In situations where the cost/benefit analysis of using physics-based damage propagation algorithms is not favorable and when sufficient test data are available that map out the damage space, one can employ data-driven approaches. In this investigation, we evaluate different algorithms for their suitability in those circumstances. We are interested in assessing the trade-off that arises from the ability to support uncertainty management, and the accuracy of the predictions. We compare here a Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), and a Neural Network-based approach and employ them on relatively sparse training sets with very high noise content. Results show that while all methods can provide remaining life estimates although different damage estimates of the data (diagnostic output) changes the outcome considerably. In addition, we found that there is a need for performance metrics that provide a comprehensive and objective assessment of prognostics algorithm performance.
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This dataset contains data comparison files (based on unix diff) representing the updates of the print collection. The diff-files are based on the CSV in this dataset. Filename contains the versions of the diff.
OSCAL Deep Diff is a CLI application and library that can produce schema-agnostic comparisons of JSON artifacts. The purpose of this tool is to compare OSCAL artifacts.
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1996443 Global export shipment records of Diff,cotton with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
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The size and share of the market is categorized based on Application (Database Administrators, Data Engineers, Software Developers) and Product (Schema comparison, Data comparison, Synchronization tools) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).
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596 Global import shipment records of Diff Pin with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains data comparison files (based on unix diff) representing the updates of the print collection. The diff-files are based on the CSV in this dataset. Filename contains the versions of the diff.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
6609 Global export shipment records of Diff,pinion with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
tttx/ttt-diff-buffer-data-step2-hp10 dataset hosted on Hugging Face and contributed by the HF Datasets community
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_p_stRT/_I_STRT/_S_04_stPanTr/_A_01_evigene_1_3cvs-gffmerged
This online application gives manufacturers the ability to compare Iowa to other states on a number of different topics including: business climate, education, operating costs, quality of life and workforce.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains data comparison files (based on unix diff) representing the updates of the print collection. The diff-files are based on the CSV in this dataset. Filename contains the versions of the diff.
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
tttx/ttt-diff-buffer-data-step2-hp12 dataset hosted on Hugging Face and contributed by the HF Datasets community
Official statistics are produced impartially and free from political influence.
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The top table shows the average classifier performance for cross-validation on the 9-locus public STR data. The bottom table is the performance for the same test, but on a 9-locus subset of our ground-truth training data. While overall performance is lower than the 15-locus cross-validation test on our ground-truth data (Table 1), the two data sets perform similarly here, indicating that increasing the number of markers in the data set can significantly improve performance.
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235 Global export shipment records of Diff Parts with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.
This dataset contains pre-processed data from the discrimination of Same and Different syllable pairs. Data are in the EEGLAB (Matlab toolbox) format, with each dataset consisting of a pair of files (.fdt and .set together constitute a single file). ICA has been performed to identify neural components, and all identified components remain in the data. There is also an Excel spreadsheet listing the behavioral accuracy for participants. Stimulus markers 24 are for Same trials, 26 are for Different trials, and 25 is for the control condition (passively listening to white noise).
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815 Global import shipment records of Act Diff with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
In situations where the cost/benefit analysis of using physics-based damage propagation algorithms is not favorable and when sufficient test data are available that map out the damage space, one can employ data-driven approaches. In this investigation, we evaluate different algorithms for their suitability in those circumstances. We are interested in assessing the trade-off that arises from the ability to support uncertainty management, and the accuracy of the predictions. We compare here a Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), and a Neural Network-based approach and employ them on relatively sparse training sets with very high noise content. Results show that while all methods can provide remaining life estimates although different damage estimates of the data (diagnostic output) changes the outcome considerably. In addition, we found that there is a need for performance metrics that provide a comprehensive and objective assessment of prognostics algorithm performance.