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https://olympias.lib.uoi.gr/jspui/handle/123456789/38362Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Γεροδήμος, Θεοφάνης | el |
| dc.date.accessioned | 2024-09-09T08:07:25Z | - |
| dc.date.available | 2024-09-09T08:07:25Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/38362 | - |
| dc.identifier.uri | http://dx.doi.org/10.26268/heal.uoi.18068 | - |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
| dc.subject | Φασματοσκοπία εκπομπής ακτίνων-Χ, Ανάλυση μεγάλων δεδομένων, Μηχανική Μάθηση | el |
| dc.title | Big data analysis techniques in X-ray fluorescence imaging spectroscopy | en |
| dc.type | doctoralThesis | * |
| heal.type | doctoralThesis | el |
| heal.type.en | Doctoral thesis | en |
| heal.type.el | Διδακτορική διατριβή | el |
| heal.secondaryTitle | Τεχνικές ανάλυσης μεγάλων δεδομένων στην απεικονιστική φασματοσκοπία φθορισμού ακτίνων-Χ | el |
| heal.classification | Μεγάλα δεδομένα -- Ανάλυση | el |
| heal.language | en | el |
| heal.access | account | el |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Πολυτεχνική Σχολή. Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2024-06-14 | - |
| heal.abstract | Big data presents exciting opportunities and huge challenges for data scientists. That is why this field is very promising and considered cutting-edge in many scientific disciplines. The ability to perform complex calculations and extract information from large datasets is a valuable research tool in both basic and applied research. The size and complexity (high dimensionality) of big data present unique challenges, including scalability and storage limitations, noise over clustering, misleading correlations, and randomness errors or problems during measurements. All these challenges require innovative computational and statistical approaches. X-ray fluorescence spectroscopy (XRF) is an analytical method widely used in many fields due to its nondestructive and noninvasive nature. XRF spectroscopy allows multi-elemental determination, requires no processing of the sample, provides fast results, and is environmentally friendly. XRF is based on detecting the specific radiative transitions emitted by the atoms of a material when it is exposed to a primary X-ray beam. The rapid technological development in the last two decades in the field of X-ray tubes, optical devices, as well as energy dispersive detectors has resulted in the rapid development of Xray fluorescence imaging spectroscopy. In X-ray fluorescence imaging spectroscopy, the ionizing beam scans the target, allowing the elemental composition of the target to be determined based on its position coordinates. The elemental composition is determined from the spectrum recorded in each spatial area during scanning. The number of spectra during a scan depends on the beam spot, the pixel size, and the size of the object, and can exceed a million. Furthermore, each spectrum contains information in thousands of channels. Consequently, the analysis of big data produced by XRF imaging spectroscopy is crucial due to the complexity and volume of data generated during the process. The techniques for analyzing the experimental data can improve the strength of this analytical method. The XRF spectra are high-dimensional, containing vast amounts of data points (spectra) for each sample analyzed. This complexity arises from the need to detect and measure the intensity of emitted X-rays across a broad spectrum of energies. Advanced analytical techniques, such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Non-negative Matrix Factorization (NMF), are essential for dimensionality reduction and data simplification. These techniques enable researchers to manage and interpret the high-dimensional data effectively, extracting meaningful patterns and correlations that would be otherwise hidden/unrevealed. The application of clustering algorithms like k-means and machine learning models, including neural networks, further enhances the capability to process and analyze large XRF datasets. These models facilitate the identification of distinct elements within samples and improve the accuracy of qualitative and quantitative analysis. Moreover, integrating machine deep learning architectures like convolutional neural networks (CNNs) and auto encoders, expands the analytical power of XRF spectroscopy. These algorithms enable the automation of data processing, reducing the need for manual intervention and special knowledge and increasing the efficiency of the analytical workflow. Chapters 1 and 2 introduce the basics of X-rays and X-ray fluorescence (XRF) spectroscopy. They cover the main theory of X-rays, the instrumentation of XRF spectrometers, and the challenges associated with analyzing and interpreting the complex data generated. In Chapters 3 and 4 advanced analytical techniques, such as dimensionality reduction and machine learning algorithms, are discussed that help researchers extract meaningful information from this big data. Subsequent chapters delve deeper into the application of these advanced analytical methods in specific case studies, illustrating the practical benefits of big data analysis in XRF spectroscopy. In Chapter 5, we demonstrate the effectiveness of big data analysis techniques collected during the application of macroscopic scanning X-ray fluorescence spectroscopy to a Byzantine religious icon (Macroscopic XRF, MA-XRF). By comparing methods of X-ray fundamental parameter analysis and statistical data analysis, we demonstrate how these approaches can extract detailed information about pigments used, painting techniques, and the state of preservation of historical artifacts. In Chapter 6, the fusion of datasets acquired from the same object applying asynchronously scanning micro-X-ray fluorescence and multispectral imaging spectroscopy (MSI) is explored. Specifically, the study of stamps was chosen, due to the increased requirements of spatial resolution. This chapter highlights the enhanced analytical capabilities achieved through data fusion, emphasizing the importance of aligning and co-registering datasets from different imaging techniques to obtain more comprehensive insights. Chapter 7 examines the application of Artificial Intelligence (AI) methods to the analysis of datasets acquired by applying MA-XRF on a 19th-century religious image. This chapter illustrates how clustering algorithms, factorization methods, and supervised machine learning techniques can provide detailed and rapid analysis, making advanced data interpretation accessible even to non-experts. Chapter 8 focuses on using convolutional neural networks (CNNs) to analyze MA-XRF datasets from religious panel paintings. It highlights CNNs' effectiveness in accurately identifying and mapping elemental transitions, thus enabling automated spectral analysis and providing support for novice and experienced analysts. Chapter 9 presents two case studies of machine learning applied to art conservation analysis. Firstly, investigates the potential of CNN classifiers to analyze complex multilayer samples in paintings. By training CNNs to predict paint layers from scanning XRF spectra, this chapter demonstrates the capability of these networks to reveal the stratigraphy of artworks, offering valuable tools for art conservation and study. Secondly, investigates the per-pixel correlation between RGB images and XRF spectra using a deep autoencoder, providing a comprehensive understanding of the relationship between visual and spectral data in artworks before restoration and conservation. In conclusion, big data analysis in XRF spectroscopy is a crucial tool for analyzing the large datasets acquired during the measurements. By leveraging advanced data analysis techniques and machine learning algorithms, researchers can more effectively handle the complexity and volume of XRF data, leading to more accurate and reliable results. This synergy between domains like XRF spectroscopy and big data analysis ultimately drives advancements in material science, quality control, cultural heritage studies, and various industrial applications. | en |
| heal.advisorName | Αναγνωστόπουλος, Δημήτριος | el |
| heal.committeeMemberName | Αναγνωστόπουλος, Δημήτριος | el |
| heal.committeeMemberName | Λύκας, Αριστείδης | el |
| heal.committeeMemberName | Καρύδας, Ανδρέας Γερμανός | el |
| heal.committeeMemberName | Ματίκας, Θεόδωρος | el |
| heal.committeeMemberName | Παπαγεωργίου, Δημήτριος | el |
| heal.committeeMemberName | Μαστροθεόδωρος, Γεώργιος | el |
| heal.committeeMemberName | Παπαδάκης, Βασίλειος | el |
| heal.academicPublisher | Πανεπιστήμιο Ιωαννίνων. Πολυτεχνική Σχολή. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.academicPublisherID | uoi | el |
| heal.numberOfPages | 249 | el |
| heal.fullTextAvailability | true | - |
| Appears in Collections: | Διδακτορικές Διατριβές - ΜΕΥ | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Gerodimos_Theofanis_phd_thesis_v4.pdf | 10.81 MB | Adobe PDF | View/Open |
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