Results 111 to 120 of about 39,851 (271)

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

On infinite dimensional stochastic differential games

, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

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, Debaprasad Dutta, Simant R. Upreti   +2 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

Domain‐adapted driving scene understanding with uncertainty‐aware and diversified generative adversarial networks

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel.
Yining Hua, Jie Sui, Hui Fang, Chuan Hu, Dewei Yi   +4 more
wiley   +1 more source

Bottlenecks‐Breaking in Zinc‐Iodine Batteries Toward Practical Implementation: A Review and Perspective

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Aqueous zinc–iodine batteries (Zn–I2Bs) offer promise for grid storage due to safety and cost advantages yet face critical bottlenecks: severe self‐discharge (polyiodide shuttling and HER), limited energy density, sluggish kinetics, and zinc anode instability.
Jia‐Lin Yang, Jun‐Ming Cao, Dai‐Huo Liu, Xing‐Long Wu   +3 more
wiley   +1 more source

Entropy‐Driven Innovations: Entropy Stabilized Oxides for High‐Performance Next‐Generation Lithium–Sulfur Batteries

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This article explores high‐entropy‐stabilized oxides (HEOs) as novel functional materials for addressing critical issues in lithium–sulfur (Li–S) batteries, including lithium polysulfide (LPS) shuttling, inadequate conductivity, and slow redox kinetics.
Hassan Raza, Jialiang Wei, Zhicai Wang, Zhaoyang Lv, Junye Cheng, Jia Xu, Liang An, Yuliang Liu, Sergey Ryazantsev, Shukui Li, Guangping Zheng   +10 more
wiley   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

Energy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan, Suresh Panchanathan, Chan Choon Kit, Elumalai Perumal Venkatesan, Nasim Hasan   +4 more
wiley   +1 more source