A Deep Learning-Based Hybrid Data Fusion Method for Object Recognition

2019 ◽  
pp. 390-402
Author(s):  
Weishan Zhang ◽  
Zongchao Zheng ◽  
Yuanjie Zhang ◽  
Liang Xu ◽  
Jiehan Zhou
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Data Fusion ◽  
Fusion Method ◽  
Hybrid Data

scholarly journals A Survey on Deep Learning for Multimodal Data Fusion

2020 ◽  
Vol 32 (5) ◽  
pp. 829-864 ◽  
Author(s):  
Jing Gao ◽  
Peng Li ◽  
Zhikui Chen ◽  
Jianing Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Fusion ◽  
High Volume ◽  
Fusion Method ◽  
Learning Models ◽  
Multimodal Data ◽  
Fusion Methods ◽  
Multimodal Data Fusion ◽  
Future Topics

With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to multimodal big data, contain abundant intermodality and cross-modality information and pose vast challenges on traditional data fusion methods. In this review, we present some pioneering deep learning models to fuse these multimodal big data. With the increasing exploration of the multimodal big data, there are still some challenges to be addressed. Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method and to motivate new multimodal data fusion techniques of deep learning. Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. Then the current pioneering multimodal data fusion deep learning models are summarized. Finally, some challenges and future topics of multimodal data fusion deep learning models are described.

Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Qi Tian ◽  
Shenghan Guo ◽  
Erika Melder ◽  
Linkan Bian ◽  
Weihong “Grace” Guo
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Additive Manufacturing ◽  
Data Fusion ◽  
Infrared Camera ◽  
Fusion Method ◽  
Thermal Images ◽  
Melt Pool ◽  
Porosity Detection

Abstract Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM.

A Deep Learning Based Data Fusion Method for Degradation Modeling and Prognostics

2020 ◽  
pp. 1-15
Author(s):  
Feng Wang ◽  
Juan Du ◽  
Yang Zhao ◽  
Tao Tang ◽  
Jianjun Shi
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Fusion Method ◽  
Degradation Modeling

Reinforcement learning based data fusion method for multi-sensors

2020 ◽  
Vol 7 (6) ◽  
pp. 1489-1497
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Jie Zou
Keyword(s):  
Reinforcement Learning ◽  
Data Fusion ◽  
Fusion Method

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 21642-21652
Author(s):  
Murtadha D. Hssayeni ◽  
Behnaz Ghoraani
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Physiological Data
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