为敌
发表于 2025-3-25 03:21:55
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.
影响深远
发表于 2025-3-25 09:34:05
http://reply.papertrans.cn/25/2419/241811/241811_22.png
钳子
发表于 2025-3-25 14:48:20
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.
一大群
发表于 2025-3-25 19:37:36
http://reply.papertrans.cn/25/2419/241811/241811_24.png
adhesive
发表于 2025-3-25 22:17:58
http://reply.papertrans.cn/25/2419/241811/241811_25.png
Notify
发表于 2025-3-26 02:23:15
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
Nebulous
发表于 2025-3-26 06:36:25
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.
上釉彩
发表于 2025-3-26 10:38:46
http://reply.papertrans.cn/25/2419/241811/241811_28.png
mortgage
发表于 2025-3-26 16:40:06
http://reply.papertrans.cn/25/2419/241811/241811_29.png
infarct
发表于 2025-3-26 16:54:25
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).