Results 51 to 60 of about 22,233,261 (243)

Expanding Genetic Code to Generate Human Brain Organoids with Both Vasculature and Microglia‐Like Cells

Advanced Science, EarlyView.
Using genetic code expansion, we engineered vascularized human cerebral organoids (vhCOs) with microglia‐like cells and blood‐brain barrier features. vhCOs recapitulate neurovascular interactions, regional identities, and neuronal subtypes resembling the fetal brain.
Haishuang Lin, Yaqing Wang, Hu Du, Yuxin Qin, Haoyu Zhang, Peng Wang, Lin Wei, Jianhua Qin   +7 more
wiley   +1 more source

Closing the Loop: High‐Precision 3D Photofabrication in Living Tissues

Advanced Science, EarlyView.
Writing 3D microstructures inside living tissue demands more than laser access; It demands information. Hierarchical sensing captures thermal and mechanical states across pulse‐train, voxel, and structure timescales, feeding a controller that steers the laser in real time.
Amirbahador Zeynali, Mangirdas Malinauskas, Giuseppe Chirico   +2 more
wiley   +1 more source

Model Predictive Control with Integral Action: A simple MPC algorithm [PDF]

Modeling, Identification and Control, 2015
A simple Model Predictive Control (MPC) algorithm of velocity (incremental) form is presented.The proposed MPC controller is insensitive to slowly varying system and measurement trends and thereforehas integral action.
David Di Ruscio
doaj   +1 more source

Learning regime‐dependent governing equations: A symbolic decision tree approach

AIChE Journal, EarlyView.
Abstract Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data‐driven discovery of regime‐dependent governing equations essential for predictive modeling, optimization, and control. We propose symbolic decision trees for the data‐driven discovery of regime‐dependent governing equations.
Ilias Mitrai, Tongjia Liu, Gabriel E. Sanoja   +2 more
wiley   +1 more source

Extended model predictive control: robust controller with matching condition

Nihon Kikai Gakkai ronbunshu, 2020
This paper describes a new robust controller based on Model Predictive Control (MPC). MPC is one of the useful control methods because it enables to design time response intuitively and to consider the constraint of state and input. However, MPC does not
Kai MASUDA, Kenji UCHIYAMA
doaj   +1 more source

Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

AIChE Journal, EarlyView.
Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate ...
Huitian Yu, Jiewen Zhao, Heiko Briesen
wiley   +1 more source

APPLICATION OF MODEL PREDICTIVE CONTROL (MPC) TUNING STRATEGY IN MULTIVARIABLE CONTROL OF DISTILLATION COLUMN [PDF]

, 2007
A model predictive control strategy is proposed for multivariable nonlinear control problem in a distillation column. The aim is to provide a solution to nonlinear control problem that is favorable in terms of industrial implementation.
Wahid, A., Ahmad, A.
core   +2 more sources

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Energy and AI
Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads.
Dylan Wald, Olga Doronina, Kathryn Johnson, Ryan King, Michael Sinner, Kevin Griffin, Rohit Chintala, Deepthi Vaidhynathan, Jibonananda Sanyal, Marc Day   +9 more
doaj   +1 more source

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source