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Advances in electrical capacitance tomography.

机译：电容层析成像技术的进步。

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Electrical tomography techniques for process imaging are very prominent for industrial applications due to their low cost, safety, high capture speed, and suitability for different vessel sizes. Among electrical tomography techniques, electrical capacitance tomography has been the subject of extensive recent research due to its noninvasive nature and capability of differentiating between different phases based on permittivity distribution. Research in electrical capacitance tomography is inherently interdisciplinary, and areas of research in it can be categorized as: (1) sensor design, (2) hardware electronics, (3) and image reconstruction. Work presented in this dissertation includes developments in image reconstruction and sensor design.; Work on image reconstruction presented in this dissertation include developments of both forward and inverse solutions. A feed forward neural network based forward solver has been developed for fast and relatively accurate forward solutions. The forward solver has been integrated into a Hopfield optimization reconstruction technique to provide a fully non-linear image reconstruction process. In addition, a 3D volume image reconstruction has been developed by extending the 2D neural network multi objective image reconstruction technique (NN-MOIRT) to 3D applications, and inclusion of new objective functions tailored for 3D imaging.; Developments on sensor related topics provided in this dissertation are 3D capacitance sensor designs for 3D imaging and non-invasive capacitance sensors for simultaneous permittivity/conductivity imaging. In the former case, a 3D sensor with axial variation in field distribution has been used for volume imaging based on the developed Hopfield 3D optimization image reconstruction. In the latter case, an extension of the conventional capacitance sensor based on capacitance and power measurements has been provided for simultaneous imaging of permittivity and conductivity distributions.
机译：用于过程成像的电子断层扫描技术因其低成本，安全性，高捕获速度以及对不同容器尺寸的适用性而在工业应用中非常重要。在电层析成像技术中，电容层析成像由于其非侵入性的性质和基于介电常数分布区分不同相的能力而成为最近广泛研究的主题。电容层析成像的研究本质上是跨学科的，其研究领域可以归类为：（1）传感器设计，（2）硬件电子学，（3）和图像重建。本文的工作包括图像重建和传感器设计方面的发展。本文提出的图像重建工作包括正解和逆解的发展。已经开发了基于前馈神经网络的前向求解器，用于快速和相对准确的前向求解。前向求解器已集成到Hopfield优化重建技术中，以提供完全非线性的图像重建过程。此外，通过将2D神经网络多目标图像重建技术（NN-MOIRT）扩展到3D应用程序，并包括为3D成像量身定制的新目标功能，已经开发了3D体积图像重建。本文提供的传感器相关主题的发展是用于3D成像的3D电容传感器设计和用于同时介电常数/电导率成像的非侵入性电容传感器。在前一种情况下，基于已开发的Hopfield 3D优化图像重建，已使用具有轴向分布场变化的3D传感器进行体积成像。在后一种情况下，已经提供了基于电容和功率测量的常规电容传感器的扩展，用于同时对介电常数和电导率分布进行成像。

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