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Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning
Blaž Pongrac, Dušan Gleich, Marko Malajner, Andrej Sarjaš, 2023, izvirni znanstveni članek

Opis: This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas.
Ključne besede: ground penetrating radar, cross-hole, L-band, deep learning, convolutional neural network, soil moisture estimation
Objavljeno v DKUM: 03.04.2024; Ogledov: 119; Prenosov: 9
.pdf Celotno besedilo (3,22 MB)
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3.
Prediction of the form of a hardened metal workpiece during the straightening process
Tadej Peršak, Jernej Hernavs, Tomaž Vuherer, Aleš Belšak, Simon Klančnik, 2023, izvirni znanstveni članek

Opis: In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.
Ključne besede: sustraightening process, hardened workpiece, manufacturing, U-Net convolutional neural network, modeling, point cloud, digital sustainability
Objavljeno v DKUM: 02.04.2024; Ogledov: 115; Prenosov: 14
.pdf Celotno besedilo (10,52 MB)
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4.
Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism
Blaž Pongrac, Dušan Gleich, 2023, izvirni znanstveni članek

Opis: The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.
Ključne besede: synthetic aperture radar, speckle, speckle suppression, despeckling, deep learning, convolutional neural network
Objavljeno v DKUM: 21.02.2024; Ogledov: 152; Prenosov: 10
.pdf Celotno besedilo (13,70 MB)
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Frequency range optimization for continuous wave Terahertz imaging
Blaž Pongrac, Andrej Sarjaš, Dušan Gleich, izvirni znanstveni članek

Opis: S krajšimi valovnimi dolžinami kot mikrovalovi in večjo globino prodora v material kot infrardeča svetloba, valovi v TeraHertz-nem (THz) spektru ponujajo edinstvene možnosti testiranja materialov. THz tehnologija ponuja neinvazivna in nedestruktivna testiranja v obliki spektroskopije in slikanja. Najbolj uporabljeni sistemi za THz slikanje so sistemi spektroskopije v časovni domeni. Vendar sistemi spektroskopije frekvenčne domene ponujajo odlično frekvenčno ločljivost in so primerni za biomedicinske aplikacije. THz-no slikanje na podlagi spektroskopije v frekvenčnem prostoru je časovno kompleksno in ima pomanjkljivosti zaradi napak pri generiranju THz valov. V tem članku je predstavljen nov princip enodimenzionalnega zajemanja s THz valovi. Predlagana optimizacija frekvenčnega območja temelji na konvolucijski nevronski omreži. Predstavljena je frekvenčna optimizacija za določitev optimalnega frekvenčnega območja za zajem podatkov. Optimalno frekvenčno območje ali pasovna širina morata biti dovolj široka za učinkovito zaznavanje faze in morata biti na presečišču več spektralnih odtisov v opazovanem mediju. Presek spektralnih odtisov je ocenjen z uporabo predlaganega algoritma za optimizacijo frekvenčnega območja, ki temelji na konvolucijski nevronski mreži in algoritmu za občutljivost okluzije. Predlagani algoritem izbira samodejno najobčutljivejši frekvenčni pas THz spektra in omogoča zelo hitre zajeme za pregled in klasifikacijo objektov.
Ključne besede: terahertz, spectroscopy, imaging, convolutional neural network, occlusion sensitivity, optimization
Objavljeno v DKUM: 07.12.2023; Ogledov: 296; Prenosov: 13
.pdf Celotno besedilo (8,82 MB)
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