Rapid manual inspection and mapping using integrated ultrasonic arrays

2021 ◽  
pp. 97-106
Author(s):  
D.I.A. Lines ◽  
K.R. Dickson ◽  
G. Filippi
Keyword(s):  
Manual Inspection ◽  
Ultrasonic Arrays

scholarly journals Artifact-Free Single Image Defogging

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 577
Author(s):  
Gabriele Graffieti ◽  
Davide Maltoni
Keyword(s):  
Learning Strategy ◽  
Real Data ◽  
Training Procedure ◽  
Single Image ◽  
Paired Data ◽  
Manual Inspection ◽  
Synthetic Datasets ◽  
Visibility Enhancement ◽  
Difficult Cases ◽  
Good Contrast

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

scholarly journals LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6387 ◽  
Author(s):  
Xiaohan Tu ◽  
Cheng Xu ◽  
Siping Liu ◽  
Shuai Lin ◽  
Lipei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Point Cloud ◽  
High Speed ◽  
Point Cloud Data ◽  
Embedded Devices ◽  
Cloud Data ◽  
Manual Inspection ◽  
Order Of Magnitude ◽  
Supply Systems

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

scholarly journals Ceramic Cracks Segmentation with Deep Learning

2021 ◽  
Vol 11 (13) ◽  
pp. 6017
Author(s):  
Gerivan Santos Junior ◽  
Janderson Ferreira ◽  
Cristian Millán-Arias ◽  
Ramiro Daniel ◽  
Alberto Casado Junior ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ceramic Plate ◽  
Ceramic Tiles ◽  
Entire Area ◽  
Manual Inspection ◽  
Optical Inspection ◽  
Proposed Model ◽  
Processing Step ◽  
Long Time ◽  
Automated Optical Inspection

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

INSPECTION OF SINGLE CRYSTAL AEROSPACE COMPONENTS WITH ULTRASONIC ARRAYS

2010 ◽  
Author(s):  
C. J. L. Lane ◽  
A. Dunhill ◽  
B. W. Drinkwater ◽  
P. D. Wilcox ◽  
Donald O. Thompson ◽  
...  
Keyword(s):  
Single Crystal ◽  
Ultrasonic Arrays

Ultrasonic arrays for non-destructive evaluation: A review

2006 ◽  
Vol 39 (7) ◽  
pp. 525-541 ◽  
Author(s):  
Bruce W. Drinkwater ◽  
Paul D. Wilcox
Keyword(s):  
Non Destructive Evaluation ◽  
Ultrasonic Arrays ◽  
Non Destructive

scholarly journals Smart Dye Inspection System in Textile Industry

2020 ◽  
Vol 8 (6) ◽  
pp. 5061-5063
Keyword(s):  
Image Processing ◽  
Reference Material ◽  
Textile Industry ◽  
High Accuracy ◽  
Inspection System ◽  
Manual Operation ◽  
Image Processing Techniques ◽  
Manual Inspection ◽  
Processing Techniques

Inspection on the dyed material in the textile industry is facing a challenging task owing to the accurate measurement of the dye concentration added. Currently manual inspection is done. It consumes more time and less accurate. The proposed work provides a solution to above problem. The image of reference material (cloth) is captured and the features are extracted using image processing techniques. The color concentration of both the reference material and the test fabric is compared. If the dye concentration of the test fabric matches with the reference material, then it is a perfect dyed cloth whereas for mismatched samples, the concentration is to be adjusted is displayed. This smart dyeing inspection system reduces the manual operation and saves time and results in high accuracy.

scholarly journals Colony size measurement of the yeast gene deletion strains for functional genomics

2021 ◽  
Author(s):  
Negar Memarian ◽  
Matthew Jessulat ◽  
Javad Alirezaie ◽  
Nadereh Mir-Rashed ◽  
Jianhua Xu ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Colony Size ◽  
Large Scale ◽  
Model Organism ◽  
Antifungal Compound ◽  
Image Analysis System ◽  
Yeast Saccharomyces Cerevisiae ◽  
Inspection Method ◽  
Manual Inspection ◽  
Growth Differences

Background Numerous functional genomics approaches have been developed to study the model organism yeast, Saccharomyces cerevisiae, with the aim of systematically understanding the biology of the cell. Some of these techniques are based on yeast growth differences under different conditions, such as those generated by gene mutations, chemicals or both. Manual inspection of the yeast colonies that are grown under different conditions is often used as a method to detect such growth differences. Results Here, we developed a computerized image analysis system called Growth Detector (GD), to automatically acquire quantitative and comparative information for yeast colony growth. GD offers great convenience and accuracy over the currently used manual growth measurement method. It distinguishes true yeast colonies in a digital image and provides an accurate coordinate oriented map of the colony areas. Some post-processing calculations are also conducted. Using GD, we successfully detected a genetic linkage between the molecular activity of the plant-derived antifungal compound berberine and gene expression components, among other cellular processes. A novel association for the yeast mek1 gene with DNA damage repair was also identified by GD and confirmed by a plasmid repair assay. The results demonstrate the usefulness of GD for yeast functional genomics research. Conclusion GD offers significant improvement over the manual inspection method to detect relative yeast colony size differences. The speed and accuracy associated with GD makes it an ideal choice for large-scale functional genomics investigations.

scholarly journals PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11845
Author(s):  
Maxime Borry ◽  
Alexander Hübner ◽  
Adam B. Rohrlach ◽  
Christina Warinner
Keyword(s):  
Dna Damage ◽  
Functional Diversity ◽  
Ancient Dna ◽  
Damage Identification ◽  
De Novo ◽  
Metagenomic Data ◽  
Cytosine Deamination ◽  
High Data ◽  
Manual Inspection ◽  
Gene Catalogue

DNA de novo assembly can be used to reconstruct longer stretches of DNA (contigs), including genes and even genomes, from short DNA sequencing reads. Applying this technique to metagenomic data derived from archaeological remains, such as paleofeces and dental calculus, we can investigate past microbiome functional diversity that may be absent or underrepresented in the modern microbiome gene catalogue. However, compared to modern samples, ancient samples are often burdened with environmental contamination, resulting in metagenomic datasets that represent mixtures of ancient and modern DNA. The ability to rapidly and reliably establish the authenticity and integrity of ancient samples is essential for ancient DNA studies, and the ability to distinguish between ancient and modern sequences is particularly important for ancient microbiome studies. Characteristic patterns of ancient DNA damage, namely DNA fragmentation and cytosine deamination (observed as C-to-T transitions) are typically used to authenticate ancient samples and sequences, but existing tools for inspecting and filtering aDNA damage either compute it at the read level, which leads to high data loss and lower quality when used in combination with de novo assembly, or require manual inspection, which is impractical for ancient assemblies that typically contain tens to hundreds of thousands of contigs. To address these challenges, we designed PyDamage, a robust, automated approach for aDNA damage estimation and authentication of de novo assembled aDNA. PyDamage uses a likelihood ratio based approach to discriminate between truly ancient contigs and contigs originating from modern contamination. We test PyDamage on both on simulated aDNA data and archaeological paleofeces, and we demonstrate its ability to reliably and automatically identify contigs bearing DNA damage characteristic of aDNA. Coupled with aDNA de novo assembly, Pydamage opens up new doors to explore functional diversity in ancient metagenomic datasets.

scholarly journals Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

2022 ◽  
Author(s):  
Miguel Steiner ◽  
Markus Reiher
Keyword(s):  
High Efficiency ◽  
Reaction Network ◽  
Decomposition Reactions ◽  
Full Resolution ◽  
Manual Inspection ◽  
Computational Procedures ◽  
Fast Electronic ◽  
Processing Errors ◽  
High Degree ◽  
Computational Catalysis

AbstractAutonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. Graphical Abstract

Condition monitoring with ultrasonic arrays at elevated temperatures

2003 ◽  
Vol 45 (2) ◽  
pp. 130-133 ◽  
Author(s):  
K Kirk ◽  
C K Lee ◽  
S Cochran ◽  
A McNab ◽  
G Shepherd
Keyword(s):  
Condition Monitoring ◽  
Elevated Temperatures ◽  
Ultrasonic Arrays
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