Results 121 to 130 of about 874,187 (329)

Enhanced Performance and In Situ TEM Investigation in High Entropy Alloy Electrode Based Memristors

Advanced Functional Materials, EarlyView.
This study utilizes in situ TEM, EDS, EELS, and APT to reveal switching in HEA/ZnO/Nb:STO RRAM. High diffusivity of Mn lowers the SET voltage to 1.5 V. Cr stabilizes the oxygen layer, while Fe, Co, and Ni stabilize the electrode. Based on these observations, the device achieves 30 ns switching, a 10⁷ memory window, and excellent endurance, being ...
Jing‐Yuan Tsai, Chun‐Wei Huang, Jui‐Yuan Chen, Che‐Hung Wang, Ping‐Hsien Wu, Jia‐Wei Chen, Yu‐Chen Yang, Tung‐huan Chou, Kun‐lin Lin, Ying‐Hao Chu, Wen‐Wei Wu   +10 more
wiley   +1 more source

A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

Measurement + Control, 2019
Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers.
K Karthikumar, V Senthil Kumar, M Karuppiah   +2 more
doaj   +1 more source

Synthesis of artificial neural networks by transconductors only [PDF]

, 1991
MehmetAli Tan
openalex   +1 more source

Artificial Compound Eye for Clear Vision in Harsh Environment

Advanced Functional Materials, EarlyView.
Artificial compound eye (CE) with exceptional imaging and motion tracking capturing the movement of a spider and swinging thread with a wide field of view. Surface modifications ensure clear vision of alphabetic letters in rain and fog. Images captured in fog with CE remains visible 3 times longer than simple eyes (SE).
Kehinde Kassim, Qiuyun Lu, Hao Xuan, Nobuo Maeda, Xingyu Li, Ben Bin Xu, Xuehua Zhang   +6 more
wiley   +1 more source

On the use of artificial neural networks to model sound quality [PDF]

, 1994
Peter C. Laux, Patricia Davies
openalex   +1 more source

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al2O3/HfO2 Dielectrics

Advanced Functional Materials, EarlyView.
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon, Dong Ho Lee, Seyoung Oh, Jongwon Yoon, Hyo‐Bae Kim, Ji‐Hoon Ahn, Woojin Park, Han Seul Kim, Byungjin Cho   +8 more
wiley   +1 more source

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

Advanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh, Ankita Mohanty, Amit Malakar, Suryasarathi Bose, Suprakas Sinha Ray   +4 more
wiley   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

Comment on “The use of artificial neural networks for the prediction of water quality parameters” by H. R. Maier and G. C. Dandy [PDF]

, 1997
Vincent Fortin, Taha B. M. J. Ouarda, Bernard Bobée   +2 more
openalex   +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