{"title":"The use of an LSTM-based autoencoder for measurement denoising in process tomography","authors":"G. Kłosowski, T. Rymarczyk, D. Wójcik","doi":"10.3233/jae-230013","DOIUrl":null,"url":null,"abstract":"The main problem with any tomography is the transformation of measurements into images. It is the so-called “inverse problem,” which, due to its indeterminacy, can never be solved perfectly. An additional factor contributing to the deterioration of the quality of tomograms is measurement noise. This article shows how to denoise electrical capacitance tomography measurements using the LSTM autoencoder. The presented model is two-staged. First, the autoencoder is trained using very noisy measurements. Then, the decoder autoencoder generates a training set to using activations ofe the latent layer. In the second stage, the LSTM network is trained, which has encoder latent layer activations at the input and pattern images at the output. The results of the experiments show that using an autoencoder to denoise the measurements improves the reconstruction quality.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":"8 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Electromagnetics and Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3233/jae-230013","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The main problem with any tomography is the transformation of measurements into images. It is the so-called “inverse problem,” which, due to its indeterminacy, can never be solved perfectly. An additional factor contributing to the deterioration of the quality of tomograms is measurement noise. This article shows how to denoise electrical capacitance tomography measurements using the LSTM autoencoder. The presented model is two-staged. First, the autoencoder is trained using very noisy measurements. Then, the decoder autoencoder generates a training set to using activations ofe the latent layer. In the second stage, the LSTM network is trained, which has encoder latent layer activations at the input and pattern images at the output. The results of the experiments show that using an autoencoder to denoise the measurements improves the reconstruction quality.
期刊介绍:
The aim of the International Journal of Applied Electromagnetics and Mechanics is to contribute to intersciences coupling applied electromagnetics, mechanics and materials. The journal also intends to stimulate the further development of current technology in industry. The main subjects covered by the journal are:
Physics and mechanics of electromagnetic materials and devices
Computational electromagnetics in materials and devices
Applications of electromagnetic fields and materials
The three interrelated key subjects – electromagnetics, mechanics and materials - include the following aspects: electromagnetic NDE, electromagnetic machines and devices, electromagnetic materials and structures, electromagnetic fluids, magnetoelastic effects and magnetosolid mechanics, magnetic levitations, electromagnetic propulsion, bioelectromagnetics, and inverse problems in electromagnetics.
The editorial policy is to combine information and experience from both the latest high technology fields and as well as the well-established technologies within applied electromagnetics.