Study on detection simplification of tomographic gamma scanning using dynamic grids applied in the emission reconstruction

Gu, Weiguo; Liu, Cheng; Qian, Nan; Wang, Dezhong

doi:10.1016/j.anucene.2013.02.031

Cited by 15 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of real-time capabilities, recent literature [22] has assessed the ability of neural networks to manage input data with a time interval sampled approximately one-tenth of the time constant of the considered system.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing

et al. 2021

View full text Add to dashboard Cite

Fluorescent penetrant inspection (FPI) is a well-assessed non-destructive test method used in manufacturing for detecting cracks and other flaws of the product under test. This is a critical phase in the mechanical and aerospace industrial sector. The purpose of this work was to present the implementation of an automated inspection system, developing a vision-based expert system to automate the inspection phase of the FPI process in an aerospace manufacturing line. The aim of this process was to identify the defectiveness status of some mechanical parts by the means of images. This paper will present, test and compare different machine learning architectures to perform the automated defect detection on a given dataset. For each test sample, several images at different angles were captured to properly populate the input dataset. In this way, the defectiveness status should be found combining the information contained in all the pictures. In particular, the system was designed for increasing the reliability of the evaluations performed on the airplane part, by implementing proper artificial intelligence (AI) techniques to reduce current human operators’ effort. The results show that, for applications in which the dataset available is quite small, a well-designed feature extraction process before the machine learning classifier is a very important step for achieving high classification accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The assay process of TGS is more complex and time-consuming than the SGS. In order to improve the detection accuracy, some new and improved gamma-ray scanning approaches, including the dynamic grids [4], [5] and semi-TGS [6] techniques were proposed mainly aiming at the 200-L waste drum to reduce the number of voxels. In order to reduce the amount of waste drums, the wastes are compacted in the 400-L drum.…”

Section: Introductionmentioning

confidence: 99%

An Improved Gamma Scanning Assay Method for the 400-L Compacted Radioactive Waste Drum Based on the Segmented Equivalent Ring Source

Dezhong

Tang

et al. 2019

IEEE Trans. Nucl. Sci.

Self Cite

View full text Add to dashboard Cite

An improved characterization assay algorithm for the 400-L low-and intermediate-level radioactive waste drum was developed based on the segmented equivalent ring source. The improved method (IM) was validated by experiments on a 200-L waste drum. The assay errors are smaller than that of segmented gamma scanning (SGS). Numerical measurements were performed on a 400-L waste drum with two densities of 1.5 and 2.5 g/cc. Three common nuclides, Ba-133, Cs-137, and Co-60, were selected. The results show that the source positions can be determined using two detectors through this method. The maximum error of the IM is several times smaller than that of SGS if the matrix is uniformly distributed in the waste drum. The mean errors are reduced by one third to half for all nuclides compared with SGS. If IM is applied in assaying the solidified wastes drum that contains one or two cylindrical iron products, the strong attenuation of gamma rays by irons and concretes will affect the determination of source positions. The assay accuracy decreases in this case, but it is overall better than that of SGS. The results indicate that IM is an accurate method for assaying the compacted waste drums with nonuniformly distributed radioactive sources.

show abstract

Research on the CdZnTe γ spectrum analysis based on an intelligent dynamic library

Yang

Zhang

et al. 2023

J Radioanal Nucl Chem

View full text Add to dashboard Cite

Study on detection simplification of tomographic gamma scanning using dynamic grids applied in the emission reconstruction

Cited by 15 publications

References 10 publications

Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing

Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing

An Improved Gamma Scanning Assay Method for the 400-L Compacted Radioactive Waste Drum Based on the Segmented Equivalent Ring Source

Research on the CdZnTe γ spectrum analysis based on an intelligent dynamic library

Contact Info

Product

Resources

About