Stator Flux Observer for Induction Motor Based on Tracking Differentiator

Voltage model is commonly used in direct torque control (DTC) for flux observing of asynchronous motor. In order to improve low-speed and dynamic performance of the voltage model, a modified low-pass filter (LPF) algorithm is proposed. Firstly, the tracking differentiator is brought in to modulate the measured stator current, which suppresses the measurement noise, and then amplitude and phase compensation is made towards the stator electromotive force (EMF), after which the stator flux is obtained through a low-pass filter. This method can eliminate the dynamic error of flux filtered by LPF and improve low-speed performance. Experimental results demonstrate effectiveness and improved dynamic performance of such method

Analysis and Detection of Outliers in GNSS Measurements by Means of Machine Learning Algorithms

L'abstract è presente nell'allegato / the abstract is in the attachmen

Radial Basis Functions: Biomedical Applications and Parallelization

Radial basis function (RBF) is a real-valued function whose values depend only on the distances between an interpolation point and a set of user-specified points called centers. RBF interpolation is one of the primary methods to reconstruct functions from multi-dimensional scattered data. Its abilities to generalize arbitrary space dimensions and to provide spectral accuracy have made it particularly popular in different application areas, including but not limited to: finding numerical solutions of partial differential equations (PDEs), image processing, computer vision and graphics, deep learning and neural networks, etc. The present thesis discusses three applications of RBF interpolation in biomedical engineering areas: (1) Calcium dynamics modeling, in which we numerically solve a set of PDEs by using meshless numerical methods and RBF-based interpolation techniques; (2) Image restoration and transformation, where an image is restored from its triangular mesh representation or transformed under translation, rotation, and scaling, etc. from its original form; (3) Porous structure design, in which the RBF interpolation used to reconstruct a 3D volume containing porous structures from a set of regularly or randomly placed points inside a user-provided surface shape. All these three applications have been investigated and their effectiveness has been supported with numerous experimental results. In particular, we innovatively utilize anisotropic distance metrics to define the distance in RBF interpolation and apply them to the aforementioned second and third applications, which show significant improvement in preserving image features or capturing connected porous structures over the isotropic distance-based RBF method. Beside the algorithm designs and their applications in biomedical areas, we also explore several common parallelization techniques (including OpenMP and CUDA-based GPU programming) to accelerate the performance of the present algorithms. In particular, we analyze how parallel programming can help RBF interpolation to speed up the meshless PDE solver as well as image processing. While RBF has been widely used in various science and engineering fields, the current thesis is expected to trigger some more interest from computational scientists or students into this fast-growing area and specifically apply these techniques to biomedical problems such as the ones investigated in the present work

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Machine learning in solar physics

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Optimisation of the SHiP Beam Dump Facility with generative surrogate models

The SHiP experiment is a proposed fixed target experiment at the CERN SPS to search for new particles. To operate optimally, the experiment should feature a zero background environment. The residual muons flying from the target are one of the largest sources of the background. To remove them from the detector acceptance, a dedicated muon shield magnet is introduced in the experiment. The shield should be optimised to deliver the best physics performance at the lowest cost. The optimisation procedure is very computationally costly and, thus, requires ded- icated methods. This thesis comprises of a detailed description of a new machine learning method for the optimisation, comparisons to existing techniques, and the application of the method to optimising the muon shield magnet. In addition, the set of technological and simulation problems affecting the optimisation is discussed in details. Finally, the set of requirements for the muon shield prototype design and verification is presented.Open Acces

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

Magnetic resonance imaging and the development of vascular targeted treatments for cancer.

The main subject of the work presented in this thesis is the further development of magnetic resonance imaging (MRI) as a non-invasive method of investigating tumour microcirculation. Two different MR techniques were used: dynamic contrast enhanced (DCE)-MRI and Blood Oxygen Level Dependent (BOLD)-MRI. Intravital microscopy was used to help interpret BOLD-MRI results. The ultimate aims were to determine whether MRI methods could be relied upon to define a drug as having vascular disrupting activity and to develop techniques to predict the effectiveness of vascular disruptive agents (VDA). In DCE-MRI, tissue enhancement is continuously monitored over several minutes after intravenous injection of contrast medium. Modelling of contrast agent kinetics generates quantitative parameters related to tissue blood flow rate and permeability, e.g. Ktrans (transfer constant). In a clinical study, patients had DCE-MRI examinations before and 24 hours after cytotoxic chemotherapy to establish whether any acute ami-vascular effects could be detected. No acute reductions in Ktrans were seen. In this project, the acute effects of the VDA, combretastatin A-4-phosphate, were investigated using DCE-MRI in SW1222 tumours in mice. Responses were seen both at a clinically relevant dose and at higher doses, and a dose-response relationship established. BOLD-MRI can detect changes in oxygenation and blood flow within tumours using deoxygenated haemoglobin as an intrinsic contrast agent. Tumours contain a variable proportion of immature vessels, which may explain differential sensitivity to VDAs. In this project, BOLD-MRI was used to assess tumour vessel maturity using consequent vasoreactivity to angiotensin II and carbon dioxide (as air-5%C02 or as carbogen) in an animal model. Intravital microscopy was used to directly observe response to these agents in mouse window chambers. Results suggest that response to vasoactive agents is useful for assessing vascular maturity in tumours but that more sensitive non-invasive imaging methods than BOLD-MRI are required for clinical use