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209 results for “probabilistic”
Probabilistic Delamination Diagnosis of Composite Materials Using a Novel Bayesian Imaging Method
In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.
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} }
Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.
Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics
A general framework for probabilistic prognosis using maximum entropy approach, MRE, is proposed in this paper to include all available information and uncertainties for RUL prediction. Prognosis metrics are used for model comparison and performance evaluation. Several conclusions can be drawn based on the results in the current investigation: The proposed MRE updating approach results in more accurate and precise prediction compared with the classical Bayesian method. The classical Bayesian method is a special case of the proposed MRE approach and MRE approach is more flexible to include additional information for inference, which cannot be handled by the classical Bayesian method. The prognosis metrics can be successfully used for algorithm comparison and can give quantitative values in model (algorithm) performance evaluation. A robustness metric measuring the updating algorithmic sensitivity to prior uncertainty is proposed and applied to both Bayesian and MRE updating approaches. The application examples show that MRE exhibits more robustness against the uncertainty introduced by parameter distribution priors in the sense of prognosis performance. It is important to realize when to apply these metrics to arrive at meaningful interpretations. For instance, use of the convergence metric makes sense only when the algorithm predictions converge (get better) with time.
PyCOMPSs Probabilistic Tsunami Forecast (PTF) - Boumerdes-2003 earthquake and tsunami test-case results
Open the record for dataset details and reuse information.
Probabilistic Hierarchical Interpolation and Interpretable Configuration for Flood Prediction
<p>Code and dataset</p>
[Review version] Probabilistic projections of granular energy technology diffusion at subnational level
<p>This is a version for review prior to the publication of the article:<br>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, <em>PNAS Nexus</em>, 2023;, pgad321, <a href="https://doi.org/10.1093/pnasnexus/pgad321">https://doi.org/10.1093/pnasnexus/pgad321</a></p> <p>You find the public data set to the article here:<br><a href="https://doi.org/10.5281/zenodo.8414845">https://doi.org/10.5281/zenodo.8414845 </a></p>
Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma
DNA and RNA were isolated from snap frozen primary tumor tissue (human and mouse), and DNA was isolated from matched normal tissue (region matched non-tumor tissue for human, tail section for mouse). A mouse leg was also injected with cardiotoxin to induce tissue damage was taken following sacrifice and was snap frozen, then processed for RNA isolation to serve as a mouse tissue-normal for RNA sequencing. All sequencing was performed at the OHSU MPSSR core.
Dataset of e-Poster titled "A Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment of Groundwater around the Ariyamangalam Dumping Site"
<p>This database contains an "excel sheet" and ".docx" files having input parameters and health risk metrics generated from a Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment Framework for Ariyamangalam dumping site.</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.