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97 results for “torque”
Calibration Dataset of Device for Measuring Forces and Torques in Flexible Connection Joints for Parabolic Trough Collector
<p>This dataset corresponds with the calibration tests of device for measuring forces and torques in flexible connection joints for parabolic trough collector. This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 823802 (SFERA-III), and it is related with the milestone number MS29 of task 10.1.B - Enhancement of sensor monitoring/calibration and measurement accuracy of laboratory test benches of RI.</p>
The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts
<p>This dataset contains the joint toque measurements of a robot manipulator (<a href="https://blog.robotiq.com/bid/64944/Collaborative-Robot-Series-KUKA-s-Light-Weight-Robot-4">KUKA LWR4+</a>) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at <a href="https://www.ce.cit.tum.de/en/lsr/home/">Chair of Automatic Control Engineering</a>, <a href="https://www.tum.de/en/">Technical University of Munich</a>, Munich, Germany, by <a href="https://sites.google.com/view/zengjie-zhang/home">Dr. Zengjie Zhang</a>, under the supervision of <a href="https://www.ce.cit.tum.de/lsr/team/dozenten/dirk-wollherr/">Dr. Dirk Wollherr</a>, in 2017. Its detailed recording procedure is explained in the following work:</p> <p>[1] <strong>Zhang Z</strong>, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. <em>IEEE Transactions on Automation Science and Engineering</em>, 2020, 18(3): 1144-1156.</p> <p>The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm.</p> <p>The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB.</p> <p>This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.</p>
High Payload Collaborative Robot - Joint States/Motor Current/TCP Force-Torque
<p>This dataset contains bag files, with data related to robot joint position, motor currents, robot tcp pose, robot tcp Force torque values etc. that were used for the design and development of a redundant collision detection for collisions with the robotic tool. There are also data with measurements from an external F/T sensor for the validation of the approach.</p>
Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn2Au
<p>Data for the publication "<strong>Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn<sub>2</sub>Au"</strong>, published in <em>Nat Commun</em> <strong>14</strong>, 6038 (2023). (https://doi.org/10.1038/s41467-023-41569-z).</p> <p>A preprint (2023) can be found on arxiv (https://doi.org/10.48550/arXiv.2305.03368).</p> <p>The datasets are provided for Figures 2-4.</p> <p>Files are provided as comma-separated text files with column headers. The value delimiter is comma " , ". The decimal separator is period " . "</p>
Transition of laser-induced terahertz spin currents from torque- to conduction-electron-mediated transport
<p>Data for the publication "Transition of laser-induced terahertz spin currents from torque- to conduction-electron-mediated transport" published in Physical Review B. The following datasets are provided: Conductivities, electro-optic THz signals vs time and corresponding Fourier amplitude spectra for thin and thick YIG, GIG, Maghemite, Magnetite and Fe, spin currents of the mentioned materials vs time and superposition of Fe and Maghemite spin currents and raw data.</p>
Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure"
<p>Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure" (<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555">https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555</a> and <a href="http://arxiv.org/abs/2201.12984">http://arxiv.org/abs/2201.12984</a>)</p>
Drag, lift, and torque correlations for axi-symmetric rod-like non-spherical particles in linear wall-bounded shear flow
<p><strong>Data linked to the manuscript: </strong><br><em>Drag, lift, and torque coefficients of fixed axi-symmetric rod-like particles in linear wall-bounded shear flow</em></p> <p><strong>Authors:</strong><br>Victor Cheron, Berend van Wachem</p> <p>Corresponding author:<br>Berend.van.Wachem@gmail.com</p> <p><strong>Files</strong><br>Temporally averaged drag, lift and torque coefficients are written in .txt files stored in the folder ResultsCoefficients.<br>Python scripts used to plot the correlations are stored in the folder PythonScript.<br>Two simulation results are provided in the folder SimulationResults.</p> <p><strong>Results and Coefficients</strong></p> <p>The .txt files are split per coefficient, aspect ratio and shear rate, which can be identified by the name of the .txt file.<br>The results obtained for the torque coefficient of the particle of aspect ratio 2.5 for a uniform flow configuration are given in the file:<br><em>Uniform-Torque-Angles-Size2-5.txt</em></p> <p>The results obtained for the lift coefficient of the particle of aspect <br>ratio 10 for a shear rate 0.2 configuration are given in the file:<br><em>Shear02-Lift-Angles-Size10.txt</em></p> <p>In the files, the results are ordered per orientation angle and particle Reynolds number. </p> <p><strong>PythonScripts</strong></p> <p>The python scripts are split among three files:<br>- Getter.py: this script reads the .txt files storing the coefficients.<br>- ManuscriptCorrelations.py : this script returns the functions to read plot the correlations for the drag, lift and torque coefficients.<br>- generalmain.py : calls the functions</p> <p>The scripts Getter.py and ManuscriptCorrelations.py are called from the script generalmain.py file. <br>This will return a 1D column vector ordering the variables used to derive the<br>correlations:<br>- Coefficients<br>- Reynolds number<br>- Orientation Angle<br>- Dimensionless distance to the wall<br>- Aspect ratio</p> <p><strong>Simulation Results</strong></p> <p>A simulation result is provided:<br>- Aspect ratio 5, particle Reynolds number 100, orientation angles 30 and 150,<br> dimensionless distance 1.</p> <p>The data of all fields (pressure, velocity, source terms from the particles) are stored in .h-files.</p> <p>A .xmf reader is provided to read the simulation results in Paraview.</p> <p>Data for one converged simulation time are provided due to storage limits.</p> <p> </p> <p><strong>Acknowledgments</strong><br>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 448292913 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 422037413 - TRR 287.</p>
CS2_ITD_ENG_6_Engine torque
<p>Piston engines generate an output torque with very high instantaneous variations, leading to severe constraints on engine, propeller and mounting frame structure (on aeronautical applications). This information is mandatory to be able to design a mounting frame able to cope with such an engine.</p>
M2/M3 Screw Torque-Angle Curve Dataset
<p>Datasets includes torque-angle curve raw data for M2 & M3 screws, includes statistical analysis, Gaussian curve fitting data, Gaussian process regresión model, and curve fitting figures for each screw type.</p> <div> <div> <div>The datasets includes torque-angle measurements for 83 screws. </div> </div> </div>
Open data of Control of damping in perpendicularly magnetized thin films using spin-orbit torques
<p>Open access data set for manuscript "Control of damping in perpendicularly magnetized thin films using spin-orbit torques" published in Physical Review B, <strong>101</strong>, 224401 (2020)</p>
Flow Magnetic Tweezers: Gyrase dynamics under 3 different external torque conditions. Part 1/3
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils as well as response of gyrase to external torque of 2, 4 and 8 positive magnet turns. Part 1/3</p>
Flow Magnetic Tweezers: Gyrase dynamics under 3 different external torque conditions. Part 3/3
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils as well as response of gyrase to external torque of 2, 4 and 8 positive magnet turns. Part 3/3</p>
Flow Magnetic Tweezers: Gyrase dynamics under 3 different external torque conditions. Part 2/3
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils as well as response of gyrase to external torque of 2, 4 and 8 positive magnet turns. Part 2/3</p>
Robot-aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance
<p>Dataset linked with the manuscript "Robot-aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance". Please see attached readme document for details</p>
Position error-free control of magnetic-domain wall devices via spin-orbit torque modulation
<p>Magnetic domain-wall devices such as racetrack memory and domain-wall shift registers facilitate massive data storage as hard disk drives with low power portability as flash memory devices. The key issue to be addressed is how perfectly the domain-wall motion can be controlled without deformation, as it can replace the mechanical motion of hard disk drives. However, such domain-wall motion in real media is subject to the stochasticity of thermal agitation with quenched disorders, resulting in severe deformations with pinning and tilting. To sort out the problem, we propose and demonstrate a new concept of domain-wall control with a position error-free scheme. The primary idea involves spatial modulation of the spin-orbit torque along nanotrack devices, where the boundary of modulation possesses broken inversion symmetry. In this work, by showing the unidirectional motion of domain wall with position-error free manner, we provide an important missing piece in magnetic domain-wall device development.</p>
Position error-free control of magnetic-domain wall devices via spin-orbit torque modulation
<p>Magnetic domain-wall devices such as racetrack memory and domain-wall shift registers facilitate massive data storage as hard disk drives with low power portability as flash memory devices. The key issue to be addressed is how perfectly the domain-wall motion can be controlled without deformation, as it can replace the mechanical motion of hard disk drives. However, such domain-wall motion in real media is subject to the stochasticity of thermal agitation with quenched disorders, resulting in severe deformations with pinning and tilting. To sort out the problem, we propose and demonstrate a new concept of domain-wall control with a position error-free scheme. The primary idea involves spatial modulation of the spin-orbit torque along nanotrack devices, where the boundary of modulation possesses broken inversion symmetry. In this work, by showing the unidirectional motion of domain wall with position-error free manner, we provide an important missing piece in magnetic domain-wall device development.</p>
Torque High Quality
Torque from an infant burial. Bobigy's necropolis. Material: iron Diameter: 12 cm https://www.youtube.com/channel/UCC880BUJ1peB1DhkdHZ_k5Q Source: Objaverse 1.0 / Sketchfab
Engineering of intrinsic chiral torques in magnetic thin films based on the Dzyaloshinskii-Moriya interaction
<p>Open data for <strong>Engineering of intrinsic chiral torques in magnetic thin films based on the Dzyaloshinskii-Moriya interaction</strong></p>
A dataset for the investigation of upper limb torque prediction from EMG signals
<h1>Motivation</h1> <div>EMG-driven exoskeleton assistance requires the use of intention detection models to associate electromyographic signals with some feature of human movement, such as angular position, velocity, or joint torque. The goal of this dataset is to provide data that allows the benchmarking of such models for a variety of movements.</div> <h1>Short description</h1> <div>This dataset includes kinematic, dynamic and electromyographic data from 17 participants (11 males, age 28.2 ± 7 years, height 175.4 ± 7 cm, weight 70 ± 11 kg). These data were collected during the performance of a sagittal plane upper limb tracking task for single joint (elbow flexion/extension) and multiple joint (elbow and shoulder flexion/extension).</div> <h1>Methodology</h1> <div>A detailed description of the data collection methodology can be found here: https://www.biorxiv.org/content/10.1101/2024.01.11.575155v1</div> <h1>Data Description</h1> <div>The data set consists of 17 folders, one for each participant. Inside each folder you will find</div> <div>- A metadata file (<strong>SXX.json</strong>) containing information about the subject: age, sex, weight, height, and upper limb masses and lengths.</div> <div>- An OpenSim model file (<strong>scaledModel.osim</strong>) containing a scaled upper limb model of the given subject.</div> <div>- A <strong>MVC</strong> folder containing EMG data from maximal voluntary contraction trials</div> <div>- A <strong>SJ</strong> folder containing trial folders for the single joint condition (elbow flexion/extension).</div> <div>- A <strong>MJ</strong> folder containing test folders for the multi-joint condition (elbow and shoulder flexion/extension)</div> <h2>EMG Data</h2> <div>The files containing EMG data have the following header</div> <div><code>TIME,DELTAnt,DELTMed,DELTPost,PECT,LATI,TRILong,TRILat,TRIMed,BICLong,BICShort,BRA,BRD</code></div> <div>This corresponds to a time stamp and EMG signals from the anterior, median and posterior detloids, pectoralis major, latissimus dorsi, long, lateral and median triceps, long and short biceps, brachioradialis and brachialis.</div> <h2>MVC data</h2> <div>The MVC folder contains two files: <strong>emgMVCElbow.csv</strong> and <strong>emgMVCShoulder.csv</strong>. They were collected during the realisation of maximum voluntary contraction tasks and contain raw EMG data sampled at 2 kHz.</div> <h2>Trial data</h2> <h3>Single-joint condition</h3> <div>Single-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular position of the elbow in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocity of the elbow in rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torque of the elbow in N.m. Sampled at 100 Hz.</div> <h3>Multi-joint condition</h3> <div>Multi-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular positions of the upper limb in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocities of the upper limb rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torques of the upper limb in N.m. Sampled at 100 Hz.</div> <div>For this trial, kinematic and torque files use the following headers:</div> <div><code>TIME,elv_angle,shoulder_elv,shoulder_rot,elbow_flexion,pro_sup,deviation,flexion</code></div> <div>The columns correspond to the OpenSim model coordinates. For sagittal plane movement, columns of interest are <strong>shoulder_elv</strong> for shoulder flexion/extension and <strong>elbow_flexion</strong> for elbow flexion/extension.</div>
Machine Learning Techniques Application for Installation Torque Prediction of Helical Piles
<p>This database contains torque observations in the installation of helical piles used as a foundation in an infrastructure construction for power transmission towers. In this way, this database trains ML models for torque prediction.</p>
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