Results 181 to 190 of about 142,095 (261)
Cascaded adaptive model predictive and PID control for integrated LFC-AVR enhancement. [PDF]
Ayman M, Attia MA, Asim AM.
europepmc +1 more source
ABSTRACT Layered 2D materials are considered as promising for memristive applications due to their ultimate vertical scalability compared to conventional semiconductor films and pronounced hysteresis properties. Bias‐resolved Raman and Photoluminescence mapping is used to quantify strain from phonon shifts and carrier density from the exciton‐trion ...
Vladislav Kurtash +4 more
wiley +1 more source
Evaluation and optimization of resource matching for perception services in power communication networks. [PDF]
Wei L, Shang L, Zhang M, Li H, Zhu X.
europepmc +1 more source
Low‐Power Control Of Resistance Switching Transitions in First‐Order Memristors
Joule losses are a serious concern in modern integrated circuit design. In this regard, minimizing the energy necessary for programming memristors should be handled with care. This manuscript presents an optimal control framework, allowing to derive energy‐efficient programming voltage protocols for resistance switching devices. Following this approach,
Valeriy A. Slipko +3 more
wiley +1 more source
Computational study of the separation of regular sphere clusters in high-Mach-number flow. [PDF]
Whalen T, Deiterding R, Laurence SJ.
europepmc +1 more source
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa +6 more
wiley +1 more source
Network carrier allocation optimization based on immune algorithm under massive concurrent access. [PDF]
Qi L.
europepmc +1 more source
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source

