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8 results for “Probabilistic systems”
Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction"
<p>The tooling, data and results for the racetrack case study in the paper <em> Components in Probabilistic Systems: Suitable by Construction, ISoLA 2020, <a href="https://doi.org/10.1007/978-3-030-61362-4_13">DOI</a></em></p>
ProbFire: a probabilistic fire early warning system for Indonesia
<p>This repository holds ProbFire model input datasets and pre-trained model weights and feature scaling parameters. Model code and description is available at https://github.com/ToFEWSI/ProbFire.</p>
IMPROVER: the new probabilistic post processing system at the UK Met Office: BAMS paper Data
<p>© Crown Copyright, Met Office</p> <p>This is the data associated with the figures in the IMPROVER BAMS paper 2023: <a href="https://doi.org/10.1175/BAMS-D-21-0273.1">https://doi.org/10.1175/BAMS-D-21-0273.1</a>.</p> <p>Gridded data is in CF-NetCDF with reasonably self explanatory metadata, other data such as for Figure 9's wind speed calibration is in CSV.</p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
Surrogate flash flooding: Probabilistic excessive rainfall predictions from the High Resolution Ensemble Forecast (HREF) system
Open the record for dataset details and reuse information.
Historical Database for Very-Short-Term Probabilistic Forecasting of System Imbalance
<p>Historical database (2014-2018) for Very-Short-Term Probabilistic Forecasting of System Imbalance.</p> <p>These data are obtained from the Belgian transmission system operator (Elia) and the European Network of Transmission system Operators (ENTSO-E).</p> <p>If you use these data, please refer to the following paper:</p> <p>J. Bottieau, L. Hubert, Z. De Grève, F. Vallée and J-F. Toubeau, “Very-Short-Term Probabilistic Forecasting for Risk-Aware Participation in the Single Price Imbalance Settlement,”.</p>
Probabilistic Model-Based Diagnosis for Electrical Power Systems
We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
Probabilistic Fault Diagnosis in Electrical Power Systems
Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power system testbed, ADAPT, using probabilistic techniques. In the context of ADAPT, we present two challenges, regarding modelling and real-time performance, often encountered in real-world diagnostic applications. To meet the modelling challenge, we discuss our novel high-level specification language which supports auto-generation of Bayesian networks. To meet the real-time challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuits typically have small footprints and are optimized for the real-time avionics systems found in spacecraft and aircraft. Using our approach, we present how Bayesian networks with over 400 nodes are auto-generated and then compiled into arithmetic circuits. Using real-world data from ADAPT as well as simulated data, we obtain average inference times smaller than one millisecond when computing diagnostic queries using arithmetic circuits that model our real-world electrical power system. Reference: O. J. Mengshoel, A. Darwiche, K. Cascio, M. Chavira, S. Poll, and S. Uckun, “Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft”, In Proc. of the Twentieth Innovative Applications of Artificial Intelligence, Conference (IAAI-08), Chicago, IL, 2008. BibTex Reference: @inproceedings{mengshoel08diagnosing, author = {Mengshoel, O. J. and Darwiche, A. and Cascio, K. and Chavira, M. and Poll, S. and Uckun, S.}, title = {Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft}, booktitle = {Proceedings of the Twentieth Innovative Applications of Artificial Intelligence Conference (IAAI-08)}, pages = {1699--1705}, address = {Chicago, IL}, year = {2008} }
ScienceDex guides
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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.
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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.