scholarly journals metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

2021 ◽  
Author(s):  
Hani Habra ◽  
Maureen Kachman ◽  
Kevin Bullock ◽  
Clary Clish ◽  
Charles R. Evans ◽  
...  
Keyword(s):  
Feature Matching ◽  
Data Sets

Detection of Crack Initiation Based on Repeat In-Line Inspection

2016 ◽  
Author(s):  
Michael Palmer ◽  
Christopher Davies ◽  
Markus Ginten ◽  
Roland Palmer-Jones
Keyword(s):  
Crack Initiation ◽  
Crack Growth ◽  
Crack Detection ◽  
Feature Matching ◽  
Data Sets ◽  
Gas Pipelines ◽  
Electromagnetic Acoustic Transducer ◽  
As Stress ◽  
Reliable Solution ◽  
Inspection Data

As Stress Corrosion Cracking (SCC) and other cracking related issues become a more recognised hazard or threat that can be monitored by in-line inspection (ILI), there have been high expectations for the pipeline inspection industry to produce a reliable solution for identifying and sizing cracks. The current leading ILI technologies provided for pipeline crack detection are Ultrasonic (UT) and Electromagnetic Acoustic Transducer (EMAT). The introduction of EMAT In-Line Inspection technologies has provided a proven solution for crack detection that can be used in gas pipelines without having to introduce a liquid couplant into the pipeline. With the development of these technologies worldwide pipeline regulators are putting more pressure on the industry to monitor integrity issues relating to cracking. For example USA pipeline operators are required by the Office of Pipeline Safety to inspect and assess their pipelines that operate within high consequence areas for integrity issues, such as SCC, and repair or replace affected pipe. The inspection options for this include the use of Inline inspection tools — “smart pigs”. These regulations in combination with the majority of pipeline incidents relating to SCC occurring in gas pipelines have led to a significant increase in the use of EMAT ILI technology in recent years. With repeat EMAT ILIs now being conducted on some pipelines there is the option to compare data sets to identify any changes between inspections. Due to the complexities of the EMAT measurement principle and the volumes of data recorded, the process of directly comparing raw signal data from two runs is still in its infancy and cannot currently be used to confirm or discount evidence of crack growth, such as the industry has seen with estimation of corrosion growth based on Magnetic Flux Leakage (MFL) technology signal comparison. However the comparison of EMAT data sets can aid the identification of crack initiation. This technical paper presents a method for identifying the initiation of crack growth (the development of newly detectable cracks) based on repeat EMAT ILI, using feature matching and comparison of raw EMAT inspection data. The implications for integrity management of the identification of newly detectable SCC are discussed, and possible future improvements are outlined. The paper includes a case study that illustrates some of the issues.

scholarly journals Spherically Optimized RANSAC Aided by an IMU for Fisheye Image Matching

2021 ◽  
Vol 13 (10) ◽  
pp. 2017
Author(s):  
Anbang Liang ◽  
Qingquan Li ◽  
Zhipeng Chen ◽  
Dejin Zhang ◽  
Jiasong Zhu ◽  
...  
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Measurement Unit ◽  
Visual Localization ◽  
Data Sets ◽  
Wide Field ◽  
Public Data ◽  
Wide Field Of View ◽  
Random Sample Consensus ◽  
Epipolar Constraints

Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios.

scholarly journals A CNN BASED HYBRID APPROACH TOWARDS AUTOMATIC IMAGE REGISTRATION

2013 ◽  
Vol 39 (3) ◽  
pp. 121-128
Author(s):  
Pattathal Vijayakumar Arun
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Spatial Information ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Cellular Neural Network ◽  
Data Sets ◽  
Spatial Analyses ◽  
Contextual Knowledge ◽  
The Past

Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network has been found to be effective in improving feature matching as well as resampling stages of registration, and complexity of the approach has been considerably reduced using corset optimization. CNN-prolog based approach has been adopted to dynamically use spectral and spatial information for representing contextual knowledge. The salient features of this work are feature point optimisation, adaptive resampling and intelligent object modelling. Investigations over various satellite images revealed that considerable success has been achieved with the procedure. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

Automated cell counting of astrocytes on patterned substrates containing aliphatic and charged properties

1995 ◽  
Vol 53 ◽  
pp. 974-975
Author(s):  
W. Shain ◽  
H. Ancin ◽  
H.C. Craighead ◽  
M. Isaacson ◽  
L. Kam ◽  
...  
Keyword(s):  
Cell Culture ◽  
Cell Attachment ◽  
Culture Method ◽  
Cell Counting ◽  
Data Sets ◽  
Nuclear Staining ◽  
Double Positive ◽  
A Cell ◽  
Wafer Test ◽  
Cell Densities

Neural protheses have potential to restore nervous system functions lost by trauma or disease. Nanofabrication extends this approach to implants for stimulating and recording from single or small groups of neurons in the spinal cord and brain; however, tissue compatibility is a major limitation to their practical application. We are using a cell culture method for quantitatively measuring cell attachment to surfaces designed for nanofabricated neural prostheses.Silicon wafer test surfaces composed of 50-μm bars separated by aliphatic regions were fabricated using methods similar to a procedure described by Kleinfeld et al. Test surfaces contained either a single or double positive charge/residue. Cyanine dyes (diIC18(3)) stained the background and cell membranes (Fig 1); however, identification of individual cells at higher densities was difficult (Fig 2). Nuclear staining with acriflavine allowed discrimination of individual cells and permitted automated counting of nuclei using 3-D data sets from the confocal microscope (Fig 3). For cell attachment assays, LRM5 5 astroglial cells and astrocytes in primary cell culture were plated at increasing cell densities on test substrates, incubated for 24 hr, fixed, stained, mounted on coverslips, and imaged with a 10x objective.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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