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52 results for “Process Monitoring”
Reproducibility Case Study and Survey: Machine Learning-based Additive Manufacturing Process Monitoring and Quality Prediction
<p><span>Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). However, the reproducibility of the proposed ML-based AM monitoring systems in published works has not been investigated due to a lack of evaluation methods. In the paper 'Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing,' we propose a reproducibility investigation pipeline and conduct two case studies to validate the pipeline. This dataset records the data generated by one of the case studies. This dataset also contains the reproducibility survey results.</span></p>
Automated processing of aerial imagery for geohazards monitoring: Results from Fagradalsfjall eruption, SW Iceland, August 2022
<p><strong>1-</strong> <strong>Dataset Summary</strong><br> Here we present a dataset of DEMs (Digital Elevation Models), orthomosaics, and lava area outlines for the August 2022 eruption at Fagradalsfjall, SW Iceland. The dataset consists of: (1) five aerial surveys collected over the course of the August 2022 Fagradalsfjall eruption, (2) one survey carried out on 14 August 2022 using Pléiades satellite stereo images, and (3) a larger aerial survey, covering the 2021 and 2022 eruption sites in late September 2022 after the volcanic activity concluded.</p> <p><strong>2- Background</strong></p> <p>The volcano at Fagradalsfjall, SW-Iceland, began erupting on 3 August 2022 at 13:20 following 10 months of quiescence. As part of the response plan, a series of photogrammetric surveys were conducted in rapid, operational mode throughout the duration of the eruption. Subsequent production of data products for natural hazards monitoring (lava maps, lava volumes, effusion rates) were calculated within hours and reported to the Icelandic Civil Defense, following a similar approach that described in Pedersen et al., 2022a and in Gouhier et al., 2022. At the start of the 2022 eruption, GCPs had not yet been placed around the new fissure, but reference data (orthomosaics and DEMs) which had been georeferenced using targets measured with differential GNSS existed of the eruption site from September 2021 from Pedersen et al. (2022b) were available to use as a reference in the new workflow instead of GCPs. Due to the urgent need from authorities for information about the new eruption, a processing method that avoids the time-consuming task of manual GCP selection using a reference image for georeferencing was preferable in this instance. Besides the acquisition of aerial photographs, the CIEST2 initiative was also re-activated to collect Pléiades stereo images in emergency mode (Gouhier et al., 2022).</p> <p><strong>3 – Overview of data collection</strong></p> <p>Table 1 contains the overview of the surveys collected and presented in this repository.</p> <p> Table 1. Summary of surveys included in this dataset, by survey date.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Date & Time</strong><br> <strong>YYYYMMDD HH:MM</strong></p> </td> <td> <p><strong>Sensor</strong></p> </td> <td> <p><strong>Platform</strong></p> </td> <td> <p><strong>Flight alt.</strong><br> <strong>(m asl)</strong></p> </td> <td> <p><strong>Images</strong></p> </td> <td> <p><strong>Surveyed</strong><br> <strong>km<sup>2</sup></strong></p> </td> </tr> <tr> <td> <p>20220803 17:05</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203*</p> </td> <td> <p>~ 850</p> </td> <td> <p>46</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>20220804 11:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 2100</p> </td> <td> <p>32</p> </td> <td> <p>35</p> </td> </tr> <tr> <td> <p>20220813 09:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 750</p> </td> <td> <p>123</p> </td> <td> <p>9</p> </td> </tr> <tr> <td> <p>20220814 13:00</p> </td> <td> <p>Pléiades</p> </td> <td> <p>PHR1B</p> </td> <td> <p>n/a</p> </td> <td> <p>2</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>20220815 08:15</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>20</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>20220816 10:06</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>19</p> </td> <td> <p>26</p> </td> </tr> <tr> <td> <p>20220926 12:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-BMW**</p> </td> <td> <p>2100</p> </td> <td> <p>~20</p> </td> <td> <p>18</p> </td> </tr> </tbody> </table> <p>* TF-203: Savannah S aircraft</p> <p>** TF-BMW: Vulcanair P68 Observer 2 aircraft, operated by Garðaflug ehf.</p> <p><strong>4- Methods</strong></p> <p><strong>4.1 Processing of the aerial photographs from 3-16 Aug 2022</strong><br> Throughout the eruption, aerial surveys were conducted using a Hasselblad A6D 100 MP camera with 35 mm focal lens, from a height of 750 – 2,100 m above ground over the active lava field from an ultralight aircraft with a window in the bottom to allow for vertical photos to be taken (see supplement of Pedersen et al., 2022a for details and images of the setup). The camera was manually triggered to give ~70% overlap, and approximate flight lines were prepared beforehand for use with a handheld GPS during the flight to give ~30 % side overlap.<br> <br> An automated processing pipeline was created in python, which leverages tools from the Ames Stereo Pipeline (ASP, Shean et al., 2016) and Agisoft Metashape stand-alone Python API (v. 1.8.4). The processing and georeferencing of the aerial data were done in three steps, with all steps being automated except for the digitization of lava outlines. First, using a very high-resolution reference orthomosaic and DEM created in September 2021 and georeferenced with ground control points (Pedersen et al., 2022b), interest points (IPs) in each image were matched with the reference dataset, using the ASP routine <em>ipfind. </em>This created GCPs for each image over stable terrain. Second, hillshades were created from both the reference DEM and the source dataset DEM and matches in IPs were found in both, creating a second round of ground control points to refine the georeferencing of the entire block. Finally, the alignment of the source DEM was refined using the <em>dem_align</em> (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM.</p> <p><strong>4.2 Processing of the Pléiades stereo images</strong><br> The Pléiades stereo images were processed using the Ames Stereo Pipeline, using the general workflow of <em>mapproject </em>and <em>parallel_stereo </em>(e.g., Deschamps-Berger et al., 2020). The <em>parallel_stereo </em>routine used default arguments, plus the following arguments:</p> <p><em>--stereo-algorithm asp_mgm -t rpcmaprpc --corr-seed-mode 3 --corr-max-levels 2 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</em></p> <p>We used the DEM from 4 Aug 2022 as the reference for <em>mapproject </em>and for the final DEM co-registration applied to the produced Pléiades DEM.</p> <p><strong>4.3 Processing of the 26 September 2022 dataset</strong><br> The survey from 26 September 2022 was collected and processed using direct georeferencing from an on-board GPS antenna. The final alignment of the block was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. Because this survey covered a much larger area, the reference DEM for the final coregistration was the <a href="https://atlas.lmi.is/mapview/?application=DEM">ÍslandsDEM </a>v.1.0 (Landmælingar Íslands, 2022).</p> <p><strong>4.4. Maps of the lava outlines, lava thickness, lava volume, Time Average Effusion Rate (TADR)</strong><br> For each survey, a differential DEM (dDEM) showing elevation changes since the 2021 eruption was created by subtracting the reference DEM (ÍslandsDEM v.1.0, which includes the post-eruption DEM from Pedersen et al., 2022a) from the source DEM. Lava outlines, lava thickness lava volume, TADR and uncertainties were calculated using the methods described in Pedersen et al., 2022a. Table 2 summarizes calculations from this dataset.</p> <p> </p> <p>Table 2. Summary of survey results calculated from August 2022 Fagradalsfjall eruption DEMs and orthomosaics.</p> <table align="center"> <thead> <tr> <th> <p><strong> Date Start</strong></p> </th> <th> <p><strong>Date End</strong></p> </th> <th> <p><strong>Time </strong></p> <p><strong>Difference</strong></p> </th> <th> <p><strong>Lava </strong></p> <p><strong>Area<sup>*</sup> End </strong></p> <p><strong>(km<sup>2</sup>)</strong></p> </th> <th> <p><strong>dh<sup>**</sup> </strong></p> <p><strong>(m)</strong></p> </th> <th> <p><strong>Volume<sup>+</sup></strong></p> <p><strong>End<br> (1e+6 m<sup>3</sup>)</strong></p> </th> <th> <p><strong>TADR<sup>++</sup><br> (m<sup>3</sup>/s)</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>20220803<br> 13:20</p> </td> <td> <p>20220803<br> 17:05</p> </td> <td> <p>0d 03h 45m</p> </td> <td> <p>0.07</p> </td> <td> <p>5.88</p> </td> <td> <p>0.43</p> <p>± 0.03</p> </td> <td> <p>32.1</p> <p>± 1.5</p> </td> </tr> <tr> <td> <p>20220803<br> 17:05</p> </td> <td> <p>20220804<br> 11:00</p> </td> <td> <p>0d 21h 40m</p> </td> <td> <p>0.14</p> </td> <td> <p>11.13 </p> </td> <td> <p>1.57</p> <p>± 0.05</p> </td> <td> <p>17.7</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220804<br> 11:00</p> </td> <td> <p>20220813<br> 09:00</p> </td> <td> <p>8d 22h 00m</p> </td> <td> <p>1.27<sup>#</sup></p> </td> <td> <p>6.90</p> </td> <td> <p>10.33</p> <p>± 0.6</p> </td> <td> <p>11.4</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220813<br> 13:08</p> </td> <td> <p>20220814<br> 13:00</p> </td> <td> <p>0d 23h 52m</p> </td> <td> <p>1.24</p> </td> <td> <p>7.28</p> </td> <td> <p>10.62</p> <p>± 0.70</p> </td> <td> <p>2.8</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220814<br> 13:00</p> </td> <td> <p>20220815<br> 08:15</p> </td> <td> <p>0d 19h 15m</p> </td> <td> <p>1.26</p> </td> <td> <p>7.46</p> </td> <td> <p>10.99</p> <p>± 0.55</p> </td> <td> <p>4.1</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220815<br> 08:15</p> </td> <td> <p>20220816<br> 10:16</p> </td> <td> <p>1d 2h 01m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.49</p> </td> <td> <p>11.13</p> <p>± 0.53</p> </td> <td> <p>2.0</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220816<br> 10:16</p> </td> <td> <p>20220821<br> 06:00<sup>##</sup></p> </td> <td> <p>4d 19h 44m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.69</p> </td> <td> <p>11.39</p> <p>± 0.44</p> </td> <td> <p>0.653</p> <p>± 0.10</p> </td> </tr> </tbody> </table> <p><sup>*</sup>Total area of the lava field since 2022-08-03 before activity started.</p> <p><sup>**</sup>dh end is the mean thickness of the lava flow-field in the end of the given period.</p> <p><sup>+</sup>Volume erupted since 2022-08-03 before activity started.</p> <p><sup>++</sup>Time-averaged discharge rate for the given period</p> <p><sup>#</sup>Extrapolated value. Survey does not cover entire active lava area.</p> <p><sup>##</sup>End Time: 21 August 2022, 6:00. This time deduced from field observations from members of the Institute of Earth Sciences, University of Iceland. Values calculated from 26 September 2022 dataset.</p> <p>Figures and visual summaries of the processing method, resulting lava volumes, and uncertainties can be found in this poster: <a href="https://ftp.lmi.is/stm/Sydney/Fagradalsfjall_Aug2022/poster_faf_automated_proc2_srg_2022.pdf">Fagradalsfjall August 2022</a>.<br> <br> Orthomosaics from this dataset are viewable online at: <a href="https://atlas.lmi.is/mapview/?application=umbrotasja">https://atlas.lmi.is/mapview/?application=umbrotasja</a></p> <p><strong>Data naming conventions:</strong></p> <ul> <li>Data type: DEM, ortho, outline, diffDEM, lavafree, lava</li> <li>Acquisition date: YYYYMMDD_HHMM</li> <li>Platform/Sensor for data collection: Pléiades (PLE), Hasselblad A6D from aircraft (A6D)</li> <li>Resolution: 2x2 m (DEMs) and 30x30 cm (Orthomosaics)</li> <li>Folders (by survey): YYYYMMDD_HHMM_platform (during eruption) or 'posteruption'_platform (sensors/platform: A6D or PLE)</li> </ul> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Raster compression system: ZSTD (<a href="http://facebook.github.io/zstd/">http://facebook.github.io/zstd/</a>)</li> <li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul>
Nurse-led Medicines' Monitoring in Care Homes: a Process Evaluation
ClinicalTrials.gov study NCT03110471. IPD Sharing: NO. Countries: 1. Publications: 13.
Models from: Non-destructive in situ monitoring of structural changes of 3D tumor spheroids during the formation, migration, and fusion process
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Data from: Hunter-engaged monitoring of the Eurasian lynx during the reinforcement process
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UCSB SONGS Mitigation Monitoring: Wetland Process Study - Elevations of Spartina in Tidal Creek and Mudflat Habitats in Restored and Natural Wetlands in Southern California
These data describe the elevations of vegetated and unvegetated areas within tidal creek and mudflat habitats at the restored San Dieguito Wetlands (Del Mar, CA), the restored San Elijo Lagoon (Solana Beach, CA), and the natural Tijuana River Estuary (San Diego, CA). Data were collected in November 2022. Transects were established in these habitats at each wetland; the elevation and vegetation type were documented for at least seven points along at least five transects per habitat type.
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Data from: Scaling of processes shaping the clonal dynamics and genetic mosaic of seagrasses through temporal genetic monitoring
Theoretically, the dynamics of clonal and genetic diversities of clonal plant populations are strongly influenced by the competition among clones and rate of seedling recruitment, but little empirical assessment has been made of such dynamics through temporal genetic surveys. We aimed to quantify 3 years of evolution in the clonal and genetic composition of Zostera marina meadows, comparing parameters describing clonal architecture and genetic diversity at nine microsatellite markers. Variations in clonal structure revealed a decrease in the evenness of ramet distribution among genets. This illustrates the increasing dominance of some clonal lineages (multilocus lineages, MLLs) in populations. Despite the persistence of these MLLs over time, genetic differentiation was much stronger in time than in space, at the local scale. Contrastingly with the short-term evolution of clonal architecture, the patterns of genetic structure and genetic diversity sensu stricto (that is, heterozygosity and allelic richness) were stable in time. These results suggest the coexistence of (i) a fine grained (at the scale of a 20 × 30 m quadrat) stable core of persistent genets originating from an initial seedling recruitment and developing spatial dominance through clonal elongation; and (ii) a local (at the scale of the meadow) pool of transient genets subjected to annual turnover. This simultaneous occurrence of initial and repeated recruitment strategies highlights the different spatial scales at which distinct evolutionary drivers and mating systems (clonal competition, clonal growth, propagule dispersal and so on) operate to shape the dynamics of populations and the evolution of polymorphism in space and time.
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Data for "Statistical Process Monitoring of Isolated and Persistent Defects in Complex Geometrical Shapes"
<p>Simulated data used in the paper "Statistical Process Monitoring of Isolated and Persistent Defects in Complex Geometrical Shapes" by Daniele Zago, Bianca Maria Colosimo, and Giovanna Capizzi.</p> <p>The files includes MATLAB code used to generate the simulated noisy meshes.</p>
Validation of a Processed EEG Device for Monitoring Sedation in PICU
ClinicalTrials.gov study NCT05969483. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Ultra-processed Food Reducing Intervention and Continuous Glucose Monitoring
ClinicalTrials.gov study NCT07175701. IPD Sharing: NO. Countries: 1. Publications: 13.
The Influence of Standardized Process Management of Laryngeal Mask Airway Placement Based on Pressure Monitoring on the Incidence of Adverse Reactions in Elderly Patients During the Perioperative Peri
ClinicalTrials.gov study NCT06954857. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Scaling of processes shaping the clonal dynamics and genetic mosaic of seagrasses through temporal genetic monitoring
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Dataset supplementing "B. Ojha, M. Schober, S. Turad, J. Jochum and H. Kohler; Gasification of biomass: Very sensitive monitoring of TAR in syngas by determination of the oxygen demand– A proof of concept,Processes 2022"
<p>Dataset supplementing "B. Ojha, M. Schober, S. Turad, J. Jochum and H. Kohler;Gasification of biomass: Very sensitive monitoring of TAR in syngas by determination of the oxygen demand– A proof of concept,Processes 2022 "</p>
Investigating multi-physical process and deformation mechanism of reservoir landslide using integrated multi-source monitoring
<p>Data to support this study are available.</p>
Dataset Used in "Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes"
<p>This is the dataset used in Ding, Zeng, and Zhou, 2006, “Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes.” <em>Journal of Quality Technology</em>, Vol. 38(3), pp. 199-216. This data file has 528 data records, two fewer than the 530 number mentioned in the paper.</p>
Adequate Depth of Anesthesia: the Anesthesiologist's Assessment and the EEG Processed Monitoring
ClinicalTrials.gov study NCT02766894. IPD Sharing: YES. Countries: 0. Publications: 3.
Electronic Partograph: A Way of Improving Partograph Use During Labour Monitoring Process in Selected District Hospitals in Bangladesh
ClinicalTrials.gov study NCT03509103. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Genetic monitoring of open ocean biodiversity: an evaluation of DNA metabarcoding for processing continuous plankton recorder samples
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.