Results 61 to 70 of about 14,713 (239)

Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

Energy and AI
The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with ...
Pu He, Jun-Hong Chen, Chen-Zi Zhang, Zi-Yan Yu, Ming-Yang Wang, Jun-Yu Chen, Jia-Le Song, Yu-Tong Mu, Kun-Ying Gong, Wen-Quan Tao   +9 more
doaj   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

From Dictionaries to Deep Learning: A Systematic Mapping Review of the Natural Language Processing Tasks Used to Analyze Sustainability Reports

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study aims at shedding light on the vast landscape of natural language processing (NLP) tasks used when analyzing sustainability reports or sustainability within integrated annual reports. A systematic literature review is carried out, identifying 160 studies of relevance.
Hannes Cordes
wiley   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
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

Adaptive Neural Sliding Mode Control of Active Power Filter

Journal of Applied Mathematics, 2013
A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode
Juntao Fei, Zhe Wang
doaj   +1 more source

Combined Prediction Energy Model at Software Architecture Level

IEEE Access, 2020
Accurate prediction of software energy consumption is of great significance for the sustainable development of the environment. In order to overcome the limitations of a single prediction method and further improve the prediction accuracy, a combined ...
Junke Li, Kai Liu, Mingjiang Li, Deguang Li   +3 more
doaj   +1 more source

Machine Learning Modeling of Drill Cuttings Transport Based on Computational Fluid Dynamics Simulation Data

Energy Science &Engineering, EarlyView.
Machine learning surrogates accurately predict drill cuttings transport from computational fluid dynamics data, with Reynolds number dominating system behavior. While tree‐based models excel in accuracy, Gaussian process regression provides physically consistent, smooth predictions essential for reliable drilling simulations.
Kathrin Skinder, Mostafa M. Abdelhafiz, Khizar Shahid, Gunther Brenner   +3 more
wiley   +1 more source

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Research of output characteristic fitting of eddy-current sensor based on radial-basis function neural network

Gong-kuang zidonghua, 2013
In view of problem that eddy-current sensor cannot reflect measured physical quantity accurately caused by higher nonlinear of output characteristic parameter, the paper proposed a scheme of using RBF neural network to fit output characteristic parameter
YOU Wen-jian, LIANG Bing, LI Yin-jun
doaj