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Dataset results
19 results for “leak detection”
Dataset for the comparison of performance of two leak detectors using hydrogen reference leaks (supplement to paper "Advancing Hydrogen Leak Detection: Design and Calibration of Reference Leaks")
<p>Excel file containing some measurements made in December 2023, using three hydrogen reference leaks, to assess the performance of two distinct leak detectors, one portable and made specifically for hydrogen and one MSLD in hydrogen-mode.</p>
Properties of leak detection sensor, Arduino and application code
<p>The dataset contains properties of a leak detection sensor; absorbance spectra and reflectance in different pH environments, ionic strength, reversibility, FT-IR (Fourier-transform infrared spectroscopy), stability, TGA (Thermogravimetric analysis), and photoisomerization data. The microprocessor and application codes are also included. The information included herein will be helpful for users of the sensor.</p>
Properties of leak detection sensor, Arduino and application code
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Data from: Using controlled subsurface releases to investigate the effect of leak variation on above-ground natural gas detection
<p class="MsoNormal"><span>Pipelines transport natural gas (NG) in all stages between production and the end user. The NG composition, pipeline depth, and pressure vary significantly between extraction and consumption. As methane (CH<sub>4</sub>), the primary component of NG is both explosive and a potent greenhouse gas, NG leaks from underground pipelines pose both a safety and environmental threat. Leaks are typically found when an observer detects a CH<sub>4</sub> enhancement as they pass through the downwind above-ground NG plume. The likelihood of detecting a plume depends, in part, on the size of the plume, which is contingent on both environmental conditions and intrinsic characteristics of the leak. To investigate the effects of leak characteristics, this study uses controlled NG release experiments to observe how the above-ground plume width changes with changes in the gas composition of the NG, leak rate, and depth of the subsurface emission. Results show that plume width generally decreases when heavier hydrocarbons are present, the leak rate is reduced, and as leak depth decreases from 0.9 to 0.6 m. The above surface CH<sub>4</sub> plume is undetectable when leaks are 1.8 m deep. As most survey methods typically prioritize leaks based on the leak size, this study shows that the effect of NG density on above-ground plume width is only 4%, equivalent to the effect of leak rate. This suggests that reported leaks in areas with heavier hydrocarbons could currently be missed or underestimated. Furthermore, this study shows that leaks from pipelines laid in covers meeting minimum depth requirements of 0.9 m could be easier to detect compared to those buried shallower. Overall, this study illustrates that leak survey protocols for flowlines and gathering lines should be different from distribution pipelines and tailored to the compositions of the transported NG to report emissions accurately.</span></p>
Data from: Using controlled subsurface releases to investigate the effect of leak variation on above-ground natural gas detection
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Supplementary Materials for LAMeD: LLM-generated Annotations for Memory leak Detection
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Minimizing Search Areas for Leak Detection in Water Distribution Networks - Code
<p>This database includes the code used to analyze and produce the results for the following research article:</p> <p>Minimizing Search Areas for Leak Detection in Water Distribution Networks by B. Snider, G. Lewis, A.S. Chen, L. Vamvakeridou-Lyroudia, S. Djordjevic, D.A. Savic. Journal of Hydroinformatics. (Accepted - awaiting publication).</p> <p> </p> <p> </p>
ECO-LEAK Technique: Early Detection of Colorectal Anastomotic Leakage by Transvaginal Ultrasound
ClinicalTrials.gov study NCT05942209. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Evaluating Air Leak Detection in Intubated Patients
ClinicalTrials.gov study NCT01857986. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validation of a System Using Aerosol Glycerine to Detect and Localize Intraoperatively Pulmonary Air Leaks
ClinicalTrials.gov study NCT05971719. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility and Accuracy of a Novel Pleural Drain Gas Analyzer in Detecting Air Leaks
ClinicalTrials.gov study NCT06548386. IPD Sharing: NO. Countries: 1. Publications: 0.
Air Leak Detection and Treatment
ClinicalTrials.gov study NCT05854654. IPD Sharing: NO. Countries: 1. Publications: 0.
Development of Advanced Leak Detection Technologies for Positive Airway Pressure Devices Therapies
ClinicalTrials.gov study NCT04741854. IPD Sharing: NO. Countries: 1. Publications: 0.
Early Detection of Postoperative Anastomotic Leak by CT
ClinicalTrials.gov study NCT03632395. IPD Sharing: Not stated. Countries: 1. Publications: 0.
White Test in Intra-operative Detection of Donor Biliary Leak in LDLTx.
ClinicalTrials.gov study NCT04451447. IPD Sharing: NO. Countries: 1. Publications: 0.
Stomasense: A New Route to the Proactive Detection and Management of Leaks Within Ostomy Pouches
ClinicalTrials.gov study NCT06914804. IPD Sharing: NO. Countries: 1. Publications: 0.
A Safety and Effectiveness Prospective Blinded Clinical Study to Validate xBar System as Monitoring Tool for Anastomotic Leaks Detection
ClinicalTrials.gov study NCT06168669. IPD Sharing: NO. Countries: 2. Publications: 0.
Using Decision Trees to Detect and Isolate Leaks in the J-2X
**Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine** Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center **Abstract** The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. **Introduction** The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool [http://www.gensym.com](http://www.gensym.com) to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro
Pulmonary air leak detection
<p>Datasets for pulmonary air leak sounds acquired from rat and swine lungs.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.