Journal ArticleDOI
Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
TLDR
This research presents a novel and scalable approach called “SmartGlass” that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and displaying information in augmented reality (AR).Abstract:
Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information wit...read more
Citations
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Journal ArticleDOI
Machine learning in manufacturing and industry 4.0 applications
TL;DR: The machine learning field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm as discussed by the authors, which encourages the usage of smart sensors, devices, and devices.
Journal ArticleDOI
Predictive maintenance using digital twins: A systematic literature review
TL;DR: A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed as mentioned in this paper .
Journal ArticleDOI
Challenges and opportunities on AR/VR technologies for manufacturing systems in the context of industry 4.0: A state of the art review
TL;DR: In this paper , the authors demonstrate the research progress and developments in the AR/VR technologies for product design and evaluation, Repair & Maintenance, Assembly, Warehouse management, Quality control, Plant layout and CNC simulation.
Journal ArticleDOI
A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields.
TL;DR: In this paper, the authors present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak, which are collated to operations processes angle and provide an exciting research agenda and four propositions derived from the framework.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.