2,733 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Contactless excitation for electric machines: high temperature superconducting flux pumps

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    With the intensification of global warming and climate change, the pace of transformation to a neutral-emission society is accelerating. In various sectors, electrification has become the absolute tendency to promote such a movement, where electric machines play an important role in the current power generation system. It is widely convinced that electric machines with very high power density are essential for future applications, which, however, can be hardly achieved by conventional technologies. Owing to the maturation of the second generation (2G) high temperature superconducting (HTS) technologies, it has been recognized that superconducting machine could be a competitive candidate to realize the vision. One significant obstacle that hinders the implementation of superconducting machines is how to provide the required magnetic fields, or in other words, how to energise them appropriately. Conventional direct injection is not suitable for HTS machines, because the current leads would bridge ambident temperature to the cryogenic environment, which can impose considerable heat load on the system and increase the operational cost. Thus, an efficient energisation method is demanded by HTS machines. As an emerging technology that can accumulate substantial flux in a closed loop without any physical contact, HTS flux pumps have been proposed as a promising solution. Among the existing developed HTS flux pumps, rotary HTS flux pumps, or so-called HTS dynamo, can output non-zero time-averaged DC voltage and charge the rest of the circuit if a closed loop has been formed. This type of flux pump is often employed together with HTS coils, where the HTS coils can potentially work in the persistent current mode, and act like electromagnets with a considerable magnetic field, having a wide range of applications in industry. The output characteristics of rotary HTS flux pumps have been extensively explored through experiments and finite element method (FEM) simulations, yet the work on constructing statistical models as an alternative approach to capture key characteristics has not been studied. In this thesis, a 2D FEM program has been developed to model the operation of rotary HTS flux pumps and evaluate the effects of different factors on the output voltage through parameter sweeping and analysis of variance. Typical design considerations, including the operating frequency, air gap, HTS tape width, and remanent flux density have been investigated, in particular, the bilateral effect of HTS tape width has been discovered and explained by looking at the averaged integration of the electric field over the HTS tape. Based on the data obtained from various simulations, regression analysis has been conducted through a collection of machine learning methods. It has been demonstrated that the output voltage of a rotary HTS flux pump can be obtained promptly with satisfactory accuracy via Gaussian process regression, aiming to provide a novel approach for future research and a powerful design tool for industrial applications using rotary HTS flux pumps. To enhance the applicability of the proposed statistical models, an updated FEM program has been built to take more parameters into account. The newly added parameters, namely the rotor radius and the width of permanent magnet, together with formerly included ones, should have covered all the key design parameters for a rotary HTS flux pump. Based on data collected from the FEM model, a well-trained semi-deep neural network (DNN) model with a back-propagation algorithm has been put forward and validated. The proposed DNN model is capable of quantifying the output voltage of a rotary HTS flux pump instantly with an overall accuracy of 98% with respect to the simulated values with all design parameters explicitly specified. The model possesses a powerful ability to characterize the output behaviour of rotary HTS flux pumps by integrating all design parameters, and the output characteristics of rotary HTS flux pumps have been successfully demonstrated and visualized using this model. Compared to conventional time-consuming FEM-based numerical models, the proposed DNN model has the advantages of fast learning, accurate computation, as well as strong programmability. Therefore, the DNN model can greatly facilitate the design and optimization process for rotary HTS flux pumps. An executable application has been developed accordingly based on the DNN model, which is believed to provide a useful tool for learners and designers of rotary HTS flux pumps. A new variant inspired by the working principles of rotary HTS flux pumps has been proposed and termed as stationary wave HTS flux pumps. The superiority of this type is that it has a simple structure without any moving components, and it utilises a controllable current-driven electromagnet to provide the required magnetic field. It has been demonstrated that the origin of the output voltage is determined by the asymmetric distribution of the dynamic resistance in the HTS tape, for which the electromagnet must be placed at such a position that its central line is not aligned with that of the HTS tape. A numerical model has been built to simulate the operation of a stationary wave HTS flux pump, based on which the output characteristics and dynamic resistance against various parameters have been investigated. Besides, accurate and reliable statistical models have been proposed to predict the open circuit voltage and effective dynamic resistance by adapting the previously developed machine learning techniques. The work presented in this PhD thesis can bring more insight into HTS flux pumps as an emerging promising contactless energisation technology, and the proposed statistical models can be particularly useful for the design and optimization of such devices

    Undergraduate Catalog of Studies, 2022-2023

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    A Taxonomy of Freehand Grasping Patterns in Virtual Reality

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    Grasping is the most natural and primary interaction paradigm people perform every day, which allows us to pick up and manipulate objects around us such as drinking a cup of coffee or writing with a pen. Grasping has been highly explored in real environments, to understand and structure the way people grasp and interact with objects by presenting categories, models and theories for grasping approach. Due to the complexity of the human hand, classifying grasping knowledge to provide meaningful insights is a challenging task, which led to researchers developing grasp taxonomies to provide guidelines for emerging grasping work (such as in anthropology, robotics and hand surgery) in a systematic way. While this body of work exists for real grasping, the nuances of grasping transfer in virtual environments is unexplored. The emerging development of robust hand tracking sensors for virtual devices now allow the development of grasp models that enable VR to simulate real grasping interactions. However, present work has not yet explored the differences and nuances that are present in virtual grasping compared to real object grasping, which means that virtual systems that create grasping models based on real grasping knowledge, might make assumptions which are yet to be proven true or untrue around the way users intuitively grasp and interact with virtual objects. To address this, this thesis presents the first user elicitation studies to explore grasping patterns directly in VR. The first study presents main similarities and differences between real and virtual object grasping, the second study furthers this by exploring how virtual object shape influences grasping patterns, the third study focuses on visual thermal cues and how this influences grasp metrics, and the fourth study focuses on understanding other object characteristics such as stability and complexity and how they influence grasps in VR. To provide structured insights on grasping interactions in VR, the results are synthesized in the first VR Taxonomy of Grasp Types, developed following current methods for developing grasping and HCI taxonomies and re-iterated to present an updated and more complete taxonomy. Results show that users appear to mimic real grasping behaviour in VR, however they also illustrate that users present issues around object size estimation and generally a lower variability in grasp types is used. The taxonomy shows that only five grasps account for the majority of grasp data in VR, which can be used for computer systems aiming to achieve natural and intuitive interactions at lower computational cost. Further, findings show that virtual object characteristics such as shape, stability and complexity as well as visual cues for temperature influence grasp metrics such as aperture, category, type, location and dimension. These changes in grasping patterns together with virtual object categorisation methods can be used to inform design decisions when developing intuitive interactions and virtual objects and environments and therefore taking a step forward in achieving natural grasping interaction in VR
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