Abstract:The article concerns the research on the properties of core-shell superparamagnetic nanoparticles in the context of their use in medicine for diagnostics and therapy. The article presents a system for impedance (AC) spectroscopy of nanoparticles with a new arrangement of receive coils. A significant modification was the position of the reference coil in relation to the receive coils as well as the method of winding and routing the wires on the carcass. The 3D printing technique was used in the production of th… Show more
“…Electrical impedance tomography [32,33], generally called electrical tomography (ET) [34,35], includes some image reconstruction techniques. The methods available in the literature are electrical resistance tomography (ERT) [36,37], electrical capacitance tomography (ECT) [38][39][40][41] and electrical impedance tomography. ECT is based on the reconstruction of the electrical permeability ε, whereas EIT is based on the reconstruction of the conductivity γ.…”
This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.
“…Electrical impedance tomography [32,33], generally called electrical tomography (ET) [34,35], includes some image reconstruction techniques. The methods available in the literature are electrical resistance tomography (ERT) [36,37], electrical capacitance tomography (ECT) [38][39][40][41] and electrical impedance tomography. ECT is based on the reconstruction of the electrical permeability ε, whereas EIT is based on the reconstruction of the conductivity γ.…”
This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.
“…However, there are several methods that can reduce the size of B [1,2]. Although these methods make it easier to obtain the inverse of the matrix A, the resolution needed to correctly recognize the shape and size of the measured object is still insufficient [3][4][5][6][7][8][9][10][11][12][13].…”
This paper presents an image reconstruction method for reflective ultrasonic tomography. The variation of the time corresponding to the first peak for the transmitter-receiver pairs is used in the transmission tomography. Commonly, all the reflected packets are used in the reflective reconstruction, but here we assume that the inside of the object is either changing or has a high absorption coefficient. Classical tomography methods are based on an equation where the system matrix is not square. Therefore, inverting such a matrix is a complex task. The solution to this problem is our simple geometrical method, which is very fast and accurate. Moreover, a very high spatial resolution can be achieved. In this case, the amount of information obtained from the system is limited only by the number of transducers used for the ultrasonic measurement.
“…The solutions presented in these publications were mainly based on traditional computer vision techniques, including the images filtered by Gabor filters [15], fast Haar transforms (FHT), and fast Fourier transforms [16] or edge detection schemes [14,17]. However, in the literature, machine learning is arousing more and more interest [18], where various solutions are used, including support vector machines, k-nearest neighbours algorithm, logistic regression, artificial neural networks (ANN) [19][20][21][22]. It can also be seen in detect defects related to the categorization of pictures.…”
The purpose of this study was to employ a previously trained (pre-trained) convolutional neural network called Resnet101 in conjunction with deep machine learning approaches in order to construct an algorithm for classifying cracks in the photos that were evaluated. Adjustments were made to the ultimate layer, which resulted in the fully connected layer being altered. Specifically, the basic 1000-output fully connected layer in Resnet101 was replaced with a binary-classification layer, which consisted of two categories: an image with cracks and an image without cracks. In this study, we investigate whether or not it is possible to use deep neural networks to accomplish the rapid and entirely automated detection of flaws by utilizing analyzed photographs as the data source. The research that was done led to the discovery that a pre-trained convolutional neural network that makes use of support vector machines to train a fully connected layer is quite an efficient option, and that the acquired forecasting algorithm allows the categorization of faults with extremely good accuracy. The proposed classification algorithm is 99 percent efficient. In material inspection tasks, this idea can be used to find cracks and other flaws in the material, such as those that could be found in a number of public structures like buildings, roads, and bridges.
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