Results 31 to 40 of about 13,903 (231)

Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network

Frontiers in Earth Science, 2023
Risk assessment is critical to ensure the safe operation of oil and gas pipeline systems. The core content of such risk assessment is to determine the failure probability of the pipelines quantitatively and accurately.
Lexin Zhao, Zhengshan Luo, Guangya Deng, Victor Shi   +3 more
doaj   +1 more source

Enabling image optimisation and artificial intelligence technologies for better Internet of Things framework to predict COVID

IET Networks, EarlyView., 2022
Abstract Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework.
Noor M Allayla, Farah Nazar Ibraheem, Refed Adnan Jaleel   +2 more
wiley   +1 more source

A Novel Kernel for RBF Based Neural Networks [PDF]

Abstract and Applied Analysis, 2014
Radial basis function (RBF) is well known to provide excellent performance in function approximation and pattern classification. The conventional RBF uses basis functions which rely on distance measures such as Gaussian kernel of Euclidean distance (ED) between feature vector and neuron’s center, and so forth.
Wasim Aftab, Muhammad Moinuddin, Muhammad Shafique Shaikh   +2 more
openaire   +4 more sources

Artificial Neural Network Approaches for Predicting the Heat Transfer in a Mini-Channel Heatsink with Alumina/Water Nanofluid [PDF]

Journal of Heat and Mass Transfer Research
This work uses artificial neural networks to evaluate heat transfer in a mini-channel heatsink using an alumina/water nanofluid. The multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are employed for the modeling.
Mohammad Mahdi Tafarroj, Seyed Soheil Mousavi Ajarostaghi, C.J. Ho, Wei-Mon Yan   +3 more
doaj   +1 more source

Convergent Decomposition Techniques for Training RBF Neural Networks [PDF]

Neural Computation, 2001
In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers.
C. BUZZI, L. GRIPPO, SCIANDRONE, MARCO
openaire   +6 more sources

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

Advanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini, Elena Adelucci, Stefano Scatigna, Lorenzo Pugi, Alice Bruni, Benedetta Carotenuto, Chiara Pecini, Filippo Cavallo   +7 more
wiley   +1 more source

GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature

IEEE Access, 2023
In this paper, the scattering parameter (S-parameter) modeling method for heterojunction bipolar transistor (HBT) at different temperatures is investigated. S-parameters of HBT at different temperatures are randomly divided into training and testing sets,
Qian Lin, Xiao-Zheng Wang, Hai-Feng Wu
doaj   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Structural parameter optimization of radial basis function neural network based on improved genetic algorithm and cost function model

Advances in Mechanical Engineering
This paper investigates the structural parameter optimization of RBF networks with the goal of economic control. The cost function and its implementation method are analyzed, and the cost function model of RBF neural network is established.
Lianhui Li, Adham Manyara, Jie Liu
doaj   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source