Results 71 to 80 of about 277,892 (265)

Balanced Domain Randomization for Safe Reinforcement Learning

Applied Sciences
Reinforcement Learning (RL) has enabled autonomous agents to achieve superhuman performance in diverse domains, including games, navigation, and robotic control.
Cheongwoong Kang, Wonjoon Chang, Jaesik Choi   +2 more
doaj   +1 more source

Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Advanced Healthcare Materials, EarlyView.
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei, Hesam Mohammadi, Shamim Golafshan, Mojtaba Alehosseini, Mahsa Mohammadzadeh, Atefeh Golshirazi, Nasim Golafshan, Tatiane Eufrásio‐da‐Silva, Alireza Dolatshahi‐Pirouz, Gorka Orive   +9 more
wiley   +1 more source

Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification

IEEE Access, 2018
One major challenge in the current brain–computer interface research is the accurate classification of time-varying electroencephalographic (EEG) signals.
Qingshan She, Bo Hu, Haitao Gan, Yingle Fan, Thinh Nguyen, Thomas Potter, Yingchun Zhang   +6 more
doaj   +1 more source

Advancing Human Skin Equivalents: The Crucial Role of Neurovascular Integration

Advanced Healthcare Materials, EarlyView.
This review discusses the importance of integrating vascular and peripheral nerve systems into human skin equivalents (HSEs) to better recapitulate native skin physiology. Recent advances in vascularized, innervated, and neurovascularized HSEs are highlighted, together with emerging bioengineering strategies, current challenges, and future ...
Hao Wu, Lorenzo Moroni, Nicolas Lebonvallet, Vincent Flacher, Paul Wieringa   +4 more
wiley   +1 more source

Conditional Sequence Modeling for Safe Reinforcement Learning

Mathematics
Offline safe reinforcement learning (RL) aims to learn policies from a fixed dataset while maximizing performance under cumulative cost constraints. In practice, deployment requirements often vary across scenarios, necessitating a single policy capable ...
Wensong Bai, Chao Zhang, Qihang Xu, Chufan Chen, Chenhao Zhou, Hui Qian   +5 more
doaj   +1 more source

Blood–Brain Barrier Regulation: Evolving From Classic Strategies to Electrochemical Ion and Reactive Oxygen Species Control

Advanced Healthcare Materials, EarlyView.
Conventional therapies suffer from poor blood–brain barrier (BBB) penetration and disordered ion/reactive oxygen species (ROS) homeostasis, hindering precise neurological treatment. Electrochemical strategies achieve accurate spatiotemporal regulation of neural homeostasis.
Xiaokang Hu, Caiyu Liu, Meijun Pang, Zhiyuan Shi, Dong Ming, Xiuyun Liu   +5 more
wiley   +1 more source

Applying machine learning to safe vascular anastomosis

JPRAS Open
Background: Machine-learning technology is currently being introduced into the medical field and has been shown to aid diagnostic imaging, patient examinations, patient-data analysis, various surgical aspects, and medical education.
Hiroki Umezawa, Akatsuki Kondo, Marie Taga, Rei Ogawa   +3 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Safe Interactive Model-Based Learning

CoRR, 2019
Control applications present hard operational constraints. A violation of these can result in unsafe behavior. This paper introduces Safe Interactive Model Based Learning (SiMBL), a framework to refine an existing controller and a system model while operating on the real environment.
Marco Gallieri, Seyed Sina Mirrazavi Salehian, Nihat Engin Toklu, Alessio Quaglino, Jonathan Masci, Jan Koutník, Faustino J. Gomez   +6 more
openaire   +3 more sources

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source