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Dataset results
229 results for “pneumaticity”
A Pneumatic Compression Device for the Treatment of Lymphedema in Patients With Stage II-IV Head and Neck Cancer After Chemoradiation
ClinicalTrials.gov study NCT05512767. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Intermittent Pneumatic Compression of the Upper Extremity for Post-Operative VTE Prophylaxis
ClinicalTrials.gov study NCT04341272. IPD Sharing: NO. Countries: 0. Publications: 0.
Osteotomy of the 1st Metatarsal for Hallux Valgus Using Pneumatic Oscillating Saw or Piezoelectric Scalpel
ClinicalTrials.gov study NCT04365712. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Comparative Study of Per-oral Endoscopic Myotomy (POEM) in Treatment - naïve Achalasia Patients Versus Patients With Previous Pneumatic Dilation
ClinicalTrials.gov study NCT07287787. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Intermittent Pneumatic Compression of the Foot vs Geko Plus R-2 Neuromuscular Electrostimulation Device in Venous Leg Ulcer Patients
ClinicalTrials.gov study NCT02912858. IPD Sharing: YES. Countries: 0. Publications: 0.
A Model-based Prognostics Approach Applied to Pneumatic Valves
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
Prognostics for Ground Support Systems: Case Study on Pneumatic Valves
Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.
A Model-Based Prognostics Approach Applied to Pneumatic Valves
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the un- derlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model- based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refuel- ing system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evalu- ate its effectiveness and robustness. The approach is demon- strated using historical pneumatic valve data from the refuel- ing system.
Application of Model Based Prognostics to Pneumatic Valves in a Cryogenic Propellant Loading Testbed
Pneumatic-actuated valves are critical components in many applications, including cryogenic propellant loading for space operations. For these components, failures need to be predicted so that components can be repaired to ensure mission success, i.e., health monitoring and fault prognostics is required. In order to develop, test, mature, and deploy valve prognostics algorithms, we have developed a testbed for pneumatic valves used in cryogenic service for propellant loading operations, in which we can inject controlled damage pro files and observe its effects on valve operation. In this paper, we focus on the prognostics of a continuously-controlled pneumatic valve. We describe the construction of the testbed, the fault injection mechanisms, and the model-based valve prognostics algorithms. Experimental results from the testbed demonstrate successful prediction of valve failure.
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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.