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841 results for “vibrations”
Supplementary material: Hyperfine-resolved rotation-vibration line list of ammonia (NH3)
<p>Supplementary material to the manuscript: P. Coles, A. Owens, J. Küpper, A. Yachmenev, A Hyperfine-resolved Rotation-Vibration Line List of Ammonia (NH<sub>3</sub>), <em>The Astrophysical Journal</em> <strong>870</strong>, 24 (2019), DOI: <a href="http://dx.doi.org/10.3847/1538-4357/aaef7e">http://dx.doi.org/10.3847/1538-4357/aaef7e</a></p> <p>Contains two archive files</p> <ol> <li><em>efg_surface.tar </em> - contains Fortran 90 program, together with the input/output examples and README file, for computing the electric field gradient tensor of NH<sub>3</sub></li> <li><em>linelist.tar</em> - contains compressed file with the rovibrational line list of NH<sub>3</sub> with quadrupole coupling components, along with programs to extract user-desired transition data and README file</li> </ol>
Decision-Making Tool for Road Preventive Maintenance Using Vehicle Vibration Data
<p>Corresponding data set for Tran-SET Project No. 18PLSU08. Abstract of the final report is stated below for reference:</p> <p>"Automated and timely road pavement damage inspection is critical to the preventive maintenance and the long-term sustainability and resilience of roads in Region 6. Current road inspection practices rely heavily on a manual process. Sensor-based methods (e.g., LiDAR scanning) are promising but can be too expensive for a wider adoption. This study employs a crowdsourcing approach of using the vibration patterns of regular vehicles in inferring specific types of road damages. A cloud-based smart phone app and system was developed to collect real-time vehicle vibrations, location data, and road damage images for training the detection model. However, there is a great challenge in using classic classification methods with crowdsourced vibration data containing high level of noises, as vehicle vibrations are greatly affected by the types and conditions of the vehicles, as well the varying driving behaviors of drivers. The study thus employed the recent developments in Deep Learning methods, including a Self-Taught Learning (STL) algorithm and Sparse Coding to tackle with the low-quality issues of collected data. A total of 310 miles of road-induced vehicle vibration data was collected in Texas and Louisiana, and the road damage detection model was trained on Texas A&M University (TAMU) supercomputing server. The results show that the features generated from Sparse Coding greatly contribute to enhancing detection performances, by addressing low-quality data issues."</p>
Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network
<p>This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu</p> <p>The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions.</p> <p>The information contained here includes:</p> <p>- Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth).</p> <p>- Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X.</p> <p>- There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information.</p> <p>- In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable.</p> <p>- Companion documents in PDF format in order to provide more detailed information on the datasets and results.</p> <p>Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence.</p> <p>www.ideko.es<br> www.dfki.de</p> <p>Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es</p> <p>October 2019</p>
Dataset related to article "Gradient-induced vibrations and motion-induced Lenz effects on conductive nonmagnetic orthopedic implants in MRI"
<p>Set of data reported in the tables and figures of the article entitled "Gradient-induced vibrations and motion-induced Lenz effects on conductive nonmagnetic orthopedic implants in MRI", published in Magnetic Resonance in Medicine with DOI:10.1002/mrm.30263.</p> <p><span>The results presented here have been developed in the framework of the 21NRM05 STASIS Project. The Project has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. </span></p>
Variational Vibrational States of Methanol (12D): Dataset
<p>This dataset collects the coefficients larger than 1.0E-3 for the direct products of the basis functions, and their excitation numbers for the vibrational wavefunction computed with b=8 and ave-L, regarding the results discussed in the paper titled "Variational Vibrational States of Methanol (12D)" by Ayaki Sunaga, Gustavo Avila, and Edit Mátyus.</p>
Dataset for the article "Assessing the Partial Hessian Approximation in QM/MM-based Vibrational Analysis"
<p>This dataset contains the raw data and Jupyter notebooks used in the article "Assessing the Partial Hessian Approximation in QM/MM-based Vibrational Analysis."</p>
Variation in plant leaf traits affects transmission and detectability of herbivore vibrational cues
<p>Many insects use plant-borne vibrations to obtain important information about their environment, such as where to find a mate or a prey, or when to avoid a predator. Plant species can differ in the way they vibrate, possibly affecting the reliability of information, and ultimately the decisions that are made by animals based on this information. We examined whether the production, transmission and possible perception of plant-borne vibrational cues is affected by variation in leaf traits. We recorded vibrations of 69 <i>Spodoptera exigua</i> caterpillars foraging on four plant species that differed widely in their leaf-traits (cabbage, beetroot, sunflower and corn). We carried out a transmission and an airborne noise absorption experiment to assess whether leaf traits influence amplitude and frequency characteristics, and background noise levels of vibrational-chewing cues. Our results reveal that species-specific leaf traits can influence transmission and potentially perception of herbivore-induced chewing vibrations. Experimentally-induced vibrations attenuated stronger on plants with thicker leaves. Amplitude and frequency characteristics of chewing vibrations measured near a chewing caterpillar were, however, not affected by leaf traits. Furthermore, we found a significant effect of leaf area, water content and leaf thickness - important plant traits against herbivory, on the vibrations induced by airborne noise. On larger leaves higher amplitude vibrations were induced, whereas on thicker leaves containing more water airborne noise induced higher peak frequencies. Our findings indicate that variation in leaf traits can be important for the transmission and possibly detection of vibrational cues.</p>
Auxetic vibration behaviours of periodic tetrahedral units with a shared edge
A very low frequency mode supported within an auxetic structure is presented. We propose a constrained periodic framework with corner-to-corner and edge-to-edge sharing of tetrahedra and develop a kinematic model incorporating two types of linear springs to calculate the momentum term under infinitesimal transformations. The modal analysis shows that the microstructure with its two degrees of freedom has both low and high frequency modes under auxetic transformations. The low frequency mode approaches zero frequency when the corresponding spring constant tends to zero. With regard to coupled eigenmodes, the stress–strain relationship of the uniaxial forced vibration covers a wide range. When excited, a very slow motion is clearly observed along with a structural expansion for almost zero values of the elastic modulus.
Whisker vibrations measured with acoustic methods
<p>Original voltage traces measured with a mouse whisker of 21.5mm long that is consecutively trimmed. </p> <p>Data are used for all figures in the manuscript</p>
Spectrogram of vibrations from inside an active beehive (Apis mellifera)
<p>A piezoelectric transducer is inserted inside an active beehive. We record the vibrations within the hive every 5 minutes for about 2 months. We extract the spectrogram from each recording. The spectrogram is a time-frequency representation that shows how the frequencies of the recording evolve over time. All spectrograms from 3865 recordings are stacked and their corresponding timestamp appears on the upper left corner. The video demonstrates the vibrational activity of the bees and any other factor that can vibrate the beehive for two months.</p>
Linearly and Nonlinearly Implicit Schemes for Energy-Stable Simulation of String Vibrations with Collisions: Refinement, Analysis, and Comparison
<p>Sound examples accompanying the manuscript submitted to the Journal of Sound and Vibration</p>
Vibration analysis metrics of a ball bearing during different operational states
<p>This labeled dataset is provided in the form of a CSV file and contains vibration analysis metrics (v-RMS, a-RMS, a-Peak, Temperature, Crest Factor) of a 6204 2RS ball bearing by the use of an IFM VVB001 vibration sensor and measurements were captured every two minutes by the sensor device.<br> An experimental assembly was set up comprised by a 0.75kW - 1450rpm Bonfiglioli BN80B4 FD motor with an i=80 reduction gear, a coupler and an axle with the ball bearing mounted on the latter for the purpose of conducting experiments based on three operational states of the bearing in regard to the level of grease applied.</p> <p>Attribute description as per the sensor’s manual and operation instructions:</p> <ul> <li>The <strong>v-RMS</strong> (effective value of the vibration velocity) measures the total load of a rotating machine. The most frequent types of overload (unbalance, alignment errors, etc.) are reflected in the v-RMS. An increased load can damage the machine in the long term (fatigue, fatigue strength) or, in extreme cases, destroy it within a short time. It is expressed in m/s.</li> <li>The <strong>a-RMS </strong>(effective value of the acceleration) detects mechanical contact of machine components. This contact typically occurs in case of wear (faulty bearing, worn out toothed wheels, etc.) or problems with lubricants (contaminated grease, water in oil, etc.). It is expressed in m/s<sup>2</sup>.</li> <li>The <strong>a-Peak</strong> monitors the maximum value of the acceleration. Shocks in the acceleration can occur once or periodically, as in a crash, for example in the event of bearing damage. a-Peak is a measure for the forces occurring on the machine. It is expressed in m/s<sup>2</sup>.</li> <li>The <strong>Crest </strong>(or crest factor) is a described characteristic value of the signal analysis. It is defined as the ratio of the maximum value to the effective value (peak/RMS). In condition monitoring the characteristic value is used for the evaluation of the bearing condition. The high-frequency signals with a short pulse duration of a bearing damage generate higher peak values in relation to the effective value. This relation can be read from the crest factor.</li> <li>The <strong>Temperature </strong>attribute is self explanatory and is expressed in degrees of Celsius.</li> <li>Labels of the <strong>Bearing State</strong> regarding the recorded measurements:<br> 1 - sealed ball bearing with recommended amount of industrial grease<br> 2 - unsealed ball bearing with no amount of grease<br> 3 - unsealed ball bearing with excess amount of grease</li> </ul> <p>This dataset was produced for the purpose of training an Artificial Neural Network model for the task of multi-class classification in the frames of a Predictive Maintenance strategy.</p>
Source data file for "Vibrational signature of hydrated protons confined in MXene interlayers"
<p>Source data file for the manuscript entitled: "Vibrational signature of hydrated protons confined in MXene interlayers"</p>
Ambient vibrations of the San Frediano bell tower in Lucca
<table> <tbody> <tr> <th> </th> <td>The San Frediano bell tower in Lucca, dating back to the thirteenth century, is located in the historic center of Lucca and is about 50 m high. This dataset hosts the velocities recorded on the tower from 29-5-15 to 3-6-2015 under ambient vibrations. The velocities were recorded by four tri-axial seismic stations installed at different positions along the tower's height. Data have been acquired continuously with a frequency of 100 sp. The recordings hosted in the dataset are given in count and can be transformed to m/s using the constant 119*10^-9 /200. This constant allows for a good approximation of the velocity values in the frequency range of interest. For more information on the transfer function of the instruments, please get in touch with the Authors. Data were acquired in the MONSTER and the SOUL project frameworks, both funded by the "Fondazione Cassa di Risparmio di Lucca", whose contribution is gratefully acknowledged.</td> </tr> </tbody> </table>
Exploratory investigation of historical decorative laminates by means of vibrational spectroscopic techniques
<p>This dataset contains the data used for the publication entitled "Exploratory investigation of historical decorative laminates by means of vibrational spectroscopic techniques".</p>
A rapidly evolving cricket produces percussive vibrations: how, who, when, and why
<p>Sexual signals are often transmitted through multiple modalities (e.g., visual and chemical), and under selection from both intended and unintended receivers. A nuanced understanding of sexual signal evolution is important since they play a critical role in diversification. We recently documented percussive substrate-borne vibrations in the Pacific field cricket (<em>Teleogryllus oceanicus</em>), a species that uses airborne acoustic and chemical signals to attract and secure mates. The airborne signals of Hawaiian <em>T. oceanicus</em> are currently undergoing rapid evolution; at least five novel male morphs have arisen in the past 20 years. Nothing is yet known about the newly discovered percussive substrate-borne vibrations, so we ask 'how' they are produced, 'who' produces them (e.g., population, morph), 'when' they produce them (e.g., whether they are plastic), and 'why' (e.g., do they play a role in mating). We show that the vibrations are produced exclusively by males and during courtship via foreleg drumming. One novel morph, purring, produces quieter airborne songs and is more likely to drum than the ancestral morph. However, drumming behavior is also contextually plastic for some males; when we removed the ability of males to produce airborne song, ancestral males became more likely to drum, whereas two novel morphs were equally likely to drum regardless of their ability to produce song. Opposite our prediction, females were less likely to mate with males who drummed. We discuss why that might be and describe what we can learn about complex signal evolution from this newly discovered behavior.</p>
Dataset for "Vibrational Predissociation Spectra of C2N- and C3N-: Bending and Stretching Vibrations"
<p>Dataset for "Vibrational Predissociation Spectra of C2N- and C3N-: Bending and Stretching Vibrations"</p>
Ambient vibrations of the Guinigi tower in Lucca
<table> <tbody> <tr> <th> <table> <tbody> <tr> <td> <p>The Guinigi Tower is located in the historic center of Lucca and dates back to the fourteenth century. The vibrations of the tower under ambient noise were continuously measured from June 25th 2021, to September 13th 2022. This dataset includes the recordings from 1st to 7th October 2021 acquired via two seismic stations (2898, 2897) SS45 by Sara Electronic Instruments. Each file contains a one-hour recording, as specified in the file name. The six columns in the file are 2898x, 2898y, 2898z, 2897x, 2897y, and 2897z. The sensor map defines the instruments' axes, their position and the tower's geometry. Measurements are expressed in m/s.</p> <p> </p> </td> </tr> </tbody> </table> </th> <td> </td> </tr> </tbody> </table>
Effect of stochastic deformation on the vibration characteristics of a tube bundle in axial flow: code and data
<p>These files accompany the following publication:</p> <p>Dolfen, H., Vandewalle, S., & Degroote, J. (2023). Effect of stochastic deformation on the vibration characteristics of a tube bundle in axial flow. Nuclear Engineering and Design, 411, 112412. <a href="https://doi.org/10.1016/j.nucengdes.2023.112412">doi:10.1016/j.nucengdes.2023.112412</a>.</p> <p>In this publication the effect of a stochastic bow deformation on the vibration characteristics of a tube bundle was investigated. The Monte Carlo and generalized Polynomial Chaos (gPC) method were used. For the latter method, the <a href="https://chaospy.readthedocs.io/en/master/">chaospy Python-package</a> was used. Further dependencies include the numpy, scipy and matplotlib Python packages. The gPC was benchmarked with the Monte Carlo method on a steady CFD case. The the gPC was used on an FSI case used to extract the output quantity of interest, the vibration characteristics. This FSI case was run in the open-source code <a href="https://github.com/pyfsi/coconut">CoCoNuT</a>. This code developed at Ghent University is Python-based and has the capability to couple existing solvers, both open-source and commercial solvers.</p> <p>The archive includes scripts to set-up the steady CFD case as well as the FSI case, the used version of CoCoNuT and some post-processing scripts. ReadMe files are provided to explain the files, and what adjustments are likely needed to make it work on a different system. For CoCoNuT to work, the 'coconut' folder should be added to the PYTHONPATH environment variable. For requirements to run CoCoNuT, refer to the <a href="http://pyfsi.github.io/coconut/">documentation</a>.</p>
Reproducibility Report for the paper "Vibration signal-assisted endpoint detection for long-stretch, ultraprecision polishing processes"
<p>This reproducibility report includes the datasets and computer code used for reproducing the results in the paper, Jin, Bukkapatnam, Hayes, and Ding, 2023, “Vibration signal-assisted endpoint detection for long-stretch, ultraprecision polishing processes,” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol.145, pp. 061007.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.
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