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ShareScore release 0.9.0
Dataset results
11 results for “learning based control”
Current Harmonics Minimization of PMSM Based on Iterative Learning Control and Neural Networks: Motor Data
<p>The provided motor data corresponds to an electrical machine with 24 stator slots and 16 poles. As is common in electrical machines, this motor generates unwanted flux and current harmonics. However, the accompanying paper presents an effective solution to suppress these harmonics through the combined use of Iterative Learning Control (ILC) and Neural Networks (NNs).</p> <p>The ILC method demonstrates proficient compensation for harmonics during operations with constant speed and current reference values. Additionally, Neural Networks are trained with data derived from ILC, proving to be highly effective in suppressing harmonics even during transient operation. The simulation model used in the study is based on flux and torque maps, dependent on dq-currents and the electrical angle. These maps are obtained from Finite Element Method (FEM) simulations of an interior permanent magnet synchronous machine (IPM) and are openly published here, intended to facilitate other researchers in making direct comparisons with their own methodologies.</p> <p>Simulation results presented in the paper confirm that the integration of ILC and NNs leads to superior elimination of current harmonics during transient operations compared to using ILC alone.<br> If you use the provided maps and motor data, kindly cite the associated paper for reference: https://doi.org/10.3390/machines11080784, https://www.mdpi.com/2075-1702/11/8/784</p>
Dataset for 'A deep learning-based quality control loop of the extrusion-based bioprinting process'
<p>Zip file containing the dataset for the paper 'A deep learning-based quality control loop of the extrusion-based bioprinting process' (available at <a href="https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620">https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620</a>). The dataset is composed of:</p> <ul> <li>A series of video folders, each having a unique ID, and containing the processed video frames.</li> <li>A master.csv file containing all the metadata for each video.</li> </ul> <p>The authors acknowledge the supported by the European Union’s Horizon 2020 research and innovation program under the project GIOTTO: “Giotto: Active ageing and osteoporosis: The next challenge for smart nanobiomaterials and 3D technologies,” grant agreement no. 814410.</p>
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments [dataset]
<p>This dataset is related to "Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments" in IEEE RA-L,2019.</p> <p> </p> <p>Data Capture<br> ========================<br> Data is obtained by manually flying the UAV through the redwood forest environment using a FrSky Taranis (Plus) Digital Telemetry Radio System. In total, 81,674 frames were captured together with the flight behaviour that comprehends flights under and above the forest canopy, navigation inside caves and on river beds, lakes and mountains.</p> <p> </p> <p>Folder Structure<br> ========================<br> |-manual_0 - manual_5: sequences containing training data</p> <p>|-test_0 - sequences containing testing data</p> <p> </p> <p>Data Protection<br> ========================<br> Gathered by simulated flight using Microsoft AirSim (2019) and released in accordance with MSR Aerial Information and Robotics Simulator (AirSim) lisence, which is described in details bellow:</p> <p> </p> <blockquote> <p>The MIT License (MIT)</p> <p>MSR Aerial Informatics and Robotics Platform<br> MSR Aerial Informatics and Robotics Simulator (AirSim)<br> Copyright (c) Microsoft Corporation<br> All rights reserved.<br> MIT License</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the ""Software""), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:<br> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.<br> THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p> </blockquote>
Data from: Machine learning-based discovery of molecular descriptors that control polymer gas permeation
Open the record for dataset details and reuse information.
Data for: Exploration-based learning of a stabilizing controller predicts locomotor adaptation
Open the record for dataset details and reuse information.
The Effect of a Mobile Application Based on the Social Cognitive Learning Theory on Medication Adherence and Hypertension Self-Efficacy in Patients With Hypertension: A Randomized Controlled Trial
ClinicalTrials.gov study NCT07050303. IPD Sharing: NO. Countries: 1. Publications: 13.
Motor Learning for the Lumbar Spine Using Sensor-based Postural Feedback: a Randomized Controlled Trial
ClinicalTrials.gov study NCT02773160. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
LC-Smart: A Deep Learning-Based Quality Control Model for Laparoscopic Cholecystectomy
ClinicalTrials.gov study NCT06732271. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of the Effects of Ordinary and Differential Learning Based Physiotherapy on Subjects With Ischemic Stroke Torso Control, Balance and Gait in the Second Phase of Rehabilitation
ClinicalTrials.gov study NCT04985162. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Impact of the Virtual Human Project on Team-based Learning: A Randomized-controlled Trial
ClinicalTrials.gov study NCT02484469. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Efficacy and Safety of an Artificial-pancreas-like Learning-based Control in Type 1 Diabetes on Multiple Daily Injection Therapy
ClinicalTrials.gov study NCT06418464. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
ScienceDex guides
Understand access before you commit
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.