Results 31 to 40 of about 6,285,273 (279)
Cell model parameter identification algorithm (MSE: mean square error, GA: genetic algorithm).
, 2015 Cell model parameter identification algorithm (MSE: mean square error, GA: genetic algorithm).
Samaneh Khakshour (732999) +3 more
core +1 more source
Performance Analysis of Distributed Incremental LMS Algorithm with Noisy Links
International Journal of Distributed Sensor Networks, 2011 Recently proposed distributed adaptive estimation algorithms for wireless sensor networks (WSNs) do not consider errors due to noisy links, which occur during the transmission of local estimates between sensors.
Azam Khalili +2 more
doaj +1 more source
Earth and Space Science, 2020 A clustering and classification method using a Gaussian mixture model (GMM) is used to summarize and simplify meteorological data from a numerical weather prediction (NWP) model.
Takeshi Watanabe +2 more
doaj +1 more source
Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective
Water Science and Technology, 2023 This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital.
Mehran Khan +3 more
doaj +1 more source
On the influence of detection tests on deterministic parameters estimation [PDF]
, 2006 In non-linear estimation problems three distinct regions of operation can be observed. In the asymptotic region, the Mean Square Error (MSE) of Maximum Likelihood Estimators (MLE) is small and, in many cases,close to the Cramer-Rao bound (CRB).
Chaumette, Eric +5 more
core +1 more source
Monolithically 3D‐Printed Spectral‐Spatial Meta‐Authenticators
Advanced Materials, EarlyView.A single height map printed in a low‐index resin by two‐photon lithography encodes multiple visible wavelengths in one metasurface without any polarization optics. A fabrication‐aware optimization folds the printable height levels into the design, so the device is printed as optimized.
Cherry Park +3 more
wiley +1 more source
Leveraging Mechanical Resonances for the Selection of Promising Materials in Complex Phase Spaces
Advanced Materials Technologies, EarlyView.A central challenge in materials science is the design of new functional materials with enhanced performance for targeted applications. This paper demonstrates that “listening” to materials through measuring their mechanical resonances is a rich, and largely untapped, platform for the rapid identification of promising novel compounds and a powerful ...
Christopher A. Mizzi +4 more
wiley +1 more source
Prediction of abrasive wear and surface hardness of printed parts by SLA technology
Ingenius: Revista de Ciencia y TecnologíaIn the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and
P. Muñoz-Valverde +4 more
doaj +3 more sources
IET Computers & Digital Techniques, 2021 In recent years, approximate circuit design targeting the error‐tolerant applications has gained significance. In this study, the authors propose a metric that ranks a stand‐alone approximate adder in terms of power savings obtained for a given mean ...
Celia Dharmaraj +2 more
doaj +1 more source
Customizable Piezo‐Transmittance‐Based Multiaxial Force Sensor With Deep‐Learning‐Assisted Sensing
Advanced Materials Technologies, EarlyView.Development of a soft tri‐axis piezo‐transmittance sensor with integrated LEDs and photodiodes enables multidirectional force sensing through deformation‐induced optical modulation. A tailored 1D convolutional neural network provides real‐time force direction and intensity prediction, achieving 99.6% classification accuracy and a mean squared error of ...
Filippo Peruzzi +6 more
wiley +1 more source