A Novel Deep Learning Based Approach for Particle Image Velocimetry with Global Motion Aggregation

Jin-cheng, Wang; Sista, Harsha; Hu, Haiyang; He, Ping; Hu, Hui

doi:10.2514/6.2023-4357

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Transformer and Attention Mechanism-assisted Particle Tracking1

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2024

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“…Furthermore, transformers have shown versatility beyond NLP, being applied to image processing, time-series forecasting, and other domains, showcasing their potential to reshape the landscape of deep learning architectures and techniques. There are comparatively few studies on the application of transformer models on particle tracking and flow reconstruction [94,95,[102][103][104]. Most of them focus heavily on flow estimation rather than individual particle detection and tracing, which is out of the scope of this article.…”

Section: Transformer and Attention Mechanism-assisted Particle Trackingmentioning

confidence: 99%

Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods

Wang,

Hong,

Chamorro

2024

Micromachines

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Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

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