Deep neural networks for 1D impedance inversion
نویسندگان
چکیده
منابع مشابه
XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification
We propose two multimodal deep learning architectures [1] that allow for cross-modal dataflow (XFlow) between the feature extractors, thereby extracting more interpretable features and obtaining a better representation than through unimodal learning, for the same amount of training data. These models can usefully exploit correlations between audio and visual data, which have a different dimensi...
متن کاملCystoscopy Image Classication Using Deep Convolutional Neural Networks
In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...
متن کاملDeep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
The mathematical problem for Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low...
متن کاملA direct inversion scheme for deep resistivity sounding data using artificial neural networks
Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need of a priori information. We introduce a two-step network approach where the first network identi...
متن کاملNeural Network Inversion for Multilayer Quaternion Neural Networks
Recently, solutions to inverse problems have been required in various engineering fields. The neural network inversion method has been studied as one of the neural network-based solutions. On the other hand, the extension of the neural network to a higher-dimensional domain, e.g., complex-value or quaternion, has been proposed, and a number of higher-dimensional neural network models have been ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ASEG Extended Abstracts
سال: 2019
ISSN: 2202-0586
DOI: 10.1080/22020586.2019.12073187