A decision-making algorithm for automatic flow pattern identification in high-speed imaging

Tomasoni, Flora; Saracoglu, Bayindir H.; Paniagua, Guillermo

doi:10.1016/j.eswa.2013.12.015

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Mohmmed et al (2016) measured the slug velocity and length of slug flow using an image processing technique. Tomasoni, Saracoglu, and Paniagua (2014) proposed a digital image processing algorithm for flow identification in high-speed imaging, which correctly identified the fuzzy flow characteristics. Dinh and Choi (1999) used a high-speed camera to identify the slug and bubbly flow in a vertical pipe, and the size of the bubble edge was calculated by image processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the spatial distribution of collectors in dust scrubber based on image processing

Xiao

et al. 2019

Journal of the Air & Waste Management Association

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The spatial distribution of the collectors in dust scrubber is key in determining the effectiveness of the dust removal process. In the present study, a high-speed camera was used to capture images of the distribution of the collectors. Some of the image information was extracted by image processing, such as the gray mean (GM), the angular second moment (ASM), and the entropy (ENT) from the gray-level co-occurrence matrix of the image. Subsequently, the spatial distribution rules of the collectors were studied by analyzing the spatial proportion, dispersion area, and uniformity and intensiveness of the collectors. It is an intuitive approach and a novel analysis method for the operating state of dust scrubber. The results show that the spatial distribution of the collectors could be better reflected by image processing methods. The dispersion area of the collectors expanded with an increase in the airflow velocity. When the initial liquid level (ILL) was higher, the collectors expanded in an approximate circular shape, and when the ILL was lower the collectors expanded in an approximate sector shape. In general, the variation trend in the spatial proportion enhanced with an increase in ILL and airflow velocity, which is consistent with the uniformity of collectors. When the liquid level was 0−20 mm and the airflow velocity was greater than 6.5 m/sec, the spatial proportion and uniformity of the collectors reached the highest degree. However, the growth rate of the spatial proportion and uniformity of the collectors slowed down and even led to negative growth when the ILL was lower and the airflow velocity was higher. The intensiveness of the collectors was great when the ILL was higher, which was free from the apparent influence of the airflow velocity and the ILL. However, when the ILL was lower, the intensiveness of the collectors was poor, intensifying as the airflow velocity and ILL increased. When the liquid level was −5−10 mm and the airflow velocity was greater than 8 m/sec, the intensiveness of the collectors reached the highest degree, indicating that a liquid level greater than 0 mm and a higher airflow velocity improved the spatial distribution of the collectors. Implications: This paper focuses on the spatial distribution of the collectors in dust scrubber. Some of the image information was extracted by image processing, such as the gray mean of the image, the angular second moment, and the entropy from the gray-level co-occurrence matrix of the image. The spatial distribution rules of the collectors were studied by analyzing the spatial proportion, the dispersion area, and the uniformity and intensiveness of the collectors.

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Section: Introductionmentioning

confidence: 99%

Analysis of the spatial distribution of collectors in dust scrubber based on image processing

Xiao

et al. 2019

Journal of the Air & Waste Management Association

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“…Additionally, for some scientific applications (e.g. [4]) it is crucial to be able to install the light source in a particular place in a complex imaging system. This requires a modular camera design and some measures for triggering with known latency.…”

Section: Factors To Consider For Highspeed Imagingmentioning

confidence: 99%

“…In today's industrial and scientific environments, highspeed imaging is predominately used for scientific analysis or test and measurement applications, with vehicle collision studies [3] probably the most commonly recognized. Other significant applications include particle imaging velocimetry (PIV) [4] whereby images are taken of fluid flows and particle velocities extracted as a tool for aerodynamic engineering, human motion analysis [5], as well as failure mode dynamics and analysis [6], impact testing, and ballistics research [7]. More recently, there has been increasing interest from the biological and nanotechnology sectors [8] to adapt high-speed techniques and leverage the advantages that high-speed technology brings, such as the visualization of MEMS devices.…”

Section: High-speed Imaging Provides Unique Insights Into Processesmentioning

confidence: 99%

Fast Mover

2015

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The realm of high-speed imaging traditionally provides some of the most visually appealing images for the non-specialist. From bullets passing through objects to the minute details of animal behaviour, the ability to freeze time opens the way to unique insights into the world around us, as well as providing a valuable tool for analysis and test in an industrial environment. Often associated with high costs and complex operation, new approaches to capturing critical high-speed events are being developed which can enable scientists and engineers to use high-speed imaging as a routine tool rather than a specialist discipline. By synchronizing high-power illumination pulses with tightly controlled image sensor exposures, while at the same time providing on-board image storage and in-situ processing, a simple and flexible highspeed imaging architecture is realized, capable of wide ranging application. High-speed imaging provides unique insights into processesFrom the seminal work of Eadweard Muybridge in the late nineteenth century analysing the gait of a galloping horse [1] to the latest advances in femto-photography [2], high-speed imaging has a rich history of providing new information about motions, events, and processes. In today's industrial and scientific environments, highspeed imaging is predominately used for scientific analysis or test and measurement applications, with vehicle collision studies [3] probably the most commonly recognized. Other significant applications include particle imaging velocimetry (PIV) [4] whereby images are taken of fluid flows and particle velocities extracted as a tool for aerodynamic engineering, human motion analysis [5], as well as failure mode dynamics and analysis [6], impact testing, and ballistics research [7]. More recently, there has been increasing interest from the biological and nanotechnology sectors [8] to adapt high-speed techniques and leverage the advantages that high-speed technology brings, such as the visualization of MEMS devices.In each case it is the insight that is obtained by viewing events happening on a timescale much faster than humans are capable of visualizing, which provides the ultimate value to the user. Factors to consider for highspeed imagingHigh-speed imaging relies on the ability to capture an optical image of a scene in a short period of time. Using shorter periods of time for scene capture, generally corresponds to an increased ability to "freeze" fast moving events. It is this ability to capture short moments in time which provides the useful diagnostic information used in applications. A typical approach to capturing an image in a short period of time is to simply decrease the exposure time of the camera. Typical image sensors used within machine vision cameras have minimum exposure times in the range 5-15 microseconds, whilst dedicated high-speed imaging systems can operate with exposure times lower than 200 ns. If the object odos imaging LimitedEdinburgh, Scotland odos imaging Limited is a technology focused company specialising in th...

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Identification of the state-space dynamics of oil flames through computer vision and modal techniques

Silva

Fleury

Martins

et al. 2015

Expert Systems with Applications

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A decision-making algorithm for automatic flow pattern identification in high-speed imaging

Cited by 8 publications

References 15 publications

Analysis of the spatial distribution of collectors in dust scrubber based on image processing

Analysis of the spatial distribution of collectors in dust scrubber based on image processing

Fast Mover

Identification of the state-space dynamics of oil flames through computer vision and modal techniques

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