Results 171 to 180 of about 8,865 (256)

Complex Versus Parsimonious Site‐Based Stochastic Ground Motion Models: Which One Is Better?

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Stochastic ground motion models (GMMs) provide a probabilistic representation of seismic input and are increasingly important for uncertainty quantification (UQ) in earthquake engineering. This study focuses on site‐based stochastic GMMs, which learn the statistical features of selected datasets of seismic records and generate statistically ...
Maijia Su, Mayssa Dabaghi, Marco Broccardo   +2 more
wiley   +1 more source

A Synchronous Triggering Method for Impact Artificial Seismic Source and Seismographs Based on Non-Contact Audio Detection. [PDF]

Sensors (Basel)
Wang W, Yang Y, Wang S, Wang Z, Hu J, Shi Y, Fu Z.   +6 more
europepmc   +1 more source

Stability Evaluation and Parametric Optimization of Coal‐Concrete Composite Bearing Systems Under Mine‐Water‐Induced Deterioration: Experiments and FEINN Analysis

Energy Science &Engineering, EarlyView.
Mine‐water immersion tests reveal pronounced coal weakening (vs. minor concrete degradation), identifying coal pillars as the stability‐limiting component in composite dams. A coupled FEINN framework quantifies extreme‐pressure stability and ranks multi‐parameter designs via a normalized multi‐indicator scheme, enabling optimized dam configuration for ...
He Wen, Hongchuan Dai, Guixin Zhang, Pengzhe Li, Tong Yang, Cang Gong, Renbo Gao   +6 more
wiley   +1 more source

Chaotic cosine and logistic map based robust image encryption with dual-stage confusion-diffusion architecture. [PDF]

Sci Rep
Amutha R, Phamila AV.
europepmc   +1 more source

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

A hybrid analytical-optimization framework for sidelobe suppression and beamwidth control in linear antenna arrays. [PDF]

Sci Rep
Elkhawaga AM, Aboualalaa M, Abd Elnaby MM.   +2 more
europepmc   +1 more source

Bidirectional Wireless Power Transfer for Electric Vehicle Energy Systems Integrating Modelling, Intelligent Control, Communication and Cybersecurity: A Review

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates the evolution of wireless electric vehicle charging from unidirectional wireless power transfer to bidirectional wireless power transfer (BWPT) and toward future intelligent, autonomous wireless energy systems.
P. V. Elumalai, Yuvaraja Shanmugam, A. Vivek Anand, Mesith Chaimanee, Choon Kit Chan, S. Prabhakar, R. Narayanamoorthi   +6 more
wiley   +1 more source

Sparse Transform and Compressed Sensing Methods to Improve Efficiency and Quality in Magnetic Resonance Medical Imaging. [PDF]

Sensors (Basel)
Villota S, Inga E.
europepmc   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

Refined trajectory smoothing and deep learning classification of human sperm motility. [PDF]

Hum Reprod
Shahali S, Mortimer ST, McLachlan R, O'Bryan MK, Zander-Fox D, Mortimer D, Ackermann K, Neild A, Nosrati R.   +8 more
europepmc   +1 more source