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101 results for “digital twin”
Supplementary Material of the paper entitled "Governing Digital Twin technology on smart and sustainable tourism"
<p>In this document, we provide some supplementary material of the paper entitled “Governing Digital Twin technology on smart and sustainable tourism’’.</p> <p>Rahmadian, E., Feitosa, D., Zwitter, A. (2022). Governing Digital Twin technology on smart and sustainable tourism.</p>
Data Augmentation for learning mechanical digital twins of voids in welding joints
<p>In Source-2_Data_Augmentation:</p> <p>Exercice1_augmentation.ipynb Jupyter Notebook for data warpping of defect images.</p> <p>Exercice2_augmentation_multimodale.ipynb Jupyter Notebook for multimodal data augmentaion (defect images and mechanical fields) via oversampling</p> <p>Exercice3_clustering.ipynb Data clustering using the k-medoids algorithm applied to mechanical dissimilarity of the defects.</p> <p>k_medoids.py is a python code of a kmedoids algorithm.</p> <p>in Data:</p> <p>All_images.npy (numpy file) contains the defect images.</p> <p>All_Stresses.npy (numpy) contains mechanical fields, All_Stresses[k,i,j,ic,it] is the instance number k of the component ic of the Cauchy stress tensor at time it. The mechanical problem is decribed in <a href="https://dx.doi.org/10.5802/crmeca.51">⟨10.5802/crmeca.51⟩</a>. <a href="https://hal.archives-ouvertes.fr/hal-03113503">⟨hal-03113503⟩.</a></p> <p>New_images_1.npy and New_Stresses_1.npy are augmented data for k=1.</p> <p>New_images_87.npy and New_Stresses_87.npy are augmented data for k=87.</p> <p>Dissimilarity_Stress.npy is the Frobenius norm of the distances between stress tensors (All_Stresses.npy).</p> <p> </p>
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
<p>This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: LIDAR Point Cloud of a Portion of UTEP Campus
<p>This Autodesk ReCap file is a combination of numerous individual LiDAR scans captured using a Leica Terrestial LiDAR system. The scan includes some black and white and some color scans. The area of campus generally focuses on the southwestern portion of campus including the Interdisciplinary Research Building, the Mining Minds roundabout, the Sun Bowl 2 Parking Lot, the University Bookstore, and the Sun Bowl Parking Garage, and roads including University Ave. and Sun Bowl Drive.</p>
Digital twin of a standard electrochemical cell for cyclic voltammetry based on Nernst-Planck-Poisson model
<p>The project contains a COMSOL file used for the simulations in the publication "<em>Digital twin of a standard electrochemical cell for cyclic voltammetry based on Nernst-Planck-Poisson model</em>".</p>
Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study
<p><span>In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by </span><span>leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (</span><span>Δ</span><span>x=117 ± 4 </span><span>μ</span><span>m, </span><span>Δ</span><span>t=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks.<br><br>Dataset A: <span>Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted </span><span>α</span><span>-actinin, Connexin43 and DAPI immunostainings </span><span>for Cellular Potts Model optimization. During data processing, cell shape<span>s (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized.<br>Dataset C corresponds to FluoVolt recordings <span>(3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples)</span>. </span></span></span></p>
Digital Twin of a Multi-Arm Robot Platform based on Isaac Sim for Synthetic Data Generation
<p>This data set is required by the following repository<br> https://github.com/AISciencePlatform/icra2023_synthetic_data_pretraining_for_robotics</p>
The Hierarchical Task data in Digital Twin Railway Systems
<p>Data sets for simulating hierarchical<strong>-</strong>task scheduling in cloudSim for digital twin railway system, with the number of tasks ranging from 100 to 1500</p>
Dataset for geometrical digital twins of the as-built microstructure of three-leaf stone masonry walls with laser scanning
<p>This repository contains the dataset from the geometrical digital twinning of the as-built microstructure of three-leaf stone masonry walls of 700mm x 700 mm x 400 mm (Height x Length x Width) with laser scanning. It includes raw and processed data and data analysis scripts. A Readme file explains the structure of the dataset and the contents of each folder. The dataset corresponds to the journal paper <strong><em>Geometrical digital twins of the as-built microstructure of three-leaf stone masonry walls with laser scanning</em></strong> published on Scientific data https://doi.org/10.1038/s41597-023-02417-3.</p> <p>Please, cite as:</p> <p>Saloustros, S., Settimi, A., Ascencio, A.C., Gamerro, J, Weinand, Y., Beyer, K. Geometrical digital twins of the as-built microstructure of three-leaf stone masonry walls with laser scanning. <em>Sci Data</em> <strong>10</strong>, 533 (2023). https://doi.org/10.1038/s41597-023-02417-3</p>
Systematic Comparison of Software Agents and Digital Twins: Differences, Similarities, and Synergies in Industrial Production: A Dataset
<p>Supplementary dataset containing extrated information regarding the capabilites, properties, purposes, and axes of RAMI 4.0 of Agents and Digital Twins.</p>
Data and Statistics for the SLR entitled "Ontologies in Digital Twins: A Systematic Literature Review"
<p>Data and statistics that are produced as part of a Systematic Literature Review (SLR) entitled "Ontologies in Digital Twins: A Systematic Literature Review". The SLR will be submitted to Future Generation Computer System journal's <a href="https://journals.elsevier.com/future-generation-computer-systems%20/call-for-papers/special-issue-on-digital-twin-for-future-networks-and-emerging-iot-applications">Special Issue </a>on Digital Twin for Future Networks and Emerging IoT Applications.</p> <p>It consists of a set of excel sheets that includes a list of reviewed articles and their analysis. A description of what each sheet contains is given below:</p> <table> <tbody> <tr> <td><strong>Sheet Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Search Statistics</td> <td>List of research databases, search tools, search queries and number of gathered results.</td> </tr> <tr> <td>Unfiltered Paper List</td> <td>List of papers that are initially collected as a result of the paper identification phase.</td> </tr> <tr> <td>Removed Duplicates</td> <td>List of papers after removing duplicates</td> </tr> <tr> <td>Marked Non-relevants (Round 1)</td> <td>Marking papers as relevant or non-relevant based on titles, abstracts and skimming</td> </tr> <tr> <td>Marked Non-relevants (Round 2)</td> <td>Identification of relevant papers by exhaustive reading</td> </tr> <tr> <td>Final Table</td> <td>Final list of table with analysis results</td> </tr> <tr> <td>Statistics</td> <td>Statistics based on the analysis results</td> </tr> </tbody> </table> <p><strong>Acknowledgement: </strong>This work has received support from The Dutch Research Council (NWO), in the scope of Digital Twin for Evolutionary Changes in water networks (DiTEC) project, file number 19454.</p>
Video for "Outlining of the Open Digital Twin Platform"
<p>Complex simulations and machine-learning models increase in application in research, industry, and governance. However, applying these systems with reasonable accuracy and efficiency requires large-scale efforts of data collection, data transformation, data analysis, and data visualization. At the same time, maintaining the required infrastructure, software, and personnel skyrockets making these tools unavailable to many potential users. The paradigm of the digital twin offers a novel perspective on how to manage the data efficiently and make these systems available more steadily at a lower cost. We introduce the first prototype of the Open Digital Twin Platform (ODTP) that is designed to be openly available to all interested parties to enable a common framework and baseline for digital twin based research. ODTP uses containerization, loose coupling, and micro-services to provide dynamically composable digital twins.<br> ODTP also provides tools for licensing resolution, privacy and access control, and reproducibility. In its first iteration presented here, ODTP implements a common mobility research pipeline of the eqasim pipeline for MATSim. These kind of programs are usually difficult to assemble and use, thus leading to dangerous versions of ``never change a running system''. ODTP converts them into an easy-to-use version making it possible to initiate mobility simulations with one click. ODTP enables the quick adding of relevant data sources and analytical pipelines related to any topic and make them easily usable, accessible and shareable to research, industry, and governance. Thus, ODTP expands the FAIR principle from data to the complete data life cycle.</p>
DARTER: Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers
<p>The DARTER dataset contains a sample (for now) of driving data (camera, LiDAR, IMU, steering wheel angle, etc.) without labels gathered at AstaZero's test track using the Chalmers ReVeRe lab's SnowFox test vehicle. DARTER stands for Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers. This research was possible thanks to the funds of a SAFER pre-study grant.</p> <p> </p> <p><strong>About DARTER:</strong></p> <p>Verification and validation (V&V) of Intelligent Transport Solutions and their components in real traffic is difficult (costs, passers-by, etc.) and running tests under all possible conditions (weather, traffic, etc.) is impossible. For these reasons, controlled proving grounds and simulations are being used to safely increase the coverage of V&V. </p> <p>The DARTER project, a SAFER pre-study, addresses two issues connected to these alternatives. On the one hand, the limited access to real proving grounds and their corresponding high-fidelity simulations of academic researchers, key in evaluating the benefits and social and environmental harms that technological advances can pose. On the other hand, the fidelity gap between the virtual and the real world, that prevents the usage of simulations at vehicle integration test level and needs to be understood and measured. </p> <p>SAFER pre-studies https://www.saferresearch.com/content/safer-pre-studies </p>
Validation of a Digital Twin Performing Strength Training
ClinicalTrials.gov study NCT04849923. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
Open the record for dataset details and reuse information.
Accelerating development in UAV network digital twins with a flexible simulation framework
Open the record for dataset details and reuse information.
Data of paper "Grid ,Hydrodynamic boundary and Uncertainty analysis of 2D-SWEs in the context of digital twins: Taking numerical simulation of river networksas an example"
<p>论文数据 “数字孪生背景下2D-SWEs的网格、水动力边界和不确定性分析:以河流网络数值模拟为例”</p>
Digital Twin based Control of a Mobile Knuckle Boom Crane, Video 4
<p>Supportive Material for the Publication: Digital Twin based Control of a Mobile Knuckle Boom Crane. Video shows obstacle avoidance in simulation and on real-system.</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.