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17 results for “defect detection”
Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
Sensor Defect Detection Datasets
<p><strong>Deprecated</strong> - Use https://zenodo.org/record/48728 for a more comprehensive version.</p> <p> </p> <p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.txt) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p>
Sensor Defect Detection Datasets with Configuration
<p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.csv) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario (data_scenario_2.csv) there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p> <p>Additionally attached is configuration data (Configurations.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</p>
CSEM-MISD - CSEM's Multi-Illumination Surface Defect Detection Dataset
<p>In automated surface visual inspection, it is often necessary to capture the inspected part under many different illumination conditions to capture all the defects. To address this issue, at <a href="http://www.csem.ch/">CSEM</a> we have acquired a real-world multi-illumination defect segmentation dataset, called CSEM-MISD and we release it for research purposes to benefit the community.</p> <p>The dataset consists of three different types of metallic parts -- washers, screws, and gears. Parts were captured in a half-spherical <a href="https://register.epo.org/application?number=EP13197867">light-dome system</a> that filtered out all the ambient light and successively illuminated it from 108 distinct illumination angles. Each 12 illumination angles share the same elevation level and the relative azimuthal difference between the adjacent illumination angles on the same level is 30 degrees. For more details, please read Sections 3 and 4 of our paper.</p> <p>The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.</p> <p>We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.</p> <p>This dataset is challenging mainly because:</p> <ul> <li>each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space;</li> <li>the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors;</li> <li>the annotations are not very precise because the exact extent of defect contours is always subjective;</li> <li>the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. </li> </ul> <p>The dataset is organized as follows:</p> <ul> <li>each sample resides in the Test, Train, or Unannotated directory;</li> <li>each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks;</li> <li>each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels;</li> <li>Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12).</li> <li>Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.</li> </ul> <p>We provide data loaders implemented in python at the project's <a href="https://github.com/DawyD/illumination-preserving-rotations">repository</a>.</p> <p>If you find our dataset useful, please cite our paper:</p> <blockquote> <p>Honzátko, D., Türetken, E., Bigdeli, S. A., Dunbar, L. A., & Fua, P. (2021). Defect segmentation for multi-illumination quality control systems. <em>Machine vision and Applications</em>.</p> </blockquote>
Dataset for the Paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities"
<p>This is the dataset for the paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities ", including the extracted data and results.</p> <p>The dataset contains the following three folders:</p> <p><strong>1. RQ1</strong>: </p> <ul> <li><strong>Security defect in Nova.xlsx</strong></li> <li><strong>Security defect in Neutron.xlsx</strong></li> <li><strong>Security defect in Qt Base.xlsx</strong></li> <li><strong>Security defect in Qt Creator.xlsx;</strong></li> </ul> <p>The RQ1 folder contains four files corresponding to the four projects (i.e., Nova and Neutron from OpenStack, Qt Base and Qt Creator from Qt), including 539 security-related review comments, in which security defects were identified by the reviewers. These instances were obtained from manual labelling after keyword-based search. The security defect type of these instances are presented to answer RQ1.</p> <p><strong>How to Read the MS Excel files in RQ1:</strong></p> <p>Each of the four MS Excel files in this folder contains 6 sheets for six years from 2017 to 2022. Each sheet has 10 columns for recoding 10 data items, among which the last four data items are used in our study to answer the RQs. We list the data items in the following table.</p> <table> <tbody> <tr> <td><strong>Data Item</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>Keyword</td> <td>The corresponding keyword of the comment.</td> <td>Keyword-based Search</td> </tr> <tr> <td>Code_change_id</td> <td>The code_change_id of the comment.</td> <td>Gerrit</td> </tr> <tr> <td>File</td> <td>The file in which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Patchset</td> <td>The patchset of the comment within the code change.</td> <td>Gerrit</td> </tr> <tr> <td>Line</td> <td>The line number in the file at which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Message</td> <td>The text of the review comment.</td> <td>Gerrit</td> </tr> <tr> <td>Security-related</td> <td>Whether the review comment is security-related (i.e., Yes or No).</td> <td>Labelling</td> </tr> <tr> <td>Security defect type</td> <td>The type of the security defect identified in the comment.</td> <td>Labelling</td> </tr> <tr> <td>Consequence</td> <td>The Consequence of the security defect.</td> <td>Extraction</td> </tr> <tr> <td>Resolution Evidence</td> <td>The information about where the identified security defect was resolved in the code</td> <td>Extraction</td> </tr> </tbody> </table> <p><strong>2. RQ2</strong>: </p> <ul> <li><strong>Extracted data for RQ2.mx22</strong></li> </ul> <p>The RQ2 folder contains the extracted data of 539 security-related review comments in <strong>Extracted data for RQ2.mx22</strong>, which was encoded and analyzed by the MAXQDA tool, investigating the treatment of security defects by developers and reviewers to answer RQ2.</p> <p><strong>3. RQ3</strong>: </p> <ul> <li><strong>Extracted data for RQ3.mx22</strong></li> </ul> <p>The RQ3 folder contains the extracted data of 161 review comments in which identified security defects were not resolved by developers in <strong>Extracted data for RQ3.mx22</strong>. which was also encoded and analyzed by the MAXQDA tool, exploring the causes of not resolving security defects to answer RQ3.</p> <p><strong>Note</strong>: The mx22 can be opened by MAXQDA 22, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free trial version of MAXQDA 2022, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p>
Detection and Characterization of Host Defense Defects
ClinicalTrials.gov study NCT00001355. IPD Sharing: YES. Countries: 1. Publications: 3.
Heidelberg Edge Perimetry (HEP) Detecting Glaucomatous Visual Field Defects
ClinicalTrials.gov study NCT02526654. IPD Sharing: NO. Countries: 0. Publications: 1.
Computer Vision-Based Algorithm for Precise Defect Detection and Classification in Photovoltaic Modules
Open the record for dataset details and reuse information.
Data for "Defect detection in atomic-resolution images via unsupervised learning with translational invariance"
<p>This dataset accompanies the paper titled <em>Defect detection in atomic-resolution images via unsupervised learning with translational invariance</em> by Yueming Guo<sup>*</sup>, Sergei V. Kalinin, Hui Cai, Kai Xiao, Sergiy Krylyuk, Albert V Davydov, Qianying Guo, Andrew R. Lupini<sup>* </sup></p>
Applying defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs
GEO Series GSE180632. Severe acute respiratory syndrome coronavirus 2. 3 samples. Type: Expression profiling by high throughput sequencing.
Study of the Safety and Efficacy of Apadenoson for Detection of Myocardial Perfusion Defects Using SPECT MPI
ClinicalTrials.gov study NCT01313572. IPD Sharing: Not stated. Countries: 5. Publications: 0.
Novel Portable Diagnostic Device for Automatic Detection of Relative Afferent Pupillary Defect
ClinicalTrials.gov study NCT02772666. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Evaluation of Different Perimetric Grids to Detect Central Visual Field Defect in Glaucoma Patients with Reduce Ganglion Cell Layer Thickness Measured by Spectral Domain OCT.
ClinicalTrials.gov study NCT06227611. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of the Safety and Efficacy of Apadenoson for Detection of Myocardial Perfusion Defects Using SPECT MPI
ClinicalTrials.gov study NCT00990327. IPD Sharing: Not stated. Countries: 2. Publications: 0.
AI-Based Radiographic Detection of Periodontal Defects
ClinicalTrials.gov study NCT07086625. IPD Sharing: NO. Countries: 1. Publications: 0.
Cone Beam Computed Tomography Versus Intraoral Digital Radiography in Detection and Measurements of Simulated Periodontal Bone Defects
ClinicalTrials.gov study NCT03729843. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Dataset related to the article "Diagnostic accuracy of subendocardial vs. transmural myocardial perfusion defect for the detection of in-stent restenosis or progression of coronary artery disease after percutaneous coronary intervention
<p>This record contains raw datarelated to the article"Diagnostic accuracy of subendocardial vs. transmural myocardial perfusion defect for the detection of in-stent restenosis or progression of coronary artery disease after percutaneous coronary intervention</p> <p> </p> <p>Background. The ADVANTAGE study demonstrated in a cohort of stented patients a diagnostic accuracy of stress myocardial CT perfusion (CTP) significantly higher than that of coronary CT angiography (CCTA) for the detection of in-stent restenosis (ISR) or CAD progression vs. quantitative coronary angiography (QCA). This is a pre-defined subanalysis of the ADVANTAGE aimed at assessing the difference in terms of diagnostic accuracy vs. QCA of a subendocardial vs. a transmural perfusion defect using static stress CTP.<br> Methods. We enrolled consecutive patients who previously underwent coronary stenting and were referred for QCA. All patients underwent stress CTP and rest CTP+CCTA. The diagnostic accuracy of CCTA and CTP were evaluated in territory-based and patient-based analyses. We compared the diagnostic accuracy of “subendocardial” perfusion defect, defined as hypo-enhancement encompassing >25% but <50% of the transmural myocardial thickness within a specific coronary territory vs. “transmural” perfusion defect, defined as hypo-enhancement encompassing >50% of the transmural thickness.<br> Results. In 150 patients (132 men, mean age 65.1±9.1 years), the diagnostic accuracy of subendocardial vs. transmural perfusion defect in a vessel-based analysis was 93.5% vs. 87.7%, respectively (p<0.0001). The sensitivity and specificity of subendocardial vs. transmural defect were 87.9% vs. 46.9% (p<0.001) and 94.9% vs. 97.9% (p=0.004), respectively. In a patient-based analysis, the diagnostic accuracy of the subendocardial vs. transmural approach was 86.6% vs. 68% (p<0.0001).<br> Conclusions. This study shows that detection of a subendocardial perfusion defect as compared to a transmural defect is significantly more accurate to identify coronary territories with ISR or CAD progression.</p> <p> </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.