Results 81 to 90 of about 59,820 (274)

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

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

Linear and Order Statistics Combiners for Pattern Classification

, 1999
Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers.
Ghosh, Joydeep, Tumer, Kagan
core   +2 more sources

AI‐driven circular economy optimization in waste management: A review of current evidence

Environmental Progress &Sustainable Energy, EarlyView.
Abstract The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic ...
David Bamidele Olawade, Ram Narayan Yadav, James O Ijiwade, Ojima Z. Wada   +3 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

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

Radial basis function network learns ceramic processing and predicts related strength and density [PDF]

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C.
Baaklini, George Y., Cios, Krzysztof J., Tjia, Robert E., Vary, Alex   +3 more
core   +1 more source

Machine Learning‐Driven Classification and Production Capacity Prediction of Tight Sandstone Reservoirs: A Case Study of the Taiyuan Formation, Ordos Basin

Energy Science &Engineering, EarlyView.
On the basis of core and log data, a Bayesian‐Optimized Random Forest model achieved 92.76% accuracy in classifying tight sandstone reservoirs. A gray relational analysis‐derived evaluation index shows > 80% consistency with actual gas zones. ABSTRACT Tight sandstone gas (TSG), an unconventional oil–gas resource, has heterogeneous reservoirs ...
Yin Yuan, Haijiao Fu, Detian Yan, Gang Wang, Shuguang Yang, Mengmeng Liu, Qingling Jiang, Xiaoming Wang, Wei Wang   +8 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  

Phase transmittance RBF neural networks

Electronics Letters, 2007
Presented is a new complex valued radial basis function (RBF) neural network with phase transmittance between the input nodes and output, which makes it suitable for channel equalisation on quadrature digital modulation systems.
D.V. Loss, M.C.F. DeCastro, P.R.G. Franco, F.C.C. DeCastro   +3 more
openaire   +1 more source