Results 61 to 70 of about 7,404,725 (288)

A Deep Reinforcement Learning Approach for Efficient, Safe and Comfortable Driving

Applied Sciences, 2023
Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the ...
Dinesh Cyril Selvaraj, Shailesh Hegde, Nicola Amati, Francesco Deflorio, Carla Fabiana Chiasserini   +4 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

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

Spacecraft Safe Proximity Policy Based on Graph Neural Network Safe Reinforcement Learning

Aerospace
Spacecraft safe proximity, as a critical component of on-orbit servicing missions, primarily encounters the following two challenges: the partial observability of the environment surrounding the service spacecraft and the necessity to evade uncertain ...
Heng Zhou, Jingxian Wang, Monan Dong, Yong Zhao, Yuzhu Bai, Rong Chen   +5 more
doaj   +1 more source

Reinforcement Learning-Based Safe Path Planning for a 3R Planar Robot

Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2022
Path planning is an essential topic of robotics studies. Robotic researchers have suggested some methods such as particle swarm optimization, A*, and reinforcement learning (RL) to obtain a path.
Mustafa Can Bingol
doaj   +1 more source

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

Safe Autonomous Driving with Latent Dynamics and State-Wise Constraints

Sensors
Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in ...
Changquan Wang, Yun Wang
doaj   +1 more source

Safe Reinforcement Learning Using Wasserstein Distributionally Robust MPC and Chance Constraint

IEEE Access, 2022
In this paper, we address the chance-constrained safe Reinforcement Learning (RL) problem using the function approximators based on Stochastic Model Predictive Control (SMPC) and Distributionally Robust Model Predictive Control (DRMPC).
Arash Bahari Kordabad, Rafael Wisniewski, Sebastien Gros   +2 more
doaj   +1 more source

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

Iterative Reachability Estimation for Safe Reinforcement Learning

Advances in Neural Information Processing Systems 36, 2023
Ensuring safety is important for the practical deployment of reinforcement learning (RL). Various challenges must be addressed, such as handling stochasticity in the environments, providing rigorous guarantees of persistent state-wise safety satisfaction, and avoiding overly conservative behaviors that sacrifice performance. We propose a new framework,
Milan Ganai, Zheng Gong, Chenning Yu, Sylvia L. Herbert, Sicun Gao   +4 more
openaire   +4 more sources