Results 91 to 100 of about 11,734,216 (296)

The schematic figure of the Long-Short Term Memory (LSTM) network.

The schematic figure of the Long-Short Term Memory (LSTM) network.
Robab Kalantari (18121294), Amirhossein Amini (10163250)   +1 more
core   +1 more source

Short-term memory as a working memory control process [PDF]

, 2013
Aben et al. (2012) take issue with the unthoughtful use of the terms “working memory” (WM) and “short-term memory” (STM) in the cognitive and neuroscientific literature.
Eddy J Davelaar, Eddy J. Davelaar, Davelaar, Eddy J.   +2 more
core   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
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, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models

Wind Energy
Driven by the challenges in measuring blade deformations, this study presents a novel machine learning methodology to predict blade tip deformation using inflow wind data and operational parameters. Using a long short‐term memory (LSTM) model and a novel
Shubham Baisthakur, Breiffni Fitzgerald
doaj   +1 more source

Smart grid forecasting method based on data preprocessing and Bi-LSTM

Diance yu yibiao
Short-term prediction plays an important role in the construction of smart grid, and profoundly affects the intelligent transformation of all aspects of power grid generation, transmission and distribution.
LI Yan, LIU Xinyue, QIAO Junjie, WANG Maotao, LIU Yifan, QI Leijie   +5 more
doaj   +1 more source

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network [PDF]

Physica D: Nonlinear Phenomena, 2020
43 pages, 10 figures, 78 ...
openaire   +3 more sources

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source

Development and experimental validation of a predictive resilient framework for cyber‐physical systems

AIChE Journal, EarlyView.
Abstract Cyber‐physical process systems integrate process dynamics, control, sensing, actuation, and communication, making them vulnerable to attacks that falsify data, alter control actions, or disrupt networks and drive operations toward unsafe states.
Luyang Liu, Ritu Ranjan, Zaman Sajid, Benjamin Wilhite, Costas Kravaris, Faisal Khan   +5 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source