Characterization of indoor radio propagation environments by cluster analysis as preprocessing for neural network model

2002 ◽  
Author(s):  
Qin Zhou ◽  
A.K.Y. Lai
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Radio Propagation ◽  
Indoor Radio ◽  
Indoor Radio Propagation

Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model

Measurement ◽  
2021 ◽  
pp. 110654
Author(s):  
Jiaxing Xin ◽  
Jinzhong Chen ◽  
Chunyu Li ◽  
Runkun Lu ◽  
Xiaolong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Oil And Gas ◽  
Gas Pipeline ◽  
Bp Neural Network Model

Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model

2018 ◽  
Vol 13 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Won-Mo Jung ◽  
In-Soo Park ◽  
Ye-Seul Lee ◽  
Chang-Eop Kim ◽  
Hyangsook Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural ◽  
Selection Of

Using deep learning to identify the severity of pipeline dents

2020 ◽  
Vol 4 (2) ◽  
pp. 90-96
Author(s):  
Ishita Charkraborty ◽  
◽  
Brent Vyvial ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Data Set ◽  
Machine Learning Model ◽  
Unseen Data ◽  
Risk Categories

With the advent of machine learning, data-based models can be used to increase efficiency and reduce cost for the characterization of various anomalies in pipelines. In this work, artificial intelligence is used to classify pipeline dents directly from the in-line inspection (ILI) data according to their risk categories. A deep neural network model is built with available ILI data, and the resulting machine learning model requires only the ILI data as an input to classify dents in different risk categories. Using a machine learning based model eliminates the need for conducting detailed engineering analysis to determine the effects of dents on the integrity of the pipeline. Concepts from computer vision are used to build the deep neural network using the available data. The deep neural network model is then trained on a sub set of the available ILI data and the model is tested for accuracy on a previously unseen set of the available data. The developed model predicts risk factors associated with a dent with 94% accuracy for a previously unseen data set.

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