Results 81 to 90 of about 188,249 (289)

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

Modelling and compensation of rate‐dependent hysteresis in piezoelectric actuators based on a modified Madelung model

Electronics Letters
Based on the principle of weight superposition, a modified Madelung model is proposed. By combining the signal delay response characteristics (SDRC), a dynamic model and its inverse model are established to describe and compensate for the rate‐dependent ...
Rui Li, Kairui Cao, Zekun Li
doaj   +1 more source

Energy-Aware Validation of the PIDA Control in the Hardware-in-the-Loop Environment

Energies
The goal of this work is to compare the effectiveness of the classical PID (Proportional Integral Derivative) controller and its extended PIDA (Proportional Integral Derivative Acceleration) version in the energy-aware context.
Marcin Jabłoński, Paweł D. Domański
doaj   +1 more source

Robust high-performance control for robotic manipulators [PDF]

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies were merged into a single control system having a control law formulation including two ...
Seraji, Homayoun
core   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Exploring feedback and feedforward in dental education using a followership model

Frontiers in Dental Medicine
ObjectivesThe study aimed to explore the feedback and feedforward quality in the clinical setting and the usability of a followership model as a framework to analyze and map students' peer feedback.MethodFeedback and feedforward from 59 fourth-year ...
Christina Gummesson, Nina Lundegren
doaj   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Development of a Path Tracker Based on a 4WS Vehicle for Low-Speed Automated Driving Systems

Applied Sciences
With the increasing demand for various autonomous driving services in urban environments, low-speed autonomous vehicles, such as autonomous shuttles and purpose-built vehicles, equipped with enhanced driving characteristics suitable for urban roads ...
Heung-Sik Park, Moon-Sik Kim
doaj   +1 more source

Public feedback - but personal feedforward?

Journal of Learning Development in Higher Education, 2012
People can learn by considering and understanding examples. With assistance, students should see and appreciate in examples strengths on which they can build, and weaknesses which they should minimise. So feedback and feedforward to students can usefully
John Cowan, YiChing Jean Chiu
doaj   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source