recognition accuracy of two-phase fillback image is more than 99%. This conclusion indicates the effectiveness of BLS to predict different two-phase flow patterns; it also provides a new solution for the pattern recognition of the flow pattern in the mining filling pipeline.
- Subjects :
- Article Subject
business.industry
Computer science
Pipeline (computing)
020208 electrical & electronic engineering
Flow (psychology)
Process (computing)
Pattern recognition
02 engineering and technology
Electrical capacitance tomography
Engineering (General). Civil engineering (General)
Roundness (object)
Visualization
Pattern recognition (psychology)
0202 electrical engineering, electronic engineering, information engineering
Feedforward neural network
020201 artificial intelligence & image processing
Artificial intelligence
TA1-2040
business
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 16878094 and 16878086
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Advances in Civil Engineering
- Accession number :
- edsair.doi.dedup.....e783c7741de073d07a883ec0c2db9cd4