Nothing Special   »   [go: up one dir, main page]

Xu et al., 2021 - Google Patents

A method of defect depth recognition in active infrared thermography based on GRU networks

Xu et al., 2021

View HTML
Document ID
114756941856030996
Author
Xu L
Hu J
Publication year
Publication venue
Applied Sciences

External Links

Snippet

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications

Similar Documents

Publication Publication Date Title
Fang et al. A method of defect depth estimation for simulated infrared thermography data with deep learning
Teng et al. Modal strain energy-based structural damage detection using convolutional neural networks
Zhang et al. A transfer residual neural network based on ResNet-50 for detection of steel surface defects
Palevičius et al. Automatic detection of cracks on concrete surfaces in the presence of shadows
Xu et al. A method of defect depth recognition in active infrared thermography based on GRU networks
Zhong et al. Structural damage features extracted by convolutional neural networks from mode shapes
Deng et al. Classification and quantitative evaluation of eddy current based on kernel-PCA and ELM for defects in metal component
Di Carolo et al. A thermoelastic stress analysis general model: study of the influence of biaxial residual stress on aluminium and titanium
Bhutada et al. Machine learning based methods for obtaining correlations between microstructures and thermal stresses
Wang et al. Automated classification of pipeline defects from ultrasonic phased array total focusing method imaging
Zhang et al. A multi-scale attention mechanism based domain adversarial neural network strategy for bearing fault diagnosis
Rezayiye et al. Thermal data augmentation approach for the detection of corrosion in pipes using deep learning and finite element modelling
Liu et al. Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L
Gumbarević et al. Application of Multilayer Perceptron Method on Heat Flow Meter Results for Reducing the Measurement Time
Li et al. A gradient-field pulsed eddy current probe for evaluation of hidden material degradation in conductive structures based on lift-off invariance
Zhang et al. The strain distribution reconstructions using GWO algorithm and verification by FBG experimental data
Xu et al. Classification of liquid ingress in GFRP honeycomb based on one-dimension sequential model using THz-TDS
Liu et al. Data-augmented manifold learning thermography for defect detection and evaluation of polymer composites
Hou et al. Influence of variation/response space complexity and variable completeness on BP-ANN model establishment: Case study of steel ladle lining
Miller et al. Detection of material degradation of a composite cylinder using mode shapes and convolutional neural networks
Shiozawa et al. Fatigue damage evaluation of short carbon fiber reinforced plastics based on phase information of thermoelastic temperature change
Addante et al. Laser thermography: An investigation of test parameters on detection and quantitative assessment in a finite crack
Wang et al. Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)
Li et al. A weighted estimation algorithm for enhancing pulsed eddy current infrared image in ecpt non-destructive testing
Shen et al. Quantitative Detection of Pipeline Cracks Based on Ultrasonic Guided Waves and Convolutional Neural Network