A method to measure non-Newtonian fluids viscosity using inertial viscometer with a computer vision system

Kornaeva, Elena; Stebakov, Ivan; Kornaev, Alexey; Dremin, Viktor; Popov, Sergey G.; Vinokurov, Andrey Y.

doi:10.1016/j.ijmecsci.2022.107967

Cited by 15 publications

(6 citation statements)

References 70 publications

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“…Quantitative parameters of the registered diaphanograms obtained earlier could be used as diagnostic criteria to build a classification model [22,23], in particular utilizing linear discriminant analysis (LDA) [24]. In addition, the application of pattern recognition theory, namely convolutional neural networks (CNN), seems promising for the differentiation of the condition of the maxillary sinuses (presence or absence of pathology) due to the method's resistance to small shifts and rotations of objects in the input images possible during diagnostic procedure [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

Bryanskaya

Dremin

Shupletsov

et al. 2023

Journal of Biophotonics

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Maxillary sinus pathologies remain among the most common ENT diseases requiring timely diagnosis for successful treatment. Standard ENT inspection approaches indicate low sensitivity in detecting maxillary sinus pathologies. In this paper, we report on capabilities of digital diaphanoscopy combined with machine learning tools in the detection of such pathologies. We provide a comparative analysis of two machine learning approaches applied to digital diapahnoscopy data, namely, convolutional neural networks and linear discriminant analysis. The sensitivity and specificity values obtained for both employed approaches exceed the reported accuracy indicators for traditional screening diagnosis methods (such as nasal endoscopy or ultrasound), suggesting the prospects of their usage for screening maxillary sinuses alterations. The analysis of the obtained values showed that the linear discriminant analysis, being a simpler approach as compared to neural networks, allows one to detect the maxillary sinus pathologies with the sensitivity and specificity of 0.88 and 0.98, respectively.

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Section: Introductionmentioning

confidence: 99%

Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

Bryanskaya

Dremin

Shupletsov

et al. 2023

Journal of Biophotonics

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“…[135] Using a cylinder immersed in the biodiesel sample, rotational viscometry measures the amount of force needed to rotate the cylinder. [136] To determine the viscosity of biodiesel, an optical viscometer measures how long it takes for a beam of light to travel a predetermined distance through the fluid. The fluid viscosity is derived from this measurement.…”

Section: Viscositymentioning

confidence: 99%

“…When determining viscosity, it is crucial to take into account both the flow rate and the capillary tube dimensions [135] . Using a cylinder immersed in the biodiesel sample, rotational viscometry measures the amount of force needed to rotate the cylinder [136] . To determine the viscosity of biodiesel, an optical viscometer measures how long it takes for a beam of light to travel a predetermined distance through the fluid.…”

Section: Physical Characterization Of Biodieselmentioning

confidence: 99%

A Review on Biodiesel Production, Analysis, and Emission Characteristics from Non‐Edible Feedstocks

Jamil

2023

ChemistrySelect

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Biodiesel produced from different edible and non‐edible feedstocks is a viable alternative option for fueling a combustion engine. However, the widespread use of edible sources for biodiesel production may lead to food versus fuel controversy. Oil from non‐edible feedstock is best possible solution for problems that inhibits biodiesel production on commercial scale. This study presents the potential of different non‐edible feedstock for biodiesel production. Several aspects such as extraction technologies used for extracting oil from non‐edible sources and biodiesel production pathways from non‐edible feedstocks are discussed. The quality of biodiesel should be assured before its commercialization by different physical and chemical characterization techniques are used. In particular, the emission comparison of biodiesel derived from the different non‐edible feedstocks is discussed. Based on this study, the non‐edible feedstocks have the potential to produce biodiesel but still it has a long way to go. This is due to non‐edible feedstock is in the R&D phase and needs more research to eliminate additional treatment steps compared to edible feedstock for economic aspects. Furthermore, there has been a trade off in emission properties of biodiesels produced from non‐edible feedstocks which must be removed or evaluated for pilot scale investigations before commercialization.

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“…Wang Xiaojie [6] et al developed a real-time fluid viscosity measurement system based on the horizontal capillary method. Kornaeva E P [7] et al measurement are implemented with the computer vision system that processes data obtained from the high-speed CMOS camera that records inertial flow in the transparent capillary illuminated with laser.…”

Section: Introductionmentioning

confidence: 99%

Research on moving liquid level detection method of viscometer in dynamic scene

Liu,

Jing,

Zhang

et al. 2024

Preprint

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In order to solve the problem of false detection of the moving liquid level caused by the vibration of the constant temperature water bath, this paper combines the Type-2 Fuzzy Gaussian Mixture Model (T2-FGMM) and Markov Random Field (MRF) to study a new background modeling method for detecting the moving liquid level in dynamic scenes. The method first considers the output of T2-FGMM as the initial labeling domain of MRF, and then combines the local energy of the labeling domain with the observation energy. The key of this method is to combine the spatiotemporal prior of T2-FGMM with the observation. Comparative experimental results show that the proposed algorithm has better dynamic background detection effect than traditional frame difference method and Vibe algorithm, and can effectively eliminate the influence of the vibration of the constant temperature water bath on the detection of the moving liquid level of the viscometer.

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A method to measure non-Newtonian fluids viscosity using inertial viscometer with a computer vision system

Cited by 15 publications

References 70 publications

Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

A Review on Biodiesel Production, Analysis, and Emission Characteristics from Non‐Edible Feedstocks

Research on moving liquid level detection method of viscometer in dynamic scene

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