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17 results for “Particle filter”
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
<p>Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)</p>
Data for figures in "Next-generation ice nucleating particle sampling on aircraft: Characterization of the High-volume flow aERosol particle filter sAmpler (HERA)"
<p>Atmospheric ice nucleating particle (INP) concentration data from the free troposphere are sparse, but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system constisting of a sampling unit and a powerful pump unit and has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min<sup>−1</sup>, which is well above typical flow rates of aircraft INP sampling systems described in the literature (~10 L min<sup>−1</sup>). Consequently, required sampling times to capture rare, high-temperature INPs (≥-15 °C) are reduced in comparison to other systems and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is controlled remotely via software so that manual filter changes in-flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to the design and principle of operation of HERA, this paper presents laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 nm to 800 nm) or flow rate (10 L min<sup>−1</sup> to 100 L min<sup>−1</sup>) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the deposition efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application.</p> <p>The given csv files contain the data for reproducing the figures in the publication. The data structure of the csv files is explained in the README file.</p>
Supplementary Data for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting
<p>This data repository is provided as a supplement to the paper *Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* written by Håvard Heitlo Holm, Martin Lilleeng Sætra and Peter Jan van Leeuwen. It contains the complete datasets (initial conditions and results of the ensemble simulations) obtained from the experiments presented therein.</p> <p>This data set is generated by, and can be further post-processed and visualized by, the code published as *metno/gpu-ocean: Supplementary Software for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* by Håvard Heitlo Holm, Martin Lilleeng Sætra and André Rigland Brodtkorb (DOI 10.5281/zenodo.3458291). </p> <p> </p>
COCO dataset and neural network weights for micro-FTIR particle detection on filters.
<h3>The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health.</h3> <p>More information about the project <a href="https://www.imptox.eu/en/">here</a>.</p> <p><strong>Description:</strong> This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Weights File (<code>neuralNetWeights_V3.pth</code>):</strong></p> <ul> <li>Format: .pth</li> <li>Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images.</li> </ul> </li> <li> <p><strong>Custom COCO Dataset (<code>uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip</code>):</strong></p> <ul> <li>Format: .zip</li> <li>Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles.</li> <li>Contents: <ul> <li><strong>Images:</strong> JPEG format images of micro-FTIR filters.</li> <li><strong>Annotations:</strong> A JSON file in COCO format providing detailed annotations of the particles in the images.</li> </ul> </li> <li>Management: The dataset can be managed and manipulated using the <a href="https://pypi.org/project/pycocotools/">Pycocotools</a> library, facilitating easy integration with existing COCO tools and workflows.</li> </ul> </li> </ol> <p><strong>Applications:</strong> The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains.</p> <p><strong>Usage Notes:</strong></p> <ul> <li>The <code>neuralNetWeights_V3.pth</code> file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2.</li> <li>The contents of <code>uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip</code> should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes.</li> <li>Code can be found on the related <a href="https://github.com/ThibaultSchowing/IMPTOX" target="_blank" rel="noopener">Github repository.</a></li> </ul> <p> </p>
Particle filter meets hybrid octrees: an octree-based ground vehicle localization approach without learning
<p>This paper proposes an accurate lidar-based outdoor localization method that requires few computational resources, is robust in challenging environments (urban, off-road, seasonal variations) and whose performances are equivalent for two different sensor technologies: scanning LiDAR and flash LiDAR. The method is based on the matching between a pre-built 3D map and the LiDAR measurements. Our contribution lies in the combined use of a particle filter with a hybrid octree to reduce the memory footprint of the map and significantly decrease the computational load for online localization. The design of the algorithm allows it to run on both CPU and GPU with equivalent performance. We have evaluated our approach on the KITTI dataset and obtained good results compared to the state of the art. This paper introduces the baseline performance on a multi-seasonal dataset we are publicly releasing to the community. We have shown that the same localization algorithms and parameters can perform well in urban environments and can be extended to off-road environments. We have also evaluated the robustness of our method when masking angular sectors of the LiDAR field of view to reproduce edgecases scenarios in urban environments where the LiDAR field is partially occulted by another vehicle (bus, truck). Finally, experiments have been carried out with two distinctive scanning and flash LiDAR technologies. The performance achieved with the flash LiDAR is close to the scanning LiDAR despite different resolutions and sensing modalities. The positioning performance is significant with 10cm and 0.12° angular RMSE for both technologies. We validated our approach in an off-road environment from a front view field of view with only 768 LiDAR points.</p>
The Effects of Filter During CPET on WOB and Aerosol Particle Concentrations
ClinicalTrials.gov study NCT04526925. IPD Sharing: NO. Countries: 1. Publications: 4.
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
The input files and processed output data for the OSSEs using particle filter for the forecast of cloud and precipitation
<p>DART_input.tar.gz contains the input files for the DART system for the control run, Exp-1 ~ Exp-5.</p> <p>WRF&WRS_namelist.tar.gz contains the input namelist files for the WPS and WRF model for the nature run, the control run, and Exp-1 ~ Exp-5.</p> <p>cloud_data.tar.gz contains the procssed output data for the WRF/DART system corresponding to the nature run, the control run, and Exp-1 ~ Exp-5. Untarring the file will generate several subdirectories named after the UTC time. For example, 202008200200 denotes the results for at 02:00 UTC, 20 August, 2020. Because the data for all times are quite large (~10G), we only uploaded the data at 02:00 UTC, 20 August, 2020, 10:30 UTC, 20 August, 2020, 19:00 UTC, 20 August, 2020, 03:30 UTC, 21 August, 2020, and 12:00 UTC, 21 August, 2020. Each subdirectory contains preassim_mean.nc and postassim_mean.nc, which mean the posterior and prior estimate of atmosphere state variables and other processed variables. Either preassim_mean.nc or postassim_mean.nc contains the cloud water path (CWP, unit:kgm-2), cloud water content (CWC, which is the sum of the mixing ratio of six cloud hydrometeors, unit: kgkg-1), RE_CLOUD(unit:um), RE_ICE(unit:um), QVAPOR(unit:kgkg-1), T(the perturbation of potential temperature, unit:K).</p> <p>rain_rate.tar.gz contains the procssed output data for the rain rate corresponding to the nature run, the control run, and Exp-4 ~ Exp-5.</p>
Data-Two-dimensional numerical study on particle motion trajectories and deposition in a channel of partial diesel particulate filter
<p>A numerical investigation on the soot laden flow of gas in a PDPF (Partial Diesel Particulate Filter) is presented based on solving the momentum equations for continuous phase in the Euler frame and the motion equations for dispersed phase in the Lagrangian frame. The interaction between the gas and particles is treated as one-way coupling for extremely dilute particle concentration, while the interaction between particles and porous wall is implemented through user-defined-subroutines. To accurately track the motion of nanoscale particles, the drag force, the Brownian excitation, and the partial slip are included in the particle motion equation. Two methods are used to verify the gas flow model and reasonable agreement for both comparisons is observed. The effects of upstream velocity, wall permeability and particle size on the filtration efficiency and deposition distribution of the particles along the wall surface of inlet channel are quantitatively studied. The results show that (1) the wall permeability plays the most primary role in determining the filtration efficiency of PDPF; (2) high upstream velocity improves filtration efficiency and drives the deposition position of particles to the rear of inlet channel; (3) the dependence of the particle deposition distribution on its own size is mainly reflected in the initial deposition position; (4) the filtration efficiency of PDPF is not proportional markedly to the gas flow into inlet channel at a low wall permeability, implying an intense separation of particles from gas streamline at the flow entrance.</p>
Data-Two-dimensional numerical study on particle motion trajectories and deposition in a channel of partial diesel particulate filter
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Input of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
Open the record for dataset details and reuse information.
Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework
This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.*
Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics
Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.
Model-based Prognostics with Fixed-lag Particle Filters
Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a physics-based model that is derived from first principles. In most applications, uncertain- ties from a number of sources cause the predic- tions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are em- ployed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a gen- eral model-based prognostics methodology using particle filters. In order to provide more accu- rate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The exper- iments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.
SAR Image Enhancement using Particle Filters
In this paper, we propose a novel approach to reduce the noise in Synthetic Aperture Radar (SAR) images using particle filters. Interpretation of SAR images is a difficult problem, since they are contaminated with a multiplicative noise, which is known as the “Speckle Noise”. In literature, the general approach for removing the speckle is to use the local statistics, which are computed in a square window. Here, we propose to use particle filters, which is a sequential Bayesian technique. The proposed method also uses the local statistics to denoise the images. Since this is a Bayesian approach, the computed statistics of the window can be exploited as a priori information. Moreover, particle filters are sequential methods, which are more appropriate to handle the heterogeneous structure of the image. Computer simulations show that the proposed method provides better edge-preserving results with satisfactory speckle removal, when compared to the results obtained by Gamma Maximum a posteriori (MAP) filter.
Model Adaptation for Prognostics in a Particle Filtering Framework
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well- designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*
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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)
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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.