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Titlebook: Cyber-Physical Systems; Digital Technologies Alla G. Kravets,Alexander A. Bolshakov,Maxim V. Sh Book 2021 The Editor(s) (if applicable) and

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樓主: 解毒藥
31#
發(fā)表于 2025-3-26 21:13:25 | 只看該作者
32#
發(fā)表于 2025-3-27 02:28:51 | 只看該作者
Esophagus: Functional/Motility Abnormalitiesonality. The correctness of algorithms work was estimated on a test sample, the method of extraction of technical functions showed the following criteria: precision—0.87, recall—0.77, F—0.82, the method of search of physical effect description showed precision—0.92.
33#
發(fā)表于 2025-3-27 06:19:22 | 只看該作者
34#
發(fā)表于 2025-3-27 13:11:38 | 只看該作者
35#
發(fā)表于 2025-3-27 14:07:55 | 只看該作者
https://doi.org/10.1007/978-3-662-39704-6physics-based models and machine learning models allows predicting the occurrence of malfunctions and predicting internal parameters of subsystems influencing the gas turbine plant’s technical health. The proposed methodology was implemented as a special software system, and it was tested on the field data from mobile gas turbine plants.
36#
發(fā)表于 2025-3-27 18:55:17 | 只看該作者
37#
發(fā)表于 2025-3-27 23:22:30 | 只看該作者
https://doi.org/10.1007/978-3-642-69619-0nalyzed for more accurate and faster forecasting. A module for predictive modeling of trends in technological development is developed, algorithms for the module for predictive modeling of trends in technological development are described.
38#
發(fā)表于 2025-3-28 03:21:36 | 只看該作者
39#
發(fā)表于 2025-3-28 09:23:11 | 只看該作者
40#
發(fā)表于 2025-3-28 14:07:51 | 只看該作者
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