Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Chen, Zhuoran; Ma, Gege; Jiang, Yuexiang; Wang, Baoliang; Soleimani, Masoud

doi:10.3390/electronics10091058

Cited by 17 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing the results with those obtained in other works [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] in the field of EIT reconstruction using machine learning methods, it can be concluded that at least some of the models presented in this work (especially the CART model) dominate the published achievements in terms of the obtained measures of reconstruction quality.…”

Section: Resultssupporting

confidence: 71%

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Baran¹,

Kozłowski

Majerek

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation.

show abstract

Section: Resultssupporting

confidence: 71%

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Baran¹,

Kozłowski

Majerek

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the past, Multi-Layer Perceptrons (MLPs) [ 43 ] and Convolutional Neural Networks (CNNs) [ 44 ] have been used for various tasks. MLPs represent the most primitive type of ANN.…”

Section: Methodsmentioning

confidence: 99%

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Irshad

Nisar

Huang

et al. 2022

Sensors

View full text Add to dashboard Cite

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP).

show abstract

“…The fourth paper is entitled Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography, and it is authored by Chen et al [6]. In this work, the authors proposed a new framework for image reconstruction for capacitively coupled electrical resistance tomography (CCERT) industrial application.…”

Section: Regularization Techniques For Machine Learning and Their App...mentioning

confidence: 99%

Special Issue: Regularization Techniques for Machine Learning and Their Applications

Kotsilieris

Anagnostopoulos

Livieris³

2022

Electronics

View full text Add to dashboard Cite

Over the last decade, learning theory performed significant progress in the development of sophisticated algorithms and their theoretical foundations. The theory builds on concepts that exploit ideas and methodologies from mathematical areas such as optimization theory. Regularization is probably the key to address the challenging problem of overfitting, which usually occurs in high-dimensional learning. Its primary goal is to make the machine learning algorithm “learn” and not “memorize” by penalizing the algorithm to reduce its generalization error in order to avoid the risk of overfitting. As a result, the variance of the model is significantly reduced, without substantial increase in its bias and without losing any important properties in the data.

show abstract

Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Cited by 17 publications

References 36 publications

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Special Issue: Regularization Techniques for Machine Learning and Their Applications

Contact Info

Product

Resources

About