Low-noise instrumentation for electromagnetic groundwater flow measurement

Bonnett, Blair; Mitchell, Ben; Frampton, Michael J.; Hayes, Michael

doi:10.1109/i2mtc.2019.8827136

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Because blood is electrically conductive, its flow velocity can be measured by the electromagnetic effect of blood flow theoretically. The fluid detection method based on the electromagnetic effect has been widely used in industrial fields such as wastewater monitoring [ 6 ], gas–water two-phase flow measurement [ 7 ] and groundwater flow measurement [ 8 ]. Additionally, this method has been introduced into the blood flow detection in the medical field in recent years.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

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Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

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Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

Entropy

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Estimating hydraulic conductivity correlation lengths of an aquitard by inverse geostatistical modelling of a pumping test

et al. 2023

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Aquitards are common hydrogeological features in the subsurface. Typically, pumping tests are used to parameterize the hydraulic conductivity of heterogeneous aquitards. However, they do not take spatial variability and uncertainty into account. Alternatively, core-scale measurements of hydraulic conductivity are used in geostatistical upscaling methods, for which their correlation lengths are needed, but this information is extremely difficult to obtain. This study investigates whether a pumping test can be used to obtain the correlation lengths needed for geostatistical upscaling and account for the uncertainty about heterogeneous aquitard conductivity. Random realizations are generated from core-scale data with varying correlation lengths and inserted into a groundwater flow model which simulates the outcome of an actual pumping test. The realizations yielded a better fit to the pumping test data than the traditional pumping test result, assuming homogeneous layers are selected. Ranges of horizontal and vertical correlation lengths that fit the pumping-test well are found. However, considerable uncertainty regarding the correlation lengths remains, which should be considered when parameterizing a regional groundwater flow model.

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