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52 results for “Process Monitoring”
Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot
<p>This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1]. The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1]. </p> <h4>Dataset Structure:</h4> <ul> <li><strong>File:</strong> <code>ForceTorqueTimeSeries.csv</code> <ul> <li><strong>Columns:</strong> <ul> <li><code>idx (1-524)</code>: Index of the recording corresponding to the assembly of one wheel.</li> <li><code>label (true/false)</code>: Indicates whether the assembly was successful (TRUE = product is OK).</li> <li><code>meas_id (1-6)</code>: Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).</li> <li><code>force_x</code>: X-component of the force measured by the sensor mounted on the delta robot's end effector.</li> <li><code>force_y</code>: Y-component of the force.</li> <li><code>force_z</code>: Z-component of the force.</li> <li><code>torque_x</code>: X-component of the torque.</li> <li><code>torque_y</code>: Y-component of the torque.</li> <li><code>torque_z</code>: Z-component of the torque.</li> </ul> </li> </ul> </li> </ul> <h4>Additional Files:</h4> <ul> <li><strong><code>IMG_3351.MOV</code>:</strong> A video demonstrating the assembly process for one batch of four wheels.</li> <li><strong><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>:</strong> Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><strong><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>:</strong> Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). <em>Anomaly detection in robotic assembly process using force and torque sensors</em> [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). <em>Edge AI integration for anomaly detection in assembly using Delta robot</em> [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>
Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring
<p>There are two files for each subject:</p> <p>1. sub##_error.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. sub##_hit.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 7 cm) in the task (segment and electrode information can be found below).</p> <p><br> The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodes</p> <p>n represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726 (850 ms after ball release) in every segment.</p> <p>datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,<br> i.e. hit/error #1 -> row 1 to 1400, hit/error #2 -> row 1401 to 2800, ..., hit/error #n -> (n-1) * 1400 + 1 to n * 1400</p> <p>electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
[Dataset] In situ laser-ultrasonic monitoring of Poisson's ratio and bulk sound velocities of steel plates during thermal processes
<p>Data generated and analyzed in the work titled "In situ laser-ultrasonic monitoring of Poisson’s ratio and bulk sound velocities of steel plates during thermal processes". See the associated publication for more context.</p> <p>All files are stored in Matlab's binary MAT-file format.</p> <ul> <li>cutOffs_ZGVs_nu_S1S2_A2A3_S3S6_A4A7.mat <ul> <li>Dispersion relation data of plates obtained from numerical calculation with a range of Poisson's ratios and otherwise arbitrary but fixed material properties.</li> <li>S1S2-, A2A3-, S3S6- and A4A7-ZGV resonance frequencies and k-values</li> <li>L1 and T1 thickness resonance frequencies</li> </ul> </li> <li>lusResults_jmat_dilatometry_data.mat <ul> <li>LUS measurement data and resulting material properties (raw displacement data recorded on the oscilloscope is stored separately to keep the file size reasonable.)</li> <li>Dilatometer measurements</li> <li>JMatPro simulation</li> </ul> </li> <li>lusOscilloscope_data.mat <ul> <li>Normal surface displacement measurement data obtained in situ with LUS and recorded with an oscilloscope</li> </ul> </li> </ul>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Groundwater monitoring with an underwater DAS1 cable, processed dataset
<p>This repository contains data to reproduce figures from the article "Groundwater monitoring with an underwater DAS cable " by Destin Nziengui Bâ, Aurélien Mordret, Olivier Coutant, Camille Jestin and Ludovic Bodet </p>
Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study
<p>Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> . </p> <p>The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data.</p> <p>`processed_covariate_data.csv`:<br> ```<br> Rows: 298<br> Columns: 7<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ age_cat: indicates age category at the time of a PEM session<br> $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire<br> $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. <br> $ bmi_cat: indicates BMI category at the time of a PEM session<br> ```</p> <p>`processed_lcms_data data.csv` contains the processed LCMS data:<br> ```<br> Rows: 298<br> Columns: 4297<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime.<br> Non-detects (below limit of detection (LOD) are coded as 1 for the compounds.<br> ....<br> ```<br> In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables.</p> <p>The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively. </p>
Monitoring valve activity in M.edulis and M.galloprovincialis: Valve signal processing
<p>This repository provides access to the metadata and scripts used to monitor valve gaping activity of bivalves using valvometry (Valve-Trek ; Technosmart Europe srl, www.technosmart.eu). The dataset contains valve activity records in csv format per individual sampled from February to May 2023 at Agon-Coutainville (Normandy, France), as well as r scripts for processing, formatting and analyzing valve gaping data.</p>
UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Vegetation Cover
These data describe estimates of the percent cover of marsh plants in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, species of marsh plants were identified and recorded under 98 uniformly spaced points within 4.5 m2 quadrats in each plot.
UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Plant Size
These data describe estimates of the condition and size of three species of salt marsh plants (Arthrocnemum subterminale, Frankenia salina, and Salicornia virginica) planted in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, the length of the longest axis and maximum perpendicular width of each planted individual was measured.
UCSB SONGS Mitigation Monitoring: Wetland Process Study – Soil Properties
These data describe physical and chemical properties of soil samples collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2019 at the San Dieguito Wetland in San Diego County, CA. Additional locations at Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh is Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA were added in 2021. Sampling occurred sporadically at various locations in each wetland. All soil samples were characterized for organic matter content and particle size. Additional properties were characterized for select soil samples.
AVANGARD | In Situ Monitoring of Welding Quality of GMAW Process
<p>In the framework of the AVANAGRD project, a series of collaborative sensors were used in a feasibility study to develop inline quality monitoring of welding processes, such as microphones, acoustic emission sensors, and thermal cameras, besides of course the arc parameters. In this video, some examples of thermal filming are shown, performed during extreme conditions of GMAW applied on T-joints.<br> Besides the art that each frame already is, it’s possible to provide key information about process stability that will be translated into welding defects.<br> Using a combination of those techniques with Artificial Intelligence, there is no limit on what we can reach related to productivity, quality, and zero-defect manufacturing.<br> The AVANGARD project (Advanced Manufacturing Solution Tightly Aligned with Business Need) is funded by the European Union within the frame of the Horizon 2020 research and development program under Grant Agreement No. 869986. <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnNoM1F3ZnBManZKUlVKT0h1WnhyMUVDY3BpUXxBQ3Jtc0ttR2FwOWVRbXY0am1NNGxWNk5JUndJdS05cnF2U2s2S1lJMEo5YzcycWdWdWRxMEdIM1hNVy1yelpYWW84M0V3anFsVGg0SDZTbFNZVHJIY2RiNnNZQ2pZY2huNE1Da2ZVY2NxZ3lkSVBvUVE0TkdkSQ&q=http%3A%2F%2Fwww.avangard-project.eu%2F&v=7HgNP1rdeHI">http://www.avangard-project.eu/</a></p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Electronic appendix for "Are we there yet? A critical experimental assessment of the application of induced polarization for monitoring geochemical processes", Strobel, C., Störiko, A, Olaf, O.A. & Mellage, A.
Open the record for dataset details and reuse information.
GMAW & WAAM Process monitoring using XARION Eta300 Ultra laser microphone
<p>Repository contains extra wide bandwidth acoustic process monitoring data of a stable and unstable gas metal arc welding (GMAW) process variant Fronius "cold metal transfer" (CMT) of G3Si1 steel wire, which is used for additive manufacturing. Additional Video acquisition of the welding process is provided.</p>
UCSB SONGS Mitigation Monitoring: Reef Process Study - Density of Giant Kelp Recruits and Cover of Dead Holdfasts
These data describe annual estimates of the density of giant kelp recruits and the percent cover of dead giant kelp holdfasts from replicate quadrats at three subtidal reefs. Data collection occurred from 2000 -2023 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA) to evaluate the ability of Wheeler North Reef to compensate for losses of kelp forest habitat and associated biota caused by the operation of the San Onofre Nuclear Generating Station (SONGS).
MONITORING AND MODELING OF HYDROLOGICAL PROCESSES IN THE SEMIARID REGION OF BRAZIL: THE CARIRI EXPERIMENTAL BASINS
<p><strong>DATASET DESCRIPTION</strong> - Two experimental basins – the Cariri basins – were installed in a typically semiarid region in the State of Paraíba, Brazil, for obtaining reliable estimates of runoff and soil erosion in different scales to evaluate the influence of the human activities and other factors over the processes of runoff and erosion. In the first basin, located in the municipality of Sumé, the field studies were carried out at three different scales: four micro-basins with an area of around 0.5 ha; nine standard Wischmeier-type erosion plots of 100 m<sup>2</sup> and seven sample plots of 1 m<sup>2</sup>. The experimental units had varied vegetal cover and management and, except the sample plots, were subjected to natural rainfall events only, and were monitored from 1982 to 1991. The total runoff and total sediment yield were determined for each of the events of precipitation. The installations of the second basin, in the near municipality of São João do Cariri, were planned for the continuation of the studies initiated at Sumé, and include erosion plots (100 m<sup>2</sup>), micro-basins, and sub-basins, which are being monitored for runoff and sediment production up to now. Among them, two nested micro-basins were monitored to detect any scale effect at the micro-basin level. Nearly 600 events of natural precipitation, that produced runoff in at least one of the experimental units, have been registered. This bulk of data was utilised to evaluate the influence of various factors, including cultivation practices. The data collected so far has been successfully used to calibrate hydrological models for plots and micro-basins. Parameters have been tested by means of cross validations among micro-basins and sub-basins.</p> <p><strong>FILENAMES </strong>– The data files are divided into three categories: Description of the equipment utilized for collecting data and their locations, the data collected from the monitored experimental basins and another with maps, figures and pictures. The file names are designated with the basin name and the content. The files describing the equipment comprise: BASINNAME_DATADESCRIPTION, where BASINNAME could be EBS or EBSJC. The files with the data collected in the experimental basins are denominated like: BASINNAME_DATANAME. The DATANAME will be one of the three that may be, precipitation, runoff and sediment yield, or climatologic data. The file with maps and other information are identified as: BASINNAME_GEOPHYSICDATANAME, and BASINNAME_PICTURES. The geophysical data refer to topographic data, soil data, land cover and the drainage network. Graphs, pictures, etc., are included in the PICTURES file.</p> <p><strong>DATAFORMAT </strong>– The data file about equipment and localization as well as the data collected in experimental units are of the type “. csv”, the geophysical data are of either “.dwg” or “.shp”. The figures and picture are in the format: .jpeg, .png or .tif.</p> <p><strong>ACKNOWLEDGEMENTS </strong>– SUDENE – the Superintendency for the Development of the Northeast of Brazil with the cooperation of ORSTOM – the French Government Agency for Technical Cooperation Overseas was responsible for implementing the program of Representative and Experimental Basins in the region beginning in the decade of 1970. Pierre Audry, Eric Cadier, Jean Leprun and Michel Molinier, hydrologists and soil scientists from France played key roles in the selection of site, installation of experimental units and beginning the operation of the EBS. Beronildo Freitas was the engineer from SUDENE responsible for technical coordination and administration. The contributions of other researchers and technical people have been listed by Srinivasan and Galvão (2003). The installation of the research catchment at São João de Cariri had the valuable collaboration of GTZ, the cooperation Agency of the Government of Germany. Dr. Ing Ubald Koch was responsible for getting all the equipment, installing them and conducting research work along with the members of the Hydrology Research Group of the Federal Universities of Paraiba and Campina Grande. Late prof. Manoel Gilberto de Barros efficiently coordinated the field work. Eduardo Figueiredo, Celso Santos and Ricardo Aragão have made note worthy contributions. The Ministry of Science and Technology of Brazil has provided the bulk of the financial support needed for the operation of the basins, through its main funding agencies of CNPq (National Council for Development of Science and Technology) and FINEP (Agency for Financing research Studies and Projects).</p>
Shark-dust: Application of high-throughput DNA sequencing of processing residues for trade monitoring of threatened sharks and rays
<p>Data repository accompanying manuscript titled of "Shark-dust: Application of high-throughput DNA sequencing of processing residues for trade monitoring of threatened sharks and rays."</p> <p>Prasetyo, A. P., Murray, J. M., Kurniawan, M. F. A. K., Sales, N. G., McDevitt, A. D., & Mariani, S. (2023). Shark-dust: Application of high-throughput DNA sequencing of processing residues for trade monitoring of threatened sharks and rays. Conservation Letters, 16, e12971. https://doi.org/10.1111/conl.12971</p>
Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018
<p>Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018</p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.