Smoothness Properties Research Articles

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools.

This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.

Stability and numerical analysis of backward problem for subdiffusion with time-dependent coefficients

Our aim is to study the backward problem, i.e. recover the initial data from the terminal observation, of the subdiffusion with time dependent coefficients. First of all, by using the smoothing property of solution operators and a perturbation argument of freezing the diffusion coefficients, we show a stability estimate in Sobolev spaces, under some smallness/largeness condition on the terminal time. Moreover, in case of noisy data, we apply a quasi-boundary value method to regularize the problem and then show the convergence of the regularization scheme. Finally, we propose a completely discrete scheme by applying the finite element method in space and backward Euler convolution quadrature in time. An a priori error estimate is established. The proof is heavily built on a perturbation argument dealing with time dependent coefficients and some nonstandard error estimates for the direct problem. The error estimate gives an useful guide for balancing discretization parameters, regularization parameter and noise level. Some numerical experiments are presented to illustrate our theoretical results.

Error estimates for a splitting integrator for abstract semilinear boundary coupled systems

Abstract We derive a numerical method, based on operator splitting, to abstract parabolic semilinear boundary coupled systems. The method decouples the linear components that describe the coupling and the dynamics in the abstract bulk- and surface-spaces, and treats the nonlinear terms similarly to an exponential integrator. The convergence proof is based on estimates for a recursive formulation of the error, using the parabolic smoothing property of analytic semigroups, and a careful comparison of the exact and approximate flows. This analysis also requires a deep understanding of the effects of the Dirichlet operator (the abstract version of the harmonic extension operator), which is essential for the stable coupling in our method. Numerical experiments, including problems with dynamic boundary conditions, reporting on convergence rates are presented.

Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference

We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning. Our improvement increases the scope of the estimator from differentiable models to non-differentiable ones without requiring manual intervention of the user; the improved estimator automatically identifies differentiable parts of a given probabilistic program using our static analysis, and applies the pathwise gradient estimator to the identified parts while using a more general but less efficient estimator, called score estimator, for the rest of the program. Our analysis has a surprisingly subtle soundness argument, partly due to the misbehaviours of some target smoothness properties when viewed from the perspective of program analysis designers. For instance, some smoothness properties, such as partial differentiability and partial continuity, are not preserved by function composition, and this makes it difficult to analyse sequential composition soundly without heavily sacrificing precision. We formulate five assumptions on a target smoothness property, prove the soundness of our analysis under those assumptions, and show that our leading examples satisfy these assumptions. We also show that by using information from our analysis instantiated for differentiability, our improved gradient estimator satisfies an important differentiability requirement and thus computes the correct estimate on average (i.e., returns an unbiased estimate) under a regularity condition. Our experiments with representative probabilistic programs in the Pyro language show that our static analysis is capable of identifying smooth parts of those programs accurately, and making our improved pathwise gradient estimator exploit all the opportunities for high performance in those programs.

The Heston stochastic volatility model has a boundary trace at zero volatility

We establish boundary regularity results in Hölder spaces for the degenerate parabolic problem obtained from the Heston stochastic volatility model in Mathematical Finance set up in the spatial domain (upper half-plane) mathbb {H} = mathbb {R}times (0,infty )subset mathbb {R}^2. Starting with nonsmooth initial data u_0in H, we take advantage of smoothing properties of the parabolic semigroup textrm{e}^{-t{mathcal {A}}}:,Hrightarrow H, tin mathbb {R}_+, generated by the Heston model, to derive the smoothness of the solution u(t) = textrm{e}^{-t{mathcal {A}}} u_0 for all t>0. The existence and uniqueness of a weak solution is obtained in a Hilbert space H = L^2(mathbb {H};mathfrak {w}) with very weak growth restrictions at infinity and on the boundary partial mathbb {H}= mathbb {R}times { 0}subset mathbb {R}^2 of the half-plane mathbb {H}. We investigate the influence of the boundary behavior of the initial data u_0in H on the boundary behavior of u(t) for t>0.

Low-Light Image Enhancement via Self-Reinforced Retinex Projection Model

Low-light image enhancement aims to improve the quality of images captured under low-lightening conditions, which is a fundamental problem in computer vision and multimedia areas. Although many efforts have been invested over the years, existing illumination-based models tend to generate unnatural-looking results (e.g., over-exposure). It is because that the widely-adopted illumination adjustment (e.g., Gamma Correction) breaks down the favorable smoothness property of the original illumination derived from the well-designed illumination estimation model. To settle this issue, a great-efficiency and high-quality Self-Reinforced Retinex Projection (SRRP) model is developed in this paper, which contains optimization modules of both illumination and reflectance layers. Specifically, we construct a new fidelity term with the self-reinforced function for the illumination optimization to eliminate the dependence of the illumination adjustment to obtain a desired illumination with the excellent smoothing property. By introducing a flexible feasible constraint, we obtain a reflectance optimization module with projection. Owing to its flexibility, we can extend our model to an enhanced version by integrating a data-driven denoising mechanism as the projection, which is able to effectively handle the generated noises/artifacts in the enhanced procedure. In the experimental part, on one side, we make ample comparative assessments on multiple benchmarks with considerable state-of-the-art methods. These evaluations fully verify the outstanding performance of our method, in terms of the qualitative and quantitative analyses and execution efficiency. On the other side, we also conduct extensive analytical experiments to indicate the effectiveness and advantages of our proposed model.

Sharp smoothing properties of averages over curves

Abstract We prove sharp smoothing properties of the averaging operator defined by convolution with a measure on a smooth nondegenerate curve $\gamma $ in $\mathbb R^d$ , $d\ge 3$ . Despite the simple geometric structure of such curves, the sharp smoothing estimates have remained largely unknown except for those in low dimensions. Devising a novel inductive strategy, we obtain the optimal $L^p$ Sobolev regularity estimates, which settle the conjecture raised by Beltran–Guo–Hickman–Seeger [1]. Besides, we show the sharp local smoothing estimates on a range of p for every $d\ge 3$ . As a result, we establish, for the first time, nontrivial $L^p$ boundedness of the maximal average over dilations of $\gamma $ for $d\ge 4$ .

Qualitative analysis of solutions to mixed-order positive linear coupled systems with bounded or unbounded delays

This paper addresses the qualitative theory of mixed-order positive linear coupled systems with bounded or unbounded delays. First, we introduce a general result on the existence and uniqueness of solutions to mixed-order linear systems with time-varying delays. Next, we obtain necessary and sufficient criteria which characterize the positivity of mixed-order delay linear coupled systems. Our main contributions are in Section 5. More precisely, by using a smoothness property of solutions to fractional differential equations and developing a new appropriated comparison principle for solutions to mixed-order delay positive systems, we prove the attractivity of mixed-order non-homogeneous linear positive coupled systems under the impact of bounded or unbounded delays. We also establish a necessary and sufficient condition to| ensure the stability of homogeneous systems. As a consequence of these results, we show the smallest asymptotic bound of solutions to mixed-order delay positive non-homogeneous linear coupled systems where disturbances are continuous and bounded. Finally, we provide numerical simulations to illustrate the proposed theoretical results.

The theorems about traces and extensions for functions from Nikolsky-Besov spaces with generalized mixed smoothness

The theory of embedding of spaces of differentiable functions studies important relations of differential (smoothness) properties of functions in various metrics and has wide application in the theory of boundary value problems of mathematical physics, approximation theory and other fields of mathematics. In this article, we prove the theorems about traces and extensions for functions from Nikolsky-Besov spaces with generalized mixed smoothness and mixed metrics. The proofs of the obtained results is based on the inequality of different dimensions for trigonometric polynomials in Lebesgue spaces with mixed metrics and the embedding theorem of classical Nikolsky-Besov spaces in the space of continuous functions.

Color image completion using tensor truncated nuclear norm with l0 total variationColor image completion using tensor truncated nuclear norm with l0 total variation

In recent years, the problem of incomplete data has been behind the appearance of several completion methods and algorithms. The truncated nuclear norm has been known as a powerful low-rank approach both for the matrix and the tensor cases. However, the low-rank approaches are unable to characterize some additional information exhibited in data such as the smoothness or feature-preserving properties. In this work, a tensor completion model based on the convex truncated nuclear norm and the nonconvex-sparse total variation is introduced. Notably, we develop an alternating minimization algorithm that combines the accelerating proximal gradient for the convex step and a projection operator for the nonconvex step to solve the optimization problem. Experiments and comparative results show that our algorithm has a significant impact on the completion process.

Stability and local attractivity for non-autonomous boundary Cauchy problems

In this paper we present results concerning the existence, stability and local attractivity for non-autonomous semilinear boundary Cauchy problems. In our method, we assume certain smoothness properties on the linear part and the local lipshitz continuity on the nonlinear perturbation. We apply our abstract results to population equations.

Asymptotic smoothness in Banach spaces, three-space properties and applications

We study four asymptotic smoothness properties of Banach spaces, denoted T p , A p , N p \mathsf {T}_p,\mathsf {A}_p, \mathsf {N}_p , and P p \mathsf {P}_p . We complete their description by proving the missing renorming characterization for A p \mathsf {A}_p . We show that asymptotic uniform flattenability (property T ∞ \mathsf {T}_\infty ) and summable Szlenk index (property A ∞ \mathsf {A}_\infty ) are three-space properties. Combined with the positive results of the first-named author, Draga, and Kochanek, and with the counterexamples we provide, this completely solves the three-space problem for this family of properties. We also derive from our characterizations of A p \mathsf {A}_p and N p \mathsf {N}_p in terms of equivalent renormings, new coarse Lipschitz rigidity results for these classes.

A spectral approach to non-linear weakly singular fractional integro-differential equations

In this work, a class of non-linear weakly singular fractional integro-differential equations is considered, and we first prove existence, uniqueness, and smoothness properties of the solution under certain assumptions on the given data. We propose a numerical method based on spectral Petrov-Galerkin method that handling to the non-smooth behavior of the solution. The most outstanding feature of our approach is to evaluate the approximate solution by means of recurrence relations despite solving complex non-linear algebraic system. Furthermore, the well-known exponential accuracy is established in \(L^{2}\)-norm, and we provide some examples to illustrate the theoretical results and the performance of the proposed method.

Theoretical Analysis of Boundary Value Problems for Generalized Boussinesq Model of Mass Transfer with Variable Coefficients

A boundary value problem is formulated for a stationary model of mass transfer, which generalizes the Boussinesq approximation in the case when the coefficients in the model equations can depend on the concentration of a substance or on spatial variables. The global existence of a weak solution of this boundary value problem is proved. Some fundamental properties of its solutions are established. In particular, the validity of the maximum principle for the substance’s concentration has been proved. Sufficient conditions on the input data of the boundary value problem under consideration, which ensure the local existence of the strong solution from the space H2, and conditions that ensure the conditional uniqueness of the weak solution with additional property of smoothness for the substance’s concentration are established.

A Novel Low Rank Smooth Flat-Field Correction Algorithm for Hyperspectral Microscopy Imaging.

A flat-field correction method is proposed for multiple measured hyperspectral microscopy imaging in this paper. As the most crucial preprocessing process in quantitative microscopic analysis, flat-field correction solves the uneven illumination caused by vignetting in microscopic images, and guarantees the precision of spatial and spectral information in hyperspectral microscopic imaging. In order to carry out flat-field correction and extract uneven illumination among groups of hyperspectral microscopic data containing hundreds of bands simultaneously, two properties of vignetting have been exploited: i) low-rank property is reflected by little information contained in vignetting; ii) local smoothness can be observed as a gradual change in brightness of vignetting, which is typically equivalent to the sparseness in spatial frequency domain. Combining the two properties above, a novel Low Rank Smooth Flat-field Correction (LRSFC) model modified from common orthogonal basis extraction is proposed, while an optimization is solved based on alternating direction multiplier method (ADMM), obtaining a unique flat-field term with low-rank and smooth properties. Qualitative and quantitative experimental assessments indicate that LRSFC does not add extra cell texture to the extracted flat-field term, whose performance appears prior to other state-of-the-art flat-field correction methods.

A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon-Cosine Wavelet.

Because of noise interference, improper exposure, and the over thickness of human tissues, the detailed information of DR (digital radiography) images can be masked, including unclear edges and reduced contrast. An image-enhancement algorithm based on wavelet multiscale decomposition is proposed to address the shortcomings of existing single-scale image-enhancement algorithms. The proposed algorithm is based on Shannon-Cosine wavelets by taking advantage of the interpolation, smoothness, tight support, and normalization properties. Next a multiscale interpolation wavelet operator is constructed to divide the image into several sub-images from high frequency to low frequency, and to perform different multi-scale wavelet transforms on the detailed image of each channel. So that the most subtle and diagnostically useful information in the image can be effectively enhanced. Moreover, the image will not be over-enhanced and combined with the high contrast sensitivity of the human eye's visual system in smooth regions, different attenuation coefficients are used for different regions to achieve the purpose of suppressing noise while enhancing details. The results obtained by some simulations show that this method can effectively eliminate the noise in the DR image, and the enhanced DR image detail information is clearer than before while having high effectiveness and robustness.

Cyclic inner functions in growth classes and applications to approximation problems

AbstractIt is well known that for any inner function $\theta $ defined in the unit disk $\mathbb {D}$ , the following two conditions: (i) there exists a sequence of polynomials $\{p_n\}_n$ such that $\lim _{n \to \infty } \theta (z) p_n(z) = 1$ for all $z \in \mathbb {D}$ and (ii) $\sup _n \| \theta p_n \|_\infty < \infty $ , are incompatible, i.e., cannot be satisfied simultaneously. However, it is also known that if we relax the second condition to allow for arbitrarily slow growth of the sequence $\{ \theta (z) p_n(z)\}_n$ as $|z| \to 1$ , then condition (i) can be met for some singular inner function. We discuss certain consequences of this fact which are related to the rate of decay of Taylor coefficients and moduli of continuity of functions in model spaces $K_\theta $ . In particular, we establish a variant of a result of Khavinson and Dyakonov on nonexistence of functions with certain smoothness properties in $K_\theta $ , and we show that the classical Aleksandrov theorem on density of continuous functions in $K_\theta $ is essentially optimal. We consider also the same questions in the context of de Branges–Rovnyak spaces $\mathcal {H}(b)$ and show that the corresponding approximation result also is optimal.

On dispersion curve coloring for mechanical metafilters

This paper formalizes smooth curve coloring (i.e., curve identification) in the presence of curve intersections as an optimization problem, and investigates theoretically properties of its optimal solution. Moreover, it presents a novel automatic technique for solving such a problem. Formally, the proposed algorithm aims at minimizing the summation of the total variations over a given interval of the first derivatives of all the labeled curves, written as functions of a scalar parameter. The algorithm is based on a first-order finite difference approximation of the curves and a sequence of prediction/correction steps. At each step, the predicted points are attributed to the subsequently observed points of the curves by solving an Euclidean bipartite matching subproblem. A comparison with a more computationally expensive dynamic programming technique is presented. The proposed algorithm is applied with success to elastic periodic metamaterials for the realization of high-performance mechanical metafilters. Its output is shown to be in excellent agreement with desirable smoothness and periodicity properties of the metafilter dispersion curves. Possible developments, including those based on machine-learning techniques, are pointed out.

Vibroacoustic response of a high-speed train extruded profile under fluid-induced vibration

The wall pressure fluctuation caused by a turbulent boundary layer is an important source of noise and vibration in high-speed trains. Considering that railway vehicles are usually made of extruded profiles, a simple and reliable model is needed to predict the vibroacoustic characteristics of extruded profiles excited by a TBL in the early stages of design. A hybrid finite element and statistical energy analysis model is established for computing the vibroacoustic response of finite-sized panels excited by a TBL. In the hybrid model, the wall pressure fluctuation of the TBL is modelled in the wavenumber domain. The panels are modelled using FEs, and acoustic waves in the fluid space below the panels are modelled using SEA. The TBL wall pressure acting on the top surface of the panels is resolved into equivalent nodal forces with discretization of the structure using FEs, achieved by calculating the cross-spectrum matrix of the wall pressure in the spectral domain and using jinc functions with smooth boundary and symmetric properties as shape functions. The reciprocity between direct and reverberant fields couples the acoustic fields to the structure. Once the response of the hybrid model is determined, sound transmission through the panels can be calculated. By following the procedures outlined in this paper, the range of wall pressure model selection for TBL has extended and reduced computational time and provides a basis for the acoustic optimization design of extruded profiles.

The ultra-thin AlN epitaxy on monolayer WS2 by helicon sputtering at 400 °C

This work demonstrates the epitaxial growth of ultra-thin AlN films grown on monolayer WS2/Sapphire by helicon sputtering. In our work, WS2 acts as a template to facilitate the initial two-dimensional (2D) AlN growth via van der Waals epitaxy (VDWE), and this growth method provides a promising semiconductor manufacturing process with low thermal budget (<450 °C) than the required for the backend-of-the-line (BEOL). The ultrathin AlN epitaxy on WS2 is characterized by synchrotron-based grazing-incidence wide-angle x-ray scattering (GIWAXS). There are two stages in AlN epitaxy on the WS2; the first is the layered AlN growth aligned with the underlying WS2 lattice. The subsequent development of AlN transforms into 3D growth, which is similar to Stranski–Krastanov (SK) mode. The ultrathin AlN films achieve atomic level smoothness and significant piezoelectric properties as indicated by the technique of atomic- and piezo force microscopy (AFM/PFM). This research broadens the application of 2D materials in assisting AlN epitaxy and has a potential avenue for hetero-epitaxial integration in developing future electronic devices.