Results 31 to 40 of about 11,064 (217)
CoRR, 2017 Most of the weights in a Lightweight Neural Network have a value of zero, while the remaining ones are either +1 or -1. These universal approximators require approximately 1.1 bits/weight of storage, posses a quick forward pass and achieve classification accuracies similar to conventional continuous-weight networks.
openaire +3 more sources
Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation
Sensors, 2022 Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs.
Yongseop Jeong +5 more
doaj +1 more source
A Lightweight Blind Obstacle Detection Network for Mobile Side [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesChina has the longest and widest distribution of blind corridors in the world, but in many cities they are virtually non-existent, with all kinds of obstacles affecting the movement of the blind. Thus, guaranteeing safe traveling for the blind has become
J. Zhao, Z. Liu, Z. Liu, A. Xu, J. Zhao
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YOLO-LWNet: A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices
Sensors, 2023 To solve the demand for road damage object detection under the resource-constrained conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an efficient lightweight road damage detection algorithm for mobile terminal devices ...
Chenguang Wu +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora +4 more
wiley +1 more source
Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting
IEEE AccessIn this study, we propose a novel lightweight detection model for rebar counting, which is rectified mobilenet lightweight feature pyramid network based on YOLO (RM-LFPN-YOLO).
Haodong Liu +4 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.This study examines how helicoidal architectures with different porosities respond to bending. Adjusting layer angle and spacing in 3D‐printed polymers reveals clear tradeoffs between stiffness, strength, and energy absorption. Experiments and simulations highlight designs that distribute stress effectively, offering pathways for optimizing lightweight
Praveenkumar Subhash Patil +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.In situ synchrotron high‐energy X‐ray diffraction reveals the real‐time high‐temperature phase evolution of a ternary V‐9Si‐6.5B alloy. The study uncovers a kinetically delayed V5SiB2 → V8SiB4 transformation governed by massive structural and chemical barriers.
Zahra Sabeti +4 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan +4 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.To enhance through‐thickness conductivity without sacrificing impregnation, large spherical graphite particles are intentionally employed in a low‐viscosity resin. Unlike finer conductive fillers, these particles remain outside the fiber bundles and accumulate in resin‐rich interlaminar regions during molding.
Keito Hosoe +6 more
wiley +1 more source