Results 31 to 40 of about 151,464 (237)

Hopfield Neural Networks for Online Constrained Parameter Estimation With Time‐Varying Dynamics and Disturbances

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Linear Superiorization for Infeasible Linear Programming

, 2016
Linear superiorization (abbreviated: LinSup) considers linear programming (LP) problems wherein the constraints as well as the objective function are linear.
A Cegielski, AR Pierro De, GT Herman, GT Herman, HA Simon, HH Bauschke, J-B Baillon, JW Chinneck, Y Censor, Y Censor, Y Censor, Y Censor   +11 more
core   +1 more source

Multimodal Mechanical Testing of Additively Manufactured Ti6Al4V Lattice Structures: Compression, Bending, and Fatigue

Advanced Engineering Materials, EarlyView.
In this experimental study, the mechanical properties of additively manufactured Ti‐6Al‐4V lattice structures of different geometries are characterized using compression, four point bending and fatigue testing. While TPMS designs show superior fatigue resistance, SplitP and Honeycomb lattice structures combine high stiffness and strength. The resulting
Klaus Burkart, Jonas Schmidt, Anastasiya Toenjes, Johanna Eisenträger   +3 more
wiley   +1 more source

Exploiting the structure effectively and efficiently in low rank matrix recovery

, 2018
Low rank model arises from a wide range of applications, including machine learning, signal processing, computer algebra, computer vision, and imaging science.
Absil, Agarwal, Ahmed, Alter, Amelunxen, Beck, Blanchard, Blumensath, Boumal, Boyd, Bunk, Cai, Cai, Cai, Cai, Cai, Cai, Candes, Candes, Candes, Candes, Candes, Candès, Candès, Candès, Candès, Carmon, Chandrasekaran, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Chen, Combettes, Davenport, Davenport, Ding, Donoho, Fazel, Ge, Ge, Ge, Goldberg, Gross, Hardt, Harrison, Huang, Jaggi, Jain, Jain, Ji, Jin, Keshavan, Kim, Kliesch, Koltchinskii, Krim, Kueng, Lanman, Li, Li, Lin, Ling, Ling, Liu, Liu, Lustig, Ma, Miao, Mishra, Mishra, Mishra, Mobahi, Mu, Negahban, Netrapalli, Ngo, Potter, Qu, Recht, Recht, Schermelleh, Srebro, Strohmer, Sun, Sun, Tanner, Tanner, Tao, Tharrault, Toh, Toh, Tomasi, Tu, Vandereycken, Wang, Wei, Wei, Wei, Wen, WenHuang, Xu, Yi, Zhang, Zhang, Zheng   +109 more
core   +2 more sources

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Modifications of iterative reconstruction algorithms for the reduction of artefacts in high resolution X-ray computed tomography [PDF]

, 2013
X-ray Computed Tomography is a non destructive technique which allows for the visualization of the internal structure of complex objects. Most commonly, algorithms based on filtered backprojection are used for reconstruction of the projection data ...
Brabant, Loes, De Witte, Yoni, Dierick, Manuel, Pauwels, Elin, Van Hoorebeke, Luc   +4 more
core  

CNN-Based Projected Gradient Descent for Consistent Image Reconstruction

, 2017
We present a new method for image reconstruction which replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN).
Gupta, Harshit, Jin, Kyong Hwan, McCann, Michael T., Nguyen, Ha Q., Unser, Michael   +4 more
core   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Algebraic Reconstruction Techniques for Tomographic Particle Image Velocimetry [PDF]

, 2007
Tomographic particle image velocimetry (Tomo-PIV) is a technique for three-component three-dimensional (3C-3D) velocity measurement based on the tomographic reconstruction of a volume intensity field from multiple two-dimensional projection.
Atkinson, C. H., Soria, J.
core   +1 more source