Error detection in digital neural networks: an algorithm-based approach for inner product protection

1994 ◽  
Author(s):  
Luca Breveglieri ◽  
Vincenzo Piuri
Keyword(s):  
Neural Networks ◽  
Error Detection ◽  
Inner Product ◽  
Product Protection

Error detection and classification in patient‐specific IMRT QA with dual neural networks

2020 ◽  
Vol 47 (10) ◽  
pp. 4711-4720 ◽  
Author(s):  
Nicholas J. Potter ◽  
Karl Mund ◽  
Jacqueline M. Andreozzi ◽  
Jonathan G. Li ◽  
Chihray Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Error Detection ◽  
Patient Specific ◽  
Imrt Qa

scholarly journals A Review of Computer Vision Techniques in the Detection of Metal Failures

2021 ◽  
Vol 349 ◽  
pp. 02021
Author(s):  
Deborah Fitzgerald ◽  
Roselita Fragoudakis
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Statistical Analysis ◽  
Error Detection ◽  
Spatial Data ◽  
Fourier Transforms ◽  
Vision System ◽  
Gabor Filtering ◽  
Destructive Testing ◽  
Occurrence Matrix

This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.

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