TL;DR
This paper considers the integration of multimodal sensor data fusion to improve the reliability of deep learning models in smart manufacturing and proposes a late and adaptive weighted data fusion architecture based on transformers and deep neural networks.
A deep learning-driven technology framework for performance optimization in manufacturing enterprises by integrating multimodal data fusion, attention-enhanced long short-term memory (LSTM) networks, deep reinforcement learning, and edge deployment is proposed.
MODERN, a deep learning framework for quality monitoring and fault isolation, which integrates these enhanced capabilities into the practice of industrial quality control is introduced and a control chart that monitors the likelihood of a product containing defects is developed.
An Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring that improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports s...
A Kronecker Fused Variational-Evolutionary Reconstruction Framework for Adaptive Intelligent Diagnostics (KROVER-AID), which enables real-time, cross-sensor, and across-sensor fault diagnosis and is a scalable and intelligent approach to predictive maintenance and fault management in next-generation smart manufacturing...
The emergence of multi-sensor Internet of Things (IoT) deployments has led to an immediate demand to have unified intelligence models that can take advantage of heterogeneous data modalities, such as time-series sensor readings, visual data, audio streams as well as contextual metadata, to form incoherent understanding...
A novel hybrid framework titled AMF-TGNet (Attention-based Multi-Level Fusion with Temporal Graph Network) under a Multi-Level Multi-Sensor Data Fusion architecture that enhances knowledge mining performance by effectively modelling cross-sensor correlations and temporal dynamics while minimizing information loss is pr...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
