☆ 4.7 Article

Characterization of the in-focus droplets in shadowgraphy systems via deep learning-based image processing method

PHYSICS OF FLUIDS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Mechanics

Penetration and aerosolization of cough droplet spray through face masks: A unique pathway of transmission of infection

Gautham Vadlamudi et al.

Summary: This study investigates the effectiveness of different types of masks in blocking droplet transmission. It shows that masks are not able to completely prevent the penetration of larger droplets and can cause atomization, leading to increased risk of virus transmission. The study also examines the filtration and entrapment of virus-like particles in masks, as well as their efficacy for susceptible individuals.

PHYSICS OF FLUIDS (2022)

添加到收藏夹

Article Mechanics

Deep-learning-based super-resolution reconstruction of high-speed imaging in fluids

Zhibo Wang et al.

Summary: A degradation and super-resolution attention model using unsupervised machine learning is proposed to reconstruct high resolution time-resolved fluid images from coarse data, validated in high-speed experiments. The model outperforms existing methods and has significant applications in obtaining flow images with high spatial and temporal resolution.

PHYSICS OF FLUIDS (2022)

添加到收藏夹

Article Mechanics

Three-dimensional measurement of the droplets out of focus in shadowgraphy systems via deep learning-based image-processing method

Zhibo Wang et al.

Summary: This paper proposes a deep learning-based method to recover sharp images and infer depth information from defocused blur droplet images. The method consists of a defocus map estimation subnetwork and a defocus deblur subnetwork, optimized with a two-stage strategy. The results demonstrate satisfactory performance in depth location estimation and droplet size measurement, with good generalization and robustness.

PHYSICS OF FLUIDS (2022)

添加到收藏夹

Article Engineering, Multidisciplinary

Machine learning shadowgraph for particle size and shape characterization

Jiaqi Li et al.

Summary: This paper introduces a robust learning-based method using a single convolution neural network for analyzing particle shadow images. The method employs a two-channel-output U-net model to generate binary particle image and particle centroid image, and achieves better performance compared to traditional methods through marker-controlled watershed segmentation. The proposed machine learning approach shows great potential for real-time particle image analysis in industrial applications.

MEASUREMENT SCIENCE AND TECHNOLOGY (2021)

添加到收藏夹

Article Engineering, Mechanical

Sensitivity analysis and measurement uncertainties of a two-camera depth from defocus imaging system

Wu Zhou et al.

Summary: This study comprehensively investigated the impact of system and particle parameters on the calibration functions and measurement uncertainties of a two-camera Depth from Defocus imaging system, with experimental verification. It provides explicit recommendations on which quantities are most influential in determining measurement accuracy and must be carefully controlled.

EXPERIMENTS IN FLUIDS (2021)

添加到收藏夹

Article Engineering, Chemical

Development of a deep learning-based image processing technique for bubble pattern recognition and shape reconstruction in dense bubbly flows

Rafael F. L. Cerqueira et al.

Summary: This study proposes a CNN-based method for the shape reconstruction of bubbles in bubbly flows, using anchor points and boxes for bubble identification and shape reconstruction from high-speed camera images. Experimental results demonstrate the method’s generalization capability and accuracy in different gas-liquid systems, showing the effectiveness of the deep learning approach for bubble detection in high-speed camera images even in dense bubbly flow configurations.

CHEMICAL ENGINEERING SCIENCE (2021)

添加到收藏夹

Article Optics

Planar rainbow refractometry

Can Li et al.

Summary: This study extends traditional rainbow refractometry configurations to measure particle positions in a plane, using a novel calibration method to address challenges. Experimental results demonstrate achieved horizontal and vertical position accuracies, as well as refractive index uncertainty.

OPTICS LETTERS (2021)

添加到收藏夹

Article Mechanics

Bubble recognizing and tracking in a plate heat exchanger by using image processing and convolutional neural network

Qianwen Wang et al.

Summary: A new method is proposed to recognize and track bubbles in a plate heat exchanger (PHE) using convolutional neural network (CNN) and improved three-frame difference (ITFD) method, achieving precise identification and tracking of individual bubble's spatiotemporal behavior, as well as calculation of bubble positions, velocities, and dimensionless parameters.

INTERNATIONAL JOURNAL OF MULTIPHASE FLOW (2021)

添加到收藏夹

Article Mechanics

Reversed role of liquid viscosity on drop splash

Haixiang Zhang et al.

Summary: The influence of liquid viscosity on the splashing behavior of a drop impacting a smooth surface was investigated. It was found that liquid viscosity plays a reversed role on the drop splash, promoting splash in low-viscosity cases and suppressing splash in high-viscosity cases. The increase in liquid viscosity contributes to a lower splashing angle and influences both aerodynamic and viscous forces, resulting in a proposed splash criterion for smooth surfaces.

PHYSICS OF FLUIDS (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows

Yewon Kim et al.

Summary: Researchers have developed an automated bubble detection and mask extraction tool using deep learning-based image processing, with a training dataset and customized loss function to ensure high accuracy under various experimental conditions. The tool's processing speed is over twice as fast as conventional approaches, without the need for tedious parameter tuning.

SCIENTIFIC REPORTS (2021)

添加到收藏夹

Article Mechanics

Experimental velocity data estimation for imperfect particle images using machine learning

Masaki Morimoto et al.

Summary: This study introduces a method using supervised machine learning to estimate velocity fields from particle images with missing regions due to experimental limitations. The machine learning models show promising results in estimating velocity fields in missing regions compared to conventional methods, with significantly higher data density.

PHYSICS OF FLUIDS (2021)

添加到收藏夹

Article Thermodynamics

Measurement of transient evaporation of an ethanol droplet stream with phase rainbow refractometry and high-speed microscopic shadowgraphy

Can Li et al.

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Spray drop measurements using depth from defocus

Zhou Wu et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2020)

添加到收藏夹

Article Engineering, Chemical

BubCNN: Bubble detection using Faster RCNN and shape regression network

Tim Haas et al.

CHEMICAL ENGINEERING SCIENCE (2020)

添加到收藏夹

Article Mechanics

Bubble patterns recognition using neural networks: Application to the analysis of a two-phase bubbly jet

Igor Poletaev et al.

INTERNATIONAL JOURNAL OF MULTIPHASE FLOW (2020)

添加到收藏夹

Article Physics, Multidisciplinary

What Determines the Drop Size in Sprays?

Stefan Kooij et al.

PHYSICAL REVIEW X (2018)

添加到收藏夹

Article Biochemical Research Methods

TrackMate: An open and extensible platform for single-particle tracking

Jean-Yves Tinevez et al.

METHODS (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Detection of bubbles as concentric circular arrangements

Nataliya Strokina et al.

MACHINE VISION AND APPLICATIONS (2016)

添加到收藏夹

Article Chemistry, Analytical

Spray Droplet Characterization from a Single Nozzle by High Speed Image Analysis Using an In-Focus Droplet Criterion

Sofija Vulgarakis Minov et al.

SENSORS (2016)

添加到收藏夹

Article Engineering, Chemical

An integrative image measurement technique for dense bubbly flows with a wide size distribution

Ashish Karn et al.

CHEMICAL ENGINEERING SCIENCE (2015)

添加到收藏夹

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

添加到收藏夹

Article Energy & Fuels

Spray characterization of gasoline-ethanol blends from a multi-hole port fuel injector

T. N. C. Anand et al.

FUEL (2012)

添加到收藏夹

Review Mechanics

Optical Particle Characterization in Flows

Cameron Tropea

ANNUAL REVIEW OF FLUID MECHANICS, VOL 43 (2011)

添加到收藏夹

Article Engineering, Multidisciplinary

Drop size distribution measured by imaging: determination of the measurement volume by the calibration of the point spread function

Nicolas Fdida et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2010)

添加到收藏夹

Article Optics

A digital image analysis technique for quantitative characterisation of high-speed sprays

Julian T. Kashdan et al.

OPTICS AND LASERS IN ENGINEERING (2007)

添加到收藏夹

Article Engineering, Multidisciplinary

Recognition of highly overlapping ellipse-like bubble images

M Honkanen et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2005)

添加到收藏夹

Article Engineering, Multidisciplinary

Determination of in-focus criteria and depth of field in image processing of spray particles

KU Koh et al.

ATOMIZATION AND SPRAYS (2001)

添加到收藏夹

Article Engineering, Multidisciplinary

Volumetric characterization of dispersed two-phase flows by digital image analysis

A Lecuona et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.