A method for identifying incomplete codewords of gas meters

A gas meter and codeword technology, applied in character recognition, character and pattern recognition, instruments, etc., can solve the problem of low accuracy of incomplete codeword recognition, and achieve the effect of solving insufficient training and improving loss function.

Active Publication Date: 2021-10-29
NORTHWEST UNIV
View PDF10 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above problems, the object of the present invention is to provide a gas meter incomplete code word recognition method, which effectively solves the difficult problem of low recognition accuracy of incomplete code words

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for identifying incomplete codewords of gas meters
  • A method for identifying incomplete codewords of gas meters
  • A method for identifying incomplete codewords of gas meters

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0067] The incomplete gas meter codeword recognition method of the present invention and other machine learning algorithms SVM, CNN and PSO-SVM are used to recognize the incomplete gas meter codeword respectively, and the recognition results shown in Table 1 are obtained. The operating system used is: Linux (GPU-NVIDIA GTX1060), and the deep learning library used is: TensorFlow.

[0068] Among them, see figure 1 , the convolutional neural network model in the method of the present invention comprises an input layer, a convolutional layer I, a pooling layer I, a convolutional layer II, a pooling layer II, a fully connected layer I, a fully connected layer II and In the output layer, the number of neurons in the input layer is 28*28; the convolution kernel scale of the convolutional neural network model is 3*3, and the step size of the convolution and pooling operations of the model is (1, 1 ), the activation function is RELU, the attribute value of adding padding is same, the ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a gas meter incomplete code word recognition method, which combines the common characteristics of the gas meter complete code word and the incomplete code word, improves the loss function of the network model based on the convolutional neural network, and realizes the use of the gas meter complete code word. The goal of using codewords to train and identify incomplete codeword network models of gas meters fundamentally solves the problem of insufficient training of deep learning models due to insufficient data volume.

Description

technical field [0001] The invention belongs to the technical field of natural gas metering, and relates to a method for identifying an incomplete code word of a gas meter. Background technique [0002] At present, among the natural gas metering tools, the traditional gas meter meter is basically a membrane type, that is, a large number of wheel-type code meters exist and use, which hinders the development and progress of information management to a certain extent. The traditional wheel-type gas meter is mainly Natural gas is counted by manual meter reading. Later, some scholars proposed feedforward (Back Propagation, BP) neural network, support vector machine (Support Vector Machine, SVM), convolutional neural network (Convolutional Neural Network, CNN), particle swarm Optimize machine learning methods such as SVM (Particle Swarm Optimization, PSO) to directly recognize the codewords of the gas meter codeword image to realize the automation of meter reading, but there are s...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/20G06K9/62
CPCG06V10/22G06V30/10G06F18/29
Inventor 张蕾苗成强卜起荣冯筠王红玉
Owner NORTHWEST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap