Shape-based defect classification for Non Destructive Testing

The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.Comment: 5 pages, IEEE International Worksho

A Nondestructive Distributed Sensor System for Imaging in Industrial Tomography

The proposed solution is based on the construction of a cyber-physical system for acquiring, processing, and reconstructing the image of measurement data. Industrial tomography enables observation of physical and chemical phenomena without the need of internal penetration. Process tomography gives the possibility to analyze processes taking place inside the facility without disturbing the production, analysis, and detection of obstacles, defects, and various anomalies. The presented measuring system has a specially designed measuring structure (including electrodes, thanks to which it is an innovative solution in the field, particularly effective in analysis). Knowledge of the characteristics of each tomographic technique allows to choose the appropriate method of image reconstruction. The inverse problem is the process of identifying optimization or synthesis, wherein the objective is to determine the parameters describing the data field

METODY PARAMETRYCZNE W ROZWIĄZYWANIU PROBLEMU ODWROTNEGO DLA MONITOROWANIA PRZEPŁYWÓW MATERIAŁÓW SYPKICH

The article presents the parametrisation-based methods of monitoring of the process of gravitational silo discharging with aid of capacitance tomography techniques. Proposed methods cover probabilistic Bayes’ modelling, including spatial and temporal analysis and Markov chain Monte Carlo methods as well as process parametrisation with artificial neural networks. In contrast to classical image reconstruction-based methods, parametric modelling allows to omit this stage as well as abandon the associated reconstruction errors. Parametric modelling enables the direct analysis of significant parameters of investigated process that in turn results in easier incorporation into the control feedback loop. Presented examples are given for the gravitational flow of bulk solids in silos.Niniejszy artykuł przedstawia parametryczne metody rozwiązywania problemu odwrotnego w tomografii pojemnościowej na przykładzie monitorowania procesu przepływu materiałów sypkich przy użyciu tomografii pojemnościowej. Wybrane metody obejmują modelowanie probabilistyczne Bayesa, w tym przestrzenne i czasowe oraz metody Monte Carlo łańcuchów Markowa, a także parametryzację procesu z użyciem sztucznych sieci neuronowych. W odróżnieniu od klasycznych metod opartych na algorytmach rekonstrukcji obrazu parametryzacja pozwala na pominięcie tego etapu, a co za tym idzie brak dodatkowych błędów związanych z rekonstrukcją. Parametryzacja pozwala na bezpośrednią analizę istotnych parametrów badanego procesu, przez co łatwiejsze jest użycie tych wyników w pętli sprzężenia zwrotnego sterowania. Przykłady rozpatrywane w tekście są opisane dla procesu grawitacyjnego opróżniania materiałów sypkich przechowywanych w silosach

Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Development Of Intelligent Gas-Oil Flow Process Interpreter Based On Generic Primary Electrode Of Electrical Capacitance Tomography Sensor

Electrical Capacitance Tomography (ECT) is a technique used to obtain information about the distribution of materials inside a vessel by measuring variations in the dielectric properties of the material distributions. Previous research works on ECT flow regime classification and material fraction estimation have employed Artificial Neural Networks (ANNs) approach focusing on fixed ECT sensor parameters, and hence producing inefficient process interpreter systems. Therefore, this research aims to develop intelligent process interpreter systems which function to accommo- date a range of ECT primary electrode sensor sizes. For the purpose, Multilayer Perceptron (MLP) ANNs have been trained with different types of datasets to investigate the best method in producing generic intelligent gas-oil flow regime classifier and oil fraction estimator. The Principal Component Analysis (PCA) technique has also been used to reduce the dimensionality of input, reduce training time and improve the systems’ performances. The developed intelligent gas-oil classifier has given 93.93% average correct classification accuracy from ECT data of generic primary electrode. This accuracy value is higher than the average classification accuracy of intel- ligent classifier trained with fixed ECT primary electrode size which is 37.45%, for the same test dataset. The developed intelligent oil fraction estimator has produced 3.05% mean absolute er- ror (MAE) for generic ECT data of various flow regimes. This MAE is 3.25% lower than the MAE produced by the best non-generic intelligent oil fraction estimator, based on the same dataset. The satisfactory research results reveal that the performances of generic intelligent gas-oil clas- sifier and oil fraction estimator are better than the non-generic gas-oil classifier and estimator for process interpretation tasks

Compressed sensing techniques for radial Ultra-short Echo Time (UTE) magnetic resonance imaging

This thesis proposes two techniques, namely Compressed Sensing (CS) and self-gating, for pre-clinical (CMRI) to reduce scan time and RF exposure to mouse heart, simply experimental procedures, and improve imaging quality. The proposed CS technique reduces the number of radial trajectories in Ultra-short Echo Time (UTE) CMRI scans on a 7 Tesla MRI machine to acquire 13% to 38% of the fully sampled k-space data. To reconstruct the image, the Non-Uniform Fast Fourier Transform (NUFFT) is utilized in each iteration of the l1-norm optimization algorithm of the CS to reduce error and aliasing. Experimental results with a phantom and a mouse heart samples show that the image quality of the proposed NUFFT-CS reconstructions, measured by the Peak Signal to Noise ratio (PSNR) and structural similarity (SSIM), is obviously better than those of traditional zero-filling method and regridding-CS method. Comparing the images of the CS technique with the reconstructions of fully sampled data, the quality degradation is illegible while the scan time is largely reduced. The proposed self-gating technique extracts the cardiac cycle information directly from the UTE CMRI measurements that are acquired without Electrocardiography (ECG) trigger. The proposed detector filters the k0 lines in the no-trigger UTE MRI scans to extract the cardiac cycle features, and automatically detects the peaks of the filtered signal as the cycle start points. The reconstruct cardiac images based on the self-gating signals reflect the cardiac cycle clearly and the scan time in MRI exams is reduced by 40% to 70%. The proposed self-gating detector differs from existing k0-line detector in the filter design and in the combination with NUFFT image reconstruction. Future research in this direction may include thorough analysis of the detector performance and may combine self-gated MRI with CS reconstruction. --Abstract, page iv

Efficient Autonomous Navigation for Planetary Rovers with Limited Resources

Rovers operating on Mars are in need of more and more autonomous features to ful ll their challenging mission requirements. However, the inherent constraints of space systems make the implementation of complex algorithms an expensive and difficult task. In this paper we propose a control architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the current ExoMars navigation method, enhancing the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform long traverses by following a roughly safe path planned by operators on ground. The control architecture implementing the proposed navigation mode has been tested during a field test campaign on a planetary analogue terrain. The experiments evaluated the proposed approach, autonomously completing two long traverses while avoiding hazards. The approach only relies on the optical Localization Cameras stereobench, a sensor that is found in all rovers launched so far, and potentially allows for computationally inexpensive long-range autonomous navigation in terrains of medium difficulty