Results 81 to 90 of about 8,582 (299)
Model‐Enabled Knowledge Transfer Across Cell Lines, Culture Scales and Conditions
Biotechnology and Bioengineering, EarlyView.ABSTRACT Mechanistic models are central to quantitative understanding and optimization of Chinese hamster ovary (CHO) cell culture processes, but their utility is often restricted by parameter sets calibrated for specific cell lines, scales, or operating conditions.
Luxi Yu +2 more
wiley +1 more source
IET Radar, Sonar & Navigation, 2022 A new variational Bayesian‐based adaptive distributed fusion unscented Kalman filter (ADFUKF‐VB) algorithm is proposed for the problem of state estimation in a distributed fusion target tracking system with unknown sensor measurement losses.
Zhentao Hu +3 more
doaj +1 more source
A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control
The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao +2 more
wiley +1 more source
, 2011 Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services.
Liu, X +5 more
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Bayesian inverse ensemble forecasting for COVID‐19
Canadian Journal of Statistics, EarlyView.Abstract Variations in strains of COVID‐19 have a significant impact on the rate of surges and on the accuracy of forecasts of the epidemic dynamics. The primary goal for this article is to quantify the effects of varying strains of COVID‐19 on ensemble forecasts of individual “surges.” By modelling the disease dynamics with an SIR model, we solve the ...
Kimberly Kroetch, Don Estep
wiley +1 more source
Journal of Modern Power Systems and Clean Energy, 2020 Distribution state estimation (DSE) is an essential part of an active distribution network with high level of distributed energy resources. The challenges of accurate DSE with limited measurement data is a well-known problem.
Alan Louis +3 more
doaj +1 more source
Distributed adaptive Kalman filter based on variational Bayesian technique
, 2019 In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed
Hong, Y., Hu, Xiaoming,, Hu, C.
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Deep Underground Science and Engineering, EarlyView.This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu +4 more
wiley +1 more source
Diffusion nonlinear Kalman filter with intermittent observations
, 2017 In this article, we consider the distributed nonlinear state estimation over sensor networks under the diffusion Kalman filter paradigm, where data only exchanges among the neighbourhoods of sensors.
Ning Li, Guoqing Wang, Yonggang Zhang
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Spatio‐Temporal Dual‐Encoder Transformer for Short‐Term Regional Wind Power Forecasting
Energy Science &Engineering, EarlyView.ST‐DualFormer separates temporal and spatial encoding to model complex dependencies in regional wind power forecasting. The fused dual‐stream representation enables accurate short‐term regional forecasts from multi‐farm meteorological and historical power data. The method achieved 5.25% nMAE and 7.53% nRMSE for three‐day‐ahead forecasting on real‐world
Jianfeng Che +4 more
wiley +1 more source