Results 151 to 160 of about 3,299,598 (299)

A new hybrid optimization approach using PSO, Nelder-Mead Simplex and Kmeans clustering algorithms for 1D Full Waveform Inversion. [PDF]

PLoS One, 2022
Aguiar Nascimento R, Neto ÁB, Bezerra YSF, do Nascimento HAD, Lucena LDS, de Freitas JE.   +5 more
europepmc   +1 more source

Ultra‐Low Power Consumption and Highly Durability in Sm:HfO2 Thin Film Ferroelectric Memristor for Edge Detection

Advanced Electronic Materials, EarlyView.
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with
Tengyu Li, YongRui Wang, Weifeng Zhang, Jikang Xu, Xiaobing Yan   +4 more
wiley   +1 more source

Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints

Applied Sciences
Seismic multi-parameter full-waveform inversion (FWI) integrating velocity and density parameters can fully use the kinematic and dynamic information of observed data to reconstruct underground models. However, seismic multi-parameter FWI is a highly ill-
Cen Cao, Deshan Feng, Jia Tang, Xun Wang
doaj   +1 more source

Application of Optimal Basis Functions in Full Waveform Inversion

, 2004
In full waveform inversion, the lack of low frequency information in the inversion results has been a long standing problem. In this work, we show that by using mixed basis functions this problem can be resolved satisfactorily.
SUN, Gang, CHANG, Qianshun, SHENG, Ping
core   +1 more source

Synchronization of Analog Neuron Circuits With Digital Memristive Synapses: An Hybrid Approach

Advanced Electronic Materials, EarlyView.
An hybrid circuit mimicking neural units coupled using memristive synapses is introduced. The analog neurons provide flexibility and robustness, and the digital memristive coupling guarantees the full reconfigurability of the interconnection. The onset of a synchronized spiking behavior in two circuits mimicking the Izhikevich neuron is discussed from ...
Lamberto Carnazza, Francesco Maria Esposito, Carlo Famoso, Arturo Buscarino   +3 more
wiley   +1 more source

Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning

Earth and Space Science
Obtaining reliable low‐frequency seismic data is crucial for effectively reducing cycle‐skipping in full waveform inversion. However, acquiring high signal‐to‐noise ratio low‐frequency information from field data remains a challenge.
Ningcheng Cui, Tao Lei, Wei Zhang
doaj   +1 more source

Full waveform inversion based on deep learning and the phase-preserving symplectic partitioned Runge-Kutta method

Frontiers in Earth Science
To obtain more accurate full waveform inversion results, we present a forward modeling method with minimal phase error, low numerical dispersion, and high computational efficiency.
Yanjie Zhou, Xianxiang Leng, Xueyuan Huang, Xijun He, Tianming Cao   +4 more
doaj   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

sFWI: physics-informed score-based generative modeling for robust full waveform inversion

Machine Learning: Science and Technology
Full waveform inversion (FWI) is an important geophysical imaging technique with applications in hydrocarbon exploration, subsurface carbon storage, and earthquake hazard assessment. However, FWI’s efficacy is greatly affected by its ill-posed, nonlinear
Zicheng Gai, Yanfei Wang
doaj   +1 more source

A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images

Advanced Intelligent Discovery, EarlyView.
A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski, Chenyan Wang, Xinrui Wang, Huaxiao Yang, Zhao Qin, Zhen Ma   +5 more
wiley   +1 more source