Results 171 to 180 of about 24,451 (262)

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles. [PDF]

Sensors (Basel), 2019
Jo K, Lee S, Kim C, Sunwoo M.
europepmc   +1 more source

Embedded Systems for Intelligent Vehicles

EURASIP Journal on Embedded Systems, 2007
Samir Bouaziz, Paolo Lombardi, Roger Reynaud, Gunasekaran S. Seetharaman   +3 more
openaire   +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

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

Advanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim, Seoa Kim, Erik A. Wu, Darren H. S. Tan, Shawn Peng, Xiaolin Li   +5 more
wiley   +1 more source

Emerging Materials and Future Strategies for Solid Oxide Electrochemical Cells

Advanced Energy Materials, EarlyView.
Solid oxide electrochemical cells operate under strongly coupled electrochemical and thermodynamic conditions, where performance is constrained by interactions among crystal structure, defect chemistry, and interfacial evolution. This review, based on a structure‐defect‐property‐durability framework, reveals the roles of lattice symmetry and defect ...
Qiuchun Lu, Yufei Zhang, Han Zhao, Pooi See Lee, Daniel H.C. Chua   +4 more
wiley   +1 more source

Redox‐Mediated Aluminum–Air Fuel Cells With Suppressed Hydrogen Evolution for Durable Power Generation

Advanced Energy Materials, EarlyView.
The redox‐mediated aluminum–air fuel cell (RM‐AAFC) integrates a soluble redox mediator, 7,8‐dihydroxy‐2‐phenazine sulfonic acid (DHPS), to facilitate the oxidation of aluminum and inhibit hydrogen evolution reaction (HER) through competitive reactions between DHPS reduction and HER on the Al surface.
Yuxi Song, Hang Zhang, Zhongheng Fu, Zeyun Gu, Lingchao Xia, Haoyang Guo, Jiahao Zhuang, Yizhuo He, Linghang Meng, Zhi Wei Javier Ang, Qing Wang   +10 more
wiley   +1 more source

High‐Performance Elastomeric Lithium Metal Anodes Enabled by a Lithiophilic Monolayer‐Assembled Nano‐Crumpled Micro‐Concave Architecture

Advanced Energy Materials, EarlyView.
We report an elastomeric lithium metal anode integrating hierarchical nano–micro architectures with uniformly assembled lithiophilic amine functionalities. This design enables synergistic stress and interfacial regulation, effectively suppresses dendritic growth, and ensures stable cycling, highlighting elastomeric anodes as a promising platform for ...
Sungha Choi, Donghyeon Nam, Geon Choi, Seonho Lee, Seungeun Paik, Yongkwon Song, Seungyeop Choi, Jaejin Lim, Woojae Chang, Jeong Gon Son, Daegun Kim, Giwon Lee, Sungjun Park, Yong Min Lee, Seoin Back, Yongmin Ko, Jinhan Cho   +16 more
wiley   +1 more source

Radar Sensing for Intelligent Vehicles in Urban Environments. [PDF]

Sensors (Basel), 2015
Reina G, Johnson D, Underwood J.
europepmc   +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