Results 141 to 150 of about 70,734 (340)

Optimal model‐based design of experiments for parameter precision: Supercritical extraction case

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study investigates the process of chamomile oil extraction from flowers. A parameter‐distributed model consisting of a set of partial differential equations is used to describe the governing mass transfer phenomena in a cylindrical packed bed with solid chamomile particles under supercritical conditions using carbon dioxide as a solvent ...
Oliwer Sliczniuk, Pekka Oinas
wiley   +1 more source

A Highly Efficient Compressive Sensing Algorithm Based on Root-Sparse Bayesian Learning for RFPA Radar

Remote Sensing
Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar.
Ju Wang, Bingqi Shan, Song Duan, Qin Zhang   +3 more
doaj   +1 more source

Regularized Bayesian Compressed Sensing In Ultrasound Imaging

, 2012
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Basarab, Adrian, Dobigeon, Nicolas, Kouamé, Denis, Tourneret, J.-Y.   +3 more
openaire   +1 more source

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Optimization ofk‐space trajectories for compressed sensing by Bayesian experimental design [PDF]

, 2009
Matthias Seeger, Hannes Nickisch, R Pohmann, Bernhard Schölkopf   +3 more
openalex   +1 more source

Model-based decentralized Bayesian algorithm for distributed compressed sensing [PDF]

, 2021
Razieh Torkamani, Hadi Zayyani, R. A. Sadeghzadeh   +2 more
openalex   +1 more source

Simultaneous Bayesian Compressive Sensing And Blind Deconvolution

, 2012
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Amizic, Bruno, Katsaggelos, A., Molina, Rafael, Spinoulas, Leonidas, Vega, MIguel   +4 more
openaire   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source

Bayesian Compressive Sensing Assisted Space–Time Block Coded Quadrature Spatial Modulation [PDF]

, 2018
Lixia Xiao, Pei Xiao, Yue Xiao, Ibrahim Hemadeh, Abdelrahim Mohamed, Lajos Hanzo   +5 more
openalex   +1 more source