Results 101 to 110 of about 57,740 (314)

Recycling of Thermoplastics with Machine Learning: A Review

Advanced Functional Materials, EarlyView.
This review shows how machine learning is revolutionizing mechanical, chemical, and biological pathways, overcoming traditional challenges and optimizing sorting, efficiency, and quality. It provides a detailed analysis of effective feature engineering strategies and establishes a forward‐looking research agenda for a truly circular thermoplastic ...
Rodrigo Q. Albuquerque, Florian Stephan, Annalena Pongratz, Christian Brütting, Katharina Krause, Holger Ruckdäschel   +5 more
wiley   +1 more source

The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication

Advanced Functional Materials, EarlyView.
Advances in integrating artificial intelligence into 3D bioprinting are systematically reviewed here. Machine learning, computer vision, robotics, natural language processing, and expert systems are examined for their roles in optimizing bioprinting parameters, real‐time monitoring, quality control, and predictive maintenance.
Joao Vitor Silva Robazzi, Irem Deniz Derman, Deepak Gupta, Logan Haugh, Yogendra Pratap Singh, Vaibhav Pal, Yasar Ozer Yilmaz, Suihong Liu, Andre Luis Dias, Rogerio Andrade Flauzino, Ibrahim Tarik Ozbolat   +10 more
wiley   +1 more source

Deep auto-encoder neural networks in reinforcement learning [PDF]

, 2010
Sascha Lange, Martin Riedmiller
openalex   +1 more source

Additively Manufactured 3D Auxetic Metamaterials for Structurally Guided Capacitive and Resistive Tactile Sensing

Advanced Functional Materials, EarlyView.
A 3D‐architected auxetic metamaterial is used to construct capacitive and resistive tactile sensors via digital light processing‐based additive manufacturing. The inward deformation of the proposed structure under compression amplifies local strain, enhancing sensing performance.
Mingyu Kang, Hong‐Gap Choi, Keun Park, Soonjae Pyo   +3 more
wiley   +1 more source

Understanding Functional Materials at School

Advanced Functional Materials, EarlyView.
This review outlines strategies for effectively teaching nanoscience in schools, focusing on challenges such as scale comprehension and curriculum integration. Emphasizing inquiry‐based learning and chemistry core concepts, it showcases hands‐on activities, digital tools, and interdisciplinary approaches.
Johannes Claußnitzer, Jürgen Paul
wiley   +1 more source

Learning to Walk Via Deep Reinforcement Learning

Robotics: Science and Systems XV, 2019
RSS 2019, https://sites.google.com/view/minitaur-locomotion/
Haarnoja, Tuomas, Ha, Sehoon, Zhou, Aurick, Tan, Jie, Tucker, George, Levine, Sergey   +5 more
openaire   +2 more sources

Biosupercapacitors for Human‐Powered Electronics

Advanced Functional Materials, EarlyView.
Biosupercapacitors are emerging as biocompatible and integrative energy systems for next‐generation bioelectronics, offering rapid charge–discharge performance and mechanical adaptability. This review systematically categorizes their applications from external to organ‐level systems and highlights their multifunctional roles in sensing, actuation, and ...
Suhyeon Kim, Seungho Baek, Woojung Jung, Hayeon Bae, Junyoung Mun, Yong Ho Kim, Sunkook Kim, Jung Ho Kim   +7 more
wiley   +1 more source

Improving sample efficiency and exploration in upside-down reinforcement learning

Journal of Information and Intelligence
Supervised learning has been demonstrated to be a stable approach for training deep neural networks. Upside-down reinforcement learning solves reinforcement learning problems by using supervised learning, but this method suffers from weak sample ...
Mohammadreza Nakhaei, Reza Askari Moghadam   +1 more
doaj   +1 more source

Enhanced Switching Performance in Single‐Crystalline PbTiO3 Ferroelectric Memristors for Replicating Synaptic Plasticity

Advanced Functional Materials, EarlyView.
This study demonstrated single‐crystalline PbTiO3‐based memristors with atomically sharp interfaces, well‐ordered lattices, and minimal lattice mismatch. The devices exhibited an ON/OFF ratio exceeding 105, high stability, and rich resistance‐state modulation.
Haining Li, Zhiqiang Liao, Risa Kataoka, Md Sarker Shamim, Takeshi Kijima, Hiroyasu Yamahara, Hitoshi Tabata, Munetoshi Seki   +7 more
wiley   +1 more source

Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning

Energy and AI
Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks.
Xuanang Zhang, Xuan Wang, Ping Yuan, Hua Tian, Gequn Shu   +4 more
doaj   +1 more source