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Dataset results
98 results for “CNN”
Bach10scoreinformed Lasagne/Theano CNN model
<p>This dataset accompanies the paper:<br> M.Miron, J.Janer,E.Gomez,"Monaural score-informed source separation for classical music using convolutional neural networks", ISMIR 2017, http://mtg.upf.edu/node/3806</p> <p>The code is available at the github repository: https://github.com/MTG/DeepConvSep/tree/master/examples/bach10_scoreinformed</p> <p>We include the trained CNN model for the proposed approach, which can be used to separate Bach chorales with the code provided at the github repository. </p> <p> </p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Channel State Information (CSI) analysis for predictive maintenance using Convolutional Neural Network (CNN)
<p>Dataset manual:</p> <p>This dataset contains CSI amplitude values for rotating motors in an office environment. Details of the experiments may be found in the corresponding paper published in the DATA'19 workshop, SenSys (<a href="https://doi.org/10.1145/3359427.3361917">https://doi.org/10.1145/3359427.3361917</a>). </p> <p>Folder structure:<br> The folders for servo motor and stepper motor contains separate folders for network reconnection conditions (w_recc: with reconnections, wo_recc: without recconnections) and load conditions (w_load: with load and wo_load: without load). The data is stores as Matlab files with .mat extentions. </p> <p>File structure:<br> In each file name, the digits after the '_' at the end of the file name correspond to the speed of the motor. In case of stepper motor these numbers could be directly interpreted as rpm. Ex: table_inj_with_load_5_0.mat corresponds to stationary motor (0 rpm) and table_inj_with_load_5_250.mat corresponds to motor rotating with 250 rpm speed. In the case of servo motor these numbers should be mapped with the following table in order to get the speeds.</p> <p>0: 0 rpm<br> 50: 14.45 rpm<br> 100: 8.02 rpm<br> 150: 5.38 rpm<br> 200: 4.05 rpm<br> 250: 3.26 rpm<br> 300: 2.67 rpm<br> Ex: table_inj_with_load_50.mat corresponds to motor running with 14.45 rpm.</p> <p>Each file has 3 columns, each corresponding to CSI value, labels (speed/last digits in the file name) and the data sample number (not in sequence as a result of packer loss) respectively. CSI values are typically a matrix of size 3000*180 (3000 CSI samples for 3sec data @1kHz sampling rate and 180 channels for 6 antenna pairs @ 30 subcarrier data per antenna).</p>
Tempo-CNN Training Datasets (LMD Tempo, GiantSteps MTG Tempo, EBall)
<p>Global tempo annotations used for training of the tempo estimation CNN presented in <a href="https://doi.org/10.5281/zenodo.1492353">A Single-step Approach to Musical Tempo Estimation using a Convolutional Neural Network</a>.</p>
Supplementary materials for: CNN-based surrogate for the phase field damage mode
<p>We investigate the generalization of a CNN-based surrogate for the phase field model in predicting both damage and maximum/peak load, given the image of an arbitrary 2D microstructure of a unidirectional fiber-reinforced composite. We first discuss the phase field model and the numerical procedure to generate training and test data from synthetic microstructures with different volume fractions and fiber radii. We next present a two-stage approach for predicting peak load, achieved by first transforming a given fiber-encoded microstructure image to a continuous damage field; and second, predicting peak load from the damage field. A key finding is that the direct approach for predicting peak load from the microstructure image using a standard regression model fails to generalize. Instead, the damage field, even if imperfectly predicted, provides valuable cues for the CNN in generalizing across new microstructures. We describe several case studies to demonstrate the capability of the surrogate model to predict damage and peak load and to interpolate over fiber radii and volume fractions. </p>
Performance monitoring leveraging advanced AI technique with CNN
<p>The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting time of the signal, the resolution of images, and the number of images. 2) The second step is to study damage prediction with four different stress levels. The FE model is used to simulate structural performance with different delamination conditions. Moreover, except for field test results, the artificial delamination model is created. We performed numerous finite element (FE) simulation to create inputs for CNN for damage detection. The result shows improved NDT results, and CNN can achieve structural performance prediction. We performed six tasks based on these objectives: Task 1. literature review; Task 2. Collect data from bridges; Task 3. perform filed test NDT results; Task 4. Develop FE model based on field test results; Task 5. development of a machine learning model for damage prediction.</p>
GBMatch_CNN - additional data
<p>In this repository, you can find additional data for our "GBMatch_CNN" project. Please refer to the github-page for further information: https://github.com/tovaroe/GBMatch_CNN</p> <p>Included are two datasets:</p> <p>1. GBMatch_included_tiles.7z includes all H&E tiles that were used for training the CNNs and a full annotation csv.</p> <p>2. IHC_geojsons.7z includes QuPath annotations of immunohistochemically stained slides.</p>
Supplementary materials for: CNN-based surrogate for the phase field damage mode
Open the record for dataset details and reuse information.
Results of CNN-based pollen analysis
Open the record for dataset details and reuse information.
Mask R-CNN for characterization of yardang landforms
<p>This includes the source code and datasets for the automated characterization of yardang landforms using Mask R-CNN. Codes and datasets used in the manuscript will be submitted to the Journal of Geophysical Research: Earth Surface are included. The readability of codes and other documents will be updated soon.</p>
Rezultati CNN, LSTM, RF, GRU, XGB modela za modeliranje dnevnih koncentracija čestica u zraku
<p>Rezultati CNN, LSTM, RF, GRU, XGB modela za modeliranje dnevnih koncentracija čestica u zraku u pogleu koeficijenta determinaciej i srednje apsolutne pogreške.</p>
Baseline models and optimized CNN models for 8 datasets
<p>Datasets:</p> <ul> <li>Asirra</li> <li>CIFAR-10</li> <li>CIFAR-100</li> <li>GTSRB</li> <li>HASYv2</li> <li>MNIST</li> <li>STL-10</li> <li>SVHN</li> </ul>
Written news coverage by CNN and FOX on China's COVID-19 epidemic from January 1, 2020, to May 31, 2021.
<p><span><span> </span>The researchers applied Python software to FOX's health section, CNN's health section, with "Coronavirus + China" </span><span>、</span><span>"Covid-19 + China" as keywords, to capture 4272 and 4167 articles on CNN and FOX from January 1, 2020 to May 31, 2021, respectively.</span></p>
Demodulation Combining 1D-CNN and Bi-LSTM Network over Strong Solar Wind Turbulence Channel
<p>The data in this dataset is derived MATLAB simulation dataset by us to illustrate the GMSK signal demodualtion over strong solar wind turbulence using deep learing.<br> Disclaimer<br> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.<br> Description of the dataset<br> One file per is provided as a csv file with the following features:<br> train_dataset: Including data_kb2 and data_awgn. data_kb2 is the GMSK modulated fading signal data and lable data under the influence of strong solar wind turbulence, data_awgn is the GMSK modulated signal data and lable data under the influence of Gaussian white noise only.In which per mod_data and lable_data have 21000 csv file, respectively. And per csv file has 800 data. Among them ,the lable is the original binary number. The train_dataset is used to train neural network demodulator model.<br> test_dataset:Including test_data and test_lable.The test_data and test_lable have 6 file respectively. And they are used to test the trained neural network demodulator model.<br> test_data:<br> kb2_Tb0d5_m1d4: GMSK data at the BTb=0.5 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d5_m1d2:GMSK data at the BTb=0.5 , solar wind turbulence scintillation index m=1.2.<br> kb2_Tb0d3_m1d4:GMSK data at the BTb=0.3 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d3_m1d4: GMSK data at the BTb=0.3 , solar wind turbulence scintillation index m=1.2.<br> Awgn_Tb0d5:GMSK at data the BTb=0.5 under the influence of Gaussian white noise only.<br> Awgn_Tb0d5:GMSK data at the BTb=0.3 under the influence of Gaussian white noise only.<br> test_lable:<br> kb2_Tb0d5_m1d4: GMSK lable data at the BTb=0.5 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d5_m1d2:GMSK lable data at the BTb=0.5 , solar wind turbulence scintillation index m=1.2.<br> kb2_Tb0d3_m1d4:GMSK labe data at the BTb=0.3 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d3_m1d4: GMSK labe data at the BTb=0.3 , solar wind turbulence scintillation index m=1.2.<br> awgn_Tb0d5:GMSK lable data at the BTb=0.5 under the influence of Gaussian white noise only.<br> awgn_Tb0d5:GMSK labe data at the BTb=0.3 under the influence of Gaussian white noise only.<br> </p>
Thwaites CNN Classified
<p>First attempt at Thwaites classification</p>
GJI:Learning source, path, and site effects: CNN-based Onsite Intensity Prediction for Earthquake Early Warning
<p>Dataset used for the study. Submitting to GJI. Wish me luck.</p>
The CNN classifier at the order level for fungal classification
<p>This classifier was trained using the CNN model and the WI-CBS mould ITS barcode dataset for fungal classification.</p>
The CNN classifier at the genus level for fungal classification
<p>The CNN classifier at the genus level for fungal classification using the WI-CBS ITS barcodes for training.</p>
The CNN classifier at the family level for fungal classification
<p>This CNN classifier was trained using the WI-CBS ITS barcode dataset for fungal classification at the family level.</p>
A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM
<p>The dataset used in the article 'A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM.'</p>
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.