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   Articles with "angular field" as a keyword



Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field

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recommendations! Published in 2020 at "Automation in Construction"

DOI: 10.1016/j.autcon.2020.103390

Abstract: Abstract Manual load carrying without sufficient rest may cause work-related musculoskeletal disorders (WMSDs) and needs to be monitored at construction sites. While previous studies have been able to predict load-carrying modes using multiple wearable inertial… read more here.

Keywords: angular field; load; construction; gramian angular ... See more keywords

A Gramian Angular Field for Constructing Graph-Based GNNs and Its Applications in Rolling Bearing Defect Detection

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recommendations! Published in 2024 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2024.3458409

Abstract: Graph neural networks (GNNs) have unique advantages in terms of processing non-Euclidean spatial data with significant differences and effectively diagnosing faults. However, GNNs typically rely on high-quality graph structures. The current graph construction methods often… read more here.

Keywords: graph based; angular field; gramian angular; field constructing ... See more keywords

Analysis on angular field of view of holographic image dependent on hologram numerical aperture in holographic display

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recommendations! Published in 2020 at "Optical Engineering"

DOI: 10.1117/1.oe.59.3.035103

Abstract: Abstract. We investigate the angular field of view (AFOV) of a holographic image reconstructed from the digital Fresnel hologram in holographic display. The theoretical analysis clarifies that the AFOV of a holographic image is fundamentally… read more here.

Keywords: hologram numerical; holographic display; field view; angular field ... See more keywords

Intelligent Fault Diagnosis for Rotating Machinery Utilizing Gramian Angular Field-Parallel Convolutional Neural Network and Gated Recurrent Unit Networks

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recommendations! Published in 2025 at "Applied Sciences"

DOI: 10.3390/app15169217

Abstract: To address the limitations of traditional fault diagnosis methods for rotating machinery, which heavily rely on single-dimensional vibration data and fail to fully exploit the deep features of time-series data, this study proposes an innovative… read more here.

Keywords: angular field; gramian angular; fault diagnosis; network ... See more keywords