IA Scholar Query: Application of Grid computing for designing a class of optimal periodic nonuniform sampling sequences.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgThu, 17 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Nonuniform Sampled-Data Control for Synchronization of Semi-Markovian Jump Stochastic Complex Dynamical Networks with Time-Varying Delays
https://scholar.archive.org/work/3juwaxclobhsnib5nqlduverfm
In this paper, the problem of exponential synchronization of semi-Markov jump stochastic complex dynamical networks using nonuniform sampled-data control with random delayed information exchanges among dynamical nodes are discussed. In particular, it is considered that random delayed information exchanges follow a Bernoulli distribution, in which stochastic variables are used to model randomness. To achieve exponential synchronization, we designed a nonuniform sampled-data control approach. By constructing an appropriate Lyapunov–Krasovskii functional and using the Wirtinger inequality, sufficient criteria were obtained in terms of linear matrix inequalities. Finally, numerical examples were implemented to demonstrate the effectiveness and superiority of the proposed design techniques.N. Sakthivel, Yong-Ki Ma, M. Mounika Devi, G. Manopriya, V. Vijayakumar, Mooyul Huh, Chittaranjan Henswork_3juwaxclobhsnib5nqlduverfmThu, 17 Nov 2022 00:00:00 GMTSparse Fourier Transform over Lattices: A Unified Approach to Signal Reconstruction
https://scholar.archive.org/work/mztpqjklzvbjjjw6o4ju6raz5q
We revisit the classical problem of band-limited signal reconstruction – a variant of the Set Query problem – which asks to efficiently reconstruct (a subset of) a d-dimensional Fourier-sparse signal (x(t)_0 ≤ k), from minimum noisy samples of x(t) in the time domain. We present a unified framework for this problem, by developing a theory of sparse Fourier transforms over lattices, which can be viewed as a "semi-continuous" version of SFT, in-between discrete and continuous domains. Using this framework, we obtain the following results: ∙ *High-dimensional Fourier sparse recovery* We present a sample-optimal discrete Fourier Set-Query algorithm with O(k^ω+1) reconstruction time in one dimension, independent of the signal's length (n) and ℓ_∞-norm (R^* ≈x_∞). This complements the state-of-art algorithm of [Kapralov, STOC 2017], whose reconstruction time is Õ(k log^2 n log R^*), and is limited to low-dimensions. By contrast, our algorithm works for arbitrary d dimensions, mitigating the exp(d) blowup in decoding time to merely linear in d. Our algorithm also works for the semi-continuous case where frequencies lie on a lattice. ∙ *High-accuracy Fourier interpolation* We design a polynomial-time (1+ √(2) +ϵ)-approximation algorithm for continuous Fourier interpolation. This bypasses a barrier of all previous algorithms [Price and Song, FOCS 2015, Chen, Kane, Price and Song, FOCS 2016] which only achieve c>100 approximation for this basic problem. Our algorithm relies on several new ideas of independent interests in signal estimation, including high-sensitivity frequency estimation and new error analysis with sharper noise control.Zhao Song, Baocheng Sun, Omri Weinstein, Ruizhe Zhangwork_mztpqjklzvbjjjw6o4ju6raz5qTue, 15 Nov 2022 00:00:00 GMTInference for Dependent Data with Learned Clusters
https://scholar.archive.org/work/jnxn6cpccrdshk4zhj4qmxeyi4
This paper presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the paper allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The paper gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data.Jianfei Cao, Christian Hansen, Damian Kozbur, Lucciano Villacortawork_jnxn6cpccrdshk4zhj4qmxeyi4Mon, 14 Nov 2022 00:00:00 GMTWhat's the Situation with Intelligent Mesh Generation: A Survey and Perspectives
https://scholar.archive.org/work/qwng2t3pifflfplqzaqb2v7mmq
Intelligent mesh generation (IMG) refers to a technique to generate mesh by machine learning, which is a relatively new and promising research field. Within its short life span, IMG has greatly expanded the generalizability and practicality of mesh generation techniques and brought many breakthroughs and potential possibilities for mesh generation. However, there is a lack of surveys focusing on IMG methods covering recent works. In this paper, we are committed to a systematic and comprehensive survey describing the contemporary IMG landscape. Focusing on 110 preliminary IMG methods, we conducted an in-depth analysis and evaluation from multiple perspectives, including the core technique and application scope of the algorithm, agent learning goals, data types, targeting challenges, advantages and limitations. With the aim of literature collection and classification based on content extraction, we propose three different taxonomies from three views of key technique, output mesh unit element, and applicable input data types. Finally, we highlight some promising future research directions and challenges in IMG. To maximize the convenience of readers, a project page of IMG is provided at .Zezeng Li, Zebin Xu, Ying Li, Xianfeng Gu, Na Leiwork_qwng2t3pifflfplqzaqb2v7mmqFri, 11 Nov 2022 00:00:00 GMTSpectral analysis for noise diagnostics and filter-based digital error mitigation
https://scholar.archive.org/work/ks3hmdsekfhj5avahjrnidnlem
We investigate the effects of noise on parameterised quantum circuits using spectral analysis and classical signal processing tools. For different noise models, we quantify the additional, higher frequency modes in the output signal caused by device errors. We show that filtering these noise-induced modes effectively mitigates device errors. When combined with existing methods, this yields an improved reconstruction of the noiseless variational landscape. Moreover, we describe the classical and quantum resource requirements for these techniques and test their effectiveness for application motivated circuits on quantum hardware.Enrico Fontana, Ivan Rungger, Ross Duncan, Cristina Cîrstoiuwork_ks3hmdsekfhj5avahjrnidnlemThu, 10 Nov 2022 00:00:00 GMTMultiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems
https://scholar.archive.org/work/bskrpxpfwrct7kzdjvbjnhknsu
Surface electromyogram (sEMG) is arguably the most sought-after physiological signal with a broad spectrum of biomedical applications, especially in miniaturized rehabilitation robots such as multifunctional prostheses. The widespread use of sEMG to drive pattern recognition (PR)-based control schemes is primarily due to its rich motor information content and non-invasiveness. Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with inevitable interferences that distort intrinsic characteristics of the signal, precluding existing signal processing methods from yielding requisite motor control information. Therefore, we propose a multiresolution decomposition driven by dual-polynomial interpolation (MRDPI) technique for adequate denoising and reconstruction of multi-class EMG signals to guarantee the dual-advantage of enhanced signal quality and motor information preservation. Parameters for optimal MRDPI configuration were constructed across combinations of thresholding estimation schemes and signal resolution levels using EMG datasets of amputees who performed up to 22 predefined upper-limb motions acquired in-house and from the public NinaPro database. Experimental results showed that the proposed method yielded signals that led to consistent and significantly better decoding performance for all metrics compared to existing methods across features, classifiers, and datasets, offering a potential solution for practical deployment of intuitive EMG-PR-based control schemes for multifunctional prostheses and other miniaturized rehabilitation robotic systems that utilize myoelectric signals as control inputs.Oluwarotimi Williams Samuel, Mojisola Grace Asogbon, Rami Khushaba, Frank Kulwa, Guanglin Liwork_bskrpxpfwrct7kzdjvbjnhknsuThu, 10 Nov 2022 00:00:00 GMTToward the discovery of matter creation with neutrinoless double-beta decay
https://scholar.archive.org/work/ljpl6qmdtvgtnddevsvxgaahmq
The discovery of neutrinoless double-beta decay could soon be within reach. This hypothetical ultra-rare nuclear decay offers a privileged portal to physics beyond the Standard Model of particle physics. Its observation would constitute the discovery of a matter-creating process, corroborating leading theories of why the universe contains more matter than antimatter, and how forces unify at high energy scales. It would also prove that neutrinos and anti-neutrinos are not two distinct particles, but can transform into each other, with their mass described by a unique mechanism conceived by Majorana. The recognition that neutrinos are not massless necessitates an explanation and has boosted interest in neutrinoless double-beta decay. The field stands now at a turning point. A new round of experiments is currently being prepared for the next decade to cover an important region of parameter space. In parallel, advances in nuclear theory are laying the groundwork to connect the nuclear decay with the underlying new physics. Meanwhile, the particle theory landscape continues to find new motivations for neutrinos to be their own antiparticle. This review brings together the experimental, nuclear theory, and particle theory aspects connected to neutrinoless double-beta decay, to explore the path toward - and beyond - its discovery.Matteo Agostini and Giovanni Benato and Jason A. Detwiler and Javier Menéndez and Francesco Vissaniwork_ljpl6qmdtvgtnddevsvxgaahmqThu, 10 Nov 2022 00:00:00 GMTNdBaInO4 based triple (electronic, ionic and protonic) conductor for solid oxide fuel cell applications
https://scholar.archive.org/work/5wjpqdmzdng7xpuchinkyo2zfu
Oxide-ion conducting materials have never failed to attract intensive attention due to their potential to be used for the applications of solid oxide fuel cell (SOFC) devices. With the aim of reducing the operating temperatures of SOFC devices to the intermediate high temperature range (500oC-800 oC), the design and synthesis of a new structure family to be used as the electrolyte material could be crucial. In this thesis, the potential of calcium-doped layered perovskite compounds, BaNd1-xCaxInO4-x/2 (where x is the Ca content), as protonic conductors was experimentally investigated. The single phase of monoclinic crystal structure with the P21/c symmetry was confirmed in the as-synthesized BaNd1-xCaxInO4-x/2 solid solutions by XRD characterisations. The acceptor-doped ceramics exhibited improved total conductivities that were 1-2 orders of magnitude higher than those of the parent material, BaNdInO4. The highest total conductivity of 2.6 x 10-3 Scm-1 was obtained for the BaNd0.8Ca0.2InO3.90 sample at a temperature of 750 oC in air. Electrochemical impedance spectroscopy measurements of the x = 0.1 and x = 0.2 substituted samples showed higher total conductivity under humid environments than those measured in a dry environment over a large temperature range (250 oC-750 oC). At 500 oC, the total conductivity of the 20% substituted sample in humid air (~3% H2O) was 1.3 x 10-4 Scm-1. The incorporation of water vapour decreased the activation energies of the bulk conductivity of the BaNd0.8Ca0.2InO3.90 sample from 0.755(2) eV to 0.678(2) eV in air. The saturated BaNd0.8Ca0.2InO3.90 sample contained 2.2 mol% protonic defects, which caused an expansion in the lattice according to the in-situ X-ray diffraction data. Combining studies of the impedance behaviour with 4-probe DC conductivity measurements obtained in humid air which showed a decrease in the resistance of the x=0.2 sample, it could be concluded that experimental evidence indicates that BaNd1-xCaxInO4-x/2 exhibits triple (oxygen-ion, proton and hole) conduct [...]Yu Zhou, Stephen Skinner, China Scholarship Councilwork_5wjpqdmzdng7xpuchinkyo2zfuMon, 07 Nov 2022 00:00:00 GMTOptimizing illumination patterns for classical ghost imaging
https://scholar.archive.org/work/62wqq7feifdr7pbnifmyrdqbrq
Classical ghost imaging is a new paradigm in imaging where the image of an object is not measured directly with a pixelated detector. Rather, the object is subject to a set of illumination patterns and the total interaction of the object, e.g., reflected or transmitted photons or particles, is measured for each pattern with a single-pixel or bucket detector. An image of the object is then computed through the correlation of each pattern and the corresponding bucket value. Assuming no prior knowledge of the object, the set of patterns used to compute the ghost image dictates the image quality. In the visible-light regime, programmable spatial light modulators can generate the illumination patterns. In many other regimes -- such as x rays, electrons, and neutrons -- no such dynamically configurable modulators exist, and patterns are commonly produced by employing a transversely-translated mask. In this paper we explore some of the properties of masks or speckle that should be considered to maximize ghost-image quality, given a certain experimental classical ghost-imaging setup employing a transversely-displaced but otherwise non-configurable mask.Andrew M. Kingston and Lindon Roberts and Alaleh Aminzadeh and Daniele Pelliccia and Imants D. Svalbe and David M. Paganinwork_62wqq7feifdr7pbnifmyrdqbrqMon, 07 Nov 2022 00:00:00 GMTEmbracing Off-the-Grid Samples
https://scholar.archive.org/work/xeg6zy2kjrahhp72h6r5qdmzx4
Many empirical studies suggest that samples of continuous-time signals taken at locations randomly deviated from an equispaced grid (i.e., off-the-grid) can benefit signal acquisition, e.g., undersampling and anti-aliasing. However, explicit statements of such advantages and their respective conditions are scarce in the literature. This paper provides some insight on this topic when the sampling positions are known, with grid deviations generated i.i.d. from a variety of distributions. By solving the basis pursuit problem with an interpolation kernel we demonstrate the capabilities of nonuniform samples for compressive sampling, an effective paradigm for undersampling and anti-aliasing. For functions in the Wiener algebra that admit a discrete s-sparse representation in some transform domain, we show that 𝒪(slog N) random off-the-grid samples are sufficient to recover an accurate N/2-bandlimited approximation of the signal. For sparse signals (i.e., s ≪ N), this sampling complexity is a great reduction in comparison to equispaced sampling where 𝒪(N) measurements are needed for the same quality of reconstruction (Nyquist-Shannon sampling theorem). We further consider noise attenuation via oversampling (relative to a desired bandwidth), a standard technique with limited theoretical understanding when the sampling positions are non-equispaced. By solving a least squares problem, we show that 𝒪(Nlog N) i.i.d. randomly deviated samples provide an accurate N/2-bandlimited approximation of the signal with suppression of the noise energy by a factor ∼1/√(log N).Oscar López, Özgür Yılmazwork_xeg6zy2kjrahhp72h6r5qdmzx4Fri, 04 Nov 2022 00:00:00 GMT2D MoS2 Nanopores: Wafer-scale Fabrication and Monolayer Stability for Long-term Single-Molecule Sensing
https://scholar.archive.org/work/2opi6lnqjrgtbdt7klo45a2obu
These studies are considered early-stage milestones that would set the stage for new development in nanopore sensors, nicely illustrated (Fig. 1 .3) by Lee et al.[10] By this time, nanopores were about to diverge into biological nanopores and solid-state nanopores.[10-14] Figure . 1.4 shows a thickness comparison of some well-known biological pores and solid-state nanopores. The first solid-state nanopore for DNA detection was devel- The solid-state nanopores offer many advantages compared to biological ones, such as robustness, tunable pore size, stability over a wide range of voltages and concentrations, and the possibility of direct integration with the electronics. This is because the biological lipid membrane is inherently fragile and sensitive to voltages applied during the translocation experiment (<200 mV). This has been addressed using mechanically stable polymers and engineering membranes support. Figure 1 .4 shows a thickness comparison from commonly studied nanopores.Mukeshchand Thakurwork_2opi6lnqjrgtbdt7klo45a2obuMon, 31 Oct 2022 00:00:00 GMTAccelerating Carbon Capture and Storage Modeling using Fourier Neural Operators
https://scholar.archive.org/work/ffylvsezojh6bdqzaxjb2n44ym
Carbon capture and storage (CCS) is an important strategy for reducing carbon dioxide emissions and mitigating climate change. We consider the storage aspect of CCS, which involves injecting carbon dioxide into underground reservoirs. This requires accurate and high-resolution predictions of carbon dioxide plume migration and reservoir pressure buildup. However, such modeling is challenging at scale due to the high computational costs of existing numerical methods. We introduce a novel machine learning approach for four-dimensional spatial-temporal modeling, which speeds up predictions nearly 700,000 times compared to existing methods. It provides highly accurate predictions under diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework, Nested Fourier Neural Operator (FNO), learns the solution operator for the family of partial differential equations governing the carbon dioxide-water multiphase flow. It uses a hierarchy of FNO models to produce outputs at different refinement levels. Thus, our approach enables unprecedented real-time high-resolution modeling for carbon dioxide storage.Gege Wen, Zongyi Li, Qirui Long, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Bensonwork_ffylvsezojh6bdqzaxjb2n44ymMon, 31 Oct 2022 00:00:00 GMTThe Brain Imaging Data Structure (BIDS) Specification
https://scholar.archive.org/work/zrtrryke3jdg5gbhcr2piifevy
This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions. To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version. See Appendix I for a list of the BIDS contributors who jointly created this specification.BIDS-Contributorswork_zrtrryke3jdg5gbhcr2piifevySat, 29 Oct 2022 00:00:00 GMTSurfactant migration on polymeric substrates
https://scholar.archive.org/work/dzp267m7dfhgbk2kmeoqtsteyq
Many industrial nonwoven polymeric fabrics are coated with surfactants to provide improved wettability which is an essential attribute for disposable hygiene products, like facemasks, wipes, absorbent materials and baby nappies. These surfactant coatings on polyolefinic nonwovens appear to be typically not permanent and this fugitive nature of the surfactants is a concern for the industry. However, the interaction between organic species and complex semi-amorphous polymers as used in nonwoven products is an industrially important but poorly understood research area. Experimental studies reported here have established the mechanisms by which surfactants interact with polyolefinic surfaces, provide visualisation of 3D surfactant distributions on these nonwovens as well as their wettability, and report on the processes responsible for surfactant migration/loss from polyolefins. A novel confocal microscopy method is reported here for the non-invasive imaging of the 3D distributions of surfactants on polymeric nonwovens. Optical contrast was achieved by introducing a fluorescent dye via vaporisation at elevated temperatures, which preferentially dissolves into the hydrophilic surfactant regions of the nonwoven sample. The method is quantitative and allows the patch wise heterogenic distribution of surfactant coatings on complex 3D nonwoven materials to be visualised. To understand the interaction between surfactants and nonwoven polyolefins, several chemical properties and physicochemical descriptors of nonwoven materials were determined including wettability, specific surface area, surface energy, solvent sorption kinetics, and their surface elemental composition. Specific surface area BET measurements demonstrated that industrial nonwovens are characterised by generally low specific surface area values, in the range 0.1 - 4 m2/g and that inverse gas chromatography (IGC) offered best sensitivity and precision. The wettability of polyolefin surfaces is well described by the dispersive contribution of surface free ener [...]Jona Ramadani, Daryl Williams, Engineering And Physical Sciences Research Council, Procter & Gamble (Firm)work_dzp267m7dfhgbk2kmeoqtsteyqFri, 28 Oct 2022 00:00:00 GMTCellular Automata: Temporal Stochasticity and Computability
https://scholar.archive.org/work/3iy5cks2jvfczojqwqmbijur2y
In this dissertation, we study temporally stochasticity in cellular automata and the behavior of such cellular automata. The work also explores the computational ability of such cellular automaton that illustrates the computability of solving the affinity classification problem. In addition to that, a cellular automaton, defined over Cayley tree, is shown as the classical searching problem solver. The proposed temporally stochastic cellular automata deals with two elementary cellular automata rules, say f and g. The f is the default rule, however, g is temporally applied to the overall system with some probability τ which acts as a noise in the system. After exploring the dynamics of temporally stochastic cellular automata (TSCAs), we study the dynamical behavior of these temporally stochastic cellular automata (TSCAs) to identify the TSCAs that converge to a fixed point from any seed. We apply each of the convergent TSCAs to some standard datasets and observe the effectiveness of each TSCA as a pattern classifier. It is observed that the proposed TSCA-based classifier shows competitive performance in comparison with existing classifier algorithms. We use temporally stochastic cellular automata to solve a new problem in the field of cellular automata, named as, affinity classification problem which is a generalization of the density classification problem . We show that this model can be used in several applications, like modeling self-healing systems. Finally, we introduce a new model of computing unit developed around cellular automata to reduce the workload of the Central Processing Unit (CPU) of a machine to compute. Each cell of the computing unit acts as a tiny processing element with attached memory. Such a CA is implemented on the Cayley Tree to realize efficient solutions for diverse computational problems.Subrata Paulwork_3iy5cks2jvfczojqwqmbijur2yThu, 20 Oct 2022 00:00:00 GMTAsymptotic Analysis of Conditioned Stochastic Gradient Descent
https://scholar.archive.org/work/b6dw2zly5nfcnisgnbubkntq5a
In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called conditioned SGD, based on a preconditioning of the gradient direction. Using a discrete-time approach with martingale tools, we establish the weak convergence of the rescaled sequence of iterates for a broad class of conditioning matrices including stochastic first-order and second-order methods. Almost sure convergence results, which may be of independent interest, are also presented. When the conditioning matrix is an estimate of the inverse Hessian, the algorithm is proved to be asymptotically optimal. For the sake of completeness, we provide a practical procedure to achieve this minimum variance.Rémi Leluc, François Portierwork_b6dw2zly5nfcnisgnbubkntq5aThu, 20 Oct 2022 00:00:00 GMTContamination Level Monitoring Techniques for High-Voltage Insulators: A Review
https://scholar.archive.org/work/bwfg5ucax5ewzfhqdtoougiobi
Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility.Luqman Maraaba, Khaled Al-Soufi, Twaha Ssennoga, Azhar M. Memon, Muhammed Y. Worku, Luai M. Alhemswork_bwfg5ucax5ewzfhqdtoougiobiMon, 17 Oct 2022 00:00:00 GMTMachine learning algorithms for three-dimensional mean-curvature computation in the level-set method
https://scholar.archive.org/work/gxhzvgxi7javve6ley6axv2fuy
We propose a data-driven mean-curvature solver for the level-set method. This work is the natural extension to ℝ^3 of our two-dimensional strategy in [arXiv:2201.12342][1] and the hybrid inference system of [DOI: 10.1016/j.jcp.2022.111291][2]. However, in contrast to [1,2], which built resolution-dependent neural-network dictionaries, here we develop a pair of models in ℝ^3, regardless of the mesh size. Our feedforward networks ingest transformed level-set, gradient, and curvature data to fix numerical mean-curvature approximations selectively for interface nodes. To reduce the problem's complexity, we have used the Gaussian curvature to classify stencils and fit our models separately to non-saddle and saddle patterns. Non-saddle stencils are easier to handle because they exhibit a curvature error distribution characterized by monotonicity and symmetry. While the latter has allowed us to train only on half the mean-curvature spectrum, the former has helped us blend the data-driven and the baseline estimations seamlessly near flat regions. On the other hand, the saddle-pattern error structure is less clear; thus, we have exploited no latent information beyond what is known. In this regard, we have trained our models on not only spherical but also sinusoidal and hyperbolic paraboloidal patches. Our approach to building their data sets is systematic but gleans samples randomly while ensuring well-balancedness. We have also resorted to standardization and dimensionality reduction as a preprocessing step and integrated regularization to minimize outliers. In addition, we leverage curvature rotation/reflection invariance to improve precision at inference time. Several experiments confirm that our proposed system can yield more accurate mean-curvature estimations than modern particle-based interface reconstruction and level-set schemes around under-resolved regions.Luis Ángel Larios-Cárdenas, Frédéric Gibouwork_gxhzvgxi7javve6ley6axv2fuySun, 16 Oct 2022 00:00:00 GMTMetaplectic Geometrical Optics
https://scholar.archive.org/work/pmekw35cwzbnvayv4xarecutp4
Ray optics is an intuitive and computationally efficient model for wave propagation through nonuniform media. However, the underlying geometrical-optics (GO) approximation of ray optics breaks down at caustics, erroneously predicting the wave intensity to be infinite and thereby limiting the predictive capabilities of GO-based codes. Full-wave modeling can be used instead, but the added computational cost brings its own set of tradeoffs. Developing more efficient caustic remedies has therefore been an active area of research for the past few decades. In this thesis, I present a new ray-based approach called 'metaplectic geometrical optics' (MGO) that can be applied to any linear wave equation. Instead of evolving waves in the usual x (coordinate) or k (spectral) representation, MGO uses a mixed X=Ax+B representation. By continuously adjusting the coefficients A and B along the rays via sequenced metaplectic transforms (MTs) of the wavefield, corresponding to symplectic transformations of the ray phase space, one can ensure that GO remains valid in the X coordinates without caustic singularities. The caustic-free result is then mapped back onto the original x space using metaplectic transforms, as demonstrated on a number of examples. Besides outlining the basic theory, this thesis also presents specialized fast algorithms for MGO. These algorithms focus on the MT, which is a unitary integral mapping that can be considered a generalization of the Fourier transform. First, a discrete representation of the MT is developed that can be computed in linear time when evaluated in the near-identity limit; finite MTs can then be implemented by iterating K≫ 1 near-identity MTs. Second, an algorithm based on Gauss–Freud quadrature is developed for efficiently computing finite MTs along their steepest-descent curves, which may be useful in catastrophe-optics applications beyond MGO.Nicolas A. Lopezwork_pmekw35cwzbnvayv4xarecutp4Thu, 06 Oct 2022 00:00:00 GMTHybrid Methodology Based on Symmetrized Dot Pattern and Convolutional Neural Networks for Fault Diagnosis of Power Cables
https://scholar.archive.org/work/2ghkpzxy5napfnsc7mms6w5h2y
This study proposes a recognition method based on symmetrized dot pattern (SDP) analysis and convolutional neural network (CNN) for rapid and accurate diagnosis of insulation defect problems by detecting the partial discharge (PD) signals of XLPE power cables. First, a normal and three power cable models with different insulation defects are built. The PD signals resulting from power cable insulation defects are measured. The frequency and amplitude variations of PD signals from different defects are reflected by comprehensible images using the proposed SDP analysis method. The features of different power cable defects are presented. Finally, the feature image is trained and identified by CNN to achieve a power cable insulation fault diagnosis system. The experimental results show that the proposed method could accurately diagnose the fault types of power cable insulation defects with a recognition accuracy of 98%. The proposed method is characterized by a short detection time and high diagnostic accuracy. It can effectively detect the power cable PD to identify the fault type of the insulation defect.Meng-Hui Wang, Hong-Wei Sian, Shiue-Der Luwork_2ghkpzxy5napfnsc7mms6w5h2yWed, 05 Oct 2022 00:00:00 GMT