Results 101 to 110 of about 8,432 (220)
Dynamic models of brain imaging data and their Bayesian inversion
, 2010 This work is about understanding the dynamics of neuronal systems, in particular with respect to brain connectivity. It addresses complex neuronal systems by looking at neuronal interactions and their causal relations.
Sousa Cardoso Costa Marreiros, A.
core
A Chance‐Constrained Model for a Production Routing Problem With Uncertain Availability of Vehicles
Networks, EarlyView.ABSTRACT The Production Routing Problem (PRP) is a complex integrated problem that allows for the achievement of competitive advantages, such as better management of inventory, reduction in operational costs and lead times, improvement in efficiency and customer service, and better response to market changes.
Alline Zanette +2 more
wiley +1 more source
Estimation of inverse mean: An orthogonal series approach
In this article, we propose the use of orthogonal series to estimate the inverse mean space. Compared to the original slicing scheme, it significantly improves the estimation accuracy without losing computation efficiency, especially for the ...
Yin, Xiangrong, Wang, Qin
core
Pest Management Science, EarlyView.In the present study, relationship between knockdown and mortality of insecticides was examined against the adult female housefly, Musca domestica. Type‐I pyrethroid insecticides showed a weak linkage, as early knocked down flies did not always result in mortality, while type‐II pyrethroids and organophosphates exhibited strong correlation between the ...
Junho Yoon, Jun‐Hyung Tak
wiley +1 more source
A structurally localized ensemble Kalman filtering approach
Quarterly Journal of the Royal Meteorological Society, EarlyView.We derive an inherently localized ensemble Kalman filtering (EnKF) approach, avoiding the need for any auxiliary localization technique. The idea is to first use the variational Bayesian optimization to approximate the (continuous) state analysis probability density function (pdf) by a product of independent marginal pdfs corresponding to small ...
Boujemaa Ait‐El‐Fquih +1 more
wiley +1 more source
Sufficient Dimension Reduction with Orthogonality Constraint through Manifold Optimization
Electronic Thesis or DissertationSufficient Dimension Reduction (SDR) aims to identify a central subspace (CS), where the projection of predictors onto the subspace retains all information about the response variable without loss of information, thereby ...
Wang, Hui
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Hybrid physics–data‐driven modeling for sea ice thermodynamics and transfer learning
Quarterly Journal of the Royal Meteorological Society, EarlyView.Icepack–NN, a machine‐learning‐based hybrid version of the sea‐ice column model Icepack, is developed to correct state‐dependent forecast errors arising from misspecified snow thermodynamics, using neural networks applied online within the physical model.
G. De Cillis +7 more
wiley +1 more source
Quality and Reliability Engineering International, EarlyView.ABSTRACT Upon establishing multiple response optimization (MRO) or dual response optimization (DRO), response surface methodology (RSM) acts as a conventional model‐driven framework for optimizing small‐sized experimental data to derive an input condition in which a decent response is expected.
Dong‐Hyun Koo +2 more
wiley +1 more source
An adaptive estimation of MAVE
Minimum average variance estimation (MAVE, Xia et al. (2002) [29]) is an effective dimension reduction method. It requires no strong probabilistic assumptions on the predictors, and can consistently estimate the central mean subspace.
Yao, Weixin, Wang, Qin
core
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT This paper presents novel results regarding the input–output finite‐time stability (IO‐FTS) of nonlinear quadratic systems (NLQSs), a class of dynamical models particularly useful for the study of robotic systems, biochemical reaction networks, and other processes of interest to the applied sciences.
Alessio Merola +5 more
wiley +1 more source