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Titlebook: Cyber-Physical Systems and Control II; Dmitry G. Arseniev,Nabil Aouf Conference proceedings 2023 The Editor(s) (if applicable) and The Aut

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樓主: otitis-externa
21#
發(fā)表于 2025-3-25 03:21:55 | 只看該作者
Arbeitsrecht und Gesellschaftsrechtrm of a linear or nonlinear equation or a system of such equations. The local linear approximation of the measurands dependencies was used to obtain these expressions. It was also analyzed how this nonlinearity neglecting affects the error estimation accuracy in such cases.
22#
發(fā)表于 2025-3-25 09:34:05 | 只看該作者
23#
發(fā)表于 2025-3-25 14:48:20 | 只看該作者
Control of Plane Poiseuille Flow Using the Kreiss Constantlinear flow model. The maximum transient energy growth and the Kreiss constant both convey information regarding this transient behavior. A Kreiss-constant-minimizing controller is obtained and its effectiveness for the plane Poiseuille flow control problem is demonstrated.
24#
發(fā)表于 2025-3-25 19:37:36 | 只看該作者
25#
發(fā)表于 2025-3-25 22:17:58 | 只看該作者
26#
發(fā)表于 2025-3-26 02:23:15 | 只看該作者
Conference proceedings 2023 held from 29 June to 2?July 2021 in St.?Petersburg, Russia. The CPS&C’2021 Conference continues the series of international conferences that began in 2019 when the first International Conference on Cyber-Physical Systems and Control (CPS&C’2019) took place...Cyber-physical systems (CPSs) considered
27#
發(fā)表于 2025-3-26 06:36:25 | 只看該作者
Brief von Galois an Auguste Chevalierared-divergence. For comparison purposes, we also study the symmetric Hellinger distance measure. A lot of numerical experiments on synthetic and real datasets illustrating the proposed modifications are provided and analyzed.
28#
發(fā)表于 2025-3-26 10:38:46 | 只看該作者
29#
發(fā)表于 2025-3-26 16:40:06 | 只看該作者
30#
發(fā)表于 2025-3-26 16:54:25 | 只看該作者
Robust Models of Distance Metric Learning by Interval-Valued Training Dataeme points of a polyhedron produced by the probability distribution constraints. The approaches can be regarded as a framework for developing a set of robust distance metric learning models which can be constructed on the basis of the Mahalanobis distance (linear transformation) as well as of Siamese neural networks (non-linear transformation).
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