Results 101 to 110 of about 26,356,550 (281)
Advanced Materials, EarlyView.Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh +5 more
wiley +1 more source
KOMBINASI KPCA DAN EUCLIDEAN DISTANCE UNTUK PENGENALAN CITRA WAJAH
Rekayasa, 2011 Permasalahan machine learning dan pattern recognition bukan merupakan penelitian yang baru. Seiring dengan perkembangan teknologi, semakin berkembang pula teknik dan algoritma yang digunakan untuk menyelesaikan permasalahan machine learning dan pattern ...
Rima Tri Wahyuningrum
doaj +1 more source
Efficient Iterative Dynamic Kernel Principal Component Analysis Monitoring Method for the Batch Process with Super-large-scale Data Sets. [PDF]
ACS Omega, 2021 Wang Y, Yu H, Li X.
europepmc +1 more source
Advanced Materials Technologies, EarlyView.A mask‐free and cost‐effective UV‐pico‐second laser‐based microfabrication method is proposed to fabricate large‐area biodegradable microelectrode arrays and pressure sensors. These devices demonstrate low impedance, good conformability, excellent biocompatibility, and rapid degradation, providing a new route for next‐generation transient electronics ...
Bhavani Prasad Yalagala +5 more
wiley +1 more source
Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure [PDF]
, 2013 A fundamental problem in structural damage detection is to define an efficient feature to calculate a damage index. Furthermore, due to perturbations from various sources, we also need to define a rigorous threshold whose overtaking indicates the ...
HAJRYA, Rafik, MECHBAL, Nazih
core +1 more source
On the Convergence of Eigenspaces in Kernel Principal Component Analysis
, 2005 This paper presents a non-asymptotic statistical analysis of Kernel-PCA with a focus different from the one proposed in previous work on this topic (\cite{ShaWilCriKan02CDL}, chapter \ref{KPCA1chap}). Here instead of considering the reconstruction error of KPCA we are interested in approximation error bounds for the eigenspaces themselves.
Zwald, Laurent, Blanchard, Gilles
openaire +2 more sources
Advanced Materials Technologies, EarlyView.Dense tactile streams from across the humanoid body converge on collide in a central wiring and data bottleneck. By relocating computation closer to and then into the skin itself, near‐ and in‐sensor architectures, together with neuromorphic computing, chart a path toward perception‐native electronic skin, in which the conversion of stimulus into ...
Mijin Kim +6 more
wiley +1 more source
Principal Component Analysis of Crop Yield Response to Climate Change [PDF]
The objective of this study is to compare the effects of climate change on crop yields across different regions. A Principal Component Regression (PCR) model is developed to estimate the historical relationships between weather and crop yields for corn ...
Shurley, W. Donald +4 more
core
Unlocking Terahertz Steganography: Authentication of Hidden Images in Flexible Media
Advanced Materials Technologies, EarlyView.Hidden information in multilayered devices, such as steganographic portraits, is detected and authenticated using reflection‐mode terahertz time‐domain imaging. Image reconstruction relies on a dedicated figure‐of‐merit obtained by integrating the signal intensity over the time‐window associated with the reflection from the metallic mirror backing the ...
Tiziana Ritacco +4 more
wiley +1 more source
MachinesThe feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction ...
Yan He, Linzheng Ye, Yao Liu
doaj +1 more source