Results 151 to 160 of about 145,155 (289)

Memory‐Reduced Convolutional Neural Network for Fast Phase Hologram Generation

Advanced Intelligent Systems, EarlyView.
This article reports a lightweight convolutional neural network framework using INT8 quantization to efficiently generate 3D computer‐generated holograms from a single 2D image. The quantized model reduces memory usage and computational cost, accelerates inference speed, and maintains high output quality, enabling real‐time holographic display on low ...
Chenliang Chang, Chenzhou Zhao, Bo Dai, Qi Wang, Dawei Zhang, Songlin Zhuang, Chao Ping Chen   +6 more
wiley   +1 more source

Frequency-aligned loss and spectral filtering improve long-range influenza forecasting. [PDF]

Front Public Health
Feng T, Luo C, Huang Y.
europepmc   +1 more source

Median Mean Squared Error (MSE) loss comparison among the five best model sets for training (80 controls), validation (20 controls), test (10 controls), and A-T set (16 participants with A-T).

Catalina Saini (22145787), Cristian Salazar-Vilches (22145790), Caroline C.V. Blanchard (22145793), William P. Whitehouse (12245545), Denis Parra (4164562), Rob A. Dineen (19146665), Stefan Pszczolkowski (20407400)   +6 more
openalex   +1 more source

A Data‐Centric Approach to Quantifying the Forward and Inverse Relationship Between Laser Powder Bed Fusion Process Parameters and as‐Built Surface Roughness of IN718 Parts

Advanced Intelligent Systems, EarlyView.
This study introduces the first inverse machine learning model to predict laser powder bed fusion process parameters for targeted surface roughness of Inconel 718 parts. Unlike prior approaches, it incorporates spatial surface characteristics for improved accuracy.
Samsul Mahmood, Bart Raeymaekers
wiley   +1 more source

Forecasting mean deviation in glaucoma patients using an irregular autoregressive time series method. [PDF]

Sci Rep
Childress C, Gardiner SK, Pathak M.
europepmc   +1 more source

Distribution of mean squared error (MSE) values calculated for the reconstructions of synthetic test data without (top) and with (bottom) additional Gaussian noise for the varying undersampling factors, R.

Johanna Topalis (22633911), Jakob Dexl (22633914), Katharina Jeblick (22633917), Rabea Klaar (15281581), Christopher Kurz (815721), Timo Löhr (22633920), Andreas Mittermeier (22633923), Balthasar Schachtner (22633926), Anna Theresa Stüber (22633929), Tobias Weber (6330914), Philipp Wesp (22633932), Jens Ricke (74030), Max Seidensticker (659591), Guillaume Landry (815668), Michael Ingrisch (465023), Olaf Dietrich (629688)   +15 more
openalex   +1 more source

BMPCQA: Bioinspired Metaverse Point Cloud Quality Assessment Based on Large Multimodal Models

Advanced Intelligent Systems, EarlyView.
This study presents a bioinspired metaverse point cloud quality assessment metric, which simulates the human visual evaluation process to perform the point cloud quality assessment task. It first extracts rendering projection video features, normal image features, and point cloud patch features, which are then fed into a large multimodal model to ...
Huiyu Duan, Kang Fu, Sijing Wu, Yunhao Li, Zicheng Zhang, Qiang Hu, Xiongkuo Min, Guangtao Zhai   +7 more
wiley   +1 more source

Machine learning-based prediction of well performance parameters for wellhead choke flow optimization. [PDF]

Sci Rep
Akbari A, Ghazi F, Kazemzadeh Y.
europepmc   +1 more source

Distribution of mean squared error (MSE) (top) and structural similarity index measure (SSIM) (bottom) values calculated for the reconstructions of synthetic test data with the ML approach and NUFFT reconstruction with Gaussian filters with varying σ for R = 6.

Johanna Topalis (22633911), Jakob Dexl (22633914), Katharina Jeblick (22633917), Rabea Klaar (15281581), Christopher Kurz (815721), Timo Löhr (22633920), Andreas Mittermeier (22633923), Balthasar Schachtner (22633926), Anna Theresa Stüber (22633929), Tobias Weber (6330914), Philipp Wesp (22633932), Jens Ricke (74030), Max Seidensticker (659591), Guillaume Landry (815668), Michael Ingrisch (465023), Olaf Dietrich (629688)   +15 more
openalex   +1 more source

Machine Learning‐Based Standard Compact Model Binning Parameter Extraction Methodology for Integrated Circuit Design of Next‐Generation Semiconductor Devices

Advanced Intelligent Systems, EarlyView.
This study presents a neural network‐based methodology for Berkeley Short‐Channel IGFET Model–Common Multi‐Gate parameter extraction of gate‐all‐around field effect transistors, integrating binning adaptive sampling and transformer neural networks to efficiently capture current–voltage and capacitance–voltage characteristics.
Jaeweon Kang, Johyeon Kim, Sueyeon Kim, Hyunbo Cho, Jongwook Jeon   +4 more
wiley   +1 more source