Noninvasive Conductivity and Temperature Sensing Using Magnetic Induction Spectroscopy Imaging

Muttakin, Imamul; Soleimani, Masoud

doi:10.1109/tim.2020.3016435

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…The eddies must then pass through the central volume of wide objects. Therefore, the eddy current density here was higher on average throughout the object compared with typical MIT excitation by circular coils [3][4][5][6][7][13][14][15][16][17][18][19][20][21][22][23]. Improved central area sensitivity (>20 dB) was the result of this spatial prefiltering [8].…”

Section: Electro-mechanical Mit Scanner Setupmentioning

confidence: 77%

“…For this reason, the practical implementation of 3D MIT has been considered here for the first time, to detect and localize perturbations throughout the depth of a weakly conductive ("biomedical") test body. With the exciter and receiver geometries previously used [3][4][5][6][7][13][14][15][16][17][18][19][20][21][22][23], the sensitivity in the central areas of a weakly conductive body is critically low [3][4][5][6][7]. In addition to the attenuation and blurring of the induction field over distance, there is another effect that further reduces the sensitivity at the center of the body: the induced eddy currents run in closed loops within the body, and the current density thus tends to be strongest near the surface, but decreases toward the center region (and along the axis of the coils), where it vanishes and no longer provides any information [8].…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body

Klein

Erni

Rueter

2021

Sensors

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Magnetic induction tomography (MIT) is a contactless, low-energy method used to visualize the conductivity distribution inside a body under examination. A particularly demanding task is the three-dimensional (3D) imaging of voluminous bodies in the biomedical impedance regime. While successful MIT simulations have been reported for this regime, practical demonstration over the entire depth of weakly conductive bodies is technically difficult and has not yet been reported, particularly in terms of more realistic requirements. Poor sensitivity in the central regions critically affects the measurements. However, a recently simulated MIT scanner with a sinusoidal excitation field topology promises improved sensitivity (>20 dB) from the interior. On this basis, a large and fast 3D MIT scanner was practically realized in this study. Close agreement between theoretical forward calculations and experimental measurements underline the technical performance of the sensor system, and the previously only simulated progress is hereby confirmed. This allows 3D reconstructions from practical measurements to be presented over the entire depth of a voluminous body phantom with tissue-like conductivity and dimensions similar to a human torso. This feasibility demonstration takes MIT a step further toward the quick 3D mapping of a low conductive and voluminous object, for example, for rapid, harmless and contactless thorax or lung diagnostics.

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Section: Electro-mechanical Mit Scanner Setupmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body

Klein

Erni

Rueter

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…This measurement system has a signal-to-noise ratio (SNR) between 60-90 dB, where measurement at low-frequency opposite-coil has the lowest SNR; and high-frequency adjacent-coil has the highest SNR. The detailed configuration was described in [26]. For the following work (see Fig.…”

Section: Machine Learning Methods Using Mutual Induction Datamentioning

confidence: 99%

Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

Muttakin

Soleimani

2021

IEEE Sensors J.

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Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or postprocessing stages. Additionally, in some cases, the displayed image may not be essential when quantifying the void fraction or porosity. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained providing 98% accuracy on this dataset. On the other hand, CNN classification using multi-dimensional data produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

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“…This measurement system has signal-tonoise ratio (SNR) between 60-90 dB, where measurement at low-frequency opposite-coil has the lowest SNR; and highfrequency adjacent-coil has the highest SNR. The detailed configuration was described in [24]. For the following work (see Fig.…”

Section: Machine Learning Methods Using Mutual Induction Datamentioning

confidence: 99%

Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach

Muttakin¹,

Soleimani²

2021

Preprint

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Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or complex computation degrading its real-time capability. Additionally, in some cases, the actual image may not be essential whereas the void fraction or porosity needs to be estimated. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to approximately 86% accuracy on this dataset. CNN classification using multi-dimensional data with more classes produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

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Noninvasive Conductivity and Temperature Sensing Using Magnetic Induction Spectroscopy Imaging

Cited by 12 publications

References 36 publications

Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body

Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body

Interior Void Classification in Liquid Metal Using Multi-Frequency Magnetic Induction Tomography With a Machine Learning Approach

Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach

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