This study employs industrial cameras to capture video streams and utilises a deep learning‐based approach to locate and track packing boxes on the production line, mapping them into a digital twin space. This addresses the issue of discontinuous target information acquisition inherent in photoelectric sensors and enhances simulation accuracy ...
Jiashun Li +3 more
wiley +1 more source
Explainable Multitask Burnout Prediction Using Adaptive Deep Learning (EMBRACE) for Resident Physicians: Algorithm Development and Validation Study. [PDF]
Alam S, Alam MAU.
europepmc +1 more source
Identificação de reservatórios utilizando Learning Vector Quantization
Michelle C. Kuroda +3 more
openaire +1 more source
PASENet: Snowy Scene 3D Object Detection With Pillar‐Wise Attention and Semantic Enhancement
LiDAR‐based 3D object detection suffers from degraded performance in snowy conditions due to false returns and occlusions. To address the overcomes, we propose pillar‐wise attention and semantic enhancement network (PASENet), an end‐to‐end network featuring pillar‐wise attention for noise suppression and a semantic enhancement branch to improve feature
Yutian Wu +3 more
wiley +1 more source
Real-Time Fluorescence-Based COVID-19 Diagnosis Using a Lightweight Deep Learning System. [PDF]
Bae HJ, Kim J, Jeong D.
europepmc +1 more source
A Robust Multi‐Oriented License Plate Detector and A Derived End‐to‐End License Plate Recognizer
This is a research paper on license plate detection and recognition. A new center‐aware license plate detection and end‐to‐end license plate recognition framework is proposed for robust and efficient license plate detection and recognition under unconstrained scenarios.
Xudong Fan, Wei Zhao
wiley +1 more source
AiM: urban air quality forecasting with grid-embedded recurrent MLP model. [PDF]
Chatterjee K +9 more
europepmc +1 more source
Imitation Learning from a Single Demonstration Leveraging Vector Quantization for Robotic Harvesting [PDF]
Antonios Porichis +4 more
openalex +1 more source
ACT‐Agent: Affinity‐Cross Transformer for Point Cloud Registration via Reinforcement Learning
1. We proposed a point cloud registration framework combining imitation learning and reinforcement learning, which formulates the registration problem as a Markov decision process with discrete action sequences. 2. We designed the Affinity‐Cross Transformer module to enhance the point cloud feature representation and interaction capabilities. 3.
Fengguang Xiong +8 more
wiley +1 more source

