| Texto completo | |
| Autor(es): |
Duran, Guilherme C.
;
Ueda, Edson K.
;
Sato, Andre K.
;
Martins, Thiago C.
;
Tsuzuki, Marcos S. G.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IFAC JOURNAL OF SYSTEMS AND CONTROL; v. 34, p. 18-pg., 2025-12-25. |
| Resumo | |
Electrode displacement is a common source of error in Electrical Impedance Tomography (EIT), particularly in long-term or dynamic measurements where stable electrode contact is difficult to maintain. This study proposes a comprehensive machine learning framework to detect, classify, and correct electrode displacements prior to image reconstruction. The approach combines tree-based classifiers-such as XGBoost and LightGBM-and Convolutional Neural Networks (CNNs) to identify both the presence and location of displaced electrodes. These models were evaluated across a series of classification tasks with increasing complexity, demonstrating high accuracy even in scenarios involving multiple simultaneous displacements with different magnitudes. For the rectification of distorted voltage measurements, several Denoising Autoencoder (DAE) configurations were investigated. Electrode-specific DAEs trained on all displacement magnitudes achieved an average Mean Squared Error (MSE) reduction of 84.5%, while shift-based DAEs offered computational efficiency for coarse corrections. A hybrid strategy combining fast shift-based and high-accuracy electrode-specific models proved effective and scalable. The pipeline was validated using both synthetic datasets and real EIT measurements, confirming its robustness and generalization capabilities. These results support the use of learning-based correction schemes to improve the reliability and consistency of EIT in practical applications affected by electrode movement. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. (AU) | |
| Processo FAPESP: | 09/07173-2 - Aplicação do recozimento simulado à tomografia por impedância elétrica para a obtenção de imagens absolutas |
| Beneficiário: | Marcos de Sales Guerra Tsuzuki |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 24/06094-1 - Eletrodos Deslocados em TIE e sua Influência nas Imagens Absolutas Reconstruídas |
| Beneficiário: | Marcos de Sales Guerra Tsuzuki |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 09/14699-0 - Aplicação do Recozimento Simulado com Vizinhança Adaptativa à Tomografia de Impedância Elétrica para Obtenção de Imagens Absolutas |
| Beneficiário: | Thiago de Castro Martins |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 17/07799-5 - Recozimento simulado intervalar implementado em GPGPU para obter imagens absolutas em tomografia por impedância elétrica |
| Beneficiário: | Marcos de Sales Guerra Tsuzuki |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |