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73 results for “parameter optimization”
56th CIRP Conference on Manufacturing Systems, CIRP CMS '23, South Africa - Enhancing SLM Stainless Steel: Model-Based Parameter Optimization
<p> SLM (selective laser melting) has faced challenges in ensuring consistent mechanical properties for various geometries in the past. However, the authors have developed a system that solves this issue by calculating the optimal process parameters for each scan vector on each layer. This approach ensures consistent melt pool geometry, derived from the most robust in conduction welding mode. The results from the single melt-line experiments and the presented thermal conduction solver demonstrate the effectiveness of this approach in achieving a stable melt pool geometry in stainless steel (1.4404 or 316L). Furthermore, a rigorous testing regimen has been implemented to evaluate the system under different changing boundary conditions, to further demonstrate its usefulness. By utilizing this system, manufacturers will achieve consistent and high-quality mechanical properties for small lot sizes, including spare and obsolescent parts.</p>
Optimization of Time Parameters of Priming iTBS for Poststroke Motor Rehabilitation
ClinicalTrials.gov study NCT06612437. IPD Sharing: NO. Countries: 1. Publications: 1.
Optimizing parameters for using the parallel auditory brainstem response (pABR) to quickly estimate hearing thresholds
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Structure parameter optimization and bearing limit analysis of the expansion unit of three-roller tube expander
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Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
<p>These are the datasets used for the publication <a href="https://arxiv.org/abs/2005.01530">https://arxiv.org/abs/2005.01530</a>. The bin files are the actual data, and the other files are the configuration files used to generate the datasets.</p> <p>In more detail, the Forward_Params*.cnf files were used in combination with FDES (<a href="https://doi.org/10.1016/j.ultramic.2015.07.005">https://doi.org/10.1016/j.ultramic.2015.07.005</a>) to generate diffraction patterns.</p> <p>The Reconstruction_Params*.cnf files were used with the code outlined in the main publication to generate the simulated results.</p> <p>The Simulation*.bin files are the raw simulated CBEDs, stored in a binary format. The eCount portion of the filename corresponds to the Poissonian statistics applied to simulate real data. The ground truth file is upsampled, with larger diffraction pattern sizes, while the eCountInf has infinite dose, and therefore no noise.</p> <p>If you want more information, please contact the corresponding author of the publication, at vandenbroek@physik.hu-berlin.de. </p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012) with uncertainty on the a priori plasma parameters obtained from the Plasma Line</p> <p>- Part VII) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Envelopes for the article "Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes" and the python notebook that shows the algorithm usage.
<p>The github repository is available at https://github.com/djozinovi/goodnessOfFitEnv</p>
Dataset for the paper "Parameter study and optimization of floating wind-wave co-generation system based on the Taguchi method"
<p>The Dataset is the result files for the paper "Parameter study and optimization of floating wind-wave co-generation system based on the Taguchi method"</p>
Flexible Automation and Intelligent Manufacturing 2024, Taiwan - Optimization of Thermal Induction Welding Parameters in LPBFM for Enhanced Adhesion Control
<p>Laser powder bed fusion of metals (LPBFM) is an additive manufacturing process in which powdered alloy is selectively fused together to form near net shape parts. A method for qualifying a model to optimize thermal conduction welding parameters to omit lack of fusion defects is proposed. The approach takes into account: material, geometry and previously experimentally derived, robust process parameters.</p> <p><br>It entails carrying out specific experiments to generate data for calibration and validation of the model for fine-tuning of process parameters to compensate for the changing boundary conditions throughout the build. The objective is to achieve defined adhesion criteria for the meltpools of each laser scan vector.</p> <p><br>The method transforms the build job preparation of LPBFM into an optimization problem by determining the required precision and defining requirements for the model approach to be applicable on the shopfloor. The work contributes to a model-based quality assurance of LPBFM technology.</p>
Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"
<p><strong>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</strong></p> <p>This publication contains the research data and example scripts for the paper “Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction” [1]. The research data is found in the directory <code>research_data</code>, the example scripts are found in the directory <code>example_scripts</code>.</p> <p>The research data contains all necessary information to be able to reconstruct the figures and values given in the paper, as well as all result figures shown. Where possible, the directories contain the necessary scripts to recreate the results themselves, up to stochastic variations.</p> <p>The example scripts are intended to show how one can (i), perform a least-square type optimization of a model function (here we focus our efforts on the analytical model functions MGH17 and Gauss3, as described in the paper) using various methods (BTVO, LM, BO, L-BFGS-B, NM, including using derivative information when applicable), and (ii), perform Markov chain Monte Carlo (MCMC) sampling around the found maximum likelihood estimate (MLE) to estimate the uncertainties of the MLE parameter (both using a surrogate model of the actual model function, as well as using the actual model function directly).</p> <p> </p> <p><strong>Research data</strong></p> <p>Contained are directories for the experimental problem GIXRF, and the two analytical model functions MGH17 and Gauss3. What follows is a listing of directories and the contents:</p> <ul> <li><code>gauss3_optimization</code>: Optimization logs for the Gauss3 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_optimization</code>: Optimization logs for the MGH17 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_mcmc_analytical</code>: Scripts for the creation of the plots (does not use an optimization log), as well as plots shown in the paper. This uses the model function directly to perform the MCMC sampling.</li> <li><code>mgh17_mcmc_surrogate</code>: Optimization log of the MGH17 function used for the creation of the MCMC plots, scripts for the creation of the plots (use the optimization log), as well as plots shown in the paper. This uses a surrogate model to perform the MCMC sampling.</li> <li><code>gixrf_optimization</code>: <code>benchmark.py</code> file to perform the optimization, the optimization logs for the various methods (BTVO, LM, BO, L-BFGS-B, NM), .npy files and scripts used for the creation of the plots, and the plots shown in the paper.</li> <li><code>gixrf_mcmc_supplement</code>: optimization log used for the creation of the plot, pickle file used for the creation of the plot, script to create the MCMC plot.</li> <li><code>gixrf_optimum_difference_supplement</code>: optimization logs of BTVO optimization of the GIXRF problem, scripts to create the difference/error plots shown for the GIXRF problem in the supplement, and the plots themselves.</li> </ul> <p><strong>Employed software for creating the research data</strong></p> <p>The software used in the creation is:</p> <ul> <li>JCMsuite Analysis and Optimization toolkit, development version, commit d55e99b (the closest commercial release is found in JCMsuite version 5.0.2)</li> <li>A list of Python packages installed (excerpt from <code>conda list</code>, name and version) <ul> <li>corner 2.1.0</li> <li>emcee 3.0.2</li> <li>jax 0.2.22</li> <li>jaxlib 0.1.72</li> <li>matplotlib 3.2.1</li> <li>numba 0.40.1</li> <li>numpy 1.18.1</li> <li>pandas 0.24.1</li> <li>python 3.7.11</li> <li>scikit-optimize 0.7.4</li> <li>scipy 1.7.1</li> <li>tikzplotlib 0.9.9</li> </ul> </li> <li>JCMsuite 4.6.3 for the evaluation of the experimental model</li> </ul> <p> </p> <p><strong>Example scripts</strong></p> <p>This directory contains a few sample files that show how parameter reconstructions can be performed using the JCMsuite analysis and optimization toolbox, with a particular focus on the Bayesian target-vector optimization method shown in the paper.</p> <p>It also contains example files that show how an uncertainty quantification can be performed using MCMC, both directly using a model function, as well as using a surrogate model of the model function.</p> <p>What follows is a listing of the contents of the directory:</p> <ul> <li><code>mcmc_mgh17_analytical.py</code>: performs a MCMC analysis of the MGH17 model function directly, without constructing a surrogate model. Uses <code>emcee</code>.</li> <li><code>mcmc_mgh17_surrogate.py</code>: performs a MCMC analysis of the MGH17 model function by constructing a surrogate model of the model function. Uses the JCMsuite analysis and optimization toolbox.</li> <li><code>opt_gauss3.py</code>: performs a parameter reconstruction of the Gauss3 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>opt_mgh17.py</code>: performs a parameter reconstruction of the MGH17 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>util/model_functions.py</code>: contains the MGH17 and Gauss3 model functions, their (automatic) derivatives, and objective functions used in the optimizations.</li> </ul> <p><strong>Requirements to execute the example scripts</strong></p> <p>These scripts have been developed and tested under Linux, Debian 10. We have tried to make sure that they would also work in a Windows environment, but can unfortunately give no guarantees for that.</p> <p>We mainly use Python to run the reconstructions. To execute the files, a few Python packages have to be installed. In addition to the usual scientific Python stack (NumPy, SciPy, matplotlib, pandas, etc.), the packages <code>jax</code> and <code>jaxlib</code> (for automatic differentiation of Python/NumPy functions), <code>emcee</code> and <code>corner</code> (for MCMC sampling and subsequent plotting of the results) have to be installed.</p> <p>This can be achieved for example using pip, e.g.</p> <pre><code>pip install -r requirements.txt</code></pre> <p>Additionally, JCMsuite has to be installed. For this you can visit [2] and download a free trial version.</p> <p>On Linux, the installation has to be added to the PATH, e.g. by adding the following to your <code>.bashrc</code> file:</p> <pre><code>export JCMROOT=/FULL/PATH/TO/BASE/DIRECTORY export PATH=$JCMROOT/bin:$PATH export PYTHONPATH=$JCMROOT/ThirdPartySupport/Python:$PYTHONPATH</code></pre> <p> </p> <p><strong>Bibliography</strong></p> <p>[1] <span>M. Plock</span>, <span> K. Andrle</span>, <span> S. Burger</span>, <span> P.-I. Schneider</span>, <span>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</span>. <em>Adv. Theory Simul.</em> <strong><span>5</span></strong>, 2200112 (2022).</p> <p>[2] https://jcmwave.com/</p>
Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection
<p> <span>The data presented are related to the paper entitled</span> <span>Optimal Estimation of </span><span>Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection</span><span>, available in </span><span>Energies journal. Here, data are included to show the results of an Under Frequency Load Shedding</span> <span>(UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained</span> <span>in six cases that were considered and analyzed are shown. In this case, each case represents a different</span> <span>frequency response configuration in the event of generation loss, taking into account the presence or</span> <span>absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of</span> <span>virtual inertia by the VSC-HVDC link. Data for each example contains: state of the relay, threshold,</span> <span>position in every delay, load shed, and relay configuration parameters. Data were obtained through</span> <span>Digsilent Power Factory and Python simulations. The purpose of this dataset is that other researchers</span> <span>can reproduce the results reported in our paper</span></p>
Optimized Ensemble Algorithm of SVMD-SVR+BiLSTM for Short-Term Forecasting of Earth Rotation Parameters
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Intraoperative Haemodynamic Optimization Comparing Flow and Pressure Parameters
ClinicalTrials.gov study NCT02104687. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Estimated Oxygen Extraction Versus Dynamic Parameters for Perioperative Hemodynamic Optimization
ClinicalTrials.gov study NCT04053595. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Developing Optimal Parameters for Hyperpolarized Noble Gas and Inert Fluorinated Gas MRI of Lung Disorders
ClinicalTrials.gov study NCT02748798. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Optimization of Cochlear Implant MAP-parameters in Children
ClinicalTrials.gov study NCT03694340. IPD Sharing: NO. Countries: 1. Publications: 5.
Optimizing Parameters of Low-Intensity Focused Ultrasound for Cortical Modulation in Stroke Patients
ClinicalTrials.gov study NCT05016531. IPD Sharing: NO. Countries: 1. Publications: 1.
Optimizing Acquisition Parameters and Interpretive Methods of FDG-PET/CT With Rb-82
ClinicalTrials.gov study NCT03048097. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Unveiling the optimal parameters for cellulolytic characteristics of Talaromyces verruculosus SGMNPf3 and its secretory enzymes
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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.