Analysis of microtomographic images in automatic defect localization and detection

The paper presents a fast method of fully automatic localization and classification of defects in aluminium castings based on computed microtomography images. In the light of current research and based on available publications, where such analysis is made on the basis of images obtained from standard radiography (x-ray), this is a new approach which uses microtomographic images (μ-CT). In addition, the above-mentioned solutions most often analyze a pre-separated portion of an image, which requires the initial operator interference. The authors’ own pre-processing methods, which allow to separate the element area and potential defect areas from μ-CT images, and methods of extraction of selected features describing these areas have been proposed in the solution discussed here. A neural network trained using the Levenberg–Marquardt method with error backpropagation has been used as a classifier. The optimal network structure 20–4–1 and a set of 20 features describing the analysed areas have been determined as a result of performed tests. The applied solutions have provided 89% correct detection for any defect size and 96.73% for large defects, which is comparable to the results obtained from methods using x-ray images. This has confirmed that it is possible to use μ-CT images in automatic defect localization in 3D. Thanks to this method, quantitative analysis of aluminium castings can be carried out without user interaction and fully automated

The Magic of Vision: Understanding What Happens in the Process

How important is the human vision? Simply speaking, it is central for domain\ua0related users to understand a design, a framework, a process, or an application\ua0in terms of human-centered cognition. This thesis focuses on facilitating visual\ua0comprehension for users working with specific industrial processes characterized\ua0by tomography. The thesis illustrates work that was done during the past two\ua0years within three application areas: real-time condition monitoring, tomographic\ua0image segmentation, and affective colormap design, featuring four research papers\ua0of which three published and one under review.The first paper provides effective deep learning algorithms accompanied by\ua0comparative studies to support real-time condition monitoring for a specialized\ua0microwave drying process for porous foams being taken place in a confined chamber.\ua0The tools provided give its users a capability to gain visually-based insights\ua0and understanding for specific processes. We verify that our state-of-the-art\ua0deep learning techniques based on infrared (IR) images significantly benefit condition\ua0monitoring, providing an increase in fault finding accuracy over conventional\ua0methods. Nevertheless, we note that transfer learning and deep residual network\ua0techniques do not yield increased performance over normal convolutional neural\ua0networks in our case.After a drying process, there will be some outputted images which are reconstructed\ua0by sensor data, such as microwave tomography (MWT) sensor. Hence,\ua0how to make users visually judge the success of the process by referring to the\ua0outputted MWT images becomes the core task. The second paper proposes an\ua0automatic segmentation algorithm named MWTS-KM to visualize the desired low\ua0moisture areas of the foam used in the whole process on the MWT images, effectively\ua0enhance users\u27understanding of tomographic image data. We also prove its\ua0performance is superior to two other preeminent methods through a comparative\ua0study.To better boost human comprehension among the reconstructed MWT image,\ua0a colormap deisgn research based on the same segmentation task as in the second\ua0paper is fully elaborated in the third and the fourth papers. A quantitative\ua0evaluation implemented in the third paper shows that different colormaps can\ua0influence the task accuracy in MWT related analytics, and that schemes autumn,\ua0virids, and parula can provide the best performance. As the full extension of\ua0the third paper, the fourth paper introduces a systematic crowdsourced study,\ua0verifying our prior hypothesis that the colormaps triggering affect in the positiveexciting\ua0quadrant in the valence-arousal model are able to facilitate more precise\ua0visual comprehension in the context of MWT than the other three quadrants.\ua0Interestingly, we also discover the counter-finding that colormaps resulting in\ua0affect in the negative-calm quadrant are undesirable. A synthetic colormap design\ua0guideline is brought up to benefit domain related users.In the end, we re-emphasize the importance of making humans beneficial in every\ua0context. Also, we start walking down the future path of focusing on humancentered\ua0machine learning(HCML), which is an emerging subfield of computer\ua0science which combines theexpertise of data-driven ML with the domain knowledge\ua0of HCI. This novel interdisciplinary research field is being explored to support\ua0developing the real-time industrial decision-support system

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following

Three-dimensional eddy current pulsed thermography and its applications

Ph. D. Thesis.The measurement and quantification of defects is a challenge for Non-DestructiveTesting and Evaluation (NDT&E). Such challenges include the precise localisation and detection of surface and sub-surface defects, as well as the quantification of such defects. This work first reports a three-dimensional (3D) Eddy Current Pulsed Thermography (ECPT) system via integration with an RGB-D camera. Then, various quantitative measurements and analyses of defects are carried out based on the 3D ECPT system. The ECPT system at Newcastle University has been prooven to be an effective nondestructive testing (NDT) method in surface and sub-surface detection over the past few years. Based on the different numerical or analytical models, it has achieved precise defect detection on the rail tracks, wind turbines, carbon fibre reinforced plastic (CFRP) and so on. The ECPT system has the advantage of fast inspection and a large lift-off range. However, it involves a trade-off between detectable defect size and inspection area compared with other NDT methods. In addition, there are challenges of defect detection in a complex structure. Thus, the quantification of defects gives a higher requirement of the measurement the object geometry information. Furthermore, the analysis of thermal diffusion requires a precise 3D model. For this reason, a 3D ECPT system is proposed that adds each heat pixel with an exact X-Y-Z coordinate. In this work, first, the 3D ECPT system is built. A feature-based automatic calibration of the infrared camera and the RGB-D camera is proposed. Second, the software platform is built. A fast 3D visualization is completed with multi-threading technology and the Point Cloud Library. Lastly, various studies of defect localization, quantification and thermal tomography reconstruction are carried ou

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom