2,499 research outputs found
Symmetries of Riemann surfaces and magnetic monopoles
This thesis studies, broadly, the role of symmetry in elucidating structure. In particular, I investigate the role that automorphisms of algebraic curves play in three specific contexts; determining the orbits of theta characteristics, influencing the geometry of the highly-symmetric Bringâs curve, and in constructing magnetic monopole solutions. On theta characteristics, I show how to turn questions on the existence of invariant characteristics into questions of group cohomology, compute comprehensive tables of orbit decompositions for curves of genus 9 or less, and prove results on the existence of infinite families of curves with invariant characteristics. On Bringâs curve, I identify key points with geometric significance on the curve, completely determine the structure of the quotients by subgroups of automorphisms, finding new elliptic curves in the process, and identify the unique invariant theta characteristic on the curve. With respect to monopoles, I elucidate the role that the Hitchin conditions play in determining monopole spectral curves, the relation between these conditions and the automorphism group of the curve, and I develop the theory of computing Nahm data of symmetric monopoles. As such I classify all 3-monopoles whose Nahm data may be solved for in terms of elliptic functions
MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN
Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment.
To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamiltonâs principle along with Galerkinâs decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution
Conformance Checking-based Concept Drift Detection in Process Mining
One of the main challenges of process mining is to obtain
models that represent a process as simply and accurately as
possible. Both characteristics can be greatly influenced by
changes in the control flow of the process throughout its life
cycle.
In this thesis we propose the use of conformance metrics to
monitor such changes in a way that allows the division of the
log into sub-logs representing different versions of the process
over time. The validity of the hypothesis has been formally
demonstrated, showing that all kinds of changes in the process
flow can be captured using these approaches, including
sudden, gradual drifts on both clean and noisy environments,
where differentiating between anomalous executions and real
changes can be tricky
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds 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 modiïŹed Proportional ConïŹict 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 classiïŹers, 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, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation.
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 classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well
Non-Hermitian Topological Magnonics
Dissipation in mechanics, optics, acoustics, and electronic circuits is
nowadays recognized to be not always detrimental but can be exploited to
achieve non-Hermitian topological phases or properties with functionalities for
potential device applications. As elementary excitations of ordered magnetic
moments that exist in various magnetic materials, magnons are the information
carriers in magnonic devices with low-energy consumption for reprogrammable
logic, non-reciprocal communication, and non-volatile memory functionalities.
Non-Hermitian topological magnonics deals with the engineering of dissipation
and/or gain for non-Hermitian topological phases or properties in magnets that
are not achievable in the conventional Hermitian scenario, with associated
functionalities cross-fertilized with their electronic, acoustic, optic, and
mechanic counterparts, such as giant enhancement of magnonic frequency combs,
magnon amplification, (quantum) sensing of the magnetic field with
unprecedented sensitivity, magnon accumulation, and perfect absorption of
microwaves. In this review article, we address the unified approach in
constructing magnonic non-Hermitian Hamiltonian, introduce the basic
non-Hermitian topological physics, and provide a comprehensive overview of the
recent theoretical and experimental progress towards achieving distinct
non-Hermitian topological phases or properties in magnonic devices, including
exceptional points, exceptional nodal phases, non-Hermitian magnonic SSH model,
and non-Hermitian skin effect. We emphasize the non-Hermitian Hamiltonian
approach based on the Lindbladian or self-energy of the magnonic subsystem but
address the physics beyond it as well, such as the crucial quantum jump effect
in the quantum regime and non-Markovian dynamics. We provide a perspective for
future opportunities and challenges before concluding this article.Comment: 101 pages, 35 figure
Multiphase flow measurement and data analytic based on multi-modal sensors
Accurate multiphase flow measurement is crucial in the energy industry. Over
the past decades, separation of the multiphase flow into single-phase flows has
been a standard method for measuring multiphase flowrate. However, in-situ, non-invasive, and real-time imaging and measuring the key parameters of multiphase
flows remain a long-standing challenge. To tackle the challenge, this thesis first
explores the feasibility of performing time-difference and frequency-difference imaging
of multiphase flows with complex-valued electrical capacitance tomography (CVECT).
The multiple measurement vector (MMV) model-based CVECT imaging algorithm
is proposed to reconstruct conductivity and permittivity distribution simultaneously,
and the alternating direction method of multipliers (ADMM) is applied to solve
the multi-frequency image reconstruction problem. The proposed multiphase flow
imaging approach is verified and benchmarked with widely adopted tomographic
image reconstruction algorithms. Another focus of this thesis is multiphase flowrate
estimation based on low-cost, multi-modal sensors. Machine learning (ML) has
recently emerged as a powerful tool to deal with time series sensing data from multi-modal sensors. This thesis investigates three prevailing machine learning methods,
i.e., deep neural network (DNN), support vector machine (SVM), and convolutional
neural network (CNN), to estimate the flowrate of oil/gas/water three-phase flows
based on the Venturi tube. The improvement of CNN with the combination of long-short term memory machine (LSTM) is made and a temporal convolution network
(TCN) model is introduced to analyse the collected time series sensing data from the
Venturi tube installed in a pilot-scale multiphase flow facility. Furthermore, a multi-modal approach for multiphase flowrate measurement is developed by combining
the Venturi tube and a dual-plane ECT sensor. An improved TCN model is built
to predict the multiphase flowrate with various data pre-processing methods. The
results provide guidance on data pre-processing methods for multiphase flowrate
measurement and suggest that the proposed combination of low-cost flow sensing
techniques and machine learning can effectively translate the time series sensing
data to achieve satisfactory flowrate measurement under various flow conditions
Homological algebra and moduli spaces in topological field theories
This is a survey of various types of Floer theories (both in symplectic
geometry and gauge theory) and relations among them.Comment: 56 pages 12 Figure
This Year's Nobel Prize (2022) in Physics for Entanglement and Quantum Information: the New Revolution in Quantum Mechanics and Science
The paper discusses this yearâs Nobel Prize in physics for experiments of entanglement âestablishing the violation of Bell inequalities and pioneering quantum information scienceâ in a much wider, including philosophical context legitimizing by the authority of the Nobel Prize a new scientific area out of âclassicalâ quantum mechanics relevant to Pauliâs âparticleâ paradigm of energy conservation and thus to the Standard model obeying it. One justifies the eventual future theory of quantum gravitation as belonging to the newly established quantum information science. Entanglement, involving non-Hermitian operators for its rigorous description, non-unitarity as well as nonlocal and superluminal physical signals âspookilyâ (by Einsteinâs flowery epithet) synchronizing and transferring some nonzero action at a distance, can be considered to be quantum gravity so that its local counterpart to be Einsteinâs gravitation according to general relativity therefore pioneering an alternative pathway to quantum gravitation different from the âsecondary quantizationâ of the Standard model. So, the experiments of entanglement once they have been awarded by the Nobel Prize launch particularly the relevant theory of quantum gravitation grounded on âquantum information scienceâ thus granted to be nonclassical quantum mechanics in the shared framework of the generalized quantum mechanics obeying rather quantum-information conservation than only energy conservation. The concept of âdark phaseâ of the universe naturally linked to the very well confirmed âdark matterâ and âdark energyâ and opposed to its âlight phaseâ inherent to classical quantum mechanics and the Standard model obeys quantum-information conservation, after which reversible causality or the mutual transformation of energy and information are valid. The mythical Big Bang after which energy conservation holds universally is to be replaced by an omnipresent and omnitemporal medium of decoherence of the dark and nonlocal phase into the light and local phase. The former is only an integral image of the latter and borrowed in fact rather from religion than from science. Physical, methodological and proper philosophical conclusions follow from that paradigm shift heralded by this yearâs Nobel Prize in physics. For example, the scientific theory of thinking should originate from the dark phase of the universe, as well: probably only approximately modeled by neural networks physically belonging to the light phase thoroughly. A few crucial philosophical sequences follow from the break of Pauliâs paradigm: (1) the establishment of the âdarkâ phase of the universe as opposed to its âlightâ phase, only to which the Cartesian dichotomy of âbodyâ and âmindâ is valid; (2) quantum information conservation as relevant to the dark phase, furthermore generalizing energy conservation as to its light phase, productively allowing for physical entities to appear âex nihiloâ, i.e., from the dark phase, in which energy and time are yet inseparable from each other; (3) reversible causality as inherent to the dark phase; (4) the interpretation of gravitation only mathematically: as an interpretation of the incompleteness of finiteness to infinity, for example, following the Gödel dichotomy (âeither contradiction or incompletenessâ) about the relation of arithmetic to set theory; (5) the restriction of the concept of hierarchy only to the light phase; (6) the commensurability of both physical extremes of a quantum and the universe as a whole in the dark phase obeying quantum information conservation and akin to Nicholas of Cusaâs philosophical and theological worldview
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